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The AP2 clathrin adaptor complex links protein cargo to the endocytic machinery but it is unclear how AP2 is activated on the plasma membrane . Here we demonstrate that the membrane-associated proteins FCHo and SGIP1 convert AP2 into an open , active conformation . We screened for Caenorhabditis elegans mutants that phenocopy the loss of AP2 subunits and found that AP2 remains inactive in fcho-1 mutants . A subsequent screen for bypass suppressors of fcho-1 nulls identified 71 compensatory mutations in all four AP2 subunits . Using a protease-sensitivity assay we show that these mutations restore the open conformation in vivo . The domain of FCHo that induces this rearrangement is not the F-BAR domain or the µ-homology domain , but rather is an uncharacterized 90 amino acid motif , found in both FCHo and SGIP proteins , that directly binds AP2 . Thus , these proteins stabilize nascent endocytic pits by exposing membrane and cargo binding sites on AP2 . Clathrin-mediated endocytosis is a conserved and ubiquitous process for internalizing material from the cell surface . The Adaptor Protein-2 ( AP2 ) complex serves as a bridge between cargo at the plasma membrane and clathrin . The AP2 core complex contains binding sites for membrane phospholipids and endocytic cargo while the appendages bind clathrin and accessory proteins that coordinate endocytosis ( Traub and Bonifacino , 2013 ) . AP2 thereby binds target proteins on the surface of the cell and assembles the machinery necessary for internalization of cargo . AP2 can adopt functionally different conformations . The first crystal structure of the core complex revealed that the binding pockets for cargo and membrane were partially occluded ( Collins et al . , 2002 ) . This structure was proposed to represent a closed , inactive conformation of AP2 . Cocrystallization with cargo peptides led to partially open or fully open conformations ( Kelly et al . , 2008; Jackson et al . , 2010 ) . The open conformation places the cargo- and membrane-binding pockets in coplanar face of the complex and is therefore thought to be the active form of AP2 . It has been difficult to determine whether AP2 reorganization is an obligatory process in vivo . What regulates the switch from closed to open conformation ? One model proposes that AP2 can open by simply binding the peptide motifs of cargo proteins and phosphatidylinositol 4 , 5-bisphosphate ( PIP2 ) on the plasma membrane ( Honing et al . , 2005; Kelly et al . , 2014 ) . Alternatively , AP2 might require association with clathrin and phosphatidylinositol-3-phosphate to bind cargo at the plasma membrane ( Rapoport et al . , 1997 ) . Another model suggests that the open form of AP2 is induced by phosphorylation ( Fingerhut et al . , 2001; Olusanya et al . , 2001; Conner and Schmid , 2002; Ricotta et al . , 2002; Honing et al . , 2005 ) . Alternatively it is possible that the complex is activated by one of the many other clathrin-associated proteins . The most likely of these proteins would be one that precedes AP2 at sites of endocytosis . Examples include Epidermal growth factor receptor substrate 15 ( Eps15 ) , intersectin , and most recently , Fer/CIP4 Homology domain only ( FCHo ) proteins ( Syp1p in yeast ) ( Stimpson et al . , 2009; Taylor et al . , 2011 ) . The role of FCHo at endocytic sites is poorly defined . In syp1 mutants , which encodes the yeast homolog of FCHo , endocytic patches are less frequent , but still progress to coated pits ( Reider et al . , 2009; Stimpson et al . , 2009 ) . When FCHo proteins were knocked down in tissue culture cells , AP2 failed to bind membrane ( Henne et al . , 2010 ) . However , others found that knockdown of FCHo did not prevent AP2 association with the membrane ( Umasankar et al . , 2012 ) but that there is an increased tendency for endocytic events to abort ( Cocucci et al . , 2012 ) with flat clathrin plaques forming rather than clathrin-coated pits ( Mulkearns and Cooper , 2012 ) . These studies suggest that FCHo might regulate AP2 during the formation of a clathrin-coated pit . On the other hand , there is evidence that FCHo may be acting in a parallel endocytic pathway with ESCRT0 in Caenorhabditis elegans ( Mayers et al . , 2013 ) . In fish , FCHo appears to act in BMP signaling during development ( Umasankar et al . , 2012 ) . Thus it is unclear whether FCHo proteins function via AP2 or in parallel to AP2 in clathrin coat assembly , or in an entirely unrelated pathway . Here , we report that FCHo directly activates AP2 by promoting the open conformation . In FCHo mutants in the nematode C . elegans , AP2 is functionally inactive and endocytosis of surface cargo is reduced . However , the requirement for FCHo can be bypassed by mutations in AP2 that specifically destabilize the closed conformation of AP2 . FCHo is comprised of an F-BAR domain ( Henne et al . , 2007 ) , a linker region , and a C-terminal μ-homology domain related to the medium subunit of AP2 ( Reider et al . , 2009; Stimpson et al . , 2009; Taylor et al . , 2011 ) . The region of FCHo that is required for activation of AP2 is not the F-BAR or the μ-homology domain but rather a conserved region found in the linker called the AP2 activator domain ( APA ) . This small domain from all metazoan orthologs of FCHo proteins , including SH3-containing GRB2-like protein 3-interacting protein 1 ( SGIP1 ) , binds AP2 and is sufficient to activate the AP2 complex in vivo in the absence of the endogenous FCHo protein . We propose that the FCHo/SGIP class of proteins evolved to promote endocytosis by binding to , and stabilizing the open conformation of AP2 . Mutations in the AP2 complex alpha and mu subunits in C . elegans ( encoded by the apa-2 and apm-2 genes ) result in animals with pleiotropic phenotypes including reduced body length ( Dpy ) , egg-laying defects ( Egl ) and uncoordinated locomotion ( Unc ) . In addition , they exhibit a unique ‘jowls’ phenotype , in which the mutants exhibit bulges in the cuticle on either side of the head ( Gu et al . , 2013 ) . Deletion of the sigma subunit ( aps-2 ) produces a similar ‘jowls’ phenotype ( Figure 1A and Figure 1—figure supplement 1B ) , while the beta subunit is shared by both AP1 and AP2 in C . elegans and mutations in apb-1 are lethal . We screened for mutants with the jowls phenotype and identified multiple mutations in three genes coding for alpha adaptin , mu2 adaptin and the nematode homolog of FCHo ( Figure 1—figure supplement 2 ) . We generated a deletion allele fcho-1 ( ox477 ) by transposon excision ( Figure 1—figure supplement 1A ) ; all six mutant alleles of fcho-1 produced defects strikingly similar to mutants lacking AP2 subunits , including the ‘jowls’ phenotype ( Figure 1A and Figure 1—figure supplement 1B ) , suggesting that AP2 function is compromised in the absence of FCHo . 10 . 7554/eLife . 03648 . 003Figure 1 . Loss of FCHo compromises AP2 activity . ( A ) Animals cropped to highlight jowls ( red arrrowheads , anterior up ) shared by fcho-1 and AP2 subunit mutants ( apa-2 , apm-2 , aps-2 ) . ( B ) Left , representative confocal micrographs of coelomocytes in worms expressing GFP-tagged alpha subunit . Images represent maximum projections of Z-slices through ∼1/2 of a coelomocyte . Numbers indicate the coefficient of variance of pixel intensities across coelomocytes ( excluding the cell periphery ) . p < 0 . 01 unpaired , two-tailed t-test . Right , normalized histograms of pixel intensities ( logarithmic scale ) . Arrow indicates higher intensity pixels that are missing in fcho-1 mutants . ( C ) Time-lapse montages of FRAP experiments on coelomocytes expressing alpha:GFP in adult hermaphrodites . The outlined membrane region was photobleached at time = 0 . ( D ) FRAP assay . Average recovery curves and time constants of fluorescence after photobleaching . p < 0 . 01 unpaired , two-tailed t-test on data from 12 fcho ( + ) coelomocytes and 14 fcho ( − ) coelomocytes . ( E ) Cargo assay . Micrographs of intestinal cells ( anterior left ) expressing a GFP-tagged transmembrane cargo internalized by AP2 . The cargo is a truncated CD4 transmembrane construct with a YxxΦ cargo recognition motif ( Figure 1—figure supplement 1C ) . The average pixel intensity along an intestinal basal-lateral membrane in fcho ( + ) animals ( n = 11 ) is 972 ± 85 arbitrary units ( au ) and 5610 ± 416 au in fcho ( − ) mutants ( n = 12 ) . p < 0 . 01 unpaired , two-tailed t-test . Data in ( B ) , ( D ) and ( E ) represent the mean ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 00310 . 7554/eLife . 03648 . 004Figure 1—figure supplement 1 . FCHO-1 and AP2 regulate the same pathways . ( A ) Diagram of the fcho-1 locus in C . elegans indicating four point mutations and two deletions associated with a loss-of-function phenotype . The targeted deletion ( ox477 ) was generated by mobilizing the Mos1 transposon and repairing the broken chromosome with a recombinant template that replaces the first eight exons with a positive selection ( unc-119 rescue ) . ox477 was used exclusively throughout this study as fcho ( − ) . ( B ) Images of animals cropped in Figure 1A . fcho ( − ) and AP2 subunit ( − ) animals are dumpy and egg-laying defective . Red arrowheads point to jowls . ( C ) Diagram of artificial GFP-CD4 AP2 cargo . GFP was flanked by two 12 amino acid flexible linkers and inserted between a secretion signal peptide from C . elegans PAT-3 and a modified human CD4 truncated to include two immunoglobulin domains , the transmembrane domain , and eight amino acids from the cytoplasmic domain ( Feinberg et al . , 2008 ) . The four amino acid YxxΦ motif from the C-terminus of the C . elegans lysosome-associated membrane glycoprotein , LMP-1 , was appended . ( D ) Cargo assay ( amount of GFP-tagged cargo on intestinal cell membrane ) . *p < 0 . 01 , unpaired , two-tailed t-test compared to WT , n ≥ 9 . ( E ) Brood size assay . Number of fertilized embryos produced by individual hermaphrodites of the indicated genotype . p < 0 . 05 , p < 0 . 01 , unpaired , two-tailed t-test compared to WT , n ≥ 8 . Values for WT and apa-2 samples were previously published ( Gu et al . , 2013 ) . ( F ) Starvation assay . Days required for a worm population to expand and consume the bacterial food . p < 0 . 01 , unpaired , two-tailed t-test compared to WT , n = 12 . Data in ( D ) , ( E ) and ( F ) represent the mean ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 00410 . 7554/eLife . 03648 . 005Figure 1—figure supplement 2 . Recessive alleles isolated from genetic screen for ‘jowls’ phenotype * independently identified ‘jowls’ mutant . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 005 In C . elegans , the FCHO-1 protein is localized to the plasma membrane and binds to AP2 in a complex with Eps15 and intersectin ( Mayers et al . , 2013 ) . To determine if FCHo is required to recruit AP2 to the plasma membrane we examined fluorescently-tagged alpha adaptin . In the wild type , AP2 is found in concentrated patches on the plasma membrane ( Figure 1B ) . In fcho-1 mutants , AP2 is associated with the plasma membrane ( Figure 1C ) , but does not form clusters , consistent with previous reports ( Henne et al . , 2010; Cocucci et al . , 2012; Mayers et al . , 2013 ) . To measure the kinetics of membrane association we performed in vivo Fluorescence Recovery After Photobleaching ( FRAP ) on coelomocytes . Coelomocytes are scavenger cells which exhibit high levels of endocytosis ( Sato et al . , 2014 ) . The fluorescence signal recovered approximately three times faster after bleaching in the absence of FCHo ( Figure 1C , D ) . Thus , FCHo stabilizes patches of AP2 on the membrane and limits its mobility , consistent with previous studies ( Henne et al . , 2010; Cocucci et al . , 2012 ) . To determine whether clathrin-mediated endocytosis is compromised in fcho-1 mutants , we assayed endocytosis of a fluorescently tagged transmembrane protein . This molecule is comprised of a GFP-tagged CD4 protein with a tyrosine cargo recognition motif ( Figure1—figure supplement 1C ) . Tyrosine motifs comprised of YxxΦ , where x is any amino acid , and Φ is a large hydrophobic residue , bind the mu2 subunit of AP2 and are required for AP2-mediated internalization ( Ohno et al . , 1995; Owen and Evans , 1998 ) . In wild-type worms , very little CD4-GFP is expressed on the surface of intestinal cells ( Figure 1E ) . However , this cargo accumulates on the cell surface in mutants lacking AP2 subunits and in fcho-1 mutants , suggesting that AP2-dependent endocytosis is defective in fcho-1 mutants ( Figure 1—figure supplement 1D ) . In addition the phenotype is not enhanced in double mutants indicating that FCHO-1 acts in the same pathway as AP2 . To identify components downstream of FCHo , we performed a genetic screen for mutations that suppress a null mutation in fcho-1 . To increase the probability of getting missense mutations we used the mutagen N-ethyl-N-nitrosourea ( ENU ) , which can generate transversions and can therefore swap charges , or hydrophilic and hydrophobic amino acids . In addition , we designed a multigenerational screen to select for subtle improvements in fitness . Wild-type animals grow rapidly and starve a culture plate in 5 days , whereas fcho-1 mutants exhibit reduced fecundity ( Figure1—figure supplement 1E ) and require twice as long to consume the same amount of food ( Figure 1—figure supplement 1F ) . We selected for suppressors that rapidly starved plates , and identified 71 dominant mutations that confer increased fitness to fcho-1 mutants and suppressed the jowls phenotype . All of these suppressed strains contained second site missense mutations in one of the four subunits of AP2 ( Figure 2—figure supplement 2 ) and none exhibited loss-of-function phenotypes for these adaptin genes ( Figure 2—figure supplement 1A–C ) . These mutations all occur at conserved amino acids , and cluster at sites likely to stabilize the closed ( inactive ) conformation when placed on the crystal structures of AP2 ( Figure 2A–C ) ( Collins et al . , 2002; Kelly et al . , 2008; Jackson et al . , 2010 ) . These mutations can be classified into four groups: ( 1 ) residues that lie in the bowl-like interface between the mu2 subunit and the other three subunits , ( 2 ) residues that stabilize the insertion of the N-terminus of the beta subunit into the cargo binding motif of sigma , ( 3 ) residues in the alpha subunit that are found in the helical solenoid that lies across the top of the complex , and ( 4 ) the phosphorylation site on the mu2 subunit . It is likely that these mutations destabilize the closed conformation of AP2 , suggesting that the open conformation of AP2 may bypass the requirement for FCHo . In other words , these mutations would promote an open conformation of AP2 , suggesting that AP2 may dwell in the closed state in the absence of FCHo . 10 . 7554/eLife . 03648 . 006Figure 2 . Mutations in AP2 closed conformation interfaces suppress fcho ( − ) . Predicted location of the mutated worm residues within the inactive ( PBD ID: 2VGL ) and active ( PBD ID: 2XA7 ) crystal structures of the vertebrate AP2 core complex . Alpha is blue , beta is green , mu2 is pink , and sigma2 is cyan . The residue numbers are from the worm subunits and parentheses indicate the corresponding vertebrate residue . * designates mutations isolated twice , and † designates mutations isolated thrice . ‡ designates mutations that were combined to re-establish a salt bridge between beta and mu ( See Figure 5 ) . ( A ) Mutations at the contact interface between the mu domain and the other three subunits . These contacts are disrupted upon opening . To visualize the contact surface in the closed conformation , the mu domain has been flipped to the right . Small renderings ( left ) show the closed ( Collins et al . , 2002 ) and open conformations ( Jackson et al . , 2010 ) ; the plasma membrane would be below the complex in this view . The K411E mutation on the mu domain ( white residue ) was not isolated from the fcho-1 suppressor screen , but was engineered ( See Figure 5 ) . ( B ) Mutations in the latching mechanism formed by the N-terminus of beta and the di-leucine motif binding-pocket of sigma2 . ( C ) Mutations in alpha cluster along the hinge region that flexes during opening . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 00610 . 7554/eLife . 03648 . 007Figure 2—figure supplement 1 . Suppression of fcho-1 by missense mutations in individual AP2 subunits . For ( A–C ) , the AP2 mutation identified in each suppressed fcho-1 mutant strain is indicated . Only a subset of the mutations were assayed . See Figure 2—figure supplement 2 for complete list of suppressor mutations . All data represent the mean ± SEM . ( A ) Starvation assay ( days required for a worm population to expand and consume the bacterial food ) . The AP2; fcho-1 double mutants all exhibit faster starvation rates compared to fcho-1 mutants alone ( p < 0 . 01 , unpaired , two-tailed t-test n ≥ 9 ) . ( B ) Cargo assay . Fluorescence from GFP-tagged cargo on plasma membrane of intestinal cells . Left and right panels indicate experiments conducted on different days . n ≥ 5; p < 0 . 05 and p < 0 . 01 , unpaired , two-tailed t-test compared to fcho ( − ) alone; † data reported in Figure 1E caption . ( C ) FRAP assay . Average time constants for fluorescence recovery after photobleaching of GFP-tagged AP2 . n ≥ 3; p < 0 . 05 , unpaired , two-tailed t-test compared to fcho ( − ) alone . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 00710 . 7554/eLife . 03648 . 008Figure 2—figure supplement 2 . Dominant mutations in AP2 subunits identified in fcho-1 suppressor screen . ( A ) mu-‘cup’ contact mutations . * spontaneous fcho-1 suppressor . ( B ) sigma-beta contact mutations . ( C ) alpha backbone mutations . ( D ) phosphorylation site mutations . ( E ) other mutations near inter-subunit contacts . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 008 To determine if AP2 remains in the closed conformation in fcho-1 mutants we devised an in vivo protease assay . The mu2 subunit of AP2 becomes sensitive to trypsin when the complex is incorporated into clathrin coats ( Matsui and Kirchhausen , 1990; Aguilar et al . , 1997 ) . The protease-sensitive segment is contained within ∼15 residues that are not resolved in crystal structures ( Owen and Evans , 1998; Heldwein et al . , 2004 ) , but the boundaries of this segment are apposed to the internal face of the sigma subunit in the closed structure , and are exposed on the exterior of the complex in the open structure ( Figure 3A ) . Because this poorly conserved region tolerates the insertion of various tags ( Nesterov et al . , 1999; Jackson et al . , 2010 ) , we inserted a TEV cleavage site in this sequence , and replaced the endogenous apm-2 gene with the protease-sensitive version of mu2 tagged with HA ( Figure 3—figure supplement 1A ) . We used a temperature-inducible promoter to transiently drive expression of TEV protease in these transgenic worms . After heat-shock induction of the protease , the level of the full-length subunit declined and a smaller 25kd N-terminal fragment accumulated over a 8 hr period ( Figure 3B , C and Figure 3—figure supplement 1B , C ) The mu2 cleavage rate was slower in fcho-1 mutants even though the protease was induced to a similar level ( Figure 3B , C and Figure 3—figure supplement 1B-D ) . TEV-sensitivity was also demonstrated for a FLAG-tagged version of mu2 , which was used in the structure-function experiments described below ( Figure 3—figure supplement 1 ) . These data suggest that a larger fraction of AP2 is in the closed , protease-resistant state in fcho-1 mutants . 10 . 7554/eLife . 03648 . 009Figure 3 . FCHo promotes the protease-sensitive open conformation of AP2 in vivo . ( A ) A TEV protease site was inserted into a surface loop of the mu domain . The dashed line connects the boundaries of the unstructured region within two conformations of the AP2 complex . ( B ) Western blot of whole animal lysates expressing the HA-tagged mu2 subunit depicted in Figure 3—figure supplement 1A . The amount of full-length subunit ( top ) decreases following heatshock . Anti-histone blot is below . Each sample is comprised of 100 larval 4 stage animals . ( C ) Quantification of mu2 proteolysis . Intensity of anti-HA signal relative to histone , normalized to time 0 . p < 0 . 05 , unpaired , two-tailed t-test compared to fcho ( + ) values at same time point , n = 4 . Data represent the mean ± SEM . See Figure 3—figure supplement 1 for results using FLAG-tagged version of mu2 . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 00910 . 7554/eLife . 03648 . 010Figure 3—figure supplement 1 . Schematic of TEV Protease assay and results from FLAG-tagged version of mu2 . ( A ) Cartoon of modified mu2 including epitope tag , sigma-homology domain , mu domain and TEV protease site insertion . The protease site is predicted to become more accessible upon AP2 activation . ( B ) Western blots of whole worm lysates . Animals were heat-shocked for one hour to induce TEV protease expression . Full-length ( intact ) and proteolyzed ( cleaved ) mu2 subunits were detected using anti-FLAG ( top ) while protease expression was monitored with anti-TEV ( middle ) . Anti-histone blot ( bottom ) used for normalization . Each sample is comprised of 100 larval 4 stage animals . ( C ) Quantification of the proteolysis . Intensity of intact mu2 relative to histone , normalized to time 0 . Similar results were obtained using an HA-tagged mu2 subunit ( Figure 3B , C ) . ( D ) Protease levels over time . Intensity of TEV band relative to histone . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 010 We also examined the conformation of AP2 in the strains carrying the fcho-1 suppressors using the TEV protease assay ( Figure 4A , C ) . We tested two suppressors each for the alpha , beta and mu2 subunits , and each led to an increase in mu2 cleavage in the double mutants compared to fcho ( − ) alone ( Figure 4C ) . These data suggest that AP2 is in the closed conformation in the absence of FCHo and that single amino acid changes in the complex are sufficient to tip the equilibrium toward the active conformation . Indeed , the amount of rescue observed in the cargo assay ( Figure 4B ) was grossly correlated with protease sensitivity . However , none of these mutations fully restored cargo endocytosis , even though all of the suppressors rescued growth and morphology ( Figure 4A and not shown ) . Only the mutation that resulted in a profoundly protease–hypersensitive complex ( μE306K ) increased cargo internalization with high significance in fcho-1 mutants . These findings indicate that subtle conformational changes favoring active AP2 satisfied an organismal requirement for FCHO-1 without fully compensating for the endocytic defect of fcho-1 mutants . 10 . 7554/eLife . 03648 . 011Figure 4 . AP2 mutations restore the active conformation in fcho-1 mutants . Listed mutations correspond to the worm residues . ( A ) Starvation assay ( days required for a worm population to expand and consume the bacterial food ) . ( B ) Cargo assay ( amount of GFP-tagged cargo on intestinal cell membrane ) . For ( A ) and ( B ) , data represent the mean ± SEM for n ≥ 10 . Significance determined by unpaired , two-tailed t-test compared to fcho ( − ) , p < 0 . 05 and p < 0 . 01 . ( C ) in vivo TEV protease assay . Samples collected for Western blot analysis 8 hr after heatshock ( as in Figure 3B ) . Numbers indicate band intensity of full-length mu2 ( anti-HA , top ) relative to histone ( bottom ) , normalized to the fcho ( + ) sample . ( D ) Blot for phosphorylated threonine-160 ( T156 in vertebrates ) in the linker region of the mu2 subunit . Samples collected before heatshock . Numbers indicate band intensity of phosphorylated T160 ( top ) relative to histone ( bottom ) , normalized to fcho ( + ) sample . For ( C ) and ( D ) , each sample is comprised of 100 larval 4 stage animals . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 01110 . 7554/eLife . 03648 . 012Figure 4—figure supplement 1 . Activation of AP2 strengthens membrane association , enables cargo binding , and stabilizes the location of mu2 phosphorylation . ( A ) and ( B ) are adapted from ( Jackson et al . , 2010 ) . ( A ) Side views of the AP2 complex showing that the mu2 linker ( pink dashed line ) containing the phosphorylation site ( T160 ) transitions from a disordered state ( closed , left ) to ordered helix ( open , right ) upon activation . The conformational rearrangement is associated with enhanced membrane binding and exposure of a binding pocket for transmembrane cargo ( YxxΦ ) . ( B ) Location of threonine-160 ( 156 in vertebrates ) of mu2 linker in open conformation of AP2 . The residue is at the end of a helix ( pink ) that nestles into a groove in the beta subunit ( green ) . Mutations isolated as fcho-1 suppressors are indicated , as is the hydroxyl group ( red ) predicted to be phosphorylated . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 012 The vertebrate mu2 subunit is phosphorylated in a clathrin-dependent manner at threonine-156 by Adaptor-Associated Kinase ( AAK1 ) ( Pauloin and Thurieau , 1993; Conner and Schmid , 2002; Conner et al . , 2003; Jackson et al . , 2003 ) . The phosphorylated core complex binds cargo motifs and phosphoinositides with higher affinity ( Honing et al . , 2005 ) . In the fcho-1 suppressor screen we isolated multiple mutations in the equivalent residue ( T160 ) of the worm mu2 subunit , including a mutation to the phosphorylation-defective amino acid alanine ( Figure 4A , Figure 2—figure supplement 2D ) . Mutating this residue to the phosphomimetic residue glutamate also suppressed fcho-1 mutants ( Figure 4A ) . Note that cargo internalization was compromised in both the phosphorylation-defective and phosphomimetic mutants ( Figure 4B ) , which is consistent with previous reports ( Olusanya et al . , 2001; Semerdjieva et al . , 2008; Jackson et al . , 2010 ) . Our data suggest that the presence of a threonine at this position stabilizes the inactive state and that any change to this residue is likely to destabilize the inactive state of AP2 . To assay phosphorylation of the mu2 subunit , we used an antibody specific to phosphorylated T160 . We found that AP2 is phosphorylated in the wild-type , and is hypo-phosphorylated in the fcho-1 mutant ( Figure 4D ) . All of the suppressor mutations we tested increased phosphorylation relative to the fcho-1 mutants ( except of course the T160 mutations themselves ) . Increased phosphorylation was also associated with increased protease sensitivity of the mu2 subunit in the TEV assay ( Figure 4C ) . These data demonstrate that the open state is phosphorylated , and that FCHO-1 is not absolutely required for phosphorylation , but rather AP2 in the active state is sufficient to induce phosphorylation . Nevertheless , this threonine residue is not completely exposed in the crystal structure of the open conformation ( Figure 4—figure supplement 1 ) ( Jackson et al . , 2010 ) so it is unclear whether the side chain would be accessible to AAK1 in this state . The protease sensitivity of the suppressor mutations indicates that the closed structure determined by X-ray crystallography is an authentic structure in vivo , and that these mutations destabilize the closed state of AP2 . Nevertheless , it is possible that the mapping of these mutations onto the crystal structure is coincidental . To verify that the closed structure has in vivo significance we identified a mutation among our suppressors that would disrupt a salt bridge in the closed conformation , and used the crystal structure to predict a compensatory mutation that would restore the salt bridge . In the closed conformation β ( E361 ) forms a salt bridge to μ ( K411 ) ( Figure 2A; Figure 5A; Figure 5—figure supplement 1 ) . We therefore analyzed mutations in β ( E361K ) and μ ( K411E ) that break this salt bridge , and found that both suppressed the fcho-1 mutant phenotype ( Figure 5B ) . These mutations also increased protease sensitivity relative to the fcho-1 mutant , and increased phosphorylation of threonine-160 ( Figure 5D , E ) . Similar to previous results ( Figure 4 ) , only the mutation that produced an acutely open complex ( μK411E ) significantly rescued the cargo-recycling defect of fcho-1 mutants ( Figure 5C ) . We then constructed the double mutant containing both the βE361K and μK411E mutations which should restore the salt bridge . The two mutations together no longer suppressed the fcho-1 growth phenotype or cargo retrieval defect of the fcho-1 mutants , and reversed the protease sensitivity of the single mutants . Phosphorylation of T160 in the double mutant was reduced relative to the μK411E single mutant but was not fully restored to fcho ( − ) levels . These results confirm that the closed form as determined by crystallography predominates in the fcho-1 mutant and that destabilizing the closed form can bypass the requirement for FCHO-1 . 10 . 7554/eLife . 03648 . 013Figure 5 . Charge swaps activate and inactivate AP2 in vivo . ( A ) Predicted location of residues stabilizing an important inter-subunit salt bridge within the inactive ( PBD ID: 2VGL ) crystal structure of the vertebrate AP2 core complex . Alpha is blue , beta is green , and mu2 is pink . The residue numbers are from the worm subunits . See Figure 2A and Figure 5—figure supplement 1 for localization on interfaces . ( B ) Starvation assay ( days required for a worm population to expand and consume the bacterial food ) . AP2 mutations indicated above . ( C ) Cargo assay ( amount of GFP-tagged cargo on intestinal cell membrane ) . For ( B ) and ( C ) , data represent the mean ± SEM for n ≥ 10 . Significance determined by unpaired , two-tailed t-test , p < 0 . 01 . ( D ) in vivo TEV protease assay . Samples collected for western blot analysis ( anti-HA ) 8 hr after heatshock ( As in Figure 3B ) . Numbers indicate band intensity normalized to the fcho ( + ) sample . ( E ) Blot for phosphorylated threonine 160 . Samples collected before heatshock . Numbers indicate band intensity normalized to fcho ( + ) sample . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 01310 . 7554/eLife . 03648 . 014Figure 5—figure supplement 1 . An Inter-subunit salt bridge is broken in the active conformation of AP2 . Predicted location of the modified worm residues within the inactive ( PBD ID: 2VGL ) and active ( PBD ID: 2XA7 ) crystal structures of the vertebrate AP2 core complex . Alpha is blue , beta is green , mu2 is pink , and sigma is cyan . The residue numbers are from the worm subunits . The residues are hidden in both of these views . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 014 Which domain of FCHo activates AP2 ? FCHO-1 is composed of a membrane-binding F-BAR domain ( Henne et al . , 2007 ) , a linker region , and a C-terminal μ-homology domain related to the medium subunit of AP2 ( Reider et al . , 2009 ) ( Figure 6A ) . We generated single copy transgenic animals expressing proteins deleting each of these domains in the fcho-1 null background . We found that the N-terminal F-BAR domain is dispensable for rescue of all fcho-1 phenotypes ( Figure 6B and Figure 6—figure supplement 1A-D ) . Constructs lacking the C-terminal μ-homology domain ( μHD ) failed to rescue cargo endocytosis , but increased the growth rate , protease sensitivity and phosphorylation of the mutants ( Figure 6B ) . However , deletions that extend into the linker domain failed to rescue fcho-1 mutants . The linker domain was previously found to bind the AP2 complex using pulldown assays ( Umasankar et al . , 2012 ) , suggesting that the activation of AP2 by FCHO-1 observed here could be via a direct interaction . 10 . 7554/eLife . 03648 . 015Figure 6 . A Conserved region of FCHo proteins is necessary and sufficient to rescue fcho-1 mutants . ( A ) FCHo homologs showing conserved domains . Amino acid numbers indicated above . The AP2 Activator ( APA ) domain is aligned below . Amino acids colored by Clustal X scheme and shaded by conservation . Membrane Phospholipid-binding domain ( MP ) , μ-Homology Domain ( μHD ) . ( B ) Structure/function analysis of worm FCHO-1 . ( C ) Quantification of fcho-1 mutant rescue with APA domains from worm ( Ce ) , mouse ( Mm ) , and human ( Hs ) orthologs expressed as extrachromosomal arrays . See Figure 7—figure supplement 1A for results of the starvation assay when the APA domains are expressed from single-copy transgenes . For ( B ) and ( C ) , protease assay performed with FLAG-tagged mu2 subunit as in Figure 3—figure supplement 1B . Numbers indicate band intensity of full-length mu2 ( top ) relative to the histone control ( bottom ) and normalized to the fcho ( + ) sample . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 01510 . 7554/eLife . 03648 . 016Figure 6—figure supplement 1 . The APA domain of FCHO-1 Is sufficient to organize AP2 on the membrane . ΔBAR represents worm FCHO-1 ( amino acids 288–968 ) lacking the F-BAR domain expressed from a single-copy transgene while APA represents amino acids 454–565 overexpressed by an extrachromosomal ( See Figure 6 ) . ( A ) Distribution of the alpha-GFP pixel intensities in maximum Z-projections of coelomocyte halves as in Figure 1B ( † data reproduced from Figure 1B ) . Arrow indicates higher intensity pixels that are missing in fcho-1 mutants . ( B ) The percent coefficient of variance of the alpha-GFP pixel intensities in coelomocytes ( † data reproduced from Figure 1B ) . ( C ) FRAP assay . Fluorescence of membrane regions of coelomocytes expressing alpha-GFP photobleached at time = 0 . ( D ) Time constants for recovery of fluorescence after photobleaching . Data represent mean ± SEM of n = 9–15 coelomocytes for ( A ) and ( B ) , and n = 8–20 coelomocytes for ( C ) and ( D ) ; *p < 0 . 01 unpaired , two-tailed t-test compared to fcho ( − ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 016 The linker domain of FCHO-1 is poorly conserved in general , but there is a small region of ∼90 amino acids that is shared with other FCHo homologs and the vertebrate protein SGIP1 ( Figure 6A ) ( Reider et al . , 2009; Uezu et al . , 2011 ) . Expression of this short fragment alone was capable of rescuing fcho-1 mutants , including growth rate , endocytosis of cargo , and morphology ( Figure 6C , Figure 7—figure supplement 1A , and not shown ) . This fragment is also sufficient to immobilize AP2 on the membrane in the photobleaching assay and to cluster AP2 into presumptive endocytic pits ( Figure 6—figure supplement 1 ) . Moreover , the equivalent domains from mouse FCHo2 , mouse SGIP1 , and human FCHo1 also rescued fcho-1 mutants ( Figure 6C ) , though the fragment from mouse FCHo1 rescued poorly ( Figure 7—figure supplement 1A ) . These results suggest that this small 90 amino acid region , called the AP2 Activator motif ( APA ) , encompasses a large fraction of FCHO-1 function in vivo . To determine whether the APA domain binds AP2 , we expressed the worm and mammalian APA domains fused to a HaloTag in tissue culture cells ( HEK293 ) and pulled down the APA fragment using chloroalkane beads . Silver-stained gels ( Figure 7A ) and Western blot analysis ( Figure 7—figure supplement 1B ) suggested the presence of all four AP2 subunits in the pulldowns . To systematically identify the binding partners we performed a mass spectrometry analysis of the pulldowns using Multi-Dimensional Protein Identification Technology ( MudPIT ) . The majority of the peptide reads were from the bait itself ( Figure 7B ) , but the most abundant interacting partner for the APA domains from mouse FCHo2 and SGIP1 was the AP2 complex ( ∼10% of peptides , Figure 7B and Figure 7—figure supplement 2 ) . When the bait included the entire FCHo2 or SGIP1 proteins ( or FCHo2 without the BAR domain ) , AP2 was still enriched; but additional components known to bind the μ-homology domain , such as Eps15 , were also isolated ( Figure 7—figure supplement 1C ) . The interaction of the APA domain with AP2 likely occurs in vivo as well , since fluorescently tagged APA colocalizes with AP2 on the membrane in coelomocytes , and membrane association is lost in mutants lacking the mu2 subunit ( Figure 7—figure supplement 1D ) . 10 . 7554/eLife . 03648 . 017Figure 7 . The APA domain binds AP2 . In ( A ) and ( B ) APA domains from FCHo homologs were expressed as HaloTag fusions in HEK293T cells ( Ce , C . elegans; Mm , M . musculus ) . ( A ) Silver-stained gel of affinity-purified proteins following proteolytic cleavage from the HaloTag . Arrows indicate bands of presumed identity . ( B ) The top ten human proteins purified using two different APA baits , as detected by MudPIT mass spectrometry . Nonspecific proteins also found in the control were removed . Values represent the mean % distributed Normalized Spectral Abundance Factor ( dNSAF × 100 ) from three independent experiments . The values of all four AP2 subunits were totaled to determine the amount of complex in each sample . Multiple isoforms of alpha , beta , and phosphatidylinositol 5-phosphate 4-kinase type-2 ( PIP4K2 ) were summed . WD repeat-containing protein 48 ( WDR48 ) , epidermal growth factor receptor substrate 15 ( EPS15 ) , adaptin ear-binding coat-associated protein 2 ( NECAP2 ) , and serine/threonine-protein kinase PLK1 ( PLK1 ) . See Figure 7—figure supplement 2 for complete results . ( C ) APA pulldowns using bacterially expressed proteins . Purified HaloTag with ( HT + APA ) and without ( HT ) the APA domain from mouse SGIP1 were incubated with purified AP2 fragments followed by TEV protease cleavage to release the APA bait . Silver-stained gel of the eluted proteins . Note that the alpha/sigma and beta/mu hemicomplexes are soluble in our hands and that the AP2 appendage ( ear ) domains exhibit non-specific binding in this assay . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 01710 . 7554/eLife . 03648 . 018Figure 7—figure supplement 1 . The APA domain links FCHo proteins to the AP2 complex . ( A ) Starvation assay of fcho-1 mutant worms expressing APA domains as single-copy transgenes . Data represent mean ± SEM of n = 10 plates . p < 0 . 05 and p < 0 . 01 compared to fcho ( − ) worms , determined by unpaired , two-tailed t-test . ( B ) Western blot for AP2 subunits purified from HEK293T cells using FCHo1 linker regions and APA domains as bait . APA domain from FCHo2 included for comparison ( far right ) . Blots were incubated with antibodies for all four subunits and detected using secondary antibodies for mouse ( top ) and rabbit ( bottom ) . Weak binding of the mouse FCHo1 APA fragment to AP2 ( See also Figure 7—figure supplement 2 ) was associated with weak rescue of fcho-1 mutants in ( A ) . This result was not consistent with conservation of the APA domain ( Figure 6A ) and a previous report that the linker region of human FCHo1 binds AP2 ( Umasankar et al . , 2012 ) . Indeed , the linker regions from mouse , human and zebrafish FCHo1 homologs affinity-purified AP2 ( left ) , and the APA domain from human FCHo1 bound AP2 ( middle ) and rescued fcho-1 mutants better than the mouse fragment in ( A ) . Note the cloned human fragment of APA is slightly larger than the mouse FCHo1 fragment , and this may account for differences in binding and rescue . For ( A ) and ( B ) , C . elegans ( Ce ) , M . musculus ( Mm ) , H . sapiens ( Hs ) , and Danio rerio ( Dr ) . The APA domains are dark blue and the poorly conserved linker regions are gray . ( C ) Top ten human proteins purified using regions of FCHo proteins in addition to the APA domain as bait . ΔBAR lacks the N-terminal 278 amino acid F-BAR domain . Results from a single experiment reported as in Figure 7B . Epidermal growth factor receptor substrate 15 ( EPS15 ) , epidermal growth factor receptor substrate 15-like ( EPS15L ) , sorting nexin-9 ( SNX9 ) , intersectin-1 ( ITSN1 ) , and growth factor receptor-bound protein 2 ( GRB2 ) . See Figure 7—figure supplement 2 for complete results . ( D ) Localization of APA in coelomocytes . Flourescently-tagged alpha subunit and APA domain from mouse SGIP1 were imaged in animals with and without the mu2 subunit . Intensity of each signal along the indicated line is plotted . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 01810 . 7554/eLife . 03648 . 019Figure 7—figure supplement 2 . Top proteins detected by MudPIT analysis . The top 10 proteins ( in addition to bait ) detected by MudPIT in samples purified from HEK293 cells using mouse FCHo2 and SGIP1 proteins ( full-length and fragments ) as bait . The ranked frequency of detection for each non-bait ( H . sapiens ) protein is indicated ( Five leftmost columns ) . The amount of each protein present in the sample is reported as the percent distributed Normalized Spectral Abundance Factor ( %dNSAF ) along with the corresponding values from C . elegans FCHO-1 APA and control ( HaloTag alone ) samples ( 16 rightmost columns ) . Note that results from three separate experiments using the mouse APAs and negative control are reported . The wide middle column describes the Halo-tagged baits and associated human proteins . The subunits of the AP-2 complex ( yellow ) are detected in the top 10 proteins of most purifications . The AP2A2 and AP1B1 isoforms ( light yellow ) are less abundant ( detected with one peptide unique to these isoforms ) . M . musculus ( Mm ) , C . elegans ( Ce ) , AP2 activator domain ( APA ) , full-length ( FL ) , lacking the F-BAR domain ( ΔBAR ) , not-detected ( X ) . See ‘Materials and methods’ for more information . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 019 We demonstrated that the interaction between the APA domain and the AP2 core is direct using purified recombinant proteins in pulldown assays ( Figure 7C ) . The APA domain does not appear to bind the appendages of the large adaptins , nor to the mu domain alone . Rather , it bridges the complex since the APA bait binds both the alpha/sigma and beta/mu hemicomplexes . Together these data suggest that the APA domain in FCHo homologs from worms , mice and humans binds AP2 to destabilize the closed conformation and promote the active conformation . There is broad agreement that FCHo acts early and it acts to stabilize nascent clathrin-coated pits via AP2 . The AP2 complex associates with the plasma membrane with different lifetimes; some AP2 clusters are aborted rapidly ( between 5–16 s ) whereas others develop into fully committed pits ( 90 s ) ( Loerke et al . , 2009 ) . In the absence of FCHo , the lifetime of AP2 at the membrane in cultured cells is quite brief ( <10 s ) , whereas overexpression of FCHo stabilizes AP2 and promotes the growth of clathrin-coated pits with long lifespans ( >25 s ) ( Henne et al . , 2010; Cocucci et al . , 2012 ) . We observe a similar phenotype in fcho-1 mutants: although AP2 still associates with the plasma membrane , it does not form clusters . Moreover , the dwell time of AP2 on the membrane is shorter; in fcho-1 mutants worms , the lifespan of AP2 on the membrane is reduced from 35 to 10 s . These results are strikingly similar to the observations of Cocucci et al ( 2012 ) . Previously it was thought that FCHo stabilizes AP2 on the membrane indirectly by binding Eps15 and intersectin , and these proteins in turn bind AP2 and stabilize the formation of a clathrin-coated pit ( Henne et al . , 2010 ) . Our data suggest that FCHo acts directly on AP2: a 90 amino acid segment of the linker domain binds the AP2 complex in pulldowns from HEK293 cells . The linker region of human FCHo1 was previously shown to interact with AP2 ( Umasankar et al . , 2012 ) . This interaction was thought to be via the appendage domain of the alpha subunit of AP2 , however we find that the interior of the linker binds the core complex . This AP2 activator domain ( APA ) is conserved and this fragment from the nematode FCHO-1 protein or from the mammalian FCHo1 , FCHo2 and SGIP1 proteins can rescue fcho-1 mutant worms . It is curious to note that while the APA domain is conserved in metazoan orthologs of FCHO-1 , it is absent in the yeast ortholog Syp1p ( Reider et al . , 2009 ) . What is the function of the μ-homology and F-BAR domains in C . elegans ? Although the APA domain rescues fcho-1 mutants to grossly wild-type morphology and behavior , it does not fully rescue at a cellular level: endocytosis of cargo is not restored to wild-type levels by expression of the APA fragment . Full rescue is only observed when the rescuing constructs include both the APA domain and the μ-homology domain . We have confirmed that the μ-homology domains of FCHo proteins bind Eps15 and Eps15-like proteins in pulldowns from HEK293 cells by extending the bait proteins to include this domain ( Figure 7—figure supplement 1C and Figure 7—figure supplement 2 ) . It is therefore likely that binding of FCHo to Eps15 is required for endocytosis of cargo . It has been demonstrated that FCHo forms an independent complex with Eps15 and Intersectin and that this complex functions in the recruitment of cargo to clathrin-coated pits ( Mayers et al . , 2013 ) . The F-BAR domain binds membrane and is required to recruit FCHo to the cell surface in both yeast and tissue culture cells ( Reider et al . , 2009; Stimpson et al . , 2009; Henne et al . , 2010 ) . By contrast , neither the F-BAR domain of FCHO-1 or the membrane association domain of SGIP are required for rescue of fcho-1 mutants . The dispensable nature of the F-BAR domain conflicts with models in which this domain must bend the membrane for clathrin-coated pit formation ( Henne et al . , 2010 ) , and instead suggests that the most important feature of the F-BAR domain is its ability to localize the APA domain to the membrane . Apparently in C . elegans , the APA domain of FCHo can be recruited to the membrane via interactions with other proteins independent of the F-BAR domain . Nevertheless , the presence of a membrane-binding motif in all FCHo and SGIP proteins demonstrate that membrane interactions are important and conserved . Different crystal structures of AP2 suggest that the complex can adopt multiple conformations . AP2 can assume a closed and inert conformation in which membrane- and cargo-binding domains are inaccessible ( Collins et al . , 2002 ) , in the unlatched or open conformations AP2 can bind the plasma membrane and the recognition motifs of cargo ( Kelly et al . , 2008; Jackson et al . , 2010 ) . The gain-of-function mutations in AP2 that bypass the requirement for fcho-1 provide in vivo support for these conformational changes . These mutations can be sorted into three classes based on the regions affected: the latch , the bowl and the hinge . In the unlatched state , the N-terminus of the beta subunit disconnects from the alpha and sigma2 subunits and exposes the dileucine-motif binding pocket ( Kelly et al . , 2008 ) . Among the bypass suppressors of fcho-1 were seven residues at the contact interface of the latched state . In the open structure , the mu domain is expelled from the bowl formed by the other subunits , and about half of the suppressors ( 34/71 ) were found in contact residues between mu2 and the bowl . The alpha hinge domain flexes as the bowl collapses in the open state , and 19 mutations in the alpha hinge were identified . We isolated 7 other mutations in residues that reside near inter-subunit contacts in the closed conformation . These mutations are all consistent with a destabilization of the closed state , and are in fact in an open state as determined by the exposure of a TEV protease site in vivo . The activated AP2 mutations that result in the most open ( protease sensitive ) and phosphorylated AP2 complex fully rescue the morphological and growth defects of the fcho-1 deletion . Nevertheless , these mutations do not fully restore clearance of an artificial cargo from the surface of the intestine . It is likely that the activated AP2 mutations cannot recapitulate all of the normal functions of the AP2 complex , since it is known that FCHo has other functions beyond its actions on AP2 , for example via interactions with Eps15 or Disabled-2 ( Figure 7—figure supplement 2 and Figure 7—figure supplement 1C ) ( Reider et al . , 2009; Henne et al . , 2010; Uezu et al . , 2011; Mulkearns and Cooper , 2012; Umasankar et al . , 2012; Mayers et al . , 2013 ) . Nor do these mutants exhibit enhanced endocytosis or membrane association in an otherwise wild-type background ( Figure 8 ) . It is possible that compensatory mechanisms counteract the open state of these AP2 mutants . Alternatively , our endocytosis assay may be at its detection limit because fluorescence from the artificial cargo is close to background levels in the wild-type . 10 . 7554/eLife . 03648 . 020Figure 8 . fcho-1 bypass mutants do not exhibit hyperactive AP2 . Mutations were examined in an fcho ( + ) background . ( A ) Cargo assay . Amount of GFP-tagged cargo on intestinal cell membrane . ( B ) FRAP assay . Time constants for recovery of alpha-GFP fluorescence after photobleaching . ( C ) The percent coefficient of variance of alpha-GFP pixel intensities in coelomocytes . Data represent mean ± SEM of n = 9–13 intestinal cells for ( A ) and n = 9–14 coelomocytes for ( B ) and ( C ) ; p < 0 . 01 unpaired , two-tailed t-test compared to fcho ( + ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03648 . 020 The only mysterious suppressors are the mutations at the phosphorylation site on the mu2 subunit . The other suppressors led to an increase in phosphorylation of T160 , consistent with phosphorylation promoting the open state ( Fingerhut et al . , 2001; Olusanya et al . , 2001; Ricotta et al . , 2002 ) . However , mutation to a phosphodefective , as well as a phosphomimetic , amino acid caused AP2 to adopt the open state . These data are most consistent with dephospho-threonine at this position stabilizing the closed state . Phosphorylation then does not cause the open state but rather is a result of the open state . This conclusion is supported by the observation that clathrin assembly stimulates AAK1 to phosphorylate mu2 ( Conner et al . , 2003; Jackson et al . , 2003 ) . How then does FCHo promote the AP2 cycle ? The formation of a closed form of AP2 is probably required to unbind membranes from newly endocytosed vesicles and to scan the membrane for new sites of endocytosis . The coincidental presence of FCHo , cargo , and PIP2 can then stabilize the open state , and the conformational changes in the complex then nucleate recruitment of clathrin and other pit components ( Figure 4—figure supplement 1A ) ( Jackson et al . , 2010; Kelly et al . , 2014 ) . Worm strains were cultured and maintained using standard methods ( Brenner , 1974 ) . A complete list of strains and mutations used is in the Extended Strains List ( Supplementary file 1A ) . C . elegans late L4s were mutagenized for 4 hr at 22°C in 0 . 2 mM EMS . ∼50 , 000 haploid genomes were screened in the F2 and F3 generation to isolate animals exhibiting the jowls phenotype . Genomic DNA was prepared from the offspring of these animals for amplification and subsequent sequencing of AP2 subunits . Mu2 subunit ( apm-2 ) primer pairs were oGH408-9 , oGH411-452 , and oGH412-3 . Alpha subunit ( apa-2 ) primer pairs were oGH414-5 , oGH416-7 , and oGH418-9 . Sigma2 subunit ( aps-2 ) primer pair was oGH430-2 . Beta subunit primer pairs were oGH441-2 , oGH443-4 , oGH445-6 and oGH447-8 . To identify fcho-1 mutants , PCR products corresponding to the coding sequences of the gene were amplified and sequenced using primer pairs oGH420-1 , oGH423-50 , oGH424-5 , oGH428-9 , and oGH433-51 . Oligonucleotide sequences are listed in Supplementary file 1C . The targeted deletion allele of fcho-1 was generated by mobilizing a Mos1 transposon from the gene ( ttTi3855 ) and repairing the double strand break with a DNA template that replaces the first eight exons of fcho-1 with an unc-119 ( + ) transgene in an unc-119 ( ed3 ) mutant strain ( Frokjaer-Jensen et al . , 2010 ) . The repair template plasmid was generated by Three-Fragment Multisite Gateway ( Invitrogen , Carlsbad , CA ) . The proximal targeting arm ( 2 . 1 kb ) was amplified ( oligos oMT1-2 , Mengyao Tan , University of Utah , Salt Lake City , UT ) and cloned into the [2–3] donor . The distal arm ( 2 . 1 kb ) was amplified with oMT3-4 and cloned into the [4–1] donor . The targeting arm entry clones were assembled with a [1–2] entry containing unc-119 ( + ) ( pRL8 , Rachel Lofgren , University of Utah ) in the [4–3] destination using LR clonase ( Invitrogen ) . The resulting repair template was injected into ttTi3855 II; unc-119 ( ed3 ) III worms along with additional plasmids ( transposase and array markers ) as previously described in ( Frokjaer-Jensen et al . , 2010 ) The molecular identity of the dx34 allele was determined by whole genome sequencing ( Illumina , San Diego , CA ) ; dx34 deletes the 3ʹ end of fcho-1 , the downstream gene vig-1 and the 5ʹ end of jip-1 . Additional mutant alleles of fcho-1 ( ox500[Q634X] , ox504 [frameshift] , ox620[K872X] , ox619[W882X] ) were isolated in the jowls screen ( See above ) . Worm strains , plasmids and oligonucleotide sequences are listed in Supplementary file 1 . fcho-1 ( ox477 ) mutants were mutagenized for 4 hr at 22°C in 0 . 5 mM ENU . After washing with M9 buffer , ∼100 L4 to young adult animals were pipetted onto 10 cm Normal Growth Media ( NGM ) agar plates previously seeded with 1 ml of a dense bacterial culture ( OP50 ) . After starvation , ∼2 × 2 cm pieces of each plate were transferred to a fresh NGM agar plate with bacteria . This process was repeated 3–4 times to select for genotypes with greater fitness than the starting strain . ∼10 worms were selected from each plate exhibiting a faster rate of food consumption than sibling plates . Genomic DNA was prepared from the offspring of these animals for amplification and sequencing of AP2 subunits . Males of fcho-1 suppressor strains were generated by heatshock and crossed to fcho-1 mutant hermaphrodites to score dominance and sex chromosomal linkage of the suppressor mutations in the F1 offspring . ox618 was isolated as a spontaneous suppressor . Worm strains are listed in Supplementary file 1A . Worms were immobilized for fluorescence microscopy by placing them in a 1:1 mixture of a 1 μm polystyrene bead slurry ( Polysciences , Warrington , PA ) and 2× PBS pH 7 . 4 on 8–10% agarose pads ( Kim et al . , 2013 ) . Worms were allowed to equilibrate on the slide for 5 min before data were acquired . Data acquisition from each slide did not last longer than 20 min to ensure the health of the worms . Worms expressing alpha adaptin-GFP ( oxSi254 ) were imaged on an Ultraview VOX spinning disk confocal microscope ( Perkin Elmer , Waltham , MA ) with a 100× oil immersion objective ( Carl Zeiss , Jena , Germany ) . A z-stack of half of the coelomocyte was acquired at maximum speed ( 200 ms exposure per slice ) with 0 . 2 µm spacing . Fluorescence was excited with a 488 nm laser and was filtered through a 500–550 nm bandpass filter to an EMCCD ( C9100-23B , Hamamatsu Photonics , Hamamatsu , Japan ) . The images were then analyzed using a set of custom written plugins ( available at http://research . stowers . org/imagejplugins ) in ImageJ ( http://rsbweb . nih . gov/ij/ ) . We started by creating a maximum intensity projection of the data . Next , the user specified an ROI inside the coelomocyte that does not include the outermost membrane . The mean intensity and standard deviation of the ROI was then measured for each time point . The coefficient of variance ( %CV ) was then calculated and averaged for all time points and all worms per sample . Additionally , histograms of pixel intensities inside the coelomocytes were taken from the exact same ROI as the %CV measurements . The mode of a logarithmic histogram with a bin size of 1000 was used as the background intensity . The mode was then subtracted from the histogram . A value of 15 , 000 was then added back to the image and a new logarithmic histogram with bin size of 1000 was measured . This was necessary to avoid zero or negative values in the histogram . The bin width of the histogram was set to 1000 for all images , while the number of bins was allowed to vary . For each sample , all of the histograms were aligned to the mode and summed . Each point of the resulting histogram was then normalized to the integral of the histogram . FRAP data was recorded either on a Perkin Elmer Ultraview VOX spinning disk with a 63× 1 . 2 NA C-Apochromat water immersion objective or an LSM 780 confocal microscope with a 40× 1 . 1 NA C-Apochromat water immersion objective ( Carl Zeiss ) . On both microscopes , fluorescence was excited with a 488 nm laser and emission was collected in the 500–550 nm range . On the LSM 780 , emission was collected in photon counting mode range with a temporal resolution of 2 s and a pinhole close to 1 airy unit . Four images were acquired prior to bleaching of a manually selected ROI with maximum 488 nm laser power . On the spinning disk , emission was collected with an EMCCD camera ( Hamamatsu C9100-23B ) . Five images were acquired at maximum speed ( exposure 100–200ms ) to determine the average fluorescence intensity of the coelomocyte membrane . Bleaching was then achieved over a user specified ROI , using a 488 nm laser ( the duration of bleaching was less than five seconds ) . Recovery images were then captured for 3 min with a rate of 1 image per second . Data was analyzed using a custom written plugin ( available at http://research . stowers . org/imagejplugins ) for ImageJ ( http://rsbweb . nih . gov/ij/ ) . This macro first registered the image sequences to compensate for movement of the worm/coelomocyte . Then the user specified the bleach ROI . The mean intensity of the ROI was then plotted per time point . A fit to the data was achieved with a one component fluorescence recovery model . The tau ( inverse rate ) values from the fits were then averaged for all worms per sample . For average plots of fluorescence recovery , curves were normalized so that the minimum value was 0 and the maximum value was 1 prior to averaging . Worms expressing the synthetic fluorescent cargo ( GFP-CD4-YASV; oxSi484 ) were imaged on a LSM 780 confocal microscope ( Carl Zeiss ) . A single cross-sectional Z-plane through the intestine was recorded . All of the images for a data set were recorded in a single session using the same laser settings . The images were analyzed in ImageJ . A segmented line was drawn on the basal-lateral membrane connecting intestinal segments 2 and 3 , or rarely , 3 and 4 . The average intensity along the line was recorded . For each genotype , 10–12 L4 worms were singled to culture plates and transferred to a fresh plate every 12 hr . The transfers stopped when the worm burst ( due to an egg-laying defect such as in AP2 mutants ) or the worm started laying unfertilized oocytes ( such as wild-type ) . The fertilized embryos from each animal were counted to determine the brood size . If the worm was lost during the transfer , the data were discarded . NGM agar plates ( 60 mm ) were seeded with 0 . 45 ml of bacterial culture ( OP50 expanded overnight in 2xYT at 37°C without shaking ) . The bacterial lawns were grown at 22°C for 3 days . Three young adult hermaphrodites were placed on the bacteria and propagated at 22°C . Plates were examined daily until the worm population had consumed all of the bacteria and dispersed . Multiple ( usually 10 ) plates were scored for each genotype . All structural representations in this paper were prepared with the PyMOL molecular graphics system , version 1 . 5 . 0 . 4 ( Schrödinger , New York , NY; www . pymol . org ) . PyMOL visualization scripts are available at https://github . com/jorglab/Vu_AP2 . A mini-gene encoding the mu2 subunit of AP2 ( apm-2 ) was constructed using the Multisite Gateway System ( Invitrogen ) . The apm-2 cDNA was amplified by PCR using primers oGH634 and oGH635 and recombined with the [1–2] donor vector using BP clonase ( Invitrogen ) to generate the entry vector pGH442 . The latter half of the cDNA was replaced with two genomic fragments corresponding to the last seven exons that were amplified with primer pairs oGH618-9 and oGH620-1 . The PCR products were subsequently cloned into the apm-2 cDNA entry vector amplified with oGH616-7 using the Gibson assembly protocol ( Gibson et al . , 2009 ) to generate pGH443 . A TEV protease cleavage site ( ENLYFQGS ) was inserted after the codon encoding alanine-240 using primers oGH756-7 and the Gibson reaction to generate pGH444 . One version of the [1–2] apm-2 entry vector was appended at the amino terminus with an HA tag ( YPYDVPDYA ) followed by a flexible linker ( GTGGTGGSGGTG ) by sequential amplification using primers oGH753-699 and oGH737-699 followed by recombination with the [1–2] donor to generate pGH445 . A 3X FLAG tag ( DYKDHDGDYKDHDIDYKDDDDK ) was attached to a separate version of the [1–2] apm-2 entry vector using primers oGH814-5 followed by the Gibson reaction to generate pGH446 . 1 . 3 kb of the apm-2 promoter region was amplified using oGH785-6 and recombined with the [4–1] donor vector to generate pGH461 . The 3ʹ untranslated region ( UTR ) of apm-2 was amplified with oGH797-519 and cloned into the [2–3] donor via the BP recombination reaction to generate pGH462 . The entry clones were recombined with the [4–3] destination vector pCFJ606 ( Christian Frøkjær-Jensen , University of Utah ) using LR clonase ( Invitrogen ) to generate the complete apm-2 minigene in a MosSCI targeting vector ( Frokjaer-Jensen et al . , 2008 ) . The HA-tagged version is pGH447 and the Flag-tagged version is pGH448 . Mutations to the apm-2 coding sequence of HA-tagged [1–2] apm-2 minigene ( pGH445 ) were introduced by PCR with primer pairs containing the mutation followed by the Gibson reaction to re-close the plasmid . The primer pairs and resulting [1–2] entry clones are oGH937-8 ( pGH449 ) for E306K , oGH943-4 ( pGH450 ) for K411E , oGH947-8 ( pGH451 ) for R440S , oGH929-30 ( pGH452 ) for T160A , and oGH931-2 ( pGH453 ) for T160E . LR recombination with pGH461 , pGH462 , and pCFJ606 , generated the MosSCI targeting vectors pGH454 for E306K , pGH455 for K411E , pGH456 for R440S , pGH457 for T160A , and pGH458 for T160E . To generate an inducible TEV protease , a codon-optimized protease sequence containing two artificial introns was synthesized as two gBlocks ( IDT , Coralville , IA ) and assembled with the hsp-16 . 41 promoter and unc-54 3ʹUTR to generate pGH459 by digesting pWD141 ( M Wayne Davis , University of Utah ) with BstBI and EcoRV to generate a vector backbone for the Gibson reaction . The subsequent Phsp:TEV:unc-54UTR sequence was amplified ( oGH806-7 ) and inserted into pCFJ150 amplified with oGH751-2 using Gibson assembly to generate a MosSCI targeting vector called pGH460 . See Supplementary file 1 for oligonucleotide sequences , plasmids , and worm strains . The full-length fcho-1 cDNA ( 2 . 9 kb ) was amplified ( oGH323-4 ) and recombined with the [1–2] donor vector of the Multisite Gateway ( Invitrogen ) three-fragment system using BP clonase ( Invitrogen ) . A tagRFP containing three artificial introns ( Stefan Eimer , University of Freiburg , Freiburg im Breisgau , Germany ) , an engineered S158T mutation ( Rob Hobson , University of Utah ) and flanked by flexible linkers ( N-term: STSGGSGGTGGS; C-term: GGTGGTGGSGGTG ) was amplified ( oCF590-1 ) and inserted after the start codon of the fcho-1 cDNA using oGH350-352 to open the vector and the Gibson reaction to close it . An HA tag was inserted into the N-terminal linker of TagRFP by digestion with KpnI followed by ligation with two annealed oligos encoding the tag ( oGH372-3 ) . The resulting [1–2] entry clone encoding HA_TagRFP_wormFCHO-1 ( 1-968 ) was pGH389 . Deletions of fcho-1 coding sequence corresponding to amino acids 1-287 ( F-BAR ) , 1-535 ( F-BAR + APA ) , 688-968 ( μHD ) , and 287-968 ( APA + μHD ) were introduced to the [1–2] entry using PCR and Gibson assembly . The resulting plasmids were: pGH475 for FCHO-1 ( 288-968 ) , pGH388 for FCHO-1 ( 536-968 ) , pGH476 for FCHO-1 ( 1-687 ) , and pGH477 for FCHO-1 ( 1-286 ) . For APA expression , sequences corresponding to the same regions used as bait in tissue culture cells ( See previous section ) were amplified ( oGH793-4 for C . elegans FCHO-1 , oGH808-9 for Mus musculus FCHo2 , oGH810-1 for M . musculus FCHo1 , oGH812-3 for M . musculus SGIP1 , and oGH1035-6 for Homo sapiens FCHo1 ) and inserted after the C-terminal linker of TagRFP using vector primers oGH649 and oGH781 with Gibson assembly . The resulting [1–2] entry clones were: pGH478 for worm FCHO-1 ( 454-565 ) , pGH479 for mouse FCHo2 ( 306-394 ) , pGH480 for mouse FCHo1 ( 304-393 ) , pGH481 for mouse SGIP1 ( 97-184 ) , and pGH482 for human FCHo1 ( 305-402 ) . All [1–2] entry clones were recombined with a [4–1] entry containing the ubiquitous dpy-30 promoter , the unc-54 3ʹUTR in a [2–3] entry and one of two [4–3] destination vectors ( pCFJ201 or pCFJ212 ) using LR clonase ( Invitrogen ) to generate MosSCI targeting vectors ( Frokjaer-Jensen et al . , 2008 ) . The resulting plasmids were: pGH394 for FCHO-1 ( 1-968 ) , pGH483 for FCHO-1 ( 288-968 ) , pGH393 for FCHO-1 ( 536-968 ) , pGH484 for FCHO-1 ( 1-687 ) , pGH485 for FCHO-1 ( 1-286 ) pGH486 for FCHO-1 ( 454-565 ) , pGH487 for mouse FCHo2 ( 306-394 ) , pGH488 for mouse FCHo1 ( 304-393 ) , pGH489 for mouse SGIP1 ( 97-184 ) , and pGH490 for human FCHo1 ( 305-402 ) . See Supplementary file 1 for oligonucleotide sequences , plasmids , and worm strains . Heatshock was performed by sealing worm plates with Parafilm and submerging them in a 34°C circulating water bath for 1 hr . For each sample , 100 or 200 L4 stage animals were selected and placed in microfuge tubes containing M9 buffer +0 . 001% Triton X-100 . The worms were washed once with M9 buffer +0 . 001% Triton X-100 and collected by centrifugation ( 1000×g , 30 s ) and placed on ice . All but ∼10 μl of the buffer was removed and 10 μl of LDS Sample buffer ( 4× , Novex , Invitrogen ) with ∼100 mM fresh dithiothreitol was added . Samples were frozen in liquid N2 and stored at −80°C . Samples were then sonicated at 0°C for 6 min at 100% amplitude in a cup horn ( Branson ) and denatured at 99°C for 5 min . Entire lysates were loaded into NuPage 4–12% Bis-Tris Gels ( Novex ) for electrophoresis followed by transfer to nitrocellulose membranes using the iBlot system ( Novex ) . For anti-HA blots , membranes were blocked in Tris Buffered Saline with 0 . 1% Tween 20 ( TBST ) and 5% milk powder . Anti-HA-Peroxidase High Affinity ( 3F10 , Roche , Indianapolis , IN ) was diluted 1:200 in TBST with 1% milk powder . Peroxidase was detected with ChemiGlow ( Protein Simple , San Jose , CA ) and imaged on a G:BOX ( Syngene ) . For all other antigens , blocking and antibody incubations occurred in Odyssey Blocking Buffer ( LI-COR , Lincoln , NE ) . Primary antibodies and dilutions include mouse anti-FLAG ( 1:1000 , M2 , Sigma-Aldrich , St . Louis , MO ) , rabbit anti-APM1 ( phospho T156 , 1:1000 , Abcam 109397 , Cambridge , England ) , rabbit anti-histone H3 ( 1:100000 , Abcam 1791 ) , and rabbit-anti TEV protease ( 1:500 , Rockland Immunochemicals , Limerick , PA ) . Fluorescent secondary antibodies include goat anti-mouse IRDye 680LT ( 1:20 , 000 , LI-COR ) and goat anti-rabbit IRDye 800CW ( 1:15 , 000 , LI-COR ) . All washes were performed in TBST . Band intensities were quantified using ImageStudioLite ( LI-COR ) . For preparation of samples for MudPIT analysis , sequences corresponding to the APA domains of FCHo proteins were inserted following the HaloTag ( Promega , Madison , WI ) sequence in a modified version of pcDNA5/frt ( Banks et al . , 2014 ) . The plasmid was linearized with PacI/PmeI and assembled using the Gibson reaction with each APA domain amplified from cDNA . The corresponding amino acids , primers and plasmids were 454–565 of C . elegans FCHO-1 ( NM_061546 . 3; oGH828-9; pGH463 ) , 306-394 of M . musculus FCHo2 ( NM_172591 . 3; oGH830-1; pGH464 ) , 304-393 of M . musculus FCHo1 ( NM_028715 . 3; oGH832-3; pGH465 ) , and 97-184 of M . musculus SGIP1 ( AB262964 . 1; oGH834-5; pGH466 ) . To identify proteins interacting with additional regions of FCHo proteins , amino acids 1-809 ( oGH886-7; pGH467 ) and 263-809 ( oGH887-892; pGH468 ) of M . musculus FCHo2 and 1-854 ( oGH890-1; pGH469 ) of M . musculus SGIP1 were cloned . For western blot analysis of FCHo1 interactions , additional sequences encoding amino acids 267-609 of M . musculus FCHo1 ( oGH1019-20; pGH470 ) , 305-402 ( oGH1039-40; pGH471 ) and 267-609 ( oGH1021-2; pGH472 ) of H . sapiens FCHo1 ( NM_001161357 . 1 ) , and 295-390 ( oGH1041-2; pGH473 ) and 260-609 ( oGH1023-4; pGH474 ) of Danio rerio FCHo1 ( XM_005166937 . 1 ) were also cloned into the same mammalian expression vector . Plasmids and oligonucleotides are listed in Supplementary files 1B , C , respectively . 150 mm dishes of HEK293T cells ( ∼80% confluent , Tissue Culture Core , Stowers Institute for Medical Research , Kansas City , MO ) were transfected with 10 μg of plasmid using Lipofectamine 2000 ( Invitrogen ) . 24–36 hr later , cells were washed with PBS , scraped from the dishes , collected by centrifugation and frozen at −80°C . Cell pellets were lysed and bound to HaloLink Magnetic Beads ( Promega ) according to the manufacturer's instructions . After washing the beads , complexes were released by incubation ( 2–3 hr at 22°C while shaking ) with AcTEV protease ( 2 units in 100 μl , Invitrogen ) to digest the cleavage site between the HaloTag and bait proteins . 20 μl of samples destined for MudPIT analysis were separated by electrophoresis and visualized using the Silver Stain Plus Kit ( Bio-Rad , Hercules , CA ) . The remaining 80 μl were precipitated using trichloroacetic acid . For western blot analysis of FCHo1 interactors , 20 μl of purified complexes were electrophoresed , transferred , and blotted as described above using the following primary antibodies: mouse anti-alpha adaptin ( 610501; 1:2000; BD Biosciences , San Jose , CA ) , rabbit anti-AP2B1 ( 151961; 1:1000; Abcam ) , rabbit anti AP2M1 ( 75995; 1:1000; Abcam ) and rabbit anti-AP2S1 ( 128950; 1:10000; Abcam ) . TCA-precipitated proteins were urea-denatured , reduced , alkylated and digested with endoproteinase Lys-C ( Roche ) followed by modified trypsin ( Promega ) ( Washburn et al . , 2001; Florens and Washburn , 2006 ) . Peptide mixtures were loaded onto 250 µm fused silica microcapillary columns packed with strong cation exchange resin ( Luna , Phenomenex , Torrance , CA ) and 5-μm C18 reverse phase ( Aqua , Phenomenex ) , and then connected to a 100 µm fused silica microcapillary column packed with 5-μm C18 reverse phase ( Aqua , Phenomenex ) ( Florens and Washburn , 2006 ) . Loaded microcapillary columns were placed in-line with a Quaternary Agilent 1100 series HPLC pump and a LTQ linear ion trap mass spectrometer equipped with a nano-LC electrospray ionization source ( ThermoScientific , San Jose , CA ) . Fully automated 10-step MudPIT runs were carried out on the electrosprayed peptides , as described in ( Florens and Washburn , 2006 ) . Tandem mass ( MS/MS ) spectra were interpreted using SEQUEST ( Eng et al . , 1994 ) against a database consisting of 30 , 499 non-redundant human proteins ( NCBI , 2012-08-27 release ) , 160 usual contaminants ( human keratins , IgGs , and proteolytic enzymes ) , as well the mouse and C . elagans FCHo constructs and the mouse SGIP1 sequences . To estimate false discovery rates ( FDR ) s , the amino acid sequence of each non-redundant protein entry was randomized to generate a virtual library . This resulted in a total library of 61 , 327 non-redundant sequences against which the spectra were matched . Peptide/spectrum matches were sorted and selected using DTASelect ( Tabb et al . , 2002 ) with the following criteria set: Spectra/peptide matches were only retained if they had a DeltCn of at least 0 . 08 , and minimum XCorr of 1 . 8 for singly- , 2 . 0 for doubly- , and 3 . 0 for triply-charged spectra . In addition , peptides had to be fully tryptic and at least seven amino acids long . Combining all runs , proteins had to be detected by at least two such peptides , or one peptide with two spectra . Under these criteria the averaged FDRs at the protein and peptide levels were 0 . 24% ± 0 . 2 and 0 . 44% ± 0 . 3 , respectively . Peptide hits from multiple runs were compared using CONTRAST ( Tabb et al . , 2002 ) . To estimate relative protein levels , distributed Normalized Spectral Abundance Factors ( dNSAFs ) were calculated for each detected protein/protein group , as described in ( Zhang et al . , 2010 ) . The open source BioConductor package plgem in R was used to statistically compare the proteins detected in the FCHo and SGIP1 samples to negative controls ( Pavelka et al . , 2008 ) . Proteins were considered significantly enriched compared to the control datasets if their p-values for power law global error model signal-to-noise ( PLGEM-STN ) ratios were lower than 0 . 001 , and they were detected in at least 2 out of 3 replicate analyses of the FCHo and SGIP1 purifications . The top 10 proteins ( in addition to bait ) were ranked based on decreasing PLGEM-STN values for FCHo2 APA , decreasing STN and dNSAF values for SGIP1 APA and full length ( FL ) SGIP1 , and decreasing dNSAF values for FCHo2 FL and FCHo2 without the F-BAR domain ( ΔBAR ) . The His-tagged beta-mu hemicomplex expression vector is pGH424: Sequence corresponding to trunk domain of mouse AP2 beta 1 ( amino acids 1–591 , NM_001035854 ) was amplified from mouse brain cDNA ( Elena Gracheva , University of California , San Francisco , CA ) with primers oGH368 and oGH676; the fragment was inserted into the pETduet-1 vector amplified using oGH332 and oGH336 using the Gibson assembly reaction . This places the coding sequence downstream of the first T7 promoter . The C-terminus is tagged with a three amino acid linker ( GSS ) followed by a hexa histidine-tag ( His-tag ) . The coding sequence of mouse AP2 mu1 ( amino acids 1–435 , NM_009679 ) was amplified ( oGH370 + oGH371 ) and inserted downstream of the second T7 promoter ( oGH338 + oGH339 ) using the Gibson reaction . The His-tagged mu domain of AP2 mu1 expression construct is pGH441: This construct was generated by amplifying a portion of pGH424 ( oGH571 + oGH921 ) starting with the linker and mu domain of the mu2 protein , continuing around the plasmid backbone , and ending with the first T7 promoter . This PCR product removed the beta trunk along with the sigma-homology domain of mu while appending a His-tag to the N-terminus of the linker , and was circularized using the Gibson assembly reaction . The His-tagged beta appendage domain expression construct is pGH491: Sequence encoding the C-terminus of mouse AP2 beta 1 ( amino acids 592–951 ) was amplified using oGH1161-2 and recombined with the His-tag , T7 promoter and vector backbone portion of the His-tagged mu domain construct ( pGH441 amplified with pGH571 and oGH339 ) using Gibson assembly . The His-tagged alpha appendage expression construct is pGH492: cDNA corresponding to the linker + appendage domain ( amino acids 622–938 ) of mouse AP2 alpha 2 ( NM_007459 ) was amplified ( oGH1163-4 ) and cloned using the same strategy as the beta appendage expression construct ( pGH491 , above ) . The His-tagged HaloTag-APA expression vector is pGH493: The His-tag , T7 promoter and backbone regions of the mu domain construct ( pGH441 amplified with pGH853 and oGH1165 ) was recombined with PCR products corresponding to HaloTag ( amplified using oGH1166-7 ) and the APA domain of mouse SGIP1 ( oGH1169 + oGH861 ) using the Gibson reaction . The control construct to express the His-tagged HaloTag alone in bacteria is pGH494: The HaloTag coding sequence was amplified ( oGH1166 + oGH1171 ) and recombined with the His-tag , T7 promoter and backbone regions of the AP2 mu domain construct ( pGH441 amplified with oGH1170 + oGH1165 ) . The vector expressing the trunk domain of mouse AP2 alpha 2 with a C-terminal GST tag along with mouse AP2 sigma 2 ( Collins et al . , 2002 ) was a gift from Volker Haucke ( FMP , Berlin , Germany ) . Plasmids and oligonucleotides are listed in Supplementary files 1B , C , respectively . Each AP2 hemicomplex was expressed independently . Expression vectors were transformed into Rosetta ( DE3 ) pLysS cells ( EMD Millipore , Billerica , MA ) and grown overnight at 20°C in LB containing 200 μM IPTG , chloramphenicol ( 34 μg/ml ) , and ampicillin ( 100 μg/ml ) for the His-tagged proteins and kanamycin ( 25 μg/ml ) for the alpha/sigma hemicomplex . Cells were collected by centrifugation , washed with distilled water and re-pelleted prior to rapid freezing . Cell pellets were resuspended in lysis buffer ( 50 mM HEPES , 300 mM NaCl , 10 mM imidazole , pH 7 . 5 ) and incubated with 1 mg/ml lysozyme at 4°C prior to sonication and centrifugal clarification . To purify His-tagged proteins , lysates were incubated with Talon cobalt resin ( Clontech , Mountain View , CA ) , washed with lysis buffer containing 20 mM imidazole and eluted with lysis buffer containing 200 mM imidazole . To purify the alpha/sigma hemicomplex , lysate was incubated with glutathione agarose ( Pierce , Thermo Fisher Scientific , Rockford , IL ) and then washed with lysis buffer prior to eluting with lysis buffer containing 10 mM reduced glutathione . Purified proteins were dialyzed in 25 mM HEPES , 100 mM KCl , pH 7 . 5 and stored as frozen aliquots at −80°C . For the pulldown assay , 80 pmoles of purified HaloTag ± APA bait were diluted along with 40 pmoles of recombinant AP2 prey and 10 μl of magnetic HaloLink beads ( 20% slurry ) in 1x TBS containing 0 . 05% IGEPAL CA-630 ( 1 ml total volume for each pulldown ) and nutated overnight at 4°C . Beads were washed with 1× TBS +0 . 05% IGEPAL CA-630 and bound proteins were cleaved from the HaloTag by incubation with AcTEV protease ( 30 µl at 50 units/ml for 60 min at 22°C ) . 50% of this elution was separated by SDS-PAGE and silver-stained along with 25% of the prey input for comparison . AP2 hemicomplexes are soluble under these conditions . To purify AP2 for crystallographic studies it was found that hemicomplexes were insoluble ( Collins et al . , 2002 ) . Note that the protein concentrations used in our pulldown assay ( ∼10 μg/ml ) are roughly 1000-fold lower than those indicated for the crystallization ( ∼10 mg/ml ) .	All cells are enveloped by a plasma membrane . To interact with the outside world , cells constantly recycle the molecules found in , or on , this barrier . This is accomplished by drawing in small patches of the membrane containing these ‘cargo’ molecules via a process called endocytosis . The predominant method of endocytosis involves coating the tiny membrane pouches with a scaffold-like structure made of clathrin molecules . However , clathrin requires a set of four proteins ( known as the adaptor protein-2 complex ) to connect the membrane and cargo to the clathrin cage . Previous studies have suggested that the adaptor protein-2 complex may exist in at least two forms: one in which the binding sites for membrane and cargo are hidden , and another where these sites are exposed . These structures were proposed to represent inactive ( closed ) and active ( open ) forms of the complex , respectively . It has been unclear whether reorganization of the adaptor complex is a necessary step in endocytosis or how it might be stimulated . Now Hollopeter et al . show that worms that lack a membrane-associated protein called FCHo are unable to cluster the adaptor protein-2 complex on their cell membranes , and their cells have difficulties taking up cargo . When the FCHo protein was missing , the adaptor protein-2 complex remained in its closed shape , suggesting that the FCHo protein is needed to switch the complex from its closed to its open structure . When Hollopeter et al . looked for worms with genetic changes that can overcome the defects caused by a lack of FCHo , they identified worms with various mutations in the genes for the adaptor protein-2 complex . These mutations altered the proteins in the complex at positions that are predicted to rearrange dramatically when the complex is activated; Hollopeter et al . confirmed that such rearrangements do occur in living worms . Furthermore , Hollopeter et al . found that giving mutant worms , which lacked the fcho gene , a small fragment of the FCHo protein causes the adaptor protein-2 complex to adopt its open structure . Similar fragments from other related membrane-associated proteins had the same effect , and these fragments all ‘cured’ the worms' endocytosis problems . The FCHo fragment directly binds the adaptor complex and Hollopeter et al . propose that FCHo proteins function to activate this complex at the sites where endocytosis occurs .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology" ]	2014	The membrane-associated proteins FCHo and SGIP are allosteric activators of the AP2 clathrin adaptor complex
The diversity of cutaneous sensory afferents has been studied by many investigators using behavioral , physiologic , molecular , and genetic approaches . Largely missing , thus far , is an analysis of the complete morphologies of individual afferent arbors . Here we present a survey of cutaneous sensory arbor morphologies in hairy skin of the mouse using genetically-directed sparse labeling with a sensory neuron-specific alkaline phosphatase reporter . Quantitative analyses of 719 arbors , among which 77 were fully reconstructed , reveal 10 morphologically distinct types . Among the two types with the largest arbors , one contacts ∼200 hair follicles with circumferential endings and a second is characterized by a densely ramifying arbor with one to several thousand branches and a total axon length between one-half and one meter . These observations constrain models of receptive field size and structure among cutaneous sensory neurons , and they raise intriguing questions regarding the cellular and developmental mechanisms responsible for this morphological diversity . The skin is the largest organ that senses external stimuli , and the responses of sensory afferents in the skin encompass a diversity of modalities richer than those any other sensory organ . Among these modalities are thermosensation , pain , itch , and many distinct types of mechanosensation . A further level of complexity arises from the distinctive anatomic distributions of the different types of cutaneous sensory afferents . For example , hairy skin has specialized mechanosensors that contact hair follicles and their associated Merkel cell clusters , whereas glabrous ( i . e . palmar ) skin is innervated by specialized mechanosensors , such as those that contact Meissner corpuscles , that are absent from hairy skin . Additionally , the spatial density of sensory afferents and the spatial scale of sensory integration vary widely over the body surface . For example , two-point discrimination for mechanical stimuli has high resolution on the fingertips and low resolution on the torso ( Iggo , 1982 ) , and sensory thresholds for hot and cold stimuli vary in a fine-grained mosaic over the body surface ( Norrsell et al . , 1999 ) . Early physiologic insights into cutaneous afferent diversity came from the discovery that different types of receptors are associated with distinct conduction velocities and pharmacologic thresholds for conduction block ( Erlanger et al . , 1924; Gasser and Erlanger , 1927 , 1929 ) . Recordings from peripheral nerves also revealed differences in stimulus threshold and in adaptation to sustained stimuli , especially among different subtypes of mechanoreceptors ( Iggo , 1982 ) . Structural insights have come from light and electron microscopic studies that have defined numerous specialized nerve endings and their associated structures and correlated them with different cutaneous sensory modalities . These structures include free endings , Pacinian and Meissner corpuscles , Raffini endings , Kraus end bulbs , Merkel cell clusters , and lanceolate and circumferential endings associated with hair follicles ( Iggo and Andres , 1982 ) . Over the past 15 years the diversity of cutaneous receptor subtypes has been placed on a foundation that is increasingly defined in molecular terms . The identification of receptor proteins—such as TRP channels for pain and temperature , Mrg receptors for itch , and various receptors for inflammatory compounds—and their localization to subsets of DRG neurons has provided the most direct molecular classification of sensory neurons ( Basbaum et al . , 2009; Liu et al . , 2009 ) . Cutaneous receptor subtypes also express distinctive sets of transcription factors , neuropeptides , neurotrophin receptors , and miscellaneous cytosolic proteins , which can be used in combination to provide an empirical classification ( Fundin et al . , 1997; Marmigère and Ernfors , 2007; Reed-Geaghan and Maricich , 2011 ) . Largely missing thus far from the field of cutaneous sensory biology is an analysis of the full morphologies of sensory afferents . While the cross-sectional microanatomy of cutaneous sensory afferents has been intensively studied , their full arbors have been technically difficult to visualize for two reasons . First , the analysis of complete arbor morphologies requires either single cell tracer filling or genetically-directed sparse expression of a reporter , followed by imaging of full thickness skin to visualize individual labeled arbors against a background in which the vast majority of arbors are unlabeled . With respect to tracer filling methods , since cutaneous arbors generally reside at a substantial distance from their cell bodies in the DRG , tracer diffusion from the cell body is inefficient . Second , because skin is a relatively refractile tissue , immunostaining and imaging of full-thickness skin preparations has not been widely practiced . This second challenge has largely been solved with the recent development of protocols that give high signal-to-noise ratio immunostaining and organic solvents that render intact skin optically clear ( Li et al . , 2011 ) . In contrast to the challenges associated with visualizing sensory projections in the skin , a number of investigators have analyzed the projections of individual dorsal root ganglion ( DRG ) neurons in the dorsal laminae of the spinal cord and have correlated these projection patterns with molecular and physiologic properties ( e . g . Woodbury et al . , 2001 ) . Defining the full morphologies of cutaneous sensory afferents is of interest for many reasons . Most obviously , it would inform our understanding of receptive field size and structure . It could also reveal patterns of target innervation and territoriality that could be used to classify sensory neurons and complement classifications based on physiologic and molecular criteria ( Marmigère and Ernfors , 2007; Reed-Geaghan and Maricich , 2011 ) . In other systems , such as the vertebrate retina , the integration of morphologic and physiologic information has provided deep insights into structure , function , and evolution ( Rodieck and Brening , 1983; Masland , 2001 , 2011 ) . In the present study , we have used genetically-directed sparse labeling in adult mouse back skin to survey many hundreds of cutaneous afferent arbors and to generate a collection of complete arbor morphologies . Quantitative parametric analysis reveals 10 discrete classes within this collection , several of which have arbors of extraordinary length and complexity . Brn3a , a POU-domain transcription factor , is expressed in the vast majority of DRG and trigeminal ganglion neurons beginning at ∼E11 ( Fedtsova and Turner , 1995 ) . Neurons expressing Brn3a can be visualized using a Brn3a conditional knockout allele ( Brn3aCKOAP ) that has one loxP site in the 5′ untranslated region ( UTR ) , a second loxP site 3′ of the 3′ UTR , and an alkaline phosphatase ( AP ) reporter distal to the second loxP site ( Badea et al . , 2009 ) . Cre-mediated excision of the Brn3a coding region and 3′ UTR activates expression of AP by placing it under the control of the Brn3a promoter . In the present study , sparse Cre-mediated recombination was obtained using a Neurofilament Light Chain ( NFL ) -IRES-CreER knock-in allele and low dose Tamoxifen ( Rotolo et al . , 2008; see ‘Materials and methods’ ) . NFL-IRES-CreER was chosen as the source of Cre-recombinase because it is widely expressed in projection neurons , it is not expressed in non-neural tissue , and it produces a relatively low level of CreER . By contrast , the combination of Brn3aCKOAP with a ubiquitously expressed CreER ( ROSA26-CreER; Badea et al . , 2003 ) activates AP expression in muscle and connective tissue as well as in DRG neurons , thereby compromising the clarity with which cutaneous sensory afferents can be imaged . Brn3a+/− and Brn3a+/+ mice appear to be indistinguishable in appearance and overall health and individual Brn3a+/− DRG neuronal cell bodies do not differ in appearance or number relative to Brn3a+/+ controls ( Xiang et al . , 1996 ) . Importantly , Trieu et al . ( 2003 ) and Eng et al . ( 2004 ) have shown that , in Brn3a+/− DRG neurons , a Brn3a-dependent negative feedback regulatory system leads to nearly wild type levels of Brn3a transcripts and other Brn3a-regulated transcripts . Thus , it seems unlikely that Brn3a+/− DRG neurons differ functionally or morphologically from their wild type counterparts . The present survey of afferent arbor morphologies was conducted with back skin because this territory includes a wide variety of cutaneous sensory types and its large area facilitates the identification of well-isolated AP-stained arbors . In mature pigmented mice , melanin in skin and hair confounds full-thickness skin imaging . This difficulty was circumvented by harvesting the skin at P21 , the midpoint of the ∼2-day telogen phase of the highly synchronous first hair cycle ( Müller-Röver et al . , 2001; Alonso and Fuchs , 2006 ) . During this time window , skin pigmentation is temporarily lost ( Figure 1A ) . Titration of the Tamoxifen dose at gestational day ( GD ) 17 showed that for the Brn3aCKOAP/+; NFL-IRES-CreER/+ genotype , 200 μg , 500 μg and 1 mg of intraperitoneal ( IP ) Tamoxifen produced ∼5 , ∼50 and >500 labeled and well isolated arbors per back skin at P21 ( Figure 1B , C , and Figure 1—figure supplement 1 ) . At the highest Tamoxifen dose ( 1 mg ) , individual sensory arbors cannot be resolved ( Figure 1—figure supplement 2 ) . 10 . 7554/eLife . 00181 . 003Figure 1 . Genetically-directed sparse labeling of cutaneous sensory afferents . ( A ) Shaved back skin at P16 , P21 , and P27 shows the nadir of pigmentation at P21 . ( B ) Isolated AP+ arbors that were included in the arbor area survey are represented by convex pink polygons on a P21 Brn3aCKOAP/+; NFL-IRES-CreER/+ back skin . A , anterior; P , posterior . ( C ) Plot showing the fraction of total skin area occupied by isolated AP+ arbors vs the number of AP+ arbors included in the arbor area survey . ( D ) Number of cutaneous arbors of each type analyzed for arbor area ( upper pie chart ) and traced ( lower pie chart ) . No MCA arbors were traced . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 00310 . 7554/eLife . 00181 . 004Figure 1—figure supplement 1 . Eight skins showing well-separated cutaneous sensory afferent territories at P21 . Isolated AP+ arbors that were included in the arbor area survey are represented by convex pink polygons on a P21 Brn3aCKOAP/+; NFL-IRES-CreER/+ back skin . A , anterior; P , posterior . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 00410 . 7554/eLife . 00181 . 005Figure 1—figure supplement 2 . P21 skin with a high density of AP+ cutaneous sensory arbors . Back skin from a mouse that was exposed to Tamoxifen in utero following injection of the mother with 1 mg at GD17 . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 005 A total of 101 P21 Brn3aCKOAP/+; NFL-IRES-CreER/+ back skins were analyzed following maternal exposure to 100 , 200 , 250 , or 500 μg of Tamoxifen at GD17 . With an average surface area of 15 . 53 cm2 per skin , this corresponds to a total of 1569 cm2 examined for AP stained sensory arbors . The fraction of back skin surface area occupied by well-separated AP+ sensory arbors varied from ∼0 . 2% to ∼15% . A total of 719 arbors that appeared by visual inspection to be free from overlap were characterized further ( Figure 1C , D ) . To analyze arbor morphologies in detail , we traced 77 full arbors for nine different arbor types ( Figure 1D ) using Neuromantic , a publicly available reconstruction program . The set of labeled sensory neurons is predicted to correspond to the intersection of the expression domains of the Brn3a and NFL genes . Although Brn3aCKOAP is expressed in nearly all DRG neurons ( Badea et al . , 2012 ) , expression of the NFL-IRES-CreER knock-in allele presumably mirrors the abundance of the neurofilament light chain and is therefore enriched in sensory neurons with large axon diameters . Thus the current survey likely covers only a fraction of the morphologic diversity of cutaneous sensory arbors . We also note that the abundances of different arbor types within this set is not related in any simple way to the actual abundances of these types within the skin because ( 1 ) variations in the level of NFL-IRES-CreER expression in different neuronal types will bias their representation , ( 2 ) larger arbors tend to be under-represented because the probability of arbor overlap increases with size , and ( 3 ) the representation of the most abundant arbor classes was limited by the investigators to a number sufficient for statistically robust analysis . In the paragraphs that follow we describe the identification and characterization of 10 morphologically distinct cutaneous arbor types . These types were initially distinguished by visual inspection . Subsequent statistical analyses of their morphologic properties ( arbor area , axon length , number of axon branches , type of sensory ending specialization , number of hair follicles contacted , and lamination depth within the skin ) have confirmed those divisions . At present , the correspondence between several of these morphologic types and previously characterized sensory neuron types cannot be made with certainty , and for this reason we will refer to the 10 types using names that are based strictly on their morphologies . These are ( in alphabetical order ) : bushy ending ( BE ) , high density follicle-associated lanceolate ending ( HD-FALE ) , isolated follicle-associated circumferential ending ( I-FACE ) , isolated follicle-associated lanceolate ending ( I-FALE ) , large area follicle-associated circumferential ending ( LA-FACE ) , large area free ending ( LA-FE ) , low density follicle-associated lanceolate ending ( LD-FALE ) , Merkel cell associated ( MCA ) , small area follicle-associated circumferential ending ( SA-FACE ) , and thick ending ( TE ) . ( Here we use ‘lanceolate endings’ to mean longitudinally oriented lanceolates , i . e . parallel to the follicle axis; transverse lanceolate endings have also been described ( Fundin et al . , 1997 ) . ) Individual arbors are referred to by type , followed by an individual identifier in parenthesis , for example I-FALE ( A13-11 ) . In the figures that follow , all of the images and arbor traces are viewed in the plane of the skin unless otherwise noted . The smallest afferent arbors appear to target single Merkel cell clusters ( touch domes ) or single hair follicles . In the latter category ( I-FACE and I-FALE ) , most of the follicles appear to be guard ( tylotrich ) hairs based on their large diameter ( ∼50 μm ) . The assumption that MCA , I-FACE , and I-FALE afferents target single Merkel clusters or single follicles must be qualified by noting that all cutaneous sensory axons enter the dermis from subdermal nerves and are , therefore , invariably broken during the skin dissection , generally within several hundred microns of their terminal arbors . Since we cannot rule out the possibility that one or more additional branches emerge from the parent axon at a point proximal to its broken end , we refer to the follicle targets as ‘isolated’ rather than ‘single’ . This caution is given extra weight by evidence that an individual axon can innervate more than one Merkel cell cluster based on neurobiotin filling experiments in neonatal mice . In these experiments several slowly adapting type I ( SAI ) low threshold mechanoreceptor axons were observed to innervate two adjacent Merkel cell clusters ( Woodbury and Koerber , 2007 ) . However , in our P21 skin preparations 20/20 labeled MCA arbors targeted only isolated Merkel cell clusters , with no other labeled MCA arbors seen within a radius of >350 μm . The top row of images in Figure 2A shows two arbors with lanceolate endings that encompass almost the entire circumference [I-FALE ( A13-11 ) ] or approximately half of the circumference [I-FALE ( A13-12 ) ] of an individual guard hair follicle . Two examples of arbors that innervate all or nearly all Merkel cells within a single semi-circular Merkel cell cluster are seen in the bottom row of images in Figure 2A . Like Merkel cell clusters , the MCA terminal arbors encompass the posterior 50–75% of the circumference of the central follicle . Figure 2B shows tracings of three I-FALE arbors and four I-FACE arbors , the latter circumferentially wrapping three isolated guard hair follicles and one smaller follicle . 10 . 7554/eLife . 00181 . 006Figure 2 . Arbors that innervate Merkel cell clusters ( MCA ) and isolated follicles ( I-FACE and I-FALE ) . ( A ) Upper panels , two arbors that consist of lanceolate endings surrounding an isolated follicle ( I-FALE ) . Shown for I-FALE ( A13-11 ) are two Z-planes with superficial ( left ) and deep ( right ) layers , with a Z-stacked image . Lower panels , two arbors that innervate an isolated Merkel cell cluster ( MCA ) . Shown for MCA ( A13-10 ) are four Z-planes from superficial ( left ) to deep ( right ) layers , with a Z-stacked image . ( B ) Tracings of three I-FALE arbors and four I-FACE arbors . Arrows indicate the afferent axon . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 006 A distinct class of arbors , which we refer to as bushy ending ( BE ) , have dense and finely branched processes that encompass an area of 0 . 5–1 mm in diameter and ramify at a depth of 5–10 μm from the skin surface ( Figure 3 ) . As with all arbor types described here , the terminal branches of the BE arbors ramify within a narrow stratum ( Figure 3B; top image ) . ( The analysis of arbor depth within the dermis is noted at various points in the ‘Results’ section and is presented quantitatively in Figure 8D . ) BE arbors have total axon lengths of ∼10 cm with ∼1 , 000 branch points ( Figure 3F ) . Additional BE arbors , including a comparison of two independent traces of the same BE arbor are shown in Figure 3—figure supplements 1–3 . 10 . 7554/eLife . 00181 . 007Figure 3 . Arbors with bushy endings ( BE ) . ( A ) Arbor BE ( A12-15 ) , showing Z-planes with superficial ( left ) and deep ( right ) layers . The single axon that gives rise to this arbor is seen at the left in the deep layer image . ( B ) Tracing of BE ( A12-15 ) in side view ( top ) and en face ( bottom ) . Arrows in ( B ) and ( D ) indicate the afferent axon . ( C ) Higher magnification images of a portion of BE ( A12-6 ) at two Z-planes . ( D ) , ( E ) Tracings of BE ( A13-35 ) and BE ( A40-52 ) . ( F ) Relationships between arbor area , number of axon branch points , and axon length for six traced BE arbors . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 00710 . 7554/eLife . 00181 . 008Figure 3—figure supplement 1 . Additional BE arbor tracing . Top: Arbor BE ( A11-10 ) showing superficial ( left ) , and deep ( right ) layers . Bottom: two independent tracings of BE ( A11-10 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 00810 . 7554/eLife . 00181 . 009Figure 3—figure supplement 2 . Additional BE arbor tracing . Tracing of arbor BE ( A10-8 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 00910 . 7554/eLife . 00181 . 010Figure 3—figure supplement 3 . Additional BE arbor tracing . Tracing of arbor BE ( A40-61 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 010 Another distinctive type of arbor , which we refer to as thick ending ( TE ) , has only 20–80 branch points and exhibits relatively sparse coverage of an irregular area of 300–500 μm in diameter at a depth of ∼20 μm from the skin surface ( Figure 4G–J ) . As its name implies , a distinctive feature of the TE class is a thickening of the terminal 50–100 μm of its branches , a feature that is presumably related to its sensory function ( Figure 4G ) . 10 . 7554/eLife . 00181 . 011Figure 4 . Arbors with low density follicle-associated lanceolate endings ( LD-FALE ) , small area follicle-associated circumferential endings ( SA-FACE ) , and thick endings ( TE ) . ( A ) , ( B ) LD-FALE ( A40-35 ) image ( A ) and trace ( B ) . ( C ) Tracings of LD-FALE ( A40-31 ) and ( A40-61 ) . Arrows in ( C ) , ( F ) , and ( H ) indicate the afferent axon . ( D ) Higher magnification images at three Z-planes ( left to right , superficial to deep ) of LD-FALE ( A12-15 ) showing three lanceoalate endings . ( E ) SA-FACE ( A11-28 ) , showing Z-planes with superficial ( top ) and deep ( bottom ) layers . ( F ) Tracings of SA-FACE ( A11-28 ) and SA-FACE ( A23-62 ) . ( G ) TE ( A40-7 ) , showing three Z-planes from superficial ( left ) to deep ( right ) layers . Arrows indicate the thickened nerve terminals of the TE arbors . ( H ) Five TE tracings . TE ( A40-7 ) , shown in panel G , is at the upper left . ( I ) , ( J ) Relationships between arbor area , number of axon branch points , and axon length for 7 SA-FACE , 7 LD-FALE , and 14 TE traced arbors . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 011 Two types of arbors that innervate multiple follicles with lanceolate endings were observed . One type , low-density follicle-associated lanceolate endings ( LD-FALE ) , has a sparsely branched arbor that innervates between 2 and 36 follicles ( Figure 4A–D ) . These arbors encompass territories of 1–2 mm in diameter , but with total axon lengths of only 5–35 mm and with only 5–40 branch points ( Figure 4I ) . ( A quantitative analysis of the number of follicles innervated by this and several other arbor types are presented in Figure 8C . ) Among LD-FALE arbors , some branches terminate with thickened endings that lack the full C-shape of the typical follicle-associated lanceolate endings; these may represent endings that contact only a small fraction of a follicle's circumference or that are in the process of growing around or retracting from a target follicle . In nearly every instance , the opening of the ‘C’ faces the anterior of the mouse ( Figure 8E ) . The most commonly encountered arbor type , high density follicle-associated lanceolate ending ( HD-FALE ) , accounts for ∼50% of all AP+ arbors ( Figure 1D ) . The lanceolate endings of HD-FALE arbors localize ∼50 μm beneath the skin surface , and each arbor contacts ∼90% of the follicles within an area that ranges from ∼200 μm to ∼1 mm in diameter ( Figure 5 ) . Although most HD-FALE arbors have areas less than 106 μm2 ( mean ± SD of 3 . 4 ± 3 . 2 × 105 μm2 ) and innervate fewer than 50 follicles ( mean ± SD of 30 ± 22 ) , this type varies substantially , with arbor areas ranging from 105 μm2 to 2 . 5 × 106 μm2 and the number of follicles innervated ranging from 6 to 153 ( Figure 5D ) . For the 12 traced arbors , the mean axon length ( excluding the lanceolate endings ) was ∼2 × 104 μm and the mean number of branch points was ∼75 . As seen with I-FALE and LD-FALE arbors , individual HD-FALE endings wrap around their target follicles to variable extents ( Figure 5A–C ) , covering the full circumference or as little as ∼30% of the circumference . 10 . 7554/eLife . 00181 . 012Figure 5 . Arbors with high density follicle-associated lanceolate endings ( HD-FALE ) . ( A ) Arbor HD-FALE ( A21-55 ) showing superficial ( left ) , intermediate ( center ) , and deep ( right ) layers . The single axon that gives rise to this arbor is seen at the lower right of the deep arbor image . ( B ) Higher magnification images at three Z-planes ( left to right , superficial to deep ) of HD-FALE ( A12-17 ) showing multiple lanceolate endings . ( C ) Tracings of eight HD-FALE arbors . HD-FALE ( A21-55 ) , shown in panel A , is in the upper left . Arrows indicate the afferent axon . ( D ) For all HD-FALE arbor areas analyzed , arbor area and number of follicles innervated per arbor are shown in the top two plots . In the second plot , the inset shows the determination of the number of follicles per μm2; this value is shown in the plot as a green line . The bottom three plots show the relationships between arbor area , number of axon branch points , and axon length for the 12 traced HD-FALE arbors . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 012 Two types of arbors that innervate multiple follicles with circumferential endings were observed . One type , which we refer to as small area follicle-associated circumferential ending ( SA-FACE ) was rarely observed ( 8 out of 719 arbors ) . SA-FACE follicles contact few follicles ( mean ± SD of 3 . 8 ± 1 . 1 ) , generally have fewer than 20 branch points , and have diameters of ∼200 to ∼500 μm ( Figure 4E , F , I ) . By contrast , arbors designated as large area follicle-associated circumferential endings ( LA-FACE ) contact many follicles ( mean ± SD of 197 ± 73 ) , including occasional guard hair follicles , within territories of 1–2 mm in diameter ( Figure 6 ) . These arbors have axon lengths ( excluding the circular endings ) that cluster in the 8–15 cm range , with ∼200 branch points per arbor . The fraction of follicles innervated within the arbor territory ranges from 19% to 79% with a mean of 57% . There is a clear trend for larger arbors to exhibit a lower mean density of innervated follicles ( Figure 6E ) . 10 . 7554/eLife . 00181 . 013Figure 6 . Arbors with large area follicle-associated circumferential endings ( LA-FACE ) . ( A ) , ( B ) LA-FACE ( A11-16 ) image ( A ) and trace ( B ) . Arrowheads in panels B–D indicate guard hair follicles . Arrows in panels B and C indicate occasional nerve endings not associated with follicles . ( C ) Tracing of LA-FACE ( A40-62 ) . ( D ) High magnification images at three Z-planes ( left to right , superficial to deep ) of LA-FACE ( A12-11 ) . ( E ) Top , relationships between arbor area , number of axon branch points , and axon length for 10 traced LA-FACE arbors . Bottom , number of follicles innervated per arbor vs . arbor area for 34 LA-FACE arbors . The mean number of follicles per unit area of back skin is shown by the green line ( see Figure 5D inset ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 013 The largest arbors appear to comprise a single morphologic class , referred to as large arbors with free endings ( LA-FE; Figure 7 , Figure 7—figure supplements 1 and 2 ) . In this class , the axons are arranged in two narrowly stratified tiers: an inner arbor at a depth of ∼70 μm and an outer arbor at a depth of ∼10 μm from the skin surface are connected by >50 vertical branches ( Figures 7A , B and 8D ) . The inner arbor exhibits sparse but relatively uniform coverage of an area ∼3 mm in diameter with a total axon length of ∼10–15 cm . The outer arbor exhibits denser and relatively uniform coverage of an area ∼4 mm in diameter with a total axon length of 50–100 cm ( Figure 7B–D , Figure 7—figure supplements 1 and 2 ) . By way of comparison , we note that the torso of a P21 mouse is only ∼4 cm in length . The axons appear to lack terminal specializations . The relatively uniform coverage within the outer arbor is not achieved by a strict self-avoidance mechanism , as there are numerous examples of axon crossings within the same arbor . However , within the inner arbor , axon crossings are rare . 10 . 7554/eLife . 00181 . 014Figure 7 . Arbors with large areas and free endings ( LA-FE ) . ( A ) High magnification images at five Z-planes ( left to right , superficial to deep ) of LA-FE ( A12-9 ) . ( B ) Arbor LA-FE ( A12-36 ) , showing Z-planes with superficial ( left ) and inner ( right ) layers . A distinct inner arbor is in focus in the right panel . ( C ) Tracings of the full arbor of LA-FE ( A12-36 ) ( left ) , and its inner arbor alone ( right ) . ( D ) Relationships between arbor area , number of axon branch points , and axon length for seven traced LA-FACE arbors , with separate analyses for the inner arbor and full arbor . Note that the axon length scale extends to 1 m . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 01410 . 7554/eLife . 00181 . 015Figure 7—figure supplement 1 . Additional LA-FE arbor tracing . Tracing of the full arbor of LA-FE ( A12-21 ) ( left ) , and its inner arbor alone ( right ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 01510 . 7554/eLife . 00181 . 016Figure 7—figure supplement 2 . Additional LA-FE arbor tracing . Tracing of the full arbor of LA-FE ( A20-7 ) ( left ) , and its inner arbor alone ( right ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 016 In the descriptions above , clear qualitative distinctions between most arbor types are apparent based on the presence or absence of follicle-associated endings , the type of specialized structure elaborated by the sensory endings , and whether or not the arbor targeted single guard hair follicles or Merkel clusters . The quantitative analysis of continuous variables in Figure 8 reinforces those distinctions and , in addition , provides a clear separation between the two pairs of arbor types that are most closely related by qualitative criteria , LD-FALE vs HD-FALE and SA-FACE vs LA-FACE . 10 . 7554/eLife . 00181 . 017Figure 8 . Parametric analysis of arbor area , arbor depth , axon length , and number of follicles innervated across arbor classes . ( A ) Arbor areas plotted on linear ( top ) and log10 ( bottom ) scales for the seven arbor types with arbor areas larger than the surround of a single follicle ( i . e . arbor types other than I-FALE , I-FACE , and MCA ) . ( B ) Axon length vs arbor area . Note the break in the horizontal and vertical axes and the compressions in scale beyond the break . ( C ) Comparisons among the four arbor classes that innervate multiple follicles ( HD-FALE , LD-FALE , LA-FACE , SA-FACE ) : number of follicles innervated vs arbor area , and innervation index ( defined as number of innervated follicles per arbor area ) vs arbor area . ( D ) Arbor depth within the skin . The skin surface corresponds to 0 μm . On the back skin at P21 , the mean depth of a follicle bulb is ∼100 μm . ( E ) Orientations of follicle-associated C-shaped lanceolate endings from 34 LD-FALE arbors relative to the orientation of their associated follicle . In wild type mice at P21 , hair follicles on the back are oriented from anterior to posterior , with mean deviations from that axis of less than 10 degrees ( Wang et al . , 2010 ) . Red bars show mean and standard deviation . VNE , vector orientation for the nerve ending; VHF , vector orientation for the hair follicle . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 01710 . 7554/eLife . 00181 . 018Figure 8—figure supplement 1 . Differential interference contrast images of parts of BE , LA-FE , LA-FACE , and HD-FALE arbors at different Z-planes . The focal plane of the upper left panel is at the level of the dermis–epidermis junction . The focal plane of the two lower left panels is at the midpoint of the hair follicle . The focal plane of the three right panels is at the follicle bulb . Arrows in right panels point to proximal axon segments deep within the dermis . DOI: http://dx . doi . org/10 . 7554/eLife . 00181 . 018 Figure 8A compares arbor areas on both linear and log10 scales . This analysis reveals different degrees of clustering of area values within different arbor types , with BE , LA-FACE , and LA-FE types showing the tightest clustering . By contrast , variation in arbor areas within HD-FALE , LD-FALE , and TE types spans two orders of magnitude . Figure 8A also shows that the areas of SA-FACE and LA-FACE arbors are non-overlapping . Extending this analysis to the correlation of axon length and arbor area illustrates striking differences in arbor density , with the high density BE arbors showing the highest length:area ratio and the sparse LD-FALE arbors showing the lowest ratio ( Figure 8B ) . This plot also shows a clear separation between LA-FACE and LA-FE arbor types and between these and other types . For those arbors that contact hair follicles , the number and density of follicle contacts dictate the scale of spatial integration and the sampling density within the relevant area . Figure 8C compares these parameters for each of the four arbor types that contact multiple follicles . In this figure we introduce a new parameter , the ‘innervation index’ , defined as the number of follicles innervated per μm2 of skin . It is apparent that the two classes of arbors with circular endings , LA-FACE and SA-FACE , differ markedly in number of follicles innervated: >100 per LA-FACE arbor and <10 per SA-FACE arbor ( Figure 8C , left panels ) . SA-FACE arbors also have a lower mean innervation index than LA-FACE , and in a plot of innervation index vs arbor area the two distributions are non-overlapping ( Figure 8C , right panels ) . The two classes of arbors with lanceolate endings , HD-FALE and LD-FALE are not clearly distinguished by the number of follicles innervated ( Figure 8C , left panels ) , but show a clear separation in a plot of innervation index vs arbor area ( Figure 8C , right panels ) . Skin is a highly stratified tissue in which individual structures such as arrector pili muscles , sebaceous glands , and Merkel cells occupy distinct strata . Figures 8D and Figure 8—figure supplement 1 show that cutaneous sensory arbors are precisely stratified and that each arbor type has a characteristic depth . The outer arbors of the BE and LA-FE classes reside close to the skin surface , most likely within the epidermis , and their inner arbors reside ∼70 μm from the skin surface . TE arbors are located ∼20 μm from the skin surface . Follicle associated circular and lanceolate endings reside ∼50 μm from the skin surface for non-guard hair follicles and ∼40 μm from the skin surface for guard hair follicles . For many of the arbor types described here , it is possible to suggest a likely correspondence with physiologically or histologically defined classes of cutaneous sensory neurons ( Fundin et al . , 1997; Woodbury and Koerber , 2007; Li et al . , 2011 ) . MCA arbors likely correspond to the A-beta slowly adapting type I/low threshold mechanoreceptors ( SA I/SA LTMRs ) that contact Merkel cell clusters/touch domes . I-FALE arbors that surround guard hair follicles likely correspond to A-beta rapidly adapting ( RA ) LTMRs . HD-FALE arbors likely correspond to A-beta RA LTMRs that innervate awl/auchenne hair follicles . I-FACE , LA-FACE , and SA-FACE arbors likely correspond to the peptidergic and/or nonpeptidergic circumferential free nerve endings described by Fundin et al . ( 1997 ) ; the physiologic correlates of this class are , at present , unknown . TE arbors could correspond to the thickened endings of myelinated fibers referred to by Rice and colleagues as ‘fuzzy endings’ or ‘club endings’ , which are suggested to represent mechanoreceptors ( Fundin et al . , 1997 ) . The two types of arbors with free endings—BE and LA-FE arbors—come close to and may partially penetrate the epidermis ( Figure 8D ) , a characteristic that is shared with a variety of peptidergic and nonpeptidergic fibers with free endings , including C-type nociceptors and thermoreceptors . BE arbors bear a particularly close resemblance to the arbors of MrgprB4-expressing neurons , one of a large number of Mrgpr-expressing neurons that together mediate the sense of itch ( Liu et al . , 2007 , 2009 ) . Tests of these proposed correlations should become possible with methods currently under development . Without using genetic tools , a direct correlation between sensory arbor morphology and single unit physiology is technically challenging , although it has been achieved for neonatal mouse SA I mechanosensory afferents by recording from and then neurobiotin filling individual DRG neurons in an ex vivo skin/DRG/spinal cord preparation ( Woodbury and Koerber , 2007 ) . As elegantly demonstrated in a study of low threshold mechanosensory neurons , the combined use of this ex vivo preparation and genetically-based cell identification permits both sparse labeling of processes for morphologic analysis and labeling of cell bodies for correlation with electrophysiology ( Li et al . , 2011 ) . The geometries of sensory arbors make clear predictions about receptive field size and structure . For those I-FALE and I-FACE arbors that target single guard hair follicles , the clear prediction is that sensory information is gathered at single follicle resolution with a sampling pattern based on the regular mosaic of guard hairs . At the opposite extreme , LA-FE neurons are predicted to have receptive fields of 3–5 mm diameter with dense and relatively homogeneous coverage by sensory endings . BE neurons are predicted to have receptive fields of 0 . 5–1 mm diameter with an even higher density of sensory endings . Spatial resolution is also predicted to be relatively low for HD-FALE arbors , each of which contacts most follicles within its ∼0 . 5 to ∼1 mm diameter territory; these afferents presumably sense hair deflections . Many HD-FALE endings cover only part of the circumference of their associated follicle , suggesting that two or three HD-FALE arbors might co-innervate a single follicle and , therefore , that deflection of a single hair might elicit synchronous responses in several HD-FALE arbors . For any given HD-FALE and LD-FALE arbor , the C-shaped territories of different lanceolate clusters generally reside on the same side of their associated follicles ( Figures 4A–C and 8E ) . This asymmetric localization suggests a corresponding asymmetry in mechanosensory responses . By analogy with the responses of auditory and vestibular sensory cells , if hair deflection toward a lanceolate ending ( ‘compression’ ) elicits a response opposite to that of hair deflection away from the lanceolate ending ( ‘extension’ ) , then the asymmetric locations of lanceolate endings would necessarily provide information about the direction of hair movement . A coherent wave of mechanical stimulation—the sort delivered by a large object moving over the body surface—would presumably elicit a response of the same polarity from multiple similarly oriented C-shaped endings . If such a direction-selective response exists it would provide the somatosensory system with an information stream analogous to that provided to the visual system by direction selective retinal ganglion cells ( Vaney et al . , 2012 ) , except that the proposed directionality of the cutaneous sensor would derive from structural asymmetry at the nerve ending/hair follicle complex rather than by spatiotemporal comparisons among neurons . The pattern of spatially asymmetric C-shaped lanceolate clusters is especially striking among LD-FALE arbors because the innervated follicles are spaced at large intervals ( ∼0 . 5 mm ) , a distance that would appear incompatible with any direct communication between endings . If , as seems probable , the LD-FALE neurons sense coherent hair deflections with low spatial resolution , then these neurons would represent excellent candidates for the C-fiber neurons that subserve affective touch stimuli of the sort elicited by a mother licking her pups ( Andrew , 2010 ) . Similar C-fiber responses to low force , slowly moving stimuli—referred to as ‘pleasant touch’—have also been recorded from human hairy skin by microneurography ( Johansson et al . , 1988; Löken et al . , 2009 ) . A pattern of dense follicle coverage is seen in LA-FACE arbors , with ∼60% of follicles contacted in a territory of ∼2 mm diameter . Although AP histochemistry cannot resolve individual fibers within a tightly packed circumferential bundle , there are clear differences among follicles in the number of AP+ circumferential fibers , since some follicles appear to be encircled by only one or two fibers whereas others exhibit a thick band of AP staining that is at least 10 times the intensity of an individual fiber . If we suppose that each follicle receives roughly the same total number of circumferential fibers , then these data suggest that many follicles receive fibers from two or more LA-FACE arbors , as described above for HD-FALE arbors . For arbors that contact follicles with lanceolate or circumferential endings , we can estimate the coverage factor—defined as the mean area covered by a particular type of arbor multiplied by the number of arbors of that type per unit area—based on the percentage of follicles contacted within the arbor territory and the patterns of partial occupancy of the contacted follicles . ( For the calculations that follow , we remind the reader that all of the data are derived from mice at P21 , and it is possible that arbor territories are still being refined at this age . ) The average HD-FALE arbor contacts >80% of follicles within its territory and its sensory endings occupy on average >50% of the circumference of each follicle . If we assume that each follicle's circumference is fully and uniformly occupied by lanceloate endings—an assumption supported by high resolution light and electron microscopic imaging ( Casserly et al . , 1994; Takahashi-Iwanaga , 2000; Li et al . , 2011 ) —then the coverage factor for HD-FALE arbors would be between one and two . This number might be somewhat higher since it appears from the work of Li et al . ( 2011 ) that lanceolate endings from different arbors interdigitate extensively , with the result that an individual follicle may well accommodate more than two half-circles of lanceolate endings . A somewhat less precise estimate of the coverage factor can be made for LA-FACE arbors since the extent of coverage of an individual follicle by AP-stained circumferential endings is more difficult to estimate than the extent of coverage by clusters of lanceolate endings . On the assumption that every follicle receives a level of circumferential fiber coverage that roughly corresponds to the highest levels observed in our samples , and with each LA-FACE arbor encircling ∼60% of follicles within its territory , we estimate a coverage factor of 2–4 . Following the same logic , LD-FALE arbors could potentially have coverage factors of >30 since they innervate hair follicles that are spaced at intervals of ∼0 . 5 mm ( Figure 4A–C ) . However , this estimate represents an upper limit , since LD-FALE arbors are presumably competing for follicle targets with HD-FALE arbors . For arbor types without follicle-associated endings , the individual morphologies offer no clues regarding the coverage factor . The diverse morphologies of cutaneous afferents raise a host of questions regarding developmental mechanisms . In particular , the mechanisms that determine laminar specificity , target specificity , and arbor size and branching density among cutaneous afferents are unknown . While these and related questions have been addressed in a wide variety of neurobiological systems , they may be especially tractable in systems in which arbors are confined to one or several parallel planes , as in the inner plexiform layer of the vertebrate retina and the Drosophila larval body wall ( Masland , 2001 , 2011; Jan and Jan , 2010 ) . For sensory arbors that contact multiple follicles , competition among arbors of the same type for a limited pool of targets or target territories—such as the finite circumference of each follicle , around which the lanceolate endings are packed—could determine the size of individual arbor territories . Competition of this sort has been described among lanceolate endings that innervate the same guard hair follicle ( Casserly et al . , 1994 ) , and it is analogous to the competition observed among motor axons at the developing neuromuscular junction ( Turney and Lichtman , 2012 ) . This model predicts that the average arbor territory will correlate inversely with , and be controlled by , the number of DRG neurons of the given class per unit area of skin . Finally , axon length and number of branch points , which vary over a 1 , 000-fold range across different arbor types , could be controlled not only by local extrinsic signals but also by regulating , at the level of gene transcription , the abundances and ratios of various cytoskeletal proteins ( Gallo , 2011 ) . Another developmental question , hinted at in the preceding section , concerns the mechanism by which C-shaped lanceolate clusters assume a uniformly polarized orientation with respect to the body axes . Hair follicles and their associated structures—Merkel cell clusters ( touch domes ) , arrector pili muscles , and sebaceous glands—are all polarized with respect to the body axes . This is macroscopically apparent in the anterior-to-posterior angle of the hair follicle itself , an organizational feature that is under the control of the planar cell polarity pathway ( Wang et al . , 2010 ) . It seems reasonable to suppose that each hair follicle could transmit polarity information to its associated lanceolate cluster , and recent experiments with mice lacking planar cell polarity signaling in the skin support this conjecture ( Chang and Nathans , unpublished observations ) . The cutaneous sensory system has a number of favorable experimental attributes that facilitate the integration of experimental approaches . First , the primary sensory neurons are abundant , large , and anatomically segregated ( in the DRG ) where they can be studied or dissected either during development or in adulthood . Second , the sensory arbors are embedded within a large two-dimensional tissue ( the skin ) that by virtue of its surface location is readily accessible to controlled stimulation or surgical and pharmacologic manipulations . And third , it is the only sensory system in which single unit recordings can be made from primary sensory neurons in an awake human to correlate physiology with psychophysics ( e . g . , Vallbo and Johansson , 1984; Vallbo et al . , 1984; Löken et al . , 2009 ) . Of the three points noted above , the localization of sensory arbors within the skin is of greatest relevance to the present study . Unlike previous histochemical and immunolocalization studies on skin that have generally been conducted with vertically sectioned tissue ( e . g . Fundin et al . , 1997; Zylka et al . , 2005 ) , we have worked exclusively with intact skin . This preparation has a number of favorable attributes . First , murine skin is sufficiently thin that intradermal structures can be imaged in their entirety , thus obviating the need for physical sectioning . Second , the two-dimensional nature of the sample and the resiliency of fixed skin simplifies tissue handling , imaging , and cataloguing of structures by their location in the X–Y plane . And third , the relative ease with which large areas of skin can be surveyed in flat mounts facilitates the assembly of large datasets and the identification of relatively rare arbor types . The present morphologic survey of cutaneous afferents represents one facet of a larger program aimed at systematic structural analyses of nervous tissue . Other efforts include comprehensive descriptions of mammalian retinal cell types and their connectivities; complete reconstruction of the Drosophila optic lobe; and light microscope-level reconstruction of mouse neuromuscular units ( Sun et al . , 2002; Badea and Nathans , 2004; Kong et al . , 2005; Lin and Masland , 2006; Takemura et al . , 2008; Lu et al . , 2009; Briggman et al . , 2011 ) . Compared to other neuronal reconstruction programs , the analysis of cutaneous sensory arbors has an important simplifying feature: interactions between different arbors are minimal ( perhaps limited to co-innervation of the same target follicle ) so that a full appreciation of the neuroanatomy can largely be obtained from reconstructions of individual arbors and their epithelial targets . By contrast , in most regions of the nervous system , connectivity between large numbers of pre- and postsynaptic partners is critical to understanding neuronal function . This simplifying feature , together with the technical advantages of two-dimensional imaging and reconstruction in the skin , suggests that , in the not-too-distant future , it should be possible to obtain a comprehensive anatomic description of cutaneous afferents in mammalian skin . Brn3aCKOAP/CKOAP mice were crossed to NFL-IRES-CreER/NFL-IRES-CreER mice . At GD 17 , females carrying Brn3aCKOAP/+; NFL-IRES-CreER/+ fetuses received 100-500 μg Tamoxifen as an IP injection . Mice were sacrificed at P21 , and the back was shaved and treated with hair remover . Back skins were dissected intact and pinned dermal side up to a flat Sylguard surface using insect pins . A series of small cuts at the anterior and posterior ends of each skin served as unique identifiers . Subdermal connective tissue was removed with fine forceps , and the skins were fixed overnight in 4% paraformaldehyde in phosphate buffered saline ( PBS ) , and then heated to 70°C for 90 min to inactivate endogenous phosphatase activity . AP histochemistry and clearing in benzyl benzoate:benzyl alcohol ( 2:1 ) ( BBBA ) were performed as described in Badea et al . ( 2003 ) , ( 2012 ) . For long-term storage , skins were equilibrated in ethanol at −20°C . To survey arbor areas , a montage of each skin was first assembled from images obtained with a dissecting microscope . Convex polygons encompassing all isolated ( i . e . non-overlapping ) arbors were drawn over the image using Adobe Illustrator and each polygon area was calculated . For high resolution analyses , including axon tracing , BBBA-cleared skins were flattened between two glass gel-electrophoresis plates and isolated arbors were imaged at 10× magnification on a Zeiss apotome system . Grey-scale Z-stacks were captured in bright field mode with a 5 μm separation between adjacent layers . Following dehydration in ethanol , with the skin still pinned to the Sylguard surface , and clearing in BBBA , adult skin showed essentially no shrinkage in the X- and Y-dimensions and was reduced to ∼92% of the original thickness in the Z-dimension . Therefore ∼25 stacks were required to encompass the ∼125 μm of full thickness skin in BBBA ( original thickness ∼135 μm ) . A montage of images was captured on a Zeiss Imager Z1 and assembled with Zeiss AxioVision software . Neurites were traced using Neuromantic neuronal tracing freeware ( Darren Myat , http://www . reading . ac . uk/neuromantic ) in semi-automatic mode . As the current version of Neuromantic does not accept input files >1 GB , the files for the largest arbors ( the LA-FE arbors ) , were reduced from ∼3 GB to <1 GB by twofold reductions in X and Y resolution . Semi-automated tracing of the larger arbors ( LA-FE and LA-FACE ) requires ∼15 person-hours per arbor . As a measure of reproducibility , we compared two independent traces of the same BE arbor , BE ( A11-10 ) ( Figure 3—figure supplement 1 ) . The axon lengths and number of branch points for the two tracings were 85 , 020 vs 86 , 908 μm and 1011 vs 1071 , corresponding to differences of 2 . 17% and 5 . 60% , respectively . The Neuromantic output is a vector file: each line contains a unique number , a set of X , Y , and Z coordinates ( the vector's origin ) , and a second number that indicates the line in the file corresponding to the termination of the vector . The vector files were exported sequentially to Excel ( for reformatting ) , Rotator visualization software ( Rotator 3 . 5 ) , and Adobe Illustrator . For the analysis of neurite positions at different depths within the skin , complete serial Z-stacks through the skin were obtained with a Zeiss Imager Z1 at intervals of either 2 μm or 5 μm in differential interference contrast ( DIC ) mode for multiple examples of each arbor type . Neurite positions in the Z-dimension were determined with respect to the epidermal surface and the tips of the hair follicle bulbs . Statistical analyses were performed with Excel and Graph-Pad . Error bars in the figures indicate standard deviation ( SD ) .	Sensory neurons carry information from sensory cells in the eyes , ears and other sensory organs to the brain and spinal cord so that they can coordinate the body's response to its environment and various stimuli . The sensory organs responsible for four of the traditional senses—vision , hearing , smell and taste—are relatively small and self-contained: however , the sensory organ responsible for touch is as big as the body itself . Moreover , a variety of many different types of sensory cells in the skin allow the body to respond to temperature , pain , itches and a range of other external stimuli . Despite more than a century of research , relatively little is known about the morphology of the complex networks ( arbors ) of sensory neurons that send signals towards the central nervous system . This is mainly due to difficulties involved in imaging intact skin , the way that different arbors overlap and intermingle , and the relatively large distances that separate the bodies of neuronal cells and the farthest reaches of their arbors . Wu et al . employed an imaging method that exploits the Cre-Lox system that is already widely used in genetics . In this approach a Cre enzyme is used to remove a region of DNA that is flanked by two genetically engineered Lox sequences . Wu et al . used a gene that codes for an enzyme marker ( alkaline phosphatase ) that previous investigators had into the DNA of mice . The gene was inserted in such a way that it was only expressed in sensory neurons that innervate the skin when Cre-Lox recombination had removed an adjacent segment of DNA . Moreover , Wu et al . used this reporter gene in combination with a modified Cre enzyme that only enters the nuclei of cells in the presence of a drug ( Tamoxifen ) , so the probability that the marker gene is expressed is determined by the concentration of Tamoxifen . By administering a low level of Tamoxifen to pregnant mice , it was possible to label a very small number of sensory neurons in each embryo . Individual neurons that express the alkaline phosphatase marker were visualized with a histochemical reaction that rendered them dark purple . The remainder of the tissue remained unstained . Based on quantitative analyses of the morphologies of more than 700 arbors , Wu et al . identified 10 distinct types of neurons . Of the two types of neurons with the largest arbors , one makes contact with ∼200 hair follicles , with the nerve endings completely encircling the follicles; the other type of arbor contains several thousand branches , with a total length for all of the branches summing to as much as one meter in length . The next challenge is to study the morphologies of neurons in tissues other than the skin , and also the neurons involved in other sensory systems , and to explore the cellular and developmental mechanisms responsible for the morphological diversity found in these initial experiments .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology", "neuroscience" ]	2012	Morphologic diversity of cutaneous sensory afferents revealed by genetically directed sparse labeling
Selection for a promiscuous enzyme activity provides substantial opportunity for competition between endogenous and newly-encountered substrates to influence the evolutionary trajectory , an aspect that is often overlooked in laboratory directed evolution studies . We selected the Escherichia coli nitro/quinone reductase NfsA for chloramphenicol detoxification by simultaneously randomising eight active-site residues and interrogating ~250 , 000 , 000 reconfigured variants . Analysis of every possible intermediate of the two best chloramphenicol reductases revealed complex epistatic interactions . In both cases , improved chloramphenicol detoxification was only observed after an R225 substitution that largely eliminated activity with endogenous quinones . Error-prone PCR mutagenesis reinforced the importance of R225 substitutions , found in 100% of selected variants . This strong activity trade-off demonstrates that endogenous cellular metabolites hold considerable potential to shape evolutionary outcomes . Unselected prodrug-converting activities were mostly unaffected , emphasising the importance of negative selection to effect enzyme specialisation , and offering an application for the evolved genes as dual-purpose selectable/counter-selectable markers . Many ( if not all ) enzymes are promiscuous , meaning that in addition to their primary biological role ( s ) they can catalyse minor side reactions that have no apparent physiological relevance , either because they are too inefficient or because the substrate is not naturally encountered ( Copley , 2015 ) . From an evolutionary perspective , promiscuity can play an important role in contingency , providing a reservoir of potential functions that a cell can tap in response to changing circumstances ( O'Brien and Herschlag , 1999; Copley , 2015 ) . As demonstrated by the emergence of resistance to xenobiotic pollutants or clinical antibiotics ( O'Brien and Herschlag , 1999; Hall , 2004; Ramos et al . , 2005; Copley , 2009; Khersonsky and Tawfik , 2010 ) , a strong selection pressure can cause latent promiscuous activities to be rapidly amplified to physiologically relevant levels ( Newton et al . , 2015 ) . Catalytic transitions to an alternate substrate have been modelled experimentally using iterative rounds of random mutagenesis ( e . g . error-prone PCR ( epPCR ) ) , a powerful directed evolution strategy that enables adaptive landscapes to be explored under defined laboratory conditions ( Kaltenbach et al . , 2015; Kaltenbach et al . , 2016 ) . These laboratory evolution studies have indicated that selection for substantial increases in a promiscuous activity typically results in only weak trade-offs against the native activity; and therefore , the transition from one primary function to another tends to progress via generalist enzyme intermediates ( Kaltenbach et al . , 2016 ) . Two leading teams have offered contrasting hypotheses to explain this phenomenon . In 2005 , Tawfik and co-workers proposed that enzymes possess an innate ‘robustness’ and stability that buffers them against the potentially detrimental effects of novel mutations , coupled with a ‘plasticity’ that can amplify promiscuous functions with relatively few mutations ( Aharoni et al . , 2005 ) . More recently , Tokuriki and co-workers demonstrated that a robust native activity is not a prerequisite for weak trade-offs , and suggested that the predominance of these in the literature may instead be artefactual; a consequence of laboratory evolution studies being highly biased towards strong selection for a new function without any selection against the native activity ( Kaltenbach et al . , 2016 ) . They argue that it is unclear how specialisation can occur in this manner , and that in nature , selection might frequently exist to erode the original function . By way of example , they offer a scenario where the native and new substrate compete for the same active site ( Kaltenbach et al . , 2016 ) . In addition to their exclusive emphasis on positive selection , we note that the studies overviewed by the Tokuriki team were also heavily biased towards heterologous enzyme expression and/or a transition in activity from one exogenously applied substrate to another ( Kaltenbach et al . , 2016 ) . Thus , there has been little consideration of how the native substrate might influence the evolutionary trajectory . We were therefore motivated to study the selection of a promiscuous function within the native host environment , with particular focus on the key catalytic changes driving the transition . Recognising that the stochastic nature of iterative random mutagenesis is unlikely to yield the most efficient pathway to a selected outcome , we sought to implement simultaneous mass-mutagenesis on a massive scale that would allow us to retrospectively assess all possible intermediates of our top variants , and infer the most plausible stepwise evolutionary trajectories . We were able to achieve both these goals by employing the Escherichia coli nitro/quinone reductase NfsA as a new model system that offers several key advantages . NfsA is a member of a large bacterial superfamily comprising highly promiscuous FMN-dependent oxidoreductases that accept electrons from NAD ( P ) H and transfer them to a diverse range of substrates ( Williams et al . , 2015; Akiva et al . , 2017 ) . Expression of nfsA is governed by the soxRS regulon , and NfsA is thought to guard against oxidative stress through reduction of water-soluble quinones such as 1 , 4-benzoquinone ( Liochev et al . , 1999; Paterson et al . , 2002; Copp et al . , 2017 ) . Although most efficient with quinone substrates , NfsA is also able to reduce a wide diversity of nitroaromatic compounds ( Valiauga et al . , 2017 ) . This is generally believed to represent non-physiological substrate ambiguity , as there are relatively few nitroaromatic natural products , and in many cases nitro-reduction yields a more toxic derivative ( Winkler and Hertweck , 2007; Parry et al . , 2011; Williams et al . , 2015 ) . An important exception is that nitro-reduction of chloramphenicol transforms this antibiotic to a product that is not discernibly toxic to bacteria ( Yunis , 1988; Erwin et al . , 2007; Crofts et al . , 2019 ) . We have observed that over-expressed native NfsA confers only slight chloramphenicol protection to E . coli host cells , but reasoned that we could select for improved detoxification in an extremely high-throughput manner by plating variant libraries on chloramphenicol-amended media . Because members of the bacterial nitroreductase superfamily appear to have unusually plastic active sites ( Akiva et al . , 2017 ) , we considered that simultaneous mass-mutagenesis of up to eight active-site residues should be possible . In effect , we aimed to strip NfsA of its engine , and then select for a superior configuration of parts assembled within the empty chassis . By leaping directly to a new fitness peak , we considered that we might arrive at synergistic combinations of substitutions that would be difficult to achieve by iterative random mutagenesis approaches . We have previously conducted several different mutagenesis studies on nfsA , seeking to enhance activity with prodrugs and/or positron emission tomography ( PET ) imaging probes for cancer gene therapy applications ( Williams , 2013; Copp et al . , 2017; Rich , 2017 ) , or to assess potential collateral sensitivities between niclosamide and the antibiotics metronidazole and nitrofurantoin ( Copp et al . , 2020 ) . Based on this previous work we empirically identified eight active-site residues ( S41 , L43 , H215 , T219 , K222 , S224 , R225 , and F227; Figure 1A ) as being individually mutable and having the potential to contribute to generically improved nitroreductase activity . We then designed a degenerate gene library to enable simultaneous randomisation of each residue . As complete randomisation of target codons ( e . g . NNK degeneracy ) would have yielded an impractically large library of >1012 ( 328 or more ) gene variants , we instead used a restricted set ( Figure 1B ) . The degenerate codon NDT was preferred at most sites , as this specifies 12 different amino acids that represent a balanced portfolio of small and large , polar and non-polar , aliphatic and aromatic , and negatively and positively charged side chains ( Reetz et al . , 2008 ) . However , at positions 219 and 222 , NDT codons did not include the native NfsA residue as an option , so the alternative degeneracies NHT ( 12 unique amino acids ) and VNG ( 11 unique amino acids ) were chosen as acceptably balanced alternatives ( Figure 1B ) . In total , our library represented 430 million possible gene combinations , collectively specifying 394 million different NfsA variants . Following artificial synthesis and cloning , our library was used to transform E . coli 7NT cells ( a strain in which endogenous nitroreductase genes had been deleted ) . We ultimately recovered a total of 398 million transformed colonies , a collection predicted by GLUE ( Firth and Patrick , 2008 ) to represent 252 million different NfsA variants . Despite the drastic reconfiguration of their encoded active sites , a surprising 0 . 05% of the gene variants ( ~200 , 000 clones ) were more effective than wild-type nfsA ( i . e . enabled colony formation on LB agar amended with 3 µM chloramphenicol , the lowest concentration at which wild-type nfsA was unable to support host cell growth ) . This robust tolerance to active-site randomisation confirmed that NfsA exhibits a substantial degree of active-site plasticity . We next plated the library on ≥45 µM chloramphenicol , recovering a total of 365 colonies . Retransformation of fresh 7NT host cells with the variant-encoding plasmids was performed to eliminate any selected chromosomal mutations , followed by validation of activities in liquid growth assays . Sequencing and elimination of duplicates yielded 30 top variants that exhibited evidence of a conserved genetic response to the chloramphenicol selection . Particularly strong trends were observed at positions 41 and 219 ( where the native serine or threonine was substituted by an aromatic residue in ≥26 of the 30 cases ) , and at position 225 ( 100% substitution of the native arginine by an uncharged or acidic residue ) ( Figure 1C , Supplementary file 1a ) . Only at position 43 was the native leucine or a chemically similar residue frequently retained ( 16/30 cases ) . In EC50 assays , 7NT cells expressing these 30 variants demonstrated nearly 6- to 10-fold greater chloramphenicol tolerance than those expressing native nfsA ( Figure 1D ) . SDS-PAGE analysis revealed that there was no substantial variation in expression between these variants and native nfsA , and hence the enhanced chloramphenicol detoxification was not a consequence of elevated expression levels ( Figure 1—figure supplement 1 ) . To evaluate the impact of the active-site reconfiguration on catalytic activity , the top five chloramphenicol-detoxifying NfsA variants were purified as His6-tagged proteins and evaluated in steady-state kinetics assays . We were surprised to discover that these variants exhibited only marginal ( at most 2 . 2-fold ) improvements in chloramphenicol kcat/KM over wild-type NfsA ( Table 1 ) . However , in every case the variants were impaired in kcat ( 6–10-fold lower than NfsA ) but greatly improved in KM ( 8–13-fold lower than NfsA ) . Thus , it appeared that the in vivo improvements in chloramphenicol detoxification were driven primarily by enhanced substrate affinity . We previously observed a similar phenomenon when using an epPCR strategy to evolve NfsA for improved activation of the anti-cancer prodrug PR-104A , with all top variants exhibiting a lower kcat and lower KM for PR-104A , and none being significantly improved in kcat/KM over the native enzyme ( Copp et al . , 2017 ) . In that study , we postulated that the improved in vivo activities were a consequence of diminished competitive inhibition by native quinone substrates present in the E . coli cytoplasm; although the top variant was still active with 1 , 4-benzoquinone , we found its PR-104A reduction activity was less affected by addition of 1 , 4-benzoquinone to the reaction mix than was the case for wild-type NfsA ( Copp et al . , 2017 ) . Here , we were unable to perform the same in vitro competition assays , as both 1 , 4-benzoquinone and chloramphenicol reduction are monitored by following NADPH depletion at 340 nm . However , when assayed individually we found that 1 , 4-benzoquinone reduction was unmeasurable in each of the five top chloramphenicol-detoxifying variants ( Table 1 ) . In contrast , for wild-type NfsA , 1 , 4-benzoquinone reduction ( kcat/KM = 5 . 8 × 106; Valiauga et al . , 2017 ) is nearly 1000-fold more efficient than chloramphenicol reduction ( Table 1 ) . Our data are therefore consistent with in vivo chloramphenicol reduction having been amplified for the selected variants by the elimination of competitive quinone inhibition . We next sought to probe the contributions to improved chloramphenicol detoxification and/or diminished 1 , 4-benzoquinone reduction made by key substitutions , or combinations thereof . The top two chloramphenicol-detoxifying variants ( on the basis of EC50 measurements; 36_37 and 20_39 ) each had seven substitutions at the eight targeted positions , with both containing the wild-type residue leucine at position 43 ( 36_37 = S41Y , H215C , T219Y , K222V , S224R , R225V , F227G; 20_39 = S41Y , H215N , T219Y , K222R , S224Y , R225D , F227H; Figure 2A–C ) . Therefore , there are 126 possible intermediate forms ( 27 – 2 ) between wild-type NfsA and each selected variant . Genes encoding the 126 intermediate forms for each variant were artificially synthesised , cloned and expressed in E . coli 7NT cells , and the chloramphenicol tolerance of the resulting strains assessed in EC50 growth assays . A Python script was generated to delineate all 5040 ( 7 ! ) possible evolutionary trajectories and the output used to generate full network graphs ( Figure 2D–E , Figure 2—figure supplement 1 , Figure 2—figure supplement 2 ) . We then considered whether traditional stepwise directed evolution strategies , which require each substitution to directly improve the selected activity ( e . g . across iterative rounds of epPCR ) , could have plausibly generated either of variants 36_37 or 20_39 . For the purposes of this analysis we considered ‘improvement’ to be a > 16% increase in chloramphenicol EC50 for each step , as this was the average error across all EC50 measurements . In neither case was there a clear path from NfsA to the final variant that involved exclusively upward steps in the hypothetical evolutionary trajectory ( Figure 2D–F ) . Nevertheless , it was evident that the final two substitutions ( H215C and K222V for variant 36_37 , and K222R and S224Y for variant 20_39 ) did not contribute substantially to the overall chloramphenicol-detoxifying activity of each variant . Thus , we concluded that iterative evolutionary strategies could have plausibly generated NfsA variants exhibiting similar levels of chloramphenicol-detoxifying activity to 36_37 and 20_39 , but that there were very few accessible pathways for this ( Figure 2D–E ) . The dearth of accessible hypothetical evolutionary pathways suggested extensive epistasis , a phenomenon that several teams have previously observed when evolving enzymes ( Weinreich et al . , 2005; Poelwijk et al . , 2011; Kaltenbach , 2014; Yang et al . , 2019; Ben-David et al . , 2020 ) , where the fitness effects of certain substitutions only manifest when other substitutions have already been made . Most prominently , we noted that only one of the seven substitutions present in each of variants 36_37 and 20_39 significantly enhanced chloramphenicol detoxification when introduced on an individual basis ( Figure 3A ) . Although it was the same residue , R225 , that was substituted in each case , the substituting residues possessed very different chemical properties ( negatively charged aspartate in 20_39 versus non-polar valine in 36_37 ) . This , together with the observation that none of our top 30 selected variants had retained an arginine at position 225 ( Figure 1C , Supplementary file 1a ) , suggested that it was important for arginine 225 to be eliminated before the other active-site substitutions could make a discernible contribution to improved chloramphenicol detoxification . We also found evidence of higher-order epistasis beyond the requirement for elimination of R225 . For example , both of the top selected variants contained the substitutions S41Y and T219Y , neither of which conferred a significant improvement in chloramphenicol detoxification when introduced to NfsA individually ( Figure 3A ) or together ( Figure 3B ) . When each was introduced into an R225V or R225D background , S41Y yielded a significant increase in chloramphenicol detoxification , but T219Y did not ( Figure 3B ) . However , the combination of S41Y and T219Y together with R225V or R225D gave a further significant improvement ( Figure 3B ) . Numerous examples of sign epistasis can also readily be observed in the full network diagram ( e . g . the blue circles indicate a negative impact for certain combinations of substitutions; Figure 2D–E , Figure 2—figure supplement 1 , Figure 2—figure supplement 2 ) . For example , H215N ( present in variant 20_39 ) is detrimental to chloramphenicol detoxification activity when substituted into the R225D or R225D/S41Y backgrounds , and somewhat neutral in combination with R225D/S41Y/T291Y , but significantly enhances activity in combination with R225D/S41Y/T291Y/F227H ( Figure 3C ) . Overall , our data suggest that complex epistatic interactions render >99% of the 5040 hypothetical evolutionary pathways ( that might be traversed from wild-type NfsA to either 36_37 or 20_39 ) broadly inaccessible to iterative mutagenesis strategies . The hypothetical evolutionary trajectories depicted in Figure 2F highlight particularly pertinent intermediate combinations of mutations . We considered that interrogating the intermediate variants might shed light on the mechanistic basis of improved chloramphenicol detoxification . In particular , we wanted to determine how activity with a presumed native substrate like 1 , 4-benzoquinone was affected during the hypothetical evolutionary progression towards improved chloramphenicol detoxification . For this , the enzyme intermediates identified in the most probable stepwise evolutionary trajectory ( Figure 2F ) were purified as His-tagged proteins and in vitro kinetics assays attempted with both chloramphenicol and 1 , 4-benzoquinone ( Supplementary file 1b , Figure 2D–E , Figure 4—figure supplement 1 ) . From these data , it was evident that the first substitution of both hypothetical trajectories ( i . e . the elimination of R225 ) was sufficient to abolish nearly all 1 , 4-benzoquinone activity . A structure of NfsA with 1 , 4-benzoquinone in the active site was reported from the Hyde group that proposes the quinone is held in place through interactions between one of its carbonyl oxygens and the backbone of S41 , while the other carbonyl oxygen interacts with the guanidinium of R225 and/or the amine of Q67 ( Day , 2013 ) . Our data are consistent with R225 playing a key role in binding this substrate . Although both the R225V and R225D variants exhibited just-detectable levels of activity at a high concentration of 1 , 4-benzoquinone ( 100 µM; Figure 4A and B , Supplementary file 1b ) , no activity was discernible at lower concentrations , precluding measurement of kinetic parameters . The level of 1 , 4-benzoquinone activity remained unmeasurably low with all subsequent substitutions ( Figure 4A and B ) . The sustained loss of 1 , 4-benzoquinone activity throughout each trajectory is consistent with elimination of quinone competition promoting more effective chloramphenicol reduction . This is reinforced by examination of the chloramphenicol detoxification activities of the complete set of hypothetical evolutionary intermediates ( Figure 2D–E , Figure 2—figure supplement 1 , Figure 2—figure supplement 2 ) ; the variants that retained R225 were on average no better than wild-type NfsA at defending host cells against chloramphenicol ( mean fold improvement of 1 . 0 ± 0 . 5 ) , while cells expressing variants that contained the substitution R225V or R225D were on average able to tolerate 4 . 0 ± 2 . 4 fold higher chloramphenicol concentrations than those expressing wild-type nfsA ( Supplementary file 1c ) . The substitution S41Y that came next in both trajectories yielded a profound improvement in chloramphenicol KM , but also diminished kcat substantially ( Figure 4B and C and Figure 4F and G ) . We observed the same S41Y NfsA substitution in our previous PR-104A study , and concluded that this most likely enables planar stabilisation and stacking of nitroaromatic substrates between the isoalloxazine rings of flavin mononucleotide ( FMN ) and the introduced tyrosine ( Copp et al . , 2017 ) . It is likely that a similar phenomenon explains the improved affinity for chloramphenicol observed here , with the decrease in catalytic turnover also arising as a consequence of enhanced stabilisation of the Michaelis complex . The subsequent substitutions in each trajectory then act to ‘tune’ the system , exerting only minor effects on kcat , but overall yielding incremental improvements in chloramphenicol KM that largely mirror the improved chloramphenicol detoxification observed in vivo ( Figure 4C and E and Figure 4D and F ) . SDS-PAGE analysis confirmed that the expression levels were consistent for each intermediate variant throughout the hypothetical evolutionary progression , eliminating this as a variable exerting substantial influence on the relative activity levels in vivo ( Figure 4—figure supplement 2 ) . The above data suggest that selection of NfsA variants for enhanced chloramphenicol detoxification within E . coli was underpinned by substitution of R225 , eliminating competition with endogenous quinones , after which the improved chloramphenicol affinity conferred by additional substitutions became relevant . However , while our targeted mutagenesis strategy allowed us to comprehensively explore a defined region of sequence space , we could not rule out that other evolutionary solutions might be possible . For example , we considered that an alternative spectrum of mutations might improve chloramphenicol detoxification without eliminating quinone reduction . To investigate this , we used epPCR to introduce mutations across the entire nfsA coding sequence , then transformed E . coli 7NT cells and subjected them to selection on LB agar amended with 10 µM chloramphenicol . Growth inhibition assays were performed for 60 randomly chosen clones at a low ( 7 . 5 µM ) , medium ( 15 µM ) or high ( 30 µM ) concentration of chloramphenicol . These assays confirmed that all selected variants were able to detoxify chloramphenicol , with two appearing comparable to wild-type nfsA and the remainder providing more substantial host protection ( Supplementary file 1d ) . When each gene variant was sequenced , it was revealed that there were 50 unique sequences , each of which encoded a protein that was substituted at the R225 position ( Supplementary file 1e ) . In contrast , sequencing of 20 randomly-chosen clones grown without chloramphenicol selection identified no R225 substitutions , confirming that the epPCR library was not profoundly biased towards mutation of codon 225 ( Supplementary file 1f ) . We were surprised to note ( Supplementary file 1e ) that codon 225 was mutated exclusively at the first nucleotide position , giving substitutions R225C ( C→T , 47 times ) , R225S ( C→A , seven times ) or R225G ( C→G , six times ) , but not at the second nucleotide position ( which would have given rise to R225L/P/H ) . Of the latter three possibilities , R225L and R225H were both encoded by the degenerate NDT triplet used in our original combinatorial mutagenesis ( Figure 1B ) , but R225L was only recovered once in our top 30 variants and R225H not at all ( Supplementary file 1e ) . It may be that as individual substitutions , none of R225L/P/H confer sufficient improvement in chloramphenicol detoxification to have been selected in this experiment . Irrespective , that 100% of selected variants were mutated at codon 225 provides compelling evidence that eliminating quinone competition is a key first step towards evolving improved chloramphenicol detoxification , and that substitution of R225 is the most attainable way to achieve this . We were interested to discover how selection for improved activity with chloramphenicol had impacted unselected promiscuous activities of NfsA . We therefore used EC50 growth assays to assess the sensitivities of E . coli 7NT strains individually expressing either NfsA , variants 36_37 or 20_39 , or the hypothetical evolutionary intermediates thereof , to five structurally diverse nitroaromatic prodrugs ( Figure 5 ) . We anticipated that the loss of competitive inhibition by endogenous quinones might have generically enhanced activity with each of these prodrugs , resulting in heightened host cell toxicity . However , for four of the five prodrugs , host sensitivity was largely unchanged ( EC50 within a range of 0 . 8 to 2-fold that of the NfsA-expressing strain ) when expressing any of the variants ( Figure 5C–F ) . The exception was metronidazole , for which all variants exhibited similar gains in activity to chloramphenicol , despite the two compounds sharing little structural similarity ( Figure 5A–B ) . Moreover , the introduction of R225V or R225D substitutions into NfsA ( which largely eliminate 1 , 4-benzoquinone activity; Figure 4A–B ) did not improve reduction of all prodrugs , but only significantly enhanced activity with metronidazole and CB1954 ( Student’s t-test; Figure 5B , D ) . We therefore concluded that our selection for enhanced chloramphenicol detoxification was not driven exclusively by loss of the competing quinone activity , as this would have tended to also enhance activity with other alternate substrates . Whereas reduction of chloramphenicol is a detoxifying activity , reduction of metronidazole yields a toxic product . The serendipitous gains in metronidazole sensitivity that paralleled improved chloramphenicol detoxification inspired us to investigate whether these opposing activities might have useful molecular biology applications , by offering dual selectable and counter-selectable functionalities in a single gene . Counter-selectable markers , such as the sacB levansucrase gene from Bacillus subtilis , have multiple applications including the forced elimination of plasmids , and resolution of merodiploid constructs during allele exchange ( Stibitz , 1994 ) . However , they must typically be partnered with a selectable marker on the same DNA construct , to enable positive selection for the construct before its subsequent elimination . This occupies additional space , which is undesirable for size-restricted constructs , and means there is potential for the two genes to become separated by recombination events , leading to false positive or false negative outcomes . Because metronidazole is cheap , widely-available , and has no measurable bystander effect in E . coli ( i . e . unlike many other nitroaromatic prodrugs its toxic metabolites are confined solely to the activating cell [Chan-Hyams et al . , 2018] ) , we considered it ideally suited for counter-selection applications . We therefore tested the abilities of chloramphenicol to maintain , or metronidazole to force elimination of , plasmids bearing either 36_37 or 20_39 in E . coli 7NT . Cells were cultured for one hour in the absence of any selective compound , then plated on solid media amended with either 5 µM chloramphenicol or 10 µM metronidazole . The resulting colonies were then tested for retention or loss of the plasmid , respectively , with the expected outcome being realised in 100% of cases ( 94/94 colonies tested; Figure 5—figure supplement 1 ) . This suggests that our selected variants might indeed have useful applications as dual selectable/counter-selectable marker genes . By exploiting a powerful selection for antibiotic resistance , we were able to implement simultaneous mass-mutagenesis on an unprecedented scale , to amplify a promiscuous functionality . We acknowledge that this approach of focusing exclusively on eight key active-site residues means the reconstructed NfsA ‘engine’ is unlikely to be an optimal fit within the pre-existing chassis , and that further gains in activity would undoubtedly result by selecting residue substitutions in the second shell , or beyond . Nevertheless , we reasoned that our approach would allow us to gain comprehensive insight into key catalytic changes driving improved chloramphenicol detoxification , without being subject to the stochastic vagaries of epPCR , or the well-established phenomenon that it can only access a limited and unbalanced repertoire of residues ( on average , only 5 . 7 of the 19 alternative amino acids per codon position , with a bias towards similar residues [Hermes et al . , 1990] ) . We also initially considered that this approach might allow us to leap to a fitness peak that iterative random mutagenesis strategies would be unable to scale . However , when we examined every hypothetical evolutionary intermediate , we discovered this was not the case; although stepwise evolution would have been greatly constrained in the progression from wild-type NfsA to either of our top two variants , there were plausible trajectories to achieve these outcomes . Notably , the critical first step in any of these trajectories was the substitution of R225 , leading to near-total abrogation of the native quinone reductase activity , which was never restored and appears incompatible with the selected activity . The importance of eliminating R225 was emphasised by epPCR mutagenesis of nfsA , when 50/50 unique variants selected for improved chloramphenicol detoxification were found to have lost R225 , while 20/20 unselected variants retained it ( Supplementary files 1e , f ) . The strong trade-off between quinone and chloramphenicol reduction is a very different scenario to the predominantly weak trade-offs observed in previous laboratory evolution studies ( e . g . those reviewed by Kaltenbach et al . , 2016 ) . Even the more recent work of Ben-David et al . , 2020 who encountered an abrupt activity trade-off when they evolved the calcium-dependent lactonase mammalian paraoxonase-1 into an efficient organophosphate hydrolase , found that the native functionality could subsequently be restored and was not incompatible with the evolved one ( Ben-David et al . , 2013; Ben-David et al . , 2020 ) . By choosing to select for a novel enzymatic activity within the native cellular environment , we deliberately set out to explore the additional complexities of metabolic interference , which have potential to play dominant roles in shaping natural evolutionary outcomes . Copley , 2020 recently described an equation that succinctly summarises how the rate of a promiscuous reaction in the presence of a native substrate might be improved by ( 1 ) increasing the concentration of the enzyme; ( 2 ) increasing the ratio of promiscuous to native substrate; and/or ( 3 ) altering the active site to diminish substrate competition , by enhancing binding or turnover of the promiscuous substrate , or decreasing binding of the native substrate . In a landmark 2015 study she and co-workers experimentally demonstrated the importance of diminished substrate competition , focusing on a single key Glu to Ala substitution that enabled several orthologs of ProA ( L-gamma-glutamyl phosphate reductase , a key enzyme in proline synthesis ) to replace E . coli ArgC ( an N-acetyl glutamyl phosphate reductase required for arginine synthesis ) ( Khanal et al . , 2015 ) . Where measurable , all of the substituted variants showed decreased affinity ( increased KM ) for the native substrate; and in all but one case there was substantial improvement in kcat/KM for the promiscuous substrate as well ( Khanal et al . , 2015 ) . These findings are similar to our observation that elimination of quinone reductase activity from NfsA via substitution of R225 provided a platform for successive improvements in chloramphenicol affinity to amplify host cell resistance . Together , these examples support the proposal of Kaltenbach et al . , 2016 that during natural evolution of a promiscuous activity there is likely to be active selection against the original function , as well as our own supposition that most previous laboratory evolution studies have evaded this phenomenon by focusing on heterologous enzymes and/or exogenously applied substrates . Moreover , our observations that the unselected promiscuous activities of NfsA ( reduction of a structurally diverse panel of prodrugs ) were mostly unaffected is consistent with their central thesis , that positive selection alone does not lead to specialisation . The emerging picture is that evolution in the natural intracellular milieu involves both selection for the new function , and selection against the old . An interesting difference between our scenario and that of the Copley team is that NfsA is less essential to the fitness of its host cell than ProA ( e . g . deletion of nfsA does not impair E . coli growth even under oxidative stress from heavy metal challenge [Ackerley et al . , 2004] ) . This means that when a new stress is encountered and a promiscuous function becomes essential , as we have modelled here , the enzyme can potentially evolve without necessitating gene duplication to preserve the original function . An apparent ‘freedom to operate’ is manifest in the vast diversity of primary functionalities observed in the superfamily of nitroreductases that NfsA belongs to ( which spans activities as divergent as quinone reduction , flavin reduction to power bioluminescence , flavin fragmentation , dehalogenation and dehydrogenation [Akiva et al . , 2017] ) . Although this contrasts with the prevailing Innovation–Amplification–Divergence ( IAD ) model for natural enzyme evolution ( Bergthorsson et al . , 2007 ) , it may not be an exceptional scenario – rather , as previously argued by Newton et al . , 2015 it is likely that only a minority of enzymes in a cell are under active selection for improved catalytic activity at any time , and redundancy in metabolic networks means that there is latent evolutionary potential that can be immediately tapped to adapt to stress without the requirement of rare and costly gene duplication events . That a single mutation may suffice to rapidly amplify a desirable promiscuous activity simply by eliminating native substrate competition confers substantial ‘robustness’ at a cellular level , even if it means that individual enzymes may not be as robust as previously considered . Chloramphenicol , metronidazole , 2 , 4-dinitrotoluene and nitrofurazone were purchased from Sigma-Aldrich . CB1954 was purchased from MedKoo Biosciences . RB6145 was synthesised in-house at the Ferrier Institute , Victoria University of Wellington . To randomise the eight targeted residues of NfsA_Ec ( S41 , L43 , H215 , T219 , K222 , S224 , R225 , and F227 ) we designed a degenerate gene construct with NDT codons ( specifying Arg , Asn , Asp , Cys , Gly , His , Ile , Leu , Phe , Ser , Tyr , and Val ) at all positions other than 219 ( NHT codon , encoding Ala , Asn , Asp , His , Ile , Leu , Phe , Pro , Ser , Thr , Tyr , and Val ) and 222 ( VNG codon , encoding Ala , Gln , Glu , Gly , Leu , Lys , Met , Pro , Thr , Val , and two Arg codons ) . Initially a synthetic gene library was ordered from Lab Genius pre-cloned into plasmid pUCX ( Prosser et al . , 2013 ) , however , this only yielded 15% of the 252 million unique variants in our final collection . The remaining 85% were generated ourselves by ordering the same sequence as a gene fragment library from GenScript and ligating it into pUCX at the NdeI and SalI restriction sites . The combined libraries were used to transform E . coli 7NT , a derivative of strain W3110 bearing gene deletions of seven endogenous nitroreductases ( nfsA , nfsB , azoR , nemA , yieF , ycaK and mdaB ) and the tolC efflux pump ( Copp et al . , 2014a ) . Electrocompetent E . coli 7NT cells were generated as per Sambrook and Russell , 2001 , and the transformation efficiency was enhanced using a yeast tRNA protocol modified from Zhu and Dean , 1999 . Library selection was conducted on selective solid media containing LB agar supplemented with 100 µg . mL−1 ampicillin and either 45 or 47 . 5 µM chloramphenicol . Appropriate dilutions of the pooled library stock were spread over plates and incubated at 37°C for 40 hr . Dilutions of the library were also spread over non-selective solid media ( LB agar supplemented with 100 µg . mL−1 ampicillin ) to estimate the number of transformants included in each selection . Enzyme intermediates of NfsA_Ec 36_37 and 20_39 were ordered as synthetic gene fragments from Twist Biosciences and subsequently ligated into the NdeI and SalI restriction sites of the vectors pUCX ( for EC50 analysis ) or pET28 ( a ) + ( for purification of His6-tagged proteins ) . For growth inhibition assays , a 96-well microtitre plate with wells containing 200 µL LB medium supplemented with 0 . 2% glucose ( w/v ) and 100 µg . mL−1 ampicillin was inoculated with E . coli 7NT nitroreductase strains and incubated at 30°C with shaking at 200 rpm for 16 hr . A 15 µL sample of overnight culture was used to inoculate 200 µL of induction media ( LB supplemented with 100 µg . mL−1 ampicillin , 0 . 2% ( w/v ) glucose and 50 µM IPTG ) in each well of a fresh microtitre plate , which was then incubated at 30°C , 200 rpm for 2 . 5 hr . Aliquots of 30 µL apiece from these cultures were used to inoculate four wells of a 384-well plate ( two wells containing 30 µL induction media and two wells containing 30 µL induction media supplemented with 2 × the desired chloramphenicol concentration ) . The cultures were incubated at 30°C , 200 rpm for 4 hr . Cell turbidity was monitored by optical density at 600 nm prior to drug challenge and 4 hr post- challenge . The percentage growth inhibition was determined by calculating the relative increase in OD600 for challenged versus control wells . For EC50 growth assays , 100 µL of overnight cultures as above were used to inoculate 2 mL of induction media and incubated at 30°C , 200 rpm for 2 . 5 hr . A 30 µL sample of each culture was added to wells of a 384-well plate containing 30 µL of induction media supplemented with 2 × the final prodrug concentration . Each culture was exposed to 7–15 drug concentrations representing a 1 . 5-fold dilution series of drug and one unchallenged ( induction media only ) control . The cultures were incubated at 30°C , 200 rpm for 4 hr . Cell turbidity was monitored by optical density at 600 nm prior to drug challenge and 4 hr post-challenge . The EC50 value of technical replicates was calculated using a dose-response inhibition four-parameter variable slope equation in GraphPad Prism 8 . 0 . The EC50 values of biological replicates were averaged to provide a final EC50 value . Full details of how the evolutionary trajectory analysis was conducted are available at https://github . com/MarkCalcott/Analyse_epistatic_interactions/tree/master/Create_mutation_network ( Calcott , 2020; copy archived at swh:1:rev:62624a66324e230bf273b17591a537c691fa316f ) . Recombinant nitroreductases were cloned into the His6-tagged expression vector pET28 ( a ) + , expressed in BL21 and purified as His6-tagged proteins . Enzyme reactions were carried out in 60 µL reactions in 96-well plates with a 4 . 5 mm pathlength . All reactions were performed in 10 mM Tris HCl buffer pH 7 . 0 , 250 µM NADPH , an appropriate dilution of chloramphenicol or 1 , 4-benzoquinone substrate dissolved in DMSO ( 0–4000 µM chloramphenicol and 100 µM 1 , 4-benzoquinone ) , made up to volume with ddH2O . Reactions were initiated with the added of 6 µL of enzyme ( 8 µM or an appropriate concentration ) and the linear decrease in absorbance was monitored at 340 nm measuring the rate of NADPH depletion as an indirect measured of substrate reduction . As neither chloramphenicol nor 1 , 4-benzoquinone interfere with the absorbance at 340 nm , the extinction coefficient of NADPH at 340 nm was used ( chloramphenicol = 12 , 400 M−1cm−1 and p-benzoquinone = 6 , 220 M−1cm−1 , as two molecules of NADPH are required to reduce chloramphenicol to the hydroxylamine form , while only one is required for the reduction of p-benzoquinone to the quinol ) . Technical replicates were plotted using Graphpad Prism 8 . 0 software and non-linear regression analysis and Michaelis-Menten curve fitting was performed . E . coli 7NT pUCX::nfsA variant strains were used to inoculate 200 μL LB media supplemented with 0 . 2% ( w/v ) glucose and Amp . Cultures were incubated overnight at 30°C , 200 rpm . The next day , 100 μL of the overnight culture was used to inoculate 2 mL of LB induction medium ( LB supplemented with 0 . 2% ( w/v ) glucose , Amp and 50 μM IPTG ) . Day cultures were grown at 30°C , 200 rpm for 6 . 5 hr , after which the cultures were pelleted by centrifugation at 2500 × g for 5 min . The supernatant was decanted and the cell pellets resuspended in ~100 μL of LB medium , after which the OD600 of a 1:100 dilution was measured . Cell cultures were normalised by dilution with additional LB medium so that a 1 in 100 dilution would give an OD600 reading of 0 . 1 . A 12 μL sample of each culture was mixed with 5 × SDS loading buffer , heated at 95°C for 5 min and subjected to SDS-PAGE analysis on a 15% acrylamide gel . Error-prone PCR of NfsA_Ec was performed using a Gene Morph II kit ( Agilent ) as described by Copp et al . , 2014b . An error rate of 3 . 25 mutations per amplicon ( calculated from Supplementary file 1f ) was achieved by using 35 amplification cycles from 1 . 5 ng of purified PCR product as template per 25 μL reaction ( prepared using primers pUCX_fwd; GACATCATAACGGTTCTG and NfsA_rev; GGGTCGACTTAGCGCGTCGCCCAACCCTG ) . Amplicon size and quality were assessed by agarose gel electrophoresis , then amplicons were digested with NdeI and SalI and ligated into similarly-digested pUCX plasmid . The resulting ligation was used to transform the screening strain E . coli 7NT , generating a library of approximately 3 × 106 variants . Selection of improved variants from this library was carried out as described for the multi-site directed mutagenesis library above , but on LB agar plates supplemented with 10 µM chloramphenicol as well as 5 µM IPTG and 100 µg . mL−1 ampicillin . A single colony of an E . coli 7NT cells expressing nfsA_Ec 36_37 or 20_39 was used to inoculate a 3 mL overnight culture of LB supplemented with 100 µg . mL−1 ampicillin . The next day , 100 µL of each overnight culture was used to inoculate 10 mL fresh LB medium in a 125 mL baffled conical flask . The culture was grown at 37°C , 200 rpm for 1 hr then the OD600 of the flask was determined . An appropriate dilution of each culture was plated on agar plates containing either LB-only , or LB amended with 10 µM metronidazole or 5 µM chloramphenicol . At 10 µM metronidazole , cells expressing NfsA_Ec 36_37 or 20_39 could not grow but cells bearing no plasmid could , while the reverse scenario applied with 5 µM chloramphenicol . Plates were incubated at 37°C for 16 hr ( LB-only or LB + metronidazole ) or 40 hr ( LB + chloramphenicol ) . To confirm the presence/absence of the plasmid bearing 36_37 or 20_39 , 47 colonies from each condition were streaked on LB agar plates supplemented with 100 µg . mL−1 ampicillin and incubated at 37°C for 16 hr , with growth indicating presence of the plasmid and no growth indicating absence of the plasmid . The same 47 colonies , alongside a negative control were further tested in a PCR screen with nfsA_Ec forward and reverse specific primers ( Prosser et al . , 2013 ) . A band of approximately 720 bp indicated presence of the plasmid , while no band indicated absence of the plasmid . Unless otherwise stated , data are given as the mean ± standard deviation . The software programme GraphPad Prism 8 . 0 was used for all statistical analyses . Differences between measured EC50 values of enzyme variants were determined by an unpaired Student’s t-test . A p-value of ≤0 . 05 was considered statistically significant with *p-value ≤0 . 001 , p-value ≤0 . 01 and *p-value ≤0 . 05 .	In the cell , most tasks are performed by big molecules called proteins , which behave like molecular machines . Although proteins are often described as having one job each , this is not always true , and many proteins can perform different roles . Enzymes are a type of protein that facilitate chemical reactions . They are often specialised to one reaction , but they can also accelerate other side-reactions . During evolution , these side-reactions can become more useful and , as a result , the role of the enzyme may change over time . The main role of the enzyme called NfsA in Escherichia coli bacteria is thought to be to convert molecules called quinones into hydroquinones , which can protect the cell from toxic molecules produced in oxidation reactions . As a side-reaction , NfsA has the potential to protect bacteria from an antibiotic called chloramphenicol , but it generally does this with such low efficacy that the effects are negligible . Producing hydroquinones is helpful to the cell in some situations , but if bacteria are regularly exposed to chloramphenicol , NfsA’s role aiding antibiotic resistance could become more important . Over time , the enzyme could evolve to become better at neutralising chloramphenicol . Therefore , NfsA provides an opportunity to study the evolution of proteins and how bacteria adapt to antibiotics . To see how evolution might affect the activity of NfsA , Hall et al . generated 250 million E . coli with either random or targeted changes to the gene that codes for the NfsA enzyme . The resulting variants of NfsA that were most effective against chloramphenicol all had a change that eliminated the enzyme’s ability to convert quinones . This result demonstrates a key trade-off between roles for NfsA , where one must be lost for the other to improve . These results demonstrate the interplay between a protein’s different roles and provide insight into bacterial drug resistance . Additionally , the experiments showed that the bacteria with improved resistance to chloramphenicol also became more sensitive to another antibiotic , metronidazole . These findings could inform the fight against drug-resistant bacterial infections and may also be helpful in guiding the design of proteins with different roles .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "evolutionary", "biology", "biochemistry", "and", "chemical", "biology" ]	2020	Intracellular complexities of acquiring a new enzymatic function revealed by mass-randomisation of active-site residues
Maintaining attention at a task-relevant spatial location while making eye-movements necessitates a rapid , saccade-synchronized shift of attentional modulation from the neuronal population representing the task-relevant location before the saccade to the one representing it after the saccade . Currently , the precise time at which spatial attention becomes fully allocated to the task-relevant location after the saccade remains unclear . Using a fine-grained temporal analysis of human peri-saccadic detection performance in an attention task , we show that spatial attention is fully available at the task-relevant location within 30 milliseconds after the saccade . Subjects tracked the attentional target veridically throughout our task: i . e . they almost never responded to non-target stimuli . Spatial attention and saccadic processing therefore co-ordinate well to ensure that relevant locations are attentionally enhanced soon after the beginning of each eye fixation . The processing of vision and visuospatial attention mostly proceeds via retinotopic representations in the brain ( Cavanagh et al . , 2010; Wurtz , 2008 ) . Since each saccadic eye-movement leads to a change in the retinotopic representation of the visual scene , maintaining attention at a task-relevant spatial location across a saccade necessitates a rapid , saccade-synchronized shift of attentional modulation from the neuronal population representing the task-relevant location before the saccade to the one representing it after the saccade ( Marino and Mazer , 2016; Yao et al . , 2016 ) . Currently , perceptual measurements in humans suggest a neuronal attention shift that starts before the saccade and continues after the saccade ends ( Rolfs et al . , 2011; Szinte et al . , 2015; Golomb et al . , 2008 , 2010a , 2011; Jonikaitis et al . , 2013 ) . However , because these previous measurements used coarse temporal sampling and/or long-duration attentional probes , the precise time at which spatial attention becomes fully allocated to the task-relevant location after the saccade remains unclear . Here , using a fine-grained temporal analysis of human peri-saccadic detection performance in an attention task , we show that spatial attention is fully available at the task-relevant location within 30 milliseconds after the saccade . This rapid post-saccadic recovery of performance in our attention task indicates that retinotopic attentional shifts occur within the time required to recover from saccadic suppression of vision . Subjects almost never responded to the distractor change , indicating that they tracked the attentional target veridically throughout the task . Spatial attention and saccadic processing therefore co-ordinate well to ensure that relevant locations are attentionally enhanced soon after the beginning of each eye fixation . We measured human peri-saccadic attentional allocation by combining an endogenous spatial attention task with a visually-guided saccade . Human subjects had to make a saccade to follow a fixation point when it jumped to a new location , and concurrently , pay attention throughout the trial to a target moving random-dot pattern ( RDP ) presented eccentrically among three or five physically similar distractor RDPs ( Figure 1 , and 'Materials and methods' ) . We measured the subjects’ attentional allocation by their ability to detect a brief ( 23 . 5 ms ) change in target motion , while ignoring similar changes in the distractors . The target and distractor changes occurred at different times around the saccade , allowing us to report for the first time , peri-saccadic performance in an attention task with fine-grained temporal precision . The intervening saccade poses a challenge for the attentional system , because due to the retinotopic shift of the target location across the saccade , the attentional system needs to shift its modulatory influence from the neuronal population representing the target before the saccade to the neuronal population representing the target after the saccade . By using a fixed timing and location for the fixation point jump , we could isolate the dynamics of this attentional remapping process and minimize its interaction with the dynamics of attentional allocation to other exogenous visual events . We therefore made the saccade spatially and temporally predictable by having the fixation point jump at the same time and to the same location on each trial so that the subject could best focus on the target pattern . 10 . 7554/eLife . 18009 . 003Figure 1 . Task-design and timing . Human subjects performed a task that involved attending to a target ( marked with a white T and always at the same location ) presented as one among four ( A ) or six ( B ) moving RDPs while also making a visually guided saccade if the fixation point jumped seven degrees to the right ( 1235 ms after trial onset ) . The subjects were instructed to respond with a key-press when the target RDP briefly changed speed and direction , but to ignore similar changes in any of the remaining RDPs . Target and distractor changes occurred at different times around the saccade , enabling the measurement of peri-saccadic performance in this attention task . Two different task-difficulties were used in Experiment 2 , while six RDPs were used in Experiment 3 instead of four . There were also minor differences in timing between the three tasks . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 003 In Experiment 1 , we looked at the peri-saccadic performance of 8 subjects ( pooled data in Figure 2A , individual subject-data in Figure 2—figure supplement 1 ) . At times well before and well after the saccade , subjects almost always detected the target change and their performance was near 100% . Performance began to drop around the time the fixation point jumped ( dashed vertical line in Figure 2A ) , as expected from the previously reported diversion of pre-saccadic attentional resources towards the saccade task ( Deubel and Schneider , 1996; Montagnini and Castet , 2007; Hoffman and Subramaniam , 1995 ) and the post-saccadic retinotopic location ( Szinte et al . , 2015 ) . The performance then dropped steeply right before the saccade , as expected from the drop in visual sensitivity due to saccadic suppression ( Diamond et al . , 2000; Dorr and Bex , 2013; McConkie and Loschky , 2002 ) . Importantly , our data show ( for the first time in an attention task , to our knowledge ) that performance recovered back to baseline within 30 ms of saccade offset ( Figure 2A ) . The rapid post-saccadic recovery of performance indicates that attention is allocated to the task-relevant location within 30 ms after the saccade ends . The rapid time-course of recovery resembles that previously shown for saccadic suppression of visual performance in tasks where visual sensitivity was probed around a saccade using a briefly flashed change , but without any requirement to maintain attention on a target across a saccade ( Diamond et al . , 2000; Dorr and Bex , 2013; McConkie and Loschky , 2002 ) . This suggests that while resumption of visual function after a saccade is constrained by the recovery from saccadic suppression ( Krekelberg , 2010 ) , the peri-saccadic attentional shift necessitated by retinotopic visual processing does not impose an additional temporal cost on this recovery . The rapid post-saccadic attentional availability at the target location that we infer from our data is consistent with the only physiological data on this issue: in a mental curve-tracing task similar to ours with a fixed attentional target , attentional effects in monkey V1 emerge approximately 80 ms after the end of the saccade ( Khayat et al . , 2004 ) . Given an onset latency of approximately 30 to 50 ms in monkey V1 , MT and LIP ( Khayat et al . , 2004; Bair et al . , 2002; Bisley et al . , 2004 ) , a change occurring 30 ms after saccade offset would reach the visual cortex at approximately the time when its neurons representing the target after the saccade are attentionally enhanced . 10 . 7554/eLife . 18009 . 004Figure 2 . Rapid post-saccadic recovery of performance . ( A ) Detection-performance ( hit-rate ) of motion-direction drops around the time of the saccade and recovers within 30 ms after the saccade . The figure shows the mean detection-performance ( and 95% confidence bands ) for all trials pooled over 8 subjects calculated in non-overlapping 10 ms time-bins of the abscissa ( time of target-change relative to saccade offset ) . The inset shows the same data , focusing on the time between −100 and 100 ms . Data from individual subjects show little inter-individual variability in the time-course of recovery ( Figure 2—figure supplement 1 ) . The triangle indicates the earliest time ( 30 ms ) at which performance is statistically indistinguishable from that over the 100 to 500 ms time-period ( using Boschloo’s exact test; see 'Materials and methods' ) . The dashed vertical line indicates the mean time of fixation-point offset and the stippled vertical line indicates the mean saccade onset time . See also Figure 2—figure supplement 1 and 3 . ( B ) Similar results were obtained when two different task-difficulties were used ( data pooled over 5 subjects ) . The data from the higher-difficulty task ( in red ) show that the rapid recovery is not an artifact of a ceiling effect on performance . Data plotted using 20 ms time-bins . Figure conventions as in Figure 2A . See also Figure 2—figure supplement 2 for data from individual subjects . Figure 2—figure supplement 4 and 5 replot the same data as in Figure 2A and B and in the same format , but Figure 2—figure supplement 4 uses the time of target-change relative to saccade onset and Figure 2—figure supplement 5 only includes trials where a fixation window of 0 . 5° was used ( see corresponding legends for details ) . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 00410 . 7554/eLife . 18009 . 005Figure 2—source data 1 . Data plotted in Figure 2A and Figure 2—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 00510 . 7554/eLife . 18009 . 006Figure 2—source data 2 . Data plotted in Figure 2B and Figure 2—figure supplement 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 00610 . 7554/eLife . 18009 . 007Figure 2—source data 3 . Data plotted in Figure 2—figure supplement 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 00710 . 7554/eLife . 18009 . 008Figure 2—source data 4 . Data plotted in Figure 2—figure supplement 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 00810 . 7554/eLife . 18009 . 009Figure 2—source data 5 . Data plotted in Figure 2—figure supplement 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 00910 . 7554/eLife . 18009 . 010Figure 2—figure supplement 1 . Individual subjects – rapid post-saccadic recovery of performance . Data from the eight individual subjects whose pooled data appear in Figure 2A . Triangles indicate time at which performance reaches 80% of baseline ( see 'Materials and methods' ) ; the values of this time are 24 , 24 , 23 , 19 , 24 , 22 , 23 and 35 ms for Subjects BA , JV , JS , JK , KW , LV , MK and SP respectively . Data plotted using 20 ms time-bins . All other conventions as in Figure 2A . Related to Figure 2A . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 01010 . 7554/eLife . 18009 . 011Figure 2—figure supplement 2 . Individual subjects – rapid post-saccadic recovery of performance for two task difficulties . Data from the five individual subjects whose pooled data appear in Figure 2B . Triangles indicate time at which performance reaches 80% of baseline ( see 'Materials and methods' ) ; the values of this time are 25 , 24 , 29 , 30 , and 25 ms for the easier task and 29 , 40 , 32 , 30 , and 21 ms for the harder task for subjects JV , LV , MK , MS and TY respectively . All other conventions as in Figure 2B . Related to Figure 2B . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 01110 . 7554/eLife . 18009 . 012Figure 2—figure supplement 3 . Results from Experiment 3 , where distractor changes are more numerous and more salient also show rapid post-saccadic recovery of performance ( within 30 ms ) , and no evidence for post-saccadic retinotopic persistence or pre-saccadic predictive shifts . Pooled data in A and data from individual subjects in B . Triangles in B indicate time at which performance reaches 80% of baseline ( see 'Materials and methods' ) ; the values of this time are 28 , 35 , 30 , and 26 ms for subjects JV , JS , LV and MK respectively . All other conventions as in Figure 2A . Related to Figure 2A . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 01210 . 7554/eLife . 18009 . 013Figure 2—figure supplement 4 . Post-saccadic recovery of performance plotted relative to saccade onset . This figure is identical to Figure 2 , except that the performance ( in Experiments 1 and 2 ) is plotted as a function of the time of target change relative to saccade onset . Recovery times relative to saccade onset are 60 ms in A and 70 ms for both tasks in B . Related to Figure 2A and B . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 01310 . 7554/eLife . 18009 . 014Figure 2—figure supplement 5 . Post-saccadic recovery of performance plotted with a smaller fixation window . This figure is identical to Figure 2 , except that we only included trials where the horizontal and vertical eye-positions did not diverge by more than 0 . 5° during fixation from their median values ( see 'Materials and methods' ) . Estimated recovery times are 20 ms in A and 30 ms for both tasks in B . Despite the much smaller fixation window , A and B include 59% and 78% of the trials in Figure 2A and B respectively . Related to Figure 2A and B . DOI: http://dx . doi . org/10 . 7554/eLife . 18009 . 014 It is possible that though we report a rapid recovery in Experiment 1 , the true recovery was actually slower , but was masked by the fact that performance had already reached its maximum value of 100% within 30 ms of saccade offset . We therefore performed a similar experiment ( Experiment 2 ) with two task difficulties , where peak performance on the harder task was clearly below 100% ( Figure 2B ) . Once again , performance recovered to baseline levels within 30 ms of saccade offset in both the easier and the harder task , indicating that our estimate of a rapid recovery time for performance was genuine and not an artifact due to a ceiling effect . The recovery time-course after the saccade also did not seem to depend on saccade latency ( Figure 3 ) . Very similar performance was observed when we grouped trials based on saccade latency into three groups: putative predictive saccades ( latencies from 0 to 75 ms ) , express saccades ( latencies from 75 to 125 ms ) and regular-latency saccades ( latencies from 125 to 250 ms ) . This indicates that though various differences between these different kinds of saccades have been noted and these different kinds of saccades have been speculated to arise via different neural pathways ( Bronstein and Kennard , 1987; Chen et al . , 2013; Cotti et al . , 2009; Deubel , 1995; Gaymard et al . , 1998; Pierrot-Deseilligny et al . , 2002; Shelhamer and Joiner , 2003 ) , peri-saccadic attentional shifts seem to proceed with a similar time-course in each case . If the peri-saccadic attentional shift is not temporally well-synchronized with the saccade , attention will be peri-saccadically allocated to irrelevant spatial locations . In fact , prior findings measuring discrimination performance for attentional probes at different locations suggest that by about 75 ms before the saccade , attentional enhancement could be seen at the ‘post-saccadic’ retinotopic location ( which would be the wrong pre-saccadic spatial location ) ( Rolfs et al . , 2011 ) . Other studies report that after the saccade , attention stays at the pre-saccadic retinotopic location ( which would be the wrong post-saccadic spatial location ) for up to 100 ms after the saccade ( Golomb et al . , 2008 , 2010b ) . The predictive emergence of attention is consistent with single-neuron data from monkeys showing predictive responses in different attentional control areas of the brain ( Wurtz , 2008; Duhamel et al . , 1992 ) , while imaging data from humans have been presented as evidence for persistent retinotopic neural activity ( Golomb et al . , 2010a ) . Results from a more recent detailed study indicate that peri-saccadic attentional spread and dynamics may show complex patterns: patterns consistent with predictive shifts , transient retinotopic persistence as well as rapid post-saccadic availability of attention at the task-relevant location were seen ( Jonikaitis et al . , 2013 ) . In our data , we found no effect of predictive or delayed shifts on the rate of responding to distractor changes ( false-positives ) . In both Experiments 1 and 2 , overall , subjects responded to a distractor change on only 2 . 2% and 2% of trials respectively . Distractor changes occurred either at the distractor vertically below the target ( a control ) or at the distractor to the right of the target ( that tested post-saccadic retinotopic persistence of the pre-saccadic attentional focus ) . In the time interval immediately after the saccade ( 0–150 ms ) , the data from both Experiments showed no statistically significant increase in the rate of false-positives due to retinotopic persistence ( compared to the control location; all p-values>0 . 16 , Boschloo’s test; Supplementary file 1A ) . An additional experiment ( Experiment 3; Figure 2—figure supplement 3 and Supplementary file 1B ) where we changed the task design to test both pre-saccadic predictive shifts and post-saccadic retinotopic persistence ( while making distractor changes more numerous and salient ) also led to a false-positive rate of less than 1 . 4% and no evidence for an effect of either predictive shifts or retinotopic persistence on the false-positive rate . Subjects thus tracked the attentional target veridically throughout our task , and the peri-saccadic spread of attention to irrelevant spatial locations reported in previous studies does not seem to have any manifest effects in our task . One important difference between our task and previous tasks was that we included only one attended location within each trial , and stimuli at all other locations were distractors that the subject had to ignore . In contrast , the previous tasks required subjects to report a probe stimulus that could appear at any of the stimulus locations . There were no distractor stimuli , and attention was instead manipulated by using a dual-task ( Rolfs et al . , 2011; Golomb et al . , 2008 , 2010b ) or using an exogenous cue ( Jonikaitis et al . , 2013 ) . The fact that all stimulus locations on each trial were potential targets in the previous studies may have led the subjects to adopt a different attentional-set compared to the subjects in our study . Alternatively , the previous results may have reflected only an attentional effect on probe visibility , while the results in our task may additionally reflect the effect of attention on distractor filtering . In current theoretical accounts of attention ( Eckstein et al . , 2009; Lu and Dosher , 1998 ) , the effects of attention on distractor filtering and probe visibility correspond to the distinct effects of attention on selection/weighting and sensory signal enhancement respectively . In this scenario , distractor filtering due to the attentional selection/weighting of sensory signals across the visual field is well-synchronized to the saccade and therefore does not spread to irrelevant spatial locations . In contrast , attentional signal enhancement , but not distractor filtering , is influenced by the predictive shifts and post-saccadic retinotopic persistence of attentional modulation in the brain . As a result , in the previous tasks without a distractor filtering component , the perceptual visibility of probes at irrelevant locations was improved . In our task , any enhanced sensory signal from distractor locations would continue to be down-weighted and filtered out and the subjects would not respond to them . We emphasize that this is only one plausible explanation , and theoretical models of attention are sufficiently complex and flexible to admit alternative explanations . Even more generally , the observed differences could be a result of task-dependent ( or even entirely different ) attentional mechanisms operating in the different tasks . Extensive measurements and model-testing will be necessary to disambiguate the different possibilities . Our data represent an important advance in the ongoing discussion about the shifts of spatial attention around the time of a saccade ( Cavanagh et al . , 2010; Marino and Mazer , 2016; Rolfs and Szinte , 2016; Mayo and Sommer , 2010; Melcher , 2010 ) . We provide the first temporally fine-grained measurements of detection performance in an attention task in the critical immediate post-saccadic period ( 0 to 100 ms following saccade offset ) . Our data show that performance fully recovers soon after the end of the saccade , indicating that the correct stimulus is attended to during this immediate post-sacadic period when visual sensitivity is known to be highest ( Ibbotson and Krekelberg , 2011 ) . The rapid time-course of recovery resembles the time-course previously shown for the recovery of visual function from saccadic suppression , suggesting that the retinotopic attentional shift does not impose an additional temporal cost on the resumption of visual function after a saccade . Further , our data indicate that under our task conditions , subjects very rarely confuse a distractor stimulus for the target . Spatial attention and saccadic execution thus appear to co-ordinate well to ensure that relevant objects are attentionally enhanced soon after the beginning of each eye fixation . These findings are likely to lead to a much better understanding of the impact of peri-saccadic changes in neural activity on visual processing . 10 subjects ( 4 males , 6 females , ages 21–30 years ) participated in the study , including two of the authors ( MK and TY ) . 8 of the subjects ( excluding the two authors ) were naïve to the purpose of the Experiment . 8 , 5 and 4 subjects participated in Experiments 1 , 2 and 3 respectively; of these , 3 subjects ( including the author MK ) participated in all 3 Experiments . All subjects were right-handed and reported normal or corrected to normal vision . All naive participants received monetary compensation for each session . Each subject started the experiment with a training session to become familiar with the tasks . The experiments were performed in several blocks over one or two days . Subjects were given verbal and written instructions about the task . The study was performed in accordance with institutional guidelines for experiments with humans , adhered to the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the Georg-Elias-Müller-Institute of Psychology , University of Göttingen . Each subject gave informed written consent prior to participating in the study . Subjects were seated in a dimly lit room at a viewing distance of 57 cm from the screen with their head resting on a chin and forehead-rest . The only light source in the room was the light from the display monitor . A computer keyboard was used for recording subject responses . All aspects of the experiment were controlled by custom software running on an Apple Macintosh computer . The eye-position was monitored by an infra-red video-based eye-tracker ( iView X software running on an SMI Hi-Speed 1250 tracker , SMI GmbH , Germany ) at 1000 Hz . The stimuli were displayed on a 1600 by 1200 pixels ( 40 by 30° ) CRT monitor with a fresh rate of 85 Hz . The display background was always grey ( 40 cd/m2 ) , and all the visual stimuli were black ( 0 . 7 cd/m2 ) . We describe Experiment 1 first . Experiments 2 ( Figure 2B ) and 3 ( Figure 2—figure supplement 3 ) are variants of Experiment 1 . Data processing was done using MATLAB ( Mathworks Inc , Natwick , MA ) , except for the exact test of binomial proportions performed using the Exact package ( Calhoun , 2015 ) in R ( R Core Team , 2016 ) . We detected saccades using a standard velocity-threshold algorithm: onset ( and offset ) times were determined by when the eye velocity exceeded ( and then dropped below ) an individualized threshold ( set to between 40 and 70° per second , fixed for each subject ) . This threshold value was set to lie clearly above the peak excursions of the baseline noise in the eye-velocity traces , and the algorithm was validated by visual inspection for each subject . By considering the saccade to have ended when the velocity dropped below a threshold value well above the baseline noise ( and when the eye was still moving ) , our threshold criterion provides a conservative , i . e . early definition of saccadic end-point and therefore a longer estimate of the recovery time for perceptual performance . Our threshold-setting detected the primary saccade close to its end , but excluded post-saccadic dynamic overshoots or glissades ( Bahill et al . , 1975; Nyström et al . , 2013 ) . Setting a lower threshold and including these small eye-movements led to an even lower estimate of the recovery time of spatial attention ( around 20 ms , instead of the 30 ms we report ) . We only included trials where the subjects made a single saccade to the saccade target , and this saccade was made between 50 ms before and 450 ms after the time when the fixation point jumped . While these limits are arbitrary , they are not critical and our results remain robust for other reasonable choices , consistent with the lack of an effect of saccade latency on performance ( Figure 3 ) . Trials with a fixation break were excluded from further analysis . Early responses before the target-change were extremely rare: early responses constituted only 1 . 2 , 1 and 0 . 7% of trials in Experiments 1 , 2 and 3 respectively , even when counting all early responses that were potentially responses to the distractor change in this number . Responses to the distractor change ( false-alarms; see Results and discussion ) were also extremely rare; we considered all early responses within 800 ms of a distractor change as a response to the distractor . We could therefore exclude trials with early responses as well and simply define performance using the hit-rate ( the proportion of target-changes that were correctly detected ) . We plotted the performance as a function of the time of target-change relative to saccade offset: since the speed and direction-change lasted 2 frames ( at a refresh rate of 85 Hz ) , we used the timing of the second frame to define the time of target-change since this was the conservative choice given our focus on the rapid performance recovery after the saccade . For the pooled analyses ( Figures 2 , 3 and Figure 2—figure supplement 3A ) , we pooled the trials from all subjects and then calculated the mean and 95% Wilson-score confidence intervals ( Brown et al . , 2001 ) over successive non-overlapping time-bins of the X-axis variable ( 10 ms in Figure 2A , 20 ms in Figure 2B , 50 ms in Figure 3 and 10 ms in Figure 2—figure supplement 3A ) . To estimate the time at which performance recovered to its post-saccadic baseline , we first estimated the baseline performance ( proportion of correct trials ) from 100 to 500 ms following saccade offset and then compared this value ( using Boschloo’s exact test of binomial proportions and a one-sided p-value for the peri-saccadic performance being lower than the baseline performance ) to the performance in successive non-overlapping 10 ms time-bins from 0 to 100 ms following saccade offset . The starting-point of the first non-significant bin ( i . e . p>0 . 1 ) was taken as the time of recovery . Using a one-sided p-value and a cutoff of 0 . 1 are both conservative choices in our situation since they would only increase the estimated time of recovery . Using a cutoff of p>0 . 05 for non-significance reduced the estimated time of recovery in Experiment 1 ( Figure 2A ) to 20 ms , but did not affect any of the other estimates . Similarly , the use of Boschloo’s test also increases the power to detect a significant difference , and is therefore conservative for our purposes ( Berger , 1994; Mehrotra et al . , 2003 ) . The time estimated using 10 ms bins was further confirmed with a similar procedure using 5 ms bins . In all cases ( Figures 2A , B and 3 ) , the estimated value was 30 ms , meaning that the performance in the time-bin from both 30 to 35 ms and 30 to 40 ms was not significantly different from baseline . For Experiment 1 ( Figure 2A ) , there were at least 48 trials in each 10 ms time-bin from 0 to 40 ms . For the other experiments , the values were: Experiment 2 ( Figure 2B ) – 42 trials for the easy task , and 39 trials for the hard task and Experiment 3 ( Figure 2—figure supplement 3 ) – 31 trials . These trial numbers gave us 80% power to detect a reduction to 90% ( Experiment 1 ) , 90% ( Experiment 2 , easy ) , 81% ( Experiment 2 , hard ) and 83% ( Experiment 3 ) of the baseline value , and the estimated recovery times agreed well with the values one would estimate based on visual inspection of the curves . For the individual subjects ( Figure 2—figure supplements 1–3 ) , the time-courses appear very similar to the pooled averages . However , formal statistical testing was precluded by the small number of trials in each bin , since the estimates of recovery time based on statistical significance would be shorter than the estimate for the pooled averages ( and therefore anti-conservative ) . We therefore marked the estimated time at which the performance reached 80% of the baseline probability on the individual subject plots in Figure 2—figure supplements 1 to 3 . This value was calculated via simple linear interpolation and by visual inspection , captures the time-course of recovery quite well . We collected data from a planned number of 8 subjects in Experiment 1 . Since the data from the 8 subjects in Experiment 1 showed very similar time-courses , we collected data from a smaller number of 5 and 4 subjects respectively in the additional Experiments ( 2 and 3 ) .	When we look at a scene , our gaze does not move continuously across it . Instead , our eyes move discontinuously , shifting gaze rapidly from point to point to focus on different locations in the scene . These eye movements are known as saccades , and during them the brain temporarily and selectively stops processing visual information . In the brain , a particular area of a scene is represented by different neurons before and after a saccade . Paying attention to a relevant location in a scene across an eye movement therefore requires the brain to shift its attentional effects from the neurons that represented that location in the scene before the saccade to the set of neurons that do so after the saccade . Ideally , this shift should happen rapidly and be synchronized with the eye movement . Exactly how long it takes for attention to emerge at a relevant location after a saccade was not clear because attention had not been recorded on a fine enough time-scale immediately after an eye movement . Yao et al . have now addressed this issue in a series of experiments that asked volunteers to focus their eyes on a fixed point . The volunteers had to follow the point with their eyes as it jumped to a new location , and at the same time had to look out for a change in the movement of a pattern of random dots . The results reveal that attention is fully available at the relevant location within 30 milliseconds after the saccade . In fact , the 30-millisecond delay in the emergence of attention matches the period during which vision is suppressed during a saccade . Thus , the change in the brain’s focus of attention coordinates with the saccadic eye movement to ensure that attention can be fixed on a relevant location as soon as possible after the eye movement ends . More studies are now needed to investigate how the brain coordinates its attention and eye-movement processes to synchronize the shift in attention with the eye movement .	[ "Abstract", "Introduction", "Results", "and", "discussion", "Materials", "and", "methods" ]	[ "short", "report", "neuroscience" ]	2016	Visual attention is available at a task-relevant location rapidly after a saccade
Duplication of the yeast centrosome ( called the spindle pole body , SPB ) is thought to occur through a series of discrete steps that culminate in insertion of the new SPB into the nuclear envelope ( NE ) . To better understand this process , we developed a novel two-color structured illumination microscopy with single-particle averaging ( SPA-SIM ) approach to study the localization of all 18 SPB components during duplication using endogenously expressed fluorescent protein derivatives . The increased resolution and quantitative intensity information obtained using this method allowed us to demonstrate that SPB duplication begins by formation of an asymmetric Sfi1 filament at mitotic exit followed by Mps1-dependent assembly of a Spc29- and Spc42-dependent complex at its tip . Our observation that proteins involved in membrane insertion , such as Mps2 , Bbp1 , and Ndc1 , also accumulate at the new SPB early in duplication suggests that SPB assembly and NE insertion are coupled events during SPB formation in wild-type cells . The goal of molecular , biochemical and cell biological studies is to elucidate the function of cellular components and understand how proteins and the complexes they form interact in vivo . Few methods are available to examine large protein structures such as microtubule-organizing centers ( MTOCs ) in intact cells . However , elucidating the mechanism of MTOC assembly is important since MTOCs perform a variety of functions in the cell , including cell signaling , cilia assembly , intraflagellar transport , and chromosome segregation . Duplication of a class of MTOCs known as centrosomes ( in metazoans ) or spindle pole bodies ( SPBs , in fungi ) once per cell cycle is essential to facilitate the formation of a bipolar spindle ( Winey and O'Toole , 2001; Winey and Bloom , 2012 ) . Defects in centrosome duplication are linked to cancer and other diseases ( Chavali et al . , 2014; Godinho and Pellman , 2014 ) . Perhaps the best characterized MTOC both cytologically and molecularly is the SPB of Saccharomyces cerevisiae . Electron microscopy ( EM ) studies show that the SPB is a multilayered cylindrical organelle that is embedded in the nuclear envelope ( NE ) throughout the yeast lifecycle ( Byers and Goetsch , 1974 , 1975 ) . The outer and inner plaques of the SPB nucleate cytoplasmic and nuclear microtubules , respectively , while the central plaque is key to the structural integrity of the SPB and anchorage of the SPB to the NE via hook-like appendages visible by electron tomography ( O'Toole et al . , 1999 ) . Associated with one side of the SPB is a modified region of the NE known as the half-bridge , which is important for SPB duplication and cytoplasmic microtubule formation during G1 phase and mating ( Byers and Goetsch , 1974 , 1975; Knop and Schiebel , 1998 ) . Although SPBs and centrosomes are morphologically distinct , they share a number of components , including proteins involved in duplication ( Sfi1 and Cdc31/centrin ) , structure ( Spc110/pericentrin and Nud1/centriolin ) , microtubule nucleation ( γ-tubulin complex , composed of Tub4 , Spc97 , and Spc98 in budding yeast ) , regulators ( Mps1 and cyclin-dependent kinases; Cdks ) , and assembly principles ( Jaspersen and Winey , 2004; Kollman et al . , 2011; Winey and Bloom , 2012; Fu et al . , 2015 ) . Analysis of SPB duplication in yeast has provided key mechanistic and regulatory insights into the control of centrosome duplication in higher eukaryotes ( Fu et al . , 2015 ) . Formation of a new SPB occurs adjacent to the mother SPB . Duplication is typically broken down into three steps based on cytological examination of wild-type and mutant yeast cells ( Figure 1A ) ( Adams and Kilmartin , 2000; Jaspersen and Winey , 2004; Winey and Bloom , 2012 ) . A combination of molecular , genetic , and cytological methods has been used to determine the composition and distribution of the 18 SPB components within the organelle and to predict their function ( s ) in its duplication ( Figure 1B ) . The integral membrane proteins Mps3 and Kar1 localize to the nuclear and cytoplasmic face of the half-bridge , respectively , and likely serve as structural components that are involved in the localization of Sfi1 and Cdc31 ( Vallen et al . , 1992a , 1994; Spang et al . , 1995; Jaspersen et al . , 2002 ) . Sfi1 is a long helical protein that contains multiple binding sites along its length for the yeast centrin , Cdc31 ( Kilmartin , 2003; Li et al . , 2006 ) . Dephosphorylation of Cdk1 sites in the C-terminus of Sfi1 initiates elongation of the half-bridge to a full bridge , most likely via oligomerization of C-terminal ends of Sfi1 in an anti-parallel manner that allows free N-termini to associate directly or indirectly with components of the satellite—the new SPB precursor ( Avena et al . , 2014; Elserafy et al . , 2014 ) . ImmunoEM predicts that the satellite is composed of four of the 18 SPB components , Spc42 , Spc29 , Cnm67 , and Nud1 ( Adams and Kilmartin , 1999 ) . How the satellite assembles is unknown , but within the mother SPB core , trimers of Spc42 dimers form a hexagonal lattice that is visible by cryoEM ( Bullitt et al . , 1997 ) . The N-terminus of Spc42 associates with Spc29 and with Spc110 , a spacer protein that tethers the γ-tubulin complex to the inner plaque ( Elliott et al . , 1999; Muller et al . , 2005 ) . The C-terminus of Spc42 associates with Cnm67 , another spacer protein that associates with the signaling scaffold Nud1/centriolin and the outer plaque receptor for the γ-tubulin complex , Spc72 ( Knop and Schiebel , 1998; Gruneberg et al . , 2000 ) . Linkage of the SPB to the NE is thought to occur via Spc29 , which binds to the soluble protein Bbp1 that in turn associates with the integral membrane protein Mps2 ( Schramm et al . , 2000 ) . However , other tethering mechanisms may exist . For example , mutations in NDC1 and NBP1 result in many of the same phenotypes as BBP1 and MPS2 mutants , including defects in SPB insertion into the NE ( Winey et al . , 1991; Chial et al . , 1998; Schramm et al . , 2000; Araki et al . , 2006; Kupke et al . , 2011; Chen et al . , 2014 ) . Therefore , it is presumed that Ndc1 and Nbp1 also anchor the SPB in the NE through unknown binding interactions with the non-membrane core SPB . 10 . 7554/eLife . 08586 . 003Figure 1 . Spindle pole body ( SPB ) sub-structures can be visualized by structured illumination microscopy ( SIM ) . ( A ) Schematic of the SPB duplication pathway deduced from electron microscopy ( EM ) analysis of wild-type and mutant yeast , including the size of SPB substructures ( reviewed in Adams and Kilmartin , 2000; Jaspersen and Winey , 2004; Winey and Bloom , 2012 ) . Three steps of SPB duplication: ( 1 ) elongation of the half-bridge and deposition of the satellite; ( 2 ) maturation of the satellite into a structure known as a duplication plaque and retraction of the bridge; ( 3 ) insertion of the duplication plaque into the NE and assembly of nuclear SPB components to create duplicated side-by-side SPBs . Treatment of cells with α-factor blocks SPB duplication at the satellite-bearing stage . ( B ) Schematic of the SPB showing the locations of all 18 components based on immunoEM , FRET , yeast two-hybrid , biochemical , and genetic data . ( C ) Comparison of widefield and SIM using Spc42-GFP , which is present in the core SPB and satellite in α-factor arrested cells . Insets show the SPB from top left and bottom right cells . ( D ) SIM of asynchronous Tub4-mTurquoise2 cells . Insets show SPBs from large-budded anaphase cell . Bars , 2 µm and 200 nm ( inset ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 003 To understand the mechanism of SPB duplication and insertion into the NE , we applied structured illumination microscopy ( SIM ) and single particle averaging ( SPA ) to all 18 subunits of the SPB expressed at endogenous levels as fusions to green fluorescent protein ( GFP ) , yellow fluorescent protein ( YFP ) , mCherry ( a red fluorescent protein derivative ) , cyan fluorescent protein ( CFP ) , or mTurquoise2 ( a CFP derivative ) . In contrast to SPA approaches used in previous studies with super-resolution microscopy data ( Loschberger et al . , 2012; Szymborska et al . , 2013; Loschberger et al . , 2014; Van Engelenburg et al . , 2014; Broeken et al . , 2015 ) , we employed three-dimensional image fitting of the protein intensity distributions to facilitate image alignment and intensity quantitation in our dual color images . SPA-SIM provided unexpected insights into the early steps of SPB duplication that were not attainable using existing technologies , including the structure and timing of half-bridge elongation , the composition of the satellite and the formation of the membrane pore . Relative spatiotemporal distributions of protein density from multiple images were obtained using fudicial markers and cell cycle analysis . We find that assembly of both the bridge and satellite occurs through a series of discrete and ordered steps , beginning with end-to-end association of Sfi1 in late mitosis . Interestingly , the Sfi1 filament is not symmetrical in the elongated half-bridge and there is relatively more Cdc31 bound to the newly assembled Sfi1 . In addition to known satellite components , we find that Mps2 and Bbp1 localize to a region near the distal cytoplasmic tip of the extended half-bridge during SPB duplication , while Ndc1 is found along its length and Nbp1 is restricted primarily to the mother SPB . These data suggest that half-bridge and membrane proteins couple SPB duplication with its NE insertion . The small size of the budding yeast SPB ( 150 nm height , 80–110 nm diameter in haploids , 90–150 nm half-bridge length; [Byers and Goetsch , 1974; Winey et al . , 1991; Li et al . , 2006]; Figure 1A , B ) falls below the ∼200 nm resolution limit of conventional widefield and confocal microscopes . SIM provides a twofold increase in this resolution limit ( Gustafsson et al . , 2008 ) , and we were able to detect two foci of Spc42-GFP that were unresolvable using widefield microscopy ( Figure 1C ) . In most α-factor arrested cells , two Spc42 foci were observed; one focus was significantly brighter than the other , a phenotype anticipated for a mother SPB and a satellite that is beginning to assemble ( Figure 1A , C ) . The distance between foci ( 225 ± 10 nm ) was calculated from the center of the mother SPB to the center of the satellite so it is greater than the reported length of the extended half-bridge ( 117 ± 9 nm ) , which was measured by EM from the edge of SPB to the edge of the satellite ( Li et al . , 2006 ) . Analysis of Spc42-mMaple by photo-activated localization microscopy ( PALM ) reported a distribution of distances between the mother SPB and satellite as 80–280 nm ( Seybold et al . , 2015 ) . By SIM , two closely spaced foci of Tub4-mTurquoise2 were also detected at the spindle poles of most cells ( Figure 1D ) . The average distance between foci was 157 ± 7 nm , which is similar to the 150 nm height measured from outer to inner plaque by EM ( Byers and Goetsch , 1974; Winey et al . , 1991 ) . These results show that SPB substructures ( the inner and outer plaques ) and duplication intermediates ( satellite ) can be detected by SIM of endogenously expressed fluorescent fusion proteins , which was not possible with previous techniques . ImmunoEM analysis of Sfi1 N- and C-termini showed a distribution of gold particles along the length of the bridge with most N-terminal ends located adjacent to the mother SPB or satellite and most C-terminal ends located in the central region of the bridge . Based in part on this and on the observation that Sfi1 is able to form long ( 60–90 nm ) filaments in vitro , John Kilmartin proposed that elongation of the half-bridge may occur through end-to-end association of Sfi1 C-termini; this would generate a free Sfi1 N-terminus that could seed satellite assembly ( Figure 2A ) ( Li et al . , 2006 ) . Using C-terminally tagged Sfi1 ( Sfi1-GFP ) in combination with Spc42-mCherry , we observed a single Sfi1 focus in 97% of cells treated with α-factor ( Figure 2B , C ) . In contrast , two Sfi1 foci were observed in 62% of N-terminally tagged Sfi1 ( GFP-Sfi1 ) cells arrested under the same conditions ( Figure 2B , C ) . The orientation of the mother SPB and satellite and the relatively low fluorescence intensity of GFP-Sfi1 compared with other SPB components were the primary reasons why we do not see two foci of GFP-Sfi1 in all cells . The distance between GFP-Sfi1 foci was 187 ± 1 nm , which is longer than estimates of bridge length obtained from EM studies ( 117 ± 9 nm; Li et al . , 2006 ) . 10 . 7554/eLife . 08586 . 004Figure 2 . Structure of the half-bridge . ( A ) Top-down view of half-bridge showing the mother SPB and the Sfi1-Cdc31 filament extending in a polar fashion along the half-bridge; C-terminal end-to-end association forms an N-terminal end for satellite assembly . ( B ) Cells containing Spc42-mCherry and GFP-Sfi1 ( SLJ9741 ) or Sfi1-GFP ( SLJ10040 ) were α-factor arrested and imaged by SIM . On the left is a merged image showing the cell outline ( dashes ) . Bar , 2 µm . Single channel and merged images of the mother SPB ( arrowhead ) and satellite . Bar , 200 nm . ( C ) Cells from B were quantitated and the percentage of cells containing two Sfi1 foci and the ratio of intensity between the mother SPB/mother proximal signal and satellite/distal signal is shown . ND , not determined . Distance was determined in three dimensions between Spc42-mCherry and GFP-Sfi1 or Spc42-mCherry and Sfi1-GFP on the old and new Sfi1 filament that is proximal and distal to the mother SPB , respectively . The distance between Sfi1 foci was calculated using data in Figure 3E . Error bars , standard error of the mean ( SEM ) . ( D ) Modified schematic of bridge from A , showing Sfi1 and Cdc31 asymmetry and the bend detected by SIM . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 00410 . 7554/eLife . 08586 . 009Figure 3 . SPA-SIM analysis of the extended half-bridge . ( A , B ) YFP-Spc42-mTurquoise2 ( SLJ9442 ) cells were arrested in α-factor and imaged by SIM . 17 images of the SPB ( arrowhead ) and satellite were aligned based on mTurquoise2 fluorescence to create the projection view shown in A . Bar , 200 nm . In B , graphs show the relative fluorescence intensity along the mother-satellite ( top ) and pole ( bottom ) axis , in nm , as depicted in the schematic . ( C , D ) SIM images of cells from Figure 2B and α-factor arrested YFP-Cdc31 ( SLJ10084; representative image shown in Figure 3—figure supplement 1 ) , YFP-Kar1 ( SLJ9670 ) or Mps3-YFP ( SLJ9454 ) strains containing Spc42-mTurquoise2 were aligned and projected . In C , Spc42-mTurquoise2 or Spc42-mCherry ( red , denoted Spc42-FP ) mark the mother SPB ( arrowhead ) and satellite and localization of the indicated half-bridge component is shown in green . N is indicated . ( D ) Normalized fluorescence intensity of each protein along the mother-satellite and pole axis is plotted . ( E ) To compare positional information between samples , the maximum intensity of GFP-Sfi1 , Sfi1-GFP , YFP-Cdc31 , YFP-Kar1 , Mps3-YFP , and YFP-Spc42 distributions was determined in both axes and plotted using the center position between Spc42-mTurquoise2/mCherry at the mother SPB and satellite as the zero reference position . Localization of Kar1 and Mps3 to opposite sides of the bridge was confirmed by SIM , as shown in Figure 3—figure supplement 1 . Error bars , SEM . Based on the full-width half-maximum ( FWHM ) values of Spc42 at the mother ( 110 nm , −180 to −30 nm ) , satellite ( 110 nm; 36 to 184 nm ) and Sfi1-GFP ( −25 . 4 nm; −102 . 4 to 51 . 6 nm ) ( Table 1 ) , the bridge was divided into core/proximal , central and distal/satellite regions . The predicted position of Sfi1-GFP ( Sfi1-predicted ) based on the natural curvature of the NE of a nucleus of 1 µm is shown . ( F ) Contour map showing the distribution of fluorescent intensity at the extended half-bridge based on all images used in A and C . The C-terminally tagged Spc42 ( Spc42-FP ) in each sample is shown in red and other proteins are colored as indicated . Bar , 200 nm . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 00910 . 7554/eLife . 08586 . 010Figure 3—figure supplement 1 . Mps3 and Kar1 localize to opposite faces of the half-bridge . SIM image α-factor arrested cells ( SLJ1100 ) containing YFP-Kar1 ( green ) and Mps3-mTurquoise2 ( red ) and cells ( SLJ10084 ) containing YFP-Cdc31 ( green ) Spc42-mTurquoise2 ( red ) . Cell outline is shown with dashed lines . Bar , 2 µm . Insets show SPB region . Bar , 200 nm . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 010 Three important and novel observations were made regarding the distribution of Sfi1 on the extended half-bridge . Unlike Spc42-mCherry intensity , which is greatest at the mother SPB and shows reduced levels at the satellite , many cells exhibit increased GFP-Sfi1 fluorescence at the distal end near the satellite compared with the proximal end ( Figure 2B , C ) , suggesting asymmetry along the Sfi1 filament . The C-terminus of Sfi1 was noticeably displaced towards the cytoplasmic side of the bridge ( Figure 2B , merged panels ) and the average distance between Spc42-mCherry at the mother SPB and Sfi1-GFP in the ‘center’ of the bridge is significantly shorter than the distance between Sfi1-GFP and the satellite ( Figure 2C ) . Based on these observations , we hypothesize that newly formed Sfi1 is distinct from Sfi1 existing on the half-bridge from the previous cell cycle and that the new Sfi1 filament is built at an angle relative to the existing Sfi1 ( Figure 2D ) . These asymmetries may be important for bridge expansion , contraction , and bending , which have previously been proposed to facilitate formation and NE insertion of the new SPB ( Adams and Kilmartin , 1999 , 2000 ) . To compare the positions of Sfi1 N- and C-termini along with other half-bridge components , we developed computational methods to align dual color SIM images based on Spc42 fluorescence at the mother SPB and the satellite ( see ‘Materials and methods’ ) . Conceptually similar to single particle analysis used in many cryoEM studies , SPA-SIM involves the generation of probability profiles depicting the average position of the second SPB component after alignment of many images using Spc42 . This type of analysis incorporating a common fiducial marker is advantageous because it shows the likelihood that a protein is present in a given location based on many cells and allows for positional comparison between different proteins . As a proof-of-principle , we first aligned seventeen images from α-factor arrested cells containing a version of Spc42 fused at its N-terminus to YFP and at its C-terminus to mTurquoise2 ( YFP-Spc42-mTurquoise2 ) . Based on EM , the C-terminus of Spc42 is above the NE in intermediate layer 2 of the SPB , which is located between the central and outer plaque; the Spc42 N-terminus is located in the central plaque , which is spaced 10 . 8 nm from intermediate layer 2 towards the NE ( Bullitt et al . , 1997; O'Toole et al . , 1999; Muller et al . , 2005 ) . Because two reference points were needed for positioning , we selected C-terminal-labeled Spc42 at the mother SPB and satellite in our raw SIM images based on differences in intensity using the mTurquoise2 signal . We fit these manually selected positions to three-dimensional Gaussian functions in order to determine their centers with greater accuracy . Images were then realigned in three dimensions so that the centers of Gaussian fits were arranged along the x axis with the center point between the mother and satellite at zero . Therefore , in aligned images , the x axis represents the mother-satellite axis while the y axis represents the pole axis as depicted in Figure 3B . Examination of YFP intensity in aligned images showed a high degree of correlation in the mother-satellite axis , while in the pole axis the ∼15 nm shift in intensity maxima is consistent with the position of the N-terminus in the central plaque and the C-terminus in intermediate layer 2 ( Figure 3A , B; Table 1 ) . These data demonstrate that we have excellent color alignment in our raw images and that the use of Spc42 enables comparison of positional information between multiple images with high confidence using our alignment methods . 10 . 7554/eLife . 08586 . 011Table 1 . Probability distribution fit parameters from Figure 3DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 011SampleQuerySpc42-referencexyFWHMxFWHMyxFWHMxFWHMyYFP-Spc42−96 . 4 ( 2 . 2 ) −15 . 6 ( 0 . 6 ) 141 . 1 ( 2 . 7 ) 108 . 1 ( 1 . 4 ) −104 . 9 ( 1 . 3 ) 140 . 2 ( 3 . 0 ) 104 . 6 ( 1 . 2 ) 80 . 4 ( 2 . 8 ) −15 . 6 ( 0 . 6 ) 148 . 4 ( 5 . 0 ) 104 . 9 ( 1 . 3 ) 136 . 4 ( 5 . 5 ) Kar1-YFP−12 . 4 ( 2 . 8 ) 44 . 2 ( 0 . 6 ) 178 . 8 ( 3 . 3 ) 118 . 8 ( 1 . 1 ) −106 . 0 ( 1 . 6 ) 136 . 8 ( 4 . 6 ) 112 . 9 ( 1 . 4 ) 106 . 0 ( 1 . 6 ) 129 . 8 ( 8 . 0 ) Mps3-YFP10 . 9 ( 2 . 3 ) −58 . 5 ( 0 . 5 ) 318 . 1 ( 17 . 8 ) 158 . 1 ( 1 . 2 ) −109 . 2 ( 1 . 3 ) 158 . 0 ( 3 . 7 ) 114 . 7 ( 1 . 3 ) 109 . 2 ( 1 . 3 ) 133 . 7 ( 6 . 5 ) GFP-Sfi1−100 . 3 ( 7 . 5 ) 12 . 1 ( 0 . 8 ) 153 . 6 ( 3 . 8 ) 110 . 3 ( 2 . 2 ) −108 . 4 ( 1 . 5 ) 139 . 5 ( 3 . 2 ) 131 . 3 ( 1 . 3 ) 106 . 4 ( 8 . 3 ) 12 . 1 ( 0 . 8 ) 194 . 9 ( 14 . 5 ) 108 . 4 ( 1 . 5 ) 131 . 4 ( 6 . 1 ) Sfi1-GFP−25 . 9 ( 3 . 2 ) 29 . 4 ( 1 . 5 ) 214 . 3 ( 6 . 6 ) 153 . 9 ( 5 . 1 ) −110 . 8 ( 2 . 0 ) 147 . 0 ( 3 . 4 ) 123 . 8 ( 1 . 3 ) 110 . 8 ( 2 . 0 ) 140 . 1 ( 8 . 7 ) YFP-Cdc31−152 . 4 ( 15 . 4 ) 50 . 4 ( 2 . 3 ) 247 . 9 ( 29 . 4 ) 180 . 7 ( 7 . 6 ) −118 . 9 ( 1 . 4 ) 195 . 7 ( 3 . 7 ) 158 . 2 ( 1 . 1 ) 49 . 2 ( 6 . 9 ) 50 . 4 ( 2 . 3 ) 143 . 8 ( 14 . 1 ) 118 . 9 ( 1 . 4 ) 156 . 7 ( 4 . 8 ) Notes: All values are in nm . In all cases ‘x’ refers to the mother-satellite axis and ‘y’ refers to the pole axis . The zero x and y axis value is defined as midway between the C-terminal Spc42 reference peaks in our realignment scheme . The full-width half-maximum ( FWHM ) is 2 . 35 times the standard deviation of the Gaussian fit , and this can be converted into 95% integral values by multiplying each by 1 . 7 . In all cases the mother spindle pole body ( SPB ) peak is shown on the first line and the satellite peak , if applicable , is shown on the second line . FWHMy in cells containing two foci was determined by the averages over the two peaks . Errors are in parentheses and are standard deviations from Monte Carlo random fits as described in ‘Materials and methods’ . Alignment of GFP-Sfi1 and Sfi1-GFP images using Spc42-mCherry further demonstrated the validity of this approach and provided additional insights into its organization at the extended half-bridge that were not ascertained by inspection of individual images or by high resolution EM , PALM and stochastic optical reconstruction microscopy ( STORM ) analysis ( Kilmartin , 2003; Li et al . , 2006; Seybold et al . , 2015 ) . Not only is Sfi1-GFP displaced , but GFP-Sfi1 is also cytoplasmically shifted relative to Spc42 ( Figure 3C–E; Table 1 ) . These shifts of 29 . 4 ± 1 . 4 nm ( Sfi1-GFP ) and 12 . 1 ± 1 . 0 nm ( GFP-Sfi1 ) are significantly less than the ∼100 nm resolution achievable by SIM alone ( Gustafsson et al . , 2008 ) , suggesting that our SPA-SIM approach can provide at least a 5–10-fold improvement in intensity distribution localization . A 17 ± 2° bend in the extended half-bridge was readily apparent as was the asymmetry between old and new Sfi1 filaments and the increased intensity of GFP-Sfi1 at the satellite ( Figures 2D , 3E ) . This kink in the extended half-bridge occurs in the direction of the pole axis , which is perpendicular to the NE . It is greater than the natural bend predicted based on the curvature of the NE , which would be ∼7° based on a 1 µm nuclear diameter ( Figure 3E ) . Unlike Sfi1 , which appeared to form a filament with a distinct polarity , Mps3 and Kar1 molecules were distributed along the half-bridge in asynchronous and α-factor arrested cells ( Figure 3—figure supplement 1; data not shown ) ( Seybold et al . , 2015 ) . Probability profiles of Mps3-YFP and YFP-Kar1 showed that both proteins are displaced along the pole axis ( Figure 3C , D; Table 1 ) . Co-localization of Mps3-mTurquoise2 and YFP-Kar1 showed that the proteins are located on opposite sides of the extended bridge ( Figure 3—figure supplement 1 ) . Previous immunoEM analysis showed that Mps3 is primarily at the inner NE face of the half-bridge and Kar1 is primarily cytoplasmic ( Vallen et al . , 1992b; Jaspersen et al . , 2002 ) , so we have shown distributions according to their proper orientation ( Figure 3C–F ) . A recent half-bridge model suggests that Kar1 concentrates at a region of extended half-bridge located near the Sfi1 C-terminus ( Seybold et al . , 2015 ) . Based on the full-width half-maximum ( FWHM ) values of our Gaussian fits , YFP-Kar1 is more broadly distributed along the mother-satellite axis than Sfi1-GFP ( 179 ± 3 nm compared to 154 ± 4 nm; Table 1 ) , which was confirmed by inspection of the probability profiles and individual SIM images ( Figure 3C , F; data not shown ) . The broader distribution that we observe for Kar1 along the extended half-bridge may be important for its association with pore components of the SPB ( discussed below ) . N- or C-terminal fusions of fluorescent proteins to CDC31 were lethal when expressed under the endogenous promoter; however a cdc31∆::MET-YFP-CDC31 strain was viable and YFP-Cdc31 was detected as one or two foci in most α-factor arrested cells ( Figure 3—figure supplement 1 ) . Alignment of images using Spc42-mCherry present in the strain showed that the brighter focus ( focus 2 ) was located at a distal region of the bridge interior to the N-terminus of Sfi1 ( Figure 3C–E; Table 1 ) . Detection of YFP-Cdc31 in foci , as opposed to distributed along the bridge like YFP-Kar1 and Mps3-YFP , was unexpected based on its genetic and physical interactions with Kar1 and structural studies in vitro showing that Cdc31 binds along the tryptophan-containing repeats in the central region of Sfi1 ( Biggins and Rose , 1994; Vallen et al . , 1994; Spang et al . , 1995; Li et al . , 2006 ) . While it is possible that YFP-Cdc31 does not accurately report the localization of native Cdc31 , it is the only copy of Cdc31 in these cells and therefore is sufficient to carry out the essential function ( s ) of Cdc31 in SPB duplication . The unequal distribution of YFP-Cdc31 on the bridge may be related to Sfi1 , which also showed increased levels and possibly a different structure ( based on its length ) in the distal region of the extended bridge ( Figure 2B , D , Figure 3C–F; Table 1 ) . Our ability to observe two GFP-Sfi1 foci allowed us to ask when in the cell cycle SPB duplication initiates . During most of the cell cycle , GFP-Sfi1 appeared as a single focus; however , two foci were detected at one or both poles at the end of anaphase in many ( 63% , 25/40 ) large budded cells and at the pole in nearly all early G1 cells ( 90% , 26/29 ) ( Figure 4A ) . In these same cells , two foci of Spc42-mCherry were never seen at the SPB in anaphase cells and only 31% of the poles in early G1 cells had two Spc42 foci . The late mitotic timing of half-bridge elongation is consistent with recent studies showing that dephosphorylation of Cdk1 sites in Sfi1 by the Cdc14 phosphatase licenses a new round of SPB duplication ( Avena et al . , 2014; Elserafy et al . , 2014 ) . Our observation of an SPB duplication structure in mitosis is earlier than described by EM , perhaps because it is a transient step that is only observed in a small fraction of cells ( Byers and Goetsch , 1974 , 1975 ) . In both late mitotic and early G1 cells , an extended half-bridge was detected before the Spc42-containing satellite was observed ( Figure 4A ) , suggesting that bridge elongation is a distinct step in SPB duplication that must occur prior to formation of the satellite . 10 . 7554/eLife . 08586 . 005Figure 4 . Half-bridge elongation is a discrete step in SPB duplication . ( A ) SIM images from asynchronously growing cells ( SLJ9741 ) containing GFP-Sfi1 ( green ) and Spc42-mCherry ( red ) . A merged image showing the cell outline ( dashes ) was used together with spindle length to approximate the cell cycle position indicated . Bar , 2 µm . The SPB ( s ) are shown to the right of each cell . Arrowheads in the merged images point to the satellite . Bar , 200 nm . ( B ) GFP-SFI1 mps1-1 cells ( SHJ3829 ) were grown to log phase at 24°C , shifted to restrictive temperature ( 37°C , 4 hr ) and then prepared for immunoEM with nanogold secondary label . Shown are two representative cells with labeling at the SPB ( left ) and the distal tip of the elongated half-bridge ( right ) . Some cells also contained labeling closer to the center of the elongated half-bridge ( left ) . +: label representation . ( C ) Wild-type ( JA372 ) and mps1-1 ( JA368 ) cells containing GFP-Sfi1 and Spc42-mCherry were grown at 24°C or shifted to 37°C for 4 hr then analyzed by SIM . Because mps1-1 cells arrest in mitosis at the non-permissive temperature ( Winey et al . , 1991 ) , only large budded cells were examined . The SPBs from early mitotic wild-type cells showed co-localization of GFP-Sfi1 and Spc42-mCherry ( 95 ± 4% , n = 90 , at 24°C or 94 ± 1% , n = 117 , at 37°C ) , with 87 ± 14% ( 24°C ) and 80 ± 17% ( 37°C ) displaying co-localization at both poles . 94 ± 2% ( n = 104 ) of mitotic mps1-1 cells grown at 24°C showed the same localization as wild-type , with 86 ± 1% of cells exhibiting co-localization at both poles . At 37°C , 49 ± 7% of cells showed co-localization of GFP-Sfi1 and Spc42-mCherry , with the majority of cells displaying a single focus of each ( 40% of all mps1-1 cells at 37°C ) . 24 ± 7% and 19 ± 3% of SPBs ( n = 85 ) contained a single focus of Spc42-mCherry or two Spc42-mCherry , respectively , with two GFP-Sfi1 foci . Error bars , SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 005 Previous EM analysis of mutant alleles in the Mps1 kinase showed cells containing mps1-1 arrest with an elongated bridge lacking a satellite , which is reminiscent of certain SFI1 mutants ( Winey et al . , 1991; Kilmartin , 2003; Anderson et al . , 2007 ) . Thus , it seems likely that Mps1 regulates progression from the elongated bridge state we observed in cycling cells to the satellite-bearing stage in SPB duplication . To test this idea , we examined the localization of GFP-Sfi1 in mps1-1 cells by both immunoEM and SIM . Three pools of GFP-Sfi1 were found in the extended half-bridge of mps1-1 mutant cells by immunoEM: 5/19 gold particles were proximal to the mother SPB and 10/19 gold particles were distal . The remaining 4/19 particles were located closer to the center of the bridge ( Figure 4B ) . SIM of wild-type cells at 24°C or 37°C or mps1-1 mutants grown at 24°C showed that virtually all the early mitotic SPBs in large budded cells contained a single focus of both Spc42-mCherry and GFP-Sfi1 . In mps1-1 mutants shifted to 37°C , the frequency of this class of SPBs decreased and SPBs containing two GFP-Sfi1 foci increased , including SPBs with two Spc42-mCherry foci ( representing SPBs containing an elongated half-bridge and a satellite/duplication plaque or duplicated side-by-side SPBs ) or one Spc42-mCherry focus ( representing a SPB with an elongated bridge lacking a satellite ) ( Figure 4C ) . The mixed phenotype is consistent with molecular and genetic analysis of Mps1 showing that Mps1 function is required at multiple points in SPB duplication ( Schutz and Winey , 1998; Jones et al . , 2005; Araki et al . , 2010 ) . Collectively , these data demonstrate that half-bridge elongation is a distinct step of SPB duplication that begins in late mitosis and that the conversion from an elongated half-bridge to a satellite-bearing SPB requires the function of Mps1 . In addition to Spc42 , the satellite is predicted by immunoEM to contain three additional components of the core SPB: Spc29 , Nud1 , and Cnm67 ( Adams and Kilmartin , 1999 ) . To test if these localized to the satellite by SIM , we arrested cells at the satellite-bearing stage of SPB duplication using α-factor for 3 hr . The duration of α-factor treatment ensured that more than 95% of cells arrested in G1; however , since most yeast strains have an ∼90 min generation time at 30°C , many cells were likely in G1 for an extended period of time compared with the time normal cycling cells spend in this cell cycle stage . As shown in Figure 5A , B , Spc42-mTurquoise2 and Spc29-mTurquoise2 were observed by SIM at the mother SPB ( defined by Spc110-YFP ) and in the satellite in most cells ( 89% [n = 38] and 74% [n = 33] , respectively ) treated with mating pheromone . The fluorescence intensity ratio at the mother SPB vs the satellite was lower for Spc42-mTurquoise2 ( 1 . 9 ± 0 . 2 ) compared to Spc29-mTurquoise2 ( 2 . 8 ± 0 . 5 ) , suggesting that more Spc42 is present in the satellite relative to Spc29 and/or more Spc29 is at the mother SPB relative to Spc42 ( Figure 5B ) . 10 . 7554/eLife . 08586 . 006Figure 5 . Temporal control of satellite assembly . ( A , B ) Cells containing Spc110-YFP ( red ) and Spc42-mTurquoise2 ( SLJ8980 ) , Spc29-mTurquoise2 ( SLJ8820 ) , or Nud1-mTurquoise2 ( SLJ9099 ) ( green ) were α-factor arrested for 3 hr and imaged using SIM . The cell is shown on the left with dashes indicating the cell boundary . Bar , 2 µm . Single channel and merged images of the mother SPB and satellite ( arrowhead ) . Bar , 200 nm . ( B ) Cells from A were quantitated and the percentage of α-factor arrested cells containing two foci ( mother and satellite ) is shown , along with the distance from mother to satellite and the ratio of their intensity . The mother SPB overlaps or is adjacent to Spc110-YFP . Data for Cnm67 is an average based on data shown in Figure 5—figure supplement 1 . Errors , SEM . ( C , D ) A metaphase arrested MET-CDC20 strain ( SLJ9720 ) containing Spc42-mTurquoise2 ( green ) and Nud1-YFP ( red ) was released into the cell cycle using SC-methionine media . Aliquots were taken every 15 min to determine budding index and for analysis of satellite assembly by SIM . ( C ) Images from the 45 and 60 min time points are shown together with cells released into α-factor for 60 min . The cells are shown on the left with dashes indicating the cell boundary . Bar , 2 µm . Single channel and merged images of the SPB and the satellite ( arrowhead ) . Bar , 200 nm . ( D ) Percentage of cells with SPBs that have two foci of Spc42-mTurquoise2 ( the pole and the satellite ) and 0 ( black bar ) , 1 ( white bar ) or 2 ( red bar ) foci of Nud1 is plotted for each time point . The reciprocal plot as well as an experiment in which Nud1 is labeled with mTurquoise2 and Spc42 with YFP is in Figure 5—figure supplement 2B , C . A red and blue dot represent Nud1 and Spc42 , respectively . ( E ) As in C , a MET-CDC20 strain ( SLJ10009 ) containing Spc29-YFP and Spc42-mTurquoise2 was released and samples were collected every 10 min . The percentage of cells at 30 and 40 min after release from metaphase and the sample released into α-factor for 60 min with a single focus or two foci of either Spc42-mTurquoise2 and/or Spc29-YFP at the SPB is plotted . A blue and green dot represent Spc42 and Spc29 , respectively . At 30 min , approximately 50% of cells have a single unduplicated SPB at the pole but this decreased to 10% by 40 min . Note that none of the cells at 40 min had two foci of just Spc29-YFP . All other combinations were observed and the percentages are shown in the graph below . Reciprocal plots are in Figure 5—figure supplement 2D . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 00610 . 7554/eLife . 08586 . 007Figure 5—figure supplement 1 . Cnm67 at the satellite . ( A ) Examples of SIM images from cells containing Cnm67-YFP and Spc110-mTurquoise2 ( SLJ8034 ) , Spc110-YFP and Cnm67-mTurquoise2 ( SLJ9414 ) or Cnm67-CFP and Spc110-YFP ( SLJ9896 ) arrested in α-factor . A merged image showing the cell outline ( dashes ) is shown on the left . Bar , 2 µm . Single channel and merged images of the mother SPB and satellite ( arrow ) . Bar , 200 nm . ( B ) Cells from the strains in A as well as additional isolates of Spc110-YFP Cnm67-mTurquoise2 ( SLJ9590 and SLJ9901 ) were quantitated . The percentage of α-factor arrested cells containing two foci ( mother and satellite ) was calculated . The distance and intensity of the satellite focus to the mother SPB focus , which overlaps with Spc110 fluorescence , was also determined for cells containing two foci . Average data from these experiments is plotted in Figure 5B . A strain ( SLJ9593 ) derived from the same tetrad as SLJ9590 did not contain cells with two foci of Cnm67-mTurquoise2 . Related to Figure 5B . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 00710 . 7554/eLife . 08586 . 008Figure 5—figure supplement 2 . Temporal control of satellite assembly . ( A ) Schematic showing SPB cycle , including the metaphase arrest following depletion of CDC20 in MET-CDC20 strains and the α-factor arrest point . The color of each satellite component used in the figure is noted . ( B–D ) Metaphase-arrested MET-CDC20 strains containing Spc42-mTurquoise2 and Nud1-YFP ( SLJ9720 , B ) , Nud1-mTurquoise2 and Spc42-YFP ( SLJ9685 , C ) , and Spc42-mTurquoise2 and Spc29-YFP ( SLJ10009 , D ) were released into the cell cycle using SC-methionine media . Aliquots were taken at the indicated times for analysis of satellite assembly by SIM . Cells were also released into α-factor for the indicated time . ( B , C ) On the left , the percentage of SPBs that contain two foci Nud1 and 0 , 1 , or 2 overlapping foci Spc42 are plotted . On the right , the percentage of SPBs that contain two foci Spc42 and 0 , 1 , or 2 overlapping foci Nud1 are plotted . Note , B and C differ in the fluor; the fact that virtually identical results were obtained in both experiments suggests that mTurquoise2 and YFP have similar maturation rates and can be used to detect the newly forming satellite . ( D ) On the left , the percentage of SPBs that contain two foci Spc29 and 0 , 1 , or 2 overlapping foci Spc42 are plotted . On the right , the percentage of SPBs that contain two foci Spc42 and 0 , 1 , or 2 overlapping foci Spc29 are plotted . ( E ) Asynchronously grown cells containing Nud1-mTurquoise2 and Spc42-YFP ( SLJ9275 , left ) , Spc42-mTurquoise2 and Nud1-YFP ( SLJ9660 , center ) , Spc42-mTurquoise2 and Spc29-YFP ( SLJ8082 , center right ) , and Spc29-CFP and Spc42-YFP ( DHY47-6B/SLJ10596 , right ) were imaged by SIM . The percentages of unbudded cells with a single SPB with a single focus of Spc42 and one or two foci of Nud1 or Spc29; or a single SPB with two foci of Spc42 and one or two foci of Nud1 or Spc29 was plotted . A small fraction of cells ( 2–7% ) have other configurations that are not depicted , such as SPBs labeled with only one fluorescent protein . N > 77 in all samples . Related to Figure 5C–E . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 008 Nud1-mTurquoise2 was observed at the satellite in many ( 67% , n = 31 ) but not all α-factor arrested cells ( Figure 5A , B ) . The intensity of Nud1-mTurquoise2 at the mother SPB relative to the satellite ( 1 . 9 ± 0 . 3 ) was equivalent to that of Spc42-mTurquoise2 so it is unlikely that an inability to detect Nud1-mTurquoise2 at the satellite was the cause of the reduced number of satellites observed ( Figure 5B ) . One possibility is that Nud1 is a more peripheral satellite component that may assemble later as the satellite matures into a duplication plaque ( see below ) . The fact that the distance between the mother SPB and satellite is greater when measured by Nud1-mTurquoise2 ( 275 ± 12 nm ) than Spc29-mTurquoise2 ( 228 ± 13 nm ) or Spc42-mTurquoise2 ( 225 ± 10 nm ) ( Figure 5B ) would be consistent with this idea , particularly if the bridge adopts a bent shape ( see Figures 2D , 3E , F ) . Cnm67 showed high variability at the satellite ( ranging from 0–43% ) , even using five different strains and three distinct fluorescent protein tags ( Figure 5—figure supplement 1 ) . This variability has been observed previously with anti-Cnm67 antibodies using immunoEM ( Adams and Kilmartin , 1999 ) . The high average ratio between mother SPB and satellite for Cnm67 ( 2 . 9 ± 0 . 2 ) and its presence at the satellite in an average of 37% of cells suggests that Cnm67 is not essential for satellite assembly and it is present in lower amounts compared with Spc42 and Nud1 , which was confirmed by visual inspection of images ( Figure 5—figure supplement 1; data not shown ) . Further analysis of Cnm67 was omitted due to difficulties in reproducibly observing it at the satellite . To effectively examine the timing at which individual components assemble into the satellite , we started with a highly synchronized mitotic cell population by employing a metaphase arrest/release protocol involving depletion of the anaphase activator Cdc20 ( Figure 5—figure supplement 2A ) . Because there are currently no known cytological markers for the early steps of SPB duplication other than structures visible by EM , the synchronization ensured we compared equivalent duplication intermediates . Satellite component assembly was monitored using fluorescently tagged proteins by SIM in cells released from metaphase . In cells containing Spc42-mTurquoise2 and Nud1-YFP , we observed two foci of Spc42-mTurquoise2 as early as 45 min following release from metaphase , at about the same time a very small bud was detected ( Figure 5C , D and Figure 5—figure supplement 2B ) . Of the very small budded cells at this time point , 82% ( 77/94 ) showed two foci of Spc42-mTurquoise2 , which is indicative of satellite assembly . Most of the cells with two Spc42-mTurquoise2 foci ( 90% , 69/77 ) had a single focus of Nud1-YFP that was coincident with the more intense focus of Spc42-mTurquoise2 , which is consistent with our theory that Nud1 assembles into the satellite later than Spc42 . We observed Nud1-YFP at the SPB in the majority of SPBs at later time points ( 83% at 60 min , 48/58 ) , as illustrated in Figure 5C ( 60 min ) —both cells have two Spc42-mTurquoise2 foci , and the cell with the larger bud that has progressed further in the cell cycle contains Nud1-YFP at both poles . As a control , we also released cells from metaphase into α-factor . While we observed two foci of Spc42-mTurquoise2 in most ( 60% , 38/63 ) cells 60 min following release from metaphase , only 8% ( 5/63 ) had two foci of Nud1-YFP ( Figure 5C , D and Figure 5—figure supplement 2B ) . The reduction in the fraction of α-factor-containing SPBs with Nud1 at the satellite in this experiment compared with asynchronous cells arrested in α-factor for 3 hr ( Figure 5A , B ) was also observed in Spc42-YFP/Nud1-mTurquoise2 cells ( Figure 5—figure supplement 2C; 25% , 14/55 ) . If cells were released into α-factor for 3 hr , the fraction of cells containing two foci of Spc42-YFP and Nud1-mTurquoise2 rose to 71% ( 46/64 ) , showing that Nud1 accumulates at the satellite during a prolonged pheromone arrest ( Figure 5—figure supplement 2C ) . When Spc42-mTurquoise2/Spc29-YFP cells were synchronized and released as described above , we observed a satellite that contained Spc42-mTurquoise2 , Spc29-YFP , or both in 48% ( n = 79 ) of cells 30 min following release from metaphase ( Figure 5E and Figure 5—figure supplement 2D ) . Of these SPBs , 30/38 have two foci of Spc42-mTurquoise2 and a single Spc29-YFP focus , 2/38 have two foci of Spc29-YFP and a single Spc42-mTurquoise2 , and 6/38 have two closely spaced foci of both proteins , similar to what was observed in cells released into α-factor . This suggests that Spc42 precedes Spc29 in recruitment to the satellite; however , we cannot exclude the possibility this is due to detection issues since we were unable to create a strain containing MET-CDC20/SPC42-YFP/SPC29-mTurquoise2 . However , the observation that the percentage of SPBs containing two Spc29-YFP foci in addition to two foci of Spc42-mTurquoise2 increased from 15% at 30 min to 47% at 40 min supports that Spc42 assembles prior to Spc29 ( Figure 5E and Figure 5—figure supplement 2D ) . During metaphase arrest induced by the temperature-sensitive cdc20-1 mutant , SPB size is over twice the size as in wild-type cells in mitosis ( O'Toole et al . , 1997 ) . To exclude the possibility that accumulation of SPB components during our synchronization protocol using MET-CDC20 affected the order of satellite assembly , we examined asynchronously growing cells . Because SPB duplication occurs during G1 phase ( Byers and Goetsch , 1974 ) , we focused on unbudded cells , finding that 25–50% of cells contained a single SPB labeled with a focus of Nud1-mTurquoise2/Spc42-YFP ( 48% , n = 114 ) , Spc42-mTurquoise2/Nud1-YFP ( 41% , n = 144 ) , Spc42-mTurquoise2/Spc29-YFP ( 45% , n = 134 ) , or Spc29-CFP/YFP-Spc42 ( 25% , n = 77 ) ( Figure 5—figure supplement 2E ) . In the remaining cells , two closely spaced foci were observed . In agreement with our results described above , most contained two foci of Spc42 and one or two foci of Nud1 or Spc29 ( Figure 5—figure supplement 2E ) . Thus , the temporal order of SPB assembly appears to initiate with bridge elongation by the addition of Sfi1 , followed by Spc42 deposition followed rapidly by Spc29 and Nud1 assembly later . Interestingly , overexpression of Spc42 leads to a lateral expansion of the central layer of the core SPB into a structure sometimes referred to as the superplaque ( Donaldson and Kilmartin , 1996; Castillo et al . , 2002 ) . The ability of Spc42 overproduction alone to form this structure and its regulation by Mps1 are consistent with our data showing that Spc42 is the initial protein deposited at the satellite and that satellite formation is Mps1 dependent . We conducted a survey of the other ten SPB components by SIM to look at their different locations within the core SPB , and in some cases distinct locations in cycling cells vs cells undergoing SPB duplication ( Figure 1A ) . As depicted in Figure 6—figure supplement 1 , this was observed for Spc72-YFP and Tub4-mTurquoise2; both localize to the extended half-bridge in cells treated with α-factor , consistent with previous studies showing microtubule nucleation off the bridge during G1 and during mating ( Byers and Goetsch , 1974 , 1975; Pereira et al . , 1999 ) . Molecular and cytological studies showed that the N- and C-termini of Spc110 are located at the inner and central plaque , respectively ( Geiser et al . , 1993; Kilmartin et al . , 1993; Kilmartin and Goh , 1996; Spang et al . , 1996; Sundberg et al . , 1996 ) . We found that the C-terminus of Spc110 co-localized with calmodulin ( Cmd1 ) while the N-terminus of Spc110 overlapped with one of the two foci seen in strains containing γ-tubulin complex components Spc97-mTurquoise2 or Spc98-mTurquoise2 ( Figure 6—figure supplement 1 ) . To visualize the SPB pore ( the protein structure that surrounds the core SPB and anchors the SPB in the NE ) , we exploited the fact that SPB diameter , and thus pore diameter , increases with ploidy ( Byers and Goetsch , 1974; Bullitt et al . , 1997 ) . Localization of the membrane proteins Mps2-mTurquoise2 and Ndc1-YFP in tetraploid cells revealed a ring-like structure in some cells and linear structures in others ( Figure 6A ) . Ndc1-YFP fluorescence was also observed in puncta throughout the NE , presumably at nuclear pore complexes ( Chial et al . , 1998 ) . Ring-like structures at the SPB could be visualized in haploid and diploid cells , although the smaller pore diameter made the central region of the ring more challenging to resolve ( Figure 6A; data not shown ) . Based on the fact that Mps2 and Ndc1 are integral membrane proteins involved in SPB insertion into the NE ( Chial et al . , 1998; Munoz-Centeno et al . , 1999 ) , the donut shaped rings are likely SPBs in transverse section with Mps2 and Ndc1 localizing to the pore membrane around the SPB . The linear structures are the SPB in longitudinal section . 10 . 7554/eLife . 08586 . 012Figure 6 . Localization of SPB pore components to the membrane region and half-bridge/satellite . ( A ) Asynchronously grown Mps2-mTurquoise2/Ndc1-YFP ( SLJ8102 ) , Ndc1-mTurquoise2/Nbp1-YFP ( SLJ8263 ) , and Bbp1-mTurquoise2/Ndc1-YFP ( SLJ9231 ) were examined for evidence of a pore-like structure formed by Ndc1 and the other protein . ( B ) Location of Bbp1-mTurquoise2 ( green ) and Sfi1-YFP ( red ) in asynchronous ( SLJ9035 ) cells . ( C , D ) Cells containing Spc110-YFP ( red ) and Bbp1-mTurquoise2 ( SLJ10019 ) , Mps2-mTurquoise2 ( SLJ8084 ) , Ndc1-mTurquoise2 ( SLJ10018 ) , or Nbp1-mTurquoise2 ( green ) were α-factor arrested and imaged using SIM . In A–C , the cell is shown on the left with dashes indicating the cell boundary . Bar , 2 µm . Single channel and merged images of the SPB region ( s ) . Bar , 200 nm . In C , arrowheads point to the satellite region . ( D ) Bbp1 and Mps2 cells from C were quantitated and the percentage of α-factor arrested cells containing two foci ( mother and satellite ) is shown , along with the distance from mother to satellite and the ratio of their intensity . Error bars , SEM . The localization of other SPB components is shown in Figure 6—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 01210 . 7554/eLife . 08586 . 013Figure 6—figure supplement 1 . Localization of the γ-tubulin complex and linkers . Cells containing Spc110-YFP ( red ) and Cmd1-mTurquoise2 ( SLJ10004 ) , Spc42-mTurquoise2 ( green ) and Spc72-YFP ( SLJ8631 ) , Tub4-mTurquoise2 ( green ) and Spc42-YFP ( SLJ9384 ) , and Spc97-mTurquoise2 or Spc98-mTurquoise2 ( green ) and YFP-Spc110 ( SLJ9632 or SLJ10041 ) were α-factor arrested and imaged using SIM . The cell is shown on the left with dashes indicating the cell boundary . Bar , 2 µm . Single channel and merged images of SPB . Note the localization of Spc72-YFP and fraction of Tub4-mTurquoise2 ( presumably the pool present at the outer plaque ) to the region between Spc42 foci . Bar , 200 nm . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 013 A complex set of genetic and physical interactions suggests that Mps2 and Ndc1 anchor the SPB in the NE via binding to the soluble proteins Bbp1 and Nbp1 , respectively , which associate with components of the core SPB such as Spc29 ( Schramm et al . , 2000; Araki et al . , 2006; Sezen et al . , 2009; Casey et al . , 2012; Chen et al . , 2014 ) . Based on this data , we anticipated finding Nbp1 and Bbp1 in ring-like structures , similar to those seen with Ndc1 and Mps2 . Therefore , it was surprising that Nbp1-YFP was not observed in ring-like structures at any time during the cell cycle . Instead , it localized as a discrete focus that co-localized with a region of the Ndc1-mTurquoise2 ring ( Figure 6A ) . In most cells ( 93% , n=100 ) , Bbp1 was also observed as one or two foci; however in 7% of cells , we saw ring-like structures of Bbp1-mTurquoise2 that co-localized with Ndc1-YFP ( Figure 6A ) . The foci formed by Bbp1-mTurquoise2 were adjacent to the bridge ( Figure 6B , top panels ) . In a fraction of cells with emerging buds , two foci of Bbp1-mTurquoise2 could be seen flanking Sfi1-YFP ( Figure 6B , bottom panels ) , reminiscent of the localization pattern of satellite components . This suggests that Bbp1 and possibly other pore membrane components of the SPB may localize to early SPB duplication structures . To test the idea that components of the pore membrane localize to the newly forming SPB , we examined the localization of Bbp1 , Mps2 , Ndc1 , and Nbp1 fused to mTurquoise2 in α-factor arrested cells containing Spc110-YFP by SIM ( Figure 6C ) . Under these conditions , Mps2-mTurquoise2 and Bbp1-mTurquoise2 were visible at both ends of the bridge in 62% ( n = 21 ) and 63% ( n = 47 ) of cells examined , respectively ( Figure 6C , D ) . The distance between the two foci was approximately equal to the values for satellite components ( Figure 5B ) . In some cells , Mps2-mTurquoise2 was not observed in two foci but rather was distributed across the region that is likely the bridge , possibly because the SPB is oriented in a longitudinal section ( Figure 6C , arrowhead ) . Short linear structures were also observed in α-factor arrested cells containing Nbp1-mTurquoise2 and to a lesser extent Ndc1-mTurquoise2 ( Figure 6C ) . Collectively , these data suggest that the Nbp1 and Ndc1 may localize to the extended half-bridge region , and Mps2 and Bbp1 accumulate on the mother SPB and satellite in α-factor arrested cells . We used asynchronously dividing cells to examine the distribution of pore membrane proteins throughout the cell cycle ( using bud size , SPB number , and distance between poles to approximate cell cycle position ) to understand their distribution during SPB duplication and to provide insights into the temporal control of pore membrane assembly . As shown in Figure 7A , Bbp1-YFP was present in foci at the mother SPB and satellite in unbudded early G1 cells . Nbp1-mTurquoise2 initially localized to the mother SPB , then assembled in the region between Bbp1-YFP foci before forming two separate foci near the mother SPB and satellite as SPB duplication concluded in early S phase . The remnant of Nbp1-mTurquoise2 in the region between the two poles was no longer detected in medium budded cells , and both proteins localized to the SPB throughout the rest of the cell cycle . Similarly , Ndc1-YFP ( Figure 7B ) and Ndc1-mTurquoise2 ( Figure 7C ) also accumulated in the region between the mother SPB and the satellite in unbudded cells but later matured into two foci . These data strongly suggest that Bbp1 and Mps2 are recruited to the satellite early in its formation , ahead of Ndc1 and Nbp1 . 10 . 7554/eLife . 08586 . 014Figure 7 . Cell cycle analysis of pore membrane component localization . SIM images from asynchronously growing cells containing Nbp1-mTurquoise2 ( green ) and Bbp1-YFP ( red ) ( SLJ7699 , A ) , Bbp1-mTurquoise2 ( green ) and Ndc1-YFP ( red ) ( SLJ9231 , B ) and Ndc1-mTurquoise2 ( green ) and Spc42-YFP ( red ) ( SLJ7941 , C ) are arranged based on bud size and distance between SPBs ( or satellite structure ) , which approximates position in the cell cycle . A merged image showing the cell outline ( dashes ) is shown on the left . Bar , 2 µm . The SPB ( s ) are shown to the right of each cell . Bar , 200 nm . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 014 From our cell cycle analysis of Ndc1-Turquoise2 and Spc42-YFP ( Figure 7C ) , we noticed that Ndc1 was slightly displaced from Spc42 along the pole axis , particularly during initiation of SPB assembly ( see top and bottom SPBs ) . As the SPB duplicated , Ndc1-mTurquoise2 appeared to form a layer underneath ( towards the nucleoplasm ) and later around Spc42 , suggesting that the pore membrane forms below the satellite . To better understand the relationship between the satellite and pore membrane components , we applied SPA-SIM to images of α-factor arrested cells containing Spc110-YFP , which localizes to the central plaque of the mother SPB . As shown in Figure 8A , B and Table 2 , two foci of Mps2 , Bbp1 , Spc42 , Spc29 , Nud1 , and Cnm67 were observed along the mother-satellite axis , consistent with analysis of individual images ( Figure 5A , Figure 5—figure supplement 1A , Figure 6C ) . Although a bimodal distribution of Ndc1 was also seen , the shallow trough indicates that Ndc1 localized at the mother SPB , satellite and along the length of the extended bridge . Interestingly , Nbp1 was found primarily at the mother SPB and in bridge region proximal to the mother SPB in α-factor arrested cells . The fact that Nbp1 is the only pore membrane component not present at the satellite suggests that it may be a limiting factor restricting membrane insertion , or that its recruitment to the new SPB may require a maturation event that has not yet occurred . 10 . 7554/eLife . 08586 . 015Figure 8 . Early steps of SPB assembly involve both satellite and pore membrane proteins . ( A ) SIM Images from Figures 5A , 6C were aligned and average projections generated as in Figure 3 . The indicated mTurquoise2 protein ( green ) or Spc110-YFP ( red ) at the mother SPB ( arrowhead ) is shown along with N . Bar , 200 nm . ( B ) Normalized fluorescence intensity of each protein in A along the mother-satellite and pole axis , as depicted in the schematic , is plotted using the average Spc110-YFP position from all cells . In these plots , as in Figure 3 , the center of Spc42-mTurquoise2 was used to define the position of the 0 coordinate in the mother-satellite axis and the pole axis . FWHM values for each are listed in Table 2 . ( C ) Contour maps showing the distribution of fluorescence intensity at the extended half-bridge based images in A . Spc110-YFP in each sample is shown in red and other proteins are colored as indicated . Bar , 200 nm . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 01510 . 7554/eLife . 08586 . 016Table 2 . Probability distribution fit parameters from Figure 8DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 016SampleQuerySpc110-referencexyFWHMxFWHMyFWHMxFWHMySpc42-mT−60 . 3 ( 0 . 9 ) 0 . 073 . 0 ( 1 . 1 ) 111 . 2 ( 1 . 5 ) 109 . 4 ( 1 . 2 ) 144 . 6 ( 1 . 4 ) 60 . 3 ( 1 . 2 ) 71 . 2 ( 1 . 8 ) Spc29-mT−60 . 0 ( 0 . 9 ) −25 . 8 ( 1 . 0 ) 70 . 9 ( 1 . 1 ) 111 . 4 ( 0 . 9 ) 101 . 5 ( 1 . 2 ) 157 . 7 ( 1 . 4 ) 49 . 5 ( 1 . 3 ) 69 . 7 ( 1 . 8 ) Nud1-mT−65 . 3 ( 1 . 2 ) −11 . 6 ( 1 . 7 ) 78 . 0 ( 2 . 1 ) 112 . 3 ( 3 . 8 ) 101 . 8 ( 1 . 7 ) 148 . 2 ( 1 . 0 ) 61 . 6 ( 1 . 8 ) 73 . 5 ( 2 . 6 ) Cnm67-mT−60 . 1 ( 1 . 1 ) −19 . 0 ( 1 . 6 ) 79 . 9 ( 1 . 9 ) 91 . 0 ( 2 . 8 ) 97 . 0 ( 1 . 3 ) 140 . 1 ( 1 . 6 ) 74 . 4 ( 2 . 6 ) 61 . 6 ( 4 . 1 ) Ndc1-mT−62 . 7 ( 2 . 3 ) −34 . 2 ( 1 . 1 ) 97 . 0 ( 5 . 6 ) 130 . 9 ( 1 . 3 ) 106 . 9 ( 1 . 3 ) 137 . 4 ( 1 . 4 ) 56 . 0 ( 3 . 3 ) 56 . 2 ( 5 . 4 ) Nbp1-mT−66 . 5 ( 1 . 2 ) −48 . 8 ( 1 . 0 ) 77 . 7 ( 4 . 3 ) 106 . 8 ( 1 . 1 ) 102 . 1 ( 1 . 0 ) 119 . 1 ( 1 . 0 ) 3 . 0 ( 1 . 8 ) 50 . 6 ( 2 . 3 ) Mps2-mT−63 . 1 ( 1 . 9 ) −37 . 2 ( 1 . 4 ) 60 . 0 ( 6 . 4 ) 96 . 4 ( 2 . 1 ) 146 . 6 ( 3 . 3 ) 132 . 8 ( 2 . 0 ) 42 . 8 ( 3 . 0 ) 47 . 2 ( 6 . 2 ) Bbp1-mT−53 . 8 ( 1 . 5 ) −35 . 8 ( 1 . 5 ) 87 . 5 ( 3 . 2 ) 108 . 2 ( 2 . 2 ) 113 . 0 ( 2 . 3 ) 137 . 6 ( 2 . 6 ) 62 . 9 ( 2 . 2 ) 65 . 3 ( 2 . 8 ) Notes: All values are in nm . In all cases ‘x’ refers to the mother-satellite axis and ‘y’ refers to the pole axis . The origin is at the center point between the C-terminal Spc42 peaks as in Figure 3 . The Spc110 distributions are all centered at −44 . 2 ( 1 . 3 ) in x and −61 . 1 ( 0 . 8 ) in y . For Nbp1 , Ndc1 , and Mps2 a third Gaussian centered on the half-bridge with a broad width was added to allow for proper Gaussian fitting of the visible peaks . As in Table 1 , the FWHM is 2 . 35 times the standard deviation of the Gaussian fit , and this can be converted into 95% integral values by multiplying each by 1 . 7 . In all cases the mother SPB peak is shown on the first line and the satellite peak , if applicable , is shown on the second line . FWHMy in cells containing two foci was determined by the averages over the two peaks . Errors are in parentheses and are standard deviations from Monte Carlo random fits as described in ‘Materials and methods’ . Analysis of positional information obtained from the Gaussian fits used for SPA-SIM ( Table 2 ) showed that Nbp1-mTurquoise2 is located closest to the Spc110-YFP plane , whereas Ndc1-mTurquoise2 , Mps2-mTurquoise2 , and Bbp1-mTurquoise2 are 24–27 nm away . The peaks of Spc29-mTurquoise2 , Spc42-mTurquoise2 , Cnm67-mTurquoise2 , and Nud1-mTurquoise2 are further displaced towards the cytoplasm in the pole axis by 35 , 61 , 42 , and 50 nm , respectively . The location of these satellite components , with the exception of Spc42 , relative to the C-terminus of Spc110 is consistent with our knowledge of their positional information within the core SPB from other methods of analysis such as immunoEM , FRET , and yeast two-hybrid analysis ( Adams and Kilmartin , 1999; Jaspersen and Winey , 2004; Muller et al . , 2005; Winey and Bloom , 2012 ) . The large displacement of Spc42-mTurquoise2 is most likely due to the fact that the fits consider the position of Spc42 at both the mother SPB and satellite . The shift of the satellite components relative to the membrane proteins indicates that the pore and pore-related structures are formed near the region defined by the C-terminus of Spc110 , which is displaced in the pole axis from the satellite components . Overlays of fluorescent density further illustrate how pore proteins assemble beneath the satellite ( Figure 8C ) . Release of cells from α-factor into the cell cycle allowed us to compare the kinetics of Spc110 acquisition ( which occurs at the end of duplication after the new SPB is inserted into the NE ) with that of incorporation of Nbp1 or Ndc1 to determine if Nbp1 is indeed a limiting factor for SPB duplication as our SPA-SIM analysis suggested . Continued growth of cells allowed us to watch NE insertion and protein incorporation during the next cell cycle to ensure the α-factor arrest did not affect recruitment of Nbp1 or Ndc1 . A single Spc110-YFP focus and a single Ndc1-mTurquoise2 or Nbp1-mTurquoise2 focus was observed in Ndc1-mTurquoise2/Spc110-YFP or Nbp1-mTurquoise2/Spc110-YFP strains arrested in α-factor ( Figure 6C ) and at the 5 min time point following release ( Figure 9A , B ) . At 15 or 20 min following release , two foci of Ndc1-mTurquoise2 were detected in 32% ( 44/136 ) or 34% ( 37/110 ) of cells , respectively . In these cells , a single Spc110-YFP focus was observed , providing evidence that Ndc1 localizes to the new SPB before insertion into the NE ( Figure 9B , D ) . In contrast , only 9% ( 12/129 ) or 10% ( 17/165 ) of cells at 15 or 20 min had two foci of Nbp1-mTurquosie2 with a single focus of Spc110-YFP . Most ( 88% , 114/129 at 15 min and 79% , 104/165 at 20 min ) contained a single focus of Nbp1-mTurquoise2 and a single focus of Spc110-YFP . Even at later time points , the predominant phenotype seen was two resolved foci of Spc110-YFP that likely are SPBs in the duplicated side-by-side configuration connected by an elongated Nbp1-mTurquoise2 signal ( 18% , 19/107 at 25 min and 38% , 24/65 at 30 min ) ( Figure 9B , D ) . It is important to note that we used mTurquoise2 , which has a shorter wavelength than YFP ( and thus increased resolution with SIM ) to visualize Nbp1 . The fact that we saw two foci of Spc110-YFP suggests that biology inherent to SPB duplication , rather than the resolution of our microscope , was the underlying cause for this stretched configuration of Nbp1-mTurquoise2 . A similar pattern of Ndc1-mTurquoise2 recruitment to the new SPB prior to its insertion and Nbp1-mTurquoise2 resolution after SPB insertion was observed during the next SPB duplication cycle ( Figure 9A , B ) , although precise timing of events relative to release from α-factor varied from cell to cell due to loss of synchrony following mitosis . Although two resolved foci of Nbp1-mTurquoise2 occasionally were seen prior to NE insertion , these events were rare ( less than 10% of cells examined at all time points ) and could be due to poor detection of Spc110-YFP . Taken together with our SPA-SIM results , these data lend support to the idea that Nbp1 is the last SPB component to fully assembly onto the new pole . We propose that a membrane fenestra formed by Mps2 and Bbp1 is created below the satellite at the time of its formation and that Ndc1 is recruited later followed by Nbp1 ( Figure 10 ) . 10 . 7554/eLife . 08586 . 017Figure 9 . Localization of Ndc1 and Nbp1 during SPB insertion . Cells containing Nbp1-mTurquoise2 ( A and C , SLJ10169 ) or Ndc1-mTurquoise2 ( B and D , SLJ10018 ) along with Spc110-YFP were released from α-factor into prewarmed SC-complete media at 30°C . At 5 min time intervals , an aliquot of cells was harvested , fixed and imaged by SIM . ( A , B ) Merged images showing the outline ( dashes ) of a representative cell is on the left . Bar , 2 µm . The SPB ( s ) are to the right of each cell . Bar , 200 nm . ( C , D ) Cells at the 15 , 20 , 25 , and 30 min time points were analyzed to determine the percentage of schmooed and small budded cells that have two closely spaced foci of Spc110-YFP and/or Nbp1-mTurquoise2 ( C ) or Ndc1-mTurquoise2 ( D ) . Schematics depict the five configurations of proteins , including the signal that was stretched between mother and daughter SPB . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 01710 . 7554/eLife . 08586 . 018Figure 10 . Model for SPB duplication . Revised model of the early steps in SPB duplication based on results described . In anaphase , Sfi1 oligomerization leads to an elongated half-bridge . Next , Spc42 and Spc29 assemble at the distal cytoplasmic tip of the bridge . At the same time , the pore proteins Bbp1 ( magenta ) and Mps2 ( gray ) accumulate at the satellite region . Later , the satellite and membrane-associated region continue to mature through the addition of Nud1 ( green ) , Cnm67 ( orange ) , and Ndc1 ( orange ring ) . Nbp1 ( cyan ) remains associated primarily with the mother-proximal end of the extended half-bridge until the SPB is inserted into the NE . Note that satellite and pore-associated proteins are also present at the mother SPB . DOI: http://dx . doi . org/10 . 7554/eLife . 08586 . 018 SPA-SIM allowed us to resolve features of the extended half-bridge formed by Sfi1 and other half-bridge components that were not observed using EM , biochemical , genetic , or other super-resolution methods . A recent study that combined PALM and STORM data showed , as we have , that N-terminally tagged versions of Sfi1 appear as two foci and a single focus is observed if Sfi1 is tagged at its C-terminus ( Seybold et al . , 2015 ) . However , using SPA-SIM we were able to resolve additional structural features of the extended half-bridge due to our ability to perform quantitative analysis of multiple SPB components in three dimensions . Unexpectedly , we find that the bridge is neither linear nor symmetric , even at early stages of SPB duplication , and that Cdc31 is not localized uniformly along its length . The maximum intensity of Cdc31 binding on the extended half-bridge occurs in the central region of the new Sfi1 filament , which is located between the central and distal bridge ( Figure 3E ) . While this central repeat domain of Sfi1 has been shown in vitro to contain multiple Cdc31 binding sequences ( Kilmartin , 2003; Li et al . , 2006; Seybold et al . , 2015 ) , it is unknown why Cdc31 would preferentially associate with the newly formed Sfi1 instead of the old molecules of Sfi1 since both contain the same repeat domains . By EM , the half-bridge appears as a flat sheet and probably only has one layer of Sfi1 filaments ( Byers , 1981; Li et al . , 2006 ) . Based on the 65° rotation of Cdc31 molecules around Sfi1 repeats seen in vitro , there is thought to be limited side-to-side contact between adjacent Sfi1-Cdc31 filaments ( Li et al . , 2006 ) . Cdc31 binding to only a fraction of repeats , in particular those on the new filament , could increase the interaction between adjacent Sfi1 molecules by partially alleviating this constraint . Binding to the newly assembled Sfi1 also could be important for stabilizing the half-bridge , a theory supported by recent studies on sfi1+ and cdc31+ in fission yeast ( Lee et al . , 2014; Bouhlel et al . , 2015 ) . Initiation of a new round of SPB duplication requires elongation of the half-bridge . During most of the cell cycle , the C-terminus of Sfi1 is phosphorylated; dephosphorylation of Cdk1 sites in the C-terminus by Cdc14 results in half-bridge elongation and triggers SPB reduplication ( Avena et al . , 2014 ) . Here we show that two foci of GFP-Sfi1 are visible in late mitosis , when Cdc14 is activated ( Stegmeier and Amon , 2004 ) . Based on our analysis , this appears to be the first landmark event signaling a new round of SPB duplication . Understanding the molecular interactions between the C-termini of Sfi1 molecules emanating from the same pole and between Sfi1 C-termini in the antiparallel array will further elucidate how this important step in SPB duplication is controlled . Our finding that Bbp1 and Mps2 localize to the satellite region early in SPB duplication was unexpected; however it could explain , in part , how satellite components are anchored to the bridge and NE . Not only do Mps2 and Bbp1 interact with each other , but also Bbp1 binds to Spc29 and Kar1 and Mps2 associates with Mps3 ( Schramm et al . , 2000; Jaspersen et al . , 2006 ) . Enrichment of Mps3-YFP at the distal end of the bridge near the satellite ( Figure 3E , F ) could be due to interactions with Mps2 . The mps2-381 allele that is defective in Mps3 binding is unable to form a bridge or satellite at the non-permissive temperature , supporting the idea that Mps2 plays a role early in SPB duplication ( Jaspersen et al . , 2006 ) . A role for Bbp1 early in SPB duplication has not been reported; however , the observation that Mps1-dependent phosphorylation of Spc29 increases its binding to Bbp1 ( Araki et al . , 2010 ) might be re-interpreted in light of our finding—phosphorylated Spc29 may recruit Bbp1 to the satellite . Similarly , other regulatory events at the SPB may take on a new significance based on possible coordination between satellite and pore assembly . If pore proteins localize to the satellite region in α-factor arrested cells , why does the SPB not insert into the NE ? The answer likely is that factors needed to remodel the NE to form a stable contiguous pore membrane have not yet localized . One candidate is Ndc1 , an evolutionarily conserved integral membrane protein that is present at the pore membrane of both SPBs and NPCs ( Chial et al . , 1998 ) . Although Ndc1 was observed at the satellite region in α-factor arrested cells , yeasts are exquisitely sensitive to its dosage ( Chial et al . , 1999; Chen et al . , 2014 ) and it may not be present at high enough levels to induce stable changes in membrane architecture . A second candidate is the Ndc1 binding protein Nbp1 , which contains an ArfGAP1 lipid packing sensor domain that is able to interact with lipid head groups to induce membrane bending ( Bigay et al . , 2005; Kupke et al . , 2011 ) . Nbp1 localization in α-factor arrested cells was restricted to the mother SPB and the bridge proximal to the mother . Our observations that Nbp1 does not localize to the new SPB until that pole is inserted into the NE and that it localizes to a region within 10 nm of Spc110 are consistent with the idea that Nbp1 functions from the nucleoplasm ( Kupke et al . , 2011 ) . Analysis of cells depleted of NBP1 using a temperature-dependent degron allele showed that Nbp1 is not required for localization of Cdc31 , Kar1 , Mps3 , Sfi1 , Spc29 , or Spc42 to the mother SPB or the satellite . However , Nbp1 is partially required for Bbp1 recruitment and Mps2 stability at the new SPB without significantly affecting the mother SPB ( Araki et al . , 2006 ) . The fact that SPB insertion can occur without full resolution of Nbp1 onto a new SPB combined with its requirement for Mps2 protein stability make it tempting to speculate that Nbp1 is not required for SPB insertion per se but rather for maturation and maintenance of structures formed at the membrane of the new SPB . Cnm67 links Nud1 and Spc72 at the outer plaque with Spc42 at the central plaque ( Adams and Kilmartin , 1999; Schaerer et al . , 2001; Muller et al . , 2005 ) . Cells lacking CNM67 display defects in cytoplasmic microtubule organization but remain viable , undergoing a normal SPB duplication cycle ( Brachat et al . , 1998; Hoepfner et al . , 2000; Schaerer et al . , 2001 ) . Our observation that Cnm67 is present at variable levels at the satellite is consistent with the idea that it is not required for SPB duplication . In cells lacking CNM67 , ∼10% of cells showed Nud1 localization to the half-bridge region , suggesting that there is a Cnm67-independent mechanism of SPB localization ( Adams and Kilmartin , 1999 ) . This could explain , in part , why Nud1 is detected at the satellite region more frequently than Cnm67 in our experiments . Although Nud1 is an essential component of the outer plaque and is present at the satellite , it has no known role in SPB duplication . Instead , Nud1 serves as a signaling platform that is important for exit from mitosis ( Adams and Kilmartin , 1999; Gruneberg et al . , 2000; Rock et al . , 2013 ) . However , given that detection of Cnm67 at the satellite was challenging and varied considerably between strains , our results may underestimate levels of Cnm67 at the satellite . Our ability to determine the spatiotemporal relationship of multiple SPB components was integral to the new insights in SPB structure and duplication described here . While SIM by itself does not offer the resolution of STORM and PALM , the SPA-SIM method we developed offered unique advantages that were ideally suited to our study . Firstly , as opposed to STORM and PALM , SIM data can be acquired using standard fluorescent proteins such as GFP , YFP , CFP , mCherry , and mTurqouise2 . In yeast , where genetic manipulations are straightforward , this allows for study of endogenously tagged proteins and removes concerns over competition with native untagged proteins as well as the need for antibody staining . Because all protein can in theory be visualized , sparse labeling artifacts are effectively eliminated , which pose a major challenge for any type of quantitative single particle analysis . Secondly , SIM allows for simple and rapid measurement with two to three fluorophores . In our case , we have used this to our advantage since Spc42 or Spc110 could be included as reference points for further alignment and averaging . Our fitting uncertainty of Gaussian centers in three dimensions is less than 3 nm , while the alignment of the colors , shown for YFP-Spc42-mTurqouise2 ( Figure 3 ) , is within 12 ± 6 nm in the mother-satellite axis , which serves as a color alignment control for our SIM data and our realignment method . Furthermore , we are able to decipher shifts in density centers on the order of 10–30 nm , putting SPA-SIM on par with other super-resolution and particle averaging methods that to this point have required more sophisticated three-dimensional data acquisition methods ( Loschberger et al . , 2012; Szymborska et al . , 2013; Loschberger et al . , 2014; Van Engelenburg et al . , 2014; Broeken et al . , 2015 ) . In summary , we have used SIM and SPA-SIM to uncover important mechanistic details about a process that has been extensively studied genetically , cytologically , and biochemically . Our localization analysis of wild-type proteins provides a dynamic picture of the process of SPB duplication that can be used in the future for the study of cell cycle and SPB mutants . Furthermore , our experimental methods can be applied to multiple biological structures and thus provide a framework for how advanced microscopy methods can be applied to elucidate details of biological systems that cannot be captured in vitro . Yeast strains are derivatives of W303 and are listed in Source data 1 . Fusions to GFP , YFP , CFP , mCherry , and mTurquoise2 were created using polymerase chain reaction-based methods in SLJ1070 ( Mata/Matα bar1/bar1 ade2-1/ADE2 trp1-1/TRP1 lys2∆/LYS2 leu2-3 , 112/leu2-3 , 112 his3-11 , 15/his3-11 , 15 ura3-1/ura3-1 ) ( Gardner and Jaspersen , 2014 ) . Haploid strains were generated by sporulation and tetrad dissection . MET3-CDC20-KANMX-HO ( a gift of Marc Gartenberg , Rutgers ) was integrated into strains by digestion with AflII and transformants were selected on plates containing G418 . The N- and C-terminal-tagged versions of SFI1 were gifts of John Kilmartin ( University of Cambridge ) and were integrated into strains as described ( Avena et al . , 2014 ) . mps1-1 ( Straight et al . , 2000 ) was crossed into these strains . MET3-YFP-CDC31 and several YFP- and CFP-tagged versions of CNM67 , SPC42 , SPC29 , and SPC110 were gifts from Trisha Davis , Eric Muller , and Tennessee Yoder ( University of Washington ) . Strains were grown in minimal media supplemented with 3× adenine to mid-log phase at 30°C , with the exception of strains containing mps1-1 , which were grown at 24°C then shifted to 37°C for 4 hr . To arrest cells in G1 , 1 µg/ml or 10 µg/ml α-factor was added to bar1 or BAR1 strains , respectively , for 3 hr at 30°C . To arrest cells in metaphase , strains containing MET-CDC20 were grown in YPD for 2 . 5–3 hr at 30°C . Following three washes , cells were released into minimal media lacking methionine . 1 µg/ml α-factor was added to half of the released culture; dimethyl sulfoxide was added to the other half . Both were incubated at 30°C and samples were taken at the indicated times . Cells were fixed for 15 min in 4% paraformaldehyde ( Ted Pella ) in 100 mM sucrose and then washed two times in phosphate-buffered saline , pH 7 . 4 . An aliquot of cells was placed on a glass slide and covered with a number 1 . 5 coverslip . SIM images were acquired with an Applied Precision OMX Blaze ( GE Healthcare ) . A 60× 1 . 42 NA Plan Apo oil objective was used , and emission was collected onto two PCO Edge sCMOS cameras ( Kelheim , Germany ) with each camera dedicated to one specific channel . For CFP/mTurqouise2/YFP experiments , a 440/514/561 dichroic was used with 460–485 nm and 530–552 nm emission filters for CFP/mTurqouise2 and YFP , respectively . The 440 nm line and 514 nm line were used for excitation of CFP/mTurqouise2 and YFP , respectively . For GFP/mCherry experiments , a 405/488/561/640 dichroic was used with 504–552 nm and 590–628 nm emission filters for GFP and mCherry , respectively , using a 488 nm laser line ( GFP ) and 561 nm laser line ( mCherry ) . To limit spectral cross-talk , all SIM data was acquired in alternating excitation mode . SIM reconstruction was performed with the Applied Precision software Softworx with a Wiener filter of 0 . 001 . Color alignment from different cameras in the radial plane was performed using the color alignment slide from GE Healthcare ( Pittsburg , PA ) . In the axial direction , color alignment was performed using 100 nm tetraspeck beads ( Life Technologies , Kelheim , Germany ) . For image preparation , the SIM reconstructed images were scaled 2 × 2 with bilinear interpolation then smoothed with a Gaussian blur of pixel radius 0 . 8 . In many cases , for illustration purposes , a max projection in z over the relevant slices was done . Three-dimensional analysis of SIM images was performed with custom written macros and plugins in the open source software , ImageJ ( NIH , Bethesda , MD ) . All plugins and their source code are available at http://research . stowers . org/imagejplugins/ . In cases where the protein of interest represents two distinct spots corresponding to the mother SPB and satellite , the spots were fitted to two 3D Gaussian functions ( see below ) and realigned along the axis between these functions for further analysis and visualization . In cases where the protein represents a less distinct distribution or when the distribution along the mother-satellite axis is being queried , a secondary protein ( typically Spc42 ) was fit to two 3D Gaussian functions for further analysis and visualization . SPBs were identified in a semi-automated fashion by sum projecting the raw SIM images , blurring them with Gaussian blur with a standard deviation of 1 pixel and progressively finding all maxima with an intensity above 15% of the image maximum intensity and a minimum distance of 30 pixels from other maxima . Next 30 × 30 pixel intensity profiles along the z axis were created . SPBs with fitted channel profile maxima in the first or last slice were eliminated . Each selected SPB was then visually inspected in three dimensions for counts of those containing two spots . This methodology underestimates the number of two spot images given the lower z resolution and thus the inability of the microscope to distinguish spots where the mother-satellite axis is oriented vertically . Nevertheless , yeast nuclei rarely orient themselves this way and this method provides the best estimate of satellite formation and/or incorporation . SPBs containing two visible spots were selected for fitting initialization with the mother selected first . In samples with Spc110 labeling , the spot closest to the Spc110 was identified as the mother SPB . In samples where Spc42 was used for fitting , the brighter Spc42 spot was considered the mother SPB , as was shown in Spc42/Spc110 dual labeling experiments ( see Figure 5A , B ) . While this method has some associated error , most proteins are distributed fairly symmetrically along the mother-satellite axis so a small number of mis-oriented axes will not dramatically skew the results . After manual spot selection , the spots were fit to the sum of two 3D Gaussian functions by Levenberg–Marquardt non-linear least squares ( Bevington and Robinson , 2003 ) . Spot centers in x and y were constrained to within ± two pixels of the manually selected positions to avoid dramatic misfits . The center position in z was initialized as the maximum slice of a 3 × 3 pixel z profile and also constrained to ± two slices . After fitting , a two dimensional plane profile in both channels was created containing the fitted centers of the spots and oriented horizontally along the axis perpendicular to the mother-satellite axis . Intensities were obtained by tri-linear interpolation along this plane with a pixel size four times smaller than that of the reconstructed SIM image . These realigned profiles were summed together to create dual-color probability maps for the intensity profiles of the two proteins under study . In cases where Spc110 is labeled in the second channel , profiles were rotated so that the side of the profile with the higher Spc110 intensity ( the mother SPB ) was oriented to one side . In that way it was possible to assess the shift of the mother-satellite profile relative to the pole axis . In the same way , half-bridge profiles collected with Spc42 as a reference were oriented to one side based on the non-Spc42 signal ( or YFP in the YFP-Spc42-mTurquoise2 strain ) . Typically images are shown with the aligned channel above the non-aligned channel since this is most consistent with available structural and molecular information . The exceptions to this are Mps3 and N-terminus of Spc42 , where EM and/or FRET data demonstrates positioning below the mother-satellite axis ( Bullitt et al . , 1997; O'Toole et al . , 1999; Jaspersen et al . , 2002; Muller et al . , 2005 ) . Analysis of probability map centers for distance and angle measurements were made by multi-Gaussian fitting of average intensity profiles along either axis . Errors were estimated by standard Monte Carlo analysis of 100 randomly simulated data sets with random Gaussian noise corresponding to the variance indicated by the fit residuals ( Bevington and Robinson , 2003 ) . Given the random orientation of individual realigned images and the choice to orient them with the reference channel to one side , the mother-spindle axis was centered slightly differently for different probability maps . For creation of overlayed contour maps , these differences were eliminated by vertically shifting the probability maps with bilinear interpolation . Contour maps were then generated by thresholding each channel at 75% of its maximum intensity and outlining the resulting mask . For proteins present at both the mother and satellite , each distribution was outlined independently to avoid bias due to the intensity differences between these distributions . Centers and FWHM values for the probability distributions were generated by fitting average horizontal and vertical profiles to one-dimensional Gaussian functions . For horizontal ( x direction ) half-bridge profiles with a single peak , 30 pixels at either end of the distribution were removed to prevent mis-fitting due to variable background signals in that region . For pore protein distributions in that same direction , a third broad Gaussian function centered on the distribution was required for reasonable fitting of the distribution . Log-phase cells were high-pressure frozen in a Wohlwend Compact 02 high pressure freezer , freeze-substituted in 0 . 25% glutaraldehyde , 0 . 1% uranyl acetate in acetone , embedded in Lowicryl HM20 , and processed for immunoEM as previously described ( Giddings et al . , 2001 ) . The affinity-purified rabbit polyclonal GFP antibody was a gift from Jason Kahana and Pam Silver . Imaging was conducted using a FEI Phillips CM100 electron microscope .	Cells divide to produce two new daughter cells that each contain the same genetic material . First , the DNA of the parent cell is copied , then it must be physically separated into the daughter cells by a structure made of filaments called microtubules . To ensure that the DNA is separated into two equal parts , the microtubules must emerge from two points in the cell , known as spindle poles . Each spindle pole is made of a group ( or ‘complex’ ) of proteins and these have to be copied before the cell can divide . While we understand how DNA is copied , we do not know how cells copy proteins . The spindle pole in yeast—known as the spindle pole body—is an ideal model to study this problem because the proteins that form it have already been identified and it is easy to study yeast in the laboratory . Burns et al . developed a new method to study the spindle pole body using fluorescent protein tags and a sophisticated microscopy technique . The experiments mapped the positions of 18 proteins within the spindle pole body during its duplication . Some of these proteins enable the spindle pole to insert into the membrane that surrounds the cell's nucleus . Unexpectedly , Burns et al . observed that this set of proteins interact with the new spindle pole as it forms , instead of afterwards as was previously believed . Burns et al . 's findings suggest that the spindle pole body assembles into the membrane surrounding the nucleus at the same time as it is copied . The next challenges are to understand the details of how this works and to use the same method to study other large protein complexes in cells . Until now , highly detailed surveys of protein structures have been limited to a handful of proteins and conditions . The method developed by Burns et al . makes it possible to carry out studies that examine the movements of whole protein complexes during cell division .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "chromosomes", "and", "gene", "expression", "cell", "biology" ]	2015	Structured illumination with particle averaging reveals novel roles for yeast centrosome components during duplication
Complexins play activating and inhibitory functions in neurotransmitter release . The complexin accessory helix inhibits release and was proposed to insert into SNARE complexes to prevent their full assembly . This model was supported by ‘superclamp’ and ‘poor-clamp’ mutations that enhanced or decreased the complexin-I inhibitory activity in cell–cell fusion assays , and by the crystal structure of a superclamp mutant bound to a synaptobrevin-truncated SNARE complex . NMR studies now show that the complexin-I accessory helix does not insert into synaptobrevin-truncated SNARE complexes in solution , and electrophysiological data reveal that superclamp mutants have slightly stimulatory or no effects on neurotransmitter release , whereas a poor-clamp mutant inhibits release . Importantly , increasing or decreasing the negative charge of the complexin-I accessory helix inhibits or stimulates release , respectively . These results suggest a new model whereby the complexin accessory helix inhibits release through electrostatic ( and perhaps steric ) repulsion enabled by its location between the vesicle and plasma membranes . Neurotransmitter release is crucial for interneuronal communication and is exquisitely regulated by a sophisticated protein machinery ( Sudhof , 2013 ) . Great advances have been made in elucidating the mechanism of release ( Brunger et al . , 2009; Sorensen , 2009; Jahn and Fasshauer , 2012; Rizo and Sudhof , 2012 ) and basic aspects of this process have been reconstituted with eight central components of the release machinery ( Ma et al . , 2013 ) , leading to a model with defined roles for each component . In this model , the neuronal soluble N-ethylmaleimide-sensitive factor attachment protein receptors ( SNAREs ) synaptobrevin , syntaxin-1 and SNAP-25 form a tight four-helix bundle called the SNARE complex ( Sollner et al . , 1993; Poirier et al . , 1998; Sutton et al . , 1998 ) that brings the synaptic vesicle and plasma membranes together ( Hanson et al . , 1997 ) and is critical for membrane fusion; N-ethylmaleimide-sensitive factor ( NSF ) and soluble NSF attachment proteins ( SNAPs ) disassemble the SNARE complex ( Sollner et al . , 1993 ) to recycle the SNAREs for another round of fusion ( Mayer et al . , 1996 ) , and may favor physiological membrane fusion by disassembling syntaxin-1-SNAP-25 complexes ( Ma et al . , 2013 ) ; Munc18-1 and Munc13s orchestrate SNARE complex assembly in an NSF/SNAP resistant manner ( Ma et al . , 2013 ) , and may play a direct role in fusion ( Dulubova et al . , 2007; Li et al . , 2011b ) ; and synaptotagmin-1 acts as a Ca2+ sensor ( Fernandez-Chacon et al . , 2001 ) , likely by bridging the two membranes ( Arac et al . , 2006; Xue et al . , 2008a ) . Tight regulation of neurotransmitter release also depends critically on complexins , small soluble proteins that bind to the SNARE complex ( McMahon et al . , 1995 ) and play activating and inhibitory functions . Absence of complexins leads to a severe impairment of Ca2+-evoked exocytosis and to varied effects on spontaneous release ranging from small decreases to dramatic increases , depending on the system ( Reim et al . , 2001; Huntwork and Littleton , 2007; Xue et al . , 2008b; Maximov et al . , 2009; Hobson et al . , 2011; Martin et al . , 2011; Yang et al . , 2013 ) . These results likely arise from an interplay between stimulatory and inhibitory activities of different regions of complexins ( Xue et al . , 2007 , 2009; Cho et al . , 2010; Kaeser-Woo et al . , 2012 ) . Complexin I ( CpxI ) is largely unstructured in solution ( Pabst et al . , 2000 ) but forms a central α-helix that binds to the SNARE complex and is preceded by an accessory helix ( Bracher et al . , 2002; Chen et al . , 2002 ) ( Figure 1A ) . The central helix is crucial for both the activating and inhibitory functions of complexins , while the accessory helix inhibits release ( Xue et al . , 2007; Maximov et al . , 2009 ) ; the complexin N-terminus plays an activating function , releasing the inhibition by the accessory helix ( Xue et al . , 2010; Yang et al . , 2010 ) , and the C-terminal sequence has activating and inhibitory roles ( Kaeser-Woo et al . , 2012 ) . 10 . 7554/eLife . 02391 . 003Figure 1 . Models of complexin function . ( A ) Domain diagram of CpxI and ribbon diagram of the crystal structure of the CpxI ( 26-83 ) /SNARE complex ( PDB code 1KIL ) ( Chen et al . , 2002 ) . Selected residue numbers are indicated above the ribbon diagram and on the CpxI ( 26–83 ) ribbon in the structure . Synaptobrevin is colored in red , syntaxin-1 in yellow , SNAP-25 in blue and green ( N-terminal and C-terminal SNARE motifs , respectively ) , and CpxI ( 26-83 ) in orange ( accessory helix ) and pink ( central helix ) . The same color code is used in all panels . N and C indicate the N- and C-termini of the SNARE motifs . ( B ) Ribbon diagram of the crystal structure of the complex between the CpxI ( 26-83 ) superclamp mutant and a synaptobrevin truncated SNARE complex ( PDB code 3RK3 ) ( Kummel et al . , 2011 ) . Three complexes are shown to illustrate the zigzag array present in the crystals . ( C–E ) Models for the inhibitory activity of the complexin accessory helix . In all models , the accessory helix is proposed to prevent C-terminal assembly of a partially assembled SNARE complex either by inserting into the complex ( C ) , by binding to the synaptobrevin SNARE motif ( D ) , or by electrostatic repulsion with both membranes ( E ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 00310 . 7554/eLife . 02391 . 004Figure 1—figure supplement 1 . The interface between CpxI and the SNARE complex . A ribbon diagram of the crystal structure of the CpxI ( 26-83 ) /SNARE complex ( PDB code 1KIL ) ( Chen et al . , 2002 ) is shown , with synaptobrevin colored in red , syntaxin-1 in yellow , SNAP-25 in blue and green ( N-terminal and C-terminal SNARE motifs , respectively ) , and CpxI ( 26-83 ) in orange ( accessory helix ) and pink ( central helix ) . The side chains of CpxI ( 26-83 ) , synaptobrevin and syntaxin-1 are shown , and selected residues are labeled as a guide to see which residues interact and where were the truncations of the different proteins made . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 00410 . 7554/eLife . 02391 . 005Figure 1—figure supplement 2 . High B-factors in the accessory helix in the crystal structure of the CpxI ( 26-83 ) superclamp mutant bound to SCΔ60 . ( A ) Electron density images of the crystal structure of CpxI ( 26-83 ) superclamp mutant bound to SCΔ60 ( PDB accession code 3RK3; Kummel et al . , 2011 ) . Superimposed on the coordinates from 3RK3 is the map with coefficients 2\|mF ( obs ) −DF ( calc ) \| , contoured at the r . m . s . d . level of the map . The phases and map were calculated from the deposited 3RK3 structure factors in the program PHENIX ( Afonine et al . , 2010 ) . Note that little electron density is observed for the side chains of the accessory helix , even for the hydrophobic residues in the interface with the SNAREs ( the native L41 and the mutant L27 , F34 and A37; upper panel ) . In contrast , clear electron density is observed for the side chains of the central helix that contact the SNARE complex ( lower panel; some of these residues are labeled ) . ( B ) Plot of average atomic displacement parameters ( ADP or ‘B-factors’ ) over the residues of the CpxI ( 26-83 ) superclamp molecule in 3RK3 . The plot is color coded from blue as the minimum to red as the maximum value of the average B-factors . Note that the B-factors are much higher for the accessory helix than for the residues of the central CpxI helix that contact the truncated SNARE complex ( residues 59 to 72 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 005 Cell–cell fusion assays and reconstitution studies also indicated dual roles for complexins that likely recapitulate to some extent their physiological functions ( Giraudo et al . , 2006; Schaub et al . , 2006; Yoon et al . , 2008; Malsam et al . , 2009 , 2012; Diao et al . , 2012 ) , but in most cases these studies revealed only stimulatory or inhibitory roles . The activating function has been proposed to arise from stabilization of the SNARE complex by complexin binding ( Chen et al . , 2002 ) , from interactions of the complexin N-terminus with the C-terminus of the SNARE complex ( Xue et al . , 2010 ) , and from binding of the complexin C-terminal region to phospholipids ( Seiler et al . , 2009 ) , but these models remain to be validated . The inhibitory activity of complexins attracted much attention because several studies suggested that complexins prevent exocytosis before Ca2+ influx and synaptotagmin-1 releases the inhibition upon Ca2+ binding by displacing complexins from the SNARE complex ( Giraudo et al . , 2006; Schaub et al . , 2006; Tang et al . , 2006; Roggero et al . , 2007 ) . Later analyses showed that CpxI is not fully displaced but there is competition between synaptotagmin-1 and part of CpxI for binding to the SNARE complex on membranes ( Dai et al . , 2007; Xu et al . , 2013 ) . Genetic interaction studies in hippocampal neurons showed that complexins regulate release similarly in the absence or presence of synaptotagmin-1 , indicating that complexins function independently of synaptotagmin-1 ( Xue et al . , 2007 , 2010 ) , but this finding does not exclude the notion that complexins and synaptotagmin-1 may cooperate in regulating release . For example , the dramatic increase of spontaneous release observed in complexin nulls in Drosophila requires synaptotagmin-1 ( Jorquera et al . , 2012 ) , and absence of complexins in hippocampal neurons sensitizes release to loss of 50% of synaptotagmin-1 expression ( Xue et al . , 2010 ) . While it is clear that the complexin accessory helix inhibits release ( Xue et al . , 2007; Maximov et al . , 2009 ) , a satisfactory model for this activity has not emerged yet . We initially proposed that part of this helix might replace part of the synaptobrevin SNARE motif in trans partially assembled SNARE complexes , hindering C-terminal assembly of the complex ( Xue et al . , 2007; Figure 1C ) . A similar insertion model , but envisioning that the entire accessory helix replaces part of synaptobrevin , was proposed later and was supported by the enhanced inhibition in cell–cell fusion assays caused by replacing charged with hydrophobic residues in the accessory helix of CpxI ( 'superclamp mutants' ) and by the design of a ‘poor-clamp’ mutation ( K26A ) that impairs the inhibitory activity ( Giraudo et al . , 2009 ) . The crystal structure of a fragment of a CpxI superclamp mutant ( D27L , E34F , R37A ) bound to a SNARE complex with C-terminally truncated synaptobrevin suggested an alternative model whereby the central helix of one CpxI molecule binds to a SNARE complex and the accessory helix inserts into another SNARE complex , resulting in a zigzag array ( Kummel et al . , 2011; Figure 1B ) . However , formation of such a complex with wild type ( WT ) CpxI would be highly unfavorable thermodynamically because three charged residues would be placed into hydrophobic environments . The study described here was designed to investigate how the complexin accessory helix inhibits neurotransmitter release , testing the insertion and zigzag models as well as additional models that emerged subsequently ( Figure 1D , E ) . Using NMR spectroscopy and isothermal titration calorimetry ( ITC ) , we show that the accessory helix of CpxI does not insert into synaptobrevin-truncated SNARE complexes in solution . Furthermore , in stark contrast with the cell–cell fusion data , rescue experiments in complexin I-III triple knockout ( KO ) neurons reveal that superclamp mutations in CpxI lead to slightly stimulatory or no effects on neurotransmitter release , while the poor-clamp K26A mutation impairs release . We also find that the accessory helix of complexin from drosophila melanogaster inhibits spontaneous release more strongly than the accessory helix of mammalian CpxI , which may arise from the more negatively charged nature of the former . Indeed , a mutation that increases the negative charge of the CpxI accessory helix inhibits release and a mutation that decreases the negative charge enhances release . These results suggest a model whereby the location of the negatively charged accessory helix between the synaptic vesicle and plasma membranes causes electrostatic and perhaps steric repulsion with the membranes , thus hindering membrane fusion and neurotransmitter release ( Figure 1E ) . To analyze interactions between CpxI and soluble truncated SNARE complexes that might mimic trans SNARE complexes partially assembled between two membranes ( e . g . , Figure 1C , insertion model ) , we used 1H-15N two-dimensional transverse relaxation optimized spectroscopy ( TROSY ) heteronuclear single quantum coherence ( HSQC ) spectra , which provide a powerful tool to study protein–protein interactions . These NMR spectra can be viewed as protein fingerprints with one cross-peak for each non-proline residue in a 15N-labeled protein , and the positions and line widths of the cross-peaks are very sensitive to perturbations caused by binding to an unlabeled protein ( Rizo et al . , 2012 ) . Flexible and unstructured regions exhibit sharp cross-peaks with poor dispersion whereas structured regions have broader , well-dispersed cross-peaks , as exemplified by 1H-15N TROSY-HSQC spectra of a uniformly 2H , 15N-labeled CpxI fragment spanning the accessory and central helices [CpxI ( 26-83 ) ] . As described previously ( Chen et al . , 2002 ) , the 1H-15N TROSY-HSQC spectrum of this fragment exhibits sharp cross-peaks and poor dispersion ( Figure 2A ) because , although partially helical , the fragment is very flexible . Upon binding to a minimal SNARE complex containing the SNAREs motifs of synaptobrevin , syntaxin-1 and SNAP-25 ( below referred to as SNARE complex or SC ) , the 1H-15N TROSY-HSQC spectrum of CpxI ( 26-83 ) reveals strong broadening and a dramatic increase in dispersion for cross-peaks from the central helix ( Figure 2B , red contours ) due to stable packing of this helix against the synaptobrevin and syntaxin-1 SNARE motifs . Cross-peaks from the accessory helix , which does not contact the SNARE complex , are also perturbed by binding because the stabilization of the central helix propagates toward the accessory helix , but the perturbations are progressively smaller toward the N-terminus due to fraying of the helix and retention of the intrinsic flexibility characteristic of the isolated CpxI ( 26-83 ) fragment ( Chen et al . , 2002 ) . 10 . 7554/eLife . 02391 . 006Figure 2 . NMR analysis of interactions between 2H , 15N-labeled CpxI fragments and synaptobrevin-truncated SNARE complexes . ( A–C ) Expansions of 1H-15N TROSY-HSQC spectra of 2H , 15N-CpxI ( 26-83 ) free ( black contours ) , or bound to non-truncated SNARE complex ( red contours ) , to SCΔ68 ( blue contours ) or SCΔ62 ( green contours ) . Cross-peaks assignments for the free form are based on those described for full-length CpxI ( Pabst et al . , 2000 ) and assignments for CpxI ( 26-83 ) bound to non-truncated SNARE complex were described previously ( Chen et al . , 2002 ) . In ( B and C ) , the minimal contour levels of the different spectra were adjusted to enable visualization of the weakest cross-peaks of interest; hence , cross-peak intensities are not directly comparable . ( D and E ) Expansions of the regions containing the cross-peaks of A30 ( D ) or Q38 ( E ) of the spectra shown in panels ( A–C ) . The minimal contour levels of the different spectra were adjusted to make the cross-peak intensities directly comparable . ( F ) Expansions of 1H-15N TROSY-HSQC spectra of 2H , 15NCpxI ( 26-83 ) bound to SCΔ62 ( green contours ) or SCΔ60 ( orange contours ) . ( G ) Expansions of 1H-15N TROSY-HSQC spectra of WT 2H , 15N-CpxI ( 26-48 ) in the absence ( black contours ) or presence ( red contours ) of SCΔ60 . Because the red and black spectra are practically identical , the black spectrum was plotted at slightly lower levels to facilitate observation of the black crosspeaks behind the red ones . However , the intensities of all the cross-peaks were the same in the black and red spectra within experimental error , as illustrated by the one-dimensional traces shown above and on the right of the two-dimensional contour plots ( taken at the chemical shifts indicated by the blue arrows ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 00610 . 7554/eLife . 02391 . 007Figure 2—figure supplement 1 . Additional analysis of the interaction between 2H , 15N-labeled CpxI fragments and synaptobrevin-truncated SNARE complexes . ( A ) Chemical shift changes in the CpxI central helix of SNARE complex-bound 2H , 15N-CpxI ( 26- 83 ) caused by truncation of synaptobrevin to residue 68 , normalized by the changes caused by binding of 2H , 15N-CpxI ( 26-83 ) to the SC . The chemical shift changes were calculated as Δδ = [ ( ΔδHN ) 2 + ( 0 . 17ΔδN ) 2]1/2 , where ΔδHN and ΔδN are the differences in HN and N chemical shifts , respectively , between the spectra being compared . For ΔδCpx ( SCΔ68-SC ) , we compared 1H-15N TROSY-HSQC spectra of 2H , 15N-CpxI ( 26-83 ) bound to SC and bound to SCΔ68 . For ΔδCpx ( SC-free ) , we compared 1H-15N TROSY-HSQC spectra of 2H , 15N-CpxI ( 26-83 ) free and bound to SC . ( B ) Plot of ΔδCpx ( SCΔ68-SC ) vs ΔδCpx ( SC-free ) . ( C and D ) Ratio between the intensities of cross-peaks of 1H-15N TROSY-HSQC spectra of 2H , 15N-CpxI ( 26-83 ) bound to SCΔ68 ( C ) or SCΔ62 ( D ) vs those observed for 2H , 15N-CpxI ( 26-83 ) bound to SC . To correct for small differences in protein concentrations , the cross-peaks intensities measured for each spectra were normalized with a correction factor derived by averaging the cross-peak intensities of the five C-terminal residues ( residues 79-83 ) , which were practically unaffected by the synaptobrevin C-terminal truncations . In all the plots shown in A–D , comparisons between chemical shifts or cross-peak intensities were made only for cross-peaks that could be identified in all the relevant spectra based on the assignments available for free and SNARE complexbound Cpx ( 26-83 ) ( Figure 2A , B ) ( Chen et al . , 2002; Pabst et al . , 2000 ) and the progressive movements caused by truncations in the SNARE complex ( see Figure 2C ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 00710 . 7554/eLife . 02391 . 008Figure 2—figure supplement 2 . NMR analysis of interactions between 2H , 15N-labeled CpxI superclamp mutant fragments and synaptobrevin-truncated SNARE complexes . ( A ) Expansions of 1H-15N TROSY-HSQC spectra of D27L , E34F , R37A superclamp mutant ( supcl ) 2H , 15N-CpxI ( 26-48 ) in the absence ( black contours ) or presence ( red contours ) of SCΔ60 . Because the red and black spectra are practically identical , the black spectrum was plotted at slightly lower levels to facilitate observation of the black cross-peaks behind the red ones . However , the intensities of all the cross-peaks were the same in the black and red spectra within experimental error , as illustrated by the one-dimensional traces shown above and on the right of the two-dimensional contour plots ( taken at the chemical shifts indicated by the blue arrows ) . ( B–E ) Expansions of 1H-15N TROSY-HSQC spectra of WT 2H , 15N-CpxI ( 26-83 ) free ( black contours ) or bound to SCΔ60 ( orange contours ) , and of D27L , E34F , R37A superclamp mutant 2H , 15N-CpxI ( 26-83 ) free ( pink contours ) or bound to SCΔ60 ( blue contours ) . The minimal contour levels of the different spectra were adjusted to enable visualization of the weakest crosspeaks of interest; hence , cross-peak intensities are not comparable in general . However , the spectra of panel ( D ) were plotted at the same contour levels to allow direct comparison of crosspeak intensities for free and SCΔ60-bound 2H , 15N-CpxI ( 26-83 ) supcl . Selected well-resolved cross-peaks are labeled in the different panels . Note that no cross-peak from CpxI ( 26-83 ) supcl overlaps closely with the A30 and Q38 cross-peaks of WT Cpx ( 26-83 ) due to the mutations ( B and C ) , and three new well-resolved cross-peaks are observed for CpxI ( 26-83 ) supcl ( labeled N1–N3 in panels B–D ) . Cross-peaks N1–N3 must belong to the accessory helix where the three mutations were made and can be tentatively assigned to Q38 , A30 and A37 , respectively , based on their proximity to WT cross-peaks or the observed 15N chemical shift ( for A37 ) . The overall changes caused by SCΔ60 binding are similar for WT and superclamp mutant CpxI ( 26-83 ) ( B , D , E ) , including the effects on the intensities of the cross-peaks from the accessory helix . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 008 To test whether the CpxI accessory helix can replace in part or in full the C-terminus of the synaptobrevin SNARE motif in the SNARE complex , we acquired 1H-15N TROSY-HSQC spectra of 2H , 15N-labeled CpxI ( 26-83 ) bound to SNARE complexes where the synaptobrevin SNARE motif was truncated at residue 62 or 68 ( SCΔ62 or SCΔ68 ) . Comparison with the spectrum obtained in the presence of non-truncated SNARE complex ( Figure 2C ) showed that the well-resolved cross-peaks from several residues of the central helix ( e . g . , those of E58 , E60 , M62 , R63 and Q64 ) moved gradually to the center of the spectrum , toward their positions in free CpxI ( 26-83 ) , as the truncation was more severe . Moreover , some of these cross-peaks exhibited broadening that most likely arises from chemical exchange . In contrast , cross-peaks from more C-terminal residues of the central helix ( e . g . , those of I66 , D68 and K69 ) were less affected by the truncations . This behavior is illustrated by the chemical shift changes observed in the CpxI ( 26-83 ) central helix when comparing SC-bound vs SCΔ68-bound spectra [ΔδCpx ( SCΔ68-SC ) ] , normalized by the changes observed between free and SC-bound Cpx ( 26-83 ) [ΔδCpx ( SC-free ) ] ( Figure 2—figure supplement 1A ) . These ratios provide a measure of the destabilization of the central helix caused by the synaptobrevin C-terminal truncation . A plot of ΔδCpx ( SCΔ68-SC ) vs ΔδCpx ( SC-free ) ( Figure 2—figure supplement 1B ) also shows that cross-peaks from the C-terminus of the central helix were less affected by the C-terminal truncation . The differential effects of the truncations show that the movements toward the center of the spectrum do not result simply from incomplete binding of the CpxI ( 26-83 ) fragment to the truncated SNARE complexes , which should be almost quantitatively bound based on affinities measured by ITC ( see below ) . Instead , these data indicate that there is exchange between the normal bound state with a stable central helix and one or more states where the C-terminus of the central helix is stably packed against the SNAREs but the N-terminus of the central helix is flexible . These states become more populated for the Δ62 truncation than for Δ68 . The effect of the Δ68 truncation can be attributed to an overall destabilization of the synaptobrevin helix beyond R56 , the residue in the central polar layer of the SNARE complex that provides an approximate point of separation for two folding units corresponding to the N- and C-terminal halves of the complex ( Sorensen et al . , 2006; Gao et al . , 2012 ) . This destabilization is manifested in the 1H-15N TROSY-HSQC spectra of the truncated SNARE complex described below and is transferred to the CpxI central helix , which is not surprising because the central helix makes extensive contacts with synaptobrevin residues spanning from R47 to A69 in the non-truncated complex ( Figure 1—figure supplement 1 ) . The stronger effects on the cross-peaks of the CpxI central helix caused by the Δ62 truncation , compared to Δ68 , arise naturally from the removal of key synaptobrevin residues that interact with CpxI , including D64 , D65 and D68 . Changes in the cross-peaks corresponding to the CpxI accessory helix caused by the Δ62 and Δ68 synaptobrevin truncations were more difficult to monitor because the cross-peaks are mostly located in the crowded center of the spectrum . The truncations did cause some changes in the center of the spectrum , but the number of cross-peaks and their overall distribution remained similar ( Figure 2C ) . Cross-peaks from the accessory helix that could be identified in all the spectra exhibited some shifts in the different complexes , but in all cases they remained close to the position of the cross-peak corresponding to free CpxI ( 26-83 ) ( illustrated by the A30 and Q38 cross-peaks in Figure 2D , E ) . Note that these shifts can be induced by changes in the stability of the helix in the different complexes and that insertion of the accessory helix into the truncated SNARE complexes is expected to induce much more dramatic shifts . Moreover , such insertion should cause strong broadening in the cross-peaks from the accessory helix , but the intensities of these cross-peaks actually increased in the spectrum of Cpx ( 26-83 ) bound to SCΔ68 with respect to the SC-bound state , and increased somewhat more in the SCΔ62-bound spectrum ( Figure 2D , E , Figure 2—figure supplements 1C , D ) . These data show that the synaptobrevin C-terminal truncations increase the flexibility of the accessory helix , in correlation with the destabilization of N-terminal part of the central helix , and provide very strong evidence against the notion that the accessory helix of CpxI ( 26-83 ) inserts into the grove generated by the truncations . Since the crystal structure of the CpxI ( 26-83 ) D27L , E34F , R37A superclamp mutant bound to a SNARE complex with C-terminally synaptobrevin ( Figure 1B ) was obtained with a complex containing synaptobrevin truncated at residue 60 ( SCΔ60 ) , we also acquired 1H-15N TROSY-HSQC spectra of 2H , 15N-CpxI ( 26-83 ) in the presence of SCΔ60 . The spectra was similar to that obtained with SCΔ62 , but most cross-peaks from the central helix were broadened beyond detection ( Figure 2F ) . This behavior can be attributed to stronger chemical exchange broadening , which is particularly well manifested for the cross-peaks of K73 and K75 ( which are adjacent to the central helix ) . The well-resolved cross-peaks from the accessory helix of CpxI ( 26-83 ) bound to SCΔ60 ( e . g . , those of A30 and Q38 ) had similar intensities as those observed upon binding to SCΔ62 , showing that the accessory helix does not insert into the groove left by the Δ60 truncation . Because the zigzag array observed in the crystal structure of the CpxI ( 26-83 ) superclamp/SCΔ60 complex suggested that the accessory helix should be able to bind by itself to SCΔ60 , without the central helix , we also acquired 1H-15N TROSY-HSQC spectra of a WT 2H , 15N-CpxI fragment spanning the accessory helix [CpxI ( 26-48 ) ] . SCΔ60 did not cause substantial changes in the spectra of CpxI ( 26-48 ) ( Figure 2G ) . Because of the very high sensitivity of these spectra to binding to protein complexes such as SCΔ60 , particularly for flexible peptides such as CpxI ( 26-48 ) , even a small percentage of binding should be reflected in some cross-peak broadening . Hence , these results clearly show that CpxI ( 26-48 ) does not bind to the synaptobrevin Δ60 truncated SNARE complex in solution under the conditions of our experiments . We also acquired parallel 1H-15N TROSY-HSQC spectra of 2H , 15N-labeled fragments of the CpxI ( 26-83 ) D27L , E34F , R37A superclamp mutant ( supcl ) in the presence and absence of SCΔ60 . The data acquired with 2H , 15N-CpxI ( 26-48 ) supcl showed no binding to SCΔ60 ( Figure 2—figure supplement 2A ) , as observed for WT CpxI ( 26-48 ) . The spectrum obtained for 2H , 15N-CpxI ( 26-83 ) supcl bound to SCΔ60 was similar to that obtained for WT CpxI ( 26-83 ) , with a few differences that arise from the mutations ( Figure 2—figure supplement 2B ) and are also observed in the spectra obtained for the free CpxI fragments ( Figure 2—figure supplement 2C ) . Moreover , the overall effects of SCΔ60 binding to CpxI ( 26-83 ) suplc are similar to those observed for WT CpxI ( 26-83 ) ( Figure 2—figure supplement 2B , D , E ) and , as observed for the WT protein , the cross-peaks from the accessory helix do not exhibit dramatic shifts and/or broadening as would be expected for insertion into the groove left by the synaptobrevin truncation . Therefore , we were unable to detect an interaction between the accessory helix of CpxI ( 26-83 ) supcl and SCΔ60 under the conditions of our NMR experiments , although we cannot rule out the possibility that there is a weak interaction in solution that becomes stabilized by crystallization . To further test the insertion model , we also acquired 1H-15N TROSY-HSQC spectra of truncated SNARE complexes that were 2H , 15N-labeled at the C-terminal SNARE motif of SNAP-25 ( SNC ) or at the syntaxin-1 SNARE motif ( Syx ) , since these SNARE motifs were prediced to contact the CpxI accessory helix in synaptobrevin-truncated SNARE complexes . We first compared spectra of 2H , 15N-SNC complexes that were non-truncated or truncated at residues 62 or 76 of synaptobrevin ( 2H , 15N-SNC-SCΔ62 or 2H , 15N-SNC-SCΔ76 ) ( Figure 3A , B ) , and found that progressive truncation led to increased appearance of sharp cross-peaks in the center of the spectrum and disappearance of cross-peaks from the SNC C-terminal residues in well-resolved regions , or shifts for residues close to the polar layer ( Q174 for SNC ) . These results show that the synaptobrevin truncations lead to flexibility in the C-terminal half of SNC . For the Δ62 truncation , stable structure appears to remain only up to residue 180 . 10 . 7554/eLife . 02391 . 009Figure 3 . NMR analysis of interactions between 2H , 15N-labeled synaptobrevin-truncated SNARE complexes and CpxI fragments . ( A and B ) Expansions of 1H-15N TROSY-HSQC spectra of the non-truncated 2H , 15N-SNC-SC ( black contours ) , 2H , 15N-SNC-SCΔ76 ( green contours ) and 2H , 15N-SNC-SCΔ62 ( red contours ) . ( C and D ) Expansions of 1H-15N TROSY-HSQC spectra of 2H , 15N-SNC-SCΔ62 alone ( red contours ) or in the presence of CpxI ( 26-83 ) ( light blue; C ) or CpxI ( 26-47 ) ( dark blue; D ) . ( E and F ) Expansions of 1H-15N TROSY-HSQC spectra of 2H , 15N-Syx-SC ( black contours ) and of 2H , 15N-Syx-SCΔ62 in the absence ( red contours ) or presence ( light blue contours ) of CpxI ( 26-83 ) . Cross-peak assignments are based on those described for the non-truncated SNARE complex ( Chen et al . , 2002; Chen et al . , 2005 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 00910 . 7554/eLife . 02391 . 010Figure 3—figure supplement 1 . Additional NMR analyses of interactions between 2H , 15N-labeled synaptobrevin-truncated SNARE complexes and CpxI fragments . Expansions of 1H-15N TROSY-HSQC spectra of 2H , 15N-SNCΔ62 ( A and B ) or 2H , 15N-SNCΔ60 ( C and D ) in the absence ( red contours ) or presence ( blue contours ) of CpxI ( 26-83 ) D27L , E34F , R37A superclamp mutant ( supcl ) ( A and D ) , dmCpx ( 28-88 ) ( B ) , or CpxI ( 26-83 ) ( C ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 010 CpxI ( 26-83 ) caused only small shifts in some of the well-resolved cross-peaks of the 1H-15N TROSY-HSQC spectrum of 2H , 15N-SNC-SCΔ62 ( Figure 3C ) , in correlation with the small perturbations observed for the non-truncated SNARE complex because CpxI makes little contact with SNAP-25 ( Chen et al . , 2002 ) . Moreover , these small shifts can arise from stabilization of the synaptobrevin and syntaxin-1 helix in the truncated SNARE complex upon binding to CpxI ( 26-83 ) . Importantly , CpxI ( 26-83 ) binding induced practically no changes in the cross-peaks corresponding to the flexible C-terminus of SNC in the Δ62 SNARE complex ( Figure 3C ) , showing again that the CpxI accessory helix does not interact with the truncated SNARE complex . We also analyzed perturbations caused by the Cpx ( 26-83 ) supcl mutant or a fragment spanning the accessory and central helices of CpxI from Drosophila Melanogaster [dmCpx ( 28-88 ) ] , which inhibits release more strongly than mammalian CpxI ( Huntwork and Littleton , 2007; Xue et al . , 2009 ) , but the results ( Figure 3—figure supplement 1A , B ) were analogous to those obtained with WT CpxI ( 26-83 ) . Since the crystal structure leading to the zig-zag model ( Figure 1B ) was obtained with synaptobrevin truncated at residue 60 , we acquired additional 1H-15N TROSY-HSQC spectra with 2H , 15N-SNCΔ60 , but similar data were obtained again on addition of WT CpxI ( 26-83 ) or Cpx ( 26-83 ) supcl mutant ( Figure 3—figure supplement 1C , D ) . All these results suggest that the accessory helix of WT CpxI ( 26-83 ) , Cpx ( 26-83 ) supcl and dmCpx ( 28-88 ) do not insert into the SNARE complexes containing C-terminally truncated synaptobrevin . Finally , addition of a CpxI fragment spanning only the accessory helix [CpxI ( 26-48 ) ] caused practically no changes on the 1H-15N HSQC spectrum of 2H , 15N-SNC-SCΔ62 ( Figure 3D ) , confirming that the accessory helix does not bind by itself to the truncated SNARE complex or binds with extremely weak affinity . We also performed parallel experiments with truncated SNARE complex 2H , 15N-labeled at the syntaxin-1 SNARE motif ( 2H , 15N-Syx-SCΔ62 ) . Comparison of the 1H-15N TROSY HSQC spectra of this complex and the non-truncated complex ( Figure 3E ) revealed that the Δ62 truncation led to disappearance of most of the well-resolved cross-peaks from residues beyond the residue in the polar layer ( Q226 for Syx ) and an increase in sharp cross-peaks in the center of the spectrum . These results correlated with those obtained with 2H , 15N-SNC-SCΔ62 and indicate that the C-terminal half of the syntaxin-1 SNARE motif becomes flexible due to the Δ62 synaptobrevin truncation , although the smaller number of cross-peaks in the middle suggests that there may be broadening due to exchange between structured and flexible conformations . CpxI ( 26-83 ) caused multiple changes in the 1H-15N TROSY HSQC spectrum of the 2H , 15N-Syx-SCΔ62 complex ( Figure 3F ) that correlate with those observed for the non-truncated SNARE complex ( Chen et al . , 2002 ) . Only limited changes were observed for the sharp cross-peaks in the middle of the spectrum , which can arise from partial stabilization of the syntaxin-1 helix . Although these data are less conclusive than those obtained with the 2H , 15N-SNC-SCΔ62 complex , it is clear that CpxI ( 26-83 ) binding did not yield new well-dispersed cross-peaks that might correspond to structured syntaxin-1 C-terminal residues interacting with the CpxI ( 26-83 ) accessory helix . Hence , these data further support the conclusion that the accessory helix does not interact with C-terminally synaptobrevin truncated SNARE complex . The primary evidence reported to support the notion that the accessory helix of WT CpxI ( 26-83 ) inserts into the synaptobrevin Δ60 truncated SNARE complex , as observed by crystallography for the CpxI ( 26-83 ) D27L , E34F , R37A superclamp mutant , was obtained in competition assays monitored by ITC ( Kummel et al . , 2011 ) . In these assays , 1 . 5 equivalents of CpxI lacking the accessory helix [CpxI ( 47-134 ) ] were used to block the central helix binding site of SCΔ60 , and the heat observed on addition of CpxI ( 26-83 ) was attributed to binding of the accessory helix of CpxI ( 26-83 ) to SCΔ60 . This interpretation assumed that 1 . 5 equivalents for CpxI ( 47-134 ) were sufficient to quantitatively saturate SCΔ60 , but the validity of this assumption is unclear because removal of multiple synaptobrevin residues that interact with CpxI in the non-truncated SNARE complex is expected to considerably decrease the affinity of SCΔ60 for CpxI . To address this issue , we measured the affinity of CpxI ( 47-134 ) for SCΔ60 and the non-truncated SNARE by ITC . For the latter ( Figure 4A ) , we measured a Kd of 339 ± 9 nM ( ΔH = −32 . 6 kcal/mol; N = 0 . 95 ) , which is higher than that we obtained for CpxI ( 26-83 ) [Kd = 25 . 3 nM; Xu et al . , 2013] and may arise because of favorable long-range electrostatic interactions between the accessory helix and the SNARE complex . Importantly , binding of Cpx ( 47-134 ) to SCΔ60 was even weaker , with a Kd of 2 . 39 ± 0 . 19 μM ( ΔH = −19 . 5 kcal/mol; N = 0 . 92 ) ( Figure 4B ) . This decreased affinity implies that binding of CpxI ( 47-134 ) to SCΔ60 is not saturated upon addition of 1 . 5 equivalents of CpxI ( 47-134 ) ( arrow in Figure 4B ) . Hence , the heat observed upon addition of CpxI ( 26-83 ) to SCΔ60 blocked with CpxI ( 27-134 ) arises from completion of the titration of the central helix binding site , rather than from interactions involving the accessory helix . We confirmed this conclusion by titrating CpxI ( 47-134 ) itself on a sample containing SCΔ60 and 1 . 5 equivalents of CpxI ( 47-134 ) ( Figure 4C ) , which logically yielded data similar to those observed in the direct titration experiment of Figure 4B beyond 1 . 5 equivalents . Moreover , adding CpxI ( 26-83 ) to SCΔ60 prebound to 1 . 5 equivalents of CpxI ( 47-134 ) yielded very similar results ( Figure 4D ) , which in turn were also comparable to the data described in Kummel et al . ( 2011 ) . To test for binding of the accessory helix under conditions where the central helix binding site was more saturated , we performed experiments with SCΔ60 prebound to 3 . 0 equivalents of CpxI ( 47-134 ) ( 93% binding based on the Kd described above ) . As expected , addition of Cpx ( 26-83 ) yielded only a small amount of heat that again is the natural extension of the direct titration of the central helix binding site ( Figure 4—figure supplement 1 ) . Hence , no binding of the accessory helix of Cpx ( 26-83 ) to SCΔ60 is detected in these experiments . 10 . 7554/eLife . 02391 . 011Figure 4 . ITC analysis of binding of CpxI fragments to SNARE complexes . ( A and B ) Direct titrations of non-truncated SNARE complex ( SC; A ) or SCΔ60 ( B ) with CpxI ( 47-134 ) . ( C–E ) Competition assays where samples containing SCΔ60 and 1 . 5 equivalents of CpxI ( 47-134 ) were titrated with CpxI ( 47-134 ) ( C ) , WT CpxI ( 26-83 ) ( D ) or CpxI ( 26-83 ) D27L , E34F , R37A superclamp mutant ( supcl ) ( E ) . The arrow in panel ( B ) shows the point of the direct titration where 1 . 5 equivalents of Cpx ( 47-134 ) had been added , and the dashed line shows the heat measured at that point of the titration . The same heat ( within experimental error ) was measured at the start of the competition experiments of panels ( C–E ) . Thus , the heat measurements in the competition assays correspond to the completion of the titration ( i . e . , the tail of the direct titration of panel B ) because 1 . 5 equivalents of CpxI ( 47-134 ) were not sufficient to saturate SCΔ60 . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 01110 . 7554/eLife . 02391 . 012Figure 4—figure supplement 1 . Additional ITC analysis of binding of CpxI fragments to SNARE complexes . ( A ) Competition assay where a sample containing SCΔ60 and 3 . 0 equivalents of CpxI ( 47-134 ) was titrated with WT CpxI ( 26-83 ) . ( B ) Composite diagram where the ITC data shown in panel ( A ) ( shown here as red circles ) was shifted by 3 . 0 molar ratio units in the x axis and plotted together with ITC data obtained in a direct titration of SCΔ60 with CpxI ( 47-134 ) , shown by black circles ( analogous to that shown in Figure 4B ) . Note that the data in red constitute a natural continuation of the direct titration of the central helix binding site and hence there is not additional detectable heat caused by binding of the accessory helix of Cpx ( 26-83 ) to the groove within SCΔ60 . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 012 The equations describing competition data are more complicated than those describing a single-site binding model , but we used this simplified model to fit our ITC data to allow comparison with the results described in Kummel et al . ( 2011 ) . We obtained apparent Kd = 3 . 73 ± 0 . 57 μM and apparent ΔH = −5 . 5 kcal/mol for competition with CpxI ( 47-134 ) ( Figure 4C ) , and apparent Kd = 5 . 41 ± 1 . 48 μM and apparent ΔH = −5 . 0 kcal/mol for competition with CpxI ( 26-83 ) ( Figure 4D ) , which are clearly similar . Kummel et al . ( 2011 ) reported Kd = 16 μM and ΔH = −5 . 4 kcal/mol for competition with CpxI ( 26-83 ) . Although the Kd is somewhat different , we consider that our data do reproduce the results of Kummel et al . ( 2011 ) within experimental error , considering the approximation involved in the single-site model . However , our results show that the interpretation of the competition ITC assays needs to be revised and that these assays are unable to detect an interaction between the accessory helix of WT CpxI ( 26-83 ) and SCΔ60 , in agreement with our NMR results . We also performed competition assays with CpxI ( 26-83 ) D27L , E34F , R37A superclamp mutant and obtained very similar results to those observed with WT CpxI ( 26-83 ) ( Figure 4E; apparent Kd = 4 . 03 ± 0 . 89 μM; apparent ΔH = −5 . 1 kcal/mol ) . These results contrast with those described previously ( Kummel et al . , 2011 ) but agree with our NMR data and further suggest that even the accessory helix of the CpxI ( 26-83 ) supcl mutant does not interact with SNARE complexes containing C-terminally truncated synaptobrevin in solution or , if there is any interaction , it is weak and cannot be detected in our NMR and ITC experiments . We also investigated whether the complexin accessory helix might inhibit neurotransmitter release by replacing the syntaxin-1 SNARE motif in partially assembled SNARE complexes . For this purpose , we first acquired 1H-15N TROSY-HSQC spectra of 2H , 15N-labeled synaptobrevin SNARE motif ( 2H , 15N-Syb ) free and incorporated into non-truncated SNARE complex ( 2H , 15N-Syb-SC ) or SNARE complex with syntaxin-1 truncated at residue 236 ( 2H , 15N-Syb-SCΔ236 ) . Comparison of the spectra obtained for free 2H , 15N-Syb and 2H , 15N-Syb-SC showed again the dramatic spectral changes that occur when a flexible sequence such as 2H , 15N-Syb forms a stable complex ( Figure 5A; cross-peak assignments for 2H , 15N-Syb based on those of Syb[1-96] [Hazzard et al . , 1999] are shown in Figure 5—figure supplement 1A ) . The truncation of the syntaxin-1 C-terminus in 2H , 15N-Syb-SCΔ236 led to disappearance of most well-dispersed cross-peaks from the C-terminal half of the synaptobrevin SNARE motif and appearance of new , sharp cross-peaks in the middle of the spectrum ( Figure 5B ) . We obtained assignments for some of these cross-peaks using triple resonance experiments as described ( Chen et al . , 2002 ) and found that they generally were at similar positions to those observed for free 2H , 15N-Syb ( e . g . , for A67 , A69 , A72 , A74 , W89 and K91; compare Figure 5B , orange contours , with Figure 5—figure supplement 1A ) . These results suggest that the C-terminal half of the synaptobrevin SNARE motif is flexible in 2H , 15N-Syb-SCΔ236 as a result of the syntaxin-1 truncation . We also acquired 1H-15N TROSY-HSQC spectra for the same truncated complex but with the SNAP-25 C-terminal motif 2H , 15N-labeled ( 2H , 15N-SNC-SCΔ236 ) and again found disappearance of the well-dispersed cross-peaks from the C-terminal half of SNC with concomitant appearance of sharp cross-peaks in the middle of the spectrum ( Figure 5—figure supplement 1B ) . These changes are similar to those caused by the synaptobrevin truncation in 2H , 15N-SNC-SCΔ62 ( Figure 3B ) and show that the C-terminus of SNC also becomes flexible upon truncation of syntaxin-1 . 10 . 7554/eLife . 02391 . 013Figure 5 . NMR analysis of interactions between 2H , 15N-labeled syntaxin-1 truncated SNARE complexes and CpxI fragments . ( A and B ) Expansions of 1H-15N TROSY-HSQC spectra of free 2H , 15N-labeled synaptobrevin SNARE motif ( 2H , 15N-Syb , green contours ) , the non-truncated 2H , 15N-Syb-SC ( black contours ) and 2H , 15N-Syb-SCΔ236 ( orange contours ) . ( C ) Expansions of 1H-15N TROSY-HSQC spectra of 2H , 15N-Syb-SC in the absence ( black contours ) and presence ( red contours ) of CpxI ( 26-83 ) . ( D ) Expansions of 1H-15N TROSY-HSQC spectra of 2H , 15N-Syb-SCΔ236 in the absence ( orange contours ) and presence ( light blue contours ) of CpxI ( 26-83 ) . ( E ) Superposition of expansions of 1H-15N TROSY HSQC spectra of 2H , 15N-Syb-SC ( red contours ) and 2H , 15N-Syb-SCΔ236 ( light blue contours ) bound to CpxI ( 26-83 ) . ( F ) Expansions of 1H-15N TROSY-HSQC spectra of 2H , 15N-Syb-SCΔ236 in the absence ( orange contours ) and presence ( dark blue contours ) of CpxI ( 26-47 ) . Cross-peaks assignments for 2H , 15N-Syb-SC free and bound to CpxI ( 26-83 ) were described previously ( Chen et al . , 2002 ) . Cross-peaks assignments for 2H , 15N-Syb-SCΔ236 that were not immediately clear from those obtained for 2H , 15N-Syb-SC were obtained using triple resonance experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 01310 . 7554/eLife . 02391 . 014Figure 5—figure supplement 1 . Additional NMR analyses of interactions between 2H , 15N-labeled syntaxin-1 truncated SNARE complexes and CpxI fragments . ( A ) Expansion of a 1H-15N TROSY-HSQC spectrum of free 2H , 15N-labeled synaptobrevin SNARE motif ( 2H , 15N-Syb ) . Cross-peak assignments are based on those obtained for synaptobrevin ( 1-96 ) ( Hazzard et al . , 1999 ) . ( B ) 1H-15N TROSY-HSQC spectra of the nontruncated 2H , 15N-SNC-SC ( black contours ) and 2H , 15N-SNC-SCΔ236 ( orange contours ) . ( C ) Expansions of 1H-15N TROSY-HSQC spectra of 2H , 15N-SNC-SCΔ236 in the absence ( orange contours ) and presence ( light blue contours ) of CpxI ( 26-83 ) . Cross-peaks assignments for 2H , 15N-Syb-SC free were described previously ( Chen et al . , 2005 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 014 We next examined the changes induced by CpxI ( 26-83 ) on the 1H-15N TROSY-HSQC spectra of these complexes . For non-truncated complex ( 2H , 15N-Syb-SC ) , CpxI ( 26-83 ) caused multiple cross-peak shifts , particularly for synaptobrevin residues that contact the CpxI central helix ( Figure 5C ) , as observed previously ( Chen et al . , 2002 ) . Binding to CpxI ( 26-83 ) induced similar shifts for those residues in 2H , 15N-Syb-SCΔ236 , but in addition caused disappearance of many sharp cross-peaks in the middle of the spectrum that correspond to the flexible synaptobrevin C-terminal half ( Figure 5D ) . As a result , the 1H-15N TROSY HSQC spectrum of 2H , 15N-Syb-SCΔ236 bound to CpxI ( 26-83 ) is similar to that obtained for the non-truncated SNARE complex except that most cross-peaks corresponding to the synaptobrevin C-terminal half disappeared ( Figure 5E ) . Such disappearance most likely arises from chemical exchange between the flexible conformations characteristic of the synaptobrevin C-terminal half in 2H , 15N-Syb-SCΔ236 and a more defined structure ( s ) induced upon CpxI ( 26-83 ) binding . These results contrast with those obtained upon addition of CpxI ( 26-83 ) to 2H , 15N-SNC-SCΔ236 ( Figure 5—figure supplement 1C ) , which revealed only small cross-peak shifts for a few well-dispersed cross-peaks and no marked changes for the sharp cross-peaks in the middle of the spectrum , as observed for the synaptobrevin-truncated SNARE complex 2H , 15N-SNC-SCΔ62 ( Figure 3C ) . We also analyzed the effects of CpxI ( 26-47 ) on the 1H-15N TROSY-HSQC spectrum of 2H , 15N-SNC-SCΔ236 but observed practically no changes ( Figure 5F ) , showing that the accessory helix by itself does not bind to the syntaxin-1 truncated SNARE complex . While the results obtained with 2H , 15N-SNC-SCΔ236 show that the accessory helix of CpxI ( 26-83 ) does not interact with the SNAP-25 C-terminus in the syntaxin-1 truncated SNARE complex and hence does not insert into the complex , the data acquired with 2H , 15N-Syb-SCΔ236 suggested that the accessory helix might interact with the synaptobrevin C-terminal half , which could provide an alternative mechanism to hinder SNARE complex assembly and thus inhibit neurotransmitter release ( Figure 1D ) . To test this possibility , we analyzed the effects of SCΔ236 and other SNARE complexes with stronger truncations in the syntaxin-1 C-terminus ( SCΔ232 and SCΔ228 ) on the 1H-15N TROSY-HSQC spectrum of 2H , 15N-labeled CpxI ( 26-83 ) . The spectra obtained upon binding to SC or to SCΔ236 revealed only small shifts in a few well-resolved cross-peaks and practically no perturbations of the sharp cross-peaks in the center of the spectrum corresponding to the accessory helix ( Figure 6A ) . When we included the spectra obtained in the presence of SCΔ232 and SCΔ228 in the comparison , it became clear that a few well-resolved cross-peaks ( e . g . , those of E60 , M62 , R63 , and Q64 ) shift gradually to the center of the spectrum , toward their positions in free 2H , 15N-CpxI ( 26-83 ) , as the truncation in syntaxin-1 is more severe ( Figure 6B ) . This result is similar to that caused by truncations in the synaptobrevin C-terminus ( Figure 2C ) and can thus be attributed to increasing destabilization of the CpxI central helix as more residues are deleted in syntaxin-1 ( the effects are smaller because most of the residues deleted in syntaxin-1 do not contact CpxI[26-83]; Figure 1—figure supplement 1 ) . 10 . 7554/eLife . 02391 . 015Figure 6 . NMR analysis of interactions between 2H , 15N-labeled CpxI fragments and syntaxin-1-truncated SNARE complexes . ( A and B ) Expansions of 1H-15N TROSY-HSQC spectra of 2H , 15N-CpxI ( 26-83 ) bound to nontruncated SNARE complex ( SC; red contours ) , to SCΔ236 ( blue contours ) , SCΔ232 ( green contours ) or SCΔ228 ( purple contours ) . Cross-peaks assignments for CpxI ( 26-83 ) bound to nontruncated SNARE complex were described previously ( Chen et al . , 2002 ) . ( C and D ) Expansions of the regions containing the cross-peaks of Q38 ( C ) or A30 ( D ) of the spectra shown in panels ( A and B ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 015 As in the case of the synaptobrevin truncations ( Figure 2C ) , the syntaxin-1 truncations in the SNARE complex also caused changes in the center of the 1H-15N TROSY-HSQC spectrum of the bound 2H , 15N-CpxI ( 26-83 ) , but the number of cross-peaks and their overall distribution remained similar ( Figure 6B ) and the well-resolved cross-peaks corresponding to A30 and Q38 exhibited small gradual shifts from their positions upon binding to the non-truncated SNARE complex to their free positions as syntaxin-1 was increasingly truncated ( Figure 6C , D ) . These results strongly suggest that the shifts in the CpxI ( 26-83 ) accessory helix do not arise from interactions with the C-terminal half of the synaptobrevin SNARE motif but rather because the destabilization of the central helix caused by the truncations in syntaxin-1 is transferred into destabilization of the accessory helix . Note also that no substantial broadening of the cross-peaks from the accessory helix was observed , in contrast to the broadening beyond disappearance of cross-peaks from the synaptobrevin C-terminal half in 2H , 15N-Syb-SCΔ236 bound to CpxI ( 26-83 ) ( Figure 5D , E ) . It is likely that such disappearance arises from chemical exchange between the flexible conformations of the synaptobrevin C-terminal half in free 2H , 15N-Syb-SCΔ236 and formation of a defined helical structure that does not contact CpxI ( 26-83 ) but is nucleated by stabilization of the middle of synaptobrevin upon binding of the CpxI ( 26-83 ) central helix . Regardless of the validity of this explanation , it is clear from the behavior of the cross-peaks of the accessory helix that this helix does not interact with the synaptobrevin SNARE motif of the syntaxin-1 truncated SNARE complexes , arguing against the model of Figure 1D . In parallel with our structural studies , we tested the complexin insertion and zigzag models emerging from cell–cell fusion assays ( Giraudo et al . , 2006 ) and X-ray crystallography ( Kummel et al . , 2011 ) by examining the effects on neurotransmitter release of some of the mutations that were reported to alter the inhibitory activity of CpxI in cell–cell fusion . In particular , we analyzed the effects of the D27L , E34F , R37A superclamp mutation that strongly inhibited cell–cell fusion , and of a 'poor-clamp' mutation that decreased the clamping efficiency of CpxI ( K26A ) ( Figure 7A ) . Note that rescue studies on complexin knockdown cortical neurons found that the CpxI D27L , E34F , R37A superclamp mutation did not alter evoked neurotransmitter release and appeared to induce a modest decrease in spontaneous release , but it was unclear whether the decrease was significant , in part because of the small nature of the effect and in part because there was no direct comparison with a rescue using WT CpxI ( Yang et al . , 2010 ) . 10 . 7554/eLife . 02391 . 016Figure 7 . Rescue of the complexin KO phenotype with CpxI-superclamp , but not with a clamping deficient CpxI . ( A ) Overview of the introduced mutations in CpxI . ( B–C ) Representative traces and summary data of evoked EPSC ( B ) and synaptic responses to hypertonic sucrose solution ( RRP ) ( C ) of T-KO , K26A , D27L E34F R37A and WT-CplxI expressing hippocampal neurons . ( D ) Bar graph of the calculated vesicular release probability Pvr . ( E–F ) Analysis of short-term plasticity behavior: Example traces of a train of 5 APs at 50 Hz of T-KO , K26A , D27L E34F R37A and WT-CpxI expressing neurons from which the paired pulse ratio was calculated ( E ) and amplitudes of 50 EPSCs evoked at 10 Hz which were normalized to the first EPSCs and plotted over stimulus number ( F ) . ( G ) Spontaneous transmitter release: Representative traces of T-KO , K26A , D27L E34F R37A and WT-CpxI expressing neurons and summary data of mEPSC frequency and mEPSC amplitude . Data are expressed as mean ± SEM , p<0 . 05; p<0 . 01; p<0 . 001 . The numbers of neurons analyzed are shown within the bars . Vertical bars in the traces ( B and E ) represent 2-ms somatic depolarizations; depolarization artifact and action potentials were blanked . Time of sucrose application is indicated as horizontal line ( C ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 01610 . 7554/eLife . 02391 . 017Figure 7—figure supplement 1 . Expression of Cpx variants in hippocampal CPXI-III triple KO neurons by lentiviral transduction . ( A ) Immunocytochemical detection of cytosolic complexin-FLAG tagged protein variants within presynaptic compartments illustrated by VGlut1 colocalisation . Arrowheads point towards examples of colocalisation . Scale bar: 5 μm . ( B ) Protein expression of different Cpx variants used in this study is detected by western blotting . The amounts of viruses to reach similar expression levels in individual virus preparations are indicated . TubulinIII served as loading control . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 017 In our studies we performed rescue experiments on hippocampal glutamatergic neurons from complexin I-III triple KO mice . Expression was mediated via lentiviral transduction , and WT and mutant CpxI levels were monitored by immunocytochemistry and western blot analysis ( Figure 7—figure supplement 1 ) . CpxI-III-deficient neurons exhibit reduced vesicle released probability ( Pvr ) , increased paired-pulse ratio ( PPR ) , a facilitatory phenotype at high frequency stimulation and reduced spontaneous release frequency , all of which can be rescued with lenti-viral overexpression of WT CpxI ( Xue et al . , 2008b , 2009 ) . Hence , these synaptic parameters served us as readouts for the rescue behavior of different mutant Cplx variants . The D27L , E34F , R37A superclamp mutation that introduces hydrophobic residues in the CpxI accessory helix did not change the ability to rescue the complexin I-III KO phenotype . On the contrary the amplitudes of action potential-evoked excitatory postsynaptic currents ( EPSCs ) tended to be larger that those observed upon rescue with WT CpxI ( Figure 7B ) . As the size of the readily releasable pool ( RRP ) measured by hypertonic solution ( Rosenmund and Stevens , 1996 ) was unchanged , the calculated Pvr was also slightly increased in the rescue with the superclamp CpxI mutants ( Figure 7C , D ) . The facilitatory synaptic short-term plasticity behavior in complexin I-III triple KO neurons , measured through five EPSCs at 50 Hz and also reflected by calculation of the PPR ( EPSC2/EPSC1 ) , could be reversed to a more depressing phenotype in CpxI D27L , E34F , R37A expressing KO neurons compared to the rescue with WT CpxI ( Figure 7E ) . Similarly , depression of EPSC amplitudes during a train of 50 evoked action potentials ( AP ) at 10 Hz was slightly stronger , and the spontaneous release of vesicles tended to be higher for the super-clamp mutant than for the WT rescue neurons ( Figure 7F , G ) . Conversely , the 'non-clamping' K26A mutation impaired full rescue of the KO phenotype , as the EPSC amplitude and Pvr were reduced , and the PPR was increased ( Figure 7B–E ) . The short-term plasticity experiment applying 50 AP at 10 Hz also revealed weak rescue activity ( Figure 7E , F ) , but spontaneous release was not different from the WT rescue ( Figure 7G ) . Overall , these results are in contrast with key predictions from the insertion and zigzag models and do not correlate with the data obtained with cell–cell fusion assays ( Giraudo et al . , 2009 ) , revealing an impairment of evoked release by the K26A mutation that decreases clamping activity in the cell–cell fusion assay and small effects with a tendency to increase evoked release for the superclamp mutant . Hence , our functional data did not support the hypothesis that the inhibitory activity of the accessory helix arises from insertion into the SNARE complex to replace part of the synaptobrevin SNARE motif , in correlation with our NMR and ITC results . In search of an alternative model that could explain the inhibitory function of the complexin accessory helix , we turned our attention to complexin from Drosophila melanogaster ( dmCpx ) because a dramatic increase in spontaneous release is observed in its absence ( Huntwork and Littleton , 2007 ) and experiments with chimeras of murine CpxI and dmCpx suggested that the accessory helix of dmCpx inhibits release more strongly than that of murine CpxI in mouse hippocampal neurons ( Xue et al . , 2009 ) . To verify this conclusion , we generated a chimeric complexin with most of the sequence corresponding to murine CpxI but with the accessory helix of dmCpx ( dmAcc-CpxI; Figure 8A ) , and analyzed its influence on neurotransmitter release in complexin triple KO neurons . No significant differences in evoked EPSC amplitudes , RRP charge and Pvr were observed between neurons expressing the dmAcc-CpxI chimera or WT CpxI ( Figure 8B–D ) . The PPR analyzed from trains of five EPSCs at 50 Hz also showed no difference between chimeric and WT rescue ( Figure 8E ) . At longer stimulations , the short-term plasticity characteristics at 10 Hz revealed a slight , but not significant , decrease in depression when the chimeric dmAcc-CpxI was expressed ( Figure 8F ) . Interestingly however , we did observe a significant , ca . 30% reduction in the frequency of spontaneous release in KO neurons expressing dmAcc-CpxI compared to KO neurons expressing WT CpxI , while the amplitudes of the miniature EPSCs ( mEPSCs ) were not different ( Figure 8G ) . These results indicate that the accessory alpha helix of dmCpx contains some feature ( s ) that renders it more inhibitory than the accessory helix of mammalian CpxI . 10 . 7554/eLife . 02391 . 018Figure 8 . Inhibition of spontaneous release by the accessory alpha helix of dmCpx . ( A ) Overview of the replacement of the accessory helix of CpxI with the accessory helix sequence of drosophila Cpx . ( B ) Representative traces and summary data of evoked EPSC ( B ) and synaptic responses to hypertonic sucrose solution ( RRP ) ( C ) of T-KO , dmAcc-CpxI and WT-CpxI expressing hippocampal neurons . ( D ) Bar graph of the calculated Pvr . ( E–F ) Analysis of short-term plasticity behavior: Example traces of a train of 5 APs at 50 Hz of T-KO , dmAcc-CpxI and WT CpxI expressing neurons from which the paired pulse ratio was calculated ( E ) and amplitudes of 50 EPSCs evoked at 10 Hz which were normalized to the first EPSCs and plotted over stimulus number ( F ) . ( G ) Spontaneous transmitter release: Representative traces of T-KO , dmAcc-CpxI and WT-CpxI expressing neurons and summary data of mEPSC frequency and mEPSC amplitude . Data are expressed as mean ± SEM , p<0 . 05; **p<0 . 001 . The numbers of neurons analyzed are shown within the bars . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 018 The accessory helix of dmCpx contains only one hydrophobic residue , like mammalian CpxI ( Figure 8A ) ; therefore , hydrophobicity does not explain the enhanced inhibitory activity of the dmCpx accessory helix . Sequence comparisons suggest that the accessory helix of dmCpx is longer and more negatively charged than that of mammalian CpxI . These observations and the crystal structure of the CpxI ( 26-83 ) /SNARE complex ( Figure 1A ) lead naturally to a simple model whereby the complexin accessory helix inhibits release because it is oriented toward the area where the two membranes need to be brought together for fusion and this action is hindered by electrostatic repulsion between the accessory helix and the two membranes ( Figure 1E ) . To test this model , we made two mutants of mammalian CpxI where we changed the charge of the accessory helix , one where we added five negative charges ( CpxI-5E ) and another where we replaced three negatively charged with positive charges ( CpxI-3R ) ( Figure 9A ) . Lenti-viral expression of the positively charged CpxI-3R and WT CpxI in complexin I-III triple KO neurons did not reveal significant differences in EPSC amplitudes , but rescue with the negatively charged CpxI-5E mutant yielded slightly reduced EPSC amplitudes ( Figure 9B ) . The RRP sizes in the four different groups analyzed were not significantly different ( Figure 9C ) . Calculation of the Pvr showed similar release probabilities , with a tendency in the CpxI-3R mutant towards higher release probability , and a tendency in the CpxI-5E mutant towards lower probability ( Figure 9D ) . Consistent with the Pvr results , trains of five EPSCs at 50 Hz showed that the facilitatory synaptic short-term plasticity characteristic of the complexin I-III triple KO could be rescued in the CpxI-3R expressing neurons to the same level as the WT CpxI expressing neurons ( Figure 9E ) . However , expressing the negatively charged CpxI-5E protein did not rescue this KO phenotype to the same extent , and the PPR in CpxI-5E expressing neurons was significantly increased compared to WT CpxI expressing neurons ( Figure 9E ) . These rescue behaviors of the different charged versions of CpxI were also observed when analyzing the short-term plasticity characteristics from trains of 50 EPSCs at 10 Hz . In this case , CpxI-3R expressing neurons showed a slight increase in depression whereas CpxI-5E expressing KO neurons did not depress to the same extent as WT CpxI expressing neurons ( Figure 9F ) . Importantly , complexin I-III KO neurons expressing the positively charged CpxI-3R exhibited a considerable increase in mEPSC frequency , whereas expression of the negatively charged CpxI-5E resulted in a significant decrease in mEPSC frequency compared to the WT CpxI expressing KO neurons ( Figure 9G ) . The amplitudes of these events were not different ( Figure 9G ) . 10 . 7554/eLife . 02391 . 019Figure 9 . Inhibitory effect of the accessory alpha helix is charge dependent . ( A ) Overview of the CpxI accessory alpha helix sequence and the introduced mutations resulting in more positively charged ( CpxI-3R ) or more negatively charged ( CpxI-5E ) accessory alpha helix . ( B–C ) Representative traces and summary data of evoked EPSC ( B ) and synaptic responses to hypertonic sucrose solution ( RRP ) ( C ) of T-KO , CpxI-3R , CpxI-5E and WT-CpxI expressing hippocampal neurons . ( D ) Bar graph of the calculated Pvr . ( E–F ) Analysis of short-term plasticity behavior: example traces of a train of 5 APs at 50 Hz of T-KO , CpxI-3R , CpxI-5E and WT-CpxI expressing neurons from which the paired pulse ratio was calculated ( E ) and amplitudes of 50 EPSCs evoked at 10 Hz which were normalized to the first EPSCs and plotted over stimulus number ( F ) . ( G ) Spontaneous transmitter release: Representative traces of T-KO , CpxI-3R , CpxI-5E and WT-CpxI expressing neurons and summary data of mEPSC frequency and mEPSC amplitude . Data are expressed as mean ± SEM , p<0 . 05; p<0 . 01; p<0 . 001 . The numbers of neurons analyzed are shown within the bars . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 019 Collectively , these results indicate that the accessory alpha helix of CpxI exerts its inhibitory function at least in part through the presence of negatively charged residues and that changes towards a more positively or more negatively charged nature result in decrease or increase of the inhibitory effect on spontaneous neurotransmitter release , respectively . Absence of complexins does not alter the RRP as defined from the release induced by 500 mM hypertonic sucrose in autaptic hippocampal neurons ( e . g . , Figures 7B , 8C and 9B ) but does lead to a decrease in release caused by 250 mM hypertonice sucrose ( Xue et al . , 2010 ) , showing that complexins increase the propensity of synaptic vesicles to fuse . To examine whether changing charged residues in the accessory helix of CpxI affects fusogenicity , we compared the responses to 250 mM sucrose in complexin I-III KO neurons expressing WT CpxI , CpxI-3R or CpxI-5E . Compared to WT CpxI expressing neurons , expression of the less negatively charged mutant , CpxI-3R , led to clear increases in the fraction of the RRP released by 250 mM sucrose and the peak release rate , as well as a decrease in the response onset latency ( Figure 10A–C ) . Conversely , introduction of negatively charged residues in the CpxI-5E mutant led to responses to 250 mM sucrose that resembled those of KO neurons , with reduced fractions of RRP release and peak release rates , and increased response onset latencies ( Figure 10A–C ) . These data exhibit some correlation with the Pvr ( Figure 10C ) but correlate best with the results from spontaneous release ( Figures 9G , 10C ) , suggesting that the charge of the accessory helix has a larger influence on the ability of synaptic vesicles to fuse in the absence of Ca2+ than upon Ca2+ influx . It seems likely that the stimulatory effects of CpxI and synaptotagmin-1 in evoked release override at least to some extent the inhibition by the CpxI accessory helix . 10 . 7554/eLife . 02391 . 020Figure 10 . Fusogenicity of synaptic vesicles is influenced by the charge of the accessory alpha helix of CpxI . ( A ) Average traces of synaptic responses induced by 250 mM sucrose solution ( T-KO n = 33 , CpxI-3R n = 49 , CpxI-5E n = 39 , WT CpxI n = 50 ) . ( B ) Summary data of 250 mM sucrose solution-induced response onset latency . The numbers of neurons analyzed are shown within the bars . ( C ) Correlation plot of fraction of RRP released ( T-KO n = 33 , CpxI-3R n = 49 , CpxI-5E n = 39 , WT CpxI n = 50 ) vs peak release rate ( T-KO n = 33 , CpxI-3R n = 49 , CpxI-5E n = 39 , WT CpxI n = 50 ) , vesicle release probability ( Pvr ) ( T-KO n = 42 , CpxI-3R n = 56 , CpxI-5E n = 48 , WT CpxI n = 71 ) and spontaneous release rate ( T-KO n = 39 , CpxI-3R n = 55 , CpxI-5E n = 42 , WT CpxI n = 63 ) . Data are expressed as mean ± SEM , p<0 . 05; p<0 . 01; *p<0 . 001 . In ( C ) vertically oriented p values correspond to fraction of RRP and horizontally oriented p values correspond to peak release rate , Pvr and spontaneous release rate compared to WT rescue . DOI: http://dx . doi . org/10 . 7554/eLife . 02391 . 020 Complexins are small proteins that play stimulating and inhibitory roles in neurotransmitter release . The inhibitory function was attributed to insertion of the complexin accessory helix into the C-terminus of partially assembled SNARE complexes ( Xue et al . , 2007; Giraudo et al . , 2009; Kummel et al . , 2011 ) , but the validity of this model was unclear . In the study presented here , we were unable to detect any interaction between C-terminally-truncated SNARE complexes and the accessory helix of WT CpxI or the CpxI superclamp mutant using highly sensitive biophysical methods in solution . Moreover , we find that the effects of superclamp and poor-clamp CpxI mutations on neurotransmitter release do not correlate with their effects on cell–cell fusion assays , actually pointing in opposite directions . We also show that mutations that increase the negative charge of the accessory helix inhibit neurotransmitter release while mutations that increase its positive charge enhance release . These results strongly argue against the insertion and zigzag models for the inhibitory activity of the complexin accessory helix and suggest a simple , alternative model whereby the negative charges of the accessory helix and perhaps steric hindrance repel both membranes , thus hindering membrane fusion and neurotransmitter release . A major question that arises from our study is: Can the insertion and zigzag models be now completely ruled out ? In addressing this question , it is critical to consider the available data and the arguments that have been used to support these models:We originally proposed that part of the accessory helix might insert into partially assembled SNARE complexes ( Figure 1C ) to explain the inhibitory function of this helix ( Xue et al . , 2007 ) , but this proposal was not based on any biochemical data and the model could be questioned based on the paucity of hydrophobic residues in the accessory helix . The related model whereby the entire accessory helix inserts into a partially assembled SNARE complex was supported by the effects of superclamp and poor-clamp mutations ( e . g . , D27L , E34F , R37A , and K26A ) in cell–cell fusion assays ( Giraudo et al . , 2009 ) . However , the model envisioned that the three charged side chains replaced in the superclamp mutant ( D27 , E34 , and R37 ) insert into the hydrophobic groove left in the SNARE complex , which is very unlikely from a thermodynamic point of view . Moreover , our data show that the effects of these mutations on cell–cell fusion do not correlate with their effects on neurotransmitter release: the superclamp D27L , E34F , R37A mutation that enhanced inhibition of cell–cell fusion had little effect on release , with a slight tendency to stimulate release , while the K26A mutation that diminishes the inhibitory acitivity of CpxI in cell–cell fusion actually impairs release ( Figure 7 ) . It has been argued ( e . g . , Kummel et al . , 2011 ) that the D27L , E34F , R37A superclamp mutation inhibits neurotransmitter release in vivo based on rescue experiments on complexin knockdown neurons ( Yang et al . , 2010 ) . However , these rescue experiments revealed no effect of the D27L , E34F , R37A mutation on evoked release , and it was unclear whether a modest inhibitory effect on spontaneous release was significant . Our rescue data with this mutant using complexin I-III triple KO neurons ( Figure 7 ) are consistent with the results of the knockdown rescues considering the small nature of the effects observed , and the experimental differences between the two approaches . Thus , neither of the two studies supports the insertion or zigzag models . The crystal structure of the CpxI ( 26-83 ) D27L , E34F , R37A superclamp mutant with the Δ60 synaptobrevin-truncated SNARE complex was purported to demonstrate an alternative insertion model whereby the accessory and central helices bind to different SNARE complexes ( zigzag model ) ( Kummel et al . , 2011 ) ( Figure 1B ) . However , the binding mode observed for the mutant accessory helix is highly unlikely thermodynamically for WT CpxI for the above mentioned reason that three mutated side chains are charged and hence are unlikely to insert into a hydrophobic groove . Correspondingly , our extensive NMR analyses did not detect any interaction between the WT CpxI accessory helix and synaptobrevin-truncated SNARE complexes in solution ( Figures 2 , 3 , Figure 3—figure supplement 1C ) . We were also unable to detect any interaction in solution between truncated SNARE complexes and the accessory helix even for CpxI superclamp mutant fragments ( Figure 2—figure supplement 2 , Figure 3—figure supplement 1A , D , Figure 4E ) . A plausible explanation for these findings is that the interaction between the superclamp accessory helix and the truncated SNARE complex observed in the crystals is very weak in solution , and hence could not be observed in our assays , but is stabilized by crystallization . Note that , although the interface area with the SNAREs is larger for the accessory helix ( ca . 900 Ă2 calculated with PISA; Krissinel and Henrick , 2007 ) than for the central helix ( ca . 540 Ă2 ) , the atomic B-factors of the residues of the accessory helix in the interface with the SNAREs are much larger than those in the central helix interface , with little electronic density for the side chains of the accessory helix interface ( Figure 1—figure supplement 2 ) . Interestingly , it has been suggested that motion at a crystal packing interface is intermediate between that of a solvent accessible surface and that of a protein core , even for large interfaces ( Carugo and Argos , 1997 ) . It is also worth noting that Kummel et al . ( 2011 ) described another structure obtained with a similar CpxI mutant ( D27L , E34M , R37A ) where four of the eight crystallographically distinct complexes exhibited the same type of interaction with SCΔ60 observed for the D27L , E34F , R37A CpxI mutant , but the other four complexes had an alternative interaction of the accessory helix with the groove of the truncated SNARE complex where the register was shifted by two helical turns ( Kummel et al . , 2011 ) . A competition assay monitored by ITC was used to support the conclusion that the accessory helix of WT CpxI inserts into SCΔ60 ( Kummel et al . , 2011 ) . We can reproduce these data ( Figure 4D ) but it is clear that the underlying assumption that 1 . 5 equivalentes of CpxI ( 47-134 ) saturate SCΔ60 is incorrect ( Figure 4B , C ) and that these ITC assays do not detect an interaction of the accessory helix with SCΔ60 . Experiments with a surface force apparatus ( SFA ) were proposed to support the zigzag model ( Li et al . , 2011a ) . However , the effects caused by WT complexin II in these experiments could be subject to more than one interpretation; for instance , they are compatible with both the zigzag model and the electrostatic hindrance model that we propose here . The zigzag model was also proposed to be supported by FRET measurements showing that the distances between probes placed on CpxI and SNARE complexes increase on truncation of the synaptobrevin C-terminus ( Krishnakumar et al . , 2011; Kummel et al . , 2011 ) , consistent with the fact that the accessory helix remains close to the SNAREs in the CpxI ( 26-83 ) -SNARE complex structure ( Figure 1A ) but points away from the SNAREs in the structure of the CpxI superclamp mutant bound to SCΔ60 ( Figure 1B ) . We believe that the interpretation of the FRET measurements in terms of static structures constitutes an oversimplification because multiple evidence suggests that there is some flexibility in the accessory helix even in the non-truncated complex , including the poor dispersion of the NH cross-peaks of the accessory helix , their sharper line widths compared to cross-peaks of the central helix , the fact that the NH and Cα chemical shifts of the accessory helix change much less than those of the central helix upon SNARE complex binding and the high B factors observed in the crystal structure of the complex ( Chen et al . , 2002 ) . Indeed , such flexibility is also consistent with the distance of 20 Å measured by FRET with probes placed at residue 38 of CpxI and residue 193 of SNAP-25 of the SNARE complex ( Kummel et al . , 2011 ) , since the probes would be expected to be much closer according to the structure of the complex ( Chen et al . , 2002 ) ( the closest distance between the side chains of these residues is 10 Å ) . Moreover , our NMR data indicate that the truncations in the SNARE complex further increase the flexibility of the accessory helix ( Figure 2—figure supplement 1C , D ) and produce flexibility in the N-terminus of the CpxI central helix ( Figure 2C ) as well as in the C-terminus of SNAP-25 where the FRET donor probe was placed ( Figure 3B , Figure 3—figure supplement 1C ) . The loss in FRET efficiency observed by Kummel et al . upon truncation of the SNARE complex can be readily explained by all these increases in flexibility and the fact that SCΔ60 lacks key residues of synaptobrevin that contact CpxI without the truncation ( Figure 1—figure supplement 1 ) . Kummel et al . ( 2011 ) concluded that flexibility could not explain the FRET efficiency observed between probes placed at residue 38 of CpxI and residue 193 of SNAP-25 in SCΔ60 because there was no detectable FRET in experiments performed with a CpxI mutant containing a flexible GPGP sequence between the accessory and central helices . However , a small decrease in donor fluorescence was actually observed for this mutant ( Figure 4D of Kummel et al . , 2011 ) and , based on other measurements shown in Figure 4C and Supplementary Table 3 in Kummel et al . , 2011 , such decrease would correspond to a distance of ca . 42 Å , just 8 Å longer than the distance measured without the GPGP insertion . Considering that a flexible sequence of four residues can readily span 8 Å , that the insertion of these residues is expected to push away the fluorescence probes , and that the error in such long distances is expected to be rather large because of the low associated FRET efficiencies , the results obtained with the GPGP mutant are not inconsistent with the conclusion that increased flexbility underlies the decreased FRET caused by the synaptobrevin truncation . We believe that , although the arguments presented above and the overall available data argue strongly against the validity of the insertion and zigzag models , it might be premature to completely rule out these models given the complexity of this system . Note for instance that our NMR and ITC data were obtained with truncated SNARE complexes in solution and hence do not rule out the possibility that the CpxI accessory helix interacts with trans-SNARE complexes partially assembled between membranes . Moreover , while cell–cell fusion assays do not correlate with our electrophysiological data or with the stimulatory function of complexins in release , these assays were crucial to establish the functional interplay between complexins and synaptotagmin-1 ( Giraudo et al . , 2006 ) and did provide support for the insertion model ( Giraudo et al . , 2009 ) . Hence , we believe that it is advisable to keep an open mind about the insertion or zigzag models , but views considering either of these models proven need to be revised , and alternative models need to be considered . The model proposed here is attractive because of its simplicity and because it emerges naturally from the realization that the accessory helix of complexins is negatively charged ( see Supplementary figure 1 of Huntwork and Littleton ( 2007 ) ) , together with examination of the crystal structure of the WT CpxI ( 26-83 ) /SNARE complex . Thus , binding of the central helix to the SNARE complex places the accessory helix right between the membranes at the space where they need to be brought together for fusion ( Figure 1E ) , and such action is very likely to be hindered at least to some extent by steric and electrostatic repulsion between the negatively charged membranes and the accessory helix . Note also that the stronger inhibitory activity of the accessory helix of dmCpx , compared to the mammalian CpxI accessory helix ( Figure 8 ) , cannot arise from increased hydrophobicity , but can be explained by our electrostatic hindrance model . The model is also supported by the inhibition of release caused by the 5E mutation , as well as by the increase in release caused by the 3R mutation ( Figure 9 ) . Moreover , the impairment in release that we observed for the K26A mutant ( Figure 7; charge change = −1 ) , and the increase in spontaneous release caused in rescue experiments on complexin knockdown neurons by a K26E , L41K , E47K mutation ( charge change = +1 ) ( Yang et al . , 2010 ) also correlate in general terms with this model . However , these results need to be interpreted with caution , since it is plausible that the 5E and 3R mutations may alter the helical character of the accessary helix , and the considerable magnitude of the effects caused by the K26A and K26E , L41K , E47K mutations suggest that they do not arise only from changes in overall electrostatic potential . Thus , K26 might be involved in some additional , as yet unidentified interaction . Note that K26 does not really form part of the accessory helix in the CpxI ( 26-83 ) /SNARE complex and may be involved in releasing the inhibition caused by the accessory helix ( Xue et al . , 2010 ) . It is also important to note that the accessory helix does not act on its own and its function depends on the neighboring N-terminus . Our previous studies demonstrate an overall facilitatory effect of complexin for evoked and spontaneous release that requires binding of the complexin N-terminus back onto the SNARE complex ( Xue et al . , 2010 ) . This proposed loop may serve two functions , namely to further stabilize the trans-SNARE complex and to properly position the accessory helix near the putative fusion area to reduce spontaneous release through its electrostatic repulsion effect . Alternatively , it is plausible that the function of the accessory helix may involve interactions with other components of the release machinery . Clearly , more research will be required to further test the validity of our model , ideally using diverse approaches . Such approaches should include challenging biophysical studies involving trans-SNARE complexes formed between two membranes , reconstitution experiments that have already yielded a wealth of information on complexins ( e . g . , Schaub et al . , 2006; Yoon et al . , 2008; Malsam et al . , 2009 , 2012; Diao et al . , 2012 ) , and correlations with additional studies of neurotransmitter release in neurons . Expression vectors and protocols for expression and purification of the following protein fragments in E . coli were described previously: rat syntaxin 1A residues 191-253 , rat synaptobrevin 2 residues 29-93 , human SNAP-25 residues 11-82 and 141-203 , full-length rat CpxI and rat CpxI residues 26-83 ( Pabst et al . , 2000; Chen et al . , 2002 ) . Starting from these vectors and from a vector containing the full-length dmCpx sequence ( Xue et al . , 2009 ) , we used standard recombinant DNA techniques and custom designed primers to generate: C-terminally truncated versions of the synaptobrevin SNARE motif ( residues 29-60 [Δ60] , residues 29-62 [Δ62] , residues 29-68 [Δ68] , and residues 29-76 [Δ76] ) ; C-terminally truncated versions of the syntaxin-1 SNARE motif ( residues 191-228 [Δ228]; residues 191-232 [Δ232] and residues 191-236 [Δ236] ) ; CpxI fragments corresponding to the CpxI accessory helix ( residues 26-47 and 26-48 ) and the central helix plus C-terminus ( residues 47-134 ) ; mutant versions of the CpxI ( 26-83 ) and CpxI ( 26-48 ) fragments; and dmCpx ( 28-88 ) . Proteins were expressed in E . coli and purified as described ( Pabst et al . , 2000; Chen et al . , 2002 ) . Uniform 15N- or 2H , 15N-labeling was performed by growing E . coli BL21 ( DE3 ) in minimal medium made with H2O or D2O as the solvent , respectively , and using 15NH4Cl as the sole nitrogen source . Uniform 2H , 15N , 13C-labeling of synaptobrevin ( 29-93 ) for triple resonance experiments acquired on the 2H , 15N , 13C-Syb-SCΔ236 complex was accomplished by an analogous procedure including 13C6-glucose as the sole carbon source . Non-truncated SNARE complexes were formed with SNAP-25 ( 11-82 ) , SNAP-25 ( 141-203 ) , syntaxin-1 ( 191-253 ) and synaptobrevin ( 29-93 ) . For truncated SNARE complexes , synaptobrevin ( 29-93 ) or syntaxin-1 ( 191-253 ) were replaced by the appropriate C-terminally truncated fragment . In general , complex assembly was accomplished by incubating a mixture of the purified fragments overnight at 4°C and removing remaining unassembled fragments by concentration–dilution cycles with a 30 kDa cutoff , as described for the non-truncated complex ( Chen et al . , 2002 ) . SDS-PAGE monitored by Coomassie blue staining comparing boiled and non-boiled samples was used to confirm that the complexes were fully formed and the unassembled fragments were removed . For the complexes with the most severe truncations ( Δ60 , Δ62 , Δ228 , and Δ232 ) , which are not SDS resistant , full complex formation was monitored by non-denaturing PAGE and Coomassie blue staining . All NMR spectra were obtained at 32°C on Agilent ( Santa Clara , CA ) INOVA800 or INOVA600 spectrometers equipped with triple resonance cold-probes . 1H-15N TROSY-HSQC spectra were acquired with samples that normally contained 25-50 μM 2H , 15N-labeled CpxI fragment or SNARE complex 2H , 15N-labeled at one of the SNARE motifs in the absence or presence of a 1 . 2–1 . 5 equivalents of unlabeled SNARE complex ( truncated or non-truncated ) or unlabeled CpxI fragment . The particular fragments used for each figure are described in the corresponding figure legend . Samples containing isotopically-labeled SNARE complexes and unlabeled complexin fragments were dissolved in 25 mM Tris ( pH 7 . 4 ) containing 125 mM NaCl and 8% D2O . Samples containing isotopically labeled complexin fragmens and unlabeled SNARE complexes were dissolved in 25 mM HEPES ( pH 7 . 1 ) containing 125 mM NaCl and 8% D2O . TROSY-HNCA and TROSY-HNCOCA spectra with 2H decoupling ( Yang and Kay , 1999 ) were used to obtain partial backbone assignments for 2H , 15N , 13C-Syb-SCΔ236 complex as described ( Chen et al . , 2002 ) . All the data were processed with NMRPipe ( Delaglio et al . , 1995 ) and analyzed with NMRView ( Johnson and Blevins , 1994 ) . ITC experiments were performed using a VP-ITC system ( MicroCal; Northampton , MA ) at 37°C in PBS buffer ( 10 mM Na2HPO4 , 2 mM K2HPO4 pH 7 . 4 , 2 . 7 mM KCl , 137 mM NaCl ) containing 0 . 25 mM TCEP . For Figure 4A , B , CpxI ( 47-134 ) ( 200 μM ) was directly titrated into the chamber containing non-truncated SNARE complex or SCΔ60 ( 10-15 μM ) . For Figure 4C–E , 200 μM CpxI ( 47-134 ) , CpxI ( 26-83 ) or CpxI ( 26-83 ) superclamp mutant ( D27L , E34F , R37A ) were titrated in the chamber containing 10-15 μM SCΔ60 and 1 . 5 equivalents of CpxI ( 47-134 ) . All proteins were dialyzed in the same buffer before the experiments . The data were fitted with a nonlinear least squares routine using a single-site binding model with Origin for ITC v . 5 . 0 ( Microcal ) . For expression of CpxI variants within neuronal cells a modified lentiviral vector ( Lois et al . , 2002 ) was used in which a human Synapsin-1 promoter , driving the expression of CpxI , and a second promoter ( ubiquitin C ) , which serves as driver for the reporter gene ( EGFP ) , were employed . WT rat CpxI ( GenBank accession number: NM_022864 ) and Drosophila Cpx ( AY121629 ) cDNAs were used to generate all Cpx variants by standard recombinant DNA techniques . For immunocytochemistry a 3xFLAG epitope ( Sigma-Aldrich ) was fused at the C-terminus of CpxI . After sequence verification , the cDNAs were cloned into the lentiviral shuttle vector and lentiviral particles were prepared as described ( Lois et al . , 2002 ) . Briefly , HEK293T cells were cotransfected with 10 µg shuttle vector and the helper plasmids pCMVdR8 . 9 and pVSV . G ( 5 µg each ) with X-tremeGENE 9 DNA transfection reagent ( Roche Diagnostic ) . After 72 hr the virus containing cell culture supernatant was collected and purified by filtration . Aliquots were flash-frozen in liquid nitrogen and stored at −80°C . Viruses were titrated with WT hippocampal mass-cultured neurons . For infection , about 5 × 105–1 × 106 infectious virus units were pipetted onto 1 DIV hippocampal CpxI-III triple KO neurons per 35 mm-diameter well . Murine microisland cultures were prepared as described ( Xue et al . , 2007 ) . CpxI-III triple KO neurons were described previously ( Xue et al . , 2008b ) . Animals were handled according to the rules of Berlin authorities and the animal welfare committee of the Charité Berlin , Germany . Primary hippocampal neurons were prepared from mice on embryonic day E18 and plated at 300 cm−2 density on WT astrocyte microisland for autaptic neuron electrophysiology . For western blotting and immunocytochemistry hippocampal neurons were plated at 10 . 000 cm−2 and 5000 cm−2 , respectively , on continental WT astrocyte feeder layer . Whole cell patch-clamp recordings in autaptic neurons were performed as previously described ( Xue et al . , 2009 ) . The extracellular solution contained ( in mM ) 140 NaCl , 2 . 4 KCl , 10 Hepes , 2 CaCl2 , 4 MgCl2 , 10 Glucose ( pH adjusted to 7 . 3 with NaOH , 300 mOsm ) . The patch pipette solution contained ( in mM ) 136 KCl , 17 . 8 Hepes , 1 EGTA , 0 . 6 MgCl2 , 4 ATP-Mg , 0 . 3 GTP-Na , 12 phosphocreatine and 50 units/ml phosphocreatine kinase ( 300mOsm , pH 7 . 4 ) . Neurons were clamped at −70 mV with a Multiclamp 700B amplifier ( Molecular Devices; Sunnyvale , CA ) under control of Clampex 9 ( Molecular Devices ) at DIV 11-17 . Data were analyzed offline using Axograph X ( AxoGraph Scientific; Berkeley , CA ) and Prism 5 ( GraphPad Software; La Jolla , CA ) . Statistic significances were tested using one-way analysis of variance followed by a Tukey post hoc test to compare all groups . EPSCs were evoked by a brief 2 ms somatic depolarization to 0 mV . EPSC amplitude was determined as the average of 5 EPSCs at 0 . 1 Hz . RRP size was determined by measuring the charge transfer of the transient synaptic current induced by a pulsed 5 s application of hypertonic solution ( 500 mM sucrose in extracellular solution ) . Pvr was calculated as the ratio of the charge from an evoked EPSC and the RRP size of the same neuron . Evoking 5 or 50 synaptic responses at 50 or 10 Hz respectively in standard external solution analyzed short-term plasticity . PPR was calculated by dividing the second EPSC amplitude with the first EPSC amplitude from the average of three 50 Hz trains at 0 . 1 Hz . For analyzing mEPSCs , traces were digitally filtered at 1 kHz offline . Then the last 8 s of 5 traces of EPSCs at 0 . 1 Hz were analyzed using the template-based mEPSC detection algorithm implemented in Axograph X ( AxoGraph Scientific ) and substracted from background noise by detecting events in the last 3 s of 5 EPSCs at 0 . 2 Hz in 3 mM kynurenic acid in extracellular solution . Synaptic-vesicle fusogenicity was measured by applying 250 mM sucrose solution onto the neuron for 10 s and analyzed as described previously ( Xue et al . , 2010 ) . Briefly , to obtain the fraction of RRP released at 250 mM sucrose solution , the charge transfer of the transient synaptic current was measured and divided by the RRP size obtained by 500 mM sucrose application ( 5 s ) from the same neuron . The response onset latency was calculated between the open tip control for solution exchange and the onset of the sucrose response . The peak release rate was calculated by dividing peak amplitude of sucrose response with the RRP size of the same neuron . The spontaneous release rate was calculated by dividing the mEPSC frequency with the number of vesicles within the RRP . This number was obtained by multiplying the mEPSC charge with the RRP charge measured by 500 mM sucrose application . For detection of CpxI protein levels by western blotting , protein lysates were obtained from mass cultures of CpxI-III KO hippocampal neurons ( DIV 14 ) grown on WT astrocyte feeder layers . Briefly , cells were lysed using 50 mM Tris/HCl ( pH 7 . 9 ) , 150 mM NaCl , 5 mM EDTA , 1% Triton-X-100 , 1% Nonidet P-40 , 1% sodium deoxycholate , and protease inhibitors ( complete protease inhibitor cocktail tablet , Roche Diagnostics GmbH; Manheim , Germany ) . Proteins were separated by SDS-PAGE and transferred to nitrocellulose membranes . After blocking with 5% milk powder ( Carl Roth GmbH ) for 1 hr at room temperature , membranes were incubated with rabbit anti-CpxI/II ( 1:1000; Synaptic System ) and mouse anti-tubulinIII ( 1:750; Sigma–Aldrich ) antibodies overnight at 4°C . After washing and incubation with corresponding horseradish peroxidase-conjugated goat secondary antibodies ( all from Jackson ImmunoResearch Laboratories ) , protein expression levels were visualized with ECL Plus Western Blotting Detection Reagents ( GE Healthcare Biosciences ) . To detect synaptic localization by immunocytochemistry , lentiviral transduced neurons were washed once in PBS , fixed in 4% paraformaldehyde for 10 min at room temperature and treated 3 times 5 min with 100 mM glycine in PBS . Then cells were blocked with 5% normal goat serum and 0 . 1% Tween-20 in PBS for 1 hr and incubated with primary antibodies overnight at 4°C in blocking solution . The following antibodies were used: mouse anti-FLAG ( 1:500; Sigma-Aldrich; Saint Louis , MO ) , guinea pig anti-VGlut1 ( 1:4000; Synaptic System ) . Primary antibodies were labeled with anti-mouse Rhodamine Red and anti-guinea pig Alexa Fluor 405 ( each 1:500; Jackson Immunoresearch Laboratories; West Groove , PA ) for 1 hr at room temperature . After washing , cover slips were mounted with Mowiol 4-88 antifade medium ( Polysciences Europe GmbH; Eppelheim , Germany ) . Neurons were imaged using an Olympus IX81 microscope .	The instructions sent to , from and within the brain are rapidly transmitted along neurons in the form of electrical signals . These signals cannot pass across the small gaps—called synapses—that separate neighboring neurons . Instead , neurons release chemicals called neurotransmitters into the synapses , and these relay the signal to the next neuron . The neurotransmitters are stored inside neurons in small bubbles called vesicles . To release these neurotransmitters into the synapse , the membrane that encloses the vesicle fuses with the membrane that surrounds the neuron . To fuse the membranes , proteins embedded in the vesicle membrane interact with similar proteins in the neuron membrane to form a structure called a SNARE complex . Additional proteins control membrane fusion to ensure that the signal is passed to the other neuron at the right time and with the appropriate efficiency . Among these proteins are the complexins , which are often found attached to SNARE complexes . Although different parts of complexins can both help and hinder membrane fusion , a part known as an accessory helix is thought to have only one role—to stop the membranes from fusing together . Several models have been suggested for how the accessory helix interferes with fusion . However , after performing a range of analyses by diverse biophysical techniques , Trimbuch , Xu et al . suggest these models are unlikely to describe the process accurately . Instead , Trimbuch , Xu et al . propose a new model based on the electrostatic properties of two molecules that are both negatively charged . An accessory helix taken from a fruit fly complexin was more negatively charged than a mammalian version , and experiments showed it was also better at preventing the release of neurotransmitters . It is thought that the negative charges on the helix hold the membranes apart because the helix is located between the membranes , which are also negatively charged . Consistent with this model , Trimbuch , Xu et al . showed that the membranes fused more easily when some of the negative charges on the accessory helix were replaced with positive charges . The next challenges are to test the model further with additional studies , and to explain how other proteins work with complexins to control neurotransmitter release .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "structural", "biology", "and", "molecular", "biophysics", "neuroscience" ]	2014	Re-examining how complexin inhibits neurotransmitter release
Chromatin accessibility mapping is a powerful approach to identify potential regulatory elements . A popular example is ATAC-seq , whereby Tn5 transposase inserts sequencing adapters into accessible DNA ( ‘tagmentation’ ) . CUT&Tag is a tagmentation-based epigenomic profiling method in which antibody tethering of Tn5 to a chromatin epitope of interest profiles specific chromatin features in small samples and single cells . Here , we show that by simply modifying the tagmentation conditions for histone H3K4me2 or H3K4me3 CUT&Tag , antibody-tethered tagmentation of accessible DNA sites is redirected to produce chromatin accessibility maps that are indistinguishable from the best ATAC-seq maps . Thus , chromatin accessibility maps can be produced in parallel with CUT&Tag maps of other epitopes with all steps from nuclei to amplified sequencing-ready libraries performed in single PCR tubes in the laboratory or on a home workbench . As H3K4 methylation is produced by transcription at promoters and enhancers , our method identifies transcription-coupled accessible regulatory sites . Identification of DNA accessibility in the chromatin landscape has been used to infer active transcription ever since the seminal description of DNaseI hypersensitivity by Weintraub and Groudine more than 40 years ago ( Weintraub and Groudine , 1976 ) . Because nucleosomes occupy most of the eukaryotic chromatin landscape and regulatory elements are mostly free of nucleosomes when they are active , DNA accessibility mapping can potentially identify active regulatory elements genome-wide . Several additional strategies have been introduced to identify regulatory elements by DNA accessibility mapping , including digestion with Micrococcal Nuclease ( MNase ) ( Reeves , 1978 ) or restriction enzymes ( Jack and Eggert , 1990 ) , DNA methylation ( Gottschling , 1992 ) , physical fragmentation ( Schwartz et al . , 2005 ) and transposon insertion ( Bownes , 1990 ) . With the advent of genome-scale mapping platforms , beginning with microarrays and later short-read DNA sequencing , mapping regulatory elements based on DNaseI hypersensitivity became routine ( Crawford et al . , 2004; Dorschner et al . , 2004 ) . Later innovations included FAIRE ( Giresi et al . , 2007 ) and Sono-Seq ( Auerbach et al . , 2009 ) , based on physical fragmentation and differential recovery of cross-linked chromatin , and ATAC-seq ( Buenrostro et al . , 2013 ) , based on preferential insertion of the Tn5 transposase . The speed and simplicity of ATAC-seq , in which the cut-and-paste transposition reaction inserts sequencing adapters in the most accessible genomic regions ( tagmentation ) , has led to its widespread adoption in many laboratories for mapping presumed regulatory elements . For all of these DNA accessibility mapping strategies , it is generally unknown what process is responsible for creating any particular accessible sites within the chromatin landscape . Furthermore accessibility is not all-or-none , with the median difference between an accessible and a non-accessible site in DNA estimated to be only ~20% , with no sites completely accessible or inaccessible in a population of cells ( Chereji et al . , 2019; Oberbeckmann et al . , 2019 ) . Despite these uncertainties , DNA accessibility mapping has successfully predicted the locations of active gene enhancers and promoters genome-wide , with excellent correspondence between methods based on very different strategies ( Karabacak Calviello et al . , 2019 ) . This is likely because DNA accessibility mapping strategies rely on the fact that nucleosomes have evolved to repress transcription by blocking sites of pre-initiation complex formation and transcription factor binding ( Kornberg and Lorch , 2020 ) , and so creating and maintaining a nucleosome-depleted region ( NDR ) is a pre-requisite for promoter and enhancer function . A popular alternative to DNA accessibility mapping for regulatory element identification is to map nucleosomes that border NDRs , typically by histone marks , including ‘active’ histone modifications , such as H3K4 methylation and H3K27 acetylation , or histone variants incorporated during transcription , such as H2A . Z and H3 . 3 . The rationale for this mapping strategy is that the enzymes that modify histone tails and the chaperones that deposit nucleosome subunits are most active close to the sites of initiation of transcription , which typically occurs bidirectionally at both gene promoters and enhancers to produce stable mRNAs and unstable enhancer RNAs . Although the marks left behind by active transcriptional initiation ‘point back’ to the NDR , this cause-effect connection between the NDR and the histone marks is only by inference ( Wang et al . , 2020 ) , and direct evidence is lacking that a histone mark is associated with an NDR . Here , we show that a simple modification of our Cleavage Under Targets and Tagmentation ( CUT&Tag ) method for antibody-tethered in situ tagmentation can identify NDRs genome-wide at regulatory elements adjacent to transcription-associated histone marks in human cells . We provide evidence that reducing the ionic concentration during tagmentation preferentially attracts Tn5 tethered to the H3K4me2 histone modification via a Protein A/G fusion to the nearby NDR , shifting the site of tagmentation from nucleosomes bordering the NDR to the NDR itself . Almost all transcription-coupled accessible sites correspond to ATAC-seq sites and vice-versa , and lie upstream of paused RNA Polymerase II ( RNAPII ) . ‘CUTAC’ ( Cleavage Under Targeted Accessible Chromatin ) is conveniently performed in parallel with ordinary CUT&Tag , producing accessible site maps from low cell numbers with signal-to-noise as good as or better than the best ATAC-seq datasets . We previously introduced CUT&RUN , a modification of Laemmli’s Chromatin Immunocleavage ( ChIC ) method ( Schmid et al . , 2004 ) , in which a fusion protein between Micrococcal Nuclease ( MNase ) and Protein A ( pA-MNase ) binds sites of antibodies bound to chromatin fragments in nuclei or permeabilized cells immobilized on magnetic beads . Activation of MNase with Ca++ results in targeted cleavage , releasing the antibody-bound fragment into the supernatant for paired-end DNA sequencing . More recently , we substituted the Tn5 transposase for MNase in a modified CUT&RUN protocol , such that addition of Mg++ results in a cut-and-paste ‘tagmentation’ reaction , in which sequencing adapters are integrated around sites of antibody binding ( Kaya-Okur et al . , 2019 ) . In CUT&Tag , DNA purification is followed by PCR amplification , eliminating the end-polishing and ligation steps required for sequencing library preparation in CUT&RUN . Like CUT&RUN , CUT&Tag requires relatively little input material , and the low backgrounds permit low sequencing depths to sensitively map chromatin features . We have developed a streamlined version of CUT&Tag that eliminates tube transfers , so that all steps can be efficiently performed in a single PCR tube ( Kaya-Okur et al . , 2020 ) . However , we had not determined the suitability of the single-tube protocol for profiling low cell number samples . During the COVID-19 pandemic , we adapted this CUT&Tag-direct protocol for implementation with minimal equipment and space requirements that uses no toxic reagents , so that it can be performed conveniently and safely on a home workbench ( Figure 1—figure supplement 1 ) . To ascertain the ability of our CUT&Tag-direct protocol to produce DNA sequencing libraries at home with data quality comparable to those produced in the laboratory , we used frozen aliquots of native human K562 cell nuclei prepared in the laboratory and profiled there using the streamlined single-tube protocol . Aliquots of nuclei were thawed and serially diluted in Wash buffer from ~60 , 000 down to ~60 starting cells , where the average yield of nuclei was ~50% . We used antibodies to H3K4me3 , which preferentially marks nucleosomes immediately downstream of active promoters , and H3K27me3 , which marks nucleosomes within broad domains of polycomb-dependent silencing . Aliquots of nuclei were taken home and stored in a kitchen freezer , then thawed and diluted at home and profiled for H3K4me3 and H3K27me3 . In both the laboratory and at home , we performed all steps in groups of 16 or 32 samples over the course of a single day through the post-PCR clean-up step , treating all samples the same regardless of cell numbers . Whether produced at home or in the lab , all final barcoded sample libraries underwent the same quality control , equimolar pooling , and final SPRI bead clean-up steps in the laboratory prior to DNA sequencing . Tapestation profiles of libraries produced at home detected nucleosomal ladders down to 200 cells for H3K27me3 and nucleosomal and subnucleosomal fragments down to 2000 cells for H3K4me3 ( Figure 1A–B ) . Sequenced fragments were aligned to the human genome using Bowtie2 and tracks were displayed using IGV . Similar results were obtained for both at-home and in-lab profiles for both histone modifications ( Figure 1C–D ) using pA-Tn5 produced in the laboratory , and results using commercial Protein A/Protein G-Tn5 ( pAG-Tn5 ) were at least as good . All subsequent experiments reported here were performed at home using commercial pAG-Tn5 , which provided results similar to those obtained using batches of lab-produced pA-Tn5 run in parallel . Because the Tn5 domain of pA-Tn5 binds avidly to DNA , it is necessary to use elevated salt conditions to avoid tagmenting accessible DNA during CUT&Tag . High-salt buffers included 300 mM NaCl for pA-Tn5 binding , washing to remove excess protein , and tagmentation at 37°C . We have found that other protocols based on the same principle but that do not include a high-salt wash step result in chromatin profiles that are dominated by accessible site tagmentation ( Kaya-Okur et al . , 2020 ) . To better understand the mechanistic basis for the salt-suppression effect , we bound pAG-Tn5 under normal high-salt CUT&Tag incubation conditions , then tagmented in low salt . We used either rapid 20-fold dilution with a prewarmed solution of 2 mM or 5 mM MgCl2 or removal of the pAG-Tn5 incubation solution and addition of 50 µL 10 mM TAPS pH8 . 5 , 5 mM MgCl2 . All other steps in the protocol followed our CUT&Tag-direct protocol ( Kaya-Okur et al . , 2020; Figure 2 ) . Tapestation capillary gel electrophoresis of the final libraries revealed that after a 20 min incubation the effect of low-salt tagmentation on H3K4me2 CUT&Tag samples was a marked reduction in the oligo-nucleosome ladder with an increase in faster migrating fragments ( Figure 3A and Figure 3—figure supplement 1A–B ) . CUT&Tag profiles using antibodies to most chromatin epitopes in the dilution protocol showed either little change or elevated levels of non-specific background tagmentation that obscured the targeted signal ( Figure 3—figure supplement 2 ) , as expected considering that we had omitted the high-salt wash step needed to remove unbound pAG-Tn5 . Strikingly , under low-salt conditions , high-resolution profiles of H3K4me3 and H3K4me2 showed that the broad nucleosomal distribution of CUT&Tag around promoters for these two modifications was mostly replaced by single narrow peaks ( Figure 3B and Figure 3—figure supplement 3 ) . To evaluate the generality of peak shifts we used MACS2 to call peaks , and plotted the occupancy over aligned peak summits . For all three H3K4 methylation marks using normal CUT&Tag high-salt tagmentation conditions we observed a bulge around the summit representing the contribution from adjacent nucleosomes on one side or the other of the peak summit ( Figure 3C ) . In contrast , tagmentation under low-salt conditions revealed much narrower profiles for H3K4me3 and H3K4me2 ( ~40% peak width at half-height ) , less so for H3K4me1 ( ~60% ) , which suggests that the shift is from H3K4me-marked nucleosomes to an adjacent NDR . To determine whether free pAG-Tn5 present during tagmentation contributes , we removed the pAG-Tn5 then added 5 mM MgCl2 to tagment , and again observed narrowing of the H3K4me2 peak ( Figure 3D ‘Removal’ and Figure 3—figure supplement 1C-D ) . We also observed a narrowing if we included a stringent 300 mM washing step before low-salt tagmentation ( Figure 3D , ‘Post-wash’ ) , which indicates that peak narrowing does not require free pAG-Tn5 . Inclusion of a stringent post-wash step improves consistency relative to the Dilution or Removal protocols , although it resulted in lower yields and reduced library complexity ( Figure 3—figure supplement 1E-F ) . However , if a small amount of pAG-Tn5 was included during tagmentation we obtained higher yields with increased peak narrowing ( Figure 3D ‘Add-back’ ) . Because Tn5 is inactive once it integrates its payload of adapters , and each fragment is generated by tagmentation at both ends , it is likely that a small amount of free pA ( G ) -Tn5 is sufficient to generate the additional small fragments where tethered pA ( G ) -Tn5 is limiting , albeit with higher background . Salt ions compete with protein-DNA binding and so we suppose that tagmentation in low salt resulted in increased binding of epitope-tethered Tn5 to a nearby NDR prior to tagmentation . As H3K4 methylation is deposited in a gradient of tri- to di- to mono-methylation downstream of the +1 nucleosome from the transcriptional start site ( TSS ) ( Henikoff and Shilatifard , 2011; Soares et al . , 2017 ) , we reasoned that the closer proximity of di- and tri-methylated nucleosomes to the NDR than mono-methylated nucleosomes resulted in preferential proximity-dependent ‘capture’ of Tn5 . Consistent with this interpretation , we observed that the shift from broad to more peaky NDR profiles and heatmaps by H3K4me2 low-salt tagmentation was enhanced by addition of 1 , 6-hexanediol , a strongly polar aliphatic alcohol , and by 10% dimethylformamide , a strongly polar amide , both of which enhance chromatin accessibility ( Figure 3E–F ) . NDR-focused tagmentation persisted even in the presence of both strongly polar compounds at 55°C . Enhanced localization by chromatin-disrupting conditions suggests improved access of H3K4me2-tethered Tn5 to nearby holes in the chromatin landscape during low-salt tagmentation . Localization to NDRs is more precise for small ( ≤120 bp ) than large ( >120 bp ) tagmented fragments , and by resolving more closely spaced peaks inclusion of these compounds increased the number of peaks called ( Figure 3G ) , also for H3K4me3-tethered Tn5 ( Figure 3—figure supplement 4 ) . Using CUT&Tag , we previously showed that most ATAC-seq sites are flanked by H3K4me2-marked nucleosomes in K562 cells ( Kaya-Okur et al . , 2019 ) . However , lining up ATAC-seq datasets over peaks called using H3K4me2 CUT&Tag data resulted in smeary heatmaps , reflecting the broad distribution of peak calls over nucleosome positions flanking NDRs ( Figure 4A ) . In contrast , alignment of ATAC-seq datasets over peaks called using low-salt tagmented CUT&Tag data produced narrow heatmap patterns for the vast majority of peaks ( Figure 4B ) . To reflect the close similarities between fragments released by H3K4me2-tethered low-salt tagmentation as by ATAC-seq using untethered Tn5 , we will refer to low-salt H3K4me2 and H3K4me3 CUT&Tag tagmentation as Cleavage Under Targeted Accessible Chromatin ( CUTAC ) . We confirmed the similarity between CUTAC and ATAC-seq by aligning H3K4me2 CUT&Tag and CUTAC datasets over peaks called from Omni-ATAC data ( Figure 4C ) . In a scatterplot comparison between CUTAC and Omni-ATAC we did not detect off-diagonal clusters that would indicate a subset of peaks found by one but not the other dataset ( Figure 4—figure supplement 1 ) . To further evaluate the degree of similarity between CUTAC and ATAC-seq , we aligned the ENCODE ATAC-seq dataset over peaks called using Omni-ATAC and CUTAC , where all datasets were sampled down to 3 . 2 million mapped fragments with mitochondrial fragments removed . Remarkably , heatmaps produced using either Omni-ATAC or CUTAC peak calls for the same ENCODE ATAC-seq data showed occupancy of ~95% for both sets of peaks ( compare right panels of Figure 4B–C ) . We found ~50% overlap between ENCODE ATAC-seq peaks and peaks called from either Omni-ATAC ( 50 . 0% ) or CUTAC ( 51 . 3% ) data ( Figure 4—figure supplement 2 ) . This equivalence between H3K4me2 CUTAC and Omni-ATAC when compared to ENCODE ATAC-seq implies that CUTAC and Omni-ATAC detect the same chromatin features . This conclusion does not hold for H3K4me3 CUTAC , because similar alignment of ENCODE ATAC-seq data resulted in only ~75% peak occupancy ( Figure 4D ) and lower correlations ( Figure 4E ) , which we attribute to the greater enrichment of H3K4me3 around promoters than enhancers relative to H3K4me2 . To evaluate whether CUTAC peaks also correspond to sites of DNaseI hypersensitivity , we aligned H3K4me2 CUT&Tag and CUTAC signals over 9403 CCCTC-binding factor ( CTCF ) motifs scored as peaks of DNaseI sensitivity in K562 and HeLa cells . We excluded nucleosomal fragments by using only ≤120 bp fragments . We observed that 86% of the DNaseI hypersensitive CTCF sites are occupied by CUTAC signal relative to flanking regions ( Figure 4F ) , which suggests equivalence of CUTAC and DNaseI hypersensitive CTCF sites . We also found that the H3K4me2 CUT&Tag sample showed detectable signal at only 53% of the CTCF sites . This improvement in detection of CTCF sites by H3K4me2 CUTAC over H3K4me2 CUT&Tag illustrates the potential of using ≤120 bp CUTAC fragment data to improve the resolution and sensitivity of transcription factor binding site motif detection . To evaluate signal-to-noise genome-wide , we called peaks using MACS2 and calculated the Fraction of Reads in Peaks ( FRiP ) , a data quality metric introduced by the ENCODE project ( Landt et al . , 2012 ) . For both ENCODE ChIP-seq and our published CUT&RUN data we measured FRiP = ~0 . 2 for 3 . 2 million fragments , whereas for CUT&Tag , FRiP = ~0 . 4 , reflecting improved signal-to-noise relative to previous chromatin profiling methods ( Kaya-Okur et al . , 2019 ) . Using CUT&Tag-direct , H3K4me2 CUT&Tag FRiP = 0 . 41 for 3 . 2 million fragments and ~16 , 000 peaks ( n = 4 replicates ) , whereas tagmentation by dilution in 2 mM MgCl2 resulted in FRiP = 0 . 18 for 3 . 2 million fragments and ~15 , 000 peaks ( n = 4 ) with similar values for tagmentation by removal [FRiP = 0 . 21 , ~15 , 000 peaks ( n = 4 ) ] . In add-back experiments , we measured lower FRiP values after stringent washing conditions , suggesting increased background . We also compared the number of peaks and FRiP values for CUTAC to those for ATAC-seq for K562 cells and observed that CUTAC data quality was similar to that for the Omni-ATAC method ( Corces et al . , 2017 ) , better than ENCODE ATAC-seq ( Zhang et al . , 2020 ) , and much better than Fast-ATAC ( Corces et al . , 2016 ) , a previous improvement over Standard ATAC-seq ( Buenrostro et al . , 2013; Figure 5A ) . CUTAC is relatively insensitive to tagmentation times , with similar numbers of peaks and similar FRiP values for samples tagmented for 5 , 20 and 60 min ( Figure 5A ) . We attribute the robustness of CUT&Tag and CUTAC to the tethering of Tn5 to specific chromatin epitopes , so that when tagmentation goes to completion there is little untethered Tn5 that would increase background levels . When we measured peak numbers and FRiP values for ATAC-seq for K562 data deposited in the Gene Expression Omnibus ( GEO ) from multiple laboratories , we observed a wide range of data quality ( Figure 5B , even from very recent submissions from expert groups: Table 1 and Figure 5—figure supplement 1 ) . We attribute this variability to the difficulty of avoiding background tagmention by excess free Tn5 in ATAC-seq protocols and subsequent release of non-specific nucleosomal fragments ( Swanson et al . , 2020 ) . If low-salt tagmentation sharpens peaks of DNA accessibility because tethering to neighboring nucleosomes increases the probability of tagmentation in small holes in the chromatin landscape , then we would expect smaller fragments to dominate CUTAC peaks . Indeed this is exactly what we observe for heatmaps ( Figure 5—figure supplement 2 ) , tracks ( Figure 5—figure supplement 3 ) , peak calls and FRiP values ( Figure 5C ) . Excluding larger fragments results in better resolution yielding more peaks and higher FRIP values , both of which approach a maximum with fewer fragments . Moreover , the addition of strongly polar compounds during tagmentation provides a substantial improvement in peak calling and FRiPs ( Figure 5C , turquoise and orange curves ) . Excluding large fragments did not improve ATAC-seq peak calls and FRiP values , which indicates that tethering to H3K4me2 is critical for maximum sensitivity and resolution of DNA accessibility maps . H3K4me2/3 methylation marks active transcription at promoters ( Gilchrist et al . , 2012 ) , which raises the question as to whether sites identified by CUTAC are also sites of RNAPII enrichment genome-wide . To test this possibility , we first aligned CUT&Tag and CUTAC data at annotated promoters displayed as heatmaps or average plots . CUT&Tag H3K4me2 peaks flank NDRs more downstream on either side than H3K4me3 , confirmed by ENCODE ChIP-seq data to be the actual location of these marks ( Figure 6—figure supplement 1 ) . In contrast , CUTAC peaks are located in the NDR between flanking H3K4me2-marked chromatin ( Figure 6A ) . CUTAC sites at promoter NDRs corresponded closely to promoter ATAC-seq sites , consistent with expectation for promoter NDRs . Thus , paired CUT&Tag and CUTAC samples can replace both ChIP-seq for an active promoter mark and ATAC-seq in a single experiment with identical processing , analysis and display . To determine whether CUTAC sites are also sites of transcription initiation in general , we aligned CUT&Tag RNA Polymerase II ( RNAPII ) Serine-5 phosphate ( RNAPIIS5P ) CUT&Tag data over H3K4me2 CUT&Tag and CUTAC and Omni-ATAC peaks ordered by RNAPIIS5P peak intensity . When displayed as heatmaps or average plots , CUTAC datasets show a conspicuous shift into the NDR from flanking nucleosomes ( Figure 6B ) . Mammalian transcription also initiates at many enhancers , as shown by transcriptional run-on sequencing , which identifies sites of RNAPII pausing whether or not a stable RNA product is normally produced ( Kaikkonen et al . , 2013 ) . Accordingly , we aligned RNAPII-profiling PRO-seq data for K562 cells over H3K4me2 CUT&Tag and CUTAC and Omni-ATAC sites , displayed as heatmaps and ordered by PRO-Seq signal intensity . The CUT&Tag sites showed broad enrichment of PRO-seq signals offset ~1 kb on either side , whereas PRO-seq signals were tightly centered around CUTAC sites , with similar results for Omni-ATAC sites ( Figure 6C ) . Interestingly , alignment around TSSs , RNAPIIS5P or PRO-seq data resolved immediately flanking H3K4me2-marked nucleosomes in CUT&Tag data , which is not seen for the same data aligned on signal midpoints ( Figures 3 and 5 ) . Such alignment of +1 and −1 nucleosomes next to fixed NDR boundaries is consistent with nucleosome positioning based on steric exclusion ( Chereji et al . , 2018 ) . Furthermore , the split in PRO-seq occupancies around NDRs defined by CUTAC and Omni-ATAC implies that the steady-state location of most engaged RNAPII is immediately downstream of the NDR from which it initiated . About 80% of the CUTAC sites showed enrichment of PRO-Seq signal downstream , confirming that the large majority of CUTAC sites correspond to NDRs representing transcription-coupled regulatory elements . The correlation between sites of high chromatin accessibility and transcriptional regulatory elements , including enhancers and promoters , has driven the development of several distinct methods for genome-wide mapping of DNA accessibility for nearly two decades ( Klein and Hainer , 2020 ) . However , the processes that are responsible for creating gaps in the nucleosome landscape are not completely understood . In part this uncertainty is attributable to variations in nucleosome positioning within a population of mammalian cells such that there is only a ~20% median difference in absolute DNA accessibility between DNaseI hypersensitive sites and non-hypersensitive sites genome-wide ( Chereji et al . , 2019 ) . This suggests that DNA accessibility is not the primary determinant of gene regulation , and contradicts the popular characterization of accessible DNA sites as ‘open’ and the lack of accessibility as ‘closed’ . Moreover , there are multiple dynamic processes that can result in nucleosome depletion , including transcription , nucleosome remodeling , transcription factor binding , and replication , so that the identification of a presumed regulatory element by chromatin accessibility mapping leaves open the question as to how accessibility is established and maintained . Our CUTAC mapping method now provides a physical link between a transcription-coupled process and DNA hyperaccessibility by showing that anchoring of Tn5 to a nucleosome mark laid down by transcriptional events immediately downstream identifies presumed gene regulatory elements that are indistinguishable from those identified by ATAC-seq . The equivalence of CUTAC and ATAC at both enhancers and promoters provides support for the hypothesis that these regulatory elements are characterized by the same regulatory architecture ( Andersson et al . , 2015; Arnold et al . , 2019 ) . The mechanistic basis for asserting that H3K4 methylation is a transcription-coupled event is well-established ( Henikoff and Shilatifard , 2011; Soares et al . , 2017 ) . In all eukaryotes , H3K4 methylation is catalyzed by COMPASS/SET1 and related enzyme complexes , which associate with the C-terminal domain ( CTD ) of the large subunit of RNAPII when Serine-5 of the tandemly repetitive heptad repeat of the CTD is phosphorylated following transcription initiation . The enrichment of dimethylated and trimethylated forms of H3K4 is thought to be the result of exposure of the H3 tail to COMPASS/SET1 during RNAPII stalling just downstream of the TSS , so that these modifications are coupled to the onset of transcription ( Soares et al . , 2017 ) . Therefore , our demonstration that Tn5 tethered to H3K4me2 or H3K4me3 histone tail residues efficiently tagments accessible sites , implies that accessibility at regulatory elements is created by events immediately following transcription initiation . This mechanistic interpretation is supported by the mapping of CUTAC sites just upstream of RNAPII , and is consistent with the recent demonstration that PRO-seq data can be used to accurately impute ‘active’ histone modifications ( Wang et al . , 2020 ) . Thus CUTAC identifies active promoters and enhancers that produce enhancer RNAs , which might help explain why ~95% of ATAC-seq peaks are detected by CUTAC and vice-versa ( Figure 4B–C ) . CUTAC also provides practical advantages over other chromatin accessibility mapping methods . Like CUT&Tag-direct , all steps from frozen nuclei to purified sequencing-ready libraries for the data presented here were performed in a day in single PCR tubes on a home workbench . As it requires only a simple modification of one step in the CUT&Tag protocol , CUTAC can be performed in parallel with an H3K4me2 CUT&Tag positive control and other antibodies using multiple aliquots from each population of cells to be profiled . We have shown that three distinct protocol modifications , dilution , removal and post-wash tagmentation yield high-quality results , providing flexibility that might be important for adapting CUTAC to nuclei from diverse cell types and tissues . Although a CUT&Tag-direct experiment requires a day to perform , and ATAC-seq can be performed in a few hours , this disadvantage of CUTAC is offset by the better control of data quality with CUTAC as is evident from the large variation in ATAC-seq data quality between laboratories ( Table 1 ) . In contrast , CUT&Tag is highly reproducible using native or lightly cross-linked cells or nuclei ( Kaya-Okur et al . , 2020 ) , and as shown here H3K4me2 CUTAC maps regulatory elements with sensitivity and signal-to-noise comparable to the best ATAC-seq datasets , even better when larger fragments are computationally excluded . Although datasets from H3K4me2 CUT&Tag have lower background than datasets from CUTAC run in parallel , the combination of the two provides both highest data quality ( CUT&Tag ) and precise mapping ( CUTAC ) using the same H3K4me2 antibody . Therefore , we anticipate that current CUT&Tag users and others will find the CUTAC option to be an attractive alternative to other DNA accessibility mapping methods for identifying transcription-coupled regulatory elements . Human K562 cells were purchased from ATCC ( CCL-243 ) and cultured following the supplier’s protocol . H1 ES cells were obtained from WiCell ( WA01-lot#WB35186 ) and cultured following NIH 4D Nucleome guidelines . All tested negative for mycoplasma contamination using a MycoProbe kit . Log-phase human K562 or H1 embryonic stem cells were harvested and prepared for nuclei in a hypotonic buffer with 0 . 1% Triton-X100 essentially as described ( Skene and Henikoff , 2017 ) . A detailed , step-by-step nuclei preparation protocol can be found at protocols . io . CUT&Tag-direct was performed as described ( Kaya-Okur et al . , 2020 ) , except that all CUTAC experiments were done on a home laundry room counter ( Figure 1—figure supplement 1 ) with 32 samples run in parallel mostly over the course of a single ~8 hour day . A detailed step-by-step protocol including the three CUTAC options used in this study can be found at protocols . io . Briefly , nuclei were thawed , mixed with activated Concanavalin A beads and magnetized to remove the liquid with a pipettor and resuspended in Wash buffer ( 20 mM HEPES pH 7 . 5 , 150 mM NaCl , 0 . 5 mM spermidine and Roche EDTA-free protease inhibitor ) . After successive incubations with primary antibody ( 1–2 hr ) and secondary antibody ( 0 . 5–1 hr ) in Wash buffer , the beads were washed and resuspended in pA ( G ) -Tn5 at 12 . 5 nM in 300-Wash buffer ( Wash buffer containing 300 mM NaCl ) for 1 hr . Incubations were performed at room temperature either in bulk or in volumes of 25–50 µL in low-retention PCR tubes . For CUT&Tag , tagmentation was performed for 1 hr in 300-Wash buffer supplemented with 10 mM MgCl2 in a 50 µL volume . For CUTAC , tagmentation was performed in low-salt buffer with varying components , volumes and temperatures as described for each experiment in the figure legends . In ‘dilution’ tagmentation , tubes containing 25 µL of pA ( G ) -Tn5 incubation solution and 2 mM or 5 mM MgCl2 solutions were preheated to 37°C . Tagmentation solution ( 475 µL ) was rapidly added to the tubes and incubated for times and temperatures as indicated . In ‘removal’ tagmentation , tubes were magnetized , liquid was removed , and 50 µL of ice-cold 10 mM TAPS pH 8 . 5 , 5 mM MgCl2 was added , followed by incubation for times and temperatures as indicated . The ‘post-wash’ protocol is identical to the CUT&Tag-direct protocol except that tagmentation was performed in 10 mM TAPS pH 8 . 5 , 5 mM MgCl2 at 37°C as indicated . In ‘add-back’ tagmentation , the post-wash protocol was used with 10 mM TAPS pH 8 . 5 , 5 mM MgCl2 supplemented with pA ( G ) -Tn5 and incubated at 37°C as indicated . Following tagmentation , CUT&Tag and CUTAC samples were chilled and magnetized , liquid was removed , and beads were washed in 50 µL 10 mM TAPS pH 8 . 5 , 0 . 2 mM EDTA then resuspended in 5 µL 0 . 1% SDS , 10 µL TAPS pH 8 . 5 . Following incubation at 58°C , SDS was neutralized with 15 µL of 0 . 67% Triton-X100 , and 2 µL of 10 mM indexed P5 and P7 primer solutions were added . Tubes were chilled and 25 µL of NEBNext 2x Master mix was added and vortexed . Gap-filling and 12 cycles of PCR were performed using an MJ PTC-200 Thermocycler . Clean-up was performed by addition of 65 µL SPRI bead slurry following the manufacturer’s instructions , eluted with 20 µL 1 mM Tris-HCl pH 8 , 0 . 1 mM EDTA and 2 µL was used for Agilent 4200 Tapestation analysis . The barcoded libraries were mixed to achieve equimolar representation as desired aiming for a final concentration as recommended by the manufacturer for sequencing on an Illumina HiSeq 2500 2-lane Turbo flow cell . For datasets from GEO with fragment read lengths ≥60 bp we ran cutadapt 2 . 9 with parameters -q 20 -a AGATCGGAAGAGC -A AGATCGGAAGAGC . Paired-end reads were aligned to hg19 using Bowtie2 version 2 . 3 . 4 . 3 with options: --end-to-end --very-sensitive --no-unal --no-mixed --no-discordant --phred33 -I 10 - X 700 . Tracks were made as bedgraph files of normalized counts , which are the fraction of total counts at each basepair scaled by the size of the hg19 genome . Peaks were called using MACS2 version 2 . 2 . 6 callpeak -f BEDPE -g hs -p le-5 –keep-dup all –SPMR . Heatmaps were produced using deepTools 3 . 3 . 1 . To produce the scatterplot ( Figure 4—figure supplement 1 ) and correlation matrix ( Figure 4E ) , we first removed fragments overlapping any repeat-masked region in hg19 , then sampled 3 . 2 million fragments from each of the 11 datasets and called peaks on the merged data using MACS2 . As previously described ( Meers et al . , 2019 ) , we used a CUTAC IgG negative control , summing normalized counts within peaks and removing peaks above a threshold of the 99th percentile of normalized count sums ( 46 , 561 final peaks ) . A detailed step-by-step Data Processing and Analysis Tutorial can be found at protocols . io .	Cells keep their DNA tidy by wrapping it into structures called nucleosomes . Each of these structures contains a short section of DNA wound around a cluster of proteins called histones . Not only do nucleosomes keep the genetic code organized , they also control whether the proteins that can switch genes on or off have access to the DNA . When genes turn on , the nucleosomes unwrap , exposing sections of genetic code called 'gene regulatory elements' . These elements attract the proteins that help read and copy nearby genes so the cell can make new proteins . Determining which regulatory elements are exposed at any given time can provide useful information about what is happening inside a cell , but the procedure can be expensive . The most popular way to map which regulatory elements are exposed is using a technique called Assay for Transposase-Accessible Chromatin using sequencing , or ATAC-seq for short . The 'transposase' in the acronym is an enzyme that cuts areas of DNA that are not wound around histones and prepares them for detection by DNA sequencing . Unfortunately , the data from ATAC-seq are often noisy ( there are random factors that produce a signal that is detected but is not a ‘real’ result ) , so more sequencing is required to differentiate between real signal and noise , increasing the expense of ATAC-seq experiments . Furthermore , although ATAC-seq can identify unspooled sections of DNA , it cannot provide a direct connection between active genes and unwrapped DNA . To find the link between unspooled DNA and active genes , Henikoff et al . adapted a technique called CUT&Tag . Like ATAC-seq , it also uses transposases to cut the genome , but it allows more control over where the cuts occur . When genes are switched on , the proteins reading them leave chemical marks on the histones they pass . CUT&Tag attaches a transposase to a molecule that recognizes and binds to those marks . This allowed Henikoff et al . to guide the transposases to unspooled regions of DNA bordering active genes . The maps of gene regulatory elements produced using this method were the same as the best ATAC-seq maps . And , because the transposases could only access gaps near active genes , the data provided evidence that genes switching on leads to regulatory elements in the genome unwrapping . This new technique is simple enough that Henikoff et al . were able to perform it from home on the countertop of a laundry room . By tethering the transposases to histone marks it was possible to detect unspooled DNA that was active more efficiently than with ATAC-seq . This lowers laboratory costs by reducing the cost of DNA sequencing , and may also improve the detection of gaps between nucleosomes in single cells .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "chromosomes", "and", "gene", "expression", "tools", "and", "resources" ]	2020	Efficient chromatin accessibility mapping in situ by nucleosome-tethered tagmentation
The transmembrane chemokines CX3CL1/fractalkine and CXCL16 are widely expressed in different types of tumors , often without an appropriate expression of their classical receptors . We observed that receptor-negative cancer cells could be stimulated by the soluble chemokines . Searching for alternative receptors we detected that all cells expressing or transfected with transmembrane chemokine ligands bound the soluble chemokines with high affinity and responded by phosphorylation of intracellular kinases , enhanced proliferation and anti-apoptosis . This activity requires the intracellular domain and apparently the dimerization of the transmembrane chemokine ligand . Thus , shed soluble chemokines can generate auto- or paracrine signals by binding and activating their transmembrane forms . We term this novel mechanism “inverse signaling” . We suppose that inverse signaling is an autocrine feedback and fine-tuning system in the communication between cells that in tumors supports stabilization and proliferation . The chemokines CX3CL1/fractalkine and CXCL16 are transmembrane ( tm ) proteins ( Bazan et al . , 1997; Matloubian et al . , 2000 ) that are converted to soluble ligands by metalloproteinases , in particular ADAM10 and ADAM17 ( a disintegrin and metalloproteinase , Garton et al . , 2001; Hundhausen et al . , 2003; Abel et al . , 2004; Ludwig et al . , 2005 ) . These soluble ( s- ) chemokines induce signal transduction and chemotaxis in target cells which express their classical G protein-coupled receptors CX3CR1 and CXCR6/Bonzo; however , also firm adhesion between tm-chemokine- and chemokine-receptor-bearing cells has been reported ( Imai et al . , 1997; Wilbanks et al . , 2001 ) . Of note , both chemokines themselves , show potential signal transduction sites in their short intracellular domains , but so far signaling via the transmembrane forms has not been described ( Bazan et al . , 1997; Matloubian et al . , 2000 ) . In contrast , several transmembrane ligands themselves are known to transduce signals after binding of their respective receptor . This reverse transduction of signals by transmembrane ligands has been initially observed with ligands of the tumor necrosis factor ( TNF ) family after binding to their cognate receptors ( or to antibodies ) , and thus bidirectional responses are produced ( Ferran et al . , 1994; Eissner et al . , 2004; Lettau et al . , 2011 ) . This principle has been also described for IL-15 ( Neely et al . , 2004 ) , ephrin-ligands ( Klein , 2009 ) and semaphorins ( Zhou et al . , 2008 ) . Reverse signaling can induce several biological functions , e . g . co-stimulation , silencing , transmission of additional signals ( Eissner et al . , 2004; Sun and Fink , 2007 ) , or synapse formation and plasticity in the nervous system ( Klein , 2009 ) . Reverse signaling may thus be an alternative to an autocrine-signaling loop with classical receptors . So far , reverse signaling has only been reported for distinct transmembrane ligand molecules although signaling motifs for reverse signaling are more abundant and also found within the transmembrane variants of the chemokine family . Investigating the role of transmembrane chemokines in cancer , we detected a remarkable high synthesis in several types of tumor cells without an appropriate expression of their classical receptors ( Held-Feindt et al . , 2010; Hattermann et al . , 2013 ) . Therefore , we looked for alternative functions and receptors . We found that cells expressing high levels of a tm-chemokine also responded to their soluble counterparts without expressing the respective G protein-coupled chemokine receptor . Further detailed investigations revealed that tm-chemokines themselves , in fact , responded to soluble chemokines via a novel mechanism by binding their soluble forms and subsequently inducing signals and functional responses . We propose that this novel mechanism leads to auto- or paracrine activation of cells expressing tm-chemokines that either function as surface receptor or can be shed to generate soluble ligands of another tm-chemokine on the cell surface . By quantitative RT-PCR we detected high levels of CXCL16 and CX3CL1 in human glioma , neuroblastoma , colon carcinoma , lower levels in breast cancer cells , and LOX melanoma cells produced very low or non-detectable mRNA amounts ( Figure 1 upper panel; Δ CT in logarithmic scale , thus a 3 . 3 higher Δ CTvalue indicates a 10-fold lower expression ) . Beside tumor cells , also endothelial cells and monocytes like THP-1 cells express tm-chemokines . In contrast to this broad distribution of ligands , the expression of the receptors CXCR6 and CX3CR1 was restricted to only a few cell types , e . g . activated T-cells ( positive control , compare Ludwig et al . , 2005 ) or monocytes/monocytic THP-1 cells . The expression of CX3CL1 and CXCL16 was confirmed on protein level by immunocytochemistry ( lower panel of Figure 1 ) , which revealed high expression for the brain tumor cell lines and low expression for the breast carcinoma cell line MCF-7 . The LOX melanoma cell line showed no protein expression , and therefore was chosen as a control cell line in the subsequent experiments . CXCR6 and CX3CR1 were not detectable in the solid tumor cell lines on protein level either ( not shown ) by immunocytochemistry using antibodies that were suitable for this application in recent investigations ( Held-Feindt et al . , 2010; Hattermann et al . , 2013 ) . Although these axes of transmembrane chemokines together with their receptors can play important roles in selected tumor-stroma cell interactions , the circumscribed lack of receptors in brain tumor cells themselves contrasts the broad and high expression of chemokines that has also been shown for gliomas and schwannomas in situ ( Held-Feindt et al . , 2010; Hattermann et al . , 2013; Held-Feindt et al . , 2008 ) . 10 . 7554/eLife . 10820 . 003Figure 1 . Expression of transmembrane chemokines and their known receptors in various cell types . Top: As determined by qRT-PCR , the transmembrane chemokines CXCL16 and CX3CL1 are highly transcribed in many human tumor cell lines including glioma ( U118 , U343 , T98G , A172 , A764 ) , colon carcinoma ( HT29 ) and neuroblastoma cells ( SH-SY5Y ) , in monocytes ( THP-1 ) and in endothelial cells ( HUVEC ) , at lower levels in breast cancer cells ( MCF-7 ) , but not/negligible in LOX melanoma . OH3 small cell lung cancer cells produced CX3CL1 , but not CXCL16 . In contrast , the known receptors CXCR6 or CX3CR1 were only detectable in a sample of activated T cells or in THP-1 cells , but not in tumor or endothelial cells ( n = 3 biological replicates , single data indicated by diamonds ) . Bottom: Immunostaining of a selection of tumor cells exemplarily confirms cell specific protein expression levels of the transmembrane chemokines , and their absence in LOX melanoma cells . Micrographs were taken with exposure times of 600 ms ( CXCL16 ) or 800 ms ( CX3CL1 , secondary antibody control [sec ab] ) for each cell line . Bars indicate 20 µm , n = 3 independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 003 When cells negative for the known receptors CXCR6 and CX3CL1 ( compare Figure 1 ) were stimulated with s-CXCL16 or s-CX3CL1 , we could , however , detect a transient , dose-and time-dependent phosphorylation of kinases and further biological effects ( Figures 2A , B and 3 ) . In all tested cells that expressed tm-chemokines ( like U343 , A172 , A764 , T98G glioma cell lines , primary glioma cells , or HT29 colon carcinoma cells; not all shown ) phosphorylation of ERKs ( extracellular signal-regulated kinase , p42/p44 ) occurred after 10-40 min using concentrations from 0 . 01 to 10 nM with maximum responses at about 1 nM . By contrast , in tm-chemokine-negative and receptor-negative LOX melanoma cells no signal transduction was observed ( Figure 2C ) . These signal transduction effects on tm-chemokine-positive cells did not depend on the source of recombinant s-chemokines ( not shown ) . Furthermore , not only cell lines but also primary cells isolated from surgically dissected human gliomas were responsive , which express – as the cell lines – tm-chemokines , but not their classical receptors ( Figure 2A ) . 10 . 7554/eLife . 10820 . 004Figure 2 . Signal transduction in receptor-negative ( CXCR6- , CX3CR1- ) tumor cells after stimulation with soluble chemokines ( 1 nM s-CXCL16 or s-CX3CL1 ) . A ) As shown by Western blots after SDS-PAGE separation , receptor-negative but hence responsive cell lines like the glioma cell lines U343 , A764 , T98G and primary glioma cultures from different patients display a time- and dose-dependent phosphorylation of the kinase ERK ( extracellular signal-regulated kinase p42/p44 ) after stimulation with s-chemokines for the indicated times ( compare also Figure 2 - figure supplement 1 ) . The responsiveness coincidences with the presence or absence of the corresponding tm-chemokines; compare Figure 1 . Stimulation with epidermal growth factor ( EGF; 2 nM ) serves as a positive phosphorylation control . Re-blot against non-phosphorylated kinase ERK2 ensures equal loading of the lanes . ( B ) Immunostaining of s-chemokine-stimulated U343 or A764 glioma cells confirms the time-dependent phosphorylation of ERK ( rabbit anti-pERK 1/2 with secondary antibody Alexa-Fluor 555 ( red ) -anti-rabbit IgG; blue nuclear counterstaining with DAPI ) . Bars indicate 20 µm . ( C ) : The tm-chemokine negative LOX melanoma cells are not responsive to 1 nM s-chemokines , ERK-phosphorylation is only observed in positive control samples stimulated with epidermal or fibroblast growth factors ( EGF or FGF-2 , 2 nM ) . All shown data are representative results from 2-3 independent experiments , respectively , for biological replicates please refer to Figure 2—figure supplements 1 and 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 00410 . 7554/eLife . 10820 . 005Figure 2—figure supplement 1 . Biological replicates of western blot experiments showing time- and dose-dependent activation of ERK1/2 upon stimulation with s-chemokines in responsive glioma cell lines ( compare Figure 2A ) DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 00510 . 7554/eLife . 10820 . 006Figure 2—figure supplement 2 . Biological replicates of western blot experiments showing stimulations of non-responsive LOX melanoma cells with s-chemokines ( compare Figure 2C ) DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 00610 . 7554/eLife . 10820 . 007Figure 3 . Biological effects after stimulation of receptor-negative ( CXCR6- , CX3CR1- ) tumor cells with soluble chemokines ( 1 nM s-CXCL16 or s-CX3CL1 ) . Top: Soluble chemokines ( 1 nM ) enhance proliferation of U343 and A764 glioma cells expressing the transmembrane counterparts , but not of LOX melanoma cells that are tm-chemokine negative . Stimulation was performed for 24 hr and proliferation analyzed by WST-assay . As positive control , growth medium ( med . ) with 10% fetal calf serum ( FCS ) was used . Mean values ± standard deviations of at least 3 independent biological replicates ( indicated as diamonds ) are shown . Moreover , both s-chemokines ( 1 nM , respectively ) reduced caspase-3/7 activity evoked by the chemotherapeutic Temozolomide ( 400 µg/ml , from stock solution in dimethylsulfoxide , DMSO ) . Stimulations were performed for 48 hr , controls were supplemented with 2% DMSO ( corresponding to the solvent concentration in Temozolomide-stimulated samples ) . Caspase 3/7 activity was measured by fluorescence of the converted substrate in 3 independent biological replicates . Mean values ± standard deviations are shown . For effects of the tm-chemokine low expressing cell line compare Figure 3—figure supplement 1 . Bottom: Migration of U343 and A764 glioma cells was not influenced by stimulation with 10 nM s-CXCL16 or s-CX3CL1 in a wound healing ( ‘scratch’ ) assay performed for the indicated times . 20% FCS served as positive control . Mean values ± standard deviations are shown from 2-3 independent biological replicates , for data of biological replicates compare Figure 3—source data 1 . Representative images are shown , bars indicate 50 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 00710 . 7554/eLife . 10820 . 008Figure 3—source data 1 . Biological replicates of the scratch assay shown in Figure 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 00810 . 7554/eLife . 10820 . 009Figure 3—figure supplement 1 . Only slight activation and effects upon s-chemokine stimulation of tm-chemokine low expressing MCF-7 cells . Top: MCF-7 breast carcinoma cells were stimulated with 1 nM s-CXCL16 or s-CX3CL1 for 20 min and subjected to Western blot on ERK1/2 phosphorylation . In comparison to positive controls ( EGF and IGF-1 , 10 min ) s-chemokines exerted only slight activation of the MAP kinase pathway . In accordance , caspase 3/7 activity induced by 0 . 1 µM Staurosporine ( 15 hr ) wasis only slightly reduced by co-stimulation with s-chemokines , and hardly robust ( n = 3 independent experiments ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 009 To ensure that the observed signal transduction also triggers biological effects , we investigated the influence of s-chemokines on cell proliferation , apoptosis and migration ( Figure 3 ) . Both chemokines , s-CXCL16 and s-CX3CL1 , significantly enhanced proliferation of classical receptor-negative , tm-chemokine-positive cells like U343 or A764 glioma cells , whereas double-negative cells like LOX melanoma cells did not respond . Furthermore , stimulation with s-CXCL16 and s-CX3CL1 reduced caspase-3/7 activity that was induced by exposure to Temozolomide , a clinically used chemotherapeutic for gliomas . However , the classical biological effect of chemokines , namely cell migration , was not mediated by this signaling ( Figure 3 bottom , and Figure 3—source data 1 , Figure 3—figure supplement 1 ) . As a first hint to cell specificity and expression dependent response of the measured effects , the breast cancer cell line MCF-7 , that displayed low expression levels of tm-chemokines ( compare Figure 1 ) , showed only a slight phosphorylation signals for ERK1/2 and a slightly reduction of caspase 3/7 activity ( induced by Staurosporine ) upon stimulation with the s-chemokines ( Figure 3—figure supplement 1 ) . To exclude that signal transduction and biological effects after stimulation with s-chemokines occurred through activation of other chemokine receptors , we inhibited the classical chemokine receptor Gi/o-signaling in responsive glioma cells using Pertussis toxin . Pre-incubation with Pertussis toxin did not influence signal transduction of responsive tm-chemokine-positive cells ( Figure 4A ) . Its inhibitory effect was confirmed in stimulations of CX3CR1+-THP-1 cells . Also , an engineered peptide receptor-antagonist of fractalkine ( Hermand et al . , 2008 ) mediated ERK phosphorylation in primary glioma cells comparable to the recombinant soluble peptide ( Figure 4A , lower part ) . We additionally analyzed the expression profile of a broad panel of CXC-chemokine and decoy receptors . As shown in Figure 4B , glioma cell lines did not express CXCR3 , CXCR4 , CXCR6 , CX3CR1 , but high levels of CXCR7 , which is a receptor for CXCL11 and CXCL12 and can mediate G-protein independent signals via arrestin ( Odemis et al . , 2012 ) . However , inhibition of CXCR7 by the synthetic antagonist ( Hattermann et al . , 2010; Yamaguchi et al . , 2014 ) CCX733 exhibited no influence on tm-chemokine signaling ( Figure 4C ) . As chemokine decoy receptors ( D6 , DARC , CCX-CKR ) were expressed in glioma as well as LOX cells , they could be ruled out as mediators for tm-chemokine effects that were absent in the melanoma cells . A viral encoded putative receptor for CX3CL1 , US28 ( Matlaf et al . , 2013 ) , was detected neither in glioma cells , nor in LOX melanoma cells , and thus , can be excluded for s-CXCL16 or s-CX3CL1 signaling . Additionally , the expression of CD44 , a receptor for hyaluronic acid and further ligands , e . g . CCL5 ( Roscic-Mrkic et al . , 2005 ) was determined at comparable protein levels in glioma cells and melanoma cells . These findings as well as independence of s-chemokine-mediated signal transduction from Pertussis toxin suggest that classical G protein-coupled chemokine receptors are not involved in the described effects of s-chemokines . Furthermore , related CXC-receptors and chemokine decoy receptors are absent or can be excluded as functional receptors for s-CXCL16 or s-CX3CL1 as well as other types of receptors that require Gi/o-association . 10 . 7554/eLife . 10820 . 010Figure 4 . Inhibition experiments and transcription analysis exclude the involvement of other chemokine receptors . ( A ) Pertussis toxin ( PTX , 200 ng/ml ) inhibiting Gi/o-signaling of classical chemokine receptors has no effect on s-chemokine-mediated phosphorylation of kinases in U343 or A764 cells . However , in CX3CR1-expressing THP-1 cells ( compare Figure 1 ) CX3CL1 mediated phosphorylation of Akt ( stimulation with 1 nM for 20 min ) can be inhibited by pre-incubation with PTX . An engineered variant of CX3CL1 , the recombinant CX3CR1-antagonist F1 ( 100 nM , 20 min ) induces also signal transduction in primary glioma cells indicating a mechanism different from CX3CR1-binding ( and antagonism ) . Shown are representative Western blots after SDS-PAGE separation of lysates from stimulated U343 or A764 glioma cells stained for pERK 1/2 or pAkt ( re-blot to non-phosphorylated kinases , control of equal loading ) from 3 independent experiments , compare Figure 4—figure supplement 1 . ( B ) The transcription profile of classical chemokine receptors and chemokine decoy receptors as determined by quantitative RT-PCR shows that the chemokine receptors CXCR3 , CXCR4 , CXCR6 and CX3CR1 are absent in responsive U343 and A764 and non-responsive LOX cells . However , the atypical chemokine receptor CXCR7 that is known to signal G protein-independently is expressed in responsive cell lines and absent in LOX cells . The chemokine decoy receptors D6 and CCX-CKR are expressed at comparable levels in responsive and non-responsive cells , whereas DARC is absent ( n = 3 biological replicates , indicated by diamonds ) . Additionally , the cytomegalovirus-derived gene US28 that encodes for a putative CX3CR1 receptor could not be detected in these cell lines . The highly glycosylated protein CD44 , that may putatively sequester chemokines , was detected at comparable protein levels in U343 , A764 and LOX cells ( n = 3 ) . ( C ) To investigate the contribution of CXCR7 in s-chemokine mediated signaling U343 cells were pre-incubated with the CXCR7-antagonist CCX733 ( 100 nM , 2 hr ) and stimulated with 1 nM of s-CXCL16 or s-CX3CL1 for 20 min . Controls ± s-chemokines were pre-incubated with 0 . 1% DMSO as solvent controls . The CXCR7-antagonist CCX733 does not impair s-chemokine-mediated ERK phosphorylation . Representative Western blots from 3 independent experiments . For biological replicates of western blots please refer to Figure 4—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 01010 . 7554/eLife . 10820 . 011Figure 4—figure supplement 1 . Biological replicates of western blot experiments with s-chemokines and Pertussis toxin ( compare Figure 4A ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 011 These experiments show that soluble chemokines ( 1 ) elicit signal transduction and biological effects in cells that do not express their known receptors , ( 2 ) the effects are independent from Pertussis toxin-sensitive G-proteins and other known chemokine receptors including different decoy receptors , ( 3 ) are observed only in cells which express tm-chemokines , and ( 4 ) occur within a ( patho ) physiologically relevant concentration range and time frame . Therefore , responsive cells must have either novel receptors , or even likely respond to s-chemokines via their tm-chemokines . To identify receptors for s-chemokines , we performed binding experiments with labeled peptides ( Figure 5A , B and C ) . Fluorescent- or biotin-labeled s-CXCL16 or s-CX3CL1 bound to cells that express their tm-chemokine counterparts ( as shown by fluorescence microscopy Figure 5A and FACS analysis Figure 5B ) , but not to tm-chemokine negative LOX cells . However , when LOX cells were transfected with tm-CXCL16 or tm-CX3CL1 , binding of s-CXCL16 or s-CX3CL1 was clearly observed . Fluorescent or biotin-labeled control peptides ( e . g . lactalbumin conjugates ) did not bind to these cells ( not shown ) . 10 . 7554/eLife . 10820 . 012Figure 5 . Binding of soluble chemokines to corresponding tm-chemokines on cell surfaces . ( A ) Fluorescent soluble chemokines , either labeled directly ( cyanine3 , Cy3 ) or indirectly via biotin-label to fluorescent ( strept ) avidin ( Alexa-Fluor 488 or fluorescein-isothiocyanate , FITC ) , bind to tumor cells expressing tm-chemokines ( transfected LOX melanoma , or glioma cells , not shown ) , but not to non-transfected LOX cells as shown by light microscopy . Due to its lipophilic character , the dye Cy3 yields a whiff of background . However , this was also observed with the negative control Cy3-labeled lactalbumin . Chemokine concentrations 2 nM , bars indicate 20 µm . ( B ) FACS analysis confirmed binding signals yielded by fluorescence microscopy . LOX cells transfected with tm-CXCL16 were detectable by their binding of biotinylated CXCL16 ( 2 nM ) /Avidin-FITC , whereas mock transfected cells ( LOX-pcDNA ) were not , and LOX cells transfected with tm-CX3CL1 were labeled by biotin-CX3CL1 ( 2 nM ) /Avidin-FITC , in comparison to mock transfected cells . ( C ) A close association between s- and tm-chemokines is visible by immuno-electron microscopy of tm-chemokine-expressing/-transfected cells which were immunolabeled with anti-CXCL16 or CX3CL1 and 15 nm-gold-labeled secondary antibodies and subsequently incubated with 5 nm-gold-labeled ( directly or via biotin/streptavidin complex ) soluble chemokines ( 2 nM ) before embedding . ( D ) Chemical cross-linking by paraformaldehyde ( PFA ) shifts tm-chemokine bands to higher molecular weights . Isolated membranes of tm-chemokine overexpressing LOX cells were incubated over night with 2 nM s-chemokines , cross-linked with 1% PFA and subjected to SDS-PAGE separation and subsequent Western blotting using antibodies against the respective chemokine domains of CXCL16 and CX3CL1 . All experiments were repeated in 2 independent biological replicates , and representative photographs and immunoblots are shown . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 012 The direct association of s- to tm-chemokines was also visualized by immuno-electron microscopy ( Figure 5C ) . For this , tm-chemokines were natively stained in glioma cells , tm-chemokine transfected and non-transfected LOX melanoma cells with respective antibodies and secondary antibodies labeled with 15 nm gold particles . Subsequently , cells were incubated either with s-CXCL16 directly coupled with 5 nm colloidal gold or with biotinylated s-CX3CL1 followed by a conjugate of streptavidin and 5 nm gold particles . An association of the different-sized types of colloidal gold particles ( exemplarily shown in Figure 5C ) clearly visualizes binding of the s-chemokine to cell-anchored tm-chemokine . A direct association of s-chemokines to tm-chemokines could also be verified by chemical cross-linking ( Figure 5D ) . Isolated membranes from tm-chemokine transfected LOX cells were incubated with their corresponding s-chemokine ( omitted in controls ) and cross-linked by exposure to paraformaldehyde ( PFA ) ; then proteins were separated by SDS-PAGE and blots incubated with antibodies to soluble chemokines . Only after cross-linking in the presence of s-chemokines a shift of the bands of tm-chemokines to higher molecular masses was observed , corresponding roughly to the binding of 1-2 soluble peptides . It should be noted that also soluble chemokines alone polymerize under these conditions with cross-linker ( not shown ) . These experimental approaches show that tm-chemokines bind to their soluble counterparts at nanomolar concentrations and tm-chemokines and s-chemokines appear in close proximity on the cell surface . To further verify our hypothesis we transfected non-responsive tm-chemokine negative LOX cells with expression vectors for tm-CXCL16 or tm-CX3CL1 and investigated activation of tm-chemokine mediated signal transduction ( Figure 6 ) . Upon stimulation with soluble chemokines , transiently ( not shown ) as well as stably transfected cells showed a time dependent phosphorylation of ERK 1/2 ( Figure 6A ) , which was not the case in non-transfected cells ( compare Figure 2 ) . 10 . 7554/eLife . 10820 . 013Figure 6 . Non-responsive cells can be transformed to be responsive to s-chemokine stimulation by transfection with tm-chemokines . ( A ) LOX melanoma cells were stably transfected with tm-chemokines . Transfection efficiency was controlled by quantitative RT-PCR and immunocytochemistry ( n = 3 biological replicates as indicated by diamonds; bars represent 20 µm ) . Cells were then stimulated with s-chemokines ( 1 nM ) and cell lysates analyzed by SDS-PAGE separation and immunoblotting for phosphorylation of ERK 1/2 ( re-blots for ERK2 ensure equal loading ) . Transfected cells responded with ERK 1/2-phosphorylation in contrast to non-transfected cells ( compare Figure 2 ) . ( B ) Stably transfected LOX-cells expressing C-terminally truncated tm-chemokine variants lacking the intracellular domain ( LOX-ΔCXCL16 and LOX-ΔCX3CL1 ) cannot be activated by stimulation with s-chemokines ( 1 nM ) . Thus , the intracellular domain of the tm-chemokines seems to be critical for signaling . Successful truncation was proven by co-immunostaining with anti-CXCL16 ( extracellular chemokine domain , red ) and anti-HA ( intracellular tag of the CXCL16-expression vector , green ) , or band shift in Western blot . To obtain defined bands , proteins were deglycosylated prior to SDS-PAGE . ( C ) Shed chemokine domains found in conditioned media ( CM ) from overexpressing cells can also mediate activation of tm-chemokine transfected LOX cells . Conditioned media were obtained from confluent CXCL16 or CX3CL1 or mock ( pcDNA ) transfected cells ( LOX-CXCL16 , LOX-CX3CL1 , LOX-pcDNA ) and applied to tm-chemokine expressing LOX cells for 20 min . SDS-PAGE plus immunoblotting ( CXCL16 ) or ELISA ( CX3CL1 ) proved presence of shed chemokines in the conditioned media used for stimulation . Stimulation with conditioned media containing shed chemokine domains can activate the ERK signaling in tm-chemokine transfected cells , but not in pcDNA-transfected or not modified LOX cells . All shown results are representatives from 3 independent experiments , except for the control of successful truncation of tm-chemokines in stable clones and experiments with conditioned media that were performed twice; for examples of biological replicates compare Figure 6—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 01310 . 7554/eLife . 10820 . 014Figure 6—figure supplement 1 . Biological replicates of western blot experiments with transfected LOX melanoma cells ( compare Figure 6 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 014 As the short intracellular domain of tm-chemokines could be involved in this signal transduction , we performed corresponding experiments with C-terminally truncated tm-chemokines that lacked the complete intracellular but not the transmembrane domain ( Figure 6B ) . In fact , this truncation abolished the signal transduction effects observed with the full-length tm-chemokines . Next , to exclude experimental artifacts from recombinant s-chemokines , we wanted to verify if the naturally occurring s-chemokines , which are released by proteolytic cleavage from their tm-forms , could also elicit tm-chemokine-signaling . Since transfected cells should release s-chemokines by shedding over time , we tested first their occurrence in conditioned medium , and then assayed if conditioned , s-chemokine-containing media could induce signals in overexpressing cells that were washed prior to experiments . In fact , 24 hr- or 48 hr-conditioned media of tm-chemokine-transfected cells , but not of vector-transfected cells , contained considerable amounts of corresponding s-chemokines as detected by Western blot or ELISA ( Figure 6C ) . As expected , conditioned media from transfected , but not from control cells , induced ERK 1/2 phosphorylation; again non- or control-transfected LOX cells yielded no signal transduction ( Figure 6D ) . Regarding biological effects of s-chemokines in tm-chemokine-transfected LOX cells we could observe a rescue from Camptothecin-induced cell death events . As shown by immunocytochemistry and Western blot , stimulation of tm-CXCL16-transfected LOX cells with s-CXCL16 and tm-CX3CL1-transfected LOX cells with s-CX3CL1 significantly reduced the amount of the cleaved Poly ( ADP-Ribose ) Polymerase ( PARP ) , whereas mock-transfected LOX cells were not rescued ( Figure 7 ) . 10 . 7554/eLife . 10820 . 015Figure 7 . Stimulation with s-chemokines can mediate rescue from chemically-induced cell death in tm-chemokine-transfected LOX melanoma cells . LOX melanoma cells stably expressing tm-CXCL16 or tm-CX3CL1 ( or mock transfected LOX cells ) were treated for 18 hr with 0 . 1 µg/ml Camptothecin ( inhibitor of topoisomerase I ) to induce cell death . Simultaneous stimulation of tm-CXCL16-LOX with 1 nM s-CXCL16 or tm-CX3CL1-LOX with 1 nM s-CX3CL1 significantly reduced cell death as indicated by reduced cleavage of poly ( ADP ribose ) polymerase ( PARP ) ( shown by Western blot after SDS-PAGE , n = 2 biological replicates; or immunocytochemistry , n = 3-4 biological replicates , indicated by diamonds ) . In contrast , mock-transfected ( pcDNA ) LOX cells did not show reduced signals of cleaved PARP when stimulated with s-CXCL16 or s-CX3CL1 . Bars represent 50 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 015 As contrary approach to overexpression in non-responsive , tm-chemokine negative cells , silencing of tm-chemokines in responsible cells can further prove our concept . Therefore , we reduced tm-chemokines in responsive receptor-negative glioma cell lines and primary cultures from surgical samples by siRNA and stimulated with the corresponding soluble chemokines ( Figure 8 ) . By siRNA silencing , tm-chemokine transcript amounts were reduced up to 24-59% of controls transfected with unspecific control siRNAs . The s-chemokine-mediated ERK-phosphorylation could be observed in control siRNA-treated glioma cell lines and primary cultures but was clearly reduced in tm-chemokine silenced cells . 10 . 7554/eLife . 10820 . 016Figure 8 . Silencing of tm-chemokine expression abolishes the s-chemokine mediated activation in responsive glioma cell lines and primary cultures . Cell lines and primary cells were transfected with CXCL16 or CX3CL1 specific RNAi ( or non-specific control RNAi ) , left for recovery for 36 hr and stimulated with 1nM CXCL16 or CX3CL1 for 15 min . Samples were analyzed by SDS-PAGE separation and immunoblotting for phosphorylated ERK 1/2 , and re-probed for ERK 2 to ensure equal loading . Silencing efficiency and basic transcription level were confirmed by quantitative RT-PCR ( right panel ) . In tm-chemokine silenced cultures , no activation can be observed by incubation with the respective s-chemokine . Experiments with T98G were repeated in 2 independent biological replicates , shown primary cultures are representative examples from 2 different patient’s primary cultures ( compare Figure 8—figure supplement 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 01610 . 7554/eLife . 10820 . 017Figure 8—figure supplement 1 . Biological replicates of western blot experiments from siRNA knockdown in T98G glioma cells ( compare Figure 8 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 017 Antibodies specific for the ligand portion are known to activate signaling of transmembrane ligands such as of tm-TNF-α ( Eissner et al . , 2004probably as they can mediate the required di/multimerization . To find out , whether antibodies against the chemokine domains of CXCL16 or CX3CL1 can also induce signaling of the tm-chemokines , we stimulated stably transfected tm-CXCL16 or tm-CX3CL1 LOX cells with 0 . 1 µg/ml of the respective antibody ( Figure 9A ) ; IgG from the same species and mock-transfected cells served as controls . In fact , specific antibodies , but not controls induced ERK1/2 phosphorylation in tm-chemokine expressing cells . To test if the activation potential depends on di-/oligomerization of the tm-chemokines , we compared the efficiency of complete to monovalent antibodies . However , monovalent F ( ab ) fragments of the same antibodies generated by papain digestion and purification did not produce any effects in transfected cells ( Figure 9B ) . These experiments show that antibodies like soluble ligands induce signal transduction through tm-chemokines , and this process seems to depend on di/oligomerization of the tm-chemokines . 10 . 7554/eLife . 10820 . 018Figure 9 . Signal transduction in tm-chemokine expressing LOX cells upon stimulation with specific antibodies ( 0 . 1 µg/ml ) , but not monovalent F ( ab ) fragments . ( A ) As shown by Western blot after SDS-PAGE , stimulation of tm-CXCL16 or tm-CX3CL1 transfected LOX cells for 20 min with antibodies against the corresponding chemokine domains ( 0 . 1 µg/ml ) , yields a phosphorylation signal for ERK1/2 . Non-specific control IgG could not elicit signaling , nor could specific antibodies activate mock-transfected ( LOX-pcDNA ) cells ( positive stimulation control: 2 nM FGF-2 , n = 2-3 biological replicates , for corresponding effects in glioma cells compare Figure 9—figure supplement 1 ) . ( B ) In contrast to the intact specific antibodies , monovalent F ( ab ) fragments ( 0 . 1 µg/ml ) obtained by papain digestion and clean-up of these fragments failed to mediate ERK1/2 phosphorylation as demonstrated by Western blot and immunocytochemistry ( FGF-2 serves as positive control , n = 3 biological replicates , Bars represent 50 µm ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 01810 . 7554/eLife . 10820 . 019Figure 9—figure supplement 1 . Biological replicates of western blot experiments of stimulations with chemokine-specific antibodies ( compare Figure 9 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10820 . 019 In summary , transfection as well as silencing experiments evidence that transmembrane chemokines transduce signals when stimulated with nanomolar concentrations of corresponding soluble counterparts exerting also biological effects as exemplarily shown by rescue from apoptosis . Thereby , signaling is elicited by recombinant peptides ( chemokine domain ) or soluble chemokines produced by transfected cells themselves . Furthermore , the intracellular C-terminal domains of tm-chemokines are required for these effects . Furthermore , specific chemokine domain-directed antibodies , but not monovalent fragments induce signaling emphasizing the necessity of dimerization of the tm-chemokines to induce signaling . Thus , tm-chemokines do not only bind s-chemokines , but also induce a signal transduction and further biological effects specifically . Inverse signaling is an autocrine feedback and fine-tuning systems in the communication between cells . Here , soluble ligands that are shed from their transmembrane counterparts induce signals through binding to the transmembrane ligands . Though this was shown here for transmembrane chemokines for the first time , a broader distribution , e . g . in signaling of other transmembrane ligands , and further biological effects have to be further evaluated under normal and pathological conditions in vitro and in suitable in vivo models . Recombinant human chemokines and growth factors were from PeproTech ( Hamburg , Germany ) , R&D-Systems ( Wiesbaden , Germany ) , or Immunotools ( Friesoythe , Germany ) , Pertussis toxin ( inhibits G protein-signaling ) was from Calbiochem ( Merck , Darmstadt , Germany ) or Sigma-Aldrich ( Munich , Germany ) . The CX3CR1-antagonist F1 , an engineered N-terminally modified recombinant CX3CL1 analogue that binds to CX3CR1 but does not induce signaling , was a kind gift from Prof . Dr . Philippe Deterre , Laboratoire Immunité et Infection , INSERM , Faculté de Médcine Pitié-Salpêtrière , Paris , France ( Hermand et al . , 2008 ) . The synthetic CXCR7-antagonist CCX733 was a kind gift from Dr . Mark E . T . Penfold and Prof . Dr . Thomas J . Schall ( ChemoCentryx Inc , Mountain View , CA ) . Human glioma cell lines A172 ( Giard et al . , 1973; ATCC® CRL-1620; ECACC No . 88062428 ) and T98G ( Stein , 1979; ATCC CRL-1690; ECACC No . 92090213 ) glioma cells were purchased from LGC Standards GmbH ( Wesel , Germany ) , U343-MG ( Westermark et al . , 1973 ) and U118-MG ( Ponten and Macintyre , 1968; ATCC HTB-15; identical with the glioma cell line U138; U118 was only used for expression data ) were obtained from “Deutsches Krebsforschungszentrum” ( “Tumorbank”; Heidelberg , Germany ) . Primary glioma cells and the cell line A764 were generated by dissociation from a solid tumor and cultivation ( cell line: repeated subcultivation ) in Dulbecco's modified Eagle's medium ( DMEM; PAN Biotech , Aidenbach , Germany ) plus 10% fetal calf serum , FCS . Glioma samples were obtained in accordance with the Helsinki Declaration of 1975 with approval of the ethics committee of the University of Kiel , Germany ( file reference: D 442/11 and D 427/15 ) and after written consent of donors . The mamma carcinoma cell line MCF-7 ( Soule et al . , 1973; ATCC HTB-22; ECACC No . 86012803 ) cells and the monocytic cancer cell line THP-1 ( Tsuchiya et al . , 1982; ATCC TIM-202; ECACC No . 88081201 ) were obtained from Cell Line Service ( Eppelheim , Germany ) . The LOX melanoma cell line ( established by Fodstad et al . , 1988; and cited by Thies et al . , 2007 ) , the HT29 colon carcinoma cell line ( described by Fogh and Trempe , 1957; and obtained from European Cell Culture Collection , Porton Down , Salisbury , UK; ATCC HTB-38; ECACC No . 91072201 ) , the SH-SY5Y neuroblastoma cell line ( established by Biedler et al . , 1978; and provided by Prof . Dr . Hildebrandt , Institute of Cellular Chemistry , Medical School Hannover , Germany; ATCC CRL-2266; ECACC No . 94030304 ) , and the OH3 small cell lung cancer cell line ( established by Griffin and Baylin , 1985 and cited by Schumacher et al . , 1996 ) were gifts from Prof . Dr . Udo Schumacher , Dept . of Anatomy , University of Hamburg , Germany . These cell lines were cultivated in RPMI plus 10% FCS . Preparation of T cells has been described previously ( Ludwig et al . , 2005 ) . HUVEC cells were obtained by Promocell ( Heidelberg , Germany ) , cultivated in Endothelial growth medium ( Promocell ) and used up to passage 5 . All cell lines were kept in a master stock – working stock routine to exclude cross-contaminations and monthly checked for mycoplasma contamination by DAPI staining or PCR ( Venor GeM Mycoplasma Detection Kit , Minerva Biolabs , Berlin , Germany ) . All cell lines were free of mycoplasma contamination . For signaling experiments , cells were seeded on 25 cm2 culture flasks , cultivated in medium overnight , washed serum- and chemokine-free ( 2x , with 1 hr intervals ) and stimulated in DMEM plus 0 . 5% fatty acid-free bovine serum albumin , BSA ( Sigma-Aldrich ) , or RPMI plus 5–10% FCS . Chemokines/growth factors , antibodies or inhibitors were added from stocks in phosphate buffered saline ( PBS ) , or in dimethylsulfoxide ( DMSO ) ; in this cases controls were run with a corresponding DMSO concentration ( maximum 0 . 1% ) . For Pertussis toxin experiments , cells were pre-incubated with the inhibitor overnight , and the concentration ( 200 ng/ml ) maintained during the stimulations . For inhibition of the chemokine receptor CXCR7 , the CXCR7-specific antagonist CCX733 was added 2 hr prior to the stimulation and the concentration ( 100 nM ) maintained during the chemokine stimulation . To prepare monovalent F ( ab ) fragments , 5 µg antibody specific for CXCL16 or CX3CL1 ( PeproTech ) or control IgG ( DAKO , Glostrup , Denmark ) were incubated with 800 µg immobilized papain ( agarose resins , Pierce/Life technologies , Darmstadt , Germany ) for 6 hr at 37°C under gentle shaking ( 350 rpm ) . After centrifugation for 3 min at 5000 ×g , 20 µl of protein-A agarose ( Santa Cruz , Santa Cruz , CA ) were added to the supernatant and incubated for 1 . 5 hr at 4°C and shaking ( 350 rpm ) . After another centrifugation step ( 1000 ×g , 5 min ) , the supernatant was cleaned-up by spinning through a G25-Sephadex column ( 800 ×g , 2 min; Amersham/GE Healthcare ) . Cells were stimulated with 0 . 1 µg/ml F ( ab ) fragment for 20 min . Expression vectors for CXCL16-HA ( with a hemagglutinin HA tag ) and CX3CL1 were established as previously described ( Abel et al . , 2004 ) in a pcDNA3 . 1 backbone ( Invitrogen , Karlsruhe , Germany ) , and pcDNA 3 . 1 was used for control transfections . Transfection was performed with TurboFect ( Fermentas , St . Leon-Rot , Germany ) in serum-free DMEM without antibiotics using 4 µg of the respective expression vectors with pcDNA3 . 1 backbone and 4 µl TurboFect in a total volume of 1 ml . After 6 hr , cells were rinsed and normal growth medium ( RPMI + 10% FCS ) was added . Successful transfection was controlled by immunocytochemistry and/or qRT-PCR . Stable clones were generated by selection with 0 . 75 mg/ml G418 ( Calbiochem ) , and colonies were picked after 10–20 days , amplified and checked for expression by quantitative RT-PCR and immunocytochemistry . Site directed mutagenesis was performed with the QuikChange Site-Directed Mutagenesis Kit following the manufacturer´s advice ( Agilent Technologies , Böblingen , Germany ) . The complementary primer pairs were designed to generate a double stop codon following the transmembrane domain ( MWG-Biotech , AG Ebersberg , Germany; CXCL16: 5’-ctttcctatgtgctgtgatagaggaggagggggcag-3’ , CX3CL1: 5’-tggccatgttcacctactagtaactccagggctgccctcg-3’ ) yielding expression of C-terminally truncated tm-chemokines , respectively . Successful mutations were verified by sequencing of the plasmids ( GATC Biotech AG , Koblenz , Germany ) . Surface expression of overexpressed native or truncated tm-chemokines was verified by immunocytochemistry . Successful C-terminal truncation of CXCL16 was proven by immunocytochemistry ( see below ) using antibodies against the extracellular chemokine of CXCL16 ( PeproTech , #500-P200 ) and the HA-tag of CXCL16 ( Cell signaling , #2367 , clone 6E2 ) . Successful C-terminal truncation of CX3CL1 was proven by band shift in western blot ( see below ) with an antibody against the extracellular domain of CX3CL1 ( Pepro Tech , #500-P98 ) . Therefore , cell membranes were isolated17 and - to obtain defined bands - subjected to N-deglycosylation by PNGase F ( New England Biolabs , Ipswich , MA ) following the manufacturer’s instructions . Western blot was performed as described below . RNA was isolated with the TRIZOL reagent , digested by DNase , cDNA synthesized and real time RT-PCR was performed ( Ludwig et al . , 2005 ) using TaqMan primer probes ( Applied Biosystems , Foster City , CA , USA ) : hGAPDH ( Hs99999905_m1 ) , hCXCL16 ( Hs00222859_m1 ) , hCXCR6 ( Hs00174843_m1 ) , hCX3CL1 ( Hs00171086_m1 ) , hCX3CR1 ( Hs00365842_m1 ) , hCXCR3 ( Hs00171041_m1 ) , hCXCR4 ( Hs00607978_s1 ) , hCXCR7 ( Hs00664172_s1 ) , hD6 ( Hs01907876_s1 ) , hDARC ( Hs01011079_s1 ) , hCCX-CKR ( Hs00664347_s1 ) . A Custom TaqMan primer probe set was used for the detection of the cytomegalovirus gene encoding protein US28: US28F-5′- CGGCAACTTCTTGGTGATCTTC-3′ , US28R-5′- CATCGCCGGAGCATTGA-3′ , FAM- CCATCACCTGGCGACGTCGGA-MGB ( Matlaf et al . , 2013 ) . Cycles of threshold ( CT ) were determined with an ABI PRISM 7000 Sequence detection system . ∆CT values = CTGene of interest - CTGAPDH ( glyceraldehyde-3-phosphate dehydrogenase , housekeeping gene ) . A ∆CT value of 3 . 33 corresponds to one magnitude lower gene expression compared to GAPDH . Biologically independent replicates of cell lines and stable clones were obtained from three independent cultures , e . g . from different passages . Diamonds in the respective graphs indicate biological replicates . Expression data shown for primary cultures and silencing experiments are directly matched to the corresponding stimulation experiments . Western blotting was performed as described ( Hattermann et al . , 2010 ) . Briefly , cell lysates ( 5 µg per lane ) were separated by electrophoresis using 10% acrylamide gels , transferred to polyvinylidene fluoride ( PVDF ) membranes by blotting and incubated with primary antibodies ( rabbit anti-phospho-ERK1/2 , 1:500 , and rabbit anti- phospho-Akt , 1:500 , Cell Signaling Technology , Danvers , MA , # 9101 and #406 , and mouse-anti-CD44 MEM-85 , 1:400 , Abcam , Cambridge , UK , ab2212 ) and afterwards horseradish-peroxidase labeled secondary antibody ( goat anti-rabbit , 1:30 , 000; or goat anti-mouse , 1:30 , 000 , Santa Cruz , Santa Cruz , CA ) followed by chemo-luminescence detection ( GE Healthcare , Munich , Germany or Millipore , Darmstadt , Germany ) . To ensure equal loading amounts membranes were reactivated with methanol , stripped with ReBlot Plus Strong Antibody Strip Solution ( Millipore ) and re-probed with antibodies against the non-phosphorylated proteins ( rabbit anti-ERK2 , 1:250; Millipore #05-157 or rabbit anti-Akt , 1:500 , Cell Signaling ) . To obtain conditioned media for stimulation experiments LOX melanoma cells overexpressing CXCL16 or CX3CL1 ( or mock transfected control LOX cells ) were washed once with PBS and were incubated for 24 hr ( CX3CL1 ) /48 hr ( CXCL16 ) with 3 . 5 ml DMEM containing 0 . 5% BSA . The supernatants were centrifuged ( 5 min , 1500 rpm ) to remove cell debris . Soluble CX3CL1 was quantified by ELISA following the manufacturer´s advice ( R&D Systems ) . For CXCL16-quantification , 75 µl of the conditioned media were mixed with 25 µl of SDS-sample-buffer ( 100 mg/ml SDS , 0 . 25 M dithiothreitol , 50% glycerin , 0 . 3 M Tris/HCl pH 6 . 8 + 0 . 3% SDS ) , incubated at 97°C for 10 min and 20 µl were loaded on a 15% acrylamide gel and separated by electrophoresis . Western blotting was performed as described above ( rabbit anti-CXCL16 , 1:500; PeproTech ) . Matched experiments of s-chemokine determinations and stimulations were performed two times independently; representatives are shown . For light microscopy , cells grown on poly-D-lysine-coated cover slips were fixed with ice-cold acetone/methanol ( 1:1 ) , incubated with antibodies and nuclei counterstained with 4´ , 6-diamino-2-phenylindole ( DAPI ) as described ( Hattermann et al . , 2010 ) . Primary antibodies were: anti-CXCL16 and anti-CX3CL1 ( both from rabbit , diluted 1:100 in PBS , PeproTech ) , and anti-HA ( mouse , 1:100; Cell signaling ) ; secondary antibodies: donkey anti-rabbit or donkey anti-mouse IgG conjugated with Alexa Fluor 488 or Alexa 555 ( 1:800 , Invitrogen/ Life technologies ) . For binding experiments with CXCL16 , cells were incubated with directly Cy3-labeled CXCL16 or lactalbumin ( negative control with comparable molecular weight ) at 4°C for 60 min in the dark , washed , fixed and nuclei counterstained with DAPI . Labeling was performed using monoreactive Cy3 NHS ester ( GE Healthcare ) following the manufacturer’s instructions . Briefly , 2 µg protein was incubated with a four-fold excess of reactive dye in 0 . 2 M NaHCO3 , pH 8 . 4 ( total reaction volume 90 µl ) . The reaction was stopped by addition of 1 µl 0 . 1 M Tris , pH 7 . 3 . For binding experiments with CX3CL1 , cells were incubated with biotinylated CX3CL1 or the control peptide ( Fluorokine , R&D Systems ) at 4°C for 60 min , washed , incubated with Alexa Fluor 488 streptavidin ( Invitrogen/ Life technologies ) , washed again , fixed and nuclei were counterstained with DAPI . For flow cytometry analyses , cells were detached using 0 , 5 mM EDTA , stained with the CX3CL1 Fluorokine Assay following the manufacturer’s advice . For CXCL16 experiments , recombinant CXCL16 ( Pepro Tech ) was biotinylated with the One-step antibody biotinylation kit ( Miltenyi Biotech , Auburn , CA ) and used ( instead of biotinylated CX3CL1 ) with the Fluorokine kit components . Cells were analyzed using a FACSCanto System ( BD Bioscience , Heidelberg , Germany ) . For electron microscopy , cells seeded on coated cover slips were pre-incubated in serum free DMEM ( +0 . 5% fatty-acid free BSA ) for 30 min at 37°C , then slowly cooled down ( 30 min room temperature , 15 min 8°C , 15 min 4°C ) . All following incubation and washing steps were performed at 4°C and with pre-chilled buffers and media . Cells were briefly washed ( 3x ) with 145 mM NaCl , 5 mM KCl , 1 . 8 mM CaCl2 , 1 mM MgCl2 , 20 mM Hepes , pH 7 . 4 , and incubated with the primary antibody ( anti-CXCL16 or anti-CX3CL1 , see above , 1:100 ) for 60 min , washed again and incubated with the secondary antibody ( goat anti-rabbit with adsorbed 15 nm gold ( Au ) particles , diluted 1:40 , British BioCell International , Cardiff , UK ) for 60 min . Cover slips were washed again and incubated with 3 µg/ml either recombinant CXCL16 with adsorbed 5 nm gold particles or with biotinylated recombinant CX3CL1 ( R&D Systems ) and subsequent with streptavidin adsorbed to 5 nm gold particles ( British BioCell International ) . Cells were fixed with 4% glutaraldehyde/ 0 . 5% paraformaldehyde , embedded in Araldite , sectioned and viewed on a Zeiss EM 900 electron microscope . Samples were only slightly contrasted ( 45 min 2% osmium tetroxide before embedding and 5 min uranyl acetate ( saturated ) after sectioning ) , as strong contrasting ( e . g . with lead citrate ) would not allow for clear detection of gold particles . Thus , contrast of electron micrographs was digitally enhanced . Images shown are representative views of 3 ( ICC ) or 2 ( FACS , EM ) independent stimulations/ immunostainings . After membrane isolation ( Held-Feindt et al . , 2010 ) proteins were dissolved in 0 . 2 M triethanolamine-hydrochloride ( pH 8 . 0 ) , and 1 . 5 mg membrane-protein was incubated with 2 nM recombinant s-chemokine ( PeproTech ) over night at 4°C under slow shaking conditions . Subsequently 1% paraformaldehyde was added for cross-linking and incubated for 1 hr at room temperature . The reaction was stopped with addition of 1 M Tris ( pH 8 . 3 ) and incubation for 15 min . Electrophoresis and Western blotting was performed as described above ( rabbit anti-CXCL16 /anti-CX3CL1 , 1:500; PeproTech ) , representatives from 2 independent experiments , respectively , are shown . After cultivation of glioma cells ( primary cultures and established cell lines ) in DMEM plus 10% FCS in 6-well dishes ( 150 , 000 cells /well ) for 24 hr , cells were transfected with siCXCL16 RNA or siCX3CL1 RNA ( CXCL16 siRNA ID: s33808; CX3CL1 siRNA ID: s12630; both 50 pmol/well; Life technologies ) dissolved in a mixture of Opti-MEM Medium and lipofectamine ( Life technologies ) for 6 hr . In parallel a transfection with silencer select negative control siRNA ( Life technologies ) was performed under same conditions . After transfection cell culture medium was changed and glioma cells were cultured for another 24 hr in DMEM plus 10% FCS . Then , cells were washed 20 min for three times with DMEM plus 0 . 5% FCS and afterwards stimulated for 15 min with recombinant CXCL16 or CX3CL1 ( 10 nM; PeproTech ) dissolved in DMEM plus 0 . 5% FCS . Cells were lysed and applied for Western Blot experiments as described above , representative data from 2 independent stimulations of cell lines , and 2 different patients’ derived primary cultures are shown . Migration was analyzed in wound healing assays ( scratch assay , Hattermann et al . , 2008 ) . Briefly , 200 , 000 cells/well were seeded on 6-well dishes , grown to confluence , scratched with a pipet tip , washed and supplemented with stimuli , media with 10% fetal calf serum served as positive control . In each experiment , three scratch regions were photographed at 0 and 24 hr . Scratch areas were measured and differences between 24 and 0 hr were determined ( yielding the settled area ) . Stimuli were normalized to non-stimulated controls . To measure proliferation , 5000 cells/well were seeded on 96 well plates and grown for 24 hr . Then media were changed to DMEM containing 1% BSA ( plus respective stimuli or 10% fetal calf serum as positive control ) . After 24 hr incubation , proliferation was determined by the measurement of tetrazolium salt WST-1 cleavage ( Roche , Mannheim , Germany ) and normalized to non-stimulated control ( 3 individual wells for each stimulus ) . To investigate reduction of apoptosis , 300 . 000 cells were seeded in 25 mm2 culture flasks and cultured for 2 days to reach confluency of 80% . Apoptosis was induced by addition of Temozolomide ( 400 µg/ml , Sigma-Aldrich ) or 0 . 1 µg/ml Camptothecin applied in a stock solution in DMSO; the final solvent concentration of 2% ( Temozolomide ) , 0 . 1% ( Camptothecin ) or 0 . 1 µM Staurosporine in cultures was also used in controls . After 48 hr , caspase-3/7 activity in glioma cells was measured with 40 µM Ac-DEVD-AMC ( AMC , 7-amino-4-methylcoumarine; Bachem , Bubendorf , Switzerland ) after lysis in 100 mM NaCl , 0 . 1% CHAPS , 10 mM dithiothreitol , 1 mM EDTA , 10% glycerol , 50 mM Hepes , pH 7 . 4 . Alternatively , 18 hr after stimulation cleavage of Poly ( ADP Ribose ) Polymerase ( PARP ) was measured by Western blot ( 150 , 000 cells/25 mm2 flask , grown for 30 hr and stimulated for 18 hr ) or immunocytochemistry ( 30 , 000 cells/cover slip , grown for 30 hr and stimulated for 18 hr ) as described above using an antibody specifically detecting cleaved PARP ( Asp124 , 1:500 for WB , 1:100 for ICC; Cell Signaling ) . An antibody against GAPDH ( 1:500; Santa Cruz Biotechnology ) served as loading control for Western blot , the immunocytochemistry signal obtained by fluorescence microscopy was measured and normalized to the nuclear area yielding OD/nucleus area . Values are given as means ± standard deviations ( SD ) of independent biological replicates , respectively . Diamonds shown in figures correspond to the data of an independent biological replicate , which means the experiment was performed with cells of a different subculture at a different time point . Statistical significance was analyzed by a two-tailed Student’s t-test . p<0 . 05 , p<0 . 01 , **p<0 . 001 .	The cells that make up an animal need to communicate with each other for a variety of purposes , including controlling the growth and repair of tissues . Commonly , such signaling involves ‘ligand’ molecules binding to specific ‘receptor’ proteins embedded in the cell membrane . When a ligand docks to the right receptor protein , the parts of the receptor inside the cell change shape . This activates signaling pathways within that cell . Types of ligands called transmembrane ligands are found embedded in cell membranes . Some cancer cells have high levels of transmembrane ligands called CXCL16 and CX3CL1 but do not produce the corresponding receptors for these molecules . The part of these ligands that sits outside of the cells can also be separated from the rest of the molecule to produce a soluble ligand that can move around outside the cell . By studying cancer cells using microscopy and biochemical approaches , Hattermann , Gebhardt et al . now show that the soluble forms of CXCL16 and CX3CL1 bind to their transmembrane equivalents . This activates signaling pathways that promote cell growth and make the cancer cells more resistant to cell death . However , this signaling did not occur if the transmembrane ligands were altered to lack the part normally found inside the cell , which suggests that transmembrane CXCL16 and CX3CL1 act as receptors . It was not previously known that a soluble ligand could activate its transmembrane equivalent . Hattermann , Gebhardt et al . have named this process “inverse signaling” , and suggest that it helps to fine-tune the communication between cells . Future experiments will need to study the importance of inverse signaling in living animals and investigate how it works alongside other signaling methods .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology" ]	2016	Transmembrane chemokines act as receptors in a novel mechanism termed inverse signaling
Epidemiological evidence suggests that social interactions and especially bonding between couples influence tumorigenesis , yet whether this is due to lifestyle changes , homogamy ( likelihood of individuals to marry people of similar health ) , or directly associated with host-induced effects in tumors remains debatable . In the present study , we explored if tumorigenesis is associated with the bonding experience in monogamous rodents at which disruption of pair bonds is linked to anxiety and stress . Comparison of lung cancer cell spheroids that formed in the presence of sera from bonded and bond-disrupted deer mice showed that in monogamous Peromyscus polionotus and Peromyscus californicus , but not in polygamous Peromyscus maniculatus , the disruption of pair bonds altered the size and morphology of spheroids in a manner that is consistent with the acquisition of increased oncogenic potential . In vivo , consecutive transplantation of human lung cancer cells between P . californicus , differing in bonding experiences ( n = 9 for bonded and n = 7 for bond-disrupted ) , and nude mice showed that bonding suppressed tumorigenicity in nude mice ( p<0 . 05 ) , suggesting that the protective effects of pair bonds persisted even after bonding ceased . Unsupervised hierarchical clustering indicated that the transcriptomes of lung cancer cells clustered according to the serum donors’ bonding history while differential gene expression analysis pointed to changes in cell adhesion and migration . The results highlight the pro-oncogenic effects of pair-bond disruption , point to the acquisition of expression signatures in cancer cells that are relevant to the bonding experiences of serum donors , and question the ability of conventional mouse models to capture the whole spectrum of the impact of the host in tumorigenesis . While the psychosomatic impact of cancer in patients is extensively documented , the reciprocal effects of individuals’ social experiences in carcinogenesis receive limited attention . Both anecdotal and experiential evidence , and numerous epidemiological studies , strongly suggest that emotional factors can affect the development and progression of cancer , pointing to the sensitivity of cancer cells to signals associated with behavior , emotional state , and sociality . For example , the marital status modulates the likelihood for the development of fatal cancers , with unmarried , divorced , or widowed individuals exhibiting an increased chance of developing life-threatening disease and males being more susceptible than females to the protective effects of marriage ( Aizer et al . , 2013 ) . The ‘widowhood effect’ provides an example at which in couples , after the loss of one partner , the surviving one exhibits an increased probability for the development of various fatal pathologies ( Elwert and Christakis , 2008; Blanner et al . , 2020; Sullivan and Fenelon , 2014; Bowling , 1987; Boyle et al . , 2011 ) . Notwithstanding that high variation in death causes has been documented , cancer is recognized as a common cause of mortality ( Aizer et al . , 2013; Elwert and Christakis , 2008; Blanner et al . , 2020; Burgoa , 1998; Martikainen and Valkonen , 1996; Sex , 1973 ) . Although both sexes are influenced by widowhood , males appear more sensitive than females to widowhood-associated death ( Sullivan and Fenelon , 2014; Helsing et al . , 1981 ) . Despite the information they provide , unavoidable changes in lifestyle habits in the bereaved partner at widowhood or between single and married patients complicate the epidemiological data interpretation . Several mechanisms connecting cancer to social interactions , mental state , and bereavement have been proposed . Laboratory mice of the genus Mus , despite their power in illuminating various aspects of tumorigenesis , remain of limited value in modeling the effects of pair bonding . It is estimated that in less than 10% of mammals , including humans , individuals form pair bonds that are based on mating ( Kleiman , 1977; Lukas and Clutton-Brock , 2013; Scribner et al . , 2020 ) . Therefore , mice , by not developing long-term pair bonds , are not adequate in studying the effects of widowhood and pair-bond disruption ( Chatzistamou et al . , 2018; McDonald et al . , 2005 ) . Earlier studies in mice have shown that brain-derived signals linked to the reward system may impact tumorigenesis , whereas stress can stimulate metastases ( Ben-Shaanan et al . , 2018; Sloan et al . , 2010 ) . However , more complex behavioral traits involving social interactions in married couples or widowhood cannot be studied in mice . Peromyscus californicus is a monogamous species developing long-term , cohesive pair bonds that can influence various physiological responses ( Havighorst et al . , 2017; Perea-Rodriguez et al . , 2015; Glasper and Devries , 2005; Wright et al . , 2018 ) . Upon cyclosporine-mediated immunosuppression , similarly with other rodents , P . californicus supports the growth of human cancers , providing a potentially informative animal model for the study of pair-bond disruption in tumorigenesis in vivo ( Fingert et al . , 1984; Kaza et al . , 2018; Chatzistamou and Kiaris , 2016 ) . Initially , we asked if sera of P . californicus following the disruption of pair bonds affected the growth of cancer cells in vitro in a manner that depended on bonding history . We focused on the formation of tumor spheroids that are enriched in cells with cancer stem cell ( CSC ) -like properties , and their formation is known to reflect tumorigenic activity directly ( Visvader and Lindeman , 2012; Ishiguro et al . , 2017 ) . Sera were obtained from 14 to 17 months old virgin , bonded for about 12 months , or bond-disrupted ( after 12 months of bonding ) at the periods indicated , male P . californicus , and the efficacy of spheroid formation by A549 human lung cancer cells was assessed . A pilot study indicated that sera obtained from animals 1 week after the disruption of pair bonds resulted in the formation of larger yet less compact spheroids , suggesting a significant impact of bond disruption in spheroid morphogenesis ( Figure 1a ) . The results were confirmed and extended in a subsequent study that also included sera obtained 24 hr and 2 weeks after the disruption of pair bonds ( Figure 1b ) . In this study , sera from 9 ( B ) , 5 ( BD , 24 hr ) , 5 ( BD , 1 week ) , 4 ( BD , 2 weeks ) , and 5 ( virgin , V ) different animals were used , and microsphere formation was evaluated in two biological replicas for each ( n = 10 for BD [1 week] , BD [24 hr] , and V; n = 8 for BD [2 weeks] and n = 18 for B ) . For control media ( CM ) and plain serum-free media ( PM ) , n = 4 . As shown in Figure 1b , this activity was only marginal at 24 hr but was significant ( p<0 . 05 ) 1 week and 2 weeks after the disruption of pair bonds , implying that the factors responsible accumulated in the sera after pair-bond disruption . As compared to virgins , sera from animals at bonding resulted in the formation of smaller spheroids , albeit insignificantly , which implies that bonding may also have some protective activity , beyond the pro-oncogenic activity of bond disruption ( Figure 1b ) . The effects in spheroid size described above were obtained with sera from older animals ( 14–17 months old ) that were bonded for at least 12 months . To test whether disruption of bonds in younger animals that were bonded for shorter time periods also produced similar effects , we conducted the following study: We exposed to sera of 8–10 months old animals that were either pair-bonded for 2 months or following 2 weeks of bond disruption after 2 months of bonding , a roster of lung cancer cell lines . For this experiment , 14 animals were used that represented seven sibling pairs with each sibling allocated either to the bonded or to the bond-disrupted group . Our results indicated that consistently , in the same sibling pair , an induction of microsphere size of similar magnitude was noted for all five cell lines tested , in four of seven pairs , while this effect was only marginal in the remaining three pairs ( Figure 1—figure supplement 1 ) . The variation in spheroid size was analogous to that recorded in the results described in Figure 1c . Thus , we conclude that even shorter periods of bonding are sufficient , and the consequences of its disruption can be recorded in sera from even younger animals . More importantly , it indicates that the variation of the effects is due to the diversity of the animals and not to the differential sensitivity of the cell lines used . The effects of pair bonding in spheroid formation prompted us to explore whether bond disruption also influences the efficacy of tumorigenesis in vivo . To that end , vasectomized male P . californicus were allowed to establish pair bonds for about 2 months with their female partners and then subjected to pair-bond disruption ( n = 9 ) or were left with their partners ( n = 11 ) . Following immunosuppression by CsA animals were inoculated with A549 human lung cancer cells and tumorigenesis was monitored . Animals that did not possess bonding experiences before were used as controls ( n = 8 ) . Tumors grew originally in animals of all experimental groups and by day 15 measurable tumors were detected in 9 of 11 bonded , in 8 of 9 bond-disrupted and in 6 of 8 virgins ( Figure 2a ) . At this point , tumors were modestly – albeit not statistically significantly – larger in the bond-disrupted animals and smaller in the group of virgins ( Figure 2a ) . By day 25 , the tumors persisted in both the bond-disrupted and bonded animals , at 89% ( 8 of 9 ) and 82% ( 9 of 11 ) rate , respectively , while in virgin animals , they were detectable only in 25% ( 2 of 8 ) of the animals ( Figure 2b , c ) . In a follow-up study , we explored if differential pro-oncogenic activity persisted after growth in nude mice . Thus , tumors that were originally grown in P . californicus for at least 1 month ( n = 9 for bonded and n = 7 for bond-disrupted ) were re-transplanted in virgin nude mice ( one nude mice for each original Peromyscus tumor ) , and tumorigenesis was recorded . As shown in Figure 2d , tumors from bonded P . californicus exhibited significantly ( p=0 . 011 ) lower tumorigenicity in nude mice than those grown originally in the bond-disrupted animals , despite that histologically they remained indistinguishable ( Figure 2e ) . In line with the tumor spheroid analyses , pair bonding produced persistent changes in tumors that suppressed their growth and endured even when bonding seized . The effects of bonding history in the profile of tumor growth in vivo , combined with the spheroid formation in vitro , imply the induction of transcriptional changes in the cancer cells in a manner that depends on bonding experience ( Figure 3 , Figure 3—figure supplements 1 , 2 and 3 ) . Initially , we focused on the expression of established CSC markers and genes regulating CSC potential , such as Oct-4 , b-catenin , and CD-133 that have been identified previously in A549 cells ( Chiou et al . , 2010; Akunuru et al . , 2012; Teng et al . , 2010 ) . The analysis was performed by semiquantitative RT-PCR in 2D cultures to eliminate the effects of the clonal selection of cells in the spheroids . Differential expression analysis did not reveal considerable differences between the bonding groups , either in cells cultured in vitro with sera from animals differing in bonding history or in vivo in tumors in nude mice or Peromyscus ( Figure 3—figure supplement 1 ) . However , unsupervised hierarchical clustering indicated that these CSC markers provided a signature that predicted a relatively high accuracy bonding history of the animals ( Figure 3—figure supplement 1 ) . This observation prompted us to perform RNA sequencing and analyze expression profiles at the whole transcriptome level in human A549 lung cancer cells in the presence of sera that had been isolated from monogamous male P . californicus that were virgin ( V ) , bonded ( B ) , or subjected to disruption of pair bonds ( BD ) after bonding ( n = 6 samples/group ) . Controls ( C ) cultured in the presence of fetal bovine serum ( FBS ) were also included . Unsupervised hierarchical clustering ( Vidman et al . , 2019 ) indicated that the transcriptomes clustered well together according to the serum donors’ bonding history , except the virgin ( V ) group that exhibited the lowest discrimination ( Figure 3—figure supplement 2 ) . Differential gene expression analysis was performed as described before by using the iDEP platform ( Ge et al . , 2018 ) . This analysis showed that the majority of differentially expressed genes were detected in the comparisons involving the FBS-treated cells ( C ) , which suggests that the species origin of sera produces the most potent effects in gene expression and potentially masking the consequences of pair bonding in the regulation of the transcriptome ( Figure 3—figure supplement 3 ) . Thus , we repeated the analysis by excluding the specimens corresponding to FBS and restricted it only to the specimens that received Peromyscus sera ( Figure 3 ) . Seven genes were differentially expressed in each B vs BD and B vs V comparisons , while none were detected between the V and BD groups ( Table 1 ) . Thus , it seems that pair bonding produces more robust effects in the sera as compared to those of bond disruption . Among these genes , all of which were downregulated in the B group , five were common and included HES1 , ZFP36 , NR4A1 , FGG , and SOCS3 . Hes1 is a transcription factor that is downstream of Notch signaling , for which the pro-oncogenic activity in lung cancer has been established ( Westhoff et al . , 2009; Yuan et al . , 2015 ) . NR4A1 encodes for the orphan nuclear receptor A1 for which a strong association with unfavorable outcome in lung cancer has been shown and is involved in cancer cell migration ( Zhu et al . , 2017; Hedrick et al . , 2018 ) . SOCS3 is a suppressor of cytokine signaling and is a repressor of lung tumorigenesis ( He et al . , 2003; Lund and Rigby , 2006 ) . FGG encodes for fibrinogen gamma chain that has been linked to enhanced invasion of lung and other cancer cells ( Sahni et al . , 2008; Zhang et al . , 2019 ) . The genes that were uniquely detected in the BD vs B groups comparison were FGA and FGB , which encode for fibrinogen A and B chains ( Pieters and Wolberg , 2019 ) , while in the V vs B comparison , the oncogene Jun that enhances lung cancer cell migration ( Shimizu et al . , 2008 ) and the connective tissue growth factor that at least in lung cancer , is associated with favorable prognosis ( Chien et al . , 2006; Chang et al . , 2004 ) . Pathway enrichment analysis indicated that processes associated with differentially expressed genes were linked to the regulation of cell migration and spread , or tissue morphogenesis ( Table 2 ) . The findings on P . californicus prompted us to explore whether other Peromyscus species are also sensitive to the effects of the disruption of pair bonds . Thus , we compared the effects of sera from bonded or bond-disrupted polygamous P . maniculatus and monogamous Peromyscus polionotus , in the size and shape of A549 tumor spheroids . As shown in Figure 4 , the disruption of pair bonds altered spheroid morphology in the monogamous , but not in the polygamous species . The intensity of this effect was variable among the animals tested and was recorded in at least 6 of 12 male P . polionotus but none of P . maniculatus ( n = 12 ) tested ( p=0 . 005 , chi-square test; Figure 4—figure supplement 1 ) . Contrary to P . californicus though , at which pair-bond disruption enhanced spheroid size , in P . polionotus the primary effect was seen in the spheroids’ shape: Spheroids that formed in the presence of P . polionotus sera obtained after the disruption of pair bonds had scattered morphology , as opposed to the spheroids from P . maniculatus sera at bonding and bond disruption and those of P . polionotus at bonding that were smooth-edged . In some instances ( about 25% of animals ) , this scattered phenotype was also noted in P . polionotus sera obtained at bonding ( Figure 4—figure supplement 1 ) . Whether this difference represents the actual phenotypic difference between the two species or is due to methodological changes in the state of the cells and donor animals remains to be established . In addition , it may reflect the same effect ( cell dispersion followed by proliferation ) but recorded at different stages during the formation of the spheroids . It is also noted , that the monogamous behavior in Peromyscus has developed independently during the evolution of P . polionotus and P . californicus , and thus alternative signaling ques may have been engaged in altering the consequences of bond disruption in spheroid formation ( Jašarević et al . , 2013 ) . To that end , the signaling cascades influencing spheroid size and shape may be distinct for the two species; nevertheless , the effects of pair-bond disruption persist . The present findings exemplify the role of the context – in its wider sense – in cancer progression and underscore the significance of psychosomatic factors as modulators of cancer growth . Using a behaviorally relevant animal model , our results highlight the biological basis of the ‘widowhood effects’ and suggest that it operates as a tumor-promoting factor , beyond lifestyle changes . Our conclusions are based on the recorded effects of pair bonding in three major phenotypic characteristics of the cancer cells . Those included tumor spheroid formation established in the presence of sera from bond-disrupted animals , the expression profile of the cancer cells in vitro and in vivo that depended on the bonding history of serum donors and tumor hosts , respectively , and ultimately their tumorigenicity in the nude mice . The use of sera from outbred , genetically diverse rodents , allowed us to obtain evidence that this effect varies among individuals but persists across different lung cancer cells . This observation might be of relevance to the study of human populations that are genetically diverse and their responses to the same stimuli may be variable . In our animal model , cancer cells were implanted in tumor-free animals and the kinetics of tumorigenesis was affected by the animals’ bonding history . Whether pair bonding and disruption can also influence tumor initiation will have to be established , nevertheless , the fact that most cancers are slow-growing in patients is consistent with the effects of widowhood in influencing the progression , as opposed to the initiation of the disease . Yet , by using the in vivo experiments immunocompromised animals ( nude mice and cyclosporine administration ) , our study suffers from the absence of integration of immune responses that may be especially relevant to widowhood-associated stress . An unexpected finding was the loss of the tumors in the virgin animals as opposed to the majority of the bonded and bond-disrupted that retained them ( Figure 2b ) . A possible explanation is probably related to the differential effectiveness of immunosuppression by cyclosporine . Especially during the initial period after cancer cell inoculation , cyclosporine may have caused more potently immunosuppression in the animals that had been subjected to bonding , due to the concomitant anti-inflammatory action of oxytocin , a neurohormone with essential role in the establishment of social interactions and pair bonding ( Lutgendorf et al . , 2005; Fagundes et al . , 2011; Fuligni et al . , 2009; Yuan et al . , 2016; Carter and Perkeybile , 2018 ) . It is noted though that the high difference in the tumorigenicity between virgins and the bonded or bond-disrupted animals , renders differential immune suppression unlikely as the sole contributor for this discrepancy . Differential analysis of gene expression showed that sera from animals at bonding enriched for genes regulating cell migration and spreading , and tissue morphogenesis , features that are consistent with the recorded changes in spheroid morphology . Although for several of the differentially expressed genes , their downregulation , which was seen in the bonded group , was associated with a favorable prognosis , in some cases , it was not . For example , SOCS3 was downregulated in the bonding group , yet it is a tumor suppressor for lung and other cancers ( He et al . , 2003; Lund and Rigby , 2006 ) , which may reflect responses related to oxytocin signaling during bonding ( Matarazzo et al . , 2012 ) . Beyond its effects in the expression of individual genes , the impact of bonding history in transcription was more clearly reflected in the similarity recorded in the transcriptomic profiles of cells cultured in sera from animals with similar bonding experiences . This was especially pertinent to the bonded and bond-disrupted groups . An intriguing possibility is that this is indicative for the lowest rigidity in the transcriptomic profile induced by the serum of virgin animals , as opposed to the changes triggered by the sera of bonded and of bond-disrupted animals that remained more robust . Collectively , the results provide a mechanistic foundation for the widowhood effect and suggest that the individuals’ social , and especially bonding experiences , modify the transcriptome of lung tumors modulating oncogenic activity . As such , they advocate that cancers at widowhood represent a distinct pathological entity that may deserve targeted therapeutic strategies , which should take into consideration social interactions . Thus , preventive measures could be developed to mitigate such pro-oncogenic effects in individuals at bereavement . Whether these findings do occur and at which extent in other monogamous species , including humans , and whether they are applicable to other cancers as well as other pathologies beyond malignancy , remains to be explored . Finally , the present results also raise some concerns regarding the use of conventional animal models and their ability to accurately capture the whole spectrum of the tumorigenic process and the associated host-derived factors . Genetically diverse male P . californicus ( stock IS ) , P . polionotus ( PO stock ) , and P . maniculatus ( BW stock ) were obtained from the Peromyscus Genetic Stock Center ( Columbia , SC ) ( RRID:SCR_002769 ) . Mice were all 14–17 months old and were divided into three experimental groups: bonded , bond-disrupted , and virgin . For the tumor inoculation studies , in the bonded group , mice were paired for at least 2 months before the study began and remained paired until the end of the study . In the bond-disrupted group , mice were paired 2 months before the study started , and immediately after cancer cells injection , they were separated . In the virgin group , mice were kept individually 2 months before the study began . Vasectomy was performed to prevent pregnancy during the study . Some siblings were used and were distributed randomly in different experimental groups as described in the legend of Figure 1—figure supplement 1 . Nude mice ( male , 6–8 weeks old ) were obtained from Charles River Laboratories ( Boston , MA ) and were housed in groups of 4–5 . For serum collection used in the RNAseq studies and spheroid formation , for the bonded group , mice were paired for about 12 months . For the bond-disrupted group , we separated paired mice after 12 months of bonding and collected the sera 1 week after bond disruption . For virgin mice , we collected sera from mice housed 3/cage . Sera were obtained by retro-orbital bleeding before and after bond disruption at the indicated times . Animal studies were approved by the University of South Carolina IACUC ( Protocol # 2473-101464-102319 ) . A549 human non-small cell lung adenocarcinoma cells were originally obtained from ATCC ( Manassas , VA ) and thereafter maintained in freezing media ( 60% Dulbecco’s modified Eagle medium [DMEM] , 30% FBS , 10% dimethyl sulfoxide ) . Most recently , cells were validated by STR typing ( Biosynthesis , Lewisville , TX ) just after completion of experiments . Human H1703 squamous , H596 adenosquamous , H358 bronchioalveolar , and H292 mucoepidermoid lung cancer cells were obtained prior to their use from ATCC ( Manassas , VA ) and cultured for three passages at ATCC-recommended media prior to the performance of the spheroid assays . Cells were tested negative for mycoplasma contamination . To cause immunosuppression in P . californicus and overcome xenograft rejection , animals were treated daily with 100 mg/kg cyclosporine A ( in 90% olive oil and 10% EtOH ) s . c . starting 1 day before the implantation of cancer cells , for 2 weeks , and then every other day for the whole duration of the study ( Perea-Rodriguez et al . , 2015 ) . For cancer cell inoculation , ( 5 × 106 ) cells were injected subcutaneously into the right flank of mice in a total volume of 100 μl phosphate-buffered solution ( PBS ) . Tumor volumes were assessed by using the following formula: ( width ) 2 × length/2 . All experiments were approved by the Institutional Animal Care and Use Committee of the University of South Carolina ( approval no . 101464 ) . For re-transplantation in nude mice , tumors were harvested from P . californicus , mechanically minced at pieces of 5–10 mm3 , and were implanted into the right flank of nude mice using a trocar needle . Mice were followed up each week until 4 months . Tumor was fixed in 4% neutral buffered formalin and subsequently embedded in paraffin . Sections were stained with hematoxylin and eosin for histological assessment . Where available , a part of the tumor was snap-frozen on dry ice and stored at −80°C , for RNA extraction . Images were obtained by a Leica optical microscope . Lung cancer cells were seeded into 96-well spheroid microplates ( Corning Cat . No . 4515 ) at 2 × 103 cells/well in 100 μl of DMEM+5% FBS+5% serum of each mouse . The age of the mice , their bonding group , and the period of bonding are described in the text and corresponding figure legends . The plate was incubated at 37°C , 5% CO2 . Images were taken using an inverted microscope at 4× magnification each day until 3 days and analyzed using NIH ImageJ software to assess microsphere areas and volumes . The studies were repeated independently at least twice , and similar results were obtained . For the assessment of the spheroids that formed with P . polionotus sera , ‘scattered’ phenotype was scored when at least two outgrowths formed distal from the main spheroid . Spheroid cell viability was assayed using the LIVE/DEAD Viability/Cytotoxicity Kit ( Cat . No . L3224 ) . After 3 days of spheroid culture , wells were rinsed two times with an 80 percent-volume change of media with D-PBS . EthD-1 ( 12 μM ) and calcein AM ( 4 μM ) were added to the wells , and the cells were incubated in the dark for 30–45 min to avoid the photodynamic effect . Images were taken using a fluorescence microscope; live cells fluoresce green , whereas dead cells fluoresce red . Data were analyzed using ImageJ image analysis software . Total RNA from cell and tumor tissues were isolated using the Qiagen RNeasy Mini Kit . Equal quantities of RNA were used for making cDNA using iScript cDNA synthesis kits ( Bio-Rad ) according to the supplier’s protocol on a T100 thermal cycler ( Bio-Rad ) . Human-specific primers for CSC-related genes: OCT4 , β-catenin , and CD133 were designed using Primer3 and Primer BLAST . Quantitative real-time PCR was performed using the Bio-Rad Real-Time PCR detection system and iTaq Universal SYBR Green Supermix ( Bio-Rad ) according to the manufacturer’s instructions . Amounts of target genes mRNA were normalized to a reference gene GAPDH and were expressed as arbitrary units . The oligonucleotides used for qPCR amplification were as follows: Oct-4: GAAGGATGTGGTCCGAGTGT ( left ) and GTGAAGTGAGGGCTCCCATA ( right ) ; b-catenin: GAAACGGCTTTCAGTTGAGC ( left ) and CTGGCCATATCCACCAGAGT ( right ) ; CD-133: TTGTGGCAAATCACCAGGTA ( left ) and TCAGATCTGTGAACGCCTTG ( right ) ; GAPDH: CCATCACCATCTTCCAGGAGCG ( left ) and AGAGATGATGACCCTTTTGGC ( right ) . Hierarchical clustering analysis and presentation of expression data were performed using the Morpheus analysis software ( https://software . broadinstitute . org/morpheus ) . For the analysis , raw cpm values were either used or transformed by using the formula Log2 ( 1 + raw values ) , as described in the text . RNA sequencing was performed as described ( Chavez et al . , 2020 ) . RNAseq data were deposited to NCBI ( GSE167827 ) . Differential analysis of gene expression and enrichment pathway analysis were performed by using the iDEP platform ( He et al . , 2003 ) . The data are presented as mean ± SEM . Statistical analysis was performed by paired t-test , chi-square test , ANOVA , or log-rank ( Mantel–Cox ) test as indicated in the figure legends and text . Results were considered significant when p≤0 . 05 . All graphs were generated using GraphPad Prism software ( version 8 ) .	People’s social interactions could influence their risk of developing various diseases , including cancer , according to population-level studies . In particular , studies have identified a so-called widowhood effect where a person’s risk of disease increases following the loss of a spouse . However , the cause of the widowhood effect remains debatable , as it can be difficult to separate the impact of lifestyle changes from biological changes in the individual following bereavement . It is not possible to use laboratory mice to identify a causal biological mechanism , because they do not form long-term relationships with a single partner ( pair bonds ) . However , several species of deer mouse form pair bonds , and suffer from anxiety and stress if these bonds are broken . Naderi et al . used these mice to study the widowhood effect on the risk of developing cancer . First , Naderi et al . grew human lung cancer cells in blood serum taken from mice that were either in a pair bond or had been separated from their partner . The cancer cells grown in the blood of mice with disrupted pair bonds changed size and shape , indicating that these mice were more likely to develop cancer . This effect was not observed when the cells were grown in the blood of bonded deer mice or of another deer mouse species that does not form pair bonds . Naderi et al . also found that the activity of genes involved in the cancer cells’ ability to spread and to stick together was different in pair-bonded mice and in pair-separated mice . Next , Naderi et al . implanted lung cancer cells into the deer mice to study their effects on live animals . When cancer cells from the deer mice were transplanted into laboratory mice with a weakened immune system , the cells taken from pair-bonded deer mice were less likely to grow than the cells from deer mice with disrupted pair bonds . This suggests that the protective effects of pair bonding persist even after removal from the original mouse . These results provide evidence for a biological mechanism of the widowhood effect , where social experiences can alter gene activity relating to cancer growth . In the future , it will be important to determine whether the same applies to humans , and to find out if there are ways to mimic the effects of long-term bonds to improve cancer prognoses .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "short", "report", "cancer", "biology" ]	2021	Persistent effects of pair bonding in lung cancer cell growth in monogamous Peromyscus californicus
Chromosome segregation during mitosis depends upon Kinesin-5 motors , which display a conserved , bipolar homotetrameric organization consisting of two motor dimers at opposite ends of a central rod . Kinesin-5 motors crosslink adjacent microtubules to drive or constrain their sliding apart , but the structural basis of their organization is unknown . In this study , we report the atomic structure of the bipolar assembly ( BASS ) domain that directs four Kinesin-5 subunits to form a bipolar minifilament . BASS is a novel 26-nm four-helix bundle , consisting of two anti-parallel coiled-coils at its center , stabilized by alternating hydrophobic and ionic four-helical interfaces , which based on mutagenesis experiments , are critical for tetramerization . Strikingly , N-terminal BASS helices bend as they emerge from the central bundle , swapping partner helices , to form dimeric parallel coiled-coils at both ends , which are offset by 90° . We propose that BASS is a mechanically stable , plectonemically-coiled junction , transmitting forces between Kinesin-5 motor dimers during microtubule sliding . Accurate chromosome segregation during mitosis is known to underlie the propagation of all cellular life . This process depends upon the action of a bipolar , macromolecular machine , the mitotic spindle , which uses dynamic microtubules ( MTs ) plus multiple kinesin and dynein motors to generate the piconewton-scale forces required for mitotic movements ( Loughlin et al . , 2008; Goshima and Scholey , 2010; Walczak et al . , 2010; McIntosh et al . , 2012 ) . Of these , the MT-based motor , Kinesin-5 plays a key role , being essential for the assembly of bipolar spindles in most eukaryotic cells and driving or constraining the rate of spindle elongation ( Enos and Morris , 1990; Sawin et al . , 1992; Saunders et al . , 2007; Brust-Mascher et al . , 2009 ) . Purified , native Kinesin-5 is a ‘slow’ , plus-end-directed , bipolar , homotetrameric motor capable of using pairs of N-terminal motor domains at opposite ends of a central rod to crosslink MTs into bundles ( Cole et al . , 1994; Kashina et al . , 1996a , 1996b; Gordon and Roof , 1999; Acar et al . , 2013 ) . Moreover , single Kinesin-5 holoenzymes can crosslink and slide adjacent MTs , displaying a threefold preference for MTs in the antiparallel vs the parallel orientation ( Hentrich and Surrey , 2010; Kapitein et al . , 2005; van den Wildenberg et al . , 2008; Weinger et al . , 2011 ) . Together , these data support the hypothesis that Kinesin-5 functions via a ‘sliding filament’ mechanism , crosslinking adjacent MTs into bundles throughout the spindle ( Sharp et al . , 1999 ) , and exerting outward or braking forces on antiparallel MTs to coordinate bipolar spindle assembly and to drive or constrain poleward flux and anaphase B spindle elongation ( Valentine et al . , 2006a; Kaseda et al . , 2009; Scholey , 2009; Subramanian and Kapoor , 2012 ) . In this model , the assembly of four Kinesin-5 polypeptides into a bipolar , tetrameric minifilament is crucial , but the molecular basis of this arrangement , which is , so far as we know , unique among MT-based motor proteins , remains unclear . The kinesin superfamily of MT-based motors is organized into 14 or more families ( Vale , 2003; Lawrence et al . , 2004; Wickstead and Gull , 2006; Hirokawa et al . , 2009 ) . A characteristic feature of kinesin motors is the presence of a pair of adjacent motor domains capable of hydrolyzing ATP to walk in a hand-over-hand fashion along a MT track , thereby generating force and motion ( Vale and Milligan , 2000 ) . The adjacent motor domains are oriented by a parallel , α-helical coiled-coil rod that is capable of transmitting forces generated by the pairs of motor domains at one end of the rod to the associated cargo at the other end . The striking bipolar organization of Kinesin-5 family motor proteins means that the cargo for each motile unit ( i . e . , the pair of motor domains stepping along a MT protofilament ) is another motile unit located at the opposite end of the bipolar minifilament . Therefore , the central rod must be able to transmit forces between pairs of motor domains located at its opposite ends in order to drive or constrain MT–MT crosslinking and sliding within the mitotic spindle , but how this is accomplished remains unclear . Moreover , because the homotetrameric architecture of Kinesin-5 appears to be required for MT–MT crosslinking and for normal mitosis and cell division ( Hildebrandt et al . , 2006; Tao et al . , 2006 ) , it is plausible to think that the disruption of its bipolar , oligomeric state could inhibit the rapid divisions of proliferating cancer cells , thereby providing a strategy for cancer therapy ( Owens , 2013 ) . Therefore , understanding how Kinesin-5 adopts its bipolar architecture is an important problem whose solution could lead to medical applications in addition to advancing our understanding of the basic mechanisms of motor protein function and mitosis . Structure-function analysis of the Drosophila melanogaster Kinesin-5 , KLP61F , identified a central bipolar assembly ( BASS ) domain within the coiled-coil rod that is critical for this motor’s bipolar organization and whose deletion results in disassembled , mostly monomeric , Kinesin-5 ( Acar et al . , 2013 ) . It is likely that the ability of this domain to organize four Kinesin-5 subunits into a bipolar minifilament and to transfer forces between pairs of motor domains situated at opposite ends of a 60-nm rod is essential for Kinesin-5 activity and mitotic cell viability ( Hildebrandt et al . , 2006; Tao et al . , 2006 ) . To better understand how the BASS domain promotes the assembly of Kinesin-5 into bipolar tetramers , we determined its structure using X-ray crystallography and validated the structure using structure-based mutational analysis . Instead of the expected antiparallel arrangement of two side-by-side parallel coiled-coils proposed previously ( Kashina et al . , 1996a; Acar et al . , 2013 ) , we find that the BASS domain has a novel four-helix bundle structure , which very likely has important implications for its mechanism of action within the spindle . We purified a minimal BASS domain construct that assembles into a tetrameric , 220 Å long rod based on EM ( KLP61F residues 635–835; Figure 1 ) ( Acar et al . , 2013 ) . This BASS tetramer formed hexagonal ( P6422 ) crystals , which were used to determine its structure to 2 . 6-Å resolution using X-ray crystallography with the single wavelength anomalous dispersion ( SAD ) method from selenomethionine-and mercury-derived crystals ( Materials and methods; Table 1 ) . The asymmetric unit contains a canonical anti-parallel coiled-coil BASS domain dimer encompassing residues 640–802 of both chains , which represents the basic assembly unit of a Kinesin-5 minifilament ( Figure 1 , Figure 1—figure supplement 1 ) . This anti-parallel dimer is associated with a second dimer through a crystallographic dyad axis to form the full tetramer , whose dimer–dimer packing surface extends over a large area ( ≈7000 Å2 ) and is stabilized by sequential , alternating hydrophobic and ionic residue interfaces , each occupying three-helical turns ( Figure 2 ) . The resulting four-helix bundle , which is 22 Å wide , 26 Å high , and 260 Å long ( Figure 2A , B ) is the longest helical minifilment structure determined to date , and it displays a striking organization . It consists of a 220-Å-long central bundle that is visible by EM and matches fairly well to other anti-parallel four-helix bundles , where helical axes are separated by equal distances ( Figure 1D–E , Figure 3 , Figure 4C , panel III ) but then transitions to an asymmetric diamond-shaped bundle near its ends , where C-terminal helical axes become displaced and N-terminal helical axes are closer mediated by helical bends , termed elbows ( Figure 4 , Video 1 ) . The diamond shape helical organization at the poles brings the N-terminal ends of the helices at each pole into close juxtaposition so they emerge as parallel coiled-coil dimers , which were not resolved by EM ( Acar et al . , 2013 ) , with a 90° rotational offset with respect to each other ( Figure 4 ) . This 26-nm BASS domain lies at the center of the rigid 60-nm Kinesin-5 central rod and can direct the assembly of four KLP61F subunits into a bipolar Kinesin-5 tetramer with pairs of N-terminal motor domains on each end capable of processive hand-over-hand motility ( Figure 4; see discussion in section entitled ‘Model for the Kinesin-5 minifilament’ below ) . The unique anti-parallel BASS organization presented here is structurally novel as it does not match the initial predictions of an anti-parallel arrangement of parallel coiled-coils across the central helical rod region ( Kashina et al . , 1996a; Acar et al . , 2013 ) , yet it is entirely consistent with all previous data on the BASS domain ( Acar et al . , 2013 ) . The BASS structure may be representative of a new class of force-pliable four-helical anti-parallel junctions capable of orienting two dimeric coiled-coils in opposite directions . 10 . 7554/eLife . 02217 . 003Figure 1 . The Kinesin-5 BASS domain is an anti-parallel coiled-coil four-helix bundle that switches polypeptide partners at both ends . ( A ) Schematic domain structure of a Drosophila Kinesin-5 subunit ( KLP61F ) . The bipolar assembly ( BASS domain ) is denoted by rainbow colors . The motor domain , N-terminal coiled-coil domain , the C-terminal helical domain , and tail domain are shown in green , blue , red , and yellow respectively . ( B ) Schematic of the Kinesin-5 tetramer . ( C ) Upper panel: Gel filtration ( also known as , size exclusion or molecular sieve ) chromatography ( GFC ) of BASS and Selenium-substituted BASS ( BASS-Se ) ; lower panel , purification steps of BASS and SDS-PAGE of GFC fractions . Table 2 describes measured hydrodynamic properties of wt-BASS protein . ( D ) Negative stain electron microscopy ( EM ) of BASS tetramers . ( E ) Statistical analysis of BASS lengths measured using negative stain EM images describes an average length of 220 Å . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 00310 . 7554/eLife . 02217 . 004Figure 1—figure supplement 1 . Views of the BASS crystal unit cell displaying the packing of two BASS-dimer asymmetric units within the P6422 unit cell . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 00410 . 7554/eLife . 02217 . 005Table 1 . Crystallographic statistics table for Kinesin-5 KLP61F BASS domain structure Determination . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 005BASS NativeBASS SeMet ( Peak ) BASS-Hg ( Peak ) Data collection Resolution range ( Å ) 40 . 024–2 . 6 ( 2 . 74–2 . 6 ) 60 . 219–2 . 9 ( 3 . 06–2 . 90 ) 83 . 2–3 . 8 ( 3 . 87–3 . 80 ) * Space groupP 64 2 2P 64 2 2P 64 2 2 Wavelength ( Å ) 0 . 97950 . 97921 . 007 Unit cell ( Å ) : a , b , c138 . 65 , 138 . 65 , 105 . 79139 . 07 , 139 . 07 , 104 . 35139 . 57 , 139 . 57 , 100 . 94 Total reflections15533215207865646 Unique reflections18960 {15885}†13704 {12639}†5973 Average mosaicity0 . 521 . 091 . 10 Anomalous Multiplicity–6 . 0 ( 5 . 6 ) 6 . 2 ( 5 . 3 ) Multiplicity8 . 2 ( 8 . 5 ) 11 . 1 ( 10 . 6 ) 11 . 0 ( 10 . 2 ) * Anomalous Completeness ( % ) –100 . 0 ( 100 . 0 ) 97 . 6 ( 99 . 3 ) Completeness ( % ) 99 . 9 ( 100 . 0 ) {83 . 8}†100 . 0 ( 100 . 0 ) {92 . 4}†98 . 8 ( 99 . 4 ) * <I/σ ( I ) >10 . 8 ( 2 . 3 ) 11 . 9 ( 3 . 5 ) 18 . 3 ( 1 . 9 ) * Rmerge‡0 . 099 ( 0 . 99 ) 0 . 11 ( 0 . 68 ) 0 . 091 ( 0 . 73 ) Structure refinement Rwork0 . 22 ( 0 . 28 ) 0 . 24 ( 0 . 30 ) – Rfree0 . 25 ( 0 . 37 ) 0 . 27 ( 0 . 33 ) – Molecules per asymmetric unit22– Number of atoms23552518– Protein residues288318– Number of water molecules60– RMS bond lengths ( Å ) 0 . 0060 . 006– RMS bond angles ( ° ) 0 . 860 . 89– Ramachandran favored ( % ) 99 . 398 . 4– Ramachandran outliers ( % ) 0 . 00 . 0– Clashscore5 . 55 . 6– Mean B values ( Å2 ) Overall80 . 686 . 7– Main-chain atoms77 . 683 . 6– Side-chain atoms84 . 389 . 8– Solvent55 . 5-–Numbers represent the highest-resolution shell . †Numbers represent the truncated data after treated with ellipsoidal truncation and anisotropic scaling . ‡Rmerge = ΣhklΣi\|Ii ( hkl ) − Iav ( hkl ) \|/ΣhklΣiIi ( hkl ) . 10 . 7554/eLife . 02217 . 006Figure 2 . The crystal structure of the Kinesin-5 BASS domain tetramer: ( A ) Side view of the crystal structure of the KLP61F BASS tetramer ( residues 640–796 shown ) colored in rainbow , starting with N-termini in blue traversing to C-termini in red , respectively . Four monomers pack as anti-parallel pairs of anti-parallel coiled-coil dimers . ( B ) shows the BASS structure rotated 90° around the filament axis relative to panel A . The dimensions of the BASS tetramer bundle structure are shown . ( C ) Side view of the BASS tetramer , with two BASS anti-parallel dimers colored in blue and red , respectively . ( D ) Detailed interaction between two monomers in the BASS anti-parallel dimer . ( E ) A 60° rotated view of D . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 00610 . 7554/eLife . 02217 . 007Figure 3 . Structural comparisons of BASS structure with other four-helix bundles:BASS was compared to other four-helix bundles by superimposing α-carbon chains of these structures to the BASS structure using the lowest RMSD alignment in the program Pymol . Each of the following structures , shown in red , is superimposed onto the Kinesin-5 BASS , shown in gray: ( A ) Anti-parallel coiled-coil tetramerization domain of TrpM7 ( PDB ID: 3E7K ) . ( B ) A GCN4-like designed anti-parallel coiled-coil ( PDB ID: 1UNX ) . ( C ) A truncated neuronal SNARE complex ( PDB ID:1N7S ) ( D ) coiled-coil domain of tumor suspectibility gene 1 ( TSG1 ) ( PDB ID: 3IV1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 00710 . 7554/eLife . 02217 . 008Figure 4 . BASS tetramer consists of two regions with unique helical organizations . ( A and B ) Side view of the BASS structure , colored to mark two structural regions related by a dyad axis . The central bundle is shown in red . Two elbow regions ( shown in gold ) cause bends in N-terminal BASS helices to form the end regions . The end bundles , shown in blue , are asymmetric diamond-shaped four-helix bundles . The N-terminal helices are brought in close proximity to form parallel coiled-coils at the poles of the BASS tetramer , whereas the C-terminal helices are repositioned to be further away from the bundle center axis . Panel B is a 90-degree rotation of panel A . The lines in panel B represent regions where cross-section views of the structure are presented in part C . ( C ) Cross-section views of BASS using boundaries described in B . Panel I and V describe polar regions of the end bundles ( note that N-terminal helices are closer together ) and reveal their rotational offset by 90-degrees around the filament axis . Panels II and IV show the transition region between the central and end bundle regions with the elbows inducing a change in helical trajectory . Panel III shows a cross section of the central bundle region . Note that the helices in this region are fourfold symmetric around the central filament axis . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 00810 . 7554/eLife . 02217 . 012Video 1 . Structural organization and novel fold of BASS tetramers . This video accompanies Figure 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 012 Alternating hydrophobic and ionic interfaces specify the assembly of the four BASS monomers to form a rigid central bundle . Dimeric anti-parallel coiled-coils are stabilized by complementary hydrophobic residues between a and d positions of the heptad repeat that are guided by salt bridges in the e and g positions ( Figure 2C–E ) . The tetrameric central bundle displays a precise and unusual pattern of alternating hydrophobic and ionic interfaces between the four helices , not previously observed in other anti-parallel coiled-coil bundles ( Figures 4 , 5 , 6; Ernst and Brunger , 2003; Yadav et al . , 2005; Fujiwara and Minor , 2008; Neculai et al . . This pattern can be clearly observed by electrostatic surface representation of the BASS tetramer ( Figure 5—figure supplement 1 ) . Each interface occupies approximately three alpha-helical turns on each helix within the bundle , starting at the center with interface A of the BASS tetramer and symmetrically extending toward both ends with paired interfaces B–F ( Figures 5 and 6; Videos 2 and 3 ) . The hydrophobic central interface ( A ) is bracketed by an ionic interface ( B ) , another hydrophobic interface ( C ) , and finally another ionic interface ( D ) positioned at the ‘elbows’ marking the transition between anti-parallel four-helix bundle and parallel coiled-coils at the ends ( Figure 5; Video 2 ) . Interface A ( Figure 5 , panel IV ) is composed of Met729 , 730 and 733 from two helices and Leu725 and 726 from the anti-parallel helix . In other Kinesin-5 orthologs , conservative substitutions of the cluster of methionine residues to other bulky hydrophobic residues are universally observed ( Figure 7 ) . Interface B lies outside interface A ( Figure 5 , panel III ) , and consists of salt bridges between Arg716 and Glu715 within a single chain , and Glu715/Glu722 and Arg740 residues of anti-parallel helices . Lys737 also engages the conserved Asp723 residues of the non-partner anti-parallel helices . Arg740 and Glu722 are highly conserved and are present in other species at equivalent positions or 3–4 residues apart positioning them on the same side of the helices in the bundle ( Figure 7 ) . These ionic interfaces are solution-accessible and contain visible electron density for several ordered water and cryoprotectant molecules . On the edges of interface B , hydrophobic heptad interfaces stabilize each BASS anti-parallel dimer , involving conserved Leu725 and 718 engaging the conserved Ile736 . Next , the hydrophobic interface C is composed of Leu705 , 708 , and 712 in two helices engaging Met747 , Leu751 , and Met754 in anti-parallel helices ( Figure 5 , panel II ) . At the edge of interface C , complementary salt bridges are observed between Glu704 and 711 with His750 of the non-partner anti-parallel helices ( Figure 5 , panel II ) . Interface D lies at the elbow region , and involves two Arg761 residues engaging both Asp701 residues of the anti-parallel subunits at the center of the bundle ( Figure 5 , panel I ) . In total , these seven unique interfaces in the central bundle provide highly precise environments to form the tetramer ( Video 2 ) . Such a precise pattern of alternating complementary interfaces was not observed in other four-helical bundles , and is likely to be specifically required for the precise architecture of the Kinesin-5 bipolar minifilament . It may serve to mechanically stabilize the central rod’s organization while transmitting forces between the two pairs of N-terminal motor domains at opposite ends of the tetramer . Moreover , the ionic interfaces B and D are open molecular pockets that are critical for specifying Kinesin-5 organization and are accessible to small molecules , such as the cryoprotectants observed in our structure . These pockets provide a new potential target site for small molecules that , instead of inhibiting the activity of the conserved kinesin superfamily motor domain in the manner of monastrol and related inhibitors ( Maliga et al . , 2002 ) , may interfere with Kinesin-5 oligomerization by disrupting these salt bridges to inhibit Kinesin-5 biopolar tetrameric assembly and function during mitosis . 10 . 7554/eLife . 02217 . 009Figure 5 . The BASS central bundle is assembled through an alternating pattern of antiparallel hydrophobic and ionic interfaces . ( A ) Full side-view of the BASS structure as shown in Figure 4 , describing the regions of the BASS bundle , termed interfaces A–D . Interfaces B , C and D are twofold symmetric and extend outward from a single interface A . ( B ) Detailed side view of the left side of the central bundle region depicting interfaces D , C , B , and A , respectively . Panel ( I ) , a side view of interface D: Residues Arg761 bind Asp701 from anti-parallel helices and Arg758 binds Glu755 and Ser698 of the non-partner helices . Panel ( II ) , a side view of interface C: Leu705 , Leu708 , and Leu712 from two helices pack against Met747 , Leu751 , and Met754 of the anti-parallel helices , in a four-helical interface . Glu704 , Glu711 form salt bridges with His750 of the anti-parallel helices away from the bundle axis . Panel ( III ) , side view of interface B where Arg740 forms salt bridges with Glu715 and Glu722 of the anti-parallel helices . Lys716 forms salt bridges with Glu715 , while Lys737 forms a salt bridge to Glu723 of the non-partner anti-parallel helices . Panel ( IV ) , side view of interface A: Leu725 and Leu726 pack against Met729 , Met730 , and Met733 in four-way helical packing . ( C ) Detailed cross-section view of interfaces A , B , C , and D showing the same residues described above . Panels I–IV are cross sections of corresponding views shown in B , but rotated by either 60 , 80 or 90° across the filament axis . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 00910 . 7554/eLife . 02217 . 010Figure 5—figure supplement 1 . Surface electrostatic potential view of BASS tetramer interfaces . ( A ) Single dimer in the BASS tetramer is shown in electrostatic potential bound to a second dimer in ribbon format . Residues shown are described in Figure 5 . ( B ) 90° rotated views compared A . The surface electrostatic potential for the BASS dimer is calculated using Adaptive Poisson-Boltzmann Algorithm and displayed using a −10 to 10 threshold Kcal/mol e−1 . The electrostatic surfaces show the extremely complementary charge distribution across the surface of the two dimers at the two ionic interfaces B and D . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 01010 . 7554/eLife . 02217 . 011Figure 6 . The tetrameric BASS domain N-terminal ends swap partners to form parallel coiled-coils at the bipolar filament ends . ( A ) Full side-view of the BASS structure as shown in Figure 4 , describing the regions of the BASS bundle . Interfaces E , F and the parallel coiled-coil interface ( p-CC ) are marked . ( B ) Detailed view of the end bundle interfaces showing the left side bundle of the BASS tetramer including the p-CC and interfaces F and E , respectively . Panel ( I ) , side view of the parallel coiled-coil interfaces formed by residues in the N-terminus of BASS ( residues 650–6700 ) . Residues mediating a heptad repeat hydrophobic interaction are shown in orange and include Ile651 , Leu662 , Met665 , and Phe669 . C-terminal helices also bind this region using hydrophobic interfaces . Panel ( II ) , side view of the interface F: Tyr775 binds Tyr775 through an end-to-end ring packing , supported by His683 π–π packing against the Tyr775 ring residue . This interface positions the C-terminal helices further away from the bundle axis . The remainder of the helical bundle contains small or non-interacting residues such as Ser783 . Panel ( III ) , side view of interface E: residues Met687 , Leu691 , and Leu694 of two helices packed against Ile768 and Ile772 of the anti-parallel helices in a four-helical bundle interface . ( C ) Top-to-bottom and cross-section views of the end bundle interfaces . These views are rotated by the angle described from views shown in part B , panels I , II , III , respectively . Panel I shows a top-to-bottom view of the parallel coiled-coil of two N-terminal helices . The heptad interactions are marked a and d . In total , the ‘a’ and ‘d’ positions of three heptads are observed . Phe669 packs against Phe669 to stabilize the helical ‘swap’ in this region . Panel II is a cross-sectional view of interface F rotated 70° . Panel III shows a cross-sectional view of interface E rotated 60° . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 01110 . 7554/eLife . 02217 . 013Video 2 . The Kinesin-5 four-helical bundle is organized by alternating and symmetric hydrophobic and ionic interfaces . This video accompanies Figure 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 01310 . 7554/eLife . 02217 . 014Video 3 . At the Kinesin-5 BASS bundle ends , the N-terminal helices form parallel coiled-coils . This video accompanies Figure 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 01410 . 7554/eLife . 02217 . 015Figure 7 . BASS structure features are conserved across Kinesin-5 family: ( A ) Sequence conservation between the KLP61F BASS sequence and other Kinesin-5 orthologs . The alignment shows that many hydrophobic and ionic interfaces ( depicted in Figures 5 and 6 as A–F ) are conserved and include minor positional variations that are preserved at similar positions of the helices . ( B–H ) Structural views of interfaces A–F ( described in Figures 5 and 6 ) with sequence conservation mapped on the structure in colors corresponding to those displayed in panel A . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 015 A second striking and unexpected feature revealed by the BASS tetramer structure is the transition from this anti-parallel four-helix central bundle to an asymmetric diamond-shaped four-helix bundle near its ends ( Figures 2 , 4 , 6 and Video 3 ) . This transition promotes a swap in helical organizations , from anti-parallel coiled-coil dimers in the central bundle to parallel coiled-coil dimers at its ends . The swap transition is promoted by two helical ‘elbows’ in which Pro699 and Gly693 bend the helices by 10° which ultimately bring the N-terminal helices at each pole into close proximity , emerging as parallel coiled-coils associating directly through heptad residue packing on opposite ends with a 90° rotational offset around the filament axis ( Figures 2 , 4 and 6 ) . Near the ends of the bundle , two interfaces mediate these helical swaps: a hydrophobic interface ( E ) and a di-tyrosine interface ( F ) ( Figure 6 and video 3 ) . Interface E ( Figure 6 , panel III ) maintains a 45° rotated anti-parallel four-helix bundle organization via Met687 , Leu691 , Leu694 of two helices packing with Met768 and Ile772 of the anti-parallel helices in a four-helix hydrophobic packing interaction ( Figure 4C , panel II and V ) . Within interface F , C-terminal helices are positioned further away from center of the bundle axis , due to two Tyr residues whose rings undergo end-to-end hydrogen bonding between their hydroxyl groups . Two His683 residues of the N-terminal helices stack their imidazole rings against Tyr775 residues through π–π interactions to stabilize their end-on orientations ( Figure 6 , panel II ) . Bulky residues Tyr775 or His683 are conserved suggesting that this motif is retained with some modifications in all Kinesin-5 orthologs ( Figure 7 ) . Beyond interface F , the N-terminal helices swap organization to form parallel coiled-coils between residues 670–640 stabilized by hydrophobic a and d heptad packing interaction ( pCC interface ) . Two heptad interactions are observed between the two N-terminal helices , with two half heptads at each N- and C-terminal end . Ile651 , His658 and Met665 form the ‘d’ positions , and Asn655 , Leu662 , and Phe669 form the ‘a’ positions in these heptads . Phe669-Phe669 residues pack using π–π hydrophobic interactions that stabilize the C-termini to form parallel coiled-coil structures ( Figure 6B–C , panel I ) . In Kinesin-5 sequence alignments , the last two heptads contain a Phe residue in either a or d position , which is likely to stabilize the reorganization at the tips of the N-terminal parallel coiled-coil structures via benzyl–benzyl ring interactions ( Figure 7 ) . This suggests that the N-termini of full-length Kinesin-5 extending beyond the boundaries of the BASS tetramer likely form continuous parallel coiled-coils . To test the role of the conserved residues within interfaces A–F and the swapped parallel coiled-coils in the assembly of the BASS domain , we disrupted them by structure-based mutagenesis and assessed the oligomeric state of the resulting BASS mutant proteins using hydrodynamic analyses ( Figure 8; Tables 2 and 3 ) ( Acar et al . , 2013 ) . As expected ( Acar et al . , 2013 ) , wild-type BASS forms a monodisperse tetramer based on gel filtration chromatography and sucrose density gradient centrifugation ( Figures 1 and 8B ) . Mutations that target residues within various hydrophobic and ionic interfaces in the BASS central bundle cause defects in BASS oligomerization , observed as a shift in the protein’s elution profile from tetramers ( broken vertical line ) to monomers ( solid line ) or an intermediate , presumptively dimeric species ( Tables 2 and 3; Figure 8 ) . Strikingly , single Arg740 and Arg761 residue mutations in interfaces B and D of BASS cause potent defects in specifying BASS tetramers producing a mixture of dimer and tetramer species ( Figure 8H–I ) . In contrast , mutagenesis of the region where helix-swapping forms parallel coiled-coils at the ends of the BASS domain ( Phe669Glu BASS mutant ) remained tetrameric ( Figure 8D; Tables 2 and 3 ) . Mutagenesis of multiple interfaces across the tetramer , for example interfaces A and F ( Figure 8J–K ) ; interfaces , A , F and the parallel coiled-coil swap regions ( Figure 8L ) ; and interfaces A , B , C , D , F and the swap region ( Figure 8M ) enhanced the disruptive effects of the single mutants and produced monomeric BASS species ( Tables 2 and 3 ) . Our mutational analysis validates the proposed BASS tetrameric organization revealed by the structure by demonstrating the critical roles of the alternating interfaces in forming and stabilizing the BASS tetramer and further suggests that both hydrophobic and ionic interfaces have cumulative guiding roles in selectively organizing the BASS tetramer fold and stabilizing the Kinesin-5 tetramer minifilaments . The strong defects observed for single Arg BASS mutants ( Arg740 and 761 ) suggest that small molecules that interfere with these ionic interactions in BASS interfaces B and D can disrupt Kinesin-5 minifilament assembly and may inactivate Kinesin-5 sliding motility . 10 . 7554/eLife . 02217 . 016Figure 8 . Structure-based biochemical analysis of the BASS interfaces in stabilizing Kinesin-5 bipolar minifilaments . ( A ) Schematic view of BASS tetramer , shown in Figure 4 . The model is divided into zones marking each of the interfaces described in Figures 5 and 6 . Mutated residues are described above the model , and the interfaces described in each region are described below the model . Each of the panels below ( B–M ) includes a gel filtration chromatography elution profile on the left in which the tetramer and monomer peaks are indicated by broken and solid lines , respectively . An SDS-PAGE of the column fractions marked by volume ( mLs ) is shown on the right . A BASS degradation peak is observed under some conditions: ( B ) Wt: remains mostly tetrameric ( broken line ) . ( C ) Tyr775Arg: mainly a tetrameric ( broken line ) , with moderate amount of intermediate peak ahead of monomer peak ( solid line ) . ( D ) Phe669Glu is a tetramer . ( E ) Leu726Asp: mainly a tetrameric peak ( broken line ) , with a small intermediate peak , ahead of monomer peak ( solid line ) . ( F ) Leu726Lys: mainly a tetrameric peak ( broken line ) , with a small intermediate peak , ahead of monomer peak ( solid line ) . ( G ) Met729Glu/Met730Glu: very low tetramer peak ( broken lines ) and mostly monomer peak . ( H ) Arg761Ala: mixture of tetramer peak ( broken line ) and intermediate peak ahead of monomer peak ( solid line ) . ( I ) Arg740Ala: mixture of tetramer peak ( broken line ) and intermediate peak ahead of monomer peak ( Solid line ) . ( J ) Met729Glu/Met730Glu/Tyr775Arg: very low tetramer peak ( broken lines ) and mostly monomer peak . ( K ) Leu726Asp/Tyr775Arg: little tetramer peak ( broken lines ) and mainly intermediate peak between tetramer and monomer peak ( solid line ) . ( L ) Leu726Asp/Tyr775Arg/Phe669Glu: almost no tetramer peak ( broken lines ) , co-eluting with monomer peak ( solid line ) . ( M ) Leu726Asp-Arg740Ala-Arg761Ala-Tyr775Arg-Phe669Glu: no tetramer peak ( broken lines ) and almost completely monomer ( solid line ) . Table 3 summaries the hydrodynamic properties of BASS mutants described here . Table 2 describes hydrodynamic analysis for mutants shown in panels D and M . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 01610 . 7554/eLife . 02217 . 017Table 2 . Hydrodynamic properties of BASS and its mutantsDOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 017ProteinR-stokesS-ValueMASS/Oligomerwt5 . 524 . 0092 kDa ( tetramer ) F669E5 . 624 . 25102 kDa ( tetramer ) L725D-R740 A-R761A-Y775R-F669E3 . 772 . 0732 kDa ( monomer ) Masses were calculated from a combination of gel filtration chromatography and sucrose density gradient sedimentation . Using these approaches , the Stokes Radii ( R-Stokes ) and sedimentation value ( S-values ) were determined for each protein and used to calculate the mass . 10 . 7554/eLife . 02217 . 018Table 3 . Hydrodynamic properties of BASS and its mutants shown in Figure 8DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 018BASS proteinInterfaceElution peaksOligomerization statewtNone62 . 5 mlTetramerL726DA62 . 5 mlTetramerL726KA62 . 5 mlTetramerF669Ep-CC62 . 5 mlTetramerY775RF62 . 5 , 71 mlTetramer/dimerR761AD62 . 5 , 71 mlTetramer/dimerR740AB62 . 5 , 71 , 77 mlTetra/dimer/monomerM729E-M730EA62 . 5 , 77 mlMonomerL726D-Y775RA , F71 mlDimerM729E-M730E-Y775RA , F80 mlMonomerL726D-Y775R-F669EA , F , p-CC80 mlMonomerL726D-R740 A-R761A-Y775R-F669EA , B , D , F , pCC80 mlMonomer*The masses of these proteins were measured as described in Table 2 . To investigate how the BASS domain influences the assembly and structure of Kinesin-5 into minifilaments , we built a model of the Kinesin-5 central rod minifilament based on the structure of the BASS tetramer , Kinesin-5 sequence parameters , and the overall length of the 79-nm Kinesin-5 tetramer based on EM ( Figure 9; Materials and methods’ ) ( Kashina et al . , 1996a; Acar et al . , 2013 ) . Our model was built by superimposing dimeric parallel coiled-coils at the bipolar ends of the BASS tetramer structure ( Figure 9 ) and indicates that; ( i ) the rigid tetrameric fold of the Kinesin-5 BASS domain into bipolar tetramers dictates long-range strict structural and functional assembly of helical coiled-coils N-terminal to BASS with the helical section C-terminal to BASS ( Figure 9 ) . We modeled the Kinesin-5 motor and tail domain to lie at the ends of the central rod , consistent with EM and with them both binding MTs ( Acar et al . , 2013; van den Wildenberg et al . , 2008 ) . The anti-parallel organization of BASS dimers likely produces head-to-tail junctions at the bipolar ends of Kinesin-5 coupling two dimeric kinesin heads with the conserved C-terminal tails , by traversing in the opposite orientation within the central rod filament; and ( ii ) The Kinesin-5 sequences C-terminal to BASS may not be fully helical nor form coiled-coil structures because their length is shorter than the length of the N-terminal-to-BASS domain coiled-coils ( Figures 1 , 9 ) . Moreover , while the overall model length matches that seen by EM , the rod itself is longer than that observed , similar to the Kinesin-1 rod ( Acar et al . , 2013 ) . Further high-resolution structural studies of the longer regions of the Kinesin-5 rod filament lying outside the BASS and motor domains ( Bodey et al . , 2009 ) will be required to further refine the full Kinesin-5 bipolar tetramer model . 10 . 7554/eLife . 02217 . 019Figure 9 . Modeling the Kinesin-5 tetramer minifilament . ( A ) Model of Kinesin-5 central rod coiled-coil junction . Parallel coiled-coil structures were fit to the poles of the BASS tetramer by superimposing alpha carbons . The regions where the structures swap organization from an anti-parallel coiled-coil bundle to a parallel coiled-coil dimer are marked by arrowheads ( swaps ) . ( B ) Cartoon of a full-length Kinesin-5 minifilament based on a model for the rod structure showing the central role of the BASS tetramer in organizing the N-terminal coiled-coil registers and positioning the C-terminal region to fold onto the N-terminal coiled-coil filament . The Kinesin-5 N-terminal motor and C-terminal tail domains , both bind MTs , are organized through long range folding of the BASS tetramer at the center of the Kinesin-5 rod . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 019 The BASS domain tetramer structure is , to our knowledge , the first atomic structure of a kinesin superfamily oligomerization domain determined to date . The structure explains how Kinesin-5 assembles into bipolar , homotetrameric minifilaments , but what are the biological implications of the BASS domain structure ? Our main findings are that; ( i ) the central bundle of the BASS tetramer is a novel anti-parallel coiled-coil four-helix bundle , which is held together by a unique and alternating arrangement of hydrophobic and ionic interfaces; and ( ii ) helical bends , at the outer ends of the central bundle promote transitions into parallel coiled-coils , which are offset by 90° around the filament axis with respect to each other . One significant consequence of this structure is that the BASS tetramer orchestrates a parallel organization and rotational offset of the pairs of motor domains at each end of a Kinesin-5 minifilament resembling the organization of the two motor domains comprising the motile end of the unipolar Kinesin-1 motor . This organization may be important in allowing the adjacent motor domains on either end of a Kinesin-5 minifilament to step processively in a hand-over-hand fashion along each MT ( Vale and Milligan , 2000; Valentine et al . , 2006b; Kaseda et al . , 2009 ) , thus enabling the Kinesin-5 tetramer to slide MTs outward or to constrain their sliding apart within the mitotic spindle ( Kapitein et al . , 2005; van den Wildenberg et al . , 2008; Hentrich and Surrey , 2010; Weinger et al . 2011 ) . A second consequence is that the four helices of the BASS domain are highly intertwined , in a manner reminiscent of the plectonemic coiling of the two polynucleotide chains of a DNA double helix , far more so than the simple association of two anti-parallel side-by-side dimeric coiled-coils proposed previously ( Kashina et al . , 1996a; Acar et al . , 2013 ) . The resulting heavily intertwined four-helix bundle is likely to be torsionally stiff , and this may be crucial in setting the 90° offset between the two dimeric ends of the BASS structure . This offset could in turn contribute to the motor’s observed threefold preference for crosslinking anti-parallel vs parallel MTs ( van den Wildenberg et al . , 2008 ) but further work is required to test this idea . We hypothesize that the nature of the bundling of the BASS domain is critical for the Kinesin-5-mediated sliding filament mechanism because it functionally links the pairs of motor domains located at opposite ends of the Kinesin-5 minifilament by transmitting forces and possibly allowing allosteric signaling between them ( Figure 10 ) . Thus , we propose that the alternating pattern of interfaces revealed by the BASS structure serves not only to specify the assembly of Kinesin-5 subunits into bipolar tetramers , but also to increase central rod stability during force transmission in a manner that facilitates its ability to push apart or to restrain the sliding apart of cross-linked MTs . Measuring the magnitude of forces that the rod can transmit or withstand would be a useful test of this idea , but this is currently not technically feasible . Finally , we note that we have obtained no convincing evidence for the assembly of Kinesin-5 tetramers into higher order oligomers , and the extensive inter-twining of its four chain makes it’s dissociation into , for example , dimers during force transmission unlikely . Thus it seems reasonable to propose that the Kinesin-5 bipolar tetramer represents the functional unit of Kinesin-5 activity , consistent with motility assays ( Kapitein et al . , 2005; van den Wildenberg et al . , 2008; Weinger et al . , 2011 ) . 10 . 7554/eLife . 02217 . 020Figure 10 . The implications of BASS structure on the Kinesin-5 motility and force transfer mechanism . Schematic model of Kinesin-5 minifilament showing the potential role of BASS in force transfer between two motile ends of Kinesin-5 tetramers: the orientation of Kinesin-5 tetramers and role of BASS bipolar tetramer in transmitting the forces between two motile Kinesin-5 ends . DOI: http://dx . doi . org/10 . 7554/eLife . 02217 . 020 The conservation in amino acid sequence , structural features and length of the 4-stranded helical rod among members of the Kinesin-5 family , but not in members of other kinesin families , suggests that the properties of the BASS domain reported here are likely to be key to understanding how Kinesin-5 can either drive or constrain spindle pole separation during spindle assembly , spindle maintenance , and anaphase B spindle elongation in a broad range of organisms ( Enos and Morris , 1990; Kapitein et al . , 2005; Saunders et al . , 2007; van den Wildenberg et al . , 2008; Brust-Mascher et al . , 2009 ) . The bipolar organization of Kinesin-5 resembles that of the bipolar class 2 myosin filament that drives the sliding filament mechanism of muscle contraction , cytokinesis and other forms of motility in non-muscle cells ( Huxley , 1963; Turbedsky et al . , 2005; Billington et al . , 2013 ) . However , whereas varying numbers of myosin-2 motors can assemble into filaments of variable length , all capable of pulling actin filaments inward , we propose that the unique structure of the BASS domain allows it to organize four Kinesin-5 subunits into stable , mechanically robust bipolar tetramers of uniform length , that represent functional units capable of bearing both compressive and tensile forces as they slide cross-linked MTs outward , or resist their outward sliding . Finally , we speculate that our structure may enable the development of small molecule inhibitors targeting the ionic interfaces within the BASS central bundle , leading to the disruption of Kinesin-5 tetramer assembly and inhibiting its function in rapidly proliferating cancer cells , thereby providing a possible avenue towards therapeutic intervention ( Owens , 2013 ) . Oligonucleotide primers for shortened KLP61F ( Drosophila Kinesin-5 ) wt construct ( residues 633–835 ) were designed based on hydrodynamic and EPR analysis ( Acar et al . , 2013 ) . PCR and isothermal assembly was used to build a bacterial expression BASS construct with a C-terminal His tag and was confirmed by sequencing . Expression was performed in SoluBL21 Escherichia coli strain in 6–12 liter formats and expression induced with 0 . 5 mM IPTG overnight at 18° . Selenomethionine substituted ( Se-BASS ) BASS protein was expressed in soluBL21 E . coli strain using a metabolic labeling strategy , where growth and expression were performed using minimal media containing all amino acids but with selenomethionine replacing Met ( Van Duyne et al . , 1993 ) . BASS mutants were expressed and purified using the same methods as native BASS . Overlapping poly-oligonucleotide DNA synthesis strategy ( Life technologies , GeneArt , Germany ) was used to generate structure-based BASS mutant constructs , which were assembled with isothermal assembly and expressed using the strategy described above . BASS-containing bacterial pellets were lysed using a micro-fluidizer in ( 300 mM KCl , 50 mM HEPES , 1 mM MgCl2 , 3 mM β-mercaptoethanol with protease inhibitors including 1 mM PMSF , 1 μg/ml leupeptin , 20 μg/ml benzamidine , and 40 μg/ml Nα-p-Tosyl-L-Arg ) . The bacterial lysate was clarified by centrifugation at 18k rpm for 30 min at 4°C . Ni-NTA affinity was used to purify BASS , and passage over HiTrap Q HP cation exchange in low salt ( 70 mM KCl , 50 mM HEPES , 1 mM MgCl2 ) was used to remove contaminants where BASS eluted in the flowthrough . A second Ni-NTA affinity step was used in conjunction with 30K Amicon Filters to concentrate the BASS . The concentrated BASS tetramer was applied on a HiLoad 16/600 Superdex 200 gel filtration column using an AKTA Purifier ( GE Healthcare ) and fractions were analyzed using SDS-PAGE ( Figure 1B ) . The purified BASS protein was concentrated and used immediately for crystallization , EM and hydrodynamic analysis , or was frozen in liquid nitrogen . The mass and oligomerization state of wt or mutant BASS proteins was determined as previously described ( Acar et al . , 2013 ) . Briefly , 0 . 2 mg protein mass standards ( Ovalbumin , BSA , and Aldolase ) and 0 . 2 mg BASS protein were added at the top of 5–20% sucrose gradient prepared in Beckman 9/163 . 5 UC Tubes , and centrifuged in a Beckman Ultracentrifuge using a SW41–Ti rotor for 20 hr at 40K rpm . S-values for wt BASS or mutants were determined using a linear extrapolation from known standard S-values , calculated by identifying positions of standard and BASS proteins fractions using SDS-PAGE . S-values of BASS were combined with Stokes Radii measured using calibrated gel filtration columns ( Figure 1D ) . BASS tetramer was concentrated to 25 mg/ml for crystallization trials , which were performed using a mosquito robot ( TTPlabtech ) by mixing 100 nL drops with equal amounts of 2500 distinct solutions of home-made and commercial crystallization screens ( Qiagen ) . Multiple crystallization conditions were identified; however , 0 . 1–0 . 2 µm hexagonal-shaped crystals were obtained and refined using 4–6% ( +/− ) 2-Methyl-2 , 4-pentanediol ( MPD ) , 100 mM MES pH 6–6 . 5 . Crystals appeared in 1 week and grew to maximal size in 3 weeks . SeMet BASS protein also formed hexagonal crystals using similar conditions to native BASS . For cryo-protection , native- and SeMet BASS crystals were transferred to solutions with higher MPD concentrations or rapidly immersed in Paratone oil before freezing in liquid nitrogen by looping in nylon loops . 1 mM Mercury ( II ) nitrate was added to drop solutions containing native crystals to produce mercury-substituted crystals , which were cryo-frozen as described above . X-ray diffraction was screened using 96-position cassettes using robotic auto-mounting system at the Stanford Synchrotron Radiation Laboratory ( SSRL ) using either 14-1 or 12-2 beamlines . More than 150 native and 80 SeMet BASS crystals were screened . BASS native X-ray diffraction data were anisotropic at best , diffracting to 3 . 1 Å in one direction and 2 . 6 Å in the other directions . X-ray diffraction data were indexed with program imosfilm and scaled with Scala ( Project , 1994 ) , combined with ellipsoidal truncation and anisotropic scaling , which included 84% completeness in the overall resolution shells . We used 2 . 6 Å as the high-resolution cut-off to avoid excessive loss of completeness . Selenium edge X-ray diffraction data set was collected at 0 . 9792 Å wavelength . The diffraction was also anisotropic ( minimum Bragg spacing 2 . 9 Å , and 3 . 1 Å in the weakest , intermediate , and strongest diffracting directions ) . We used 2 . 9 Å as the high-resolution cut-off with 92% completeness in the overall resolution shells . The data set was not adequate to provide phase information directly , due to high number of Met at the hexagonal sixfold axis . Phase information was initially determined using the single anomalous dispersion ( SAD ) method , using a 3 . 8 Å Mercury diffraction data set collected at the Mercury anomalous edge at 1 . 007 Å wavelength , with the program SHELEXD ( Sheldrick 2010 ) . Five heavy atom sites were identified and refined with SHELEXD . Initial BASS electron density was used to build backbone density for short helical segments , which were then combined with the heavy atom sites to further refine phases in PHENIX suite ( Adams et al . , 2002 ) . Selenium substructures were obtained by running Phaser ( McCoy et al . , 2007 ) in its MR-SAD mode with phases from BASS-Hg poly-alanine model . These phases were subjected to automatic density modification with solvent flattening and histogram matching as implemented in PHENIX . Automatic chain tracing with the program RESOLVE yielded several helical fragments . Manual tracing in the program COOT was used to fill the gaps ( Emsley and Cowtan , 2004 ) . For the native BASS , phase was obtained by rigid body refinement using SeMet BASS as the initial model . Model building was carried out using COOT . The selenomethionine and native BASS data were refined using PHENIX program to Rfree/Rwork ( 0 . 27/0 . 24 ) and 0 . 25/0 . 22 , respectively . The stereochemical quality of the models was assessed using the program PROCHECK and MolProbity . The native BASS model includes two chains with density observed for residues 640–791 and 660–795 for the two chains . The final selenomethionine BASS model , described here , includes two BASS subunits , including residues 640–802 and 648–802 of the two chains . Residues 803–843 were disordered and not observed in the BASS structures . All structural rendering figures were generated using UCSFchimera ( Pettersen et al . , 2004 ) . The Kinesin-5 model was built using a combination of COOT , O and PyMOL programs ( DeLano , 2002 ) . We reasoned that the BASS domain is asymmetrically positioned in a phylogenetically conserved position within the Kinesin-5 primary sequence , 280 residues C-terminal to the motor domain and 120 residues N-terminal to the tail domain ( Figure 1 ) . Sequences between the BASS and motor domains ( KLP61F residues 361–640 ) are likely helical with a high coiled-coil propensity , as measured by PAIRCOILS2 , whereas sequences C-terminal to the BASS domain but upstream of the tail ( residues 796–922 ) are predicted to form short helical segments , but with very low coiled-coil prediction scores ( data not shown ) . To construct a model of the rigid Kinesin-5 central rod region , we overlaid a canonical parallel coiled-coil structure ( tropomyosin; PDB ID 1C1G ) onto the extreme N-termini of the BASS tetramer structure , where it has transitioned to a parallel coiled-coil conformation ( C-alpha r . m . s . d . = 1 . 4 Å for 14 residues overlaid per chain ) . We extended each parallel coiled-coil region by 230 residues to form a 79-nm long rod . The coordinates and structure factor data for the Selenomethionine and native Kinesin-5 BASS structures were submitted to the Protein Data Bank ( PDB ) under the ID 4PXT and 4PXU , respectively .	Successful cell division requires copies of the chromosomes containing the genetic material of a cell to be accurately copied and then separated so that when a cell divides , each new daughter cell contains exactly one copy of each chromosome . If this does not happen , the cell may malfunction or die . To separate the duplicated chromosomes , a biological machine called the mitotic spindle forms inside the cell . This has two poles , one at each end , with each pole being responsible for gathering together the chromosomes for delivery to each of the daughter cells . Large numbers of long , thin protein tubes called microtubules extend out of each pole . Some microtubules attach to the chromosomes , whilst others are responsible for pushing apart the two poles—and the chromosomes attached to them—to the opposite sides of the cell before it divides . To move the poles , motor proteins slide pairs of microtubules that are attached to opposite poles over each other . The Kinesin-5 family of motor proteins is particularly important for mitosis , because it is essential for forming the mitotic spindle and for making it work correctly . These motors assemble into motile machines that can apply a force to both of the microtubules in a sliding pair at the same time because they contain motor units at each end connected by a central rod . The structure of this central rod is crucial for the successful operation of Kinesin-5 . Scholey , Nithianantham et al . have now worked out the structure of a region of this filament called the bipolar assembly , or BASS domain . This structure is more complicated than expected: it contains four helixes made of protein that are all intertwined with each other . In addition , Scholey , Nithianantham et al . found two ‘molecular pockets’ that small molecules can access . By entering the pockets , the molecules could disrupt the structure of the BASS domain , and consequently prevent Kinesin-5 from forming the dual-ended machines required to work properly . As Kinesin-5 is required to build the mitotic spindle , this would interfere with cell division . Targeting molecules into these pockets could therefore potentially form part of an anti-cancer therapy , preventing the rapid cell divisions behind the spread of the disease .	[ "Abstract", "Introduction", "Results", "and", "discussion", "Materials", "and", "methods" ]	[ "cell", "biology", "structural", "biology", "and", "molecular", "biophysics" ]	2014	Structural basis for the assembly of the mitotic motor Kinesin-5 into bipolar tetramers
Adaptation is a salient property of sensory processing . All adaptational or gain control mechanisms face the challenge of obtaining a reliable estimate of the property of the input to be adapted to and obtaining this estimate sufficiently rapidly to be useful . Here , we explore how the primate retina balances the need to change gain rapidly and reliably when photons arrive rarely at individual rod photoreceptors . We find that the weakest backgrounds that decrease the gain of the retinal output signals are similar to those that increase human behavioral threshold , and identify a novel site of gain control in the retinal circuitry . Thus , surprisingly , the gain of retinal signals begins to decrease essentially as soon as background lights are detectable; under these conditions , gain control does not rely on a highly averaged estimate of the photon count , but instead signals from individual photon absorptions trigger changes in gain . Sensory systems encode an enormous range of input signals—for example , a rock concert is ∼1012 times louder than a just detectable whisper . Maintaining sensitivity as inputs change requires adaptational mechanisms that adjust the gain or amplification of neural signals to match their limited dynamic range to the range of the input signals a cell receives . Such gain control mechanisms operate under challenging conditions as the inputs they encounter can vary rapidly in time and locally in the space of possible stimuli . For example , visual neurons can experience >1000-fold changes in input a few times a second as the eyes move to explore a typical scene ( Frazor and Geisler , 2006 ) . Standard cameras fail to capture the full structure of such scenes because they employ a single global gain control in the form of an exposure setting , which is often poorly matched to the brightest and dimmest regions of a scene . Sensory gain controls are clearly more sophisticated . Effective gain control requires balancing the need to be rapid and local with the need to be accurate ( reviewed by Rieke and Rudd , 2009 ) . This is an example of the classic change detection problem: how many samples from a signal distribution are needed to determine whether or not the distribution has changed ( Buracas et al . , 1998; Deneve , 2008; Wark et al . , 2009 ) ? Gain control mechanisms operating near absolute visual threshold exemplify this problem . Human behavioral threshold begins to increase for backgrounds producing ∼0 . 01 isomerized rhodopsin molecules per rod per second ( R/rod/s ) ( reviewed by Donner , 1992 ) . Under these conditions , the number of photons absorbed in a small time window fluctuates widely due to the Poisson statistics that govern photon absorption . In principle , increases in behavioral threshold could solely reflect this increased noise without gain changes ( Barlow , 1965 ) . However , several studies suggest that weak backgrounds decrease the gain of retinal signals ( Donner , 1992; Brown and Rudd , 1998 ) . Retinal gain control mechanisms operating at these low light levels must work in the presence of the irreducible noise associated with random photon arrivals . At low light levels , rod-mediated signals traverse the retina through the specialized rod bipolar pathway: rods → rod bipolar cells → AII amacrine cells → cone bipolar cells → ganglion cells ( Figure 1A; for review see Bloomfield and Dacheux , 2001; Field et al . , 2005 ) . The total number of rods that convey the signal increases at each stage of this pathway ( Figure 1B ) . This convergence , together with amplification of signals by cellular and synaptic mechanisms , permits fully dark-adapted ganglion cells to generate one or more spikes to flashes producing photon absorptions in only ∼0 . 1% of the rods ( Barlow et al . , 1971; Mastronarde , 1983; P Ala-Laurila and F Rieke , unpublished ) . Left unchecked , this high gain would cause just detectable backgrounds to produce ganglion cell firing rates in excess of 100 spikes per second . Gain control mechanisms are needed to prevent such excessive firing . 10 . 7554/eLife . 00467 . 003Figure 1 . Gain control in the rod pathway . ( A ) Simplified diagram of the excitatory circuits that relay rod and cone signals to On ganglion cells . Elements exclusive to the cone pathway are shown in light colors . Chemical synapses are excitatory and are indicated by apposition of small circles or ellipses; chemical synapses in the rod bipolar circuit are found between rods and rod bipolar cells , between rod bipolar cells and AII amacrine cells , and between cone bipolar cells and ganglion cells . Electrical synapses are indicated by connected cells—for example , between cone bipolar cells and AII amacrine cells and between neighboring AII amacrine cells . ‘Gain knob’ icons indicate known or potential sites of gain control ( see ‘Introduction’ for details ) . ( B ) Table of convergence ( number of rods providing input ) and previously described gain control mechanisms at each circuit location . Convergence numbers are estimated for an On parasol ganglion cell in peripheral macaque retina . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 003 Mechanisms controlling the gain of rod-mediated signals have been identified within the phototransduction cascade in the rods themselves ( Schneeweis and Schnapf , 2000; Dunn and Rieke , 2006 ) , and at the synapse between rod bipolar cells and AII amacrine cells ( Dunn and Rieke , 2008 ) . But the greater convergence at downstream locations in the rod bipolar pathway could provide a more reliable signal to control gain . Hence , we explored how the gain of retinal signals is controlled in dim backgrounds , with three main goals: ( 1 ) to determine if the gain of retinal signals is altered by backgrounds near the onset of changes in human behavioral threshold , ( 2 ) to determine how the retinal circuit averages over space and time to detect changes in the photon flux , and ( 3 ) to identify novel sites of gain control in the rod bipolar pathway . We measured spike responses ( Figure 2A ) and excitatory synaptic inputs ( Figure 2B ) of On parasol ganglion cells in response to brief flashes superimposed on a range of backgrounds ( see also ‘Materials and methods’ and Figures 10 and 11 ) . We focused specifically on backgrounds near the onset of changes in human behavioral threshold ( 0 . 01–0 . 2 isomerizations/rod/s or R/rod/s ) . Flash strengths ( 0 . 001–0 . 006 R/rod ) were chosen to elicit responses well below saturation . Both individual responses ( Figure 2A , B ) and averages ( Figure 2C ) had a clear dependence on background . Furthermore , a cell’s excitatory synaptic input and its spike response changed similarly with changes in background ( Figure 2C ) . To quantify the effect of background on a cell’s response , we determined the response gain by dividing the response amplitude ( spike count or integrated current ) by the flash strength; the gain measures the response per R/rod . The gain of spike responses and excitatory input had a similar dependence on background ( Figure 2D ) . 10 . 7554/eLife . 00467 . 004Figure 2 . Adaptation is engaged at dim backgrounds in both excitatory inputs and spike outputs from On parasol cells . ( A ) – ( C ) responses from an individual On parasol ganglion cell to dim flashes at two different backgrounds . ( A1 ) Light stimulus ( top , 20 ms flashes at 0 s ) and raster of the cell’s spike response to several stimulus trials . ( B1 ) Excitatory synaptic currents in response to the same stimulus . ( C1 ) Mean spike rate and excitatory conductance . ( A2 ) – ( C2 ) responses to the same flash as in ( A1–C1 ) but delivered on a higher background . ( D ) Gain of the excitatory input current and spike output as a function of background for the cell in ( A–C ) . Gain of the excitatory currents was measured by integrating the current over time and dividing by the flash strength , and hence has units of nC/R/rod/s . Gain of the spike responses was measured by integrating spikes over time . Error bars are SD ( n = 20–30 trials ) . ( E ) Detection threshold ( see ‘Materials and methods’ ) calculated from excitatory input current and spike output for three different cells ( symbols ) at three backgrounds each ( gray scale ) . Dashed line represents unity . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 004 Figure 2C , D suggest that the effect of changes in background on a cell’s spike output is dominated by the changes in excitatory input . To further test this conclusion , we defined a detection threshold as the strength of the flash ( in R/rod ) eliciting a response with a signal-to-noise ratio of 1 ( see ‘Materials and methods’ ) . Noise was measured from sections of recording prior to the flash . Detection thresholds for spike responses and excitatory synaptic inputs measured in the same cell were near identical across a range of backgrounds ( Figure 2E ) . Consistent with the similarity of the spike responses and excitatory inputs , inhibitory synaptic inputs to the same stimuli were three to four times smaller than the excitatory inputs , and hence unlikely to contribute strongly to spike output ( data not shown ) . Figure 2 indicates the changes in the gain of an On parasol cell's spike output are dominated by changes in the cell's excitatory synaptic inputs . Below , we focus on how gain of the excitatory synaptic inputs is controlled . Changes in human behavioral threshold with background show several characteristic regions ( reviewed by Barlow , 1965; Donner , 1992; Rieke and Rudd , 2009 ) : ( 1 ) a ‘dark light’ region extending to ∼0 . 01 R/rod/s in which threshold is independent of background , ( 2 ) a ‘Rose–deVries’ region in which threshold increases with the square root of the background ( de Vries , 1943; Rose , 1948 ) , and ( 3 ) a ‘Weber’ region in which threshold increases proportionally with the background . Since threshold depends on both signal and noise , the relationship between these behavioral measurements and gain control is unclear . Hence , the experiments below characterize gain , noise , and threshold of the On parasol responses across a range of background intensities spanning these behaviorally defined regions . We extended experiments like those in Figure 2 to span a wide range of backgrounds ( Figure 3 ) . In this and all subsequent experiments , we adjusted the flash strength to elicit similar responses across backgrounds because the change in gain exceeded the cell’s linear response range ( see ‘Materials and methods’ and Figures 10 and 11 ) . Adjusting flash strength ( and dividing the response by the flash strength to measure gain ) allowed us to probe a ∼10 , 000-fold range of backgrounds . Across the entire range tested , the dependence of gain on background ( Figure 3A ) was well described by a modified Weber function:G=GD1+IB/I0 , where G is the gain and GD is its maximum value , IB is the background , and I0 is the background required to reduce the gain by 50% . Gain in Equation 1 is constant when IB << I0 , begins to decrease when IB nears I0 , and scales inversely with background when IB >> I0 . For the cell in Figure 3A , I0 was 0 . 076 R/rod/s ( marked by the arrowhead ) ; across cells , I0 was 0 . 08 ± 0 . 03 R/rod/s ( mean ± SD , n = 6 ) . We observed a small but consistent reduction in gain in darkness relative to its maximal value at backgrounds ∼0 . 01 R/rod/s . The reduced gain in darkness was highly dependent on the history of light exposure , and hence is only partially captured in Figure 3A because we returned to the dark condition after exposure to several backgrounds ( see ‘Recordings and light stimulation’ ) . The low dark gain reflects a nonlinear processing step that is engaged in the dark but relieved for backgrounds producing 0 . 005–0 . 01 R/rod/s; this nonlinearity and its impact on visual sensitivity is the subject of another study ( P Ala-Laurila and F Rieke , unpublished ) . 10 . 7554/eLife . 00467 . 005Figure 3 . Changes in gain , noise , and threshold with background . ( A ) – ( C ) Example data from a single cell showing gain of the dim flash response ( A ) , noise ( B ) , and threshold ( C ) as a function of background ( see ‘Materials and methods’ ) . Error bars ( A and C ) are standard deviations across two flash strengths . Colored bars indicate background segments in which the slope was computed . Dotted line ( A ) represents the fit of a modified Weber function with the half-desensitizing background indicated by an arrowhead ( see Equation 1 ) . ( D ) – ( F ) Population data reporting the slope of each parameter on a log–log scale in three background regions . Error bars are SD ( n = 4 , 6 , and 5 cells in each background segment ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 005 Two features of Figure 3A are notable . First , over a ∼1000-fold range of backgrounds gain closely followed Weber law behavior , even though multiple mechanisms contribute to gain control across this range . This indicates an orderly coordination of gain control mechanisms ( see ‘Discussion’ ) . Second , gain began to decrease for backgrounds near 0 . 01 or 0 . 02 R/rod/s—close to the onset of changes in human behavioral threshold . We summarized the onset of gain changes across cells by choosing three regions spanning equal ranges of background intensities centered below , on , or above I0 ( horizontal colored lines in Figure 3A ) . Figure 3D plots the exponent describing the dependence of gain on background ( i . e . , the slope on a log–log scale as in Figure 3A ) in each region . Gain scaled inversely with background above 0 . 3 R/rod/s , and more gradually at lower background intensities . Consistent with the ability of Equation 1 to describe the data , we did not see evidence for an extended region where gain scaled inversely with the square root of the background ( see ‘Discussion’ ) . We quantified noise at each background by projecting the current prior to the flash along the same template used to measure signal ( see ‘Analysis’ ) . Noise also changed with background , but such changes were modest across those backgrounds near the onset of changes in behavioral threshold ( Figure 3B , E ) ; specifically , noise changed <10-fold over a range of backgrounds that produced an ∼10 , 000-fold change in gain . These changes in noise with background were much less than expected from Poisson fluctuations in photon absorption and no changes in gain . If Poisson fluctuations dominated noise and gain remained constant , the standard deviation of the current should increase as the square root of the background—that is , with slope 0 . 5—since the variance of a Poisson process is proportional to the mean . Noise changed much less with background than this prediction ( Figure 3B , E ) . The largest increases in noise occurred between darkness and backgrounds <0 . 01 R/rod/s . These changes are produced by a nonlinear processing step that reduces gain in the dark ( see above; P Ala-Laurila and F Rieke , unpublished ) . The reduction in noise at backgrounds higher than 0 . 1 R/rod/s likely reflects the stronger decrease in gain at these backgrounds , which more than compensates the increase in Poisson fluctuations . Changes in noise , although modest , had a noticeable effect on detection threshold—defined as the strength of the flash eliciting a response with a signal-to-noise ratio of 1 ( Figure 3C , F; see ‘Analysis’ ) . This measure of threshold assumes a linear scaling of response with flash strength , which fails in the dark but holds for backgrounds exceeding 0 . 01 R/rod/s ( P Ala-Laurila and F Rieke , preprint ) . The threshold in Figure 3C , F corresponds to the threshold of an ideal observer of the ganglion cell's output , assuming that signal and noise are independent and additive . Thus , the threshold defined here provides a point of comparison to human behavioral threshold vs intensity curves . The threshold for On parasol responses increased for backgrounds as low as 0 . 02 R/rod/s , in close correspondence with backgrounds that produce changes in behavioral threshold ( Donner , 1992 ) . Threshold increased with a slope <1 for backgrounds between 0 . 02 and 5 R/rod/s ( Figure 3F ) . The extended region over which the slope was less than unity reflected the increase in noise at backgrounds lower than those required to produce gain changes , a region near the onset of gain changes where noise was near constant , and the decrease in noise at backgrounds where gain began to decrease proportionally with background . The experiments described above indicate that the gain of retinal signals begins to decline at backgrounds producing ∼0 . 02 R/rod/s . Gain control under these conditions is challenging because photons arrive rarely at individual rod photoreceptors; retinal responses at these backgrounds are noisy , and hence changes in background will be difficult to discern without a large degree of spatial and/or temporal averaging . At the same time , left unabated , the high gain associated with fully dark-adapted vision would cause a parasol ganglion cell to generate hundreds of spikes per second for backgrounds producing ∼0 . 02 R/rod/s . The experiments described in this section characterize several key properties of how gain is controlled—particularly the statistical feature of the input that sets gain and how the mechanism integrates over space and time . Changes in the mean background necessarily also change the magnitude of the fluctuations about the mean because of the division of light into discrete photons and the resulting statistical fluctuations in photon arrival . Such Poisson fluctuations in photon absorption are particularly large relative to the mean absorption rate at low backgrounds . Because these statistical fluctuations increase with the square root of the background , a gain control that directly depends on the magnitude of the noise would cause gain to scale inversely with the square root of the background ( Brown and Rudd , 1998 ) . The experiments below , however , indicate that gain at low backgrounds is controlled by the mean photon absorption rate and not by the magnitude of the fluctuations about the mean rate . To isolate the potential effects of fluctuations about the mean on gain , we delivered a series of flashes before , during , and after a high contrast fluctuating stimulus while maintaining a constant mean background ( Figure 4A ) . A Rose–deVries gain control mechanism based on the fluctuations about the mean predicts that gain should decrease linearly with standard deviation ( dashed line in Figure 4B ) . Instead , contrast stimuli that substantially increased the variance of a ganglion cell’s excitatory synaptic inputs tended to increase , rather than decrease , the gain of the flash response . In nine cells , stimuli that increased the standard deviation of the baseline excitatory synaptic inputs by 43 ± 17% ( mean ± SD ) produced a 18 ± 11% increase in gain ( Figure 4B ) . These data argue directly against noise controlling gain at low backgrounds , indicating instead that gain is controlled by the mean photon flux . 10 . 7554/eLife . 00467 . 006Figure 4 . Gain is controlled by the mean not the variance of the background . ( A ) Fluctuating background experiment for an example cell . From top: background , probe flashes , mean excitatory input current , mean and SEM of each flash response ( n = 10 trials ) , and standard deviation of the current . ( B ) Change in the mean of the flash response plotted against the change in the standard deviation of the baseline current caused by the fluctuating background for nine cells . Dashed line indicates the prediction for a gain control mechanism based on the noise of the background—that is , if gain was inversely proportional to the standard deviation . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 006 Gain control at low backgrounds requires averaging across rods and/or time to obtain a sufficient photon count to determine that the mean background has changed ( Rushton , 1965 ) . Too much spatial or temporal averaging could limit the effectiveness of gain control: spatial averaging across regions of a scene with different backgrounds could fail to protect retinal signals from local saturation , and temporal averaging could be ineffective if it introduces too long a delay between changes in input and changes in gain . What strategy , then , does the circuit adopt ? We start by describing the kinetics of gain changes . To test for slow changes in gain that would reflect extensive temporal averaging , we switched the background periodically between two levels while delivering flashes every second to monitor gain . The baseline firing rate and mean excitatory input changed slowly following a change in background , and had not reached an obvious steady-state level even after 200 s . Gain changes , however , occurred within a few seconds following a change in background—for example , in Figure 5—figure supplement 1 , the ∼twofold change in gain between panels B and C is largely complete by the time of the first flash response ( 1 s after the background change ) . Gain did not change noticeably in the subsequent 60–200 s ( Figure 5—figure supplement 1; see also Figures 10 and 11 ) . To resolve the initial rapid change in gain , we delivered flashes at times ranging from 0 . 1 to 2 s following either an increase or a decrease in background while measuring a cell’s excitatory synaptic inputs . We adjusted the flash strength to generate a near-constant response at the two backgrounds . Subtracting the response to the background step alone isolated responses to the flashes ( Figure 5A , bottom ) . Following an increase in background , gain fell with a time constant of 0 . 10 ± 0 . 02 s ( mean ± SEM , n = 5 ) ; following a decrease in background , gain increased with a time constant of 0 . 4 ± 0 . 1 s ( n = 6 ) . The decrease in gain at light onset tracked the development of the step response , with apparently little delay due to the gain control mechanism itself . Recovery of gain following a decrease in background took more time , as indicated by the slower kinetics at light offset . 10 . 7554/eLife . 00467 . 007Figure 5 . Kinetics of gain control . ( A ) From top: background , probe flashes , current traces for each probe flash set ( see ‘Materials and methods’ ) and for the background alone ( black ) , response obtained by subtracting the background step trace from each test response . Five test flashes where given in each epoch: two at variable times following background step onset and offset , and three at fixed times before the step , at the end of the step , and at the end of the epoch . ( B ) Gain change ( on a logarithmic scale ) as a function of the time of the probe flash following an increase or decrease in background . Lines are single exponential fits . ( C ) Time constant of the fit as in ( B ) across a population of cells . Error bars are SEM ( n = 5 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 00710 . 7554/eLife . 00467 . 008Figure 5—figure supplement 1 . Kinetics of changes in baseline and gain differ . ( A ) Baseline spike rate ( middle ) and gain of response to a brief flash ( bottom; as in Figure 2 ) . Black line ( middle ) is an exponential fit with a time constant of 97 s; lines ( bottom ) are mean gain value at each background . ( B ) and ( C ) Changes in the holding current ( middle ) and gain ( bottom ) following an increase ( B ) or decrease ( C ) in background from whole-cell recordings with a holding potential of −70 mV . Changes in holding current reflect changes in the tonic excitatory synaptic input to the cell . Gain changed within a few seconds of a change in background ( i . e . , the ∼twofold change in gain between the left and right panels is already present in the first few flash responses ) . Black lines are mean ± SEM across seven repeats of the background step . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 008 The rapid kinetics of gain changes at both light onset and offset indicate a need for considerable integration of signals across rods . For example , in the 0 . 1 s following an increase in background from 0 . 02 to 0 . 08 R/rod/s , <1 in 100 rods absorbs a photon . Thus , the observed gain changes cannot be explained by a mechanism operating on a small collection of rods; for example , a mechanism operating in single rods would only be engaged in 1 out of 100 rods , and hence could reduce overall gain at most by 1% . The halving of gain produced by this step in background requires a mechanism that integrates over at least 50 rods even if a single absorbed photon reduces gain to 0 ( Figure 6 ) . In this section , we describe experiments that probed the spatial scale of the gain control mechanism by measuring the extent to which a decrease in gain in one region of space transferred to neighboring regions . 10 . 7554/eLife . 00467 . 009Figure 6 . The trade-off between rod convergence and integration time in gain control . Rod convergence required for 50% of the neural elements to collect one or more photons is plotted as a function of background ( see ‘Materials and methods’ ) . Estimated convergence along the rod bipolar pathway is indicated along the y-axis . AII: AII amacrine cell; CB: On cone bipolar cell; RB: rod bipolar cell; RGC: peripheral parasol retinal ganglion cell . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 009 We adapted subregions within the ganglion cell receptive field while probing the gain of responses elicited at distinct locations ( Figure 7A ) . In the ‘baseline’ phase of the experiment , we flashed two different probe patterns consisting of a set of bars on a weak spatially uniform adapting background ( 0 . 02 R/rod/s ) . In the ‘adaptation’ phase of the experiment , we presented a steady adapting bar pattern ( bar intensity = 0 . 2 R/rod/s ) that overlapped exactly with one of the probe patterns ( ‘adapted’ location ) and had no overlap with the other probe pattern ( ‘unadapted’ location ) . We then repeated measurements of the responses to the two probe patterns to determine the extent to which the adapting pattern affected gain in the adapted and unadapted locations . 10 . 7554/eLife . 00467 . 010Figure 7 . Measuring the spatial scale of gain control . ( A ) Diagram of the experimental design . The baseline phase consisted of at least 20 trials each of two probe patterns flashed on a uniform dim background ( 0 . 02 R/rod/s ) . In the adaptation phase , the same two probe patterns were presented superimposed on an adapting background of bars of higher intensity ( 0 . 2 R/rod/s ) , again for at least 20 trials each . One probe location was aligned with the adapting bars ( adapted location ) , whereas the other was out of phase ( unadapted location ) . ( B ) Repeating the experiment with a variety of adapting bar sizes tests the spatial scale of adaptation . Bars up to half the diameter of the adapting subregion of the receptive field would be expected to cause a complete transfer of adaptation from the adapted to the unadapted location . Bars wider than the diameter of the adapting subregion would be expected to cause incomplete adaptation transfer . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 010 By exploring a range of widths for the adapting bars , we were able to measure the distance over which gain changes from the adapted location transferred to the unadapted location . Figure 7B illustrates the logic of the experiment . Adapting bars that are narrow compared to the adapting subregions ( circles in Figure 7B ) should produce near-identical gain changes in each subregion since the total adapting pattern will vary little between subregions . In this case , the ganglion cell should respond similarly to probes at the adapted and unadapted locations , indicating complete or near-complete transfer of gain changes . Adapting bars that are wide compared to the size of the adapting subregions should produce variable changes in gain across subregions; in this case , the ganglion cell should respond more strongly at the unadapted location , indicating incomplete transfer of gain changes . Figure 8A shows results from one such experiment . Gain changes at the adapted and unadapted location were measured by dividing responses to probes during the adaptation phase ( gray traces ) by those during the baseline phase ( black traces ) . Adaptation transfer was defined as the gain change at the unadapted location divided by the gain change at the adapted location . For the cell in Figure 8A , the narrow bars produced considerably greater adaptation transfer than the wide bars . 10 . 7554/eLife . 00467 . 011Figure 8 . The spatial scale of gain control . ( A ) Responses of an example cell to probes during the baseline phase ( black ) and the adapting phase ( gray ) of the experiment at both the adapted and unadapted locations . Adapting bars were 18 μm wide in ( A1 ) and 180 μm wide in ( A2 ) . ( B ) Adaptation transfer as a function of the width of the adapting stripes . Gray symbols are individual cells and black points are mean and SEM ( n = 7 , 9 , 7 , and 8 for bar widths in ascending order ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 011 Figure 8B collects results across bar sizes and cells . Gain changes transferred completely or near completely for bar widths up to 50 µm . Near-complete adaptation transfer at this bar width requires that the receptive field of the adapting mechanism have a diameter of at least 100 µm—so that the mechanism always samples both the bright and dark bars ( see Figure 7B ) . A receptive field of this size would contain at least 500 rods given the rod density of ∼0 . 1/µm2 ( Osterberg , 1935 ) . Bar widths of 100 µm and larger produced incomplete adaptation transfer , proving an upper bound on the extent of spatial integration . The experiments of Figures 7 and 8 indicate that gain control is mediated by a mechanism that has access to signals from hundreds of rods . This degree of spatial pooling is required to support the rapid kinetics described in Figure 5 and is present only downstream of the synapse between rod bipolar cells and AII amacrine cells ( Figure 1 ) . To further constrain the possible sites of gain control , we looked for transfer of gain changes from rod- to cone-mediated signals ( Figure 9 ) . The rod bipolar pathway shares the cone bipolar cell and its synapse onto the ganglion cell with the pathways that convey signals transduced in cones to the ganglion cells ( Figure 1A ) . Transfer of gain changes from the rod bipolar pathway to the cone pathway would indicate that gain is controlled in a circuit element shared between the two pathways—most likely the cone bipolar cell itself . Failure of gain changes to transfer would indicate that the mechanism is located at a circuit element providing input only to the rod bipolar pathway . 10 . 7554/eLife . 00467 . 012Figure 9 . Gain control in rod and cone pathways . ( A ) Response to long-wavelength light ( purple ) and matched response to short-wavelength light ( blue ) were used to derive the isolated cone response ( red ) . ( B ) Rod responses to the same flash intensity at three different backgrounds . ( C ) Cone responses recorded in the same cell as in ( B ) . ( D ) Gain of the rod and cone responses for the cell in ( B and C ) plotted against the background on a log–log scale . Gain values have been normalized by the gain at the lowest background . ( E ) Slope of the background vs gain function on a log–log scale for rod and cone responses in the same population of cells . Error bars are SEM ( n = 7 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 012 The spectral separation of the photopigments in rods and long-wavelength ( L ) cones and the considerably higher gain of rod-mediated signals allowed separation of rod- and cone-mediated inputs to a ganglion cell ( Figure 9A; Dunn et al . , 2007 ) . Long-wavelength light stimuli produced a rapid L-cone–mediated response superimposed on a slower rod-mediated response ( purple trace in Figure 9A ) . This reflects the ∼600-fold greater sensitivity of the L-cones compared to the rods for long wavelength light and a roughly equal compensating factor for the higher gain of rod-mediated signals . Stimulation with dim short-wavelength light ( <0 . 1 R/rod and <0 . 03 R/cone ) generated a nearly pure rod-mediated response ( blue trace in Figure 9A ) . Appropriate scaling of the strengths of the two flashes produced matched rod responses to long- and short-wavelength stimuli . Subtracting the rod-mediated response from the mixed response isolated the cone-mediated response ( red trace in Figure 9A ) . We separated rod- and cone-mediated responses as in Figure 9A across a range of dim backgrounds ( Figure 9B , C ) . Backgrounds that produced substantial reductions in gain of the rod-mediated signals failed to reduce the gain of the cone-mediated signals ( Figure 9B–E ) . Thus , gain changes do not transfer from the rod pathway to the cone pathway , indicating that gain is controlled at a site prior to mixing of rod and cone signals in the cone bipolar cell . The lack of transfer from rod to cone pathways , together with the requirement that the gain control mechanism have access to signals from hundreds of rods ( Figure 8 ) , makes the AII amacrine cell and/or its gap junction with On cone bipolar cells likely sites . How do the properties of gain controls at low backgrounds in primate retina relate to human behavioral threshold verses intensity ( TVI ) measurements in humans ? The region of the TVI curve in which threshold is unaffected by background light has long been associated with the intrinsic noise of the visual system ( Fechner , 1860; Barlow , 1956 ) . Threshold is increased only by backgrounds that produce a sufficient rate of photon absorption to exceed intrinsic noise . Human behavioral threshold begins to increase for backgrounds near 0 . 01 R/rod/s , similar to the backgrounds ( ∼0 . 02 R/rod/s ) at which primate ganglion cell thresholds began to increase . Both of these are considerably higher than the rate at which spontaneous activation of rhodopsin generates photon-like noise events in primate rods ( 0 . 003–0 . 004 R/rod/s ) ( GD Field and F Rieke , unpublished ) . They are , however , consistent with measures of the total noise ( i . e . , both discrete and continuous ) generated by macaque rods ( Schneeweis and Schnapf , 2000 ) . The Rose–deVries region of the TVI curve has long been assumed to reflect an increase in noise without a reduction in gain ( Barlow , 1957 ) . Consistent with this view , the gain of retinal signals measured via electroretinograms begins to decline only at backgrounds ∼fivefold higher than those that increase behavioral threshold ( Frishman et al . , 1996 ) . Several other previous experiments , however , suggest that gain is reduced in the Rose–deVries range . First , behavioral brightness matching experiments , which should be sensitive to changes in neural gain but not noise , show a robust Rose–deVries region ( Brown and Rudd , 1998 ) . Second , the spike responses of toad ganglion cells exhibit an ∼10-fold range of backgrounds over which the threshold required to reach a criterion spike rate increases in proportion to the square root of the background ( Donner et al . , 1990 ) . Experiments in cat ( Barlow and Levick , 1969; Enroth-Cugell and Shapley , 1973 ) and mouse ( Dunn et al . , 2006 ) also find adaptation at dim backgrounds , but with an exponent >0 . 5 . Our measurements of how gain control mechanisms affect both the signal and noise of the responses of On parasol ganglion cells provide a direct comparison to human psychophysics . The gain of signals in the rod pathway indeed decreased at low backgrounds , and noise increased much more slowly with increasing background than expected from Poisson fluctuations in photon absorption . The region of the ganglion cell TVI curve with slope 0 . 5 was limited to a narrow range of backgrounds . This suggests that post-retinal mechanisms contribute substantially to the more extended Rose–deVries region measured in behavioral studies . Our results place a number of constraints on the neural gain control mechanism that is engaged at the lowest backgrounds—for example , in starlight . The mechanism estimates the mean photon flux rather than its variance ( Figure 4 ) , it is rapid ( Figure 5 ) , it has access to signals from hundreds of rods ( Figure 8 ) , and it acts on rod-mediated but not cone-mediated signals ( Figure 9 ) . Previously described gain control mechanisms are sensitive to variance ( reviewed by Demb , 2008 ) , act at locations with insufficient rod convergence ( Dunn et al . , 2006; Dunn and Rieke , 2008 ) , or affect cone signals ( Dunn et al . , 2007 ) . The constraints above suggest a novel mechanism located in the AII amacrine cell or the synaptic connections it makes with cone bipolar cells ( Figure 5 ) . Recordings from these cells as well as the On cone bipolar cells to which they make gap junctions could uncover more mechanistic details . Although we focused on gain changes in the inputs to On parasol ganglion cells , a location of the site of adaptation in the rod bipolar pathway indicates that it should be shared across ganglion cell types . Consistent with this prediction , On midget ganglion cells adapted very similarly to On parasol ganglion cells ( data not shown ) . The mechanism we identify here adds to a long list of gain controls in the retinal circuitry . Considering luminance alone , retinal ganglion cells encode stimuli over 10 log units of background , far exceeding the dynamic range of any single mechanism . In the rod pathway , there are at least three gain control mechanisms engaged across backgrounds , each matched to the rod convergence at the adapting location . Backgrounds producing approximately 10–20 R/rod/s halve the gain of the phototransduction cascade in primate rods ( Schneeweis and Schnapf , 2000 ) . Backgrounds producing ∼0 . 4 R/rod/s halve the gain of the rod bipolar to AII synapse by reducing the number of releasable vesicles ( Dunn et al . , 2006; Dunn and Rieke , 2008; Oesch and Diamond , 2011 ) . And , as shown here , backgrounds ∼0 . 08 R/rod/s engage a mechanism located downstream of the rod bipolar–AII synapse; the light levels at which this mechanism is engaged are consistent with backgrounds that are just detectable given the total noise generated by macaque rods ( Schneeweis and Schnapf , 2000 ) . Impressively , these mechanisms act in concert to create a smooth dependence of gain on background in the ganglion cell ( e . g . , Figure 3A ) , even extending to backgrounds at which signaling begins to switch from rods to cones and gain control mechanisms in the cone pathway become active ( Dunn et al . , 2007 ) . At least five separate gain control mechanisms that contribute to a single smooth function relating background to gain over >5 log units on each axis ( Figure 3A ) suggest that this particular functional form for adaptation was a target of selective pressure in primate evolution . Adapting at low backgrounds is inherently dangerous as statistical fluctuations in photon absorption introduce substantial noise into the neural signals accessible to a gain control mechanism; such noise would be expected to cause gain to vary widely . The impact of noise in the signals controlling gain could be mitigated by not adapting until backgrounds increase enough to make more photons available , or by integrating over time and/or rods to reduce noise . Surprisingly , retinal gain controls appear to operate at the lowest backgrounds physically possible—that is , each absorbed photon within the pool of rods providing input to an adaptational mechanism lowers gain . Adaptation at the rod bipolar to AII synapse approaches the sensitivity limit imposed by convergence and integration time ( Dunn and Rieke , 2008; see Figure 1B ) . That is , a single absorbed photon reduces the gain of the next single photon response . Rod convergence onto AII amacrine cells is not known precisely in primate , but the onset of adaptation is consistent with a single photon absorption in the AII receptive field reducing gain . Assuming a 0 . 1 s integration time ( Figure 5 ) , a mechanism operating on a pool of 200 rods would have to reduce gain by 40% for each photon absorbed for the gain to be halved at 0 . 08 R/rod/s; a pool of 800 rods could achieve this level of gain control with a 11% gain reduction per photon ( see ‘Modeling’ ) . Vision in the dark requires high gain of the retinal circuitry conveying rod-mediated signals to ganglion cells . This high gain could easily lead to saturation of retinal signals as backgrounds increase . Indeed , the onset of retinal gain control coincides with the dimmest detectable backgrounds given the intrinsic noise of the rods . These considerations help explain why retinal gain controls are engaged by each absorbed photon , even when adaptation under these conditions is necessarily noisy . All recordings were from On parasol ganglion cells in peripheral ( eccentricity typically >30°; ∼15° to 20° for the experiments of Figure 9 ) primate retina ( Macaca nemestrina , fascicularis , and mulatta ) . Retina was obtained through the Tissue Distribution Program of the Regional Primate Center at the University of Washington . The retina was dark adapted for 1 hr while attached to the pigment epithelium and stored for up to 24 hr in a light-tight container at 32°C in Ames solution equilibrated with 95% O2/5% CO2 . All subsequent procedures were performed under infrared light ( >900 nm ) using infrared-visible converters . For recording , a roughly 4 × 4-mm piece of retina was removed from the pigment epithelium and mounted photoreceptor side down on a polylysine-coated glass coverslip forming the bottom of the recording chamber . The retina was secured by nylon wires stretched across a platinum ring . The retina was continuously superfused ( 8–10 ml/min ) with warmed ( 32–34°C ) Ames solution equilibrated with 95% O2/5% CO2 . Calibrated light stimuli were delivered from LEDs focused on the retina by the microscope condenser . Spatial stimuli for Figures 7 and 8 were delivered using an OLED array ( eMagin , Bellevue , WA ) . Stimulus intensities are given in terms of isomerizations per rod per second ( R/rod/s ) , based on the measured LED spectral output , the rod spectral sensitivity , and an assumed collecting area of 1 μm2 . Spike responses were measured in the cell-attached configuration using pipettes filled with Ames solution . Whole-cell voltage clamp recordings were made using patch pipettes filled with 105 mM CsCH3SO3 , 10 mM TEA-Cl , 20 mM HEPES , 10 mM EGTA , 5 mM Mg-ATP , 0 . 5 mM Tris-GTP , and 2 mM QX-314 ( pH ∼7 . 3 with CsOH , ∼280 mOsm ) . Reported holding voltages have been corrected for the ∼10 mV junction potential associated with this internal solution . Series resistance was typically 6–10 MΩ , and was compensated 50% . All data came from parasol ganglion cells exhibiting high sensitivity when fully dark adapted . Recordings followed previous procedures ( Trong and Rieke , 2008 ) . We first used cell-attached recordings to test the spike response to a brief flash producing 0 . 001–0 . 002 R/rod , and collected data only from cells that responded to these flashes with an average of 4–5 spikes . During experiments monitoring adaptation across a range of light levels ( e . g . , Figures 2 , 3 and 9 ) , we periodically returned to a ‘reference’ background near 0 . 05 R/rod/s to check for slow changes in sensitivity . Figures 10 and 11 illustrate the basic timeline of such experiments for cell-attached recordings of spike responses ( Figures 10 ) and whole-cell recordings of excitatory synaptic inputs ( Figures 11 ) . Figures 10B and 11B show the changes in flash strength ( gray line and right axes ) used to generate near-equal responses across backgrounds . The figures also illustrate , based on raw data , some of the key features quantified in the ‘Results’: ( 1 ) the sensitivity of gain to weak backgrounds ( Figures 10A–C and 11A–C ) ; ( 2 ) the rapid changes in gain following changes in background ( Figure 10C and 11C ) ; ( 3 ) the slow changes in baseline firing rate ( Figure 10D ) and excitatory synaptic input ( Figure 11D ) following changes in background; and ( 4 ) the changes in noise following changes in background ( Figure 11E ) . 10 . 7554/eLife . 00467 . 013Figure 10 . Gain control is stable and reproducible in spike responses . Spike responses from an example On parasol cell in primate retina . ( A ) Background light level . ( B ) Increase in spike count following a 10-ms flash ( i . e . , with the maintained spike rate before the flash subtracted ) . The test flash strength is indicated by the gray line and the right axis . Flashes were presented at 1-s intervals beginning 1 s after each background change . ( C ) Gain of the spike response in spikes per isomerization per rod ( R/rod ) . Gain was calculated by dividing the spike count in ( B ) by the flash strength . ( D ) Baseline spike rate measured prior to each flash . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 01310 . 7554/eLife . 00467 . 014Figure 11 . Gain control is stable and reproducible in excitatory input currents . ( A ) – ( D ) Excitatory synaptic currents following the same format as Figure 10 . Data are from a different cell voltage clamped at −70 mV , the approximate reversal potential for inhibitory input . ( E ) Standard deviation of the current in the time interval preceding each flash . DOI: http://dx . doi . org/10 . 7554/eLife . 00467 . 014 A time window containing the flash response was chosen for each background and flash intensity by selecting the time points at which the average response exceeded 20% of its maximal value . The response amplitude was computed as the integrated current during this time window minus a baseline current recorded before the flash . Using a fixed time window across backgrounds and flash strengths yielded similar results . Gain values in Figures 2 , 3 , 10 , and 11 and Figure 5—figure supplement 1 are the response amplitude divided by the flash strength . We projected flash responses along a template to compute flash detection thresholds ( Figures 2 and 3 ) . The template was the average current trace in the time window described above . For each trial , we defined the signal γ as the projection of the measured current along the template divided by the flash strength , and the noise ν as the projection of the current during a time window before the flash . Detection threshold θ was defined as the flash strength required to give a signal-to-noise ratio of 1 according to the following equation:θ=γ/σν . Gain and threshold of spike responses were computed analogously using the peristimulus time histogram to define the response time window for spike counts ( Figure 2 ) . Probabilities of photon absorptions depending on background , integration time , and rod convergence ( Figure 6 ) were computed from a Poisson distribution as follows . For a given background ( μ ) in units of R*/rod/s , an integration time ( τ ) , and a rod convergence ( C ) per neural element , the probability of a neural element collecting a particular number ( k ) of photons follows Poisson statistics . λ=μτCP ( k , λ ) =λke−λk ! . Figure 6 plots the combination of values of μ , τ , and C required for half of the neural elements to collect at least one photon . P ( λ ) =∑k=1∞λke−λk ! ≥0 . 5 . In the ‘Discussion’ , we compute the gain reduction achieved for several size pools of converging rods . If each photon ( k ) collected by a neural element reduces the gain for the next photon response by a constant factor ( α ) , the gain as a function of k and α can be computed as follows . γ ( k , α ) = ( 1−α ) k . Therefore , the gain of a collection of neural elements ( Γ ) collecting photons according to Poisson statistics ( normalized to Γ = 1 for maximal gain ) is computed as follows . Γ=∑k=1∞P ( k , λ ) γ ( k , α ) .	To process the sights and sounds around us , our senses must be attuned to a huge range of signals: from barely audible whispers to deafening rock concerts , and from dim glimmers of light to bright spotlights . Sensory neurons face the challenge of encoding this huge range of inputs within their much more restricted response range . Thus , neurons in our eyes and ears must continually adjust their gain or sensitivity to match changes in the light and sound inputs . These gain control processes must operate rapidly to keep up with the ever-changing input signals , but must also operate accurately so as not to distort the inputs . The trade-off between rapid and accurate gain control can be illustrated by considering how the retina processes information at low light levels . There are two main types of light-sensitive cells in the retina: rods and cones . Vision at night relies on the ability of the rods to detect single photons—the smallest unit of light . In starlight , an individual rod will register photons only rarely , and most of the time , the majority of the rods will not register any photons . Neurons in the retinal circuits that read out the rod signals receive input from hundreds or thousands of rods , and those rod inputs are highly amplified to allow detection of the responses produced when a tiny fraction of the rods absorbs a photon . But this amplification is dangerous , as it could easily saturate retinal signals when light levels increase . Gain control mechanisms are needed to avoid such saturation . Schwartz and Rieke now add to our understanding of this process by examining how the retinas of non-human primates behave in low light . They reveal that levels of background light that can only just be detected behaviorally trigger retinal gain controls; these gain controls operate when less than 1% of rods absorb a photon . Under these conditions , the physics of light itself will cause considerable variability in the stream of photons arriving at the retina , leading to high variability in the gain of retinal responses . Nonetheless , changes in gain occurred rapidly following changes in background , indicating that the underlying mechanisms spend little time averaging incident photons . Taken together , these findings will require revisiting our ideas about how adaptational mechanisms balance the competing demands of speed and reliability to help us see the world around us .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2013	Controlling gain one photon at a time
Selective elimination of unwanted synapses is vital for the precise formation of neuronal circuits during development , but the underlying mechanisms remain unclear . Using inositol 1 , 4 , 5-trisphosphate receptor type 2 knockout ( Itpr2−/− ) mice to specifically disturb somatic Ca2+ signaling in astrocytes , we showed that developmental elimination of the ventral posteromedial nucleus relay synapse was impaired . Interestingly , intracerebroventricular injection of ATP , but not adenosine , rescued the deficit in synapse elimination in Itpr2−/− mice . Further studies showed that developmental synapse elimination was also impaired in P2ry1−/− mice and was not rescued by ATP , indicating a possible role of purinergic signaling . This hypothesis was confirmed by MRS-2365 , a selective P2Y1 agonist , could also rescue the deficient of synapse elimination in Itpr2−/− mice . Our results uncovered a novel mechanism suggesting that astrocytes release ATP in an IP3R2-dependent manner to regulate synapse elimination . Synapse elimination , a process of pruning inappropriate synapses during development is essential for the formation of neuronal circuits and proper brain function ( Katz and Shatz , 1996; Paolicelli et al . , 2011; Sanes and Lichtman , 1999 ) . Disruption of this process is likely involved in many neurological diseases such as schizophrenia and autism ( Hoffman and McGlashan , 1997; Tang et al . , 2014; Tsai et al . , 2012 ) . Although activity-dependent competition among different inputs has been suggested as the key mechanisms for synapse elimination , the underlying molecular and cellular mechanisms remain unclear . In addition to the retino-geniculate and climbing fiber-Purkinje cell pathways ( Chen and Regehr , 2000; Kano and Hashimoto , 2009 ) , the principal trigeminal nucleus-ventral posteriormedial thalamic nucleus ( Pr5-VPm ) connection in mice has proven to be an excellent model in which to investigate developmental synapse elimination in the central nervous system ( Arsenault and Zhang , 2006 ) . VPm neurons receive a contra-lateral Pr5 projection and relay the information to the somatosensory cortex . At an early age like postnatal day 7 ( P7 ) , each VPm neuron receives an average of 7–8 Pr5 inputs . Most of these inputs are eliminated in an activity-dependent manner during development and the majority of VPm neurons only receive a single Pr5 input by P16 . Over the past decade , emerging evidence has suggested that astrocytes not only provide structural and metabolic support to neighboring neurons , but also participate in synapse formation , synaptic transmission , and synaptic plasticity ( Allen , 2014; Henneberger et al . , 2010; Zhang et al . , 2003 ) . Structurally , astrocytes contact 50–90% of the synapses in a given brain region ( Genoud et al . , 2006; Oliet et al . , 2001 ) and form one component of the “tripartite synapse” ( Araque et al . , 1999 ) . Functionally , intracellular Ca2+ ( [Ca2+]i ) elevation in neighboring astrocytes can be induced by activation of the synapse , triggering the release of gliotransmitters such as ATP and D-serine that modulate synaptic efficacy and plasticity ( Chen et al . , 2013; Fields and Burnstock , 2006; Henneberger et al . , 2010; Yang et al . , 2003 ) . It is generally accepted that astrocytes release gliotransmitters in a G-protein-coupled receptor/ inositol 1 , 4 , 5-trisphosphate receptor ( GPCR/IP3R ) -mediated Ca2+-dependent manner . Studies in both acute slice preparations and in vivo have shown that , in response to neuronal activity , GPCR-mediated Ca2+ signaling in astrocytes depends on the activation of IP3R2 , a subunit that is solely expressed in astrocytes ( Fiacco and McCarthy , 2004; Navarrete et al . , 2012 ) . Controversially , studies also indicated that IP3R2-mediated Ca2+ signaling in astrocytes may not be involved in the acute modulation of neuronal activity and synaptic transmission ( Agulhon et al . , 2010; Petravicz et al . , 2008 ) . Indeed , there are other Ca2+ signaling pathways in astrocytes which do not involve IP3R2 . A recent study showed that although the somatic Ca2+ signaling was eliminated , Ca2+ signaling in processes was intact in the astrocyes of Itpr2−/− mice ( Srinivasan et al . , 2015 ) . Therefore , IP3-induced Ca2+ increase is only the major source for somatic Ca2+ signaling in astrocytes . Recent studies have shown that astrocytes could mediate synapse elimination in a neuronal activity-dependent manner through two distinct pathways: either directly by phagocytosis through the MEGF10 and MERTK pathways , or by activating the classical complement cascade via astrocyte-derived transforming growth factor-β ( TGF-β ) ( Bialas and Stevens , 2013; Chung et al . , 2013 ) . Here , we hypothesized that IP3R2-dependent Ca2+ signaling in astrocytes might be involved in regulating developmental synapse elimination . The results from studies in the cerebellum suggested that Ca2+-permeableα-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors ( AMPARs ) in Bergmann glia are required for the developmental elimination of climbing fiber-Purkinje cell inputs , which provides a clue that astrocytic Ca2+ signaling might be involved in synapse elimination ( Iino et al . , 2001 ) . Unfortunately , it is difficult to draw an exclusive conclusion since Bergmann glia are morphologically distinct from astrocytes and detect synaptic activity at cerebellar synapses primarily though activation of Ca2+-permeable AMPARs as well as GPCRs . By combining electrophysiological , pharmacological , and immunohistochemical methods , we discovered that selectively disturbing [Ca2+]i signaling in astrocytes using Itpr2−/− mice impaired the developmental elimination of VPm relay synapses . Intracerebroventricular injection of ATP , but not adenosine , rescued the impaired synapse elimination in these mice . We further found that developmental synapse elimination was also impaired in P2ry1−/− mice and could not be rescued by ATP . Last , the deficit of synapse elimination in Itpr2−/− mice was rescued by intracerebroventricular injection a selective P2Y1 receptors agonist MRS-2365 . Overall , we provide direct evidence to show that astrocytes contribute to synapse elimination in an IP3R2-dependent manner through activation of purinergic signaling . To determine the role of astrocytic Ca2+ signaling in synapse elimination , we need an effective way to selectively disrupt the function of astrocytes . In mammals , astrocytes rely on IP3R2-mediated intracellular Ca2+ signaling to perform their functions . A large literature suggested that IP3R2 is the only subtype astrocytes expressed and is not expressed in microglia and neurons in the cerebrum . Thus Itpr2−/− mice could be used to study the specific roles of Ca2+ signaling in astrocytes for synapse function ( Hertle and Yeckel , 2007; Li et al . , 2015; Sharp et al . , 1999 ) . In line with previous studies , we also found that IP3R2 was co-expressed with GFAP , but not bio-markers for microglia or neurons in the brain areas we tested including hippocampus ( Figure 1—figure supplement 1 ) . Using Ca2+ imaging in acute brain slices , we next found that the ATP -induced somatic [Ca2+]i elevation in the astrocytes but not in the neurons of Itpr2−/− mice ( Figure 1—figure supplement 2b , c ) was abolished in both of the VPm and hippocampus , confirming that Ca2+ signaling was selectively impaired in astrocytes in Itpr2−/− mice . Next , we examined developmental synapse elimination by whole-cell patch recording in acute brain slices . Interestingly , we found a marked difference in the mean number of inputs received by each VPm neuron between WT and Itpr2−/− mice at P16-18 ( WT = 1 . 2 ± 0 . 02 , n = 26 cells from 4 mice; Itpr2−/− = 2 . 1 ± 0 . 10 , n = 40 cells from 6 mice; p<0 . 01 , Figure 1d ) . In WT mice , only 27% ( 7 of 26 ) of VPm relay neurons received multiple Pr5 inputs at this age ( Figure 1a , c ) , whereas most of these neurons ( 72% , 32 of 42 ) in Itpr2−/− mice received multiple Pr5 inputs ( Figure 1b , c ) . VPm relay neurons receive two major excitatory inputs: from layer VI cortex and the other from the Pr5 that express vesicular glutamate transporter 1 ( VGluT1 ) and VGluT2 , respectively ( Graziano et al . , 2008 ) . Each Pr5 input forms multiple synaptic contacts with VPm neurons and thus , the number of inputs indicates how many axonal projections while the VGluT2 staining represents number of synaptic terminals . The pruning of somatic innervations by Pr5 inputs in the VPm is always related to the elimination of VPm relay synapses , as showed by previous studies ( Takeuchi et al . , 2014; Zhang et al . , 2012 ) . To further verify that there were more synapses in KO mice , we immunostained for VGluT2 . Consistent with the electrophysiological results , we observed more VGluT2 puncta around the soma as well as the total numbers of puncta in Itpr2−/− mice ( Figure 1e , f ) , indicating that there were more synapses in KO mice . In addition , we found that neuron number did not significantly change in the VPm between WT and Itpr2−/− mice at this age ( Figure 1—figure supplement 3 ) . These results strongly suggested that astrocytic IP3R2-dependent Ca2+ signaling is required for synapse elimination . 10 . 7554/eLife . 15043 . 003Figure 1 . Developmental synapse elimination was impaired in Itpr2−/− mice at P16-17 . ( a and b ) Left panels , sample traces of membrane current in response to stimuli over a range of intensities in VPm neurons at P16-17 in WT ( a ) and Itpr2−/− mice ( b ) . Currents recorded at +40 mV are mediated by NMDA receptors , and those at -70 mV are mediated by AMPA receptors . Right panels , peak current versus stimulus intensity for WT ( a ) and Itpr2−/− mice ( b ) . ( c ) Distributions of the number of Pr5 axons innervating each VPm neuron during P16-17 in WT ( n = 26 cells ) and Itpr2−/− ( n = 42 cells ) mice . *p<0 . 001 , χ2 test . ( d ) Histogram of average number of inputs received by each VPm neuron at P16-17 in WT and Itpr2−/− mice . p<0 . 001 , unpaired Student’s t test . Error bars indicate SEM . ( e ) Sample confocal images of immunostained neurons and Pr5 axonal terminals in the VPm at P16 . Neurons were labeled by the NeuN antibody ( green ) , and Pr5 axon terminals were labeled by the VGluT2 antibody ( red ) . Inset is higher-magniﬁcation of the boxed area . Scale bar , 5 µm . ( f ) Quantification of VGluT2 puncta/soma ( left , n = 40 cells/group ) and VGluT2 puncta/neuron ( right , n = 12 sections from 4 mice/group ) for WT and Itpr2−/− mice . *p<0 . 001 , unpaired Student’s t test . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 00310 . 7554/eLife . 15043 . 004Figure 1—figure supplement 1 . Confocal images showing that IP3R2 was specifically expressed in GFAP-positive astrocytes but not GFP-positive microglia . Microglias were visualized using transgenic mice in which all microglia express GFP ( green ) under the control of the CX3CR1 promoter . Astrocytes were labeled by the specific marker GFAP ( blue ) . IP3R2 was co-localized with GFAP-positive astrocytes ( arrowheads ) but not GFP- positive microglia ( arrows ) . Scale bar , 20 µm . Images were obtained from hippocampus but not the VPm because the same antibodies ( Santa cruz , sc-7278; Millipore , AB3000 ) that worked for hippocampus did not work for VPm . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 00410 . 7554/eLife . 15043 . 005Figure 1—figure supplement 2 . Knocking out IP3R2 specifically disturbed [Ca2+]i elevation in astrocytes in both of hippocampus and the VPm . ( a ) Confocal images showing astrocytes loaded with the Ca2+ indicator Fluo-4 AM and labeled with sulforhodamine 101 ( SR101 ) in hippocampal CA1 . Merged images showed that Fluo-4-loaded cells were mainly astrocytes ( arrowheads ) . Scale bar , 20 µm . ( b ) Representative [Ca2+]i elevation in response to ATP ( 100 µM ) in astrocytes ( left ) and neurons ( right ) of hippocampus ( top ) and the VPm ( bottom ) from WT ( black line ) and Itpr2−/− ( red line ) mice . Arrow indicates ATP perfusion . ( c and d ) The peak amplitude and duration of [Ca2+]i elevation in response to ATP stimulation in neurons did not differ between Itpr2−/− and WT mice . p>0 . 05 , two-way ANOVA followed by Bonferroni post hoc test , n = 55 to 62 cellsper group . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 00510 . 7554/eLife . 15043 . 006Figure 1—figure supplement 3 . Neuron number does not change in the VPm between WT and Itpr2−/− mice . ( a ) Representative confocal images of NeuN immunostaining in the VPm of WT and Itpr2−/− mice . Scale bar , 100 µM . ml , Medial lemniscus; VPL , ventral posterolateral thalamic nucleus . ( b ) Histogram summary data showing that the neuron number is not significantly different between WT and Itpr2−/− mice ( WT = 1056 ± 56 , n = 9 sections from 3 mice; Itpr2−/− = 1031 ± 29 , n = 9 sections from 3 mice ) . p=0 . 7 , unpaired Student’s t test . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 00610 . 7554/eLife . 15043 . 007Figure 1—figure supplement 4 . Synaptic properties are not altered in Itpr2−/− mice at P16-17 . ( a ) Total input amplitudes recorded from VPm relay neurons did not differ between WT and Itpr2−/− mice ( AMPA: WT = 1 . 48 ± 0 . 17 nA , n = 20; Itpr2−/− = 1 . 49 ± 0 . 13 nA , n = 31; NMDA: WT = 0 . 78 ± 0 . 06 nA , n = 17; Itpr2−/− = 0 . 85 ± 0 . 07 nA , n = 31 ) . p>0 . 05 , two-way ANOVA . Error bars indicate SEM . ( b ) Average input amplitudes recorded from VPm relay neurons were dramatically decreased in Itpr2−/− mice ( AMPA: WT = 1 . 30 ± 0 . 18 nA , n = 21; Itpr2−/− = 0 . 84 ± 0 . 09 nA , n = 37; NMDA: WT = 0 . 64 ± 0 . 07 nA , n = 17; Itpr2−/− = 0 . 41 ± 0 . 04 nA , n = 26 ) . p<0 . 05 , two-way ANOVA . Error bars indicate SEM . ( c ) AMPAR-EPSC/NMDAR-EPSC ratio did not change in Itpr2−/− mice compared to WT mice ( WT = 1 . 97 ± 0 . 23 , n = 15; Itpr2−/− = 1 . 90 ± 0 . 14 , n = 21 ) . p=0 . 77 , unpaired Student’s t test . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 007 During development , the strengthening of immature synapses often concurrent with the removal of redundant inputs . In this study , we did not find any change in maximal AMPAR-EPSCs , N-methyl-D-aspartate receptor ( NMDA- ) EPSCs and APMAR-EPSC/NMDAR-EPSC ratios recorded at VPm relay synapses when WT mice were compared with Itpr2−/− at P16 ( Figure 1—figure supplement 4 ) . Given that the Itpr2−/− mice had more inputs and synapses , the strength of each input or individual synapse was weaker than that in WT mice ( Figure 1—figure supplement 4b ) . These data revealed that the deficit of synapse elimination was sufficient to affect synaptic strengthening during development . To distinguish whether the deficit in synapse elimination was due to the failure of developmental elimination or abnormal synaptogenesis at an early age , we tested the connectivity of the Pr5-VPm projection at P7 . We found that the number of inputs received by VPm neurons was comparable in the WT and Itpr2−/− mice ( Figure 2a–d ) . Neither the numbers of VGluT2 puncta per soma nor per neuron differed between WT and Itpr2−/− mice at this age , indicating similar numbers of synapses in these mice ( Figure 2e , f ) . These results suggested that synapse formation was normal at early postnatal stage in Itpr2−/− mice . To address the possibility that the deficit in synapse elimination we found at P16 was due to a delay in development , we assessed the synapse numbers at P30 by immunostaining for VGluT2 . We found that even at P30 , there were more Pr5-VPm synapses in Itpr2−/− mice than in WT mice ( Figure 2—figure supplement 1a , b ) . Therefore , knockout of IP3R2 disrupted developmental synapse elimination at the VPm relay synapses . 10 . 7554/eLife . 15043 . 008Figure 2 . Connectivity of Pr5-VPm pathway was comparable in WT and Itpr2−/− mice at P7 . ( a and b ) Left panels , sample traces showing membrane current in response to stimuli at a range of intensities in VPm neurons at P7 in WT ( a ) and Itpr2−/− ( b ) mice . Right panels , peak current versus stimulus intensity for WT ( a ) and Itpr2−/− mice ( b ) . ( c ) Distributions of the number of Pr5 axons innervating each VPm neuron at P7 did not differ between WT ( n = 33 cells ) and Itpr2−/− ( n = 35 cells ) mice . p=0 . 78 , χ2 test . ( d ) Histogram of average number of inputs received by each VPm neuron at P7 in WT and Itpr2−/− mice ( WT = 4 . 8 ± 0 . 3 , n = 33; Itpr2−/− = 4 . 9 ± 0 . 3 , n = 33 ) . p=0 . 74 , unpaired Student’s t test . ( e ) Sample confocal images of immunostained neurons and Pr5 axonal terminals in the VPm at P7 in WT and Itpr2−/− mice . Neurons were visualized with the NeuN antibody ( green ) , and Pr5 axonal terminals were labeled by the VGluT2 antibody ( red ) . Inset is higher-magniﬁcation of the boxed area . Scale bar , 5 µm . ( f ) Quantification of VGluT2 puncta/soma ( left , n = 24 cells/group , ) and VGluT2 puncta/neuron ( right , n = 9 sections from 3 mice/group ) for WT and Itpr2−/− mice . p>0 . 05 , unpaired Student’s t test . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 00810 . 7554/eLife . 15043 . 009Figure 2—figure supplement 1 . Deficient of synapse elimination at P30 in Itpr2−/− mice . ( a ) Sample images of neurons and VGluT2 immunostaining in P30 WT ( left panel ) and Itpr2−/− ( right panel ) mice . Neurons were visualized with the NeuN antibody ( green ) , and Pr5 axonal terminals were labeled by the VGluT2 antibody ( red ) . Inset is higher-magniﬁcation of the boxed area . Scale bar , 10 µm . ( b ) Histogram summary of VGluT2 puncta/soma ( left panel , n = 44 cells per group ) and VGluT2 puncta/neuron ( right panel , n = 9 sections from 3 mice per group ) for WT and Itpr2−/− mice at P30 . **p<0 . 001 , unpaired Student’s t test . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 009 Increasing evidence suggests that astrocytes release a number of gliotransmitters , such as glutamate , ATP , and D-serine to regulate synaptic transmission and synaptic plasticity ( Chen et al . , 2013; Henneberger et al . , 2010; Jourdain et al . , 2007 ) . Among these gliotransmitters , we were particularly interested in ATP and assumed that it might play a role in synapse elimination for the following reasons: 1 ) the basal ATP levels are reduced in Itpr2−/− mice ( Cao et al . , 2013 ) ; 2 ) astrocytes release ATP in a Ca2+-dependent manner ( Lalo et al . , 2014; Zhang et al . , 2007 ) ; and 3 ) ATP regulates synaptic plasticity ( Chen et al . , 2013 ) . We then tested whether the basal ATP level in the VPm changes during development at the time when exploratory activity increases in WT mice . A significant increase in the basal ATP level occurred at P18 compared with that at P7 in WT mice ( Figure 3a ) . This developmental up-regulation of the basal ATP level was absent in Itpr2−/− mice ( Figure 3a ) . In addition , we found that the basal ATP level was comparable between WT and Itpr2−/− mice at P7 but was significantly reduced in KO mice at P18 ( Figure 3a ) . These results suggested that , during development , the basal ATP level increases in WT but not in Itpr2−/− mice . Next , we tested whether the deficit of synapse elimination in Itpr2−/− mice could be rescued by compensatory ATP . To achieve this goal , we implanted a cannula in the left lateral ventricular of the brain ( Figure 3b ) and found that intracerebroventricular injection of ATP from P11 to P15 rescued the synapse elimination deficit ( Figure 3e , i , l , m ) . At P16-17 , unlike aCSF-treated mice , the majority of VPm neurons in Itpr2−/− mice that had received ATP ( 50 µM ) treatment was innervated by a single Pr5 input ( Figure 3l ) . Immunostaining for VGluT2 also revealed that the number of synapses significantly decreased in Itpr2−/− mice with ATP treatment ( Figure 3p , q ) . These results indicate that ATP treatment is sufficient to rescue synapse elimination deficit in Itpr2−/− mice and the removal of redundant synapses is independent of IP3R2-dependent Ca2+ signaling . Cannula implantation caused injury may induce inflammatory responses of astrocytes and microglia differentially between aCSF and ATP treated-Itpr2−/− mice , thereby affected the rescue effects . We found that reactive astrocytes and activated microglia around the site of cannula placement in aCSF and ATP treated-Itpr2−/− mice were identical ( Figure 3—figure supplement 1 ) , and ruled out this possibility . We next applied a low dose ATP ( 5 µM ) and found that the impaired synapse elimination cannot be rescued in Itpr2−/− mice , suggesting the rescue effect of ATP was in a dose-dependent manner ( Figure 3n , o ) . 10 . 7554/eLife . 15043 . 010Figure 3 . Intracerebroventricular injection of ATP from P11 to P15 rescued the synapse elimination deficit in Itpr2−/− mice . ( a ) Basal ATP levels at P8 and P17-19 in WT and Itpr2−/− mice . p<0 . 05 , two-way ANOVA followed by Bonferroni post hoc test , n = 5 mice per age . n . s , not significant . Error bars indicate SEM . ( b ) Schematic illustrating the cannula was implanted to the left lateral ventricle . ( c ) Experimental design of aCSF , ATP ( 50 µM ) , ATPγS ( 50 µM ) , and adenosine ( 50 µM ) administration and analysis . Injections were given twice per day from P11 to P15 . ( d-g ) Sample traces showing membrane current in response to stimuli with a range of intensities in VPm neurons from Itpr2−/− mice at P16-17 with a short period of aCSF ( d ) , ATP ( e ) , ATPγS ( f ) and adenosine ( g ) treatment . ( h-k ) Peak current versus stimulus intensity for aCSF ( h ) , ATP ( i ) , ATPγS ( j ) and adenosine ( k ) treatment in Itpr2−/− mice . ( l ) Distributions of the number of Pr5 axons innervating each VPm neuron at P16-17 with aCSF ( n = 29 cells ) , ATP ( n = 32 cells ) , adenosine ( n = 27 cells ) , and ATPγS ( n = 32 cells ) treatment in Itpr2−/− mice . p<0 . 01 , p<0 . 001 , χ2 test . ( m ) Histogram of average number of inputs received by each VPm neuron at P16-17 in WT and Itpr2−/− mice followed by different injections . p<0 . 001 , one-way ANOVA followed by Bonferroni post hoc test . Error bars indicate SEM . ( n ) Distributions of the number of Pr5 axons innervating each VPm neuron at P16-17 with different concentrations of ATP treatment in Itpr2−/− mice . p<0 . 001 , χ2 test . ( o ) Histogram of average number of inputs received by each VPm neuron at P16-17 in Itpr2−/− mice followed by different concentrations of ATP injection . p<0 . 001 , unpaired Student’s t test . ( p ) Sample confocal images of immunostained neurons and Pr5 axon terminals in the VPm at P16 . Inset is higher-magniﬁcation of the white boxed area . Scale bar , 5 µm . ( q ) Quantification of VGluT2 puncta/soma ( left , n = 40 cells/group ) and VGluT2 puncta/neuron ( right , n = 9 sections from 3 mice/group ) for WT , Itpr2−/− , ATP and adenosine-treated Itpr2−/− mice . p<0 . 001 , one-way ANOVA . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 01010 . 7554/eLife . 15043 . 011Figure 3—figure supplement 1 . Injury-induced inflammatory responses of astrocytes and microglia are equivalent in aCSF and ATP treated-Itpr2−/− mice . ( a ) Confocal image of a coronal brain section stained with DAPI showing the location of cannula . White dotted line indicates the cannula . Scale bar , 100 µM . ( b ) Representative confocal images illustrating reactive astrocyte and activated microglia around the site of cannula placement in aCSF and ATP treated-Itpr2−/− mice . Astrocytes were labeled by the specific marker GFAP ( green ) . Microglias were visualized by Iba1 staining ( red ) . Scale bar , 20 µM . ( c-e ) Histogram summary of the max reactive distance from the cannula ( c , n = 6 mice per group , p=0 . 93 ) , the number of reactive astrocytes ( d , n = 8 sections from 4 mice per group , p=0 . 96 ) , and the percentage of activated microglia ( e , n = 7 sections from 4 mice per group , p=0 . 09 ) near the site of cannula in aCSF and ATP treated-Itpr2−/− mice . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 011 Given that ATP is readily hydrolyzed , and to exclude the possibility that the degradation products of ATP play a role in synapse elimination , we injected the non-hydrolyzable ATP analog ATPγS and found that , ATPγS ( 50 µM ) also rescued the synapse elimination impairment in Itpr2−/− mice ( Figure 3f , j , l , m ) . These results suggested that astrocytes mediate synapse elimination via the Ca2+-dependent release of ATP . Since adenosine is a degradation product of ATP and could modulate synaptic plasticity by activating adenosine receptors ( Pascual et al . , 2005 ) , we set out to exclude the possibility that adenosine rescued the synapse elimination deficit in Itpr2−/− mice . We found intracerebroventricular injection of adenosine ( 50 µM ) throughout P11-P15 had no effect on Pr5-VPm connectivity at P16 in these mice ( Figure 3g , k , l , m ) . Thus , the synapse elimination deficit in Itpr2−/− mice was rescued by ATP , but not adenosine . Having shown that ATP rescued the synapse elimination deficit at the VPm relay synapses in Itpr2−/− mice , the next question is how this process occurs . In the CNS , ATP could activate purinegic signaling through P2X or P2Y receptors to modulate synapse function . Particularly , P2Y1 receptors have been shown participated in ATP-induced heterosynaptic long-term depression ( LTD ) in hippocampus ( Chen et al . , 2013 ) , and LTD has been suggested correlated to synapse elimination ( Bastrikova et al . , 2008; Wiegert and Oertner , 2013 ) . To test the possibility of involvement of P2Y1 receptors in synapse elimination at the VPm , we first examined the expression pattern of P2Y1 receptors and found that unlike in the hippocampus ( Zhu and Kimelberg , 2004 ) , they were only expressed in neurons in the VPm ( Figure 4—figure supplement 1 ) . We next found that ATP-induced somatic Ca2+ elevation in astrocytes was impaired in hippocampus , but not in the VPm in P2ry1−/− mice ( Figure 4—figure supplement 2 ) , further indicated that the expression pattern of P2Y1 receptors might be different in hippocampus when compared with that in the VPm . In line with immunostaining results , we also found that ATP-induced Ca2+ elevation in VPm neurons of P2ry1−/− mice was impaired ( Figure 4—figure supplement 2 ) . These results suggested that even in the VPm , the expression pattern of P2Y1 may have specificity among different cell types . Next , we determined whether synapse elimination was disrupted in P2ry1−/− mice . Compared to WT mice , the majority of VPm neurons in P2ry1−/− mice received multiple Pr5 inputs at P16 ( WT = 30% , 13 of 33 cells; P2ry1−/− = 72% , 34 of 47 cells , Figure 4g ) . Consistent with the electrophysiological results , we confirmed the synapse elimination deficit in P2ry1−/− mice with immunostaining for VGluT2 ( Figure 4i ) . There was more VGluT2 staining around the somata of VPm neurons in P2ry1−/− mice than in WT mice ( Figures 4i , j ) . Therefore , synapse elimination was also impaired in P2ry1−/− mice . To address the abnormal of synaptic connectivity in P2ry1−/− mice at P16 was actually due to developmental failure , we tested Pr5-VPm synapses at P7 . We found that the connectivity of Pr5-VPm pathway was comparable between WT and P2ry1−/− mice at P7 ( Figure 4—figure supplement 3 ) . In addition , the synapse elimination deficit in these mice was not rescued by intracerebroventricular injection of ATP from P11 to P15 ( Figure 4c , f , g , h ) . These results suggested that synapse elimination promoted by ATP may depend on P2Y1 receptors . If this is the case , one direct evidence would be the rescue of the synapse elimination defect with application of the P2Y1 agonist in Itpr2−/− mice . We next found that the deficit of synapse elimination was rescued by intracerebroventricular injection of a selective P2Y1 receptors agonist , MRS-2365 ( 20 µM ) from P11 to P15 ( Figure 5 ) . Unlike aCSF treatment , the majority of VPm neurons in Itpr2−/− mice with treatment of MRS-2365 innervated by a single Pr5 input ( Figure 5c ) . The mean number of inputs received by each VPm neuron was significantly decreased in Itpr2−/− mice with intracerebroventricular injection of MRS-2365 ( 2 . 0 ± 0 . 76 MRS-2365 versus 1 . 33 ± 0 . 48 aCSF , Figure 5d ) . Taken together , these data confirmed that ATP promotes synapse elimination via P2Y1 receptors . 10 . 7554/eLife . 15043 . 012Figure 4 . Synapse elimination was also impaired in P2ry1−/− mice at P16-17 and cannot be rescued by ATP . ( a-c ) Sample traces of membrane current in response to stimuli at a range of intensities in VPm neurons at P16-17 in WT ( a ) , P2ry1−/− ( b ) , and P2ry1−/− with ATP injection ( c ) mice . ( d-f ) Peak current versus stimulus intensity for WT ( d ) , P2ry1−/− ( e ) , and P2ry1−/− with ATP injection ( f ) mice . ( g ) Distributions of the number of Pr5 axons innervating each VPm neuron at P16-17 in WT ( n = 33 cells ) , P2ry1−/− ( n = 47 cells ) and P2ry1−/− with ATP injection ( n = 39 cells ) mice . p<0 . 01 , χ2 test . ( h ) Average number of Pr5 axonal inputs received by individual VPm relay neurons significantly increased in P2ry1−/− mice compared to WT controls . p<0 . 001 by one-way ANOVA . ( i ) Sample confocal images of immunostained neurons and Pr5 axon terminals in the VPm at P16 . Neurons were visualized with the NeuN antibody ( green ) , and Pr5 axon terminals were labeled by the VGluT2 antibody ( red ) . Inset is higher-magniﬁcation of the boxed area . Scale bar , 5 µm . ( j ) Quantification of VGluT2 puncta/soma ( left , n = 42 cells/group ) and VGluT2 puncta/neuron ( right , n = 9 sections from 3 mice/group ) in WT and P2ry1−/− mice . p<0 . 001 , unpaired Student’s t test . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 01210 . 7554/eLife . 15043 . 013Figure 4—figure supplement 1 . Expression pattern of P2Y1 receptors in the VPm . ( a ) Representative confocal images of P2Y1 immunostaining in the VPm of GFAP-GFP mice . Astrocytes were labeled using the hGFAP-GFP mice in which all astrocytes express GFP ( green ) under the control of human GFAP promoter . Arrows indicate GFP-positive astrocyte; arrowheads indicate P2Y1-positive cells . P2Y1 receptors ( red ) were not co-localized with GFP-positive astrocytes in the VPm . Scale bar , 10 µm . ( b ) Representative confocal images of P2Y1 immunostaning in the VPm of Cx3cr1 GFP/+ mice . Microglia were marked using the Cx3cr1 GFP/+ mouse line . None of the VPm microglia co-localized with P2Y1 receptors . Arrows indicate GFP-positive microglia; arrowheads indicate P2Y1-positive cells . Scale bar , 10 µm . ( c ) Representative confocal images of double-staining for NeuN and P2Y1 in the VPm of WT mice . Neurons were shown with the antibody to the neuronal marker NeuN ( green ) . P2Y1 receptors ( red ) were co-localized with VPm neurons . Arrows indicate co-localization . Scale bar , 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 01310 . 7554/eLife . 15043 . 014Figure 4—figure supplement 2 . Calcium imaging in hippocampus and the VPm of WT and P2ry1−/− mice . ( a ) Confocal images of SR101 labeled-astrocytes ( arrowhead ) and neuron ( arrow ) loaded with the Ca2+ indicator Cal-520 AM in the VPm . Scale bar , 20 µm . ( b ) Representative traces of [Ca2+]i elevation evoked by ATP ( 100 µM ) application in the VPm astrocytes ( left ) , hippocampus astrocytes ( middle ) , and VPm neurons ( right ) from WT ( black line ) and P2ry1−/− ( red line ) mice . Arrow indicates ATP perfusion . ( c and d ) Histogram summary of the peak amplitude of [Ca2+]i elevation in response to ATP stimulation in neurons and astrocytes from WT ( black ) and P2ry1−/− ( red ) mice . Cell number in each group showed above the column . p<0 . 001 , unpaired Student’s t test . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 01410 . 7554/eLife . 15043 . 015Figure 4—figure supplement 3 . The number of inputs received by each VPm neuron at P7 is equivalent between WT and P2ry1−/− mice . ( a and b ) Top: sample traces showing membrane current in response to stimuli at a range of intensities in VPm neurons at P7 in WT ( a ) and P2ry1−/− ( b ) mice . Bottom: peak current versus stimulus intensity for WT ( a ) and P2ry1−/− mice ( b ) . ( c ) Distributions of the number of Pr5 axons innervating each VPm neuron at P7 between WT ( n = 36 cells ) and P2ry1−/− ( n = 37 cells ) mice . p=0 . 48 , χ2 test . ( d ) Histogram of average number of inputs received by each VPm neuron at P7 in WT and P2ry1−/−mice ( WT = 4 . 6 ± 0 . 2 , n = 36; P2ry1−/− = 4 . 8 ± 0 . 3 , n = 37 ) . p=0 . 45 , unpaired Student’s t test . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 01510 . 7554/eLife . 15043 . 016Figure 5 . Synapse elimination was rescued by P2Y1 receptor agonist MRS-2365 in Itpr2−/− mice . ( a and b ) Left panels , sample traces of membrane current in response to stimuli over a range of intensities in VPm neurons at P16-17 in aCSF ( a ) and MRS-2365 ( b ) treated-Itpr2−/− mice . Right panels , peak current versus stimulus intensity for aCSF ( a ) and MRS-2365 ( b ) treated-Itpr2−/− mice . ( c ) Distributions of the number of Pr5 axons innervating each VPm neuron during P16-17 in aCSF ( n = 32 cells ) and MRS-2365 treated-Itpr2−/− mice ( n = 30 cells ) . p<0 . 001 , χ2 test . ( d ) Histogram of average number of inputs received by each VPm neuron at P16-17 in aCSF ( n = 32 cells ) and MRS-2365 ( n = 30 cells ) treated-Itpr2−/− mice . **p<0 . 001 , unpaired Student’s t test . Error bars indicate SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 15043 . 016 Here , by using electrophysiological recording in transgenic mice , we found that astrocytes contribute to synapse elimination through Ca2+-dependent release of ATP and activation of P2Y1 receptors . Specifically , we demonstrated that ( 1 ) developmental synapse elimination in the VPm was impaired in the Itpr2−/− mice; ( 2 ) intracerebroventricular injection of ATP and ATPγS , but not adenosine , from P11 to P15 , rescued the synapse elimination deficit in Itpr2−/− mice; ( 3 ) P2ry1−/− mice also showed impaired synapse elimination and this could not be rescued by ATP; and ( 4 ) deficient of synapse elimination in Itpr2−/− mice was rescued by intracerebroventricular injection of MRS-2365 , a selective P2Y1 receptors agonist , from P11 to P15 . In the CNS , neurons make redundant synaptic connections early in development , and most of inappropriate synapses are eliminated later to form precise neural circuitry ( Lichtman and Colman , 2000; Luo and O'Leary , 2005 ) . This process is believed to largely rely on neuronal activity , while the underlying mechanisms are still unclear . Recent studies have shown that astrocytes show a transient [Ca2+]i elevation , which depends on the GPCR/IP3R pathway , in response to neuronal activity and subsequently release gliotransmitters to modulate synaptic transmission and plasticity ( Araque et al . , 2014 ) . Therefore , astrocytic [Ca2+]i elevation is a consequence of neuronal activity . Based on these reasons , we proposed that activity-dependent competition for synapse elimination involves both postsynaptic neuronal activation and astrocytic [Ca2+]I elevation . A large literature has shown that GPCR/IP3R-dependent Ca2+ signaling is critical for astrocytes to release gliotransmitters and modulate synaptic transmission . Conversely , recent studies from McCarthy’s laboratory have suggested that IP3R2-mediated Ca2+ signaling in astrocytes may not be involved in the acute modulation of neuronal activity and synaptic transmission ( Fiacco et al . , 2007; Petravicz et al . , 2008 ) . Two studies have shown that there were still some Ca2+ fluctuations occurring in astrocytes process , although the somatic Ca2+ signaling was eliminated in Itpr2−/− mice ( Kanemaru et al . , 2014; Srinivasan et al . , 2015 ) . These results suggested that Ca2+ signaling was not totally removed in these mutants . In line with previous studies , we found that the lack of ATP-induced Ca2+ increase in astrocytes soma in these mutants . Our studies strongly suggested that astrocytic IP3R2-dependent somatic Ca2+ signaling is required for developmental synapse elimination . Considering the dynamic and complexity , the astrocytic Ca2+ signaling in the process and the soma may play different roles in gliotransmitter release and regulating neuronal functions ( Volterra et al . , 2014 ) . It is important to note that synapse elimination was only partly impaired in the Itpr2−/− and P2ry1−/− mice . Although we found both anatomical and electrophysiological evidence that these mice had significant deficits in developmental synapse elimination , some degree of synaptic pruning still occurred during P7 to P16 , suggesting that there are multiple mechanisms for controlling synapse elimination . Indeed , synaptic pruning in the VPm relay synapses occurs at several developmental stages ( Wang and Zhang , 2008 ) . Before P10 , pruning is insensitive to whisker deprivation , but then later , the elimination of redundant inputs is largely dependent on somatosensory experience . In the mutants , such as Itpr2−/− mice , the nature of sensory information transmitted from whisker to the VPm might be altered , thus causes the defects of synapse elimination . We found surprisingly , the connectivity between Pr5 and VPm in Itpr2−/− mice undergone ATP and ATPγS treatments restored to WT mice level . These results suggested that no matter sensory information had been changed or not , ATP was sufficient to rescue the impairment of synapse elimination in Itpr2−/− mice . However , the rescue effect of ATP may occur at a site between the whisker and VPm , and this possibility could not be ruled out by our study . It is about P13 that mice open their eyes and start to use their vibrissae to explore the environment . Accompanying this behavioral change , we found a significant enhancement of the basal ATP level in the VPm of WT mice between P7 and P18 ( Figure 3a ) . However , the basal ATP level failed to be up-regulated in Itpr2−/− mice during development . In line with previous studies ( Cao et al . , 2013 ) , we also found a decreased basal ATP level in the brain of Itpr2−/− mice . ATP can be released by both neurons and glia . The decreased ATP level in Itpr2−/− mice was most likely due to astrocytes since IP3R2 was not expressed in neurons and [Ca2+]i elevation did not change in neurons . Although ATP itself is a signaling molecule by activating P2X or P2Y receptors , the degraded form adenosine is also a ligand for adenosine receptors and causes synaptic depression ( Wu and Saggau , 1994 ) . The fact that adenosine injection through P11-15 did not rescue the synapse elimination deficit in Itpr2−/− mice excludes the possibility of a role for adenosine in synapse elimination . In addition to being a signal molecule , ATP is also energy currency in the brain and the phenotype of Itpr2−/− mice could be a result of global metabolic stress . This possibility is very unlikely because either ATPγS , a non-hydrolyzable ATP analog or one selective P2Y1 receptors agonist MRS-2365 could rescue the deficit of synapse elimination in Itpr2−/− mice . In our study , we concluded that ATP was not acting as an energy supply to play a role in synapse elimination . Although both of Itpr2−/− and P2ry1−/− mice showed deficits of developmental elimination of relay synapses at the VPm , it is not necessary that they are mechanistically related . That is , the phenotypes in these two mutants are two independent events . The results of P2Y1 receptors agonist injection rescued the deficient of synapse elimination in Itpr2−/− mice provided a direct linkage between IP3R2-dependent releasing of ATP and its downstream purinergic pathway . Unlike IP3R2 , studies have shown that P2Y1 receptors are widely expressed in astrocytes , microglia and neurons . Thus , loss function of P2Y1 receptors in all three types of cell may contribute to the phenomenon of impaired synapse elimination in P2ry1−/− mice . First , P2Y1 receptors have been shown to be expressed by astrocytes ( Pascual et al . , 2012 ) , therefore the finding that P2ry1−/− mice has impaired synapse elimination could be due to a failure to increase calcium levels in astrocytes in response to ATP . This possibility could be excluded by the results of astrocytes in the VPm of P2ry1-/- mice responded to ATP with a similar increase in intracellular Ca2+ when compared to cells in the WT mice ( Figure 4—figure supplement 2 ) . Additionally , we found that P2Y1 receptors were not expressed by astrocytes in the VPm ( Figure 4—figure supplement 1a ) , but abundantly expressed in the hippocampus astrocytes ( data not shown ) . Second , P2Y1 receptors have been reported to be expressed in microglia and ATP activates microglia . The phenotype of synapse elimination defects in P2ry1-/- mice could be explained by injection of ATP activates microglia-mediated synapse elimination . At present , there were some mRNA and functional clues showing that microglia expressed P2Y1 receptors , whereas the protein evidence was very few ( Fields and Burnstock , 2006 ) . In our study , we did not find any P2Y1 protein expression on microglia in both of hippocampus ( data not shown ) and the VPm ( Figure 4—figure supplement 1b ) . Therefore , the protein level of P2Y1 receptors expressed on microglia in the VPm , if any , was very low . However , we still could not totally exclude the possibility that microglia may contribute to synapse elimination through P2Y1 receptors . Further studies by using cell-type specific knockout mice are needed to answer this question . Last , ATP-mediates synapse elimination may occur through activation of neuronal P2Y1 receptors . This speculation is most likely due to P2Y1 receptors are abundantly expressed in VPm neurons ( Figure 4—figure supplement 1c ) . In addition , our Ca2+ imaging data showing that ATP-evoked Ca2+ response in VPm neurons was significantly decreased in P2ry1-/- mice . Our findings strongly suggest that ATP may activate neuronal P2Y1 receptors to promote synapse elimination . This hypothesis is supported by a previous study showing that activation of neuronal P2Y1 receptors is required for astrocytes derived ATP mediated LTD in hippocampus ( Chen et al . , 2013 ) . Due to a close linkage proposed between LTD and synapse elimination ( Wiegert and Oertner , 2013 ) , one possible model is that ATP and downstream purinergic signaling could recognize unwanted synapses . Certainly , further studies are needed to elucidate the whole picture of pathways that how activation of purinergic signal mediating synapse elimination . IP3R2-knockout ( Itpr2−/− ) mice were a kind gift from Dr . Ju Chen ( University of California , San Diego ) and maintained as a heterozygous line . Heterozygous ( Itpr2+/− ) mice were interbred to generate homozygous full-mutant mice ( Itpr2−/− ) and WT littermate controls ( Itpr2+/+ ) , which were used in experiments . Cx3cr1GFP/+ mice，hGFAP-GFP mice , and heterozygous P2ry1−/− mice were obtained from Jackson Laboratories . Sagittal slices were obtained as previously described ( Wang and Zhang , 2008 ) . Mice and littermate controls were anesthetized with sodium pentobarbital , and then perfused with ice-cold oxygenated slicing solution . The brain was removed rapidly and immersed in ice-cold slicing solution containing ( in mM ) : 110 choline chloride , 7 MgCl2·6H2O , 2 . 5 KCl , 0 . 5 CaCl2·H2O , 1 . 3 NaH2PO4 , 25 NaHCO3 , 20 glucose , saturated with 95% O2 and 5% CO2 . The brain was cut into 300 µm slices on a vibratome ( MicromHM650V ) . Slices were allowed to recover for 40 min at 32°C and then at room temperature for recording in artificial cerebrospinal fluid ( aCSF ) containing ( in mM ) : 125 NaCl , 2 . 5 KCl , 2 CaCl2·H2O , 1 . 3 MgCl2·6H2O , 1 . 3 NaH2PO4 , 25 NaHCO3 , 10 glucose . Oxygen was continuously supplied during recovery and recording . To block inhibitory synaptic transmission , picrotoxin ( 100 µM ) was added to the bath . All recordings were made at room temperature in a submerged recording chamber with constant aCSF perfusion . Patch electrodes had a resistance of 2–4 MΩ when filled with an internal solution containing ( in mM ) : 110 Cs methylsulfate , 20 TEA-Cl , 15 CsCl , 4 ATP-Mg , 0 . 3 GTP , 0 . 5 EGTA , 10 HEPES , 4 . 0 QX-314 , and 1 . 0 spermine ( pH adjusted to 7 . 2 with CsOH , 290–300 mOsm with sucrose ) . Whole-cell voltage-clamp recordings were made from the soma of VPm neurons with an Axopatch 200B amplifier and Digidata 1322A with pCLAMP 8 . 1 software ( Molecular Devices ) . Cells were visualized under an upright microscope ( BX51WI , Olympus ) with infrared optics . Signals were filtered at 2 kHz and digitized at 10 kHz . The series resistance ( Rs ) was <20 MΩ with no compensation and data were discarded when this varied by ≥ 20% . To determine the numbers of inputs to a VPm neuron , we recorded evoked EPSCs from the same cells at holding potentials of -70 and +40 mV cell over a wide range of stimulus intensity . A concentric electrode ( World Precision Instruments ) was placed on the medial lemniscal fiber bundle and stimuli ( typically from 20 to 600 µA , 100 µs ) were delivered at 0 . 1 Hz via a Grass S88 stimulator . To distinguish lemniscal synaptic responses from corticothalamic responses , paired-pulse stimulation with an interval of 100 ms was used . First , we used increments of 50–100 µA to search for step numbers . Then we used small increments of 1–10 µA near each transition point to ensure that it was actually a single step . After that , we applied a stronger stimulus , the intensity of which was at least twice than the last step . The concentration of extracellular ATP was determined with a bioluminescent ATP assay kit ( Sigma ) , as previously described ( Zhang et al . , 2007 ) . The ectonucleotidase inhibitor dipyridamole ( 10 µM ) was added to the extracellular solution throughout the experiment to decrease ATP hydrolysis . Luminescence was measured with a luminometer ( Varioskan Flash , Thermo ) according to the manufacturer’s instructions . A calibration curve was constructed from standard ATP samples and the luminescence of the incubated medium was measured as the background ATP level . To measure the ATP content in acute VPm slices , they were incubated in oxygenated ACSF for 10 min at room temperature . The ACSF was then collected for ATP assay . For quantification , the amount of protein was used for normalization and was determined using the Enhanced BCA protein assay kit ( Beyotime , China ) . Animals were anesthetized with sodium pentobarbital , and perfused with 0 . 9% saline followed by ice-cold 4% paraformaldehyde . Brains were removed and post-fixed overnight in 4% paraformaldehyde at 4°C and then transferred to 30% sucrose in PBS at 4°C for 2 days . Sagittal sections 30 or 40 µm thick were cut on a microtome ( Leica CM 1850 ) . Sections were dried , washed three times in 0 . 01M PBS and with 0 . 3% Triton-100X in 0 . 1M PBS ( 40 min ) or frozen methanol ( 10 min at -20°C ) , then blocked with 10% BSA for 1 hr at room temperature . Sections were incubated with primary antibodies as follows: VGluT2 ( guinea-pig polyclonal , 1:2000 , Millipore ) , NeuN ( monoclonal mouse , 1:500; Millipore ) , GFAP ( rabbit , 1:500 , Chemicon ) , GFAP ( monoclonal mouse , 1:500; Synaptic System ) , Iba1 ( polyclonal rabbit , 1:200; Chemicon ) , IP3R2 ( polyclonal rabbit , 1:200; Santa Cruz ) , P2Y1 ( polyclonal rabbit , 1:200; Abcom ) at 4°C for 12–24 hr . Secondary Alexa-conjugated antibodies were added at 1:1000 in 0 . 1 M PBS for 2 hr at room temperature . Images were captured using an Olympus FV-1200 inverted confocal microscope . In brief , for Ca2+ imaging in hippocampus , WT , Itpr2−/− mice , and P2ry1-/- ( P13-P16 ) were sacrificed and coronal slices of hippocampus were cut and incubated in ACSF at room temperature , saturated with 95% O2 and 5% CO2 . For Ca2+ imaging in VPm , sagittal slices containing VPm were cut . Slices were incubated with 10 µM Fluo-4/AM ( Invitrogen ) or Cal-250 ( AATBioquest ) for 45 min at room temperature in oxygenated ACSF . In these conditions , most of the loaded cells in the stratum radiatum and VPm were astrocytes as confirmed by SR101 staining . CA1 , dentate gyrus , and VPm neurons were also loaded in these conditions . Before imaging , slices were transferred to stain-free ACSF for at least 10 min to wash out the stain . Images were acquired every 3 s using an Olympus FV-1200 confocal microscope . Average fluorescence intensity was measured from analysis boxes over the cell-bodies of astrocytes and neurons . When the baseline was stable , ATP ( 100 µM ) was added to evoke intracellular Ca2+ release from astrocytes and neurons . Increases in the fluorescence intensity over baseline were calculated for each trace and are reported as ΔF/F0 . Confocal images were collected with a 60× objective ( NA 1 . 3 , water ) on an Olympus FV-1200 inverted confocal microscope . Z-stacks were obtained by 10 consecutive steps with 0 . 5 µm interval thickness . To determine the VGluT2 puncta on the soma of a thalamic VPm neuron , data were opened with Fluoview software ( Olympus ) and then exported as 8-bit tiff files . Neurons were randomly selected and VGluT2 puncta were counted manually using the Cell Counter plugin of ImageJ . In most cases , the VGlut2 puncta are separated from each other . Sometimes there are multiple puncta that stay very close , but we can still discriminate them by the little gaps among them . Occasionally , there is a big puncta that has no gap , which is then referred as one puncta . We count the puncta that contact the NeuN as puncta/soma . To quantify the average VGluT2 puncta per neuron , the density of VGluT2 puncta was analyzed using the Analyze Particles command in ImageJ ( version 1 . 43 ) . Thalamic VPm neuron number was counted manually using the Cell Counter plugin of ImageJ . The puncta/neuron is then determined by all the puncta number divided by all neuron number in a given image . For quantification of neuron density , 9 sections from 3 different animals ( 3 sections per animal ) were used . The VPm was acquired by using a 10×objective ( NA 0 . 4 ) . Thalamic VPm neuron numbers were counted manually by using the Cell Counter plugin of Image J . To calculate the density of neurons , only the area of the VPm was measured . Mice at P10 were deeply anaesthetized with isoflurane and placed into a stereotactic apparatus . A 30-gauge stainless steel infusion cannula ( RWD life science ) was implanted unilaterally in the left lateral ventricle ( -0 . 4 mm from bregma , 0 . 9 mm lateral from midline , and 2 . 5 mm vertical from the cortical surface ) and was fixed to the skull with dental cement . After one day recovery , drugs including aCSF , ATP ( 5 or 50 µM , 2 µl ) , adenosine ( 50 µM , 2 µl ) , ATPγS ( 50 µM , 2 µl ) , MRS-2365 ( 20 µM , 2 µl ) were delivered to lateral ventricle slowly by using a microsyringe pump which was connected to the infusion cannula by a PE20 tube . Injections were given twice per day at the interval of 12 hr from P11 to P15 . After experiments , the location of the cannula was verified histologically and discarded the animal which did not target to lateral ventricle . To analysis the inflammatory responses of astrocytes and microglia , fixed sections were immunolabeled for GFAP and Iba1 . For quantification the maximum distance of reactive cells to the injury site , three images were collected from each animal on a confocal microscopy by using 1 µm z-step . Astrocytes in the cortex express a low level of GFAP in normal situation . They were very sensitive to injury as indicated by an increased GFAP expression . Therefore , the distance of GFAP positive cells to injury site was measured as the distance of inflammatory responses . For quantification of reactive astrocyte , the number of GFAP positive cells within 150 µm from the injury site was counted . For quantification of microglia activation , the number of Iba1+ microglia within 150 µm from the injury site was counted . Then the percentage of amedoid-like activated microglia was calculated . All results are presented as mean ± SEM . GraphPad Prism 5 ( La Jolla , CA ) software was used for all statistical analyses . Differences between two groups were evaluated by un-paired or paired Student’s t-test . For multi-group comparisons , one-way ANOVA with Tukey’s multiple-comparisons test was used . Two-way ANOVA followed by the Bonferroni post hoc test was used for Figure 3A . Frequency distributions were analyzed with χ2 test . The significance level was set at p<0 . 05 .	Neighbouring neurons connect to each other and share information through structures known as synapses . As the brain develops , many synapses turn out to be redundant . Just like trees in a garden that need to be trimmed , these redundant synapses must be pruned in order to form the right pattern of connections between different neurons . Brain cells called astrocytes play a key role in synaptic pruning , but it is unclear exactly how astrocytes coordinate this process . One important way in which astrocytes communicate with neurons is through a process called calcium signaling , in which the movement of calcium ions into or out of the cell sets off a cascade of activity inside the astrocytes . Yang et al . have now studied developing mice that lacked a gene that is essential for calcium signaling in astrocytes . Two weeks after they were born , these mice still had redundant synapses that are normally lost after birth . However , injecting the developing brain with a substance called ATP prevented this defect and allowed synapses to be correctly pruned . This is likely to be because astrocytes also use ATP to communicate with neurons , and ATP compensated for the missing calcium signaling . The experiments also uncovered the specific structure – called the P2Y1 receptor – on the outer surface of a neuron that ATP latches on to in order to help remove synapses . Further work is now needed to reveal how activating the P2Y1 receptor coordinates synaptic removal .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2016	Astrocytes contribute to synapse elimination via type 2 inositol 1,4,5-trisphosphate receptor-dependent release of ATP
Statistical analysis of evolutionary-related protein sequences provides information about their structure , function , and history . We show that Restricted Boltzmann Machines ( RBM ) , designed to learn complex high-dimensional data and their statistical features , can efficiently model protein families from sequence information . We here apply RBM to 20 protein families , and present detailed results for two short protein domains ( Kunitz and WW ) , one long chaperone protein ( Hsp70 ) , and synthetic lattice proteins for benchmarking . The features inferred by the RBM are biologically interpretable: they are related to structure ( residue-residue tertiary contacts , extended secondary motifs ( α-helixes and β-sheets ) and intrinsically disordered regions ) , to function ( activity and ligand specificity ) , or to phylogenetic identity . In addition , we use RBM to design new protein sequences with putative properties by composing and 'turning up' or 'turning down' the different modes at will . Our work therefore shows that RBM are versatile and practical tools that can be used to unveil and exploit the genotype–phenotype relationship for protein families . In recent years , the sequencing of many organisms' genomes has led to the collection of a huge number of protein sequences , which are catalogued in databases such as UniProt or PFAM Finn et al . , 2014 ) . Sequences that share a common ancestral origin , defining a family ( Figure 1A ) , are likely to code for proteins with similar functions and structures , providing a unique window into the relationship between genotype ( sequence content ) and phenotype ( biological features ) . In this context , various approaches have been introduced to infer protein properties from sequence data statistics , in particular amino-acid conservation and coevolution ( correlation ) ( Teppa et al . , 2012; de Juan et al . , 2013 ) . A major objective of these approaches is to identify positions carrying amino acids that have critical impact on the protein function , such as catalytic sites , binding sites , or specificity-determining sites that control ligand specificity . Principal component analysis ( PCA ) of the sequence data can be used to unveil groups of coevolving sites that have a specific functional role Russ et al . , 2005; Rausell et al . , 2010; Halabi et al . , 2009 . Other methods rely on phylogeny Rojas et al . , 2012 , entropy ( variability in amino-acid content ) Reva et al . , 2007 , or a hybrid combination of both Mihalek et al . , 2004; Ashkenazy et al . , 2016 . Another objective is to extract structural information , such as the contact map of the three-dimensional fold . Considerable progress was brought by maximum-entropy methods , which rely on the computation of direct couplings between sites reproducing the pairwise coevolution statistics in the sequence data Lapedes et al . , 1999; Weigt et al . , 2009; Jones et al . , 2012; Cocco et al . , 2018 . Direct couplings provide very good estimators of contacts Morcos et al . , 2011; Hopf et al . , 2012; Kamisetty et al . , 2013; Ekeberg et al . , 2014 and capture the pairwise epistasis effects necessary to model the fitness changes that result from mutations Mann et al . , 2014; Figliuzzi et al . , 2016; Hopf et al . , 2017 . Despite these successes , we still do not have a unique , accurate framework that is capable of extracting the structural and functional features common to a protein family , as well as the phylogenetic variations specific to sub-families . Hereafter , we consider Restricted Boltzmann Machines ( RBM ) for this purpose . RBM are a powerful concept coming from machine learning Ackley et al . , 1987; Hinton , 2012; they are unsupervised ( sequence data need not be annotated ) and generative ( able to generate new data ) . Informally speaking , RBM learn complex data distributions through their statistical features ( Figure 1B ) . In the present work , we have developed a method to train efficiently RBM from protein sequence data . To illustrate the power and versatility of RBM , we have applied our approach to the sequence alignments of 20 different protein families . We report the results of our approach , with special emphasis on four families — the Kunitz domain ( a protease inhibitor that is historically important for protein structure determination Ascenzi et al . , 2003 , the WW domain ( a short module binding different classes of ligands ( Sudol et al . , 1995 , Hsp70 ( a large chaperone protein Bukau and Horwich , 1998 ) , and lattice-protein in silico data Shakhnovich and Gutin , 1990; Mirny and Shakhnovich , 2001 — to benchmark our approach on exactly solvable models Jacquin et al . , 2016 . Our study shows that RBM are able to capture: ( 1 ) structure-related features , be they local ( such as tertiary contacts ) , extended such as secondary structure motifs ( α-helix and β-sheet ) ) or characteristic of intrinsically disordered regions; ( 2 ) functional features , that is groups of amino acids controling specificity or activity; and ( 3 ) phylogenetic features , related to sub-families sharing evolutionary determinants . Some of these features involve only two residues ( as direct pairwise couplings do ) , others extend over large and not necessarily contiguous portions of the sequence ( as in collective modes extracted with PCA ) . The pattern of similarities of each sequence with the inferred features defines a multi-dimensional representation of this sequence , which is highly informative about the biological properties of the corresponding protein ( Figure 1C ) . Focusing on representations of interest allows us , in turn , to design new sequences with putative functional properties . In summary , our work shows that RBM offer an effective computational tool that can be used to characterize and exploit quantitatively the genotype–phenotype relationship that is specific to a protein family . The biological interpretation of the features inferred by the RBM guides us to sample new sequences 𝐯 with putative functionalities . In practice , we sample from the conditional distribution P⁢ ( 𝐯\|𝐡 ) , Equation ( 4 ) , where a few hidden-unit activities in the representation 𝐡 are fixed to desired values , whereas the others are sampled from Equation ( 3 ) . For WW domains , we condition on the activities of hidden units 3 and 4 , which are related to binding specificity . Fixing h3 and h4 to levels corresponding to the peaks in the histograms of inputs in Figure 3C allows us to generate sequences belonging specifically to each one of the three ligand-specificity clusters ( see Figure 5A ) . In addition , sequences with combinations of activities that are not encountered in the natural MSA can be engineered . As an illustration , we used conditional sampling to generate hybrid WW-domain sequences with strongly negative values of h3 and h4 , corresponding to a Type I-like β2-β3 binding pocket and a long , Type IV-like β1-β2 loop ( see Figure 5A and B ) . For Kunitz domains , the property ‘no 11–35 disulfide bond’ holds only for some sequences of nematode organisms , whereas the Bikunin-AMBP gene is present only in vertebrates; the two corresponding motifs are thus never observed simultaneously in natural sequences . Sampling our RBM conditioned to appropriate levels of h2 and h5 allows us to generate sequences with both features activated ( see Figure 5C and D ) . The sequences designed by RBM are far away from all natural sequences in the MSA , but have comparable probabilities ( see Figure 5E ( WW ) and Figure 5F ( Kunitz ) ) . Their probabilities estimated with pairwise direct-coupling models ( trained on the same data ) , whose ability to identify functional and artificial sequences has already been tested ( Balakrishnan et al . , 2011; Cocco et al . , 2018 andare also large ( see Appendix 1—figure 7 ) . Our RBM framework can also be modified to design sequences with very high probabilities , even larger than in the MSA , by appropriate duplication of the hidden units ( see 'Materials and methods' ) . This trick can be combined with conditional sampling ( see Figure 5E and F ) . As illustrated above , the co-occurrence of large weight components in highly sparse features often corresponds to nearby sites on the 3D fold . To extract structural information in a systematic way , we use our RBM to derive effective pairwise interactions between sites , which can then serve as estimators for contacts as approaches that are based on direct-coupling Cocco et al . , 2018 . The derivation is sketched in Figure 6A . We consider a sequence 𝐯a , b with residues a and b on , respectively , sites i and j . Single mutations a→a′ or b→b′ on , respectively , site i or j are accompanied by changes in the log probability of the sequence ( indicated by the full arrows in Figure 6A ) . Comparison of the change resulting from the double mutation with the sum of the changes resulting from the two single mutations provides our RBM-based estimate of the epistatic interaction ( see Equations ( 15 , 16 ) in 'Materials and methods' ) . These interactions are well correlated with the outcomes of the Direct-Coupling Analysis ( see Appendix 1—figure 9 ) . Figure 6 shows that the quality of the prediction of the contact maps of the Kunitz ( Figure 6B ) and the WW ( Figure 6C ) domains with RBM is comparable to state-of-the-art methods based on direct couplings ( Morcos et al . , 2011 ) ; predictions for long-range contacts are reported in Appendix 1—figure 10 . The quality of contact prediction with RBM: We further tested RBM distant contact predictions in a fully blind setting on the 17 protein families ( the Kunitz domain plus 16 other domains ) that were used for to benchmark plmDCA ( Ekeberg et al . , 2014 ) , a state-of-the-art procedure for inferring pairwise couplings in Direct-Coupling Analysis . The number of idden units was fixed to M=0 . 3⁢R , that is proportionally to the domain lengths , and the regularization strength was fixed to λ12=0 . 1 . Contact predictions averaged over all families are reported in Figure 6D for different choices of the hidden-unit potentials ( Gaussian and dReLU ) . We find that performances are comparable to those of plmDCA , but the computational cost of training RBM is substantially higher . Lattice protein ( LP ) models were introduced in the 90′⁢s to study protein folding and design ( Mirny and Shakhnovich , 2001 . In one of those models Shakhnovich and Gutin , 1990 , a ‘protein’ of N=27 amino acids may fold into ∼105 distinct structures on a 3×3×3 cubic lattice , with probabilities depending on its sequence ( see 'Materials and methods' and Figure 7A and B ) . LP sequence data were used to benchmark the Direct-Coupling Analysis in Jacquin et al . ( 2016 ) , and we follow the same approach here to assess the performances of RBM in a case where the ground truth is known . We first generate a MSA containing sequences that have large probabilities ( pnat>0 . 99 ) of folding into one structure shown in Figure 7A ( Jacquin et al . , 2016 ) . A RBM with M=100 dReLU hidden units is then learned , ( see Appendix 1 for details about regularization and cross-validation ) . Various structural LP features are encoded by the weights as in real proteins , including complex negative-design related modes ( see Figure 7C and D and the remaining weights in 'Supporting Information' ) . The performances in terms of contact predictions are comparable to state-of-the art methods on LP ( see Appendix 1—figure 11 ) . The capability of RBM to design new sequences that have desired features and high values of fitness , exactly computable in LP as the probability of folding into the native structure in Figure 7A , can be quantitatively assessed . Conditional sampling allows us to design sequences with specific hidden-unit activity levels , or combinations of features that are not found in the MSA ( Figure 7E ) . These designed sequences are diverse and have large fitnesses , comparable to those of the MSA sequences and even higher when generated by duplicated RBM ( Figure 7F ) , and well correlated with the RBM probabilities P⁢ ( 𝐯 ) ( Appendix 1—figure 6 ) . Each RBM was trained on a randomly chosen subset of 80% of the sequences in the MSA , while the remaining 20% ( the test set ) were used for validation of its predictive power . In practice , we compute the average log-probability of the test set to assess the performances of the RBM for various values of the number M of hidden units , for the regularization strength λ12 and for different hidden-unit potentials . Results for the WW and Kunitz domains and for Lattice Proteins are reported in Figure 8 and in Appendix 2 ( Model Selection ) . The dReLU potential , which includes quadratic and Bernoulli ( another popular choice for RBM ) potentials as special cases , is consistently better than the quadratic and Bernoulli potentials individually . As expected , increasing M allows RBM to capture more features in the data distribution and , therefore , improves performances up to a point , after which overfitting starts to occur . The impact of the regularization strength λ12 favoring weight sparsity ( see definition in 'Materials and methods' Equation ( 8 ) ) is two-fold ( see Figure 8A for the WW domain ) . In the absence of regularization ( λ12=0 ) weights have components on all sites and residues , and the RBM overfit the data , as illustrated by the large difference between the log-probabilities of the training and test sets . Overfitting notably results in generated sequences that are close to the natural ones and not very diverse , as seen from the entropy of the sequence distribution ( Appendix 1—figure 8 ) . Imposing mild regularization allows the RBM to avoid overfitting and maximizes the log-probability of the test set ( λ12=0 . 03 in Figure 8A ) , but most sites and residues carry non-zero weights . Interestingly , imposing stronger regularizations has low impact on the generalization abilities of RBM ( resulting in a small decrease in the test set log-probability ) , while making weights much sparser ( λ12=0 . 25 in Figure 3 ) . For regularizations that are too large , too few non-zero weights remain available and the RBM is not powerful enough to model the data adequately ( causing a drop in log-probability of the test set ) . Favoring sparser weights in exchange for a small loss in log-probability has a deep impact on the nature of the representation of the sequence space by the RBM ( see Figure 8B ) . Good representations are expected to capture the invariant properties of sequences across evolutionarily divergent organisms , rather than idiosyncratic features that are attached to a limited set of sequences ( mixture model in Figure 8C ) . For sparse-enough weights , the RBM is driven into the compositional representation regime ( see Tubiana and Monasson , 2017 ) of Figure 8E , in which each hidden unit encodes a limited portion of a sequence and the representation of a sequence is defined by the set of hidden units with strong inputs . Hence , the same hidden unit ( e . g . weights 1 and 2 coding for the realizations of contacts in the Kunitz domain in Figure 2B ) can be recruited in many parts of the sequence space corresponding to very diverse organisms ( see bottom histograms attached to weights 1 and 2 in Figure 2C , which shows that the sequences corresponding to strong inputs are scattered all over the sequence space ) . In addition , silencing or activating one hidden unit affects only a limited number of residues ( contrary to the entangled regime of Figure 8D ) , and a large diversity of sequences can be generated through combinatorial choices of the activity states of the hidden units , an approach that guarantees efficient sequence design . In addition , inferring sparse weights makes their comparison across many different protein families easier . In Figure 9 and 10 , we show some representative weights that were obtained after training RBMs with the MSAs of the 16 families considered by Ekeberg et al . ( 2014 ) ( the 17th family , the Kunitz domain , is shown in Figure 2 ) , which were chosen to illustrate the broad classes of encountered motifs; see 'Supporting information' for the other top weights of the 16 families . We find that weights may code for a variety of structural properties: In addition , weights may also reflect non-structural properties , such as: For all those motifs , the top histograms of the inputs on the corresponding hidden units indicate how the protein families cluster into distinct subfamilies with respect to the features . In summary , we have shown that RBM are a promising , versatile , and unifying method for modeling and generating protein sequences . RBM , when trained on protein sequence data , reveal a wealth of structural , functional and evolutionary features . To our knowledge , no other method used to date has been able to extract such detailed information in a unique framework . In addition , RBM can be used to design new sequences: hidden units can be seen as representation-controling knobs , that are tunable at will to sample specific portions of the sequence space corresponding to desired functionalities . A major and appealing advantage of RBM is that the two-layer architecture of the model embodies the very concept of genotype-phenotype mapping ( Figure 1C ) . Codes for learning and visualizing RBM are attached to this publication ( see 'Materials and methods' ) . From a machine-learning point of view , the values of RBM that define parameters ( such as class of potentials and number M of hidden units , or regularization penalties ) were selected on the basis of the log-probability of a test set of natural sequences not used for training and on the interpretability of the model . The dReLU potentials that we introduced in this work ( Equation ( 6 ) ) consistently outperform other potentials for generative purposes . As expected , increasing M improves likelihood up to some level , after which overfitting starts to occur . Adding sparsifying regularization not only prevents overfitting but also facilitates the biological interpretation of weights ( Figure 8A ) . It is thus an effective way to enhance the correspondence between representation and phenotypic spaces ( Figure 1C ) . It also allows us to drive the RBM operation point at which most features can be activated across many regions of the sequence space ( Figure 8E ) ; examples are provided by hidden units 1 and 2 for the Kunitz domain in Figure 2B and C and hidden unit 3 for the WW domain in Figure 3B and C . Combining these features allows us to generate a variety of new sequences with high probabilities , such as those shown in Figure 5 . Note that some inferred features , such as hidden unit 5 in Figure 2C and D and , to a lesser extent , hidden unit 2 in Figure 3B and C , are , by contrast , activated by evolutionary close sequences . Our inferred RBMs thus share some partial similarity with the mixture models of Figure 8C . Interestingly , the identification of specific sequence motifs with structural , functional or evolutionary meaning does not seem to be restricted to a few protein domains or proteins , but could be a generic property as suggested by our study of 16 additional families ( Figure 9 and 10 ) . Despite the algorithmic improvements developed in the present work ( see 'Materials and methods' ) , training RBM is challenging as it requires intensive sampling . Generative models that are alternatives to RBM , and that do not require Markov Chain sampling , exist in machine learning; they include Generative Adversarial Networks ( Goodfellow et al . , 2014 ) and Variational Auto–encoders ( VAE ) ( Kingma and Welling , 2013 . VAE were recently applied to protein sequence data for fitness prediction ( Sinai et al . , 2017; Riesselman et al . , 2018 . Our work differs in several impo rtant points: our RBM is an extension of direct-based coupling approaches , requires much less hidden units ( about 10 to 50 times fewer than were used in Sinai et al . , 2017 and Riesselman et al . , 2018 ) , has a simple architecture with two layers carrying sequences and representations , infers interpretable weights with biological relevance , and can be easily tweaked to design sequences with desired statistical properties . We have shown that RBM can successfully model small domains ( of a few tens of amino acids ) as well as much longer proteins ( of several hundreds of residues ) . The reason is that , even for very large proteins , the computational effort can be controlled through the number M of hidden units ( see 'Materials and methods' for discussion about the running time of our learning algorithm ) . Choosing moderate values of M makes the number of parameters to be learned reasonable and avoids overfitting , yet allows for the discovery of important functional and structural features . It is , however , unclear how M should scale with N to unveil ‘all’ the functional features of very complex and rich proteins ( such as Hsp70 ) . From a computational biology point of view , RBM unifies and extends previous approaches in the context of protein coevolutionary analysis . From the one hand , the features extracted by RBM identify ‘collective modes’ that control the biological functionalities of the protein , in a similar way to the so-called sectors extracted by statistical coupling analysis ( Halabi et al . , 2009 ) . However , contrary to sectors , the collective modes are not disjoint: a site may participate in different features , depending on the value of the residue it carries . On the other hand , RBM coincide with direct-coupling analysis ( Morcos et al . , 2011 when the potential 𝒰⁢ ( h ) is quadratic in h . For non-quadratic potentials 𝒰 , couplings to all orders between the visible units are present . The presence of high-order interactions allows for a significantly better description of gap modes Feinauer et al . , 2014 , of multiple long-range couplings due to ligand binding , and of outliers sequences ( Appendix 1—figure 5 ) . Our dReLU RBM model offers an efficient way to go beyond pairwise coupling models , without an explosion in the number of interaction parameters to be inferred , as all high-order interactions ( whose number , qN , is exponentially large in N ) are effectively generated from the same M×N×q weights wi⁢μ⁢ ( v ) . RBM also outperforms the Hopfield-Potts framework Cocco et al . , 2013 , an approach previously introduced to capture both collective and localized structural modes . Hopfield-Potts ’patterns’ were derived with no sparsity regularization and within the mean-field approximation , which made the Hopfield-Potts model insufficiently accurate for sequence design ( see Appendix 1—figures 14–18 ) . The weights shown in Figures 2B , 3B and 4B are stable with respect to subsampling ( Appendix 1—figure 13 ) and could be unambiguously interpreted and related to existing literature . However , the biological significance of some of the inferred features remains unclear , and would require experimental investigation . Similarly , the capability of RBM to design new functional sequences need experimental validation besides the comparison with past design experiments ( Figure 5E ) and the benchmarking on in silico proteins ( Figure 7 ) . Although recombining different parts of natural proteins sequences from different organisms is a well recognized procedure for protein design ( Stemmer , 1994; Khersonsky and Fleishman , 2016 , RBM innovates in a crucial aspect . Traditional approaches cut sequences into fragments at fixed positions on the basis of secondary structure considerations , but such parts are learned and need not be contiguous along the primary sequence in RBM models . We believe that protein design with detailed computational modeling methods , such as Rosetta ( Simons et al . , 1997; Khersonsky and Fleishman , 2016 , could be efficiently guided by our RBM-based approach , in much the same way as protein folding greatly benefited from the inclusion of long-range contacts found by direct-coupling analysis ( Marks et al . , 2011; Hopf et al . , 2012 . Future projects include developing systematic methods for identifying function-determining sites , and analyzing more protein families . As suggested by the analysis of the 16 families shown in Figure 9 and 10 , such a study could help to establish a general classification of motifs into broad classes with structural or functional relevance , shared by distinct proteins . In addition , it would be very interesting to use RBM to determine evolutionary paths between two , or more , protein sequences in the same family , but with distinct phenotypes . In principle , RBM could reveal how functionalities continuously change along the paths , and could provide a measure of viability of intermediary sequences . We use the PFAM sequence alignments of the V31 . 0 release ( March 2017 ) for both Kunitz ( PF00014 ) and WW ( PF00397 ) domains . All columns with insertions are discarded , then duplicate sequences are removed . We are left with , respectively , N=53 sites and B=8062 unique sequences for Kunitz , and N=31 and B=7503 for WW; each site can carry q=21 different symbols . To correct for the heterogeneous sampling of the sequence space , a reweighting procedure is applied: each sequence 𝐯ℓ with ℓ=1 , … , B is assigned a weight wℓ equal to the inverse of the number of sequences with more than 90% amino-acid identity ( including itself ) . In all that follows , the average over the sequence data of a function f is defined as ( 7 ) ⟨f⁢ ( 𝐯 ) ⟩M⁢S⁢A= ( ∑ℓ=1Bwℓ⁢f⁢ ( 𝐯ℓ ) ) / ( ∑ℓ=1Bwℓ ) . Sampling from P in Equation ( 5 ) is done with Markov Chain Monte Carlo methods , with the standard alternate Gibbs sampler described in the main text and in Fischer and Igel ( 2012 ) . Conditional sampling , that is sampling from P ( 𝐯\|hμ=hμc ) , is straightforward with RBM: it is achieved by the same Gibbs sampler while keeping hμ fixed . The RBM architecture can be modified to generate sequences with high probabilities ( as in Figure 5E&F ) . The trick is to duplicate the hidden units , the weights , and the local potentials acting on the visible units , as shown in Figure 11 . By doing so , the sequences 𝐯 are distributed according to ( 14 ) P2⁢ ( 𝐯 ) ∝∫∏μd⁢hμ⁢1⁢d⁢hμ⁢2⁢P⁢ ( 𝐯\|𝐡1 ) ⁢P⁢ ( 𝐯\|𝐡2 ) =P⁢ ( 𝐯 ) 2 . Hence , with the duplicated RBM , sequences with high probabilities in the original RBM model are given a boost when compared to low-probability sequences ( Figure 11 ) . Note that more subtle biases can be introduced by duplicating some ( but not all ) of the hidden units in order to give more importance in the sampling to the associated statistical features . RBM can be used for contact prediction in a manner similar to pairwise coupling models , after derivation of an effective coupling matrix Ji⁢jeff⁢ ( a , b ) . Consider a sequence 𝐯 , and two sites i , j . Define the set of mutated sequences 𝐯a , b with amino acid content: vka , b=vk if k≠i , j , a if k=i , b if k=j ( Figure 6A ) . The differential likelihood ratio ( 15 ) Δ⁢Δ⁢Ri⁢j⁢ ( 𝐯;a , a′ , b , b′ ) ≡log⁡[P⁢ ( 𝐯a , b ) ⁢P⁢ ( 𝐯a′ , b′ ) P⁢ ( 𝐯a′ , b ) ⁢P⁢ ( 𝐯a , b′ ) ] , where P is the marginal distribution in Equation ( 5 ) , measures epistatic contributions to the double mutation a→a′ and b→b′ on sites i and j , respectively , in the background defined by sequence 𝐯 ( see Figure 6A ) . The effective coupling matrix is then defined as ( 16 ) Ji⁢jeff⁢ ( a , b ) =⟨1q2⁢∑a′ , b′Δ⁢Δ⁢Ri⁢j⁢ ( 𝐯;a , a′ , b , b′ ) ⟩M⁢S⁢A , where the average is taken over the sequences 𝐯 in the MSA . For a pairwise model , Δ⁢Δ⁢Ri⁢j does not depend on the background sequence 𝐯 , and Equation ( 16 ) coincides with the true coupling in the zero-sum gauge . Contact estimators are based on the Frobenius norms of Jeff , with the Average Product Correction ( see Cocco et al . , 2018 ) . The Python 2 . 7 package for training and visualizing RBMs , which was used to obtain the results reported in this work , is available at https://github . com/jertubiana/ProteinMotifRBM ( Tubiana , 2019; copy archived at https://github . com/elifesciences-publications/ProteinMotifRBM ) . In addition , Jupyter notebooks are provided for reproducing most of the figures in this article .	Almost every process that keeps a cell alive depends on the activities of several proteins . All proteins are made from chains of smaller molecules called amino acids , and the specific sequence of amino acids determines the protein’s overall shape , which in turn controls what the protein can do . Yet , the relationships between a protein’s structure and its function are complex , and it remains unclear how the sequence of amino acids in a protein actually determine its features and properties . Machine learning is a computational approach that is often applied to understand complex issues in biology . It uses computer algorithms to spot statistical patterns in large amounts of data and , after 'learning' from the data , the algorithms can then provide new insights , make predictions or even generate new data . Tubiana et al . have now used a relatively simple form of machine learning to study the amino acid sequences of 20 different families of proteins . First , frameworks of algorithms –known as Restricted Boltzmann Machines , RBM for short – were trained to read some amino-acid sequence data that coded for similar proteins . After ‘learning’ from the data , the RBM could then infer statistical patterns that were common to the sequences . Tubiana et al . saw that many of these inferred patterns could be interpreted in a meaningful way and related to properties of the proteins . For example , some were related to known twists and loops that are commonly found in proteins; others could be linked to specific activities . This level of interpretability is somewhat at odds with the results of other common methods used in machine learning , which tend to behave more like a ‘black box’ . Using their RBM , Tubiana et al . then proposed how to design new proteins that may prove to have interesting features . In the future , similar methods could be applied across computational biology as a way to make sense of complex data in an understandable way .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "computational", "and", "systems", "biology", "physics", "of", "living", "systems", "tools", "and", "resources" ]	2019	Learning protein constitutive motifs from sequence data
The nematode Caenorhabditis elegans possesses a simple embryonic nervous system with few enough neurons that the growth of each cell could be followed to provide a systems-level view of development . However , studies of single cell development have largely been conducted in fixed or pre-twitching live embryos , because of technical difficulties associated with embryo movement in late embryogenesis . We present open-source untwisting and annotation software ( http://mipav . cit . nih . gov/plugin_jws/mipav_worm_plugin . php ) that allows the investigation of neurodevelopmental events in late embryogenesis and apply it to track the 3D positions of seam cell nuclei , neurons , and neurites in multiple elongating embryos . We also provide a tutorial describing how to use the software ( Supplementary file 1 ) and a detailed description of the untwisting algorithm ( Appendix ) . The detailed positional information we obtained enabled us to develop a composite model showing movement of these cells and neurites in an 'average' worm embryo . The untwisting and cell tracking capabilities of our method provide a foundation on which to catalog C . elegans neurodevelopment , allowing interrogation of developmental events in previously inaccessible periods of embryogenesis . Understanding how complex neural circuits and entire nervous systems form is one of the fundamental goals of neuroscience . While substantial progress has been made in identifying guidance factors in neurodevelopment ( Kolodkin and Tessier-Lavigne , 2011; Dudanova and Klein , 2013; Chilton , 2006; O'Donnell et al . , 2009 ) , how known factors interact to direct the formation of complex neural circuits remains mysterious ( Dudanova and Klein , 2013 ) . Examining the entirety of neurodevelopment in intact , living samples would be useful in understanding larger scale principles that orchestrate nervous system formation . Unfortunately , technological limitations and inherent nervous system complexity have hindered our ability to capture a 'systems-level' view of the developing brain . One model organism well-suited to systems-level neuroscience research is Caenorhabditis elegans , which possesses a simple nervous system comprising 302 neurons ( White et al . , 1986 ) , 222 of which form during embryogenesis ( Sulston et al . , 1983 ) . The adult connectome has been reconstructed , and the morphology of all adult neurons has been mapped at electron-microscopy resolution ( White et al . , 1986 ) ; the genome sequenced ( C . elegans Sequence Consortium , 1998 ) ; and the organism is genetically tractable and transparent at all life stages , enabling investigation with light microscopy . The simplicity of the C . elegans nervous system , its experimental accessibility , and the extensive knowledge base make it a promising candidate for following the development of all neurons in the embryo , and eventually understanding associated molecular mechanisms . The resulting 'neurodevelopmental atlas' would represent the first view of how an entire nervous system forms . Despite the potential of the nematode as a model , imaging neurodevelopment ( Wu et al . , 2013a ) throughout embryogenesis is challenging due to embryonic sensitivity to photodamage and photobleaching , limiting imaging to several hours on most systems; the need for subcellular spatial resolution due to the small size of the embryo; and motion blur caused by rapid embryo movement after muscular twitching begins . Once images are captured , data analysis poses new problems: while it would be easy to assemble an atlas of neuronal positions and morphology if all cells were easily identifiable in one animal , techniques that allow imaging with single-cell contrast ( such as Brainbow [Livet et al . , 2007] ) are unavailable in the nematode . Currently , any attempt to build a neurodevelopmental atlas would require imaging small numbers of non-overlapping , easily distinguishable neurons , and finding methods to combine the data from multiple embryos into a composite whole . To our knowledge , comprehensive solutions to these problems do not yet exist . Recent advances in light-sheet fluorescence microscopy ( LSFM [Santi , 2011] ) have solved many of the imaging problems outlined above . LSFM sweeps a thin sheet of light through the sample , relying on perpendicular detection of fluorescence . This geometry allows far more rapid imaging and reduced phototoxicity relative to confocal microscopy ( Huisken et al . , 2004; Holekamp et al . , 2008 ) , enabling the use of LSFM in a variety of transformative applications . These include recording whole-brain calcium signaling in larval zebrafish ( Ahrens et al . , 2013; Ito , 2013 ) , and imaging ( Wu et al . , 2011; Keller et al . , 2008 ) and tracking ( Amat et al . , 2014; Bao , 2006; Santella et al . , 2010 ) large numbers of cells in developing embryos . Multiple LSFM implementations now obviate the problems of motion blur and photo damage in worm embryos ( Wu et al . , 2011; Wu et al . , 2013; Kumar et al . , 2014; 2015 ) , and also offer sufficient spatiotemporal resolution ( sub-μm in all three spatial dimensions , sub-second volumetric imaging [Wu et al . , 2013; Kumar et al . , 2014] ) that subcellular morphology may be observed over the entire 14-hour period of embryogenesis . Despite these advances , morphological changes still pose problems when trying to follow individual cells , or when combining data from multiple embryos . To address these problems , we have generated a nematode strain that expresses fluorescent markers within specific cells , and designed software that uses these markers to computationally 'untwist' the embryo , resulting in straightened volumes that significantly ease the tracking of developmental events in later embryonic stages ( described briefly in a preliminary conference proceeding [Christensen , 2015] ) . Our open-source software is based on the NIH’s Medical Image Processing , Analysis , and Visualization ( MIPAV [McAuliffe , 2001; Haak et al . , 2015] ) platform , implemented as a standalone plugin ( http://mipav . cit . nih . gov/plugin_jws/mipav_worm_plugin . php ) . Computational untwisting algorithms have previously been used to straighten images of L1 larval worms for use in tracking nuclear position ( Peng et al . , 2008; Long et al . , 2009 ) in both two and three dimensions , but to our knowledge , these algorithms are not suitable for the nematode embryo . In addition to the untwisting capability , our plugin includes the ability to annotate and track 3D positions over time , allowing semi-automated quantification of cell and neurite positions in twisted ( and untwisted ) embryos . The positional data so derived also facilitate comparison and combination of information from multiple embryos , allowing us to create a composite model of development . We demonstrate the capabilities of our method by computationally untwisting eight nematode embryos; tracking the position of seam cell nuclei , the canal-associated neuron ( CAN ) , ALA , and AIY neuron cell bodies , and the growing neurites of the ALA neuron in the untwisted reference frame; and combining the data from multiple embryos to model the time-evolution of all these elements within the elongating embryo . We find that seam cell nuclear positions are highly stereotyped across different embryos , while the rate of elongation varies according to position along the embryo . Of the neurons we examined , ALA and AIY move in concert with neighboring seam cell nuclei , suggesting they are passively 'dragged' with the rest of the elongating worm embryo , while the CAN neurons actively migrate at a faster rate than the surrounding seam cell nuclei . Tracking ALA neurites reveals that anterior-posterior neurite outgrowth starts toward the end of elongation and continues after cells reach their final positions . Our method is the first to track cell positions in the context of the entire embryo , from the beginning of twitching until hatching . We anticipate that our software will significantly further the ability to examine C . elegans development in the post-twitching regime and lay a foundation for understanding the formation of the C . elegans nervous system . In order to computationally straighten an embryo , an essential first step is defining limits of the growing worm body , thus specifying how the embryo folds inside the eggshell . Nematode embryos undergo both bending and helical twisting around the nose-to-tail axis ( Figure 1—figure supplement 1 ) posing challenges in untwisting the embryo relative to larval or adult nematodes . Our approach uses fluorescent markers driven by cell-specific promoters to define the boundaries of the worm body . We use a seam cell marker ( SCM::GFP ) to label the 20–22 seam cell nuclei , identifying the left and right sides of the worm; and a dlg-1::GFP fusion protein to label apical gut junctions and hypodermal junctions , revealing the locations of the anterior tip of the pharynx ( hereafter referred to as the nose ) , tail , midline , and hypodermal cell boundaries ( Figure 1A , B ) . This combination of markers allows automated segmentation of seam cells and manual identification of the nose , tail , and sides of the worm , thus enabling us to model the twisted , bent embryo within the eggshell , and serving as a basis for computationally untwisting the worm ( Figure 1C ) . 10 . 7554/eLife . 10070 . 003Figure 1 . Key steps in worm untwisting . ( A ) An image of a threefold embryo in the twisted state , showing the untwisting markers . ( B ) The same image as in ( A ) with the untwisting markers labeled . Asterisks mark seam cell nuclei , and the dashed line indicates the midline marker . ( C ) The same embryo as in ( A , B ) , after untwisting . Asterisks and dashed line as in B . ( D–F ) Further detail lattice creation and splines that model embryo . ( D ) Left: same embryo volume as in ( A ) . Right: accompanying schematic showing the seam cell nuclei in the twisted embryo ( black circles ) and midline ( interior black line ) . ( E ) Lattice creation . As diagrammed in right schematic , parts ( 1 ) and ( 2 ) , the user adds points to create a lattice ( blue and yellow lines ) . After the lattice is built , the algorithm generates splines defining the edges of the worm ( orange and purple lines ) automatically . The midline is also defined with a spline ( red line at left ) . ( F ) The embryo volume and accompanying schematic showing a completed lattice and model . ( G ) The embryo volume and accompanying schematic after untwisting . All scale bars: 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 00310 . 7554/eLife . 10070 . 004Figure 1—figure supplement 1 . Helical twisting in the nematode embryo . ( A ) Evidence for helical twisting , highlighted on four pairs of consecutive seam cell nuclei . If no helical twisting occurs , yellow lines ( connecting seam cell nucleus pairs ) should appear parallel to each other when sighting down the midline of the worm ( red line ) . If helical twisting is present , yellow lines should appear to twist about the midline . Arrows denote the direction of lines for four pairs of consecutive seam cell nuclei: note obvious and apparent angular twist between pairs 1 and 4 . ( B ) Side view showing same data as in ( A ) . As before , nuclei pairs 1 and 4 appear in close to perpendicular orientation to each other , despite the roughly parallel midline . Scale bar: 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 00410 . 7554/eLife . 10070 . 005Figure 1—figure supplement 2 . DiSPIM is useful in identifying landmarks in the twisted embryo . Coarse features such as seam cell nuclei are visible in single view iSPIM ( A ) , but finer features such as junctions between hypodermal cells labeled with DLG-1::GFP are better resolved in the diSPIM ( B ) , particularly in the axial direction ( lower row ) . Scale bar: 10 μm . diSPIM , dual-view Selective Plane Illumination Microscopy; iSPIM , inverted Selective Plane Illumination Microscopy . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 00510 . 7554/eLife . 10070 . 006Figure 1—figure supplement 3 . Effects of lattice point number on untwisting results . ( A ) XZ and YZ views of an untwisted worm embryo using a lattice comprised of every other seam cell nucleus , a total of 12 points . This lattice fails to capture bends in the animal and does not create smooth left and right edges in the untwisted worm embryo . ( B ) Same as ( A ) but using a lattice built with all seam cell nuclei and the nose , a total of 22 points . This lattice still fails to capture some bends in the worm , and the extension of the tail . ( C ) Same as ( A ) but using a lattice built with all seam cell nuclei as well as additional points in highly bent regions in the worm embryo , plus a pair of points marking the tail , for a total of 28 points . Bends are accurately captured in the resulting untwisted volume . ( D ) Several additional lattice points were added to the lattice in ( C ) , along the edges of the animal , for a total of 36 points . No noticeable improvements are apparent . Scale bar: 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 00610 . 7554/eLife . 10070 . 007Figure 1—figure supplement 4 . Untwisting a larval nematode . ( A ) The twisted L2 larval volume displayed in the MIPAV volume renderer . ( B ) The twisted L2 larva after lattice-building . ( C ) The L2 larval worm after untwisting . See also Video 7 . MIPAV , Medical Image Processing , Analysis , and VisualizationDOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 007 We used a dual-view selective plane illumination microscopy ( diSPIM ) implementation of LSFM to capture images of developing embryos ( Wu et al . , 2013; Kumar et al . , 2014 ) . The diSPIM was chosen due to the combination of high-imaging speed and isotropic resolution that it provides , making the identification of cells and cellular structures in a twisted-up embryo significantly easier than with lower resolution alternatives ( such as single-view light-sheet microscopy , Figure 1—figure supplement 2 ) . After images are acquired in the diSPIM , registered , and deconvolved , a user begins untwisting by downloading and running our software ( http://mipav . cit . nih . gov/plugin_jws/mipav_worm_plugin . php , Supplementary file 1 ) . First , seam cell nuclei are automatically detected , segmented , and paired to create candidate lattices . Seam cell segmentation and lattice-building are manually verified by a user , who can also incorporate additional information derived from pharyngeal and hypodermal markers , which are difficult to automatically segment ( Figure 1D , E ) . Several possible lattices are generated , and the five most likely to be correct are displayed to the user for selection and editing of the correct lattice . The resulting lattice is used to generate a 3D model of the worm volume ( Figure 1F , Video 1 ) . In cases where automated lattice-building fails , lattices can be built manually by marking the positions of seam cell nuclei , nose , bends in the embryo , and tail . When manually building lattices , minimally 22 +2B lattice points are recommended ( 22 is the number of lattice points corresponding to seam cell nuclei , plus a pair of points to mark the nose , and B is the number of bends between seam cell nuclei in the embryo ) . Fewer lattice points than the number of seam cell nuclei gives unphysical , short volumes , and more than ~32 points does not noticeably improve quality in the untwisted volumes ( Figure 1—figure supplement 3 ) . 10 . 7554/eLife . 10070 . 008Video 1 . Sequential steps used in the automated lattice-building plugin . This animation provides a graphical representation of the computational steps used to segment seam cells , build a lattice , and straighten embryo volumes . For additional information refer to Supplementary file 1 and Appendix . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 008 The first step in creating the 3D model is to generate curves defining the center and sides of the worm . The centerline curve is uniformly sampled to generate a series of planes extending along and normal to the curve , while avoiding overlap within the model . This series of restricted planes comprises the worm model and is updated as new lattice points are added . To generate a straightened volume , the voxels in the original image that intersect with the sampling planes in the worm model are captured , and the sampling planes and associated voxels are concatenated in the head-to-tail direction to generate a straightened volume ( Figure 1G , Video 1 ) . The same process can be used to straighten images of older animals ( such as L2 larvae , Figure 1—figure supplement 4 ) . More details are provided in Supplementary file 1 and Appendix . In addition to untwisting , it is also useful to obtain the 3D position of a cell or point of interest within the nematode embryo . Thus , our software also includes an annotation capability , allowing a user to define points within the embryo for which they would like to obtain 3D coordinates both before and after untwisting ( Supplementary file 1 ) . The user adds annotations similarly to lattice points , marking the volume location where the desired point should appear . The user must also add an origin point from which the relative 3D position of all other points is calculated from . As pharyngeal labeling is consistent and bright in most diSPIM volumes , we use the nose as a standard origin in all datasets described in the paper . Once the origin and annotation points have been defined , the user can untwist the worm and obtain the 3D coordinates of each annotation point in a spreadsheet file . In order to ensure that our algorithm did not alter the distance between portions of the embryo during the untwisting process , we compared the apparent 3D distance between , or along , landmark features within twisted and untwisted embryo volumes ( embryos 1–6 , Figure 2 ) . First , we determined the distances between nuclei in seam cell pairs ( Figure 2A , B ) . If untwisting did not effect morphology , we reasoned that these distance should be conserved regardless of whether the embryo is twisted or untwisted . We measured the difference between pair distance in twisted- and untwisted datasets at every fifth or tenth time point for both the first ( H0 ) and last ( T ) pairs of seam cells in six different embryos , reasoning that the difference should be close to 0 . The apparent untwisted distance between seam cell pairs H0 and T closely mirrored the values in the twisted worm , with the population average difference across timepoints and embryos ( <μDifference , time>embryo ± population standard deviation <σDifference , time>embryo ) for H0 0 . 4 µm ± 0 . 3 µm , and for T 0 . 3 ± 0 . 2 µm ( Figure 2C , Figure 2—figure supplement 1 , Supplementary file 5 , Materials and methods ) . The largest difference at any individual timepoint between twisted and untwisted values was 1 . 7 µm for H0 and 1 . 2 µm for T . 10 . 7554/eLife . 10070 . 009Figure 2 . The untwisting algorithm accurately preserves embryo dimensions . Distances between seam cell nuclei ( left ) and pharyngeal lengths ( right ) were compared in twisted ( A ) and untwisted ( B ) worm embryos . All scalebars: 10 µm . ( C ) Comparative 3D distance measurements of seam cell nuclei pairs H0 and T ( left graphs ) and pharyngeal lengths ( right graphs ) for one representative embryo ( a comparison across six different embryos is presented in Figure 2—figure supplement 1 ) . In all cases , distance measurements in the twisted case are within 5 μm of distance measurements in the untwisted case . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 00910 . 7554/eLife . 10070 . 010Figure 2—figure supplement 1 . Untwisting does not systematically alter worm morphologyComparative 3D distance measurements of seam cell nuclei pairs H0 and T ( left graphs ) and pharyngeal lengths ( right graphs ) for six embryos . In all cases , distance measurements in the twisted case are within 10 μm of distance measurements in the untwisted case . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 010 Since the model of the twisted embryo is based on positional coordinates of the seam cell nuclei , we would expect these paired distances in twisted- and untwisted embryos to agree . For a more stringent control , we also assessed the apparent distance between nose and the pharynx-gut transition ( effectively the pharyngeal contour length ) in twisted and untwisted embryos ( Figure 2A , B ) . Although the pharynx is not used as a landmark for defining the worm model used in untwisting , we still expect its contour length to be conserved despite untwisting . Here , too , we measured a close correspondence ( typically less than 5% of the total pharyngeal length ) . The population <μDifference , time>embryo ± <σDifference , time>embryo between twisted and untwisted pharyngeal lengths was 2 . 5 µm ± 1 . 6 µm ( with the maximum difference between the untwisted and twisted values for any individual timepoint being 8 . 8 µm , Figure 2C , Figure 2—figure supplement 1 ) . We conclude that our untwisting procedure accurately captures distances present in the twisted embryo . The combination of untwisting and annotation capabilities we developed allows the analysis of overall morphological changes in a developing embryo and the precise tracking of positions for individual cells or subcellular structures . We first examined overall morphological changes in the nematode embryo . Embryos lengthened ( from 86 ± 5 µm at early 1 . 5-fold , measured from the nose to the tail , to 162 ± 19 µm within the last 30 min before hatching , measured from the nose to the last pair of seam cells , mean ± standard deviation [SD] , 5 embryos ) and narrowed in width ( measured diameter across the widest cross-section 22 ± 1 µm at early timepoints , and 16 ± 1 µm at late timepoints , mean ± SD , 5 embryos ) as they progressed from comma stage to late-3 fold ( Figure 3A–H , Figure 3—figure supplement 1; Figure 3—figure supplement 2; Video 2 ) . We used our software to manually annotate and extract the positional trajectories of seam cell nuclei during this time period , as they moved relative to the nose of the animal ( Figure 3J–3L , Figure 3—figure supplement 1 ) . We note that seam cell V5 divides late in the threefold embryo into Q and V5 daughters; in such cases , we tracked the anteriormost daughter , Q , and thus refer to V5 as Q/V5 in our paper . The motion of seam cell nuclei followed relatively simple trends that were easily evident , despite the noise present in the raw untwisted trajectories . During elongation , seam cell nuclei moved laterally ( ‘X’ motion , Figure 3J ) towards the midline , while maintaining a relatively fixed dorso-ventral position ( ‘Y’ motion , Figure 3K ) . Along the axial , head-to-tail axis , the displacement of seam cell nuclei was biphasic , showing a fast , approximately linear dependence on time , followed by a slower plateau ( ‘Z’ motion , Figure 3L ) ( Priess and Hirsh , 1986; Chin-Sang and Chisholm , 2000; Ding et al . , 2004; Norman and Moerman , 2002 ) . While embryo elongation has been examined before ( Priess and Hirsh , 1986 ) , our method is the first that allows 3D interrogation of whole , live , untwisted nematodes at arbitrary timepoints in embryogenesis ( Figure 3 , Video 2 ) . 10 . 7554/eLife . 10070 . 011Figure 3 . Morphological changes in embryonic development , as unveiled by untwisting algorithm . Selected volumetric timepoints pre ( A–D ) and post ( E–H ) untwisting , with canonical state of embryo indicated at bottom . See also Video 2 . ( I ) Cartoon of untwisted embryo , indicating coordinate system . ( J–L ) X , Y , and Z movements of circled seam cell nucleus in ( I ) . Measurements are indicated relative to the animal’s nose , fixed as the origin in all untwisted datasets . All scalebars: 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 01110 . 7554/eLife . 10070 . 012Figure 3—figure supplement 1 . Comparison of untwisted 1 . 5-fold embryos after shifting . Comparative timepoints were selected based on the H1R seam cell shifts . Max projections of volumetric images are shown . Note the underlying similarity in overall shape across animals . Scalebar: 5 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 01210 . 7554/eLife . 10070 . 013Figure 3—figure supplement 2 . Comparison of threefold embryos after shifting . Comparative timepoints were selected based on the H1R seam cell shifts . Max projections of volumetric images are shown . Note the underlying similarity in overall shape and seam cell positions across animals . Scalebar: 5 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 01310 . 7554/eLife . 10070 . 014Figure 3—figure supplement 3 . Data Post-processing . Before fitting , raw data are treated to remove obvious outliers ( top row ) and to fill in missing data ( mid , bottom rows ) . In both cases , outliers and ‘gaps’ within data are found manually , and replaced by averaging the data points immediately preceding or following the outlier or gap . Examples of raw data prior to this linear interpolation are shown at left , and examples of processed data at right . The example axial distance data shown here are derived from seam cell 3 . Red arrows indicate outliers or gaps . Data shown are from the left H2 seam cell nucleus . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 01410 . 7554/eLife . 10070 . 015Video 2 . Raw data showing an untwisted worm developing from the 1 . 5-fold stage until hatching . Despite errors in individual untwisted volumes , the overall pattern of embryonic development and elongation is clear . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 015 The strong qualitative similarities in seam cell nuclear trajectories among the five embryos we inspected led us to investigate whether data from different embryos could be combined to yield a composite model of development representing growth in an 'average' embryo . Initial examination of the axial ( nose-to-tail ) seam cell nuclear trajectories from different embryos suggested a high degree of stereotypy; except for a relative shift in time , the trajectories displayed very similar shapes ( Figure 4A ) . We thus shifted the axial data in time until the trajectories from multiple embryos overlaid ( Figure 4B , Figure 4—figure supplement 1 ) . We determined the amount of shift by using a three parameter logistic function to fit the raw axial displacement data ( Figure 4—figure supplement 2 , Tables 1 , 2 , Materials and methods ) , overlaying the data from various embryos until the inflection points in each curve were identical . 10 . 7554/eLife . 10070 . 016Figure 4 . Alignment of data from different embryos . ( A , B ) Axial seam cell nuclear trajectories from different embryos are similar in shape , but shifted in time . ( C , D ) Shifting in time aligns the trajectories . ( E , F ) Averaging the shifted trajectories . ( G , H ) Fitting the shifted trajectories . Left graphs: cartoon schematic , Right graphs: data . For clarity , we have shown the shifting , averaging , and fitting process for two embryos , but note that to construct our 'composite' model of seam cell nucleus behavior we have applied the same process to five embryos ( see 'Materials and methods' for further details ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 01610 . 7554/eLife . 10070 . 017Figure 4—figure supplement 1 . Temporal alignment of embryo data . Data from two embryos are shown before ( top ) and after ( bottom ) temporal alignment . The data derived from embryo 4 was shifted 5 timepoints to the right , following the procedure described in 'Materials and methods' . Data shown are the z positions from the right V3 seam cell nucleus . Only a portion of the data , at early timepoints , is shown to highlight the shifting procedure . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 01710 . 7554/eLife . 10070 . 018Figure 4—figure supplement 2 . Different fits for axial displacement . Different fitting models ( see also Table 2 ) for embryonic axial displacement are plotted ( red curves ) , against raw data ( blue diamonds ) . Also shown on each plot are quantitative measures of goodness of fit: the squared sum of residuals ( SSR ) , the Akaike Information Criterion ( AIC ) , and the Schwarz Criterion ( SC ) . Of the three-parameter fits , the three-parameter logistic provides the best overall fit , both from visual inspection and quantitatively ( lowest SSR , AIC , and SC scores ) . The four-parameter Morgan Mercer Flodin and Logistic curves show slightly better qualitative fits , especially at early time points , but require careful tuning of the initial parameters to converge . For all axial displacement data shown elsewhere in the paper , the three-parameter logistic curve was used as a fitting function . Although the axial displacement data shown here are derived from the left seam cell nucleus H0 , we observed the same trends for all seam cells . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 01810 . 7554/eLife . 10070 . 019Figure 4—figure supplement 3 . Variability in axial distance amongst different embryos . Comparisons in axial position vs . time for a seam cell nucleus ( right H1 , upper graph ) and for CANL ( lower graph ) . For most nuclei , as in the upper graph , positions were stereotyped to within 4 . 6 μm ( as quantified by <<σZ>time>seam cell; see also 'Materials and methods' ) . As indicated in the lower graph , we noticed CANs in embryo 5 traveled a shorter distance than in other embryo datasets ( resulting in a larger value of <σZ>time for CANL , see Supplementary file 2 ) . Data are shown after applying the shifting procedure described in 'Materials and methods' . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 01910 . 7554/eLife . 10070 . 020Figure 4—figure supplement 4 . Fits used in this paper . Examples of raw , averaged data ( derived from 4 to 5 embryos , blue dots ) and fits ( black lines ) . Linear , power , and three-parameter logistic curve examples were taken from the right H0 seam cell nucleus , the quartic polynomial example from AIYL , and the smoothing fits from CANR . See also Table 1 . Note the different ranges in ordinate axes . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 020 We applied the same time shift to the X- and Y- seam cell nuclear coordinates , finding that seam cell nuclear positions followed similar trajectories throughout elongation ( average SD calculated across all 20 seam cell nuclei and all timepoints , <<σX>time>seam cell 0 . 8 µm , <<σY>time>seam cell 0 . 7 µm , <<σZ>time>seam cell 4 . 6 µm , see also Figure 4—figure supplement 3 , Table 1 , and 'Materials and methods' ) . After shifting , we averaged ( Figure 4C ) and fitted ( Figure 4D , Figure 4—figure supplement 4 , Table 1 , Supplementary file 2 ) the embryo XYZ trajectories , thus generating positions representing the noise-free time evolution of seam cell nuclei . We note that the choice of fitting functions is somewhat arbitrary . For axial positions , the growth that we and others ( Priess and Hirsh , 1986 ) have observed leads to a sigmoidal fitting function . Amongst the various three-parameter sigmoidal functions ( Table 2 ) , we found that the three-parameter logistic function gave the best qualitative and quantitative ( Figure 4—figure supplement 2 ) agreement with the data . We fitted lateral ( ‘X’ ) seam cell nuclei positions with a two parameter power law function , and dorso-ventral ( ‘Y’ ) positions with a linear function , as empirically these functions described our data well . Despite the ad hoc nature of these fits , we found that fitted values were within 1 . 5 µm of the X , Y averaged data , and within 7 . 5 µm of the Z averaged data ( Supplementary file 3 ) . For reference , the total length of the untwisted embryo at the final time point was 162 . 0 ± 18 . 7 µm ( mean ± SD , 5 embryos ) , measured from the nose to the last pair of seam cells , and the corresponding diameter at the last time point 16 . 1 ± 1 . 3 µm , measured at the widest cross-section in the animal . 10 . 7554/eLife . 10070 . 021Table 1 . Fitting functions tested for describing axial displacement . Equations are used in Figure 4—figure supplement 2 . L: length; t: time . Other parameters and their meaning are listed in the table . For all axial coordinates in this paper , a three-parameter logistic function was used . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 021Fitting typeEquationParametersvon BertalanffyL = A ( 1-exp[-B ( t-C ) ] ) A: upper asymptotic length B: growth rate C: time at which L = 0ExponentialL = A- ( A-B ) exp ( -Ct ) A: upper asymptotic length B: lower asymptotic length C: growth rateThree-parameter GompertzL = A[exp ( -exp ( -B ( t-C ) ) ) ]A: upper asymptotic length B: growth rate C: time at which L = 0Three-parameter logisticL = A/[1+exp ( -B ( t-C ) ) ]A: upper asymptotic length B: growth rate C: inflection pointFour-parameter Morgan Mercer FlodinL = A – ( A-B ) / ( 1+ ( Ct ) D ) A: upper asymptotic length B: length at t = 0 C: growth rate D: inflection parameterFour-parameter logisticL = B + ( A-B ) /{1+exp[ ( C-t ) /D]}A: upper asymptotic length B: lower asymptotic length C: growth rate D: steepness parameter10 . 7554/eLife . 10070 . 022Table 2 . Fitting functions for each cell type . X , Y , Z trajectories were fitted as indicated functions of time ( t ) . ’ 50-point smoothing’ refers to smoothing the input data with a 50-point span , using weighted linear least squares and linear fitting . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 022Cell typeX fitY fitZ fitSeam cell nucleusPower X = atb+cLinear Y = p1t + p2Three-parameter logistic Z = A/ ( 1+exp ( -B ( t-C ) ) ) CANR/L50-point smoothing50-point smoothingThree-parameter logistic Z = A/ ( 1+exp ( -B ( t-C ) ) ) AIYR/L4th degree polynomial X = p4t4+p3t3+p2t2+p1t+p0Linear Y = p1t + p2Three-parameter logistic Z = A/ ( 1+exp ( -B ( t-C ) ) ) ALA ALA xR1/xL1 ALA xR2/xL2Linear X = p1t + p2Linear Y = p1t + p2Three-parameter logistic Z = A/ ( 1+exp ( -B ( t-C ) ) ) The averaged , fitted seam cell nuclei data allowed us to inspect the relative relationships among seam cell nuclei in an elongating embryo ( Figure 5A , Videos 3 , 4 ) . Since we fixed the nose as the stationary origin in our untwisting procedure , this location does not move in 4D representations of the fitted embryo . In this 'nose-centric' reference frame , points further from the origin also appear to move faster and farther than points closer to the origin . To better understand the growth rates of individual seam cell nuclei in relation to their neighbors , and the overall length changes within the elongating embryo in a frame-independent manner , we also computed the differences in position between adjacent pairs of nearest-neighbor seam cell nuclei over time ( Figure 5B–D , Figure 5—figure supplement 1 ) . In ‘X’ and ‘Y’ dimensions , seam cell nuclei exhibited similar movement patterns , remaining largely stationary in ‘Y’ ( Figure 5—figure supplement 1 ) , and moving inwards ( toward the origin ) in ‘X’ ( Figure 5—figure supplement 1 ) at similar rates . In contrast , seam cell nuclei movement along the ‘Z’ direction was more heterogeneous . For example , the distance between the origin and nuclei of seam cell pair H0 , measured from the fitted data , changed from 2 . 4 µm to 23 . 8 µm over elongation ( Figure 5B ) , while the distance between seam cell nuclear pairs V6 and T remained essentially constant , at 22 . 5 µm ( Figure 5D ) . Thus , the rate of increase in distance between the origin and H0 was significantly greater than the increase in distance between V6 and T , over the same period . Other adjacent nuclear pairs separated at roughly similar rates from start to end of elongation ( these pairs increased in distance 6 . 8 ± 2 . 8 µm , mean ± SD from 7 adjacent pairs of seam cell nuclei , again derived from the fitted data in Figure 5C ) . These trends were not the results of artifacts in our fitting procedure , as they were evident also in the raw , averaged data ( compare left and right graphs in Figure 5B-D ) . The apparent differences in X- and Z- pre- and post-elongating seam cell nuclei positions that we observe are consistent with the asymmetric morphology of the pre-elongating embryo . Since the embryo starts out in a tadpole-like shape with the head larger than the tail , the seam cell nuclei in the head must move a greater distance than the nuclei in the tail to achieve a uniform diameter in the elongated embryo . 10 . 7554/eLife . 10070 . 023Video 3 . Rendering of seam cell nuclear positions ( gray spheres ) in the developing embryo viewed dorsally , from the late 1 . 5-fold stage until hatching . The positions shown in the rendering are averaged , fitted values derived from five embryos , using the averaging and fitting procedure described in the text; the rendering thus represents a composite , 'best-guess' view as to seam cell evolution in a developing embryo . Times are indicated relative to the first fitted volume , and are 2 . 5 min apart . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 02310 . 7554/eLife . 10070 . 024Video 4 . The same data as in Video 2 , rendered from a side view . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 02410 . 7554/eLife . 10070 . 025Figure 5 . Variability in seam cell nucleus axial movement in the elongating embryo . ( A ) Snapshots of the elongating embryo near start ( Volume 30 , left ) and end ( Volume 113 ) of elongation . Seam cell nuclei volumes are indicated as filled spheres , L/R axes are as indicated , seam cell nuclear identities indicated at the side of each snapshot , as is the origin ( nose , ‘O’ ) . See also Videos 3 , 4 . Scalebar: 10 μm . ( B–D ) Axial differences over the course of elongation between adjacent seam cell nucleus pairs , sorted into greatest ( B ) , intermediate ( C ) , and least ( D ) bins , corresponding to red , gray , and blue coloring indicated in ( A ) . Left graphs: raw , averaged data ( as in Figure 4E , 4F ) . Right graphs: fitted data ( as in Figure 4G , 4H ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 02510 . 7554/eLife . 10070 . 026Figure 5—figure supplement 1 . Seam cell nucleus XY movement in the elongating embryo . ( A–D ) Snapshots of the elongating embryo at start ( above dashed line ) and end ( below dashed line ) of elongation , as shown in lateral ( X motion , A ) and dorsal-ventral ( Y motion , C ) views . Distances from the origin in X ( B , C ) and Y ( E , F ) are also shown for each seam cell nucleus . Both averaged ( B , E ) and fitted ( C , F ) distances are displayed . Scalebars: 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 026 Embryo elongation is thought to be dependent on an actin-based contractile mechanism ( Priess and Hirsh , 1986 ) . The complex , position-dependent motion we observed is likely inconsistent with a simple , uniform contraction , as this phenomena cannot explain our finding that different regions of the embryo elongate at markedly different rates . To our knowledge , current models of embryo elongation have not taken into account the differential elongation we observed across the worm body . We expect that incorporating additional data derived from cell positions and subcellular markers ( especially cytoskeletal [Priess and Hirsh , 1986; Gally et al . , 2009] ) in the embryo would help further refine existing models ( Ciarletta et al . , 2009 ) of embryo elongation . Currently , building a composite model of neuronal positions and morphological development in the embryo depends on pooling distinct datasets from many independent embryos . Given our experience tracking seam cell nuclei , we next turned our attention to modeling the 4D motion of neurons and neurite outgrowth in the elongating worm embryo as a proof of concept for a neurodevelopmental atlas ( Figure 6A , Videos 5 , 6 ) . Four of the five embryos used in constructing our seam cell model also had neuronal cell bodies marked with a pceh-10::GFP construct; neurons included AIYL/R , CANL/R , and ALA . We manually annotated the position of these neuronal cell bodies , then temporally aligned , averaged , and fitted the positions as we did the seam cell nuclei ( Figure 4 ) . The axial motion of these neurons was qualitatively similar to the seam cell nuclei and could be well described by the three parameter logistic function . However , their XY motion appeared different than the seam cell nuclei . For example , the lateral motion of CANs could not be easily described by a simple function , so we used a 50 point smoothing of the averaged data as our 'fit' . The ALA X motion was better described by a 4th degree polynomial than a power law , so we used the former function to fit the data ( Figure 4—figure supplement 4 , Table 1 ) . As evident by their axial displacements , ALA and AIYL/R moved similarly to nearby seam cell nuclei ( Figure 6B ) . In contrast , CANs moved faster than adjacent seam cell nuclei , suggesting a more 'active' mode of migration ( Figure 6C ) . Finally , the motion of ALA and CANs ( especially CANL ) were considerably more variable between datasets than the seam cell nuclei ( Figure 4—figure supplement 3 , Supplementary files 2 , 3 , 4 ) . While it is currently unclear whether this variability is strain-dependent or reflects underlying biology , this observation underscores the need to study multiple embryos and assess the degree to which cellular motion is stereotyped in elongating embryos . 10 . 7554/eLife . 10070 . 027Video 5 . Rendering of neurons and neurites , in the context of seam cell nuclei shown in Videos 2 , 3 . As in these videos , all positions are averaged , fitted values derived from multiple embryos . View is from dorsal perspective . Red spheres represent CAN cell bodies , yellow spheres represent AIY cell bodies , and blue spheres and lines correspond to ALA and its neurites . ALA and AIY cell bodies appear to closely track neighboring seam cells during elongation , while the CAN neurons actively migrate . ALA neurite outgrowth starts toward the end of elongation and continues after most other morphological changes have ceased . Times are indicated relative to the first fitted volume , and are 2 . 5 min apart . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 02710 . 7554/eLife . 10070 . 028Video 6 . The same data as in Video 4 , rendered from the side . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 028 To examine neurite outgrowth clearly , we created a two-color strain with GFP-labeled untwisting markers and a pceh-10::mCh construct to label neuronal cell bodies and neurites . We observed substantial mosaicism in terms of which cells were labeled from one embryo to the next with this strain . Although neurons were labeled with an extrachromosomal array and a certain degree of mosaicism could be anticipated , labeling differences from one animal to the next hindered our ability to track both ALA and CAN outgrowths . Nevertheless , we were able to obtain two datasets where the ALA neuron was labeled throughout most of our imaging period . ALA is a single neuron with a cell body located in the dorsal portion of the head; a pair of long neurites extend ventrally from this cell body into the nerve ring , and then turn and extend posteriorly along the lateral nerve cord ( White et al . , 1986 ) . Left and right ALA outgrowths could be readily identified and annotated in both twisted and untwisted embryos ( Figure 6D , Figure 6—figure supplement 1 ) . In modeling the left and right neurite shapes , we simplified them by annotating them as three distinct points ( ALA: cell body; AxR1 or AxL1: point at which the neurite turns to extend posteriorly; AxR2/L2: neurite terminus; Figure 6E ) . We then measured the 3D displacements ( relative to the nose , as before ) of each independent point , shifting , averaging and fitting the data derived from two embryos ( as outlined in Figure 4 and illustrated in Figure 6—figure supplement 1 ) , to yield a noise-free representation of the neurite ( Table 1 ) . Aligning the fitted neurites to our reference embryo allowed inspection of ALA neurite growth in the context of the elongating embryo ( Figure 6A , Videos 5 , 6 ) , revealing that neurite outgrowth continued to occur for ~240 min after the other cells assumed their final positions at the end of elongation . We also segmented growing ALA neurites at several points in development to demonstrate that straightened images can be used to generate volumetric reconstructions of cell morphology throughout development ( Figure 6—figure supplement 2 . ) We are unaware of any other work that has modeled the growth and positions of neurites in the post-twitching embryonic regime ( for C . elegans or any other model organism ) . 10 . 7554/eLife . 10070 . 029Figure 6 . Neurons and neurites in the developing embryo . ( A ) Early ( left ) and late ( snapshots ) in the elongating embryo . Gray spheres: seam cell nuclei; ALA cell body: blue sphere; ALA neurites: blue lines; AIY cell bodies: yellow spheres; CAN cell bodies: red spheres . Compare to Videos 5 , 6 . ( B ) ALA ( top ) , AIYR ( middle ) , and CANR ( bottom ) axial trajectories ( red curves ) in relation to neighboring seam cells ( blue curves ) . ALA and AIY cells maintain their relative position with respect to the rest of the elongating body , while CANs migrate faster than neighboring seam cells . ( C ) ALA cell body and neurite in the twisted embryo , highlighting morphological features ( ALA: ALA cell body; AxL1/R1: junction between ventral and posterior neurite extension; AxL2/R2: posterior tip of the ALA neurites ) . ( D ) Axial trajectory of ALA neurite tip in relation to indicated seam cells . ( E ) Top and side models of ALA in untwisted reference frame , indicating neurite bend and terminus . Compare to Figure 6—figure supplement 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 02910 . 7554/eLife . 10070 . 030Figure 6—figure supplement 1 . Shifting , averaging and fitting procedures for modeling the ALA neurite . ( A ) Axial distance ( measured from the origin point ) of the ALA cell body for two ALA datasets . Similar to seam cells , axial distance increases during elongation and then plateaus once elongation has finished . ( B ) ALA axial distance , derived from averaging the axial distances of the shifted ALA datasets . ( C ) A fitted curve describing axial motion of ALA , after averaging in ( B ) . ( D–F ) Shifting , averaging , and fitting of axial motion for the AxR1 point in the ALA neurite ( the position at which the ventral growth of the neurite changes to posterior growth ) . As there is little axial growth in this part of the neurite , axial movement mirrors that of the ALA cell body . ( G–I ) Shifting , averaging , and fitting of axial motion for AxR2 , the tip of the posteriorly-growing ALA neurite . The posterior-ward axial extension of the neurite leads to a different pattern of axial movement than for the R1 point or the ALA cell body . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 03010 . 7554/eLife . 10070 . 031Figure 6—figure supplement 2 . Segmentation of neurons and neurites in the untwisted embryo . ( A ) Exemplary data for a twofold embryo . Left column: raw data . Right column: segmented data . The red neuron is RMED , the orange neuron is ALA , and the purple neurons are the cell bodies of the AIY neurons . At this point in embryonic development , the ALA neurites have extended ventrally and begun extending posteriorly , but have not undergone much extension . ( B ) Same embryo and color-scheme as in ( A ) , but now early threefold stage . More ALA neurite extension is evident . ( C ) Same as in ( B ) , but at a later stage . The ALA neurites have extended approximately 1/3 of the way to the tail at this point . In addition , neurite extension can also be observed in the AIY and RMED neurons . Scalebar: 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 031 The C . elegans cell lineage is invariant ( Sulston et al . , 1983 ) , and tracking cells in the L1 larva has revealed that cellular positions in post-hatching animals are relatively stereotyped ( Long et al . , 2009 ) . Our work suggests that this positional stereotypy extends to the cells in the late embryo as well . However , we also found that in the case of cells or structures which actively migrate , such as the CAN neurons and ALA neurites , there seems to be greater variability in terms of end position and growth rate . To some extent this is not surprising; as these cells and neurites move longer distances than most other cells , and depend on actively finding their way in a complex environment ( as opposed to passive movement in response to elongation ) , there may be more room for variability in how they travel and reach their destinations . On a more general level , we also observed variability in the temporal shifts necessary to align each elongating embryo to the reference dataset ( embryo 1 ) . Some of this variability may be due to relatively mundane explanations: embryos were at slightly different ages when imaging began , and temperature was moderately controlled ( to within 2 to 3°C both during imaging and strain growth ) . Intrinsic developmental variability , caused by maternal effects or exposure to imaging could also have played a role in the slightly different patterns of development we observed across embryos . Expanding the work we describe here to other migrating and non-migrating neurons should make clear whether there actually is a difference in positional variability between migrating and non-migrating cells . Adding additional data to our 4D model is conceptually straightforward: strains with distinguishable neurons can be crossed into the untwisting background , untwisted , trajectories of cells and outgrowths fitted , and subsequently registered with previously derived data . 'Filling in' the positions of all neurons and outgrowths in the developing embryo would form the basis of the 4D atlas of neurodevelopment , and could be combined with functional activity mapping and gene expression data to provide a more comprehensive picture of animal development in late embryogenesis . Our untwisting and annotation plugin is designed to be flexible , so that it can be applied to most problems involving tracking position and morphology of distinguishable structures in the nematode embryo . The core of the plugin relies on defining the sides of the worm embryo; although our work uses a specific set of markers , we note that any other markers which define the edges of the worm body should also work . The annotation capability is also flexible; as it is based on manual annotation , almost any distinct structure can be annotated . Finally , while the isotropic resolution of the diSPIM is very helpful in resolving fine embryonic detail ( Figure 1—figure supplement 2 ) , our untwisting algorithm is compatible with other high-resolution imaging methods . For example , we used a super-resolution two-photon instant structured illumination microscope ( 2P ISIM ) ( Winter , 2014 ) to image and untwist a bent L2 larval worm , obtaining clear images of this relatively large specimen ( Figure 1—figure supplement 4 , Video 7 ) . Our plugin is designed specifically for untwisting nematode embryos , and as such is unlikely to be immediately applicable to other biological systems without substantial modification ( we know of few non-nematode systems that have the same vermiform shape and degree of twisting and movement ) . However , some of the more general concepts we implement , such as the benefit of aligning and pooling information derived different datasets to generate an overall 4D view of development , are likely applicable to more systems than just the worm . 10 . 7554/eLife . 10070 . 032Video 7 . Rotating view of an untwisted L2 worm . The image was imported into ImageJ and the Magenta LUT was applied to the stack . The volume shown here corresponds to the untwisted volume in Figure 1—figure supplement 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 032 Despite the power of our semi-automated approach , several areas for improvement remain . Automated lattice-building assumes the embryo has 20-22 seam cell nuclei on which the lattice is based; in early periods of elongation ( especially the 1 . 5- to 2-fold transition ) expression is absent in some seam cell nuclei , requiring manual lattice-building . In addition , time spent in editing automatic segmentation and lattice generation results in ~8 hr of manual work when untwisting an embryo spanning 100–150 timepoints . Fully automated untwisting is not currently feasible , but the development of alternative markers may enable this goal . Second , although the positions of cells and neurites in the growing embryo can be determined with micron-scale precision , and placed in context with their neighbors , additional methods are needed to place the full morphological volume of a given cell within the untwisted embryo . While our data are of sufficient quality to segment such morphology in an untwisted animal ( Figure 6—figure supplement 2 , Video 8 ) , the general question about how to combine morphological segmentations from distinct , untwisted embryos remains . New methods developed for pre-twitching embryos may prove useful in this regard ( Santella et al . , 2015 ) . 10 . 7554/eLife . 10070 . 033Video 8 . Rotating three-dimensional view of the segmentation shown in Figure 6—figure supplement 2 . The volume was segmented and rendered in Imaris . DOI: http://dx . doi . org/10 . 7554/eLife . 10070 . 033 A more significant and long-term set of technical problems for completing the neurodevelopmental atlas relates to the generation of fluorescent markers and strains that provide sparse , optically resolvable neurons . Most fluorescent strains label multiple neurons that are too close in space and time to be easily resolved – possible strategies to 'separate' these neurons might include 'Brainbow' ( Livet et al . , 2007 ) ( spectral separation of densely labeled neurons ) or heat-shock-based approaches ( Halfon et al . , 1997; Bacaj and Shaham , 2007 ) ( temporal separation of densely labeled cells ) . Even if such strains are built , the identity of the resulting neurons will need to be verified . As lineaging ( Bao , 2006 ) in C . elegans has been carried out to just before twitching begins ( Giurumescu et al . , 2012 ) , in principle neurons can be identified by matching early expression to lineage data . If expression turns on after twitching , lineaging would also need to be extended into the post-twitching regime . Such 'deep lineaging' , or tracking the coordinates of all nuclei through twitching would be a valuable and complementary effort to untwisting . Finally , we note that the expression pattern of fluorescent proteins within individual neurons could be further optimized . For almost all strains ( except DCR4209 which contained membrane-targeted mCherry ) , fluorescent proteins were expressed cytoplasmically . An improved strategy would combine such cytoplasmic labeling with membrane targeting , better filling out very thin neuronal outgrowths that otherwise might be missed due to low expression; a similar strategy was adopted in super-resolution microscopy to trace thin neurites ( Lakadamyali et al . , 2012 ) . Nematode strains were kept at 20°C , and grown on NGM media plates seeded with E . coli OP50 . The untwisting strain is SLS1 xnIS17 [dlg-1::GFP + rol-6]; wIS51 [SCM::GFP] . Strains used to construct SLS1 were FT63 [xnIS17 dlg-1::GFP + rol-6] ( Totong et al . , 2007 ) and JR667 [wIS51 SCM::GFP] ( Terns et al . , 1997; Koh and Rothman , 2001 ) . Strains were crossed together to generate an animal containing these transgenes . Strains imaged for the paper include SLS1 , DCR4209 , and DCR4221 . Strain DCR4209 contained the following transgenes: olaex2457 [P . ceh-10::mCh-PHd ( 25 ng/μL ) + unc122::RFP ( 30ng/μL ) ]; xnIS17 [dlg-1::GFP + rol-6]; wIS51 [SCM::GFP] . To create olaEX2457 , 4132 bp upstream of the transcriptional start site were isolated using the following promoters: Forward AGC TCC TGC ACT CTT CTG ATC; Reverse CAC AAG AGA AAA GTG GCT GCT TAT C . Strain DCR4221 contained the following transgenes: lqIS4 ( Wenick and Hobert , 2004 ) [ceh-10promA::GFP]; xnIs17 [dlg-1::GFP + rol-6]; wIs51 [SCM::GFP] . Detailed subcloning information for olaex 2457 can be provided upon request . As previously described , worm samples were prepared for diSPIM ( Wu et al . , 2011; Bao and Murray , 2010; Kumar et al . , 2014 ) : adult animals were placed in buffer and cut to liberate embryos , embryos transferred to poly-L-lysine-coated coverslips in the diSPIM imaging chamber , and imaged once they reached the bean-to-comma stage of embryonic development . All data were acquired on either a first-generation diSPIM ( Wu et al . , 2013 ) or a more recent fiber-coupled version ( Kumar et al . , 2014 ) . Dual-color data were taken sequentially ( first the 488-nm excitation for the GFP channel , and then 561-nm excitation for the mCherry channel ) in a plane-by-plane ( 5 ms GFP collection , 5 ms mCherry collection per axial position in the embryo ) fashion . Given 50 planes per view , and two perpendicular views , this resulted in an acquisition time of 1 s per 2-color diSPIM volume . For most datasets in this paper ( embryos 2-8 , as referred to elsewhere in the text ) , single-color volumes were acquired every 5 min , but for one datastet ( embryo 1 ) , single-color volumes were acquired every 2 . 5 min . Dual-color acquisitions were used to track ALA neurite outgrowth ( embryos 7 and 8 ) . Acquisition code , written in LabVIEW , is available at http://www . wormguides . org/dispim/dispim-downloads . For 2P ISIM imaging , we used 900-nm excitation and two 680-nm short-pass filters ( Semrock , FF01-680/SP-25 ) in our emission path to filter illumination light . L2 larvae of strain SLS1 were immobilized with 50 mM levamisole ( Sigma-Aldrich; St Louis , MO ) and imaged on an agarose pad sandwiched between two #1 . 5 coverslips . Volumetric images of the entire specimen were acquired by manual XY translation of the stage between fields of view . Each raw frame was acquired in 200 ms; data used in this paper were derived by averaging six raw frames per axial position . Axial positions were spaced 0 . 333 μm apart . Individual 3D image stacks were stitched and overlaid to reconstruct the entire L2 stage worm using a custom plugin developed for MIPAV ( available online at www . cit . nih . gov/mipav ) . After stitching , the reconstructed L2 stage worm volume was further processed with 40 iterations of Richardson-Lucy deconvolution . Cells from different embryo datasets exhibited qualitatively and quantitatively similar trajectories , so we aligned and then combined them to generate averaged , noise-free trajectories . First , coordinate trajectories ( X , Y , or Z positions ( Figure 3I ) as a function of time ) were 'cleaned' to remove obvious outliers , or to linearly interpolate gaps in the raw data ( Figure 3—figure supplement 3 ) . Second , the axial ( ‘Z’ ) coordinate of each cell was fitted to a three parameter logistic function ( Table 2 ) using Growth II ( Pisces Conservation ) or MATLAB ( Mathworks ) software , as this function provided a better fit than other three parameter growth curves , and did not require careful tuning of initial parameter values , as did the four parameter growth curves we tested ( Figure 4—figure supplement 4 , Table 1 ) . Third , we aligned datasets from embryos 2-5 ( volumes recorded every 5 min ) to embryo 1 ( volumes recorded every 2 . 5 min ) , by ( i ) determining the inflection time point ( ‘C’ in Table 1 , 2 ) for each cell’s fitted axial position and ( ii ) shifting the data an integral number of time points so that the inflection time points from embryos 2-5 agreed with the inflection point for embryo 1 . For example , for the data shown in Figure 4—figure supplement 1 for seam cell V3R nuclei , embryo 1 had inflection point 42 . 6 , and embryo 4 had inflection point 37 . 7 , so the V3R trajectory for embryo 4 was shifted 42 . 6-37 . 7 = 5 timepoints to the right , to match the trajectory for embryo 1 . The same integer time point shift was then applied to the corresponding ‘X’ and ‘Y’ coordinate trajectories for each cell . Fourth , after shifts were applied , coordinate trajectories were averaged . Finally , to generate noise-free trajectories , the average trajectories were fitted ( functions chosen for the fits are shown in Figure 4—figure supplement 4 and Table 1 ) . To examine the degree to which embryo positions agreed after the shifting procedure , we computed SD between embryo positions at each time point ( Supplementary file 2 ) . With the exceptions of the CAN neurons , the X and Y positions of cells were stereotyped to within 2 μm , and the Z positions within 10 μm . The majority of cells’ coordinate trajectories were well described by power ( X coordinate ) , linear ( Y coordinate ) , and three-parameter logistic ( Z coordinate ) functions ( Figure 4—figure supplement 4 , Tables 1 , 2 , Supplementary file 3 ) . However , two cell types , CAN and AIY , were not well described by any of the common fitting functions we surveyed ( e . g . power , exponential , Gaussian , rational functions ) . For these cells , we instead used 50 point smoothing ( for CAN X and Y coordinates ) or a quartic polynomial function ( for AIY X coordinates ) to reduce noise in the shifted , averaged trajectories . To estimate how well the curve fitting described the averaged trajectories , we calculated the absolute differences between averaged and fitted coordinates at each time point , and then calculated the means and SD of these differences across time . These data are recorded in Supplementary file 3 as μ avg-fit , time and σ avg-fit , time . We also computed the average over all seam cell nuclei of these average differences to generate <μ Xavg-Xfit , time>seam cell; <μ Yavg-Yfit , time>seam cell; and <μ Zavg-Zfit , time>seam cell resulting in values of 0 . 5 µm , 0 . 6 µm , and 3 . 7 µm . In XY , similar average statistics were found for all cell types . In the Z coordinate , CANL stood out as more variable , as its μ Zavg-Zfit , time was 10 . 2 µm ( with a corresponding SD of 9 . 3 µm ) . We suspect the deviation between CANL data and fit arises more from the inherent variability with CAN cells ( Supplementary file 2 ) than inherent problems with the fitting function choice . In several locations , we report population averages taken across some combination of seam cell nuclei , time , or embryos . We use μ and s to denote mean and SD , and <X>Y indicates an average of quantity X , across Y . For example , <μX>embryo stands for the population average across embryos , of mean X coordinate positions ( each derived from an individual embryo ) . For untwisting control measurements , we measured the difference between twisted and untwisted volumes for various distance metrics ( between seam cells and along the pharynx ) . For each embryo , we computed the mean difference μDifference , time and standard deviation σDifference , time across time , and averaged these quantities to calculate a population <μDifference , time>embryo and population <σDifference , time>embryo across embryos . To estimate inter-embryo and inter-seam cell nuclei positional ( X , Y , and Z coordinates ) variability over elongation , we shifted data from embryos until they overlaid in time , and next computed the SD between embryo positions at each aligned timepoint . Mean standard deviations <σX>time; <σY>time; and <σZ>time over all timepoints were calculated , and are reported in Supplementary file 2 . To compute <<σX>time>seam cell; <<σY>time>seam cell and <<σZ>time>seam cell , we averaged mean SD across the 20 seam cell nuclei . In accordance with eLife policy , we have made our raw annotation data and quality control measurements available: Supplementary file 4 contains the 3D positions of seam cell nuclei , neurons , and growing ALA axons . These data were used in Figures 3–6 . Data are provided before outlier removal , shifting , and fitting . Supplementary file 5 contains the quality control measurements ( distances between seam cell nuclei in the H0 and T pairs before and after untwisting , and pharyngeal contour lengths before and after untwisting ) used to generate Figure 2 .	Understanding how the brain and nervous system develops from a few cells into complex , interconnected networks is a key goal for neuroscientists . Although researchers have identified many of the genes involved in this process , how these work together to form an entire brain remains unknown . A simple worm called Caenorhabiditis elegans is commonly used to study brain development because it has only about 300 neurons , simplifying the study of its nervous system . The worms are easy to grow in the laboratory and are transparent , allowing scientists to observe how living worms develop using a microscope . Researchers have learned a great deal about the initial growth of the nervous system in C . elegans embryos . However , it has been difficult to study the embryos once their muscles have formed because they constantly twist , fold , and move , making it hard to track the cells . Now , Christensen , Bokinsky , Santella , Wu et al . have developed a computer program that allows scientists to virtually untwist the embryos and follow the development of the nervous system from its beginning to when the embryo hatches . First , images are taken of worm embryos that produce fluorescent proteins marking certain body parts . The program , with user input , labels the fluorescent cells in the images , which indicates how the embryo is bending and allows the program to straighten the worm . The program can also track how cells move around the embryo during development and show the positional relationships between different cells at different stages of development . Christensen et al . have made the program freely available for other researchers to use . The next step is to increase automation , making the software faster and more straightforward for users . Ultimately , the software could help in the challenge to comprehensively examine the development of each neuron in the worm .	[ "Abstract", "Introduction", "Results", "Discussion", "M" ]	[ "computational", "and", "systems", "biology", "developmental", "biology", "tools", "and", "resources" ]	2015	Untwisting the Caenorhabditis elegans embryo
To generate energy efficiently , the cell is uniquely challenged to co-ordinate the abundance of electron transport chain protein subunits expressed from both nuclear and mitochondrial genomes . How an effective stoichiometry of this many constituent subunits is co-ordinated post-transcriptionally remains poorly understood . Here we show that Cerox1 , an unusually abundant cytoplasmic long noncoding RNA ( lncRNA ) , modulates the levels of mitochondrial complex I subunit transcripts in a manner that requires binding to microRNA-488-3p . Increased abundance of Cerox1 cooperatively elevates complex I subunit protein abundance and enzymatic activity , decreases reactive oxygen species production , and protects against the complex I inhibitor rotenone . Cerox1 function is conserved across placental mammals: human and mouse orthologues effectively modulate complex I enzymatic activity in mouse and human cells , respectively . Cerox1 is the first lncRNA demonstrated , to our knowledge , to regulate mitochondrial oxidative phosphorylation and , with miR-488-3p , represent novel targets for the modulation of complex I activity . In eukaryotes , coupling of the mitochondrial electron transport chain to oxidative phosphorylation ( OXPHOS ) generates the majority of ATP that fulfils cellular energy requirements . The first enzyme of the electron transport chain , NADH:ubiquinone oxidoreductase ( complex I ) , catalyses the transfer of electrons from NADH to coenzyme Q10 , pumps protons across the inner mitochondrial membrane and produces reactive oxygen species ( ROS ) . Mammalian mitochondrial complex I dynamically incorporates 45 distinct subunits into a ~ 1 MDa mature structure ( Vinothkumar et al . , 2014; Guerrero-Castillo et al . , 2017 ) . It is known that oxidatively damaged subunits can be exchanged in the intact holo-enzyme ( Dieteren et al . , 2012 ) , but how this process may be regulated is poorly understood . The efficiency and functional integrity of OXPHOS are thought to be partly maintained through a combination of tightly co-ordinated transcriptional and post-transcriptional regulation ( Mootha et al . , 2003; van Waveren and Moraes , 2008; Sirey and Ponting , 2016 ) and specific sub-cytoplasmic co-localisation ( Matsumoto et al . , 2012; Michaud et al . , 2014 ) . The nuclear encoded subunits are imported into the mitochondria after translation in the cytoplasm and their complexes assembled together with the mitochondrially encoded subunits in an intricate assembly process ( Perales-Clemente et al . , 2010; Lazarou et al . , 1793; Vogel et al . , 2007 ) . Mitochondrial biogenesis is co-ordinated first transcriptionally from both genomes ( Scarpulla et al . , 2012 ) , and then post-transcriptionally by regulatory small noncoding RNAs such as microRNAs ( miRNAs ) ( Dumortier et al . , 2013; Li et al . , 2012 ) . Recently , SAMMSON a long noncoding RNA ( lncRNA ) was found to bind p32 and , within mitochondria , enhanced the expression of mitochondrial genome-encoded polypeptides ( Leucci et al . , 2016 ) . Nuclear-encoded and cytosol-located lncRNAs have not yet been implicated in regulating mitochondrial OXPHOS ( Vendramin et al . , 2017 ) despite being surprisingly numerous and often found localised to mitochondrion- and ribosome-adjacent portions of the rough endoplasmic reticulum ( van Heesch et al . , 2014 ) . It is here , on the ribosome , that turnover of miRNA-targeted mRNAs frequently occurs during their translation ( Tat et al . , 2016 ) . Here we describe a novel mammalian conserved lncRNA , termed Cerox1 ( cytoplasmic endogenous regulator of oxidative phosphorylation 1 ) . Cerox1 regulates complex I activity by co-ordinately regulating the abundance of at least 12 complex I transcripts via a miRNA-mediated mechanism . Cerox1 knockdown decreases the enzymatic activities of complexes I and IV . Conversely , elevation of Cerox1 levels increases their enzymatic activities , halves cellular oxidative stress , and protects cells against the cytotoxic effects of the complex I inhibitor rotenone . To our knowledge , Cerox1 is the first lncRNA modulator of normal mitochondrial energy metabolism homeostasis and cellular redox state . The miRNA-dependency of Cerox1 and the regulation of associated OXPHOS transcripts are supported by: ( i ) direct physical interaction of miR-488–3p with Cerox1 and complex I transcripts; ( ii ) decrease or increase in Cerox1 and complex I transcripts following miR-488–3p overexpression or inhibition , respectively; ( iii ) miR-488–3p destabilisation of wildtype Cerox1 , but not a Cerox1 transcript containing a mutated miR-488–3p miRNA recognition element ( MRE ) seed region; and , ( iv ) absence of the OXPHOS phenotypes either in cell lines deficient in microRNA biogenesis or when Cerox1’s predicted miR-488–3p response element is mutated . The miRNA-dependent role of Cerox1 illustrates how RNA-interaction networks can regulate OXPHOS and that lncRNAs represent novel targets for modulating OXPHOS enzymatic activity . Cerox1 was selected for further investigation from among a set of central nervous system-derived polyadenylated long non-coding RNAs identified by cDNA sequencing ( GenBank Accession AK079380 , 2810468N07Rik ) ( Carninci et al . , 2000; Ponjavic et al . , 2007 ) . Mouse Cerox1 is a 1 . 2 kb , two exon , intergenic transcript which shares a bidirectional promoter with the SRY ( sex determining region Y ) -box 8 ( Sox8 ) gene ( Figure 1A ) . A human orthologous transcript ( CEROX1 , GenBank Accession BC098409 ) was identified by sequence similarity and conserved synteny ( 60–70% nucleotide identity within alignable regions , Figure 1B , C ) . Both mouse and human transcripts have low protein coding potential ( Materials and methods , Figure 1—figure supplement 1A ) and no evidence for translation from available proteomic datasets . Four human or mouse data types supported CEROX1 as having an important organismal role . First , its promoter shows a greater extent of sequence conservation than the adjacent SOX8 promoter and its exons are conserved among eutherian mammals ( Figure 1B ) . Second , from expression data , its levels in primary tissues and cells are exceptionally high , within the top 13% of a set of 879 lncRNAs with associated cap analysis of gene expression ( CAGE ) clusters ( Figure 1D , E ) . Expression is particularly high in neuroglia , neural progenitor cells and oligodendrocyte progenitors ( Bergmann et al . , 2015; Mercer et al . , 2010; Zhang et al . , 2014 ) ( Figure 1—figure supplement 1B ) . Cerox1 is notable in its higher expression in the adult brain than 64% of all protein coding genes . Cerox1 expression is also developmentally regulated . For example , it is a known marker gene for type one pre-haematopoietic stem cells in the dorsal aorta ( Zhou et al . , 2016 ) and its expression is high in mouse embryos beyond the 21 somite stage ( https://dmdd . org . uk/ ) . High expression of Cerox1 in the brain was confirmed using quantitative real-time PCR ( qPCR ) for both mouse and human orthologous transcripts ( Figure 1—figure supplement 1C , D ) . Third , single nucleotide variants are significantly associated both with CEROX1 expression and anthropomorphic traits , measured in the UK Biobank . These lie within an 85 kb interval encompassing the CEROX1 locus , and 5’ regions of SOX8 and LMF1 genes . For example , rs3809674 is significantly associated with standing and sitting heights; arm fat-free mass ( left or right ) ; arm predicted mass ( left or right ) ; trunk fat-free or predicted mass; and whole body fat-free mass ( p<5×10−8 , Supplementary file 1 ) ; this variant is also a CEROX1 expression quantitative trait locus ( eQTL ) for 30 GTEx tissues ( p≤0 . 05 ) , some with absolute normalised effect sizes reaching 0 . 83 ( Figure 1—figure supplement 1E ) . Genetically determined expression change in CEROX1 therefore could explain , in part , variation in these anthropomorphic traits . These variations affect a large fraction of the human population ( minor allele frequency of 28% in 1000 Genomes data ) . An alternative interpretation that rs3809674 ( and linked variants ) act on anthropomorphic traits through LMF1 , rather than CEROX1 , is consistent with this variant being an eQTL for LMF1 , but is not consistent with the known protein function of LMF1 , a lipase maturation factor , because the associated anthropomorphic traits relate only to fat-free mass . A summary-data-based Mendelian randomization analysis that uses rs3809674 as an instrumental variable , predicts an effect of CEROX1 gene expression on glioma risk ( Melin et al . , 2017 ) . Finally , it has recently been demonstrated in a mouse model of haematopoietic lineage differentiation that Cerox1 depletion may impair the contributions of stem cell or B cell differentiation to haematopoiesis ( Delás et al . , 2019 ) . In mouse neuroblastoma ( N2A ) cells the most highly expressed Cerox1 transcript ( Figure 1—figure supplement 1F , G ) is enriched in the cytoplasmic fraction ( Figure 1F ) with a short half-life of 36 ± 16 mins ( Figure 1—figure supplement 1H ) and is mainly associated with the ribosome-free fraction ( Figure 1—figure supplement 1I ) . Expression of Cerox1 was manipulated by transient overexpression or shRNA-mediated knockdown . Twelve shRNAs were tested for the ability to knock-down Cerox1 ( Figure 2—figure supplement 1A ) . One of these ( sh92 ) decreased expression levels by greater than 60% , with the next best shRNA ( sh1159 ) decreasing expression by approximately 40% . As expected from their sharing of the Cerox1-Sox8 bidirectional promoter , CRISPR-mediated activation and inhibition of the Cerox1 locus was not specific as it led also to changes in Sox8 expression ( Figure 1A , Figure 2—figure supplement 1B ) . However , decreasing Cerox1 levels in N2A cells by shRNA or transient overexpression had no effect on the expression of neighbouring genes ( Figure 2—figure supplement 1C ) . In contrast , Cerox1 overexpression led to differential expression of 286 distal genes ( q < 0 . 05 , Bonferroni multiple testing correction; Supplementary file 2 ) , of which an unexpected and large majority ( 83%; 237 ) were upregulated ( p<10−6; binomial test ) . Our attention was immediately drawn to the considerable ( ≥20 fold ) enrichment of the mitochondrial respiratory chain gene ontology term among upregulated genes ( Figure 2A ) . The mitochondrial electron transport chain ( ETC ) consists of five multi-subunit complexes encoded by approximately 100 genes of which only 13 are located in the mitochondrial genome . The 15 ETC transcripts that show statistically significant differential expression after Cerox1 overexpression are nuclear encoded ( Figure 2B , C ) with the greatest changes observed by qPCR for complex I subunit transcripts ( Figure 2—figure supplement 1D ) . Twelve of 35 nuclear encoded complex I subunits or assembly factors transcripts increased substantially and significantly ( >40% ) following Cerox1 overexpression; we consider these to be gene expression biomarkers for Cerox1 activity in the mouse N2A system ( Figure 2C ) . In the reciprocal Cerox1 knock-down experiment , all 12 were reduced in abundance using sh92 , three significantly , with a concordant pattern observed for the less effective shRNA , sh1159 ( Figure 2—figure supplement 1E , F ) . Taken together , these results indicate that Cerox1 positively and co-ordinately regulates the levels of many mitochondrial complex I transcripts . Increased abundance of OXPHOS subunit transcripts , following Cerox1 overexpression , was found to elevate protein levels . Western blots using reliable antibodies for the key complex I catalytic core proteins NDUFS1 and NDUFS3 showed approximately 2 . 0-fold protein level increases that surpassed their ~ 1 . 4 fold transcript level changes ( median 2 . 4 and 1 . 4-fold increases [p=0 . 0013 and 0 . 002] , respectively; Figure 2D ) . Cerox1 transcript abundance is thus coupled positively to OXPHOS transcript levels and to their availability for translation , resulting in an amplification of the amount of protein produced . In summary , protein subunits of the same complex ( Complex I ) that are sustained at high abundance and with long half-lives ( Dörrbaum et al . , 2018; Mathieson et al . , 2018; Schwanhäusser et al . , 2011 ) ( Figure 2—figure supplement 1G ) , and whose transcripts are stable ( Schwanhäusser et al . , 2011; Friedel et al . , 2009; Sharova et al . , 2009; Tani et al . , 2012 ) ( Figure 2—figure supplement 1H ) and also have very high copy numbers in the cell ( Schwanhäusser et al . , 2011; Cao et al . , 2017 ) , can be increased two-fold , and co-ordinately , by the simple expediency of increasing the level of this single abundant lncRNA . These large effects on protein and mRNA copy number are associated with both metabolic and cellular phenotypes . Ten metabolites are significantly different in N2A cells overexpressing Cerox1 ( Figure 2E ) . These cells show a significant increase in the reduced glutathione to oxidized glutathione ratio ( GSH:GSSG , p=1 . 3×10−3 , Figure 2D Inset ) indicative of a more favourable cellular redox state . Cerox1-overexpressing cells also exhibited a 43% reduction in cell cycle activity , yet without a change in the proportion of live/dead cells or a deviation from normal cell cycle proportions ( Figure 2—figure supplement 1I , J , K ) . Cerox1 levels thus affect the overall timing of cell division . Increased translation of some complex I transcripts leads to increased respiration ( Shyh-Chang et al . , 2013 ) and , more specifically , to an increase in the enzymatic activity of complex I ( Alvarez-Fischer et al . , 2011 ) . To address this hypothesis we used oxidative phosphorylation enzyme assays to investigate whether changes in expression to a subset of subunits lead to a change in enzyme activity and oxygen consumption . Indeed , complex I and complex IV enzymatic activities increased substantially after Cerox1 overexpression ( by 22% , p=0 . 01; by 50% , p=0 . 003 , respectively; 2-tailed Student’s t-test; Figure 3A ) . Such rate increases for two of the eukaryotic cell’s most abundant and active enzymes were unexpected . We next measured oxygen consumption under these conditions using a Seahorse XFe24 Analyzer . These complexes’ more rapid catalytic rates resulted , unexpectedly , in large increases in: ( i ) overall basal oxygen consumption ( by 85% ) , ( ii ) ATP-linked oxygen consumption ( by 107% ) and , ( iii ) maximum uncoupled respiration ( by 59%; p=5×10−4 , p=1×10−4 , p=4×10−3 , respectively , 2-tailed Student’s t-test; Figure 3B ) . These increases in enzyme activities and mitochondrial respiration are expected to produce persistent and substantial increases beyond the already very high basal rate of ATP formation ( Rich , 2003 ) , due to the long-half lives of the Cerox1 sensitive complex I protein subunits ( Dörrbaum et al . , 2018; Mathieson et al . , 2018; Schwanhäusser et al . , 2011 ) . Conversely , after sh92-mediated Cerox1 knockdown complex I and complex IV enzymatic activities decreased significantly ( by 11% , p=0 . 03% and 19% , p=0 . 02 , respectively; Figure 3C ) , with concomitant large decreases in basal oxygen consumption ( 53% ) , ATP linked oxygen consumption ( 52% ) and maximal uncoupled respiration ( 61%; p=0 . 011 , p=0 . 034 , p=0 . 042 respectively , 2-tailed Student’s t-test; Figure 3D ) . These observed changes in enzymatic activity were not due to changes in mitochondria number because the enzymatic activities of complexes II , III and citrate synthase ( Figure 3A , B ) , and the mitochondrial-to-nuclear genome ratio ( Figure 3—figure supplement 1A , B ) , each remained unaltered by changes in Cerox1 levels . These data indicate that Cerox1 can specifically and substantially regulate oxygen consumption and catalytic activities of complex I and complex IV in mouse N2A cells . Complex I deficient patient cells experience elevated ROS production ( Pitkanen and Robinson , 1996 ) . In Cerox1 knockdown N2A cells ROS levels were increased significantly , by almost 20% ( p=4 . 2×10−6; Figure 4A ) . Conversely , in cells overexpressing Cerox1 , ROS production was nearly halved ( p=3 . 5×10−7; Figure 4A ) , and protein carbonylation , a measure of ROS-induced damage , was reduced by 35% ( p=1×10−3; Figure 4B ) . Knock-down of Cerox1 resulted in a 6 . 6% increase in protein carbonylation compared to the control ( p=0 . 05 , data not shown ) . The observed Cerox1-dependent reduction in ROS levels is of particular interest because mitochondrial complex I is a major producer of ROS which triggers cellular oxidative stress and damage , and an increase in ROS production is a common feature of mitochondrial dysfunction in disease ( Murphy , 2009 ) . We next demonstrated that the increased activities of complex I and complex IV induced by Cerox1 protect cells against the deleterious effects of specific mitochondrial complex inhibitors , specifically rotenone and sodium azide ( complex I and complex IV inhibitors , 37% and 58% respectively , p<0 . 01 ) ; conversely , Cerox1-knockdown cells were significantly more sensitive to rotenone and exposure to heat ( 12% , p<0 . 001% and 11% , p<0 . 01 respectively Figure 4C ) . Cells overexpressing Cerox1 and treated with rotenone , a complex I inhibitor , exhibited no significant difference in protein carbonylation ( data not shown ) . Taken together , these results indicate that elevation of Cerox1 expression leads to decreased ROS production , decreased levels of oxidative damage to proteins and can confer protective effects against complex I and complex IV inhibitors . Due to their positive correlation in expression and cytoplasmic localisation we next considered whether Cerox1 regulates complex I transcripts post-transcriptionally by competing with them for the binding of particular miRNAs . To address this hypothesis , we took advantage of mouse Dicer-deficient ( DicerΔ/Δ ) embryonic stem cells that are deficient in miRNA biogenesis ( Nesterova et al . , 2008 ) . We first tested Cerox1 overexpression in wildtype mouse ES cells and showed that this , again , led to an increase in transcript levels , specifically of six complex I subunits ( Figure 5A ) , of which four had previously shown significant changes in N2A cells after Cerox1 overexpression ( Figure 2C ) . In contrast , overexpression in DicerΔ/Δ cells failed to increase levels of these transcripts ( Figure 5A ) . These results indicate that Cerox1’s ability to alter mitochondrial metabolism is miRNA-dependent . Four miRNA families ( miR-138–5p , miR-28/28–5p/708–5p , miR-370–3p , and miR-488–3p ) were selected for further investigation based on the conservation of their predicted binding sites ( MREs ) in both mouse Cerox1 and human CEROX1 ( Figure 5B ) . All five MREs conserved in mouse Cerox1 and human CEROX1 for N2A-expressed miRNAs ( Figure 5B Figure 5—figure supplement 1A ) were mutated by inversion of their seed regions . This mutated Cerox1 transcript failed to alter either complex I transcript levels or enzyme activities when overexpressed in mouse N2A cells ( Figure 5C , D ) . This indicates that Cerox1’s molecular effects are mediated by one or more of these MREs . If so , then the overexpression of these miRNAs in turn would be expected to deplete Cerox1 RNA levels . Indeed , overexpression of each miRNA reduced Cerox1 levels ( Figure 5E ) . Overexpression of the tissue-restricted miRNA miR-488–3p ( Figure 5—figure supplement 1B , C; Landgraf et al . , 2007; Isakova and Quake , 2018 ) caused the greatest depletion of the Cerox1 transcript ( Figure 5E ) indicating that this MRE is likely to be physiologically relevant . Dual fluorescent RNA in situ hybridization of miR-488–3p and Cerox1 indicates the proximity of these non-coding RNAs within the N2A cell ( Figure 5F ) and that both Cerox1 and miR-488–3p are localised in the cytoplasm ( Figures 1F and 5F ) . CEROX1 transcripts are predominantly ( 94% ) localised to ribosomes ( van Heesch et al . , 2014 ) , as is the destabilisation by microRNAs of mRNAs as they are being translated ( Tat et al . , 2016 ) . Together , these observations imply that Cerox1 and mitochondrial protein mRNAs are targets of miR-488–3p on ribosomes within the rough ER that forms a network around mitochondria ( Wu et al . , 2017; Eskelinen , 2008 ) . Our previous results showed that Cerox1 abundance modulates complex I activity and transcripts ( Figures 2–4 ) and that miR-488–3p has the greatest effect in decreasing Cerox1 transcript levels ( Figure 5E ) . To determine whether miR-488–3p modulates complex I transcript levels we overexpressed and inhibited miR-488–3p in N2A cells ( Figure 6A , B ) . Results showed that miR-488–3p modulates these transcripts’ levels , with overexpression leading to a significant downregulation of all 12 Cerox1-sensitive complex I transcripts ( Figure 6A ) , whilst , conversely , miR-488–3p inhibition leads to increased expression for 10 of 12 transcripts , of which 4 ( Ndufa2 , Ndufb9 , Ndufs4 and Ndufs1 ) were significantly increased ( Figure 6B ) . To determine whether the single predicted miR-488–3p MRE in Cerox1 is required to exert its effects on complex I we created a Cerox1 transcript containing three mutated nucleotides within this MRE ( Figure 6C ) . As expected for a bona fide MRE , these substitutions abrogated the ability of miR-488–3p to destabilise Cerox1 transcript in a luciferase assay ( Figure 6D ) . Importantly , these substitutions also abolished the ability of Cerox1 , when overexpressed , to elevate complex I transcript levels ( Figure 6E ) , and to enhance complex I enzymatic activity ( Figure 6F ) . The latter observation is important because not all bona fide miRNA-transcript interactions are physiologically active ( Bassett et al . , 2014 ) . Finally , direct physical interaction between Cerox1 and miR-488–3p was confirmed by pulling-down transcripts with biotinylated miR-488–3p ( Figure 6G ) . This experiment also identified 10 of 12 complex I transcripts tested as direct targets of miR-488–3p binding . These included transcripts not predicted as containing a miR-488–3p MRE , as expected from the high false negative rate of MRE prediction algorithms ( Mazière and Enright , 2007; Yue et al . , 2009; Tabas-Madrid et al . , 2014 ) . Also as expected , the two negative control transcripts , which are not responsive to Cerox1 transcript levels and have no predicted MREs for miR-488–3p , failed to bind miR-488–3p . Considered together , these findings indicate that: ( i ) Cerox1 can post-transcriptionally regulate OXPHOS enzymatic activity as a miRNA decoy , and ( ii ) of 12 miR-488–3p:Nduf transcript interactions that were investigated , all 12 are substantiated either by responsiveness to miR-488–3p through miRNA overexpression or inhibition ( Figure 6A , B ) , or by direct interaction with a biotinylated miR-488–3p mimic ( Figure 6G ) . Consequently , our data demonstrates that miR-488–3p directly regulates the transcript levels of Cerox1 and at least 12 nuclear encoded mitochondrial complex I subunit genes ( 31% of all ) and indirectly modulates complex I activity ( Figure 6F ) in N2A cells . Fewer than 20% of lncRNAs are conserved across mammalian evolution ( Necsulea et al . , 2014 ) and even for these functional conservation has rarely been investigated . In our final set of experiments we demonstrated that CEROX1 , the orthologous human transcript , is functionally equivalent to mouse Cerox1 in regulating mitochondrial complex I activity . Similar to mouse Cerox1 , human CEROX1 is highly expressed in brain tissue , is otherwise ubiquitously expressed ( Figure 1—figure supplement 1B ) , and is enriched in the cytoplasm of human embryonic kidney ( HEK293T ) cells ( Figure 7A ) . CEROX1 is expressed in human tissues at unusually high levels: it occurs among the top 0 . 3% of all expressed lncRNAs ( Figure 7B ) and its average expression is higher than 87 . 5% of all protein coding genes ( GTEx Consortium , 2017 ) . Its expression is highest within brain tissues , particularly within the basal ganglia and cortex ( Figure 7C ) . Importantly , mitochondrial complexes’ I and III activities increased significantly following CEROX1 overexpression in HEK293T cells ( Figure 7D ) . CEROX1 overexpression had a greater effect on complex I activity than the mouse orthologous sequence and also increased the activity of complex III , rather than complex IV activity , in these cells . In addition to these observed increases in enzyme activity , basal respiration increased by 35% , ATP-linked respiration increase by 31% and maximum uncoupled respiration increased by 31% ( p=0 . 02 , p=0 . 04 , p=0 . 01 respectively , 2-tailed Student’s t-test; Figure 7E ) . The latter distinction could reflect the differences in miRNA pools between mouse and human cell lines and/or the presence of different MREs in the lncRNA and human OXPHOS transcripts . Strikingly , either reciprocal expression of mouse Cerox1 in human HEK293T cells or human CEROX1 in mouse N2A cells , recapitulates the previously observed increase in complex I activity ( Figure 7F ) . This effect of mouse Cerox1 overexpression in mouse N2A cells is greater than for human CEROX1 overexpression in these cells . The role of both Cerox1 and CEROX1 in modulating the activity of mitochondrial complex I has thus been conserved over 90 million years since the last common ancestor of mouse and human . Cerox1 is the first evolutionarily conserved lncRNA to our knowledge that has been demonstrated experimentally to regulate mitochondrial energy metabolism . Its principal location in N2A cells is in the cytoplasm ( Figure 1F ) where it post-transcriptionally regulates the levels of mitochondrial OXPHOS subunit transcripts and proteins by decoying for miRNAs ( Figure 5—figure supplement 1 ) , most particularly miR-488–3p . This microRNA shares with Cerox1 an early eutherian origin and elevated expression in brain samples ( Landgraf et al . , 2007 ) , and it previously was shown to alter mitochondrial dynamics in cancer cells ( Yang et al . , 2017 ) . Changes in Cerox1 abundance in vitro alter mitochondrial OXPHOS subunit transcript levels and , more importantly , elicit larger changes in their protein subunits levels , leading to unexpectedly large changes in mitochondrial complex I catalytic activity . The observed changes in catalytic activity are in line with the degree of change seen in diseases exhibiting mitochondrial dysfunction ( Schapira et al . , 1990; Ritov et al . , 2005; Andreazza et al . , 2010 ) . Overexpression of Cerox1 in N2A cells increases oxidative metabolism , halves cellular oxidative stress and enhances protection against the complex I inhibitor rotenone . The effect of Cerox1 on complex I subunit transcript levels can be explained by their sharing MREs with Cerox1 , and subsequent competition for miRNA binding , most notably for miR-488–3p , which buffers the OXPHOS transcripts against miRNA-mediated repression . Multiple RNA transcripts have been experimentally shown to compete with mRNAs for binding to miRNAs , thereby freeing the protein coding mRNA from miRNA-mediated repression ( Cesana et al . , 2011; Karreth et al . , 2011; Sumazin et al . , 2011; Tay et al . , 2011; Karreth et al . , 2015; Tan et al . , 2014 ) . It has been experimentally demonstrated that this miRNA:RNA regulatory crosstalk can initiate rapid co-ordinate modulation of transcripts whose proteins participate within the same complex or process ( Tan et al . , 2014 ) . Physiological relevance of this crosstalk mechanism remains incompletely understood . Furthermore , mathematical modelling ( Ala et al . , 2013; Figliuzzi et al . , 2013; Martirosyan et al . , 2016 ) and experimental investigation ( Bosson et al . , 2014; Denzler et al . , 2014 ) of the dynamics and mechanism of endogenous transcript competition for miRNA binding have resulted in contrasting conclusions . Current mathematical models do not take full account of miRNA properties , such as the repressive effect not being predictable from its cellular abundance ( Mullokandov et al . , 2012 ) , intracellular localisation such as at the rough ER ( Stalder et al . , 2013 ) , loading on the RNA-induced silencing complex ( RISC ) ( Flores et al . , 2014 ) , or AGO2’s phosphorylation status within the RISC ( Golden et al . , 2017 ) . The conclusions of experiments have also assumed that all miRNA target transcripts that contain the same number and affinity of miRNA binding sites are equivalent , that steady-state measurements are relevant to repression dynamics , and that observations for one miRNA in one experimental system are equally applicable to all others ( Smillie et al . , 2018 ) . Considered together , our lines of experimental evidence indicate that miRNA-mediated target competition by Cerox1 substantially perturbs a post-transcriptional gene regulatory network that includes at least 12 complex I subunit transcripts . This is consistent with the expression level of miR-488–3p ( Kozomara and Griffiths-Jones , 2014 ) and the high in vivo expression of both Cerox1 and OXPHOS transcripts ( Schwanhäusser et al . , 2011; Vogel et al . , 2010 ) . Human CEROX1 levels , for example , exceed those of all complex I subunit transcripts ( those in Figure 6a ) in both newly-formed and myelinating oligodendrocytes ( Forrest et al . , 2014 ) . Cerox1 could maintain OXPHOS homeostasis in cells with sustained high metabolic activity and high energy requirements . Such cells occur in the central nervous system , in which Cerox1 levels are high ( Sokoloff , 1977 ) , and in haematopoiesis where depletion of Cerox1 results in B cell depletion or myeloid enrichment ( Delás et al . , 2019 ) . Our experiments demonstrate that post-transcriptional regulation of a subset of complex I subunits by Cerox1 leads to elevated oxygen consumption . How consumption increases when there is a higher abundance of only a subset of OXPHOS transcripts remains unclear . However , this phenomenon has been observed previously in mouse dopaminergic neurons ( Alvarez-Fischer et al . , 2011 ) and primary mouse embryonic fibroblasts and pinnal tissues ( Shyh-Chang et al . , 2013 ) . Our observation of increased enzymatic activity may relate to the formation , by the complexes of the respiratory chain , of higher order supercomplexes ( Schägger and Pfeiffer , 2000; Genova and Lenaz , 2014 ) . Alternatively , the observed increases in OXPHOS activity may reflect some subunits of the complex I holo-enzyme ( including NDUFS3 and NDUFA2 ) being present as a monomer pool and therefore being available for direct exchange without integration into assembly intermediates ( Dieteren et al . , 2012 ) . This monomer pool facilitates the rapid swapping out of oxidatively damaged complex I subunits ( Dieteren et al . , 2012 ) . It is thus possible that Cerox1-mediated expansion of the monomer pool thereby improves complex I catalysis efficiency ( Figure 8 ) . However , we note that overexpression of Cerox1 results in the differential expression of 286 genes , most ( 83% ) of which are upregulated . These genes’ transcripts will be both the targets of miR-488–3p decoying by Cerox1 ( Figure 6 ) and those whose upregulation is secondary to Cerox1 ( and miR-488–3p ) mediated effects , for example relating to the observed changes in cellular metabolism and proliferation ( Figure 2—figure supplement 1I , J , K ) . More efficient ETC enzymatic activity might be relevant to mitochondrial dysfunction , a feature of many disorders that often manifests as decreases in the catalytic activities of particular mitochondrial complexes . A decrease in catalytic activity can result in elevated ROS production , leading to oxidative damage of lipids , DNA , and proteins , with OXPHOS complexes themselves being particularly susceptible to such damage ( Musatov and Robinson , 2012 ) . Parkinson’s and Alzheimer’s diseases both feature pathophysiology associated with oxidative damage resulting from increased ROS production and a cellular energy deficit associated with decreased complex I and IV activities ( a reduction of 30% and 40% , respectively ) ( Schapira et al . , 1990; Keeney et al . , 2006; Canevari et al . , 1999 ) . A deficiency in complexes II , III and to a lesser extent complex IV , has also been described in Huntington disease ( Mochel and Haller , 2011 ) . Currently no effective treatments exist that help to restore mitochondrial function despite demonstration that a 20% increase in complex I activity protects mouse midbrain dopaminergic neurons against MPP+ , a complex I inhibitor and a chemical model of Parkinson’s disease ( Alvarez-Fischer et al . , 2011 ) . We note that the highest expression of CEROX1 occurs primarily in the basal ganglia ( Figure 7C inset ) regions of which are specifically vulnerable to the progressive neurological disorders Parkinson’s ( substantia nigra pars compacta ) and Huntington’s diseases ( striatum: caudate and putamen ) . The specific energy demands of these neurons may make them particularly susceptible to damage due to an energy deficit . For instance , the dopaminergic neurons of the substantia nigra , which are especially sensitive to degeneration in Parkinson’s disease , have unusually large axonal arbours that require tight regulation of cellular energy to maintain ( Bolam and Pissadaki , 2012 ) . In addition , the medium spiny neurons of the striatum , which preferentially degenerate in Huntington disease , exhibit a high degree of axonal collateralization – a morphological trait which implies high cellular energy consumption for its maintenance ( Parent and Parent , 2006 ) – therefore causing these cells to be vulnerable to decreased cellular ATP production . CEROX1’s ability to increase mitochondrial complex I activity might be recapitulated pharmacologically to restore mitochondrial function , as an exemplar of therapeutic upregulation of gene expression ( Wahlestedt , 2013 ) . The lncRNA transcripts were assessed for coding potential using the coding potential calculator ( Kong et al . , 2007 ) , PhyloCSF ( Lin et al . , 2011 ) and by mining proteomics and small open reading frame resources for evidence of translation ( Wilhelm et al . , 2014; Kim et al . , 2014a; Bazzini et al . , 2014 ) . A lack of protein-coding potential for human CEROX1 ( also known as RP11-161M6 . 2 , LMF1-3 ) is supported by a variety of computational and proteomic data summarised in LNCipedia ( Volders et al . , 2015 ) . Expression data from somite-staged mouse embryos were acquired from ArrayExpress ( E-ERAD-401 - Strand-specific RNA-seq of somite-staged second generation genotypically wild-type embryos of mixed G0 lineage from the Mouse Genetics Project/DMDD ) . Genome wide associations were performed on the UK Biobank data as described in Canela-Xandri et al . ( 2018 ) using data from up to 452 , 264 individuals . Total RNA from twenty normal human tissues ( adipose , bladder , brain , cervix , colon , oesophagus , heart , kidney , liver , lung , ovary , placenta , prostate , skeletal muscle , small intestine , spleen , testes , thymus , thyroid and trachea ) were obtained from FirstChoice Human Total RNA Survey Panel ( Invitrogen ) . Total RNA from twelve mouse tissues ( bladder , brain , colon , heart , kidney , liver , pancreas , skeletal muscle , small intestine , stomach and testis ) were obtained from Mouse Tissue Total RNA Panel ( Amsbio ) . RNA from cell lines was extracted using the RNeasy mini kit ( Qiagen ) according to the manufacturer’s instructions , using the optional on column DNase digest . cDNA synthesis for all samples was performed on 1 μg of total RNA using a QuantiTect Reverse Transcription kit ( Qiagen ) according to the manufacturer’s instructions . RNA was extracted from samples used for the detection of miRNAs using the miRNeasy mini kit ( Qiagen ) according to the manufacturer’s instructions ( with on column DNase digest ) . All RNA samples were quantified using the 260/280 nm absorbance ratio , and RNA quality assessed using a Tapestation ( Agilent ) . RNA samples with an RNA integrity number ( RIN ) >8 . 5 were reverse transcribed . 1 μg of total RNA from the miRNA samples were reverse transcribed using the NCode VILO miRNA cDNA synthesis kit . Expression levels were determined by real-time quantitative PCR , using SYBR Green Master Mix ( Applied Biosystems ) and standard cycling parameters ( 95°C 10 min; 40 cycles 95°C 15 s , 60°C 1 min ) followed by a melt curve using a StepOne thermal cycler ( Applied Biosystems ) . All amplification reactions were performed in triplicate using gene specific primers . Multiple reference genes were assessed for lack of variability using geNorm ( Vandesompele et al . , 2002 ) . Human expression data were normalised to TUBA1A and POLR2A , whilst mouse expression data were normalised to Tbp and Polr2a . Oligonucleotide sequences are provided in Supplementary file 4 . Mouse Neuro-2a neuroblastoma cells ( N2A; RRID: CVCL_0470; ECACC 89121404 ) and human embryonic kidney ( HEK293T; RRID: CVCL_0063; ECACC 12022001 ) were sourced from the European authenticated cell culture collection . HEK293T cells were confirmed by STR profiling and cell lines were tested monthly for mycoplasma contamination . Cells were grown at 37°C in a humidified incubator supplemented with 5% CO2 . Both cell lines were grown in Dulbecco’s modified Eagle medium containing penicillin/streptomycin ( 100 U/ml , 100 ug/ml respectively ) and 10% fetal calf serum . Cells were seeded at the following densities: six well dish , 0 . 3 × 106; 48 well dish , 0 . 2 × 104; T75 flask 2 . 1 × 106 . We had three reasons for the choice of HEK293T cells for this experiment . First , they are of neural crest ectodermal origin ( Lin et al . , 2014 ) and therefore have a number of neural cell line characteristics in that they express neuronal markers ( Shaw et al . , 2002 ) . Second , they are in use as a cell culture model for neurodegenerative diseases such as Parkinson’s disease ( Falkenburger et al . , 2016; Schlachetzki et al . , 2013 ) . Third , HEK293T cells are a widely used cell line to interrogate human mitochondrial biochemistry , and exhibit complex I dependent respiration ( Kim et al . , 2014b ) . Mouse embryonic stem cells and dicer knock-out embryonic stem cells were maintained as described previously ( Nesterova et al . , 2008 ) . Cells were counted using standard haemocytometry . For flow cytometry the cells were harvested by trypsinization , washed twice with PBS and fixed in 70% ethanol ( filtered , −20°C ) . The cell suspension was incubated at 4°C for 10 min and the cells pelleted , treated with 40 μg/ml RNase A and propidium iodide ( 40 μg/ml ) for 30 min at room temperature . Cells were analysed using a FACSCalibur ( BD-Biosciences ) flow cytometer . Branched chain DNA probes to Cerox1 , Malat1 and mmu-miR-488–3p were sourced from Affymetrix . The protocol was preformed according to the manufacturer’s instructions using the QuantiGene ViewRNA miRNA ISH cell assay kit ( QVCM0001 ) for adherent cells . The following parameters were optimised: cells were fixed in 4% formaldehyde for 45 min and a 1:2000 dilution of the protease was optimal for the N2A cells . Cells were imaged using an Andor Dragonfly confocal inverted microscope , and images acquired using an Andor Zyla 4 . 2 plus camera . Cells were fractionated into nuclear and cytoplasmic fractions in order to determine the predominant cellular localization of lncRNA transcripts . Briefly , approximately 2 . 8 × 106 cells were collected by trypsinization , washed three times in PBS and pelleted at 1000 g for 5 min at 4°C . The cell pellet was resuspended in 5 volumes of lysis buffer ( 10 mM Tris-HCl , pH 7 . 5 , 3 mM MgCl2 , 10 mM NaCl , 5 mM EGTA , 0 . 05% NP40 , and protease inhibitors [Roche , complete mini] ) and incubated on ice for 15 min . Lysed cells were then centrifuged at 2000 g for 10 min at 4°C , and the supernatant collected as the cytoplasmic fraction . Nuclei were washed three times in nuclei wash buffer ( 10 mM HEPES , pH 6 . 8 , 300 mM sucrose , 3 mM MgCl2 , 25 mM NaCl , 1 mM EGTA ) , and pelleted by centrifugation at 400 g , 1 min at 4°C . Nuclei were extracted by resuspension of the nuclei pellet in 200 μl of nuclei wash buffer containing 0 . 5% Triton X-100 and 700 units/ml of DNase I and incubated on ice for 30 mins . Nucleoplasm fractions were collected by centrifugation at 17 000 g for 20 min at 4°C . RNA was extracted as described above , and RNA samples with RIN values > 9 . 0 used to determine transcript localisation . To determine the stability of the lncRNA transcripts , cells were cultured to ~50% confluency and then transcription was inhibited by the addition of 10 μg/ml actinomycin D ( Sigma ) in DMSO . Control cells were treated with equivalent volumes of DMSO . Transcriptional inhibition of the N2A cells was conducted for 16 hr with samples harvested at 0 hr , 30 mins , 1 hr , 2 hr , 4 hr , 8 hr and 16 hr . RNA samples for fractionation and turnover experiments were collected in Trizol ( Invitrogen ) and RNA purified and DNAse treated using the RNeasy mini kit ( Qiagen ) . Reverse transcription for cellular localisation and turnover experiments was performed as described earlier . The 5’ and 3’ ends of Cerox1 and CEROX1 were identified by 5’ and 3’ RACE using the GeneRacer Kit ( Invitrogen ) according to manufacturer’s instructions . As an overexpression/transfection control the pCAG-EGFP backbone was used ( RRID:Addgene_89684 ) . The EGFP was removed from this backbone , and all full length lncRNAs were cloned into the pCAG backbone . For cloning into the pCAGs vector , PCR primers modified to contain the cloning sites BglII and XhoI sites were used to amplify the full length mouse Cerox1 , whilst human CEROX1 and the mouse 5x MRE mutant were synthesized by Biomatik ( Cambridge , Ontario ) , and also contained BglII and XhoI sites at the 5’ and 3’ ends respectively . All other MRE mutants were produced using overlapping PCR site directed mutagenesis to mutate 3 bases of the miRNA seed region . All purified products were ligated into the prepared backbone and then transformed by heat shock into chemically competent DH5α , and plated on selective media . All constructs were confirmed by sequencing . Short hairpin RNAs specific to the transcripts were designed using a combination of the RNAi design tool ( Invitrogen ) and the siRNA selection program from the Whitehead Institute ( Yuan et al . , 2004 ) . Twelve pairs of shRNA oligos to the target genes and β-galactosidase control oligos were annealed to create double-stranded oligos and cloned into the BLOCK-iT U6 vector ( Invitrogen ) , according to the manufacturer’s instructions . miRNA expression constructs were generated and cloned into the BLOCK-iT Pol II miR RNAi expression vector ( Invitrogen ) according to the manufacturer’s instructions . miRNA inhibitors were sourced from Ambion and used according to manufacturer’s instructions . Transfection efficiency was initially assessed by FACS , and the optimised transfection protocol used for all further assays ( 6:1 transfection reagent to DNA ratio ) . One day prior to transfection cells were either seeded in six well dishes ( 0 . 3 × 106 cells/well ) , or in T75 flasks ( 2 . 1 × 106 cells/flask ) . Twenty-four hours later cells in six well dishes were transfected with 1 μg of shRNA , miRNA or overexpression construct and their respective control constructs using FuGENE 6 ( Promega ) according to the manufacturer’s guidelines . Cells in T75 flasks were transfected with 8 μg of experimental or control constructs . Transfected cells were grown for 48-72 hrs under standard conditions , and then harvested for either gene expression studies or biochemical characterisation . Efficacy of the overexpression and silencing constructs was determined by real-time quantitative PCR . Transcripts for the luciferase destabilisation assays were cloned into the pmirGLO miRNA target expression vector ( Promega ) and assayed using the dual-luciferase reporter assay system ( Promega ) . miRCURY LNA biotinylated miRNAs ( mmu-miR-488–3p and mmu-negative control 4 ) were purchased from Exiqon , and direct mRNA-miRNA interactions were detected using a modified version of Orom and Lund ( 2007 ) and enrichment of targets was detected by qPCR . MREs were predicted using TargetScan v7 . 0 ( Agarwal et al . , 2015; RRID: SCR_010845 ) in either the 3’UTR ( longest annotated UTR , ENSEMBL build 70; RRID: SCR_002344 ) or the full length transcript of protein coding genes , and across the entire transcript for lncRNAs . The average expression across 46 human tissues and individuals according to the Pilot one data from the GTEx Consortium ( Lonsdale et al . , 2013; RRID:SCR_013042; 98 ) was computed for both protein-coding genes and intergenic lncRNAs from the Ensembl release 75 annotation ( Flicek et al . , 2014 ) . We used the normalised number of CAGE tags across 399 mouse cells and tissues from the FANTOM5 Consortium ( http://fantom . gsc . riken . jp; Kawaji et al . , 2014 ) as an approximation of expression levels for protein-coding genes and intergenic lncRNAs from the Ensembl release 75 annotation . If multiple promoters were associated with a gene , we selected the promoter with the highest average tag number . Conserved sequence blocks in the lncRNA sequences were identified using LALIGN ( Goujon et al . , 2010 ) . Microarray analysis was performed on 16 samples ( four overexpression/four overexpression controls; four knock-down/four knock-down controls ) , and hybridizations were performed by the OXION array facility ( University of Oxford ) . Data were analysed using the web-based Bioconductor interface , CARMAweb ( Rainer et al . , 2006 ) . Differentially expressed genes ( Bonferroni corrected p-value<0 . 05 ) were identified between mouse lncRNA overexpression and control cells using Limma from the Bioconductor package between the experimental samples and the respective controls . Microarray data are accessible through ArrayExpress , accession E-MATB-6792 . Metabolites were extracted from 6-well plates by washing individual wells with ice-cold PBS and addition of cold extraction buffer ( 50% methanol , 30% acetonitrile , 20% water solution at −20°C or lower ) . Extracts were clarified and stored at −80°C until required . LC-MS was carried out using a 100 mm x 4 . 6 mm ZIC-pHILIC column ( Merck-Millipore ) using a Thermo Ultimate 3000 HPLC inline with a Q Exactive mass spectrometer . A 32 min gradient was developed over the column from 10% buffer A ( 20 mM ammonium carbonate ) , 90% buffer B ( acetonitrile ) to 95% buffer A , 5% buffer B . 10 μl of metabolite extract was applied to the column equilibrated in 5% buffer A , 95% buffer B . Q Exactive data were acquired with polarity switching and standard ESI source and spectrometer settings were applied ( typical scan range 75–1050 ) . Metabolites were identified based upon m/z values and retention time matching to standards . Quantitation of metabolites was carried out using AssayR ( Wills et al . , 2017 ) . Data were normalised using levels of 9 essential amino acids ( histidine , isoleucine , leucine , lysine , methionine , phenylalanine , threonine , tryptophan , valine ) , and errors propagated , in order to account for cell count differences . Total protein was quantified using a BCA protein assay kit ( Pierce ) . 10 μg of protein was loaded per well , and samples were separated on 12% SDS-PAGE gels in Tris-glycine running buffer ( 25 mM Tris , 192 mM glycine , 0 . 1% SDS ) . Proteins were then electroblotted onto PVDF membrane ( 40V , 3 hr ) in transfer buffer ( 25 mM Tris-HCl , 192 mM glycine , 20% methanol ) , the membrane blocked in TBS-T ( 50 mM Tris-HCl , 150 mM NaCl , 0 . 1% Tween 20 ) with 5% non–fat milk powder for 1 hr . The membrane was incubated with primary antibodies overnight at 4°C with the following dilutions: anti-NDUFS1 ( RRID: AB_2687932; rabbit monoclonal , ab169540 , 1:30 , 000 ) , anti-NDUFS3 ( RRID:AB_10861972; mouse monoclonal , 0 . 15 mg/ml , ab110246 ) , or anti-alpha tubulin loading control ( RRID: AB_2241126; mouse monoclonal , ab7291 , 1:30 , 000 ) . Following incubation with the primary antibodies , blots were washed 3 × 5 min , and 2 × 15 mins in TBS-T and incubated with the appropriate secondary antibody for 1 hr at room temperature: goat anti-rabbit HRP ( RRID:AB_2536530; Invitrogen G-21234 ) 1:30 , 000; goat anti-mouse HRP ( RRID:AB_2617137; Dako P0447 ) 1:3000 . After secondary antibody incubations , blots were washed and proteins of interested detected using ECL prime chemiluminescent detection reagent ( GE Healthcare ) and the blots imaged using an ImageQuant LAS 4000 ( GE Healthcare ) . Signals were normalised to the loading control using ImageJ ( Schneider et al . , 2012 ) . Cell lysates were prepared 48 hr post-transfection , by harvesting cells by trypsinisation , washing three times in ice cold phosphate buffered saline followed by centrifugation to pellet the cells ( two mins , 1000 g ) . Cell pellets were resuspended to homogeneity in KME buffer ( 100 mM KCl , 50 mM MOPS , 0 . 5 mM EGTA , pH 7 . 4 ) and protein concentrations were determined using a BCA protein assay detection kit ( Pierce ) . Cell lysates were flash frozen in liquid nitrogen , and freeze-thawed three times prior to assay . 300–500 μg of cell lysate was added per assay , and assays were normalised to the total amount of protein added . All assays were performed using a Shimadzu UV-1800 spectrophotometer , absorbance readings were taken every second and all samples were measured in duplicate . Activity of complex I ( CI , NADH:ubiquinone oxidoreductase ) was determined by measuring the oxidation of NADH to NAD+ at 340 nm at 30°C in an assay mixture containing 25 mM potassium phosphate buffer ( pH 7 . 2 ) , 5 mM MgCl2 , 2 . 5 mg/ml fatty acid free albumin , 0 . 13 mM NADH , 65 μM coenzyme Q and 2 μg/ml antimycin A . The decrease in absorbance was measured for 3 mins , after which rotenone was added to a final concentration of 10 μM and the absorbance measured for a further 2 mins . The specific complex I rate was calculated as the rotenone-sensitive rate minus the rotenone-insensitive rate . Complex II ( CII , succinate dehydrogenase ) activity was determined by measuring the oxidation of DCPIP at 600 nm at 30°C . Lysates were added to an assay mixture containing 25 mM potassium phosphate buffer ( pH 7 . 2 ) and 2 mM sodium succinate and incubated at 30°C for 10 mins , after which the following components were added , 2 μg/ml antimycin A , 2 μg/ml rotenone , 50 μM DCPIP and the decrease in absorbance was measured for 2 mins . Complex III ( CIII , Ubiquinol:cytochrome c oxidoreductase ) activity was determined by measuring the oxidation of decylubiquinol , with cytochrome c as the electron acceptor at 550 nm . The assay cuvettes contained 25 mM potassium phosphate buffer ( pH 7 . 2 ) , 3 mM sodium azide , 10 mM rotenone and 50 μM oxidized cytochrome c . Decylubiquinol was synthesized by acidifying decylubiquinone ( 10 mM ) with HCl ( 6M ) and reducing the quinine with sodium borohydride . After the addition of 35 μM decylubiquinol , the increase in absorbance was measured for 2 mins . Activity of Complex IV ( CIV , cytochrome c oxidase ) was measured by monitoring the oxidation of cytochrome c at 550 nm , 30°C for 3 min . A 0 . 83 mM solution of reduced cytochrome c was prepared by dissolving 100 mg of cytochrome c in 10 ml of potassium phosphate buffer , and adding sodium ascorbate to a final concentration of 5 mM . The resulting solution was added into SnakeSkin dialysis tubing ( 7 kDa molecular weight cutoff , Thermo Scientific ) and dialyzed against potassium phosphate buffer , with three changes , at 4°C for 24 hr . The redox state of the cytochrome c was assessed by evaluating the absorbance spectra from 500 to 600 nm . The assay buffer contained 25 mM potassium phosphate buffer ( pH 7 . 0 ) and 50 μM reduced cytochrome c . The decrease in absorbance at 550 nm was recorded for 3 mins . As a control the enzymatic activity of the tricarboxylic acid cycle enzyme , citrate synthase ( CS ) was assayed at 412 nm at 30°C in a buffer containing 100 mM Tris-HCl ( pH 8 . 0 ) , 100 μM DTNB ( 5 , 5-dithiobis[2-nitrobenzoic acid] ) , 50 μM acetyl coenzyme A , 0 . 1% ( w/v ) Triton X-100 and 250 μM oxaloacetate . The increase in absorbance was monitored for 2 mins . The following extinction coefficients were applied: complex I ( CI ) , ε = 6220 M−1 cm−1 , CII , ε = 21 , 000 M−1 cm−1; CIII , ε = 19 , 100 M−1 cm−1; CIV , ε = 21 , 840 M−1 cm−1 ( the difference between reduced and oxidised cytochrome c at 550 nm ) ; CS , ε = 13 , 600 mM−1 cm−1 . Cellular oxygen consumption rate was determined using the Seahorse XFe24 Analyzer ( Agilent ) . Cells were plated on poly-L-lysine coated XFe24 microplates at 50 , 000 cells per well and incubated at 37°C and 5% CO2 for 24 hr . Cells were transfected with an overexpression ( pCAG-Cerox1 or pCAG-CEROX1 ) or the Cerox1 shRNA silencing construct and assayed 24–36 hr later . Cells were washed three times with Seahorse Assay media ( Agilent ) , supplemented with 10 mM glucose and 2 mM pyruvate . After 30 min of pre-incubation in a non-CO2 37°C incubator , cells were entered into the analyser for oxygen consumption rate measurements . After basal respiration measurements , 1 μM of oligomycin was injected to inhibit ATP synthase , then 0 . 2 μM of carbonyl cyanide 4- ( trifluoromethoxy ) phenylhydrazone ( FCCP ) was injected to uncouple respiration . Finally 1 μM each of rotenone and antimycin A were injected to inhibit complex I and complex III respectively . Data was normalised to total cellular protein content using the sulforhodamine B assay ( Skehan et al . , 1990 ) . Basal respiration , ATP linked respiration and maximum uncoupled respiration were calculated from the normalised data using the Agilent Seahorse XF calculation guidelines . Briefly , basal respiration = measurement 3 ( last rate measurement before oligomycin injection ) – measurement 10 ( minimum respiration after injection of rotenone/antimycin A ) ; ATP-linked respiration = measurement 3 – measurement 4 ( minimum rate measurement after oligomycin injection ) ; maximum uncoupled respiration = measurement 7 ( maximum rate after FCCP injection ) – measurement 10 . Hydrogen peroxide production was assessed as a marker of reactive oxygen species generation using the fluorescent indicator Amplex Red ( 10 μM , Invitrogen ) in combination with horseradish peroxidise ( 0 . 1 units ml−1 ) . Total amount of H2O2 produced was normalised to mg of protein added . Protein carbonylation was assessed using the OxyBlot protein oxidation detection kit ( Merck Millipore ) , and differential carbonylation was assessed by densitometry . The cell stress assay was performed on cells seeded in 48 well plates , and assayed 12 hr later by the addition of ( final concentration ) : rotenone ( 5 μM ) , malonate ( 40 μM ) , antimycin A ( 500 μM ) , oligomycin ( 500 μM ) , sodium azide ( 3 mM ) , NaCl ( 300 mM ) , CaCl2 ( 5 . 4 mM ) for 1 hr . Cells were heat shocked at 42°C and UV irradiated using a Stratlinker UV Crosslinker for 10 min ( 2 . 4 J cm−2 ) . Cell viability was assessed by the addition of Alamar Blue ( Invitrogen ) according to the manufacturer’s instructions .	Animal cells generate over 90% of the energy they need within small structures called mitochondria . Converting food into energy requires many different proteins and cells control the relative amounts of the proteins in mitochondria to ensure this process is efficient . To make more of a given protein , the cell must copy the DNA of the gene that encodes it into another molecule known as a messenger RNA , before reading the instructions in the messenger RNA to build the protein . However , this is not the only way that a cell uses molecules of RNA . A second group of RNAs called long non-coding RNAs ( or lncRNAs ) can help regulate the production of proteins in complex ways , and each lncRNA can have an effect across multiple genes . Some lncRNAs , for example , stop a third group of RNAs – microRNAs – from blocking certain messenger RNAs from being read . Sirey et al . set out to answer whether a lncRNA might help to co-ordinate the production of the many proteins needed by mitochondria . In experiments with mouse cells grown in the laboratory , Sirey et al . identified a lncRNA called Cerox1 that can co-ordinate the levels of at least 12 mitochondrial proteins . A microRNA called miR-488-3p suppresses the production of many of these proteins . By binding to miR-488-3p , Cerox1 blocks the effects of the microRNA so more proteins are produced . Sirey et al . artificially altered the amount of Cerox1 in the cells and showed that more Cerox1 leads to higher mitochondria activity . Further experiments revealed that this same control system also exists in human cells . Mitochondria are vital to cell survival and changes that affect their efficiency can be fatal or highly debilitating . Reduced efficiency is also a hallmark of ageing and contributes to conditions including cardiovascular disease , diabetes and Parkinson’s disease . Understanding how mitochondria are regulated could unlock new treatment methods for these conditions , while a better understanding of the co-ordination of protein production offers other insights into some of the most fundamental biology .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "biochemistry", "and", "chemical", "biology", "genetics", "and", "genomics" ]	2019	The long non-coding RNA Cerox1 is a post transcriptional regulator of mitochondrial complex I catalytic activity
Chronic itch remains a highly prevalent disorder with limited treatment options . Most chronic itch diseases are thought to be driven by both the nervous and immune systems , but the fundamental molecular and cellular interactions that trigger the development of itch and the acute-to-chronic itch transition remain unknown . Here , we show that skin-infiltrating neutrophils are key initiators of itch in atopic dermatitis , the most prevalent chronic itch disorder . Neutrophil depletion significantly attenuated itch-evoked scratching in a mouse model of atopic dermatitis . Neutrophils were also required for several key hallmarks of chronic itch , including skin hyperinnervation , enhanced expression of itch signaling molecules , and upregulation of inflammatory cytokines , activity-induced genes , and markers of neuropathic itch . Finally , we demonstrate that neutrophils are required for induction of CXCL10 , a ligand of the CXCR3 receptor that promotes itch via activation of sensory neurons , and we find that that CXCR3 antagonism attenuates chronic itch . Chronic itch is a debilitating disorder that affects millions of people worldwide ( Matterne et al . , 2011; Mollanazar et al . , 2016; Dalgard et al . , 2015 ) . It is a symptom of a number of skin diseases and systemic disorders , as well as a side effect of a growing list of medications . Like chronic pain , chronic itch can be a disease in and of itself ( Ständer and Steinhoff , 2002; Oaklander , 2011; Dhand and Aminoff , 2014 ) . Unlike acute itch , which can facilitate removal of crawling insects , parasites , or irritants , persistent scratching in chronic itch disorders has no discernable benefit; scratching damages skin , leading to secondary infection , disfiguring lesions , and exacerbation of disease severity ( Mollanazar et al . , 2016; Yosipovitch and Papoiu , 2008; Ikoma et al . , 2006 ) . The most common chronic itch disorder is atopic dermatitis ( AD; commonly known as eczema ) , which affects fifteen million people in the United States alone ( Spergel and Paller , 2003 ) . Severe AD can trigger the atopic march , where chronic itch and inflammation progress to food allergy , allergic rhinitis , and asthma ( Spergel and Paller , 2003; Zheng et al . , 2011 ) . Little is known about the underlying mechanisms that drive chronic itch pathogenesis . As such , studies of human chronic itch disorders have sought to identify candidate mechanisms of disease progression . A number of studies have identified biomarkers and disease genes in itchy human AD lesions ( Ewald et al . , 2017; Choy et al . , 2012; Guttman-Yassky et al . , 2009; Suárez-Fariñas et al . , 2013; Jabbari et al . , 2012 ) . Indeed , a recent study compared the transcriptomes of healthy skin to itchy and non-itchy skin from psoriasis and AD patients , revealing dramatic changes in expression of genes associated with cytokines , immune cells , epithelial cells , and sensory neurons ( Nattkemper et al . , 2018 ) . However , due to the difficulty in staging lesion development and obtaining staged samples from patients , there is currently no temporal map of when individual molecules and cell types contribute to chronic itch pathogenesis . Furthermore , the use of human patient data does not allow for rigorous mechanistic study of how disease genes contribute to chronic itch . To this end , we used a well-characterized inducible animal model of itch to define where , when , and how these genes identified from patient data contribute to chronic itch pathogenesis . We employed the MC903 mouse model of AD and the atopic march ( Dai et al . , 2017; Li et al . , 2009; Li et al . , 2006; Zhang et al . , 2009; Moosbrugger-Martinz et al . , 2017 ) to provide a framework within which to identify the molecules and cells that initiate the development of atopic itch . The MC903 model is ideal for our approach because of its highly reproducible phenotypes that closely resemble human AD and its ability to induce the development of lesions and scratching ( Li et al . , 2009; Li et al . , 2006; Zhang et al . , 2009; Oetjen et al . , 2017; Morita et al . , 2015; Kim et al . , 2019 ) . By contrast , it is difficult to synchronously time the development of lesions in commonly used genetic models of AD , such as filaggrin mutant mice or Nc/Nga mice . Another advantage of the MC903 model is that it displays collectively more hallmarks of human AD than any one particular genetic mouse model . For example , the commonly used IL-31tg overexpressor model ( Cevikbas et al . , 2014; Meng et al . , 2018 ) lacks strong Th2 induction , ( Martel et al . , 2017 ) and itch behaviors have not yet been rigorously characterized in the keratinocyte-TSLP overexpressor model . As MC903 is widely used to study the chronic phase of AD , we hypothesized that MC903 could also be used to define the early mechanisms underlying the development of chronic itch , beginning with healthy skin . We performed RNA-seq of skin at key time points in the model . We complemented this approach with measurements of itch behavior and immune cell infiltration . The primary goal of our study was to identify the inciting molecules and cell types driving development of chronic itch . To that end , we show that infiltration of neutrophils into skin is required for development of chronic itch . Additionally , we demonstrate that neutrophils direct early hyperinnervation of skin , and the upregulation of itch signaling molecules and activity-induced genes in sensory neurons . Finally , we identify CXCL10/CXCR3 signaling as a key link between infiltrating neutrophils and sensory neurons that drives itch behaviors . Although a variety of AD- and chronic itch-associated genes have been identified , when and how they contribute to disease pathogenesis is unclear . Using RNA-seq of MC903-treated skin , we observed distinct temporal patterns by which these classes of genes are differentially expressed across the first eight days of the model ( Figure 1A–B , Figure 1—figure supplement 1A ) . Overall , we found that 62% of genes from a recent study of human chronic itch lesions ( Nattkemper et al . , 2018 ) ( Figure 1—figure supplement 1A ) and 67% of AD-related genes ( Figure 1B ) were significantly changed for at least one of the time points examined , suggesting that the MC903 mouse model recapitulates many key transcriptional changes occuring in human chronic itch and AD . MC903 dramatically alters the transcriptional profile of keratinocytes by derepressing genomic loci under the control of the Vitamin D Receptor . In line with rapid changes in transcription , proteases ( Klk6 , Klk13 , among others ) and skin barrier genes ( Cdhr1 ) changed as early as six hours after the first treatment , before mice begin scratching ( Figure 1B ) . Increased protease activity in AD skin is thought to promote breakdown of the epidermal barrier and release of inflammatory cytokines from keratinocytes ( Rattenholl and Steinhoff , 2003; Yosipovitch , 2004 ) . One such cytokine , thymic stromal lymphopoetin ( TSLP ) is a key inducer of the Type two immune response , which is characteristic of human AD and the MC903 model , via signaling in CD4+ T cells , basophils , and other immune cells ( Li et al . , 2006; Zhang et al . , 2009; Briot et al . , 2010; Demehri et al . , 2009; Gao et al . , 2010; Kim et al . , 2013 ) . Beginning at day two , before any significant itch-evoked scratching ( Figure 1C ) , immune cell infiltration ( Figure 1E–G , Figure 1—figure supplements 3A , 4A and 5A–C ) , or skin lesions ( data not shown ) ( Morita et al . , 2015 ) were observed , we saw increases in Tslp , as well as several other epithelial-derived cytokines , including the neutrophil chemoattractant genes Cxcl1 , Cxcl2 , Cxcl3 , and Cxcl5 ( Figure 1D ) . To ask whether upregulation of these chemokine genes was dependent on protease activity , we treated human keratinocytes with the protease-activated receptor two agonist SLIGRL . SLIGRL treatment triggered increased expression of several of these chemokine genes , including IL8 , the human ortholog of mouse Cxcl1/Cxcl2 , and CXCL2 ( Figure 1—figure supplement 6A ) . These increases occurred after a few hours of exposure to SLIGRL , suggesting that increased protease activity can rapidly trigger increases in neutrophil chemoattractants in skin , similar to what we observe in MC903-treated mouse skin . Unexpectedly , in the skin we observed early changes in a number of transcripts encoding neuronal outgrowth factors ( Ngf , Artn ) and axon pathfinding molecules ( Slit1 , Sema3d , Sema3a ) , some of which are directly implicated in chronic itch ( Hidaka et al . , 2017; Kou et al . , 2012; Tominaga and Takamori , 2013; Tominaga et al . , 2007; Tominaga and Takamori , 2014; Figure 1—figure supplement 7A ) , prior to when mice began scratching . We thus used immunohistochemistry ( IHC ) of whole-mount skin to examine innervation at this time point . We saw increased innervation of lesions at day two but not day one of the model ( Figure 1H–I , Figure 1—figure supplement 8A ) . Our RNA-seq data showed elevation in skin CGRP transcript Calca , along with other markers of peptidergic nerve endings , specifically at day 2 . Indeed , we saw an increase in CGRP+ innervation of skin at day 2 ( Figure 1J , Figure 1—figure supplement 9A ) , which suggests that elevation of neuronal transcripts in skin is due to hyperinnervation of peptidergic itch and/or pain fibers . The increased innervation was surprising because such changes had previously only been reported in mature lesions from human chronic itch patients ( Nattkemper et al . , 2018; Haas et al . , 2010; Kamo et al . , 2013; Oaklander and Siegel , 2005; Schüttenhelm et al . , 2015; Pereira et al . , 2016; Tominaga et al . , 2009 ) . Our findings suggest that early hyperinnervation is promoted by local signaling in the skin and is independent of the itch-scratch cycle . By day five , mice exhibited robust itch behaviors ( Figure 1C ) and stark changes in a number of AD disease genes ( Figure 1A–B ) . For example , loss-of-function mutations in filaggrin ( FLG ) are a major risk factor for human eczema ( Palmer et al . , 2006; Sandilands et al . , 2007 ) . Interestingly , Flg2 levels sharply decreased at day five . In parallel , we saw continued and significant elevation in neutrophil and basophil chemoattractant genes ( Cxcl1 , 2 , 3 , 5 , and Tslp , Figure 1D ) . Using flow cytometry , we observed a number of infiltrating immune cells in the skin at day 5 . Of these , we neutrophils were the most abundant immune cell subtype ( Figure 1E , Figure 1—figure supplement 3A ) . It was not until day eight that we observed the classical AD-associated immune signature in the skin , ( Gittler et al . , 2012 ) with upregulation of Il4 , Il33 and other Th2-associated genes ( Figure 1B , Figure 1D ) . We also observed increases in the T cell chemoattractant genes Cxcl9 , Cxcl10 , and Cxcl11 ( Figure 1D ) , which are thought to be hallmarks of chronic AD lesions in humans ( Oetjen and Kim , 2018; Mansouri and Guttman-Yassky , 2015 ) . Neutrophils and a number of other immune cells that started to infiltrate on day five were robustly elevated in skin by day eight , including basophils ( Figure 1F ) , CD4+ T cells ( Figure 1G , Figure 1—figure supplement 4A ) , eosinophils ( Figure 1—figure supplement 5C ) , and mast cells ( Figure 1—figure supplement 5B ) , but not inflammatory monocytes ( Figure 1—figure supplement 5A ) . CD4+ T cells are ubiquitous in mature human AD lesions ( Guttman-Yassky and Krueger , 2017 ) and promote chronic AD itch and inflammation . More specifically , they play a key role in IL4Rα-dependent sensitization of pruriceptors in the second week of the MC903 model ( Oetjen et al . , 2017 ) . Thus , we were quite surprised to find that itch behaviors preceded significant CD4+ T cell infiltration . Therefore , neutrophils drew our attention as potential early mediators of MC903 itch . While neutrophil infiltration is a hallmark of acute inflammation , it remains unclear whether neutrophils contribute to the pathogenesis of chronic itch . Moreover , neutrophils release known pruritogens , including proteases , reactive oxygen species , and/or histamine , inflammatory lipids , and cytokines that sensitize and/or activate pruriceptors ( Dong and Dong , 2018; Hashimoto et al . , 2018 ) . Increased levels of the prostaglandin PGE2 and the neutrophil-specific leukotriene LTB4 have also been reported in skin of AD patients ( Fogh et al . , 1989 ) . Indeed , by mass spectrometry , we observed increases in several of these inflammatory lipids , PGD2 and PGE2 , as well as LTB4 and its precursor 5-HETE ( Figure 1—figure supplement 10A ) in MC903-treated skin , implicating neutrophils in driving AD itch and inflammation . Thus , we next tested the requirement of neutrophils to itch in the MC903 model . We first asked whether neutrophils , the most abundant population of infiltrating immune cells in this chronic itch model , were required for MC903-evoked itch . Systemic depletion of neutrophils using daily injections of an anti-Gr1 ( aGr1 ) antibody ( Ghasemlou et al . , 2015; Sivick et al . , 2014 ) dramatically attenuated itch-evoked scratching through the first eight days of the model ( Figure 2A ) . Consistent with a key role for neutrophils in driving chronic itch , our depletion strategy significantly and selectively reduced circulating and skin infiltrating neutrophils on days five and eight , days on which control , but not depleted mice , scratched robustly ( Figure 2B; Figure 2—figure supplement 1A–C ) . In contrast , basophils and CD4+ T cells continued to infiltrate the skin following aGr1 treatment ( Figure 2C–D ) , suggesting that these cells are not required for early MC903 itch . We next used the cheek model of acute itch ( Shimada and LaMotte , 2008 ) to ask whether neutrophil recruitment is sufficient to trigger scratching behaviors . As expected , we observed significant and selective recruitment of neutrophils to cheek skin within 15 min after CXCL1 injection ( Figure 2—figure supplement 2A–B ) . CXCL1 injection also triggered robust scratching behaviors ( Figure 2E ) on a similar time course to neutrophil infiltration ( Figure 2—figure supplement 2B ) . Thus , we next acutely depleted neutrophils with aGr1 to determine whether neutrophils were required for CXCL1-evoked acute itch . Indeed , aGr1-treatment rapidly reduced circulating neutrophils ( Figure 2—figure supplement 2C ) and resulted in a dramatic loss of CXCL1-evoked itch behaviors ( Figure 2C ) . This effect was specific to neutrophil-induced itch , as injection of chloroquine , a pruritogen that directly activates pruriceptors to trigger itch , still triggered robust scratching in aGr1-treated animals ( Figure 2—figure supplement 3A ) . Given that CXCL1 has been shown to directly excite and/or sensitize sensory neurons , ( Deftu et al . , 2017; Deftu et al . , 2018 ) it is possible that the mechanism by which CXCL1 elicits itch may also involve neuronal pathways . However , our results show that CXCL1-mediated neutrophil infiltration is sufficient to drive acute itch behaviors , and that neutrophils are necessary for itch in the MC903 model . We also examined MC903-evoked itch behaviors in mice deficient in Crlf2 , the gene encoding the TSLP Receptor ( TSLPR KO mice; Carpino et al . , 2004 ) . TSLPR is expressed by both immune cells and sensory neurons and is a key mediator of AD in humans and in mouse models ( Li et al . , 2009; Li et al . , 2006; Zhang et al . , 2009; Demehri et al . , 2009; Briot et al . , 2009 ) . Surprisingly , MC903-treated TSLPR KO mice displayed robust scratching behaviors through the first eight days of the model ( Figure 2F ) . In contrast to our results in aGr1-injected mice , TSLPR KO mice displayed robust neutrophil infiltration ( Figure 2G ) , but completely lacked basophil and CD4+ T cell infiltration into the skin ( Figure 2H–I , Figure 2—figure supplement 4A ) , and additionally displayed a reduction in mast cells ( Figure 2—figure supplement 4A ) . These results suggest that basophils and CD4+ T cells are not required for early itch and further support an inciting role for neutrophils . Previous studies have shown that TSLP drives the expression of Type two cytokines and related immune cells that promote itch and inflammation in mature AD skin lesions ( Li et al . , 2009; Li et al . , 2006; Zhang et al . , 2009; Demehri et al . , 2009; Briot et al . , 2009 ) . Consistent with a later role for TSLP signaling in AD , we did observe a significant reduction in itch-evoked scratching in TSLPR KO mice in the second week of the model ( Figure 2F ) . Thus , our data support a model in which neutrophils are necessary for initiation of AD and itch behaviors early in the development of AD , whereas TSLPR signaling mediates the recruitment of basophils and CD4+ T cells to promote later stage itch and chronic inflammation . The incomplete loss of itch behaviors on day 12 in the TSLPR KO animals ( Figure 2F ) raised the question of whether neutrophils might also contribute to itch during the second week of the MC903 model . To directly answer this question , we measured neutrophil infiltration and itch-evoked scratching on day 12 in mice that received either aGr1 or PBS on days 8–11 of the model to selectively deplete neutrophils solely during the second week . Neutrophil depletion in the second week with aGr1 robustly decreased skin-infiltrating neutrophils ( Figure 2J ) , and substantially reduced scratching behaviors at day 12 ( Figure 2K ) , supporting a role for neutrophils in chronic itch . Interestingly , we observed a 79% mean reduction in time spent scratching after neutrophil depletion at day 12 , whereas loss of TSLPR effected a 44% reduction in time spent scratching . We speculate that neutrophils and TSLP signaling comprise independent mechanisms that together account for the majority of AD itch . In order to ascertain whether neutrophils could be salient players in other models of AD , and not just MC903 , we measured neutrophil infiltration into ear skin in the 1-fluoro-2 , 4-dinitrobenzene ( DNFB ) model of atopic dermatitis , which relies on hapten-induced sensitization to drive increased IgE , mixed Th1/Th2 cytokine response , skin thickening , inflammation , and robust scratching behaviors in mice ( Zhang et al . , 2015; Kitamura et al . , 2018; Solinski et al . , 2019a ) . Indeed , neutrophils also infiltrated DNFB- but not vehicle-treated skin ( Figure 2—figure supplement 5A ) . Taken together , these observations are complementary to published datasets showing evidence for neutrophil chemokines and transcripts in human AD lesions ( Ewald et al . , 2017; Choy et al . , 2012; Guttman-Yassky et al . , 2009; Suárez-Fariñas et al . , 2013; Jabbari et al . , 2012 ) . Overall , our data support a key role for neutrophils in promoting AD itch and inflammation . But how do neutrophils drive AD itch ? Itchy stimuli are detected and transduced by specialized subsets of peripheral somatosensory neurons . Thus , to answer this question we first profiled the transcriptional changes in somatosensory neurons in the MC903 model , which were previously unstudied . In general , little is known regarding neuronal changes in chronic itch . Our initial examination of early hyperinnervation and changes in axon guidance molecules in skin suggested that neurons are indeed affected early on in the MC903 model , before the onset of itch-evoked scratching behaviors . In contrast to the skin , where we saw many early transcriptional changes , we did not see any significant transcriptional changes in the trigeminal ganglia ( TG ) until five days after the first treatment , and in total only 84 genes were differentially expressed through the eighth day ( Figure 3A–B ) . These hits included genes related to excitability of itch sensory neurons , ( Dong and Dong , 2018; Usoskin et al . , 2015 ) neuroinflammatory genes , ( Takeda et al . , 2009 ) and activity-induced or immediate early genes ( Figure 3A ) . Interestingly , we observed enrichment of neuronal markers expressed by one specific subset of somatosensory neurons that are dedicated to itch ( Il31ra , Osmr , Trpa1 , Cysltr2 , and Nppb ) , termed ‘NP3’ neurons ( Dong and Dong , 2018; Usoskin et al . , 2015; Huang et al . , 2018; Solinski et al . , 2019b ) . Similar to what has been reported in mouse models of chronic pain , we observed changes in neuroinflammatory ( Bdnf , Nptx1 , Nptx2 , Nptxr ) and immune genes ( Itk , Cd19 , Rag , Tmem173 ) . However , these transcriptional changes occurred just a few days after itch onset , in contrast to the slow changes in nerve injury and pain models that occur over weeks , indicating that neuropathic changes may occur sooner than previously thought in chronic itch . These changes occurred in tandem with the onset of scratching behaviors ( Figure 1C ) , suggesting that the early molecular and cellular changes we observed by this time point may be important for development or maintenance of itch-evoked scratching . The changes we observed in immune-related genes in the TG were suggestive of infiltration or expansion of immune cell populations , which has been reported in models of nerve injury and chronic pain , but has never been reported in chronic itch . To validate our observations , we used IHC to ask whether CD45+ immune cells increase in the TG . We observed a significant increase in TG immune cell counts at day eight but not day five ( Figure 3C–F , Figure 3—figure supplement 1A–D ) . Because we observed such dramatic expression changes in the TG on day eight of the model , we postulated that the CNS may also be affected by this time point . Thus , we performed RNA-seq on spinal cord segments that innervate the MC903-treated rostral back skin of mice . To date , only one study has examined changes in the spinal cord during chronic itch ( Shiratori-Hayashi et al . , 2015 ) . The authors showed that upregulation of the STAT3-dependent gene Lcn2 occurred three weeks after induction of chronic itch and was essential for sustained scratching behaviors . Surprisingly , we saw upregulation of Lcn2 on day eight of the MC903 model and , additionally , we observed robust induction of immediate early genes ( Fos , Junb , Figure 3G ) , suggesting that MC903 itch drives activity-dependent changes in the spinal cord as early as one week after beginning treatment . Together , our findings show that sustained itch and inflammation can drive changes in the PNS and CNS much sooner than previously thought , within days rather than weeks after the onset of scratching . We next set out to explore how loss of neutrophils impacts the molecular changes observed in skin and sensory neurons in the MC903 model , and which of these changes might contribute to neutrophil-dependent itch . To ask how neutrophils promote itch in the MC903 model , we examined the transcriptional changes in skin and sensory ganglia isolated from non-itchy neutrophil-depleted animals and from the TSLPR KO mice , which scratched robustly . A number of AD-associated cytokines that were upregulated in control MC903 skin were not upregulated in TSLPR KO and neutrophil-depleted skin . For example , Il33 upregulation is both neutrophil- and TSLPR-dependent ( Figure 4A , Figure 4—figure supplement 1A ) . By contrast , upregulation of epithelial-derived cytokines and chemokines Tslp , Cxcl1 , Cxcl2 , Cxcl3 , and Cxcl5 was unaffected by either loss of TSLPR or neutrophil depletion ( Figure 4B ) , suggesting these molecules are produced by skin cells even when the MC903-evoked immune response is compromised . Consistent with previous studies , Il4 upregulation was completely dependent on TSLPR but not neutrophils , establishing a role for TSLP signaling in the Type two immune response . Among the hundreds of MC903-dependent genes we examined , only a handful of genes were uniquely affected by neutrophil depletion . One such gene was Cxcl10 , a chemokine known to be released by skin epithelial cells , neutrophils , and other myeloid cells ( Hashimoto et al . , 2018; Ioannidis et al . , 2016; Kanda et al . , 2007; Koga et al . , 2008; Michalec et al . , 2002; Padovan et al . , 2002; Tamassia et al . , 2007 ) . Cxcl10 expression was increased in TSLPR KO but not neutrophil-depleted skin ( Figure 4B , Figure 4—figure supplement 1A ) . CXCL10 has been previously shown to drive acute itch in a model of allergic contact dermatitis via CXCR3 signaling in sensory neurons , ( Qu et al . , 2015 ) and is elevated in skin of AD patients ( Mansouri and Guttman-Yassky , 2015 ) . Expression of Cxcl9 and Cxcl11 , two other CXCR3 ligands that are elevated in AD but have an unknown role in itch , was also decreased in AD skin of neutrophil-depleted mice ( Figure 4B ) . We hypothesized that neutrophil-dependent upregulation of CXCL10 activates sensory neurons to drive itch behaviors . Consistent with this model , neutrophil depletion attenuated the expression of activity-induced immediate early genes ( Vgf , Junb ) in the TG , suggestive of neutrophil-dependent sensory neuronal activity ( Figure 4C , Figure 4—figure supplement 1B ) . We found that neutrophils also contributed to other sensory neuronal phenotypes in the model . For example , we observed that expression of Lcn2 , a marker of neuropathic itch , and activity-induced genes Fos and Junb were not increased in spinal cord isolated from neutrophil-depleted animals , indicating that neutrophil-dependent scratching behaviors may indeed drive changes in the CNS ( Figure 4D ) . We also observed that neutrophil-depleted animals displayed no skin hyperinnervation at day two ( Figure 4E ) . This result was surprising because we did not observe significant neutrophil infiltration at this early time point , but these data suggest that low numbers of skin neutrophils are sufficient to mediate these early effects . To test our model wherein CXCL10 activates CXCR3 to drive neutrophil-dependent itch , we first asked whether this CXCR3 ligand is in fact released in MC903-treated skin . We performed ELISA on cheek skin homogenate and found that CXCL10 protein was increased in MC903-treated skin from uninjected wild-type and TSLPR KO animals , but not in skin from neutrophil-depleted mice ( Figure 4F ) . To test whether CXCR3 signaling directly contributes to AD itch , we asked whether acute blockade of CXCR3 using the antagonist AMG 487 ( Qu et al . , 2015 ) affected scratching behaviors in the MC903 model . We found that the CXCR3 antagonist strongly attenuated scratching behaviors on days five , eight , and twelve ( Figure 4G ) , with the greatest effect at day eight . In contrast , CXCR3 blockade did not attenuate scratching behaviors in naive mice injected with the pruritogen chloroquine ( Figure 4G ) , demonstrating that CXCR3 signaling contributes to chronic itch but is not required for scratching in response to an acute pruritogen . Thus , we propose that neutrophils promote chronic itch in atopic dermatitis via upregulation of CXCL10 and subsequent activation of CXCR3-dependent itch pathways ( Figure 5 ) . There is great interest in unraveling the neuroimmune interactions that promote acute and chronic itch . Here , we show that neutrophils are essential for the early development of MC903-evoked itch . We further show that the recruitment of neutrophils to the skin is sufficient to drive itch behaviors within minutes of infiltration . While neutrophils are known to release a variety of pruritogens , their roles in itch and AD were not studied ( Hashimoto et al . , 2018 ) . Only a few studies have even reported the presence of neutrophils in human AD lesions ( Choy et al . , 2012; Koro et al . , 1999; Mihm et al . , 1976; Shalit et al . , 1987 ) . Neutrophils have been implicated in psoriatic inflammation and inflammatory pain , ( Sumida et al . , 2014; Perkins and Tracey , 2000; Guerrero et al . , 2008; Cunha et al . , 2003; Finley et al . , 2013; Carreira et al . , 2013; Levine et al . , 2006; Schön et al . , 2000 ) where they are thought to rapidly respond to tissue injury and inflammation , ( Oyoshi et al . , 2012 ) but they have not been directly linked to itch . There is a strong precedence for immune cell-neuronal interactions that drive modality-specific outcomes , such as itch versus pain , under distinct inflammatory conditions . In allergy , mast cells infiltrate the upper dermis and epidermis and release pruritogens to cause itch , ( Solinski et al . , 2019b; Meixiong et al . , 2019 ) whereas in tissue injury , mast cell activation can trigger pain hypersensitivity ( Chatterjea and Martinov , 2015 ) . Likewise , neutrophils are also implicated in both pain and itch . For example , pyoderma gangrenosum , which causes painful skin ulcerations recruits neutrophils to the deep dermal layers to promote tissue damage and pain ( Hashimoto et al . , 2018 ) . In AD , neutrophils are recruited to the upper dermis and epidermis , ( Choy et al . , 2012; Shalit et al . , 1987 ) and we now show that neutrophils trigger itch in AD . Adding to the complex and diverse roles of neutrophils , neutrophils recruited to subcutaneous sites during invasive streptococcal infection alleviate pain by clearing the tissue of bacteria ( Pinho-Ribeiro et al . , 2018 ) . Several potential mechanisms may explain these diverse effects of neutrophils . First , the location of the inflammatory insult could promote preferential engagement of pain versus itch nerve fibers ( Hashimoto et al . , 2018 ) . This is supported by observations that neutrophil-derived reactive oxygen species and leukotrienes can promote either itch or pain under different inflammatory conditions ( Salvemini et al . , 2011; Bautista et al . , 2006; Liu and Ji , 2012; Caceres et al . , 2009 ) . Second , it has been proposed that there are distinct functional subsets of neutrophils that release modality-specific inflammatory mediators ( Wang , 2018 ) . Third , the disease-specific inflammatory milieu may induce neutrophils to specifically secrete mediators of either itch or pain . Indeed , all three of these mechanisms have been proposed to underlie the diverse functions of microglia and macrophages in homeostasis , tissue repair , injury , and neurodegenerative disease ( Hammond et al . , 2018 ) . It will be of great interest to the field to decipher the distinct mechanisms by which neutrophils and other immune cells interact with the nervous system to drive pain and itch . In addition to neutrophils , TSLP signaling and the Type two immune response plays an important role in the development of itch in the second week of the MC903 model . Dendritic cells , mast cells , basophils , and CD4+ T cells are all major effectors of the TSLP inflammatory pathway in the skin . We propose that neutrophils play an early role in triggering itch and also contribute to chronic itch in parallel with the TSLP-Type two response . While we have ruled out an early role for TSLP signaling and basophils and CD4+ T cells in early itch , other cell types such as mast cells , which have recently been linked directly to chronic itch , ( Solinski et al . , 2019b; Meixiong et al . , 2019 ) and dendritic cells may be playing an important role in setting the stage for itch and inflammation prior to infiltration of neutrophils . Given the large magnitude of the itch deficit in the neutrophil-depleted mice , we were surprised to find fewer expression differences in MC903-dependent , AD-associated genes between neutrophil depleted and non-depleted mice than were observed between WT and TSLPR KO mice . One of the few exceptions were the Th1-associated genes Cxcl9/10/11 ( Ewald et al . , 2017; Brunner et al . , 2017 ) . We found that induction of these genes and of CXCL10 protein was completely dependent on neutrophils . While our results do not identify the particular cell type ( s ) responsible for neutrophil-dependent CXCL10 production , a number of cell types present in skin have been shown to produce CXCL10 , including epithelial keratinocytes , myeloid cells , and sensory neurons ( Hashimoto et al . , 2018; Ioannidis et al . , 2016; Kanda et al . , 2007; Koga et al . , 2008; Michalec et al . , 2002; Padovan et al . , 2002; Tamassia et al . , 2007 ) . In support of a role for neutrophils in promoting chronic itch , we observed striking differences in neutrophil-dependent gene expression in the spinal cord , where expression of activity-induced genes and the chronic itch gene Lcn2 were markedly attenuated by loss of neutrophils . Moreover , we also demonstrate that depletion of neutrophils in the second week of the MC903 model can attenuate chronic itch-evoked scratching . In examining previous characterizations of both human and mouse models of AD and related chronic itch disorders , several studies report that neutrophils and/or neutrophil chemokines are indeed present in chronic lesions ( Ewald et al . , 2017; Choy et al . , 2012; Guttman-Yassky et al . , 2009; Suárez-Fariñas et al . , 2013; Jabbari et al . , 2012; Nattkemper et al . , 2018; Li et al . , 2017; Saunders et al . , 2016; Andersson , 2015; Liu et al . , 2019; Malik et al . , 2017 ) . Our observations newly implicate neutrophils in setting the stage for the acute-to-chronic itch transition by triggering molecular changes necessary to develop a chronic , itchy lesion and also contributing to persistent itch . Additionally , we demonstrate a novel role of CXCR3 signaling in MC903-induced itch . The CXCR3 ligand CXCL10 contributes to mouse models of acute and allergic itch ( Qu et al . , 2015; Qu et al . , 2017; Jing et al . , 2018 ) ; however , its role in chronic itch was previously unknown . We speculate that the residual itch behaviors after administration of the CXCR3 antagonist could be due to TSLPR-dependent IL-4 signaling , as TSLPR-deficient mice display reduced itch behaviors by the second week of the model , or due to some other aspect of neutrophil signaling , such as release of proteases , leukotrienes , prostaglandins , or reactive oxygen species , all of which can directly trigger itch via activation of somatosensory neurons ( Hashimoto et al . , 2018 ) . Our observations are in alignment with a recent study showing that dupilumab , a new AD drug that blocks IL4Rα , a major downstream effector of the TSLP signaling pathway , does not significantly reduce CXCL10 protein levels in human AD lesions ( Hamilton et al . , 2014 ) . Taken together , these findings suggest that the TSLP/IL-4 and neutrophil/CXCL10 pathways are not highly interdependent , and supports our findings that Il4 transcript is robustly upregulated in the absence of neutrophils . Additionally , targeting IL4Rα signaling has been successful in treating itch and inflammation in some , but not all , AD patients ( Simpson et al . , 2016 ) . We propose that biologics or compounds targeting neutrophils and/or the CXCR3 pathway may be useful for AD that is incompletely cleared by dupilumab monotherapy . Drugs targeting neutrophils are currently in clinical trials for the treatment of psoriasis , asthma , and other inflammatory disorders . For example , MDX-1100 , a biologic that targets CXCL10 , has already shown efficacy for treatment of rheumatoid arthritis in phase II clinical trials ( Yellin et al . , 2012 ) . While rheumatoid arthritis and AD have distinct etiologies , ( Scott et al . , 2010 ) our body of work indicates that CXCL10 or CXCR3 may be promising targets for treating chronic itch . Our findings may also be applicable to other itch disorders where neutrophil chemoattractants and/or CXCL10 are also elevated , such as psoriasis and allergic contact dermatitis . Overall , our data suggest that neutrophils incite itch and inflammation in early AD through several mechanisms , including: 1 ) directly triggering itch upon infiltration into the skin , as shown by acute injection of CXCL1 , and , 2 ) indirectly triggering itch by altering expression of endogenous pruritogens ( e . g . induction of Cxcl10 expression; Hashimoto et al . , 2018; Ioannidis et al . , 2016; Kanda et al . , 2007; Koga et al . , 2008; Michalec et al . , 2002; Padovan et al . , 2002; Tamassia et al . , 2007 ) . Together , these direct and indirect mechanisms for neutrophil-dependent itch may explain why neutrophils have a dramatic effect on scratching behaviors on not only days eight and twelve but also day five of the model , when neutrophils are recruited in large numbers , but CXCR3 ligands are not as robustly induced . More generally , our study provides a framework for understanding how and when human chronic itch disease genes contribute to the distinct stages of AD pathogenesis . Our analysis of MC903-evoked transcriptional changes suggests we may be able to extend findings in the model not only to atopic dermatitis , but also to related disorders , including specific genetic forms of atopy . For example , we provide evidence that MC903 treatment may also model the filaggrin loss-of-function mutations , which are a key inciting factor in human heritable atopic disease ( Palmer et al . , 2006; Sandilands et al . , 2007 ) . There are many rich datasets looking at mature patient lesions and datasets for mature lesions in other mouse models of chronic itch ( Ewald et al . , 2017; Choy et al . , 2012; Guttman-Yassky et al . , 2009; Jabbari et al . , 2012; Nattkemper et al . , 2018; Oetjen et al . , 2017; Liu et al . , 2019; Liu et al . , 2016 ) . Our study adds a temporal frame of reference to these existing datasets and sets the stage for probing the function of AD disease genes in greater detail . Furthermore , we have mapped the time course of gene expression changes in primary sensory ganglia and spinal cord during chronic itch development . We show that the MC903 model recapitulates several hallmarks of neuropathic disease on a time course much shorter than has been reported for chronic itch , or chronic pain . Nervous system tissues are extremely difficult to obtain from human AD patients , and thus little is known regarding the neuronal changes in chronic itch disorders in both mouse models and human patients . Our findings can now be compared to existing and future datasets examining neuronal changes in chronic pain , diabetic neuropathy , shingles , neuropathic itch , psoriasis , and other inflammatory disorders where neuronal changes are poorly understood but may contribute to disease progression . The early changes we see in skin innervation , sensory ganglia , and spinal cord dovetail with recent studies examining neuroimmune interactions in other inflammatory conditions , ( Pinho-Ribeiro et al . , 2018; Baral et al . , 2018; Pinho-Ribeiro et al . , 2017; Blake et al . , 2018 ) which all implicate early involvement of sensory neurons in the pathogenesis of inflammatory diseases . All mice were housed in standard conditions in accordance with standards approved by the Animal Care and Use Committee of the University of California Berkeley ( 12 hr light-dark cycle , 21 °C ) . Wild-type C57BL/6 mice were obtained from Charles River or Jackson Laboratories and raised in-house . TSLPR KO mice were kindly provided by Dr . Steven Ziegler ( Crlf2tm1Jni; Carpino et al . , 2004 ) and backcrossed onto C57BL/6 . All experiments were performed under the policies and recommendations of the International Association for the Study of Pain and approved by the University of California Berkeley Animal Care and Use Committee . Where appropriate , genotypes were assessed using standard PCR . MC903 ( Calcipotriol; R and D Systems ) was applied to the shaved mouse cheek ( 20 μl of 0 . 2 mM in ethanol ) or rostral back ( 40 µl of 0 . 2 mM in ethanol ) once per day for 1–12 days using a pipette . 100% ethanol was used . All MC903 studies were performed on 8–12 week old age-matched mice . Behavior , RNA-seq , flow cytometry , and immunohistochemistry were performed on days 1 , 2 , 3 , 5 , eight and/or 12 . For AMG 487 experiments in the MC903 model , 50 µL 3 . 31 mM AMG 487 ( Tocris ) or 20% HPCD-PBS vehicle was injected subcutaneously one hour prior to recording behavior ( Qu et al . , 2015 ) . Spontaneous scratching was manually scored for the first 30 min of observation . Both bout number and length were recorded . Behavioral scoring was performed while blind to experimental condition and mouse genotype . On days 1 ( six hours post-treatment ) , 2 , 5 , or eight post-treatment , mice treated with MC903 and vehicle were euthanized via isoflurane and cervical dislocation . Cheek skin was removed , flash-frozen in liquid nitrogen , and cryo-homogenized with a mortar and pestle . Ipsilateral trigeminal ganglia were dissected and both skin and trigeminal ganglia were homogenized for three minutes ( skin ) or one minute ( TG ) in 1 mL RNAzol RT ( Sigma-Aldrich ) . Thoracic spinal cord was dissected from mice treated with 40 µL MC903 or ethanol on the shaved rostral back skin and homogenized for one minute in 1 mL RNAzol . Large RNA was extracted using RNAzol RT per manufacturer’s instructions . RNA pellets were DNase treated ( Ambion ) , resuspended in 50 µL DEPC-treated water , and subjected to poly ( A ) selection and RNA-seq library preparation ( Apollo 324 ) at the Functional Genomics Laboratory ( UC Berkeley ) . Single-end read sequencing ( length = 50 bp ) was performed by the QB3 Vincent G . Coates Genomic Sequencing Laboratory ( UC Berkeley ) on an Illumina HiSeq4000 . See Supplementary file 1 for number of mice per experimental condition and number of mapped reads per sample . Data are available at Gene Expression Omnibus under GSE132173 . Reads were mapped to the mm10 mouse genome using Bowtie2 and Tophat , and reads were assigned to transcripts using htseq-count ( Langmead et al . , 2009; Langmead and Salzberg , 2012 ) . For a given time point , replicate measurements for each gene from treated and control mice were used as input for DESeq ( R ) and genes with padjusted <0 . 05 ( for skin and spinal cord ) or padjusted <0 . 1 ( for trigeminal ganglia ) for at least one time point were retained for analysis ( Anders and Huber , 2012; Anders et al . , 2013 ) . For the skin dataset , we collated a set of AD-related immune cell markers , cytokines , atopic dermatitis disease genes , neurite outgrown/axonal guidance genes , and locally expressed neuronal transcripts , and from this list visualized genes that were significantly differentially expressed for at least one time point . For the trigeminal ganglia dataset , we plotted all genes that were significantly differentially expressed for at least one time point . Genes from these lists were plotted with hierarchical clustering using heatmap2 ( R ) ( Hill , 2019 ) . Genes were clustered into functional groups and significance was evaluated using a permutation test . Briefly , we first tabulated the absolute value of the log2 fold change of gene expression ( between MC903 and EtOH ) of each gene in a given group of n genes in turn , and then we calculated the median of these fold change values , ztrue . We then drew n random genes from the set of all genes detected in the samples and computed the median log2 fold change as above using this null set , znull . Repeating the latter 10 , 000 times established a null distribution of median log2 fold change values; we took the proportion of resampled gene groups that exhibited ( ztrue ≥znull ) as an empirical p-value reporting the significance of changes in gene expression for a given group of n genes . Skin samples were collected from the cheek of mice at the indicated time points with a 4- or 6 mm biopsy punch into cold RPMI 1640 medium ( RPMI; Gibco ) and minced into smaller pieces with surgical scissors . When ear skin was collected , whole ears were dissected postmortem into cold RPMI and finely minced with scissors . For isolation of immune cells , skin samples were digested for 1 hr at 37 °C using 1 U/mL Liberase TM ( Roche ) and 5 µg/mL DNAse I ( Sigma ) . At the end of the digestion , samples were washed in FACS buffer ( PBS with 0 . 5% FCS and 2 mM EDTA ) and filtered through a 70 or 100 µm strainer ( Falcon ) . Cells were stained with LIVE/DEAD fixable stain Aqua in PBS ( Invitrogen ) , then blocked with anti-CD16/32 ( UCSF Core ) and stained with the following fluorophore-conjugated antibodies ( all from eBiosciences unless stated otherwise ) in FACS buffer: cKit-Biotin ( clone ACK2; secondary stain with SA-FITC ) , CD11b-violet fluor 450 ( Tonbo; clone M1/70 ) , Ly6C-PerCP/Cy5 . 5 ( clone HK1 . 4 ) , CD49b-PE/Cy7 ( clone DX5 ) , CD45 . 2-APC/Cy7 ( clone 104 ) , FceRI-PE ( MAR-1 ) , Ly6G-AF700 ( clone 1A8 ) . 10 µL of counting beads ( Invitrogen ) were added after the last wash to measure absolute cell counts . For measurement of CD4+ T cells , 6 mm skin biopsy punch samples were digested for 30 min at 37 °C using Collagenase VIII ( Sigma ) . At the end of the digestion , cells were washed in RPMI buffer ( RPMI with: 5% FCS , 1% penicillin-streptomycin , 2 mM L-glutamine , 10 mM HEPES buffer , 1 mM sodium pyruvate ) . Cells were blocked with anti-CD16/32 ( UCSF Core ) and stained with the following fluorophore-conjugated antibodies in FACS buffer ( PBS with 5% FCS and 2 mM EDTA ) : CD45-APC-eFluor780 ( clone 30-F11; eBiosciences ) , CD11b-PE/Cy7 ( clone M1/70; BD Biosciences ) , B220-PE/Cy7 ( clone RA3-6B2; Tonbo Biosciences ) , CD11c-PE/Cy7 ( clone N418; eBiosciences ) , CD3-FITC ( clone 145–2 C11; eBiosciences ) , CD8-BV785 ( clone 53–6 . 7; Biolegend ) , CD4-PE ( clone GK1 . 5; BD Biosciences ) , gdTCR-AF647 ( clone GL3; Biolegend ) . 10 µL of counting beads ( Invitrogen ) were added after the last wash to measure absolute cell counts , and samples were resuspended in DAPI LIVE/DEAD ( Invitrogen ) . Blood samples were collected from saphenous vein or from terminal bleed following decapitation . Red blood cells were lysed using ACK lysis buffer ( Gibco ) , and samples were washed with FACS buffer ( PBS with 0 . 5% FCS and 2 mM EDTA ) , and blocked with anti-CD16/32 . Cells were stained with Ly6G-PE ( 1A8; BD Biosciences ) , CD11b-violet fluor 450 ( M1/70 , Tonbo ) , Ly6C-PerCP/Cy5 . 5 ( HK1 . 4 , Biolegend ) , and aGr1-APC/Cy7 ( RB6-8C5 , eBiosciences ) . For all experiments , single cell suspensions were analyzed on an LSR II or LSR Fortessa ( BD Biosciences ) , and data were analyzed using FlowJo ( TreeStar , v . 9 . 9 . 3 ) software . Normal human epidermal keratinocytes from juvenile skin ( PromoCell #C-12001 ) were cultured in PromoCell Keratinocyte Growth Medium two and passaged fewer than five times . Cells were treated for three hours at room temperature with 100 μM SLIGRL or vehicle ( Ringer’s + 0 . 1% DMSO ) . Total RNA was extracted by column purification ( Qiagen RNeasy Mini Kit ) . RNA was sent to the Vincent J . Coates Sequencing Laboratory at UC Berkeley for standard library preparation and sequenced on an Illumina HiSeq2500 or 4000 . Sequences were trimmed ( Trimmomatic ) , mapped ( hg19 , TopHat ) and assigned to transcripts using htseq-count . Differential gene expression was assessed using R ( edgeR ) ( Hill , 2019 ) . Data are available at Gene Expression Omnibus under GSE132174 . Staining was performed as previously described ( Hill et al . , 2018; Marshall et al . , 2016 ) . Briefly , 8 week old mice were euthanized and the cheek skin was shaved . The removed skin was fixed overnight in 4% PFA , then washed in PBS ( 3X for 10 min each ) . Dermal fat was scraped away with a scalpel and skin was washed in PBST ( 0 . 3% Triton X-100; 3X for two hours each ) then incubated in 1:500 primary antibody ( Rabbit anti beta-Tubulin II; Abcam #ab18207 or Rabbit anti-CGRP; Immunostar #24112 ) in blocking buffer ( PBST with 5% goat serum and 20% DMSO ) for 6 days at 4°C . Skin was washed as before and incubated in 1:500 secondary antibody ( Goat anti-Rabbit Alexa 594; Invitrogen #R37117 ) in blocking buffer for 3 days at 4°C . Skin was washed in PBST , serially dried in methanol: PBS solutions , incubated overnight in 100% methanol , and finally cleared with a 1:2 solution of benzyl alcohol: benzyl benzoate ( BABB; Sigma ) before mounting between No . 1 . 5 coverglass . Whole mount skin samples were imaged on a Zeiss LSM 880 confocal microscope with OPO using a 20x water objective . Image analysis was performed using a custom macro in FIJI . Briefly , maximum intensity z-projections of the beta-tubulin III or CGRP channel were converted to binary files that underwent edge-detection analysis . Regions were defined by circling all stained regions . Region sizes and locations were saved . TG were dissected from 8- to 12 week old adult mice and post-fixed in 4% PFA for one hour . TG were cryo-protected overnight at 4°C in 30% sucrose-PBS , embedded in OCT , and then cryosectioned at 14 μm onto slides for staining . Slides were washed 3x in PBST ( 0 . 3% Triton X-100 ) , blocked in 2 . 5% Normal Goat serum + 2 . 5% BSA-PBST , washed 3X in PBST , blocked in endogenous IgG block ( 1:10 F ( ab ) anti-mouse IgG ( Abcam ab6668 ) + 1:1000 Rat anti-mouse CD16/CD32 ( UCSF MAB Core ) in 0 . 3% PBST ) , washed 3X in PBST and incubated overnight at 4°C in 1:1000 primary antibody in PBST + 0 . 5% Normal Goat Serum + 0 . 5% BSA . Slides were washed 3x in PBS , incubated 2 hr at RT in 1:1000 secondary antibody , washed 3X in PBS , and then incubated 30 min in 1:2000 DAPI-PBS . Slides were washed 3x in PBS and mounted in Fluoromount-G with No . 1 . 5 coverglass . Primary antibodies used: Mouse anti-CD45 ( eBioscience #14-054-82 ) and Chicken anti-Peripherin ( Abcam #39374 ) . Secondary antibodies used: Goat anti-Chicken Alexa 594 ( ThermoFisher #A11042 ) and Goat anti-Mouse Alexa 488 ( Abcam #150117 ) . DAPI ( ThermoFisher #D1306 ) was also used to mark nuclei . Imaging of TG IHC experiments was performed on an Olympus IX71 microscope with a Lambda LS-xl light source ( Sutter Instruments ) . For TG IHC analysis , images were analyzed using automated scripts in FIJI ( ImageJ ) software ( Hill , 2019 ) . Briefly , images were separated into the DAPI , CD45 , and Peripherin channels . The minimum/maximum intensity thresholds were batch-adjusted to pre-determined levels , and adjusted images were converted to binary files . Regions were defined by circling all stained regions with pre-determined size-criteria . Region sizes and locations were saved . Neutrophils were acutely depleted using intraperitoneal injection with 250 µg aGR1 in PBS ( clone RB6-8C5 , a gift from D . Portnoy , UC Berkeley , or from Biolegend ) , 16–24 hr before behavioral and flow cytometry experiments . Depletion was verified using flow cytometry on blood collected from terminal bleed following decapitation . For longer depletion experiments using the MC903 model , mice were injected ( with 250 µg aGR1 in PBS or PBS vehicle , i . p . ) beginning one day prior to MC903 administration and each afternoon thereafter through day 7 of the model , or on days 8–11 for measurement of day 12 itch behaviors , and blood was collected via saphenous venipuncture at days 3 , 5 , or by decapitation at day eight to verify depletion . Neutrophil-depleted or uninjected mice were treated with MC903 or ethanol for 7 days . On day 8 , 6 mm biopsy punches of cheek skin were harvested , flash-frozen in liquid nitrogen , cryo-homogenized by mortar and pestle , and homogenized on ice for three minutes at maximum speed in 0 . 5 mL of the following tissue homogenization buffer ( all reagents from Sigma unless stated otherwise ) : 100 mM Tris , pH 7 . 4; 150 mM NaCl , 1 mM EGTA , 1 mM EDTA , 1% Triton X-100 , and 0 . 5% Sodium deoxycholate in ddH2O; on the day of the experiment , 200 mM fresh PMSF in 100% ethanol was added to 1 mM , with one tablet cOmplete protease inhibitor ( Roche ) per 50 mL , and five tablets PhosSTOP inhibitor ( Roche ) per 50 mL buffer . Tissues were agitated in buffer for two hours at 4°C , and centrifuged at 13 , 000 rpm for 20 min at 4°C . Supernatants were aliquoted and stored at −80°C for up to one week . After thawing , samples were centrifuged at 10 , 000 rpm for five minutes at 4°C . Protein content of skin homogenates was quantified by BCA ( Thermo Scientific ) and homogenates were diluted to 2 mg/mL protein in PBS and were subsequently diluted 1:2 in Reagent Diluent ( R and D Systems ) . CXCL10 protein was quantified using the Mouse CXCL10 Duoset ELISA kit ( R and D Systems; #DY466-05 ) according to manufacturer’s instructions . Plate was read at 450 nm and CXCL10 was quantified using a seven-point standard curve ( with blank and buffer controls ) and fitted with a 4-parameter logistic curve . Itch behavioral measurements were performed as previously described ( Shimada and LaMotte , 2008; Wilson et al . , 2011; Morita et al . , 2015 ) . Mice were shaved one week prior to itch behavior and acclimated in behavior chambers once for thirty minutes at the same time of day on the day prior to the experiment . Behavioral experiments were performed during the day . Compounds injected: 1 µg carrier-free CXCL1 ( R and D systems ) in PBS , 3 . 31 mM AMG 487 ( Tocris , prepared from 100 mM DMSO stock ) in 20% HPCD-PBS , 50 mM Chloroquine diphosphate ( Sigma ) in PBS , along with corresponding vehicle controls . Acute pruritogens were injected using the cheek model ( 20 µL , subcutaneous/s . c . ) of itch , as previously described ( Shimada and LaMotte , 2008 ) . AMG 487 ( 50 µL ) or vehicle was injected s . c . into the rostral back skin one hour prior to recording of behavior . Behavioral scoring was performed as described above . Skin was collected from the cheek of mice post-mortem with a 6 mm biopsy punch and immediately flash-frozen in liquid nitrogen . Lipid mediators and metabolites were quantified via liquid chromatography-tandem mass spectrometry ( LC-MS/MS ) as described before ( von Moltke et al . , 2012 ) . In brief , skin was homogenized in cold methanol to stabilize lipid mediators . Deuterated internal standards ( PGE2-d4 , LTB4-d4 , 15-HETE-d8 , LXA4-d5 , DHA-d5 , AA-d8 ) were added to samples to calculate extraction recovery . LC-MS/MS system consisted of an Agilent 1200 Series HPLC , Luna C18 column ( Phenomenex , Torrance , CA , USA ) , and AB Sciex QTRAP 4500 mass spectrometer . Analysis was carried out in negative ion mode , and lipid 30 mediators quantified using scheduled multiple reaction monitoring ( MRM ) mode using four to six specific transition ions per analyte ( Sapieha et al . , 2011 ) . The DNFB model was conducted as described previously ( Solinski et al . , 2019a ) . Briefly , the rostral backs of isofluorane-anesthetized mice were shaved using surgical clippers . Two days after shaving , mice were treated with 25 µL 0 . 5% DNFB ( Sigma ) dissolved in 4:1 acetone:olive oil vehicle on the rostral back using a pipette . Five days after the initial DNFB sensitization , mice were challenged with 40 µL 0 . 2% DNFB or 4:1 acetone:olive oil vehicle applied to the outer surface of the right ear . Twenty-four hours after DNFB or vehicle challenge , mice were euthanized and ear skin was harvested for flow cytometry . Different control experimental conditions ( e . g . uninjected versus PBS-injected animals ) were pooled when the appropriate statistical test showed they were not significantly different ( Supplementary file 2 ) . For all experiments except RNA-seq ( see above ) , the following statistical tests were used , where appropriate: Student’s t-test , one-way ANOVA with Tukey-Kramer post hoc comparison , and two-way ANOVA with Tukey Kramer or Sidak’s post-hoc comparison . Bar graphs show mean ± SEM . Statistical analyses were performed using PRISM seven software ( GraphPad ) . For all p values , =0 . 01 < p<0 . 05 , =0 . 001 < p<0 . 01 , =0 . 0001 < p<0 . 001 , and **=p < 0 . 0001 .	Chronic itch is a debilitating disorder that can last for months or years . Eczema , or atopic dermatitis , is the most common cause for chronic itch , affecting one in ten people worldwide . Many treatments for the condition are ineffective , and the exact cause of the disease is unknown , but many different types of cells are likely involved . These include skin cells and inflammation-promoting immune cells , as well as nerve cells that detect inflammation , relay itch and pain information to the brain , and regulate the immune system . Learning more about how these cells interact in eczema may help scientists find better treatments for the condition . So far , a lot of research has focused on static ‘snapshots’ of mature eczema lesions from human skin or animal models . These studies have identified abnormalities in genes or cells , but have not revealed how these genes and cells interact over time to cause chronic itch and inflammation . Now , Walsh et al . reveal that immune cells called neutrophils trigger chronic itch in eczema . The experiments involved mice with a condition that mimics eczema , and showed that removing the neutrophils in these mice alleviated their itching . They also showed that dramatic and rapid changes occur in the nervous system of mice suffering from the eczema-like condition . For example , excess nerves grow in the animals’ damaged skin , genes in the nerves that detect sensations become hyperactive , and changes occur in the spinal cord that have been linked to nerve pain . When neutrophils are absent , these changes do not take place . These findings show that neutrophils play a key role in chronic itch and inflammation in eczema . Drugs that target neutrophils , which are already used to treat other diseases , might help with chronic itch , but they would need to be tested before they can be used on people with eczema .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience", "immunology", "and", "inflammation" ]	2019	Neutrophils promote CXCR3-dependent itch in the development of atopic dermatitis
It has been suggested that Staufen ( Stau ) is key in controlling the variability of the posterior boundary of the Hb anterior domain ( xHb ) . However , the mechanism that underlies this control is elusive . Here , we quantified the dynamic 3D expression of segmentation genes in Drosophila embryos . With improved control of measurement errors , we show that the xHb of stau– mutants reproducibly moves posteriorly by 10% of the embryo length ( EL ) to the wild type ( WT ) position in the nuclear cycle ( nc ) 14 , and that its variability over short time windows is comparable to that of the WT . Moreover , for stau– mutants , the upstream Bicoid ( Bcd ) gradients show equivalent relative intensity noise to that of the WT in nc12–nc14 , and the downstream Even-skipped ( Eve ) and cephalic furrow ( CF ) show the same positional errors as these factors in WT . Our results indicate that threshold-dependent activation and self-organized filtering are not mutually exclusive and could both be implemented in early Drosophila embryogenesis . During the development of multicellular systems , the expression of patterning genes dynamically evolves and stochastically fluctuates ( Dubuis et al . , 2013; Gregor et al . , 2007a; Gregor et al . , 2007b; Jaeger et al . , 2004; Kanodia et al . , 2009; Liu et al . , 2013; Yang et al . , 2018 ) . The high degree of accuracy ( Dubuis et al . , 2013; Gregor et al . , 2007a ) and robustness ( Houchmandzadeh et al . , 2002; Inomata et al . , 2013; Liu et al . , 2013; Lucchetta et al . , 2005 ) that developmental patterning achieves is intriguing . Two hypotheses have been proposed to explain these traits: one is the threshold-dependent positional information model , that is the French flag model , which assumes that the positional information is faithfully transferred from precise upstream patterning ( He et al . , 2008; Wolpert , 2011; Gregor et al . , 2007a ) ; the other is the self-organized filtering model , which assumes that noisy upstream patterning needs to be refined to form downstream patterning with sufficient positional information ( Dubuis et al . , 2013; Houchmandzadeh et al . , 2002; Jaeger et al . , 2004; Kanodia et al . , 2009; Manu et al . , 2009 ) . The two models have often been thought to be mutually exclusive , and which one is implemented in a particular developmental system has been extensively debated . Recently , the two models have also been suggested to collaborate in some developmental patterning systems , but this is still a hypothesis and more molecular-based concrete examples remain to be illustrated ( Green and Sharpe , 2015 ) . The Drosophila embryo is an excellent model system in which to address this question . The blueprint of the adult Drosophila body plan is established during the first 3 hr of patterning before gastrulation in embryos . In particular , the adult body segments can be mapped with the expression pattern of the segmentation genes along the anterior and posterior ( AP ) axis . The hierarchic segmentation gene network consists of four layers of patterning genes ( Gregor et al . , 2007a; Jaeger , 2011 ) : maternal morphogen such as bicoid ( bcd ) ( Liu et al . , 2013; Porcher and Dostatni , 2010; Struhl et al . , 1989 ) , zygotic gap genes such as hunchback ( hb ) ( Struhl et al . , 1992 ) , pair rule genes such as even-skipped ( eve ) ( Goto et al . , 1989 ) , and segmentation polarity genes ( Swantek and Gergen , 2004 ) . They form increasingly refined developmental patterns along the AP axis until the positional information carried by the patterning genes reaches the single-cell level , that is 1% of embryo length ( EL ) , in the variability of the expression pattern ( Dubuis et al . , 2013 ) . The question of which hypothesis is valid in the dynamic transmission of positional information during Drosophila embryogenesis has been controversial , and the Hb boundary has been an important subject in these investigations ( Gregor et al . , 2007a; Houchmandzadeh et al . , 2002; Huang et al . , 2017; Lucas et al . , 2018; Staller et al . , 2015; Tran et al . , 2018 ) . Lying directly downstream of maternal gradients , zygotic Hb forms a steep posterior boundary in the anterior domain ( xHb ) on the dorsal side at approximately the middle of the embryo , with a variability of 1% EL . The variability of xHb has long been thought to depend on the gene staufan ( stau ) ( Houchmandzadeh et al . , 2002 ) . As shown in previous work , stau was the only gene that dramatically increased the variability of xHb from 1% EL to more than 6% EL for protein profiles ( Houchmandzadeh et al . , 2002 ) and 4% EL for mRNA profiles ( Crauk and Dostatni , 2005 ) . By contrast , the variability of xHb remains almost the same as that of the wild type ( WT ) with knockout of nearly all the genes that potentially interact with Hb , including nos , Kr , and kni , or even deletion of the whole or half of the chromosome ( Houchmandzadeh et al . , 2002 ) . Hence , stau could be the key to understanding the potential noise-filtering mechanism . However , the mechanism that underlies of the role of stau has been elusive . It is well known that Stau is an RNA-binding protein and it does not interact with Hb directly . But Stau is unique among the maternal factors affecting anteroposterior patterning , as it is necessary for not only the localization of the bcd mRNA at the anterior pole ( Ferrandon et al . , 1994; Struhl et al . , 1992 ) but also the spatially constrained translation of nos mRNA at the posterior pole ( St Johnston et al . , 1991 ) . Hence , Stau is important for establishing the Bcd gradient that activates the transcription of Hb ( Struhl et al . , 1989 ) and the Nos gradient that represses the translation of maternal Hb ( Wang and Lehmann , 1991 ) . The role of Bcd in regulating the variability of the Hb boundary remains controversial . On the one hand , Houchmandzadeh et al . , 2002 suggested that the variability of xHb was independent of upstream Bcd gradients , as the average Bcd gradients of two groups of embryos overlapped , although their Hb moved anteriorly or posteriorly compared with the average Hb profile , and the variability of xHb was much smaller than the positional error derived from the Bcd gradient noise . On the other hand , He et al . , 2008 showed that the shift of xHb correlated with the average Bcd gradient showing different concentrations at xHb , that the variability of xHb was equivalent to the positional error derived from the Bcd gradient noise , and that this variability increased when the Bcd gradient profile was altered to show increased flatness toward the mid-embryo ( He et al . , 2010 ) . In addition , the variability of xHb seemed to be significantly less in He’s measurement ( He et al . , 2008 ) than in Houchmandzadeh’s measurement ( Houchmandzadeh et al . , 2002 ) . Some of these controversial points might be clarified if we could further reduce the spatial and temporal measurement errors in determining developmental patterning . It has been estimated that the orientation-related error could be as high as 20–50% of the total measurement error for Bcd gradients ( Gregor et al . , 2007a ) and gap genes ( Dubuis et al . , 2013 ) . This is because the dynamic developmental profiles vary spatially in the asymmetric 3D embryos , yet traditionally they are measured in a selected plane of the manually oriented embryo ( Gregor et al . , 2007a; Houchmandzadeh et al . , 2002 ) . Here , we developed measurement methods to quantify the dynamic 3D expression of patterning genes in Drosophila embryos and applied these methods to measure the positional errors of the segmentation genes at different levels . We focused on one stau– allele , stauHL54 , which is reported to induce the largest variability in xHb location ( Houchmandzadeh et al . , 2002 ) . Surprisingly , we discovered that in stau– mutants , xHb moves posteriorly by more than 10% EL to the WT position in nc14 , and that the variability of xHb location is comparable with that in the WT . Moreover , the upstream Bcd gradients show gradient intensity noise equivalent to those in the WT from nc12 to nc14 , and the downstream Even-skipped ( Eve ) and cephalic furrow ( CF ) show the same positional errors as those in the WT . We also constructed a minimal model and revealed that the extremely large shift of the Hb boundary in stau– mutants originates from both the flattened maternal Hb profiels and the altered Bcd gradients , with a ~ 65% decrease in amplitude and a 17% increase in length constant in stau– mutants compared with the WT . Our results suggest that the threshold-dependent model could be valid at early nc14 , but that the gene network then adjusts , and the filtering mechanism is implemented at least from the maternal Bcd gradient to Hb . Some factors other than stau , which remain to be discovered , could play important roles in filtering dynamic positional information . We observed a large dynamic shift of xHb in stau– mutants using quantitative imaging with carefully controlled measurement errors . To control the measurement errors , we quantified the Hb expression pattern using 3D imaging with light-sheet microscopy ( for more details , see 'Materials and methods' , Figure 1—figure supplement 1 , and Figure 1—videos 1 and 2 ) on fixed and immunostained embryos and staged each embryo with ~1 min temporal precision using the depth of the furrow canal ( FC ) ( Figure 1—figure supplement 2 ) . The average temporal Hb expression profiles of the WT ( Figure 1—figure supplement 3 ) and of stau– mutants ( Figure 1A ) are shown on heat maps projected from 3D embryo images . From the heat map , we extracted the average of the normalized dorsal profile of Hb with a time-step of 5 min in nc14 ( Figure 1B and C ) and measured xHb . Consistent with previous results ( Dubuis et al . , 2013 ) , xHb of the WT remains nearly constant 20 min into nc14 . Notably , in the first 20 min of nc14 , it moves posteriorly by 3% EL ( Figure 1D ) . By contrast , at the dorsal side of the embryo , xHb of stau– mutants starts at 37 . 6 ± 1 . 9% EL at 2 . 5 min into nc14 , then dynamically moves posteriorly by 10 . 3% EL within 60 min , and finally stabilizes at 47 . 9 ± 0 . 9% EL , close to the WT boundary position 47 . 8 ± 0 . 9% EL at 42 . 5 min into nc14 ( Figure 1D and Figure 1—figure supplement 4C and E ) . Besides stauHL54 , another stau– allele , stauD3 , also shows a large shift of xHb: 42 . 8 ± 1 . 5% EL to 47 . 9 ± 1 . 8% EL from 12 . 5 min to 52 . 5 min into nc14 on the dorsal side ( Figure 1—figure supplement 5 ) . It is well known that stauD3 is a strong allele with a fully penetrant abdominal segmentation phenotype ( Lehmann and Nüsslein-Volhard , 1991 ) , hence the observed large shift of xHb in stau– mutants is not an allelic specific feature for stauHL54 , which could be hypomorphic , that is with partial function loss . Moreover , xHb differs significantly in different orientations , for example , the ventral boundary moves from 32 . 7 ± 2 . 4% EL by 10 . 9% EL to 43 . 6 ± 1 . 2% EL , with an anterior shift of approximately 6% EL compared with the dorsal boundary ( Figure 1D and Figure 1—figure supplement 4A , D and F ) . By contrast , the difference of xHb between the dorsal and ventral sides in the WT is only 3% EL ( Figure 1D ) . These results suggest that it is crucial to control spatial and temporal measurement errors in order to assess the variability of the Hb boundaries . Interestingly , the spatial measurement error of xHb seems to be the smallest on the dorsal profiles ( Figure 1—figure supplement 6A and B ) . Although xHb dynamically moves posteriorly by more than 10% EL in stau– mutants , this shift is reproducible from embryo to embryo . Within a short time window , the variability of xHb in stau– mutants is comparable with that of the WT ( Figure 1E ) . Furthermore , even for all of the data from the whole nc14 , after de-trending the dynamic shift , the standard deviations of xHb for stau– mutants is 1 . 67 ± 0 . 16% EL ( errors are estimated with bootstrap ) , similar to 1 . 45 ± 0 . 14% EL for the WT . Moreover , the variance difference between the two fly lines is not statistically significant ( p=0 . 31 in a two-sample F-test for equal variances ) ( Figure 1F–G ) . These results seem to contradict previous measurements , which suggest that the variability of xHb in stau– mutants is significantly higher than that of the WT ( Houchmandzadeh et al . , 2002; He et al . , 2008 ) . We suspect that the apparent difference in variability of xHb might result from the different control of the measurement errors ( Figure 1—figure supplement 6C and D ) . For instance , the temporal control in He’s measurements ( He et al . , 2008 ) could be better than Houchmandzadeh’s measurements ( Houchmandzadeh et al . , 2002 ) , and the spatial control could be better in our measurements than in He’s measurements ( He et al . , 2008 ) . In stau– mutants , xHb significantly varies as the development time or embryo orientation changes , so the measured variability of xHb is very sensitive to spatial or temporal measurement errors . For example , the dynamic shift of xHb has a strong effect on the calculation of the variability of xHb . If we pool all of the measured xHb on the dorsal side , the standard deviation of xHb is 3 . 5 ± 0 . 3% EL for stau– mutants , much greater than the WT ( 1 . 7 ± 0 . 2% EL ) . Moreover , the large shift of xHb from the dorsal to the ventral side also strongly affects the measured variability of xHb . If we pool all of the measured xHb values on both the dorsal and ventral side , the standard deviation of xHb for stau– mutants can even increase to 4 . 5 ± 0 . 3% EL ( Figure 1—figure supplement 6C ) . The variability of xHb also depends on whether it is scaling with the embryo length ( Houchmandzadeh et al . , 2002; He et al . , 2008 ) . Consistent with the comparable variability , stau– mutants and the WT are also similar in the scaling of the Hb boundary . On the one hand , the normalized relative Hb boundary positions are nearly constant for both stau– mutants and the WT ( Figure 1H ) . On the other hand , the normalized absolute Hb boundary positions with respect to the anterior pole are proportional to the embryo length ( R2 = 0 . 69 and 0 . 66 for the WT and stau– mutants , respectively , Figure 1I ) . Notably , the scaling of xHb of stau– mutants is significant only if we de-trend the dynamic shift , that is normalize the absolute Hb boundary position of each embryo by the average position in the corresponding developmental time . Without this normalization , the scaling of the absolute Hb boundary position of stau– mutants ( R2 = 0 . 26 ) is inferior compared with the WT ( R2 = 0 . 71 ) , consistent with previous results ( He et al . , 2008 ) . To understand the origin of the variability of the Hb boundary , we measured its upstream Bcd gradients . Using live imaging with a two-photon microscope ( TPM ) , we found that the average Bcd-GFP gradient of stau– mutants at 16 min into nc14 shows reduced amplitude but an increased length constant ( Figure 2A ) . Compared with the WT , in stau– mutants the amplitude of the Bcd-GFP gradient is only 48 ± 7% , but the length constant is increased by approximately 13% from 18 . 6% EL to 21 . 1% EL ( Figure 2A ) . If we take the GFP maturation effect into consideration ( Little et al . , 2011; Liu et al . , 2013 ) , the difference of the length constants can be even greater , increasing by 17% from 15 . 5% EL of the WT to 18 . 2% EL in stau– mutants ( Figure 3—figure supplement 1D ) . This apparent increase of the measured length constant could result from more extensively distributed bcd mRNA in the embryos of stau– mutants ( Ferrandon et al . , 1994; Petkova et al . , 2014 ) , consistent with the simulation based on the synthesis-diffusion-degradation ( SDD ) model ( Grimm et al . , 2010; Figure 3—figure supplement 1A–B ) . Interestingly , the Bcd gradient noise of stau– mutants is comparable with that of the WT ( Figure 2B ) . The relative noise of the Bcd-GFP gradient in the range of 0 . 1~0 . 6 EL is 18 . 0 ± 2 . 0% , which is very close to 17 . 9 ± 1 . 8% of the WT . Hence , it seems to be consistent with the comparable variability of the Hb boundary . However , these results are different from previous measurements ( He et al . , 2008; Houchmandzadeh et al . , 2002 ) , which might result from different measurement errors . Another concern is that Bcd-dependent hb transcription turns off less than 10 min into nc14 ( Liu and Ma , 2013; Liu et al . , 2016 ) , and that the time window for Bcd interpretation could be earlier than nc14 ( Huang et al . , 2017; Bergmann et al . , 2007 ) ; hence Hb expression could be largely produced by translating the previously produced mRNA . It is therefore necessary to measure the dynamic Bcd gradient noise in developmental time earlier than the conventional measurement time , that is 16 min into nc14 . By improving the imaging technique and image analysis method ( Figure 2—figure supplement 1 ) , we assessed the Bcd-GFP gradient noise in both stau– mutants and the WT . The average nuclear Bcd-GFP gradient rises to a maximum in nc14 and then slightly falls off ( Figure 2C ) . This is consistent with the results of another live imaging experiment ( Gregor et al . , 2007b ) but is significantly different from the measurement of the fixed embryos ( Little et al . , 2011 ) because of the GFP maturation effect ( Little et al . , 2011; Liu et al . , 2013 ) . Importantly , the gradient intensity noise of stau– mutants and the WT remains comparable from nc12 to 60 min into nc14 ( Figure 2D–F ) . It is also interesting to discover that the Bcd-GFP gradient noise in both fly lines remains at nearly the same level from nc13 to nc14 ( Figure 2F ) . Notably , the measured gradient noise at 16 min into nc14 is slightly higher than that measured with TPM ( Figure 2B ) , as the measurement error increases in the dynamic Bcd gradient measurements ( e . g . , a lower signal to noise ratio is due to the fast imaging speed and higher background ) . The measurement error , however , should change at a similar level for both stau– mutants and the WT , and hence their gradient noise is still comparable . As in stau– mutants , the amplitude of the Bcd-GFP gradient in the fly line with only half of the Bcd dosage ( Bcd1 . 0 ) decreased by half compared with the WT ( Liu et al . , 2013 ) . According to a previous study , for the fly line Bcd1 . 0 , the Hb boundary position might be determined according to the threshold-dependent activation model in early nc14 . Then , it dynamically moves posteriorly toward the Hb position of the WT but stops midway in later nc14 ( Liu et al . , 2013 ) . By contrast , the Hb position in stau– mutants moves all the way to the WT position . It is interesting to investigate whether the threshold-dependent activation model is also valid in early nc14 in stau– mutants . To test this speculation , we first needed to correct the GFP maturation effect to obtain the total Bcd-GFP gradient from the observed Bcd-GFP gradient measured with live imaging , as it takes tens of minutes for the newly synthesized Bcd-GFP to fluoresce . On the basis of the SDD model incorporating maturation correction and the spatial distribution of bcd mRNA ( Little et al . , 2011; Petkova et al . , 2014 ) , we calculated the maturation correction curves for the Bcd-GFP gradient in both stau– mutants and the WT ( Figure 3—figure supplement 1A ) . Interestingly , the Bcd-GFP concentrations at the respective Hb boundary position are almost the same at 16 min into nc14 ( Figure 3 ) , suggesting that the threshold-dependent model might still apply in early nc14 . However , as the xhb of stau– mutants moves posteriorly in a much greater range than that of the WT , and as the Bcd gradient changes only slightly in both fly lines , the exact time window in which this model applies remains to be determined . In addition to examining the threshold-dependent model , it was also interesting to test whether the noise-filtering model plays a role in early Drosophila embryogenesis . We measured the positional information transmission of the other patterning genes interacting with Hb in stau– mutants . As another gap protein , Kr , forms a strip adjacent to the Hb boundary , and the two genes suppress each other ( Jaeger , 2011 ) . In stau– mutants , the Kr strip shows a width of 29% EL , wider than that in the WT ( 17% EL ) at the beginning of embryogenesis , then it narrows to 20% EL ( Figure 4—figure supplement 1A ) . It then moves posteriorly together with the Hb boundary in early nc14 but ends its movement late in nc14 ( Figure 4A ) , suggesting that the late shift in the Hb boundary is independent of Kr . As one of the downstream pair rule proteins , Eve forms only four instead of seven strips in stau- mutants ( Figure 4—figure supplement 1B ) . The dynamic shift in the four strips is also shown in Figure 4B . At 56 min into nc14 , the cephalic furrow ( CF ) position in the stau- mutants is 28 . 2 ± 1 . 1% EL , while the CF of the WT is 34 . 3 ± 1 . 4% EL . To understand the dynamic transmission of positional information , we compared the positional noise of all the measured AP patterning markers from Bcd to CF during the time course of nc14 ( Figure 5 ) . Consistent with previous results , the positional error of the Hb boundary of the WT decreases by approximately two-fold to a minimum at approximately 45 min into nc14 and then slightly increases ( Dubuis et al . , 2013 ) . The stau– mutants show a trend that is similar to that in the WT . The positional error of xHb in early nc14 is slightly less than that of the Bcd gradient for stau– mutants and the WT . The minimal positional noise of the Kr front boundary , the first peak of Eve , and CF are comparable with the minimal positional error of the Hb , that is , approximately 1% EL . Notably , the stau– mutants and the WT are comparable in their minimal positional errors in these different levels of AP patterning genes ( Figure 5 ) . These results suggest that positional noise is filtered from Bcd to Hb and is then relayed between different levels of the patterning genes . In addition to the noise-filtering phenomenon , it is interesting to note that the shifts in the patterning markers differ . The Hb boundary moves posteriorly by more than 10% EL to nearly the WT position . For the other gap genes that interact with Hb , the front boundary of Kr moves posteriorly by only 5% EL and stops 3% EL away from the WT position . For the downstream genes , the first peak of Eve moves very little , and its final position ( corresponding to the CF position ) is 6% EL away from the WT position . These results indicate that the large dynamic shift of Hb seems to be dampened instead of faithfully relayed in the other patterning genes . By contrast , the shift accumulates in the fly lines that have altered Bcd dosages ( Liu et al . , 2013 ) . For example , in the fly line Bcd1 . 0 , which has almost the same Bcd dosage as stau– mutants , the Hb boundary position moves posteriorly by approximately 2 . 5% EL in nc14 and stops at 7% EL away from the WT position . The first peak of Eve moves posteriorly further by 0 . 8% EL , so the CF position is closer to the WT position . It has been suggested that this additive shift results from the dynamic integration of maternal positional information ( Liu et al . , 2013 ) . It remains to be investigated whether a new mechanism may account for the dynamic positional transmission in stau– mutants . Maternal mutants become more accessible with the CRISPR-Cas9 technique ( Bassett and Liu , 2014 ) . The balancer stabilizes the maternal mutant fly line but prevents the random mutation from being rescued by homologous recombination . Hence , the accumulated mutations often deteriorate the maternal mutant fly line in the fly stock . In this work , instead of running rescue genetics experiments , we generated a fresh stau– mutant fly line from the WT by using the CRISPR-Cas9 technique . The newly generated stauHL54 mutant fly line shows the correct expression patterns of Bcd , Hb , Kr , and Eve compared with the original stau– mutant fly line ( Figure 4—figure supplement 2 ) , and the cuticle patterns of stauHL54 and stauD3 are consistent with the published record ( Lehmann and Nüsslein-Volhard , 1991; Figure 4—figure supplement 3 ) . To reveal the true biological noise , 3D imaging is very helpful in controlling spatial measurement errors . Compared with the two-photon microscopy used in previous studies for 3D imaging of embryos ( Fowlkes et al . , 2008 ) , the light-sheet microscope should provide better-quality 3D reconstruction by combining images taken from two opposite directions with higher imaging speed ( Krzic et al . , 2012 ) . However , if strong scattering or absorbing objects exist in samples , caution is needed to alleviate or properly correct the potential striper shadow artifacts ( Mayer et al . , 2018 ) . The conventional 2D expression profiles can be extracted with the 3D imaging analysis tools developed by us and the others ( Fowlkes et al . , 2008; Heemskerk and Streichan , 2015 ) . Using the heat map projected from 3D imaging data , we can conveniently evaluate the measurement errors in embryo orientations . Interestingly , most of the time , the measurement errors on the dorsal side are smaller than those on the ventral side and the symmetric two sides on the coronal plane ( Figure 1—figure supplement 6A and B ) . This is because the Hb boundary moves in a smaller range around the dorsal side ( Figure 1A and Figure 1—figure supplement 3 ) . Nevertheless , even with 10° uncertainty in the orientation , the measurement errors on the dorsal side could still be as high as 0 . 2% EL , at least 20% of the positional error of the Hb boundary , which is 1% EL . For dynamically evolved patterning , it is also important to determine the embryo age precisely . Both the depth of the FC and nuclear shape/size are good measures of the embryo age in nc14 . The former has better time resolution in the late developmental stage . Notably , the dynamic shift in FC could vary in different fly lines , as the shifting curve measured with w1118 is different from that described by previously published results measured with Oregon-R ( Figure 1—figure supplement 2A; Dubuis et al . , 2013 ) . When compared with the imaging of fixed embryos , live imaging has advantages in potentially higher temporal resolution determined by the imaging speed . Only in nc14 , the densely packed nuclei on the embryo surface can be imaged in one plane . Hence , for convenience in controlling the measured error , the Bcd gradient noise is often measured only in a selected plane at a timepoint of 16 min into nc14 . However , before this measurement time , the Bcd-dependent regulation of Hb has already been shut off ( Liu and Ma , 2013 ) . Moreover , considering the dynamics of downstream patterning genes , it is important to measure the dynamics of the Bcd gradient noise . We found that the maximum projection from a five-layer and 1-μm-spaced z-stack image could significantly alleviate the measurement errors . To accumulate sufficient samples to measure the gradient noise , it is also very important to stabilize the imaging condition and to correct the potential intensity drift between different experimental sessions ( Figure 2—figure supplement 1C and D ) . With this imaging improvement as well as automatic image analysis , we successfully showed that the Bcd-GFP gradient noise is almost constant in the interphase from nc13 to nc14 ( Figure 2F ) . As the fluorescence intensity observed in live imaging increases during this period , this result may suggest that the Bcd gradient noise is not dominated by the Poissonian noise . On the basis of the quantitative spatial-temporal gene expression data , it has long been known that the gap gene profiles , for example , the central Kr domain and the posterior Kni and Gt domains , show substantial anterior shifts during nc14 ( Jaeger et al . , 2004 ) . These dynamic shifts are proposed to originate from the asymmetric cross-regulation between gap genes , that is , posterior gap genes repress their adjacent anterior gap genes but not vice versa ( Huang et al . , 2017; Jaeger et al . , 2004 ) . The shift amount is less than 5% , much less than the shift of the Hb boundary in stau– mutants . The Hb boundary and the other patterning features , such as the Kr central strip and Eve peaks , also move dynamically with differing Bcd dosages ( Liu et al . , 2013 ) . For example , for the fly line with only half of the Bcd dosage in the WT , the shift in the CF is only approximately 40% of the predicted value based on the threshold-dependent model . The Hb boundary moves posteriorly toward the WT position by approximately 4% EL from an initial position close to the one predicted by the threshold-dependent model . The posterior shift of the Hb boundary has also been observed in nos– mutants ( Petkova et al . , 2019 ) . Among all the reported shifts of the patterning markers in fly embryos , the shift in the Hb boundary in stau– mutants is the largest at more than 10% EL . The shift significantly slows down in late nc14 , as the majority of the shift , ~7% EL , finishes in the first 30 mins of nc14 . By contrast , most of the shifts observed in the other cases occur in the later 30 mins of nc14 and have been proposed to originate from the cross-regulation between gap genes ( Jaeger et al . , 2004; Liu et al . , 2013 ) . Hence , a mechanism other than the cross-regulation between gap genes could contribute to the large shift of the Hb boundary in stau– mutants . Moreover , the slope of the Hb boundary in stau– mutants is less than that of the WT in the first 30 mins in nc14 and later increases to nearly the same as that of the WT ( Figure 5—figure supplement 1 ) . This might be related to the modification of the maternal gradients from both poles ( Figure 6A and B ) . To test this idea , we constructed a mathematical model to calculate the dynamic shift in xHb in both stau– mutants and the WT . To illustrate the Stau effect , we neglected the cross-regulation between different gap genes and only considered the activation from Bcd to Hb and the self-activation of Hb . We assume that stau– mutants and the WT share the same gene regulation function for Hb , and that the only difference comes from the distorted expression profiles of Bcd and maternal Hb in stau– mutants ( for more details , see 'Materials and methods' , Figure 6A–C , and Figure 6—figure supplement 1A ) . On the one hand , the amplitude of the Bcd gradient is reduced by 65% , and the length constant is increased by 17% . On the other hand , the maternal Hb profile is flattened as the Nos gradient is removed . This model fits well with the measured dynamic shift of xHb in both stau– mutants and the WT ( Figure 6D ) , indicating that the synergy effect resulting from the altered Bcd gradients and maternal Hb in stau– mutants can account for the much larger shift of xHb compared with that in the WT . The depletion of the Nos gradient by the stau– mutant is necessary for the observed larger shift of xHb in stau– mutants . If only the amplitude of Bcd gradients is altered , this model predicts that the shift of xHb is much smaller . If we rescue the Nos gradient , that is keep the maternal Hb in stau– mutants the same as that in the WT , the simulation based on the fitted model shows that xHb starts at 35% EL , but only moves posteriorly by ~1% EL ( Figure 6D ) . A very similar result is also observed in the simulated dynamics of xHb in the fly line Bcd1 . 0 , of which the Bcd dosage is only half of that in the WT . The shift amount of xHb in Bcd1 . 0 is only 1%EL , smaller than the experimental value of 4% EL ( Figure 6D ) . Although the initial position of xHb might agree with the experiment , the simulated shift slows down significantly in later nc14 , inconsistent with the unchanged shifting speed in the experiment . We speculate that the shift in late nc14 could be attributed to the cross-regulation between gap genes , as suggested in previous studies ( Liu et al . , 2013 ) . Moreover , this model predicts that the initial position of xHb varies as the Bcd gradient is tuned . Our measurement shows that Bcd gradients on the ventral side decrease in the amplitude and increase in the length constant compared with those on the dorsal side ( Figure 6—figure supplement 1B ) , consistent with previous results ( Gregor et al . , 2007a ) . This difference could be attributed to the observed anterior shift of xHb on the ventral side , which is approximately 6% EL for stau– mutants but 3% EL for the WT ( Figure 6—figure supplement 1C ) . This model also suggests that the dynamic change of Bcd gradients ( Little et al . , 2011 ) influences the dynamic shift of xHb , as it predicts that xHb could continue to move posteriorly ( Figure 6—figure supplement 1D and E ) if a static Bcd gradient is applied . This prediction is inconsistent with the experimental observation that stau– mutants and the WT show reduced shift speeds and stabilized xHb at the end of nc14 . Hence , the current model suggests that the ultra-large shift of xHb in stau– mutants can be attributed to the distorted Bcd and Nos gradients due to the loss of Stau function , if self-activated Hb is activated by Bcd according to the threshold-dependent model at early nc14 . However , this simplified model without cross-regulation of the gap genes fails to predict the small shift of xHb in nos– mutants ( Houchmandzadeh et al . , 2002; Petkova et al . , 2019 ) . Thus , a comprehensive model incorporating both the maternal factors and the gap genes is still needed to dissect the sophisticated role of Stau in regulating Hb patterning . The large dynamic shift in the Hb boundary raises the question of how the positional information is transferred in the patterning system . The term ‘positional information’ was first coined by Wolpert , 1969 . On the basis of this concept , developmental patterning is instructed by the concentration of a single static morphogen gradient , and the interpretation of the morphogen gradient follows the threshold-dependent model . Each cell ‘acquires’ its position inside the embryo by ‘reading’ the morphogen concentration and accordingly activates downstream genes to form the cell-fate map ( Jaeger and Reinitz , 2006; Wolpert , 2011; Wolpert , 2016 ) . This model provides a simple molecular-based mechanism for developmental pattern formation . It has prevailed in developmental biology , especially after the identification of a series of morphogens starting with Bcd ( Rogers and Schier , 2011; Struhl et al . , 1989 ) . However , this model has long been challenged because gradient noise could disrupt patterning precision ( Houchmandzadeh et al . , 2002; Jaeger et al . , 2007 ) . Without precise morphogen gradients as inputs , the developmental pattern could also form via a noise-filtering mechanism resulting from cross-regulation between genes ( Manu et al . , 2009 ) . This idea is rooted in Turing’s seminal idea: periodic patterns can spontaneously form in a self-organizing reaction-diffusion system , for example , a slow diffusive activator and a fast diffusive inhibitor ( Corson and Siggia , 2012; Turing , 1952 ) . Recently , an increasing number of developmental patterning systems have been found to implement a Turing-like mechanism ( Economou et al . , 2012; Goryachev and Pokhilko , 2008; Raspopovic et al . , 2014 ) . Usually , these two classic mechanisms have been thought to be mutually exclusive . Hence , great effort has been made to distinguish which mechanism is implemented in a particular developmental system . However , whether early fly embryogenesis follows the threshold-dependent model ( Gregor et al . , 2007a; He et al . , 2008 ) or the noise-filtering model ( Houchmandzadeh et al . , 2002; Manu et al . , 2009 ) has been controversial because it has not been easy to run quantitative tests . First , it is well known that the gap protein profiles dynamically change in nc14 ( Dubuis et al . , 2013; Jaeger et al . , 2004 ) . Hence , the developmental system is not in a steady state , and the concentration of these transcription factors could continue to change with a time scale not much longer than the degradation time of the downstream gene . As a result , the developmental pattern cannot be regarded as the instant readout of simultaneous transcription factors but instead should be seen as an accumulation of the product generated in an early time window . Second , the gap gene integrates multiple maternal factors ( Jaeger , 2011; Liu et al . , 2013 ) . Hence , we need to measure the combined positional information of all the upstream genes and the dynamic positional information of the downstream genes . On the one hand , however , the conventional measurement method without sufficient control of spatial and temporal measurement error is rather limited in its ability to measure expression dynamics . On the other hand , the combined positional information is difficult to calculate because the regulatory function is still unknown , although it might be estimated on the basis of the optimal decoding hypothesis ( Petkova et al . , 2019 ) . By developing 3D measurements with reduced measurement errors , we observe that the positional errors decrease from approximately 2 . 0% EL at early nc14 to approximately 1 . 0% EL in the middle of nc14 in stau– mutants . A slight decrease in the positional errors was also observed in the WT ( Figure 5 ) . Interestingly , even for stau– mutants with an ultra-large dynamic shift in the Hb boundary , the threshold for Bcd in activating Hb still appears to be the same as that of the WT at 16 min into nc14 . These results suggest that the threshold-dependent positional information model probably acts upstream of the self-organized filtering mechanism during Drosophila embryogenesis . This scenario is actually one of the most basic ‘building blocks’ of the interaction between the two mechanisms ( Green and Sharpe , 2015 ) . We expect that studying the combination of the two mechanisms could be the key to revealing the mechanism of precise and robust pattern formation via dynamic transmission of positional information . Our newly developed measurement methods , together with recent developed dynamic measurement ( Bothma et al . , 2018; Dubuis et al . , 2013; Durrieu et al . , 2018; Garcia et al . , 2013; Huang et al . , 2017; Lucas et al . , 2013 ) and modeling ( Verd et al . , 2017 ) tools , will facilitate the characterization of the dynamic transmission of positional information . Bcd-GFP intensity was measured in the fly strains bcd-egfp;+;bcdE1and bcd-egfp;stauHL54;bcdE1 using live imaging . The expression profiles of Hb , Kr , and Eve in immunostained embryos were measured with w1118 , stauHL54 ( w;stauHL54 ) and stauD3 ( w;stauD3 ) mutants . The two stau– mutants were generated from w1118 directly by corresponding gene editing of the RNA-binding domain of stau using CRISPR/Cas9 ( Bassett and Liu , 2014 ) : a point mutation ( replacing the base T in the intron of the fifth exon with base A ) for stauHL54 , and a replacement of the second to fifth exon regions with a knock-in rfp marker for stauD3 . All embryos were collected at 25°C , dechorionated , and then heat-fixed in 1x TSS ( NaCl , Triton X-100 ) . After at least 5 min , the embryos were transferred from the scintillation vial to Eppendorf tubes and vortexed in 1:1 heptane and methanol for 1 min to remove the vitelline membrane . They were then rinsed and stored in methanol at −20°C . The embryos were then stained with primary antibodies , including mouse anti-Hb ( Abcam , ab197787 ) , guinea pig anti-Kr , and rat anti-Eve ( gifts from John Reinitz ) . Secondary antibodies were conjugated with Alexa-647 ( Invitrogen , A21240 ) , Alexa-488 ( Invitrogen , A11006 ) , and Alexa-555 ( gift from John Reinitz ) . To prevent cross-binding between the rat and mouse primary antibodies , the embryos were incubated in first guinea pig and then rat primary antibodies , followed by their respective secondary antibodies . Subsequently , the embryos were treated in blocking buffer before the mouse primary and secondary antibodies were applied . Finally , the embryos were stained with DAPI ( Invitrogen , D1306 ) . Embryos that were stained and washed together in the same tube were mounted in agarose ( Invitrogen E-Gel EX Agarose gels , 1~1 . 5% ) in a capillary tube with an inner diameter of 1 mm ( Brand , 701904 ) . Samples were imaged on a Zeiss Z1 light-sheet microscope . Images were taken with a W Plan Apo 20X/1 . 0 water immersion objective and with sequential excitation wavelengths of 638 , 561 , 488 and 405 nm . The thickness of the light sheet was 4 . 6 or 4 . 35 μm . Before the fluorescence imaging , each embryo was adjusted to the maximal sagittal plane via bright-field imaging . For each embryo , two z-stacks of images ( 1920 × 1920 pixels , with 16 bits and a pixel size of 286 nm at 0 . 8 magnified zoom ) with 1 μm spacing were taken from two opposite sides by rotating the embryos 180° ( Figure 1—figure supplement 1A ) . Several factors are key to improving image quality . i ) Light sheet illumination setting: chose the ‘Dual Side when Experiment’ and the ‘Online Dual Side Fusion’ , then adjusted the left and right beam path to get the optimal images . ii ) Pivot scan setting: the ‘Pivot scan’ was activated to reduce the shadows that might otherwise be cast by optically dense structures within the embryos . iii ) Laser power and exposure time: adjust laser power and exposure time to maintain an adequate signal to noise ratio and to avoid severe photo-bleaching . Image analysis routines were implemented in customized MATLAB codes ( MATLAB , 2018 ) with five steps . i ) Image-registration and fusion . Two z-stacks of raw images of each embryo were acquired from opposite directions by rotating 180o . The two stacks were registered with the autocorrelation algorithm ( Figure 1—figure supplement 1B ) , which is based on the correlation coefficient reflecting the similarity between two images . Next , the fused images were obtained by averaging the two raw images at the same position and opposite direction ( Figure 1—figure supplement 1B ) . This fusion process compensates the dependence of the intensity on the imaging depth ( Figure 1—figure supplement 1E ) . ii ) 3-D reconstruction . The 3D embryos were reconstructed with these fused z-stack images by the 3D interpolation algorithm ( Figure 1—figure supplement 1C ) . iii ) Segmentation at different angles . A plane across the AP axis with the largest area was identified . Then , starting from this plane , 36 sides of 2D images were extracted from the 3D embryos by rotating slices with 5° intervals at different orientations along the AP axis ( Figure 1—figure supplement 1C ) . iv ) Profile extraction . The gene expression profiles were extracted from these slices by morphology image processing to generate the embryo mask , extract the intensity of nuclei surrounding the boundary of embryos mask , and obtain the profile at different embryo orientations . v ) Heat map construction . The 36 Hb intensity profiles of one embryo were normalized from 0 to 1 , then placed in a heat map , in which the dorsal side was located in the bottom quarter and the ventral side was located in the upper quarter ( Figure 1—figure supplement 1D ) . Embryos were collected at 25°C for 1 hr , dechorionated in 100% bleach ( 4% NaClO ) for 3 min , glued on a glass slide and covered with halocarbon oil ( Halocarbon 700 ) . The Bcd gradient in the mid-coronal plane at 16 min into nc14 was measured as reported previously . In brief , embryos were imaged at 22°C on a TPM that was built in house . The excitation laser was 25 mW in average power and 970 nm in wavelength . The objective was a Zeiss 25X ( NA = 0 . 8 in air ) oil/water immersion objective . Emission fluorescence was collected with a gallium-arsenide-phosphide ( GaAsP ) with a quantum yield of more than 40% and dark counts of less than 4000/s at 25°C . For each embryo , three images ( 512*512 pixels with a pixel size of 460 nm , bit depth of 12 bits , and scan speed of 4 ms/line ) were taken sequentially along the A-P axis and stitched together . In each session , embryos from the fly strains bcd-egfp;+;bcdE1and bcd-egfp;stauHL54;bcdE1 were mounted on the same slides and measured side by side . For the dynamic Bcd gradient measurements , the embryos were glued on the cover glass of a Petri dish of 30 mm in diameter ( NEST , 801002 ) and covered with halocarbon oil . They were imaged in the mid-sagittal plane at 25°C on a Nikon A1RSi+ confocal microscope with a Nikon Plan Apo λ 20X/0 . 75 air objective . The fluorescence was excited at a wavelength of 488 nm and collected with a GaAsP detector . A maximum-intensity-projected z-stack of five images ( 1024 × 1024 pixels with a pixel size of 620 nm , bit depth of 12 bit , and spacing of 1 μm ) around the largest plane was acquired at each time point . Each of the images was averaged on two sequential acquisitions . In each session , at most three embryos were picked for imaging to guarantee the time resolution ( 80 s , scan speed of 2 . 3 s/frame ) . The background was the average of the background fluorescence measured with ten w1118 embryos under the same conditions . To correct for the potential imaging differences in different sessions , the control samples were prepared with the hand-peeling protocol to preserve the fluorescence of the Bcd-GFP of the collected embryos . Embryos at the interphase of nc13–nc14 were selected and imaged in advance of each imaging session . Imaging analysis was processed with customized MATLAB codes ( MATLAB , 2018 2018a ) . Hb patterning is treated as a one-dimensional reaction-diffusion system with no-flux boundary condition: ( 1 ) ∂h ( x , t ) ∂t=f ( I ( x , t ) , h ( x , t ) ) −β⋅h ( x , t ) +D∂2h ( x , t ) ∂x2where β and D denote the degradation rate and diffusion constant of Hb , respectively , h ( x , t ) denotes the concentration of Hb at time t and the AP axis coordinate x , and f ( I , h ) is the gene regulatory function ( GRF ) that determines the synthesis rate of Hb . I ( x , t ) represents the net regulation effect of the maternal factors , which are generally spatial-dependent and time-variant . The maternal factor Nos represses the translation of maternal hb ( Wang and Lehmann , 1991 ) , so we assume that Nos only affects the initial distribution of Hb , hx , 0=hm⋅S ( kx ( x-x0 ) ) , where Sξ=1/1+exp⁡ξ . Another maternal factor Bcd constantly activates the expression of Hb during early embryogenesis . Hence , we assume Ix , t=b ( x , t ) , where b ( x , t ) denotes the nuclear Bcd gradient . The nuclear Bcd gradient is dynamic and its amplitude decays after nc12 ( Little et al . , 2011 ) . As an approximation , we assume bx , t=bm⋅e-x/λ⋅Tt;ω0 , t0 , where T ( t;ω0 , t0 ) ={1 , t≤t0exp ( −ω0 ( t−t0 ) ) , t>t0 , denoting that the Bcd profile decays with the linear decay rate ω0 in an isotropic manner after t0 ( time offset from the onset of nc14 ) . Besides the activation from Bcd , Hb is also activated by itself ( Lopes et al . , 2008 ) . Considering that the P2 enhancer of hb has at least six binding sites for Bcd ( Driever et al . , 1989 ) and three for Hb ( Treisman and Desplan , 1989 ) , and given the high binding cooperativity of Bcd or Hb , we assume that the GRF in Eq . 1 takes the all-or-nothing strategy by following a coupled Hill function: ( 2 ) f ( b , h ) =αb ( bb0 ) nb+αh ( hh0 ) nh1+ ( bb0 ) nb+ ( hh0 ) nhwhere b0 and h0 are the activation thresholds for Bcd and Hb , respectively , nb and nh are the Hill coefficients for Bcd and Hb , respectively , and αb and αh denote the scaling factor for the production of Hb from the activation of Bcd and the self-activation of Hb , respectively . The experiment showed that stau– mutants are different from the WT in Bcd gradients and maternal Hb gradients: the amplitude of Bcd gradients of stau– mutants ( bmstau- ) ) is ~35% of that of the WT ( bmwt ) ) ; the length constant of Bcd gradients of stau– mutants ( λstau- ) ) is ~17% larger than that of the WT ( λwt ) ) ; and the initial distribution of Hb of stau– mutants is uniform across the embryo due to the lack of repression from the Nos gradient . Hence , we set all the parameters of stau– mutants to be the same as those for the WT except for bmstau-=0 . 35⋅bmwt , λstau-=1 . 17⋅λwt , and ( c ) x0stau-→+∞ . This model has a total 16 parameters , nine of them are fixed on the basis of the experimental values from references or our measurements , and the others need to be optimized from data fitting ( Figure 6—source data 1 ) . To obtain seven free parameters , we fit the simulated xhb as a function of t to the experimental dynamics of xhb for both stau– mutants and the WT . The error function weighted during the early rising phase of the WT , the final position difference between stau– mutants and the WT , and the final changing rate of xhb helps to capture the key dynamic characteristics and to avoid potential over fittings . Using optimized parameters , we simulated the dynamics of xhb for Bcd1 . 0 by setting bm1x=0 . 5⋅bmwt . We also simulated xhb on the ventral side by setting bmventral=0 . 62⋅bmdorsal and λventral=1 . 1⋅λdorsal to obtain the best fitting to the experimental results , that is , 6% EL and 3% EL for stau– mutants and the WT , respectively ( Figure 6—figure supplement 1B ) . To test the effect of the dynamics of Bcd gradients on the formation of the Hb boundary , we set ω0→0 and ran the simulation using the same optimized parameters or ran the parameter optimization again to obtain the best fit .	Broadly speaking , all individuals of any animal species share a highly consistent shape and structure . Despite this , the activity of the genes that control these body patterns can vary significantly . There are currently two models that have been proposed for how noisy systems of genes , and the proteins they code , can produce consistent body patterns . The first , suggests the noise is essentially self-compensating so stably produces the same result , while the second invokes localized self-organizing systems that help to refine the structural details . In the early stages of development for the fruit fly , Drosophila melanogaster , one of the proteins that controls body patterns is called Hunchback ( often just Hb for short ) . The Hb proteins are largely found at the front-end of the fly embryo , with a sharp drop near the middle . Normally the position of the drop in Hb varies between flies by around 1% of the total length of the fly embryo . Previous work has linked a gene called staufan ( or stau for short ) to the distribution of Hb in flies but the mechanism involved is unknown . Yang , Zhu , Kong et al . have now used a technique called light sheet microscopy to accurately measure the location of Hb proteins in fruit fly embryos . Without the stau gene , the average position of the drop in Hb proteins underwent a larger shift towards the rear at a key stage in development . Despite this altered behavior , the extent of variation between flies did not change . Similarly , the variation of other genes that control Hb location and that are controlled by Hb remained unchanged . As such , it seems stau affects Hb positioning but has no impact on variation between individuals . These findings suggest that both models for controlling variation in fly development could still be relevant and may operate together . This study also provides a new method for the more precise measurement of systems like these that may offer insights into the mechanisms involved in early embryonic development .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology", "computational", "and", "systems", "biology" ]	2020	The dynamic transmission of positional information in stau- mutants during Drosophila embryogenesis
PapC ushers are outer-membrane proteins enabling assembly and secretion of P pili in uropathogenic E . coli . Their translocation domain is a large β-barrel occluded by a plug domain , which is displaced to allow the translocation of pilus subunits across the membrane . Previous studies suggested that this gating mechanism is controlled by a β-hairpin and an α-helix . To investigate the role of these elements in allosteric signal communication , we developed a method combining evolutionary and molecular dynamics studies of the native translocation domain and mutants lacking the β-hairpin and/or the α-helix . Analysis of a hybrid residue interaction network suggests distinct regions ( residue ‘communities’ ) within the translocation domain ( especially around β12–β14 ) linking these elements , thereby modulating PapC gating . Antibiotic sensitivity and electrophysiology experiments on a set of alanine-substitution mutants confirmed functional roles for four of these communities . This study illuminates the gating mechanism of PapC ushers and its importance in maintaining outer-membrane permeability . Gram-negative pathogens commonly express a vast variety of complex surface organelles that are involved in different cellular processes . One of these organelles , known as pili ( or fimbriae ) , forms a class of virulence factors involved in host cell adhesion and recognition , invasion , cell mobility , and biofilm formation . P pili from uropathogenic Escherichia coli are specifically required for the colonization of the human kidney epithelium , a critical event in the kidney infection process ( pyelonephritis ) ( Roberts et al . , 1994 ) . P pili are assembled on the bacterial outer membrane ( OM ) via the chaperone/usher ( CU ) pathway ( Thanassi et al . , 1998 ) , which is often used as a model system to elucidate the mechanism of pilus biogenesis ( Waksman and Hultgren , 2009 ) . The biogenesis of pili via the CU pathway is a highly ordered process that comprises sequential steps . The chaperone protein ( PapD ) brings the pilins to the bacterial OM where they are assembled into a pilus at a transmembrane pore protein known as the usher ( PapC ) . The usher ( ∼800 residues ) is composed of five domains ( Figure 1A ) : a periplasmic N-terminal domain ( NTD ) , an OM central translocation domain ( TD ) that comprises a translocation pore domain ( TP ) , interrupted by a conserved Ig-like plug domain ( PD ) , and two domains at the periplasmic C-terminal end ( CTD1 and CTD2 ) ( Thanassi et al . , 2002; Ng et al . , 2004; Capitani et al . , 2006; Phan et al . , 2011; Geibel et al . , 2013 ) . The structure of the apo TD ( Figure 1B , C ) consists of a 24-stranded kidney-shaped β-barrel where the PD is inserted into the loop connecting two β-strands ( β6–β7 ) , occluding the luminal volume of the pore ( Remaut et al . , 2008; Huang et al . , 2009 ) . In the activated form of another archetypal member of the usher family , FimD , the PD is located outside the pore lumen in the periplasm , next to the NTD ( Phan et al . , 2011; Geibel et al . , 2013 ) . In addition to the PD , there are two secondary structure elements that uniquely characterize the large β-barrel structures of the usher TD ( Figure 1B ) . The first element is a β-hairpin that creates a large gap in the side of the β-barrel , a feature unprecedented in previously known OM β-barrel structures ( Remaut et al . , 2008 ) . This element ( located between strands β5 and β6 of the barrel , Figure 1C ) folds into the barrel lumen and constrains the PD laterally inside the barrel pore . Mutants lacking the β-hairpin show an increased pore permeability suggesting that the β-hairpin has a role in maintaining the PD in a closed conformation ( Volkan et al . , 2013 ) . The second element is an α-helix ( located on the loop between β13 and β14 , Figure 1B ) , which caps the β-hairpin from the extracellular side . Mutants lacking the α-helix , or in which the interface between the helix and the PD is disrupted , present a remarkable increase in pore permeability , comparable with that of the mutant lacking the PD , suggesting a role for the helix in maintaining the PD in a closed state ( Mapingire et al . , 2009; Volkan et al . , 2013 ) . 10 . 7554/eLife . 03532 . 003Figure 1 . PapC usher organization and detail of its translocation domain . ( A ) A diagram of the domain organization of PapC usher . NTD ( dark-blue ) represents the N-terminal domain , CTD1 ( light-violet ) and CTD2 ( dark-violet ) represent the C-terminal domains; TD represents the translocation domain , comprising the TP ( translocation pore , light-blue ) and the PD ( plug domain , magenta ) . ( B and C ) : Ribbon representation of the starting model of the native translocation domain ( TD ) of PapC with the labels ‘N’ and ‘C’ indicating the N and C termini of the translocation channel . The β-barrel , PD ( including the P-linkers ) , β-hairpin , and α-helix ( including the H-linkers ) are coloured blue , magenta , orange , and yellow , respectively . The outer membrane position is represented schematically with the labels ‘E’ , ’M’ , and ‘P’ indicating the extracellular side , the membrane , and the periplasmic side , respectively . Side view of the TD ( B ) is shown with the α-helix , β-hairpin , H-linker1 , H-linker2 , P-linker1 , P-linker2 , and PD , labelled . Extracellular top view of the TD ( C ) is shown with the barrel β strands labelled β1 through β24 and with the PD strands labelled βA through βF . The figures were created with Chimera ( Pettersen et al . , 2004 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 00310 . 7554/eLife . 03532 . 004Figure 1—figure supplement 1 . MD simulations of the native PapC TD and its mutants . ( A ) Cutaway view across the membrane plane of the native PapC TD starting model in a POPE/POPG lipid bilayer ( sim1 , t = 0 ) . Molecular surface of PapC TD is coloured as in Figure 1 , the lipids are shown in grey with the lipid head group coloured by element , the water is coloured by element , and the ions ( the Na+ in blue and the CL− in yellow ) are represented as sphere . The Cα-RMSD values for each system from the starting structure ( t = 0 ) for the native TD ( B ) , the hairpin mutant ( C ) , helix mutant ( D ) , and helix-hairpin mutant ( E ) are plotted as a function of time . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 004 The mutant lacking both the β-hairpin and the α-helix is defective for pilus biogenesis ( Mapingire et al . , 2009 ) . It has been observed in other OMP β-barrels that such secondary structure elements ( e . g . , an α-helix that protrudes inside the barrel or packs against the transmembrane strands ) can use complex allosteric mechanisms to mediate their function ( Naveed et al . , 2009 ) . These are often combinations of large conformational changes ( ‘global motions’ ) dictated by the overall architecture ( including movement of secondary structure elements ) and smaller changes ( ‘local motions’ , such as the motion of recognition loops and side-chain fluctuations ) ( Liu and Bahar , 2012 ) . Additionally , it has been shown that important residues in terms of evolution ( highly-coevolved or conserved ) could have a pivotal role in mediating such allosteric communications ( Suel et al . , 2003; Tang et al . , 2007 ) . In this study , to understand the allosteric mechanism leading to the plug displacement in PapC and the involvement of the α-helix and β-hairpin , we used a hybrid computational approach and verified our results experimentally . By combining sequence conservation analysis , mutual information-based coevolution analysis , and all-atom molecular dynamics ( AA-MD ) , we modelled the interaction network within the native PapC TD as well as within different mutants lacking the α-helix , β-hairpin , and both . This unique computational approach allowed us to identify residues that are likely to be involved in the transmission of the allosteric signal between the α-helix , β-hairpin elements and the plug . These residues were investigated by site-directed mutagenesis , functional studies , and planar lipid bilayer electrophysiology . The results confirmed the involvement of 4 of the 5 distinct communities of residues in modulating the usher's channel activity and gating , suggesting that they all participate in the allosteric mechanism controlling plug displacement . The changes in the non-covalent interactions ( hydrogen bonds and salt bridges ) between all residue pairs were analysed within the native TD by calculating their non-covalent interaction score ( NCI score ) ( see ‘Materials and methods’ ) . A non-covalent residue–residue interaction network ( RIN ) comprising 492 nodes ( residues ) and 1350 edges ( interactions ) was then constructed as a weighted undirected graph for the native TD ( Figure 2 ) and the three mutant systems ( Figure 2—figure supplement 1A–C ) , with the weight for each edge given by the corresponding NCI score ( Table 2 ) . All four RINs have properties typical of small-world networks ( Atilgan et al . , 2004; Haiyan and Jihua , 2009; Taylor , 2013 ) , with significant higher clustering coefficient compared to a corresponding random network and a higher mean short path length ( Table 2 ) . Within the constructed non-covalent native RIN , we identified 246 weak-to-strong interactions ( connecting 362 nodes ) with an NCI score of at least 0 . 3 . Among these , 231 nodes connected by 133 edges showed an NCI score greater than 0 . 6 ( i . e . , strong interaction ) of which 78 involve residues that are part of the barrel strands ( 58 . 6% ) . 10 . 7554/eLife . 03532 . 006Figure 2 . The native TD and its non-covalent interaction network ( non-covalent RIN ) . ( A ) Ribbon representation of the starting model of the native translocation domain ( TD ) of PapC with the labels ‘N’ and ‘C’ indicating the N and C termini of the translocation channel . The β-barrel , PD , P-linker1 , P-linker2 , β-hairpin , and α-helix ( including the H-linkers ) are coloured grey , magenta , light purple , dark purple , orange , and yellow , respectively . The α-helix , β-hairpin , P-linker1 , P-linker2 , and PD are labelled . ( B ) Non-covalent RIN representation of the native translocation domain ( TD ) of PapC visualized with Cytoscape 2 . 8 . 2 ( Smoot et al . , 2011 ) based on RINalyzer plug-in analysis ( Doncheva et al . , 2011 ) ( see Figure 2—figure supplement 1 for the RINs of the TD mutants ) . The nodes ( representing residues ) are coloured by structural element as in ( A ) Edges ( connecting two residues ) are shown in blue , the edge width is proportional to its NCI score from lower to higher values . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 00610 . 7554/eLife . 03532 . 007Figure 2—figure supplement 1 . Non-covalent interaction network ( non-covalent RIN ) for PapC TD mutants . Non-covalent RIN representation of the hairpin mutant ( A ) , helix mutant ( B ) , and helix-hairpin mutant ( C ) translocation domain ( TD ) of PapC visualized as in Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 00710 . 7554/eLife . 03532 . 008Figure 2—figure supplement 2 . Combined RIN of the difference in non-covalent interaction score ( ΔNCI score ) . RINs showing the difference in non-covalent interaction score ( ΔNCI score ) between the native TD system and the hairpin mutant ( A ) , helix mutant ( B ) , and helix-hairpin mutant ( C ) . The combined RIN ( D ) was created by merging the three RINs . The nodes ( representing residues ) are coloured as in Figure 2A . Edges ( connecting two residues ) are shown in blue , with edge width proportional to its corresponding ΔNCI score ( from lower to higher values ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 00810 . 7554/eLife . 03532 . 009Table 2 . Summary of the residue–residue interaction networks ( RINs ) parameter . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 009RINFull RINCCrC/CrLLrL/LrNative PapC TD1350 ( 492 ) 0 . 3840 . 01232 . 006 . 673 . 781 . 76Hairpin mutant1196 ( 485 ) 0 . 3680 . 01133 . 457 . 203 . 901 . 84Helix mutant1225 ( 476 ) 0 . 3620 . 01132 . 906 . 673 . 901 . 71Helix-hairpin mutant854 ( 466 ) 0 . 2620 . 00832 . 758 . 104 . 701 . 72Descriptions of the items are: RIN , residue–residue interaction networks of the different model systems; Full RIN , number of edges in the RIN , in parenthesis the number of node; C , average clustering coefficient; Cr , average clustering coefficient for the random networks with the same size; C/Cr , average clustering coefficient ratio ( as used in Atilgan et al . , 2004 ) ; L , average shortest path length; Lr , average shortest path length for the random networks with the same size; L/Lr , average shortest path length ratio ( as used in Atilgan et al . , 2004 ) . Comparative analysis between the RINs of native and mutants systems revealed slight changes , suggesting a rearrangement in the interaction network . To better understand the mutation-induced changes in network components , we calculated the difference in non-covalent interaction score ( ΔNCI score ) between the native TD system and each of the mutant systems ( the weakened interactions are shown in Figure 2—figure supplement 2A–C ) . This information was then added as a weighted undirected edge to the pre-existing native non-covalent RIN ( the ΔNCI edges are shown in Figure 2—figure supplement 2D ) . Interestingly , 24% of the strong interactions in the native RIN were weakened relative to the RIN of the mutant lacking the β-hairpin , 22 . 6% relative to the mutant lacking the α-helix , and 23 . 3% relative to the mutant lacking both , suggesting that interactions between nodes that are not part of the deleted secondary structure elements were consistently weakened in the absence of these elements . We first extracted evolutionary information from a multiple sequence alignment of the PapC TD family . The patterns of conservation in the TD using Consurf ( Ashkenazy et al . , 2010 ) analysis suggested that the highly conserved residues ( score 9 ) tend to be clustered in two specific regions of the usher ( Figure 3A ) . The first cluster mapped onto the PD and the P-linkers ( P-linker1 residues 248–263; P-linker2 residues 325–335 ) connecting it to the TP . The second cluster ( which included the majority of the highly-conserved residues ) mapped onto one side of the TP ( strand β1–14 and β24 ) . It includes residues: ( i ) near the periplasmic side of the β-barrel within β1–4 strands and β24 strand; ( ii ) on the extracellular side of the barrel ( within β5–10 ) ; ( iii ) in the β-hairpin region ( β-hairpin and β7–9 ) ; and ( iv ) in the area of β10–14 capped by the α-helix region , which comprises the α-helix and its linkers—H-linker1 ( residues 445–450 ) and H-linker2 ( 461–468 , respectively ) . Surface representation of the TD reveals a continuous patch of conserved residues facing the lipid bilayer , including β13 , the extracellular half of β14 and the periplasmic half of β12 ( Figure 3B ) . Intriguingly , this patch ( ‘β13 conserved patch’ ) reaches the full height of the pore from the α-helix region to a functionally important loop located between β12 and β13 strands ( Farabella , 2013; Volkan et al . , 2013 ) . 10 . 7554/eLife . 03532 . 010Figure 3 . Evolutionary analysis of PapC TD . ( A–B ) Sequence conservation calculated with Consurf ( Ashkenazy et al . , 2010 ) and mapped onto the initial model of the native PapC TD ( sim1 , t = 0 ) . Amino acid conservation scores are classified into nine levels . The colour scale for residue conservation goes from cyan ( non-conserved: grade 1 ) to maroon ( highly conserved: grade 9 ) , unreliable positions are coloured light yellow . ( A ) Ribbon representation of the model with the highly conserved residues ( grade9 ) shown as spheres and key elements labelled . ( B ) Molecular surface of the model with β12–β14 labelled . ( C–D ) Sequence co-evolution calculated with PyCogent ( Knight et al . , 2007; Caporaso et al . , 2008 ) . ( C ) The co-evolving residues are mapped onto the initial model of the native PapC TD ( sim1 , t = 0 ) . ( D ) The co-evolution network as visualized with Cytoscape 2 . 8 . 2 Cytoscape 2 . 8 . 2 ( Smoot et al . , 2011 ) based on RINalyzer plug-in analysis ( Doncheva et al . , 2011 ) . Edges ( connecting two co-evolved residues ) are shown in blue , and nodes ( representing coevolved residues ) are coloured by structural element . The PD , P-linker1 , P-linker2 , β-hairpin , and α-helix are indicated schematically and coloured as in Figure 2 . The node size is proportional to its degree of connectivity . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 010 In addition to investigating conservation , we performed an analysis to identify the coevolutionary relationships between residues in the structure . Using normalized mutual information ( NMI ) analysis ( Martin et al . , 2005 ) with a Z-score cut-off = 4 ( see ‘Materials and methods’ ) to detect the intra-molecular coevolved residues within PapC TD , a coevolutionary RIN containing 100 coevolved residues ( nodes ) and 357 connections ( edges ) was derived ( Figure 3D ) . Mapping the network onto the PapC TD structure showed that many of the residues involved are also connected spatially and are clustered in the same regions where the highly-conserved residues were found ( P-linkers , the PD , and the barrel wall capped by the α-helix , in close proximity to the β-hairpin ) ( Figure 3C , D ) . The obtained coevolutionary RIN showed a significant clustering coefficient compared to a corresponding random network ( of 0 . 493 vs 0 . 187 , respectively ) and a comparable mean short path length ( 3 . 15 vs 2 . 57 , respectively ) ( Daily et al . , 2008 ) . We constructed one hybrid RIN in which the attributes for the nodes and edges are defined by the properties described above ( non-covalent networks and evolutionary analysis , see ‘Materials and methods’ ) . Starting from the secondary structure elements ( that uniquely characterise the barrel–the α-helix and β-hairpin ) in this hybrid RIN , we used a multi-step procedure to reconstruct a pathway of communication between them ( Figure 4 ) . 10 . 7554/eLife . 03532 . 011Figure 4 . Detection of allosteric hot spots . A flowchart representing the multistep procedure used to identify allosteric hot spots . First , a sub-network of the protein hybrid RIN was generated starting from the α-helix and β-hairpin . Then , filters based on the evolutionary information and on the interactions analysis were applied ( see Figure 4—figure supplement 1 ) resulting in a sub-network of ‘hot spot’ residues . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 01110 . 7554/eLife . 03532 . 012Figure 4—figure supplement 1 . Contribution of each filter in the detection of allosteric hot spots . Venn diagrams illustrating the contribution of each filter in the node set detection . ( A ) The dynamic filter resulting from the intersection of the ΔNCI score between the native TD and each of the mutants . ( B ) The relative combination of the dynamic filter set and the set from the evolutionary filter to the final hot spots sub-network . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 012 This initial large sub-network is formed by 208 nodes ( residues ) connected by 456 NCI edges ( in the native RIN ) . Applying the dynamic filter ( independently ) on the edges , based on the difference in non-covalent interaction score between the native TD and each of the mutants ( ΔNCI > 0 ) , revealed that in each case a large part of network has weakened interactions ( hairpin mutant: 200 nodes , 438 NCI edges; helix mutant: 199 nodes , 437 NCI edges; helix-hairpin: 202 nodes , 443 NCI edges ) ( Figure 4—figure supplement 1A ) . The application of the evolutionary filter revealed that only a small part of the sub-network is made of evolutionary important residues ( 75 nodes connected by 104 native NCI edges ) . Combining the filters ( Figure 4—figure supplement 1B ) resulted in 69 nodes connected by 100 NCI edges ( thus representing interacting residues in the native PapC network ) . The residues of this sub-network ( 14% of all residues in the TD ) were considered ‘hot spots’ in the communication pathway of PapC TD ( ‘hot spot’ sub-network ) . Mapping them onto the structure revealed that they are located close together in a continuous area within PapC TD . We analysed the community structure of the hot spot sub-network using the edge-betweenness clustering algorithm ( Girvan and Newman , 2002; Morris et al . , 2010 ) . This analysis shows that the sub-network has a modular structure , with a modularity index of 0 . 73 ( maximum value of the modularity index is 1 ) , which is typical of 3D-structure based RIN ( Newman and Girvan , 2004; Sethi et al . , 2009 ) . Here , a total of 11 communities containing two or more residues were identified , from which only five communities are composed by more than five residues . For further analysis , we chose to consider only these five largest communities , which are located: between β7–9 and the P-linkers ( C1 ) ; between the β-hairpin and the conserved region at the base of the α-helix ( β12–14 ) ( C2 ) ; between β12–β13_loop and the P-linker1 ( C3 ) ; between the β-hairpin , P-linker2 and the PD ( βE–F ) ( C4 ) ; and on the tip of the PD ( βE–F loop and βA–B loop ) ( C5 ) ( Table 3 and Figure 5 ) . 10 . 7554/eLife . 03532 . 013Table 3 . Communities in the hot spot sub-network . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 013CommunityResiduesC1E247 , D249 , Y329 , L330 , T331 , G334 , Q335 , R337 , K339 , E361 , S363 , W364 , G365 , L366 , S371 , L372C2R237 , D402 , S420 , Y441 , R442 , F443 , S444 , K468 , E469 , M470 , E475 , W496C3Y260 , Y425 , S426 , K427 , T437 , F438 , A439C4S233 , R303 , G304 , L306 , V308 , F320 , T324 , A325 , V327C5E269 , E312 , N314 , G315 , R316 , K318Descriptions of the items are: Community , the name of the community; Residues , residues that are part of the community . 10 . 7554/eLife . 03532 . 014Figure 5 . PapC TD communities . The communities of the hot spot sub-network are shown as surface by colours and indicated schematically ( C1 to C5 ) . The inset shows a close up of the identified core residues located in β7 , β8 , the P-linkers , the β-hairpin , the conserved region at the base of the α-helix , in the junction between β12–β13_loop . The core residues are labelled in bold and numbered according to the X-ray structure of the apo PapC TD ( PDB id: 2vqi ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 014 We selected a number of key residues from the communities ( core hot spot residues ) , which link different elements within each community , for further experimental investigations ( Figure 5 ) . These were found in communities C1–C4: in C1 , residues linking the P-linkers and the barrel wall that possibly help in maintaining the P-linkers in a closed configuration ( P-linker1:D249 , P-linker2:Y329 , P-linker2:T331 , β7:R337 , and β8:S363 ) ; in C2 , residues that bridge the base of the α-helix ( the extracellular end of the β13 conserved patch ) and β-hairpin ( β-hairpin:R237 , β12:S420 , β13:R442 , β13:S444 ) ; in C3 , residues on the interface between P-linker1 and β12–β13_loop ( P-linker1:Y260 , β12–β13_loop:K427 , β13:T437 , β13:F438 ) ; and in C4 , residues that are part of the interface with P-linker1 and the periplasmic end of the β13 conserved patch ( P-linker2:A325 , P-linker2:V327 ) . To test experimentally if the key hot spot residues identified above ( linking elements within each community ) contribute to allosteric signalling within PapC , we constructed a set of single alanine substitution mutations ( Table 4 ) . Each of the mutants was present at a similar level in the OM compared to the wild-type PapC usher , and the mutations did not affect the ability of the usher to form a stable β-barrel in the OM ( data not shown ) . The functionality of the PapC substitution mutants was assessed by ability to assemble P pili on the bacterial surface . P pili bind to receptors on human red blood cells , and assembly of functional P pili was determined using a hemagglutination assay ( HA ) . Seven ( D249A , T331A , R442A , S444A , Y260A , K427A , T437A ) of the 14 tested mutants exhibited greater than twofold defects in agglutination titers compared with wild-type PapC , with 4 of the mutants ( D249A , R442A , Y260A , and K427A ) exhibiting no agglutination activity ( HA titer = 0 ) ( Table 4 ) . The defective mutants were in key residues from communities C1 , C2 , and C3 , confirming roles for these communities in proper usher function . 10 . 7554/eLife . 03532 . 015Table 4 . Analysis of PapC substitution mutants . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 015PapCCommunityHA titerAntibiotic sensitivitySDSErythromycinVancomycinWT641566D249AC101566Y329AC1321566T331AC124151510R337AC1641566S363AC1641566R237AC26416615S420AC2321566R442AC201566S444AC2241466Y260AC3015146K427AC301466T437AC3241466F438AC33214126V327AC464201416Descriptions of the items are: PapC , the PapC construct tested; Community , the name of the community to which the mutated residue belongs; HA ( hemagglutination assay ) titer , the maximum fold dilution of bacteria able to agglutinate human red blood cells; Antibiotic sensitivity , the diameter of zone of inhibition ( mm ) around filter disc impregnated with SDS ( 750 µg ) , erythromycin ( 15 µg ) , or vancomycin ( 20 µg ) . The antibiotic sensitivity measurement includes the filter disc ( 6 mm diameter ) . We next used an antibiotic sensitivity assay to screen the PapC substitution mutants for effects on channel activity of the usher . The OM of Gram-negative bacteria has low permeability to detergents such as SDS and to antibiotics such as erythromycin and vancomycin , providing resistance to these molecules . In its resting state , the usher TP is gated closed by the PD , preserving integrity of the OM . Mutations that disrupt channel gating by the PD will result in the opening of the large TD channel , leading to increased sensitivity of the bacteria to antibiotics . Bacteria expressing five of the PapC substitution mutants ( T331A , R237A , Y260A , F438A , and V327A ) exhibited increased sensitivity to one or more of the tested molecules ( Table 4 ) . Y329A , R337A , S363A , and S420A did not appear to perturb the allosteric signalling within PapC , showing the same antibiotic sensitivity phenotype and ability to assemble pili of the native PapC ( Table 4 ) . However , the hemagglutination assay and antibiotic sensitivity assay are screening tools , and as such , they lack in sensitivity to pick up smaller changes in the ability to assemble pili or channel activity of the usher . In total , 10 mutated PapC TDs were found to be affected either in their ability to trigger hemagglutination or in their permeability to SDS or antibiotics ( T331A , D249A in C1; R237A , R442A , S444A in C2; Y260A , K427A , T437A , F438A in C3; and V327A in C4 ) . We attempted to purify those mutants in view of examining their channel activity using planar lipid bilayer electrophysiology ( which is a more sensitive assay ) . Unfortunately , only seven of these 10 mutants yielded protein stable enough ( as wild-type ) in detergent solutions to carry out the planned experiments ( T331A in C1; R237A , S444A in C2; K427A , T437A , F438A in C3; and V327A in C4 ) . During the OM extraction procedure , D249A , R442A , and Y260A were not stable enough due to the loss of the membrane bilayer environment and inability to maintain their native conformation in detergents . Insertion of PapC purified proteins ( see ‘Materials and methods’ ) was promoted in planar lipid bilayers by clamping the membrane potential to −90 mV . As soon as channel activity was observed , the potential was briefly returned to zero and the chamber stirring stopped to minimize further insertions . 10-min long recordings of channel activity at + and −90 mV , and at + and −50 mV were performed . The typical electrophysiological signature of the wild-type PapC usher is characterized by prolonged dwell times at a low current level , representing the closed state of the usher , and brief transitions of various current amplitudes . These transitions represent short-lived openings of various conductance , ranging from 50 to 600 pS ( ‘transient-mixed’ behaviour , TM ) ( Figure 6A ) . Although it is not possible to know exactly how many individual pores were inserted into the bilayer , the observed fluctuations of various sizes are taken to represent various conformational states of a single pore . As documented previously , the openings of the ‘transient-mixed’ behaviour appear rather small and may be due to the jiggling of the plug within the TP and/or the thermally induced mobility of various domains of the protein , such as the NTD and CTDs or loops ( Mapingire et al . , 2009 ) . Occasionally , and more so at higher membrane potential , very large and sustained openings ( ‘large-open’ behaviour , LO ) are observed in wild-type PapC usher ( Figure 6B ) . These openings have a conductance of ∼3–4 nS , which is similar to the monomeric conductance of the mutant lacking the PD ( Mapingire et al . , 2009 ) and are interpreted as representing a full displacement of the PD from a single monomer . Prolonged opening of intermediate conductance ( 0 . 5–1 nS ) can also be observed and may represent partial PD displacement . 10 . 7554/eLife . 03532 . 016Figure 6 . Kinetic signatures of channel activity in wildtype and mutant PapC ushers and frequency of PD displacement . Fifty-second segments of recordings obtained in planar lipid bilayers were selected to illustrate the behaviour of the different proteins . ( A ) Recording from the wild-type PapC usher showing the characteristic ‘transient-mixed’ behaviour . ( B ) Recording from the wild-type PapC usher showing an example of spontaneous large openings due to plug displacement . Note the large amount of current fluctuations during the openings , and the ‘transient-mixed’ behaviour in between such events . Examples of similar large openings ( C and D ) are shown for the V327A and T331A mutants , respectively . ( E ) A recording from the K427A shows that the channel barely displays any activity at this voltage . The voltage was +90 mV for all panels . The current level for the closed channels is marked as ‘C’ , and openings are seen as upward deflections of the traces; current levels corresponding to fully open monomeric or dimeric forms are denoted by ‘M’ and ‘D’ , respectively . Note that the traces are plotted as conductance , rather than current , vs time and the scale bars are given in nS . ( F ) The percent of sweeps displaying ‘large-open behaviour’ ( LO ) indicative of PD displacement is shown for WT and each mutant at the indicated voltages . The number of individual bilayers investigated in each case is given above the bars . DOI: http://dx . doi . org/10 . 7554/eLife . 03532 . 016 Because the electrophysiological behaviour of wild-type PapC usher is quite variable , and in attempt to quantify the propensity at spontaneous PD displacement , we have counted the number of 10-min long recordings ( sweeps ) that show ‘large-open’ behaviour , and we report the percent of such sweeps in various conditions . The frequency of observing these large openings in wild-type PapC usher is ∼20% at ±50 mV , but increases to ∼60% at ±90 mV . The application of a larger transmembrane voltage is likely to disrupt the interactions between key residues involved in keeping the PD in place , leading to a more frequent spontaneous displacement of the latter . 3 of the 7 analysed mutants , V327A , T331A , and F438A , showed an increased propensity at displaying large openings , relative to the wild-type PapC usher , as illustrated for V327A and T331A ( Figure 6C , D ) . This was particularly true at ±90 mV where the percent of sweeps with large openings reaches values of 75–90% ( Figure 6F ) . The T331A mutant was consistently more prone to open than WT and any other mutants , which led to the occasional simultaneous opening of several monomers ( Figure 6D ) . Two of the seven mutants R237A and T437A still opened occasionally to the 3 nS level , but the frequency of sweeps with such events was slightly diminished relative to the wild-type PapC usher at ±90 mV ( Figure 6F ) suggesting that these mutants are likely to be insignificantly different from the wild-type PapC usher . The K427A and S444A mutants showed a decreased frequency ( or complete absence ) of large openings . The K427A mutant almost never showed ‘large-open’ behaviour at ±50 and ±90 mV , indicating an extremely closed channel ( Figure 6E , Figure 6E , F ) . The S444A mutant was even less prone to open , with 0% occurrence of PD displacement at ±50 mV in the 11 bilayers that we investigated ( Figure 6F ) . However , increased activity with fast flickers and occasional more prolonged openings could be seen for both mutants if the membrane potential was switched to voltages in the ±100–150 mV range , indicating that the channels are present in the bilayer , but require higher voltages for activation . In this study , we provide a first deep insight into the allosteric regulation of the gating mechanism of the usher family . Using PapC TD as model system , we developed an integrative approach combining computational modelling , sequence conservation analysis , mutual information-based coevolution analysis and information from AA-MD simulations , to study the potential involvement of particular secondary structure elements ( the α-helix and β-hairpin ) in the allosteric communication . The construction of a hybrid interaction network and the use of network analysis allowed us to identify communities of residues within the TD that potentially mediate this process . Antibiotic sensitivity and electrophysiology experiments on a set of alanine-substitution mutants confirmed that residues located in the P-linkers , the β-hairpin , and β13 conserved patch ( part of four communities ) alter channel gating and that residues located in P-linker2 , β12–β13_loop , and β13 conserved patch ( both periplasmic end and extracellular end ) are sensitive to plug displacement . Therefore , we suggest that the β13 conserved patch acts as a regulator of β12–β13_loop ( the latch ) , mediating channel opening . Furthermore , our study shows how the integration of different computational approaches based on evolution , structure , and dynamics of proteins , into a hybrid network can unveil communication pathways within proteins . Such an integrative approach can guide the experimental investigation by pinpointing key candidates involved in the transmission of the allosteric signal . We built four model systems based on the X-ray structure of the TD ( residues 1–492 ) of PapC at 3 . 2 Å resolution ( PDB ID: 2vqi [Remaut et al . , 2008] ) . The starting model for the simulation of the native TD ( sim1 , native ) was generated by adding the missing loops to the X-ray structure using the dope_loopmodel method ( Shen and Sali , 2006 ) in MODELLER-9v7 ( Sali and Blundell , 1993 ) . Additionally , three mutant model systems were constructed based on the native model: a mutant lacking the β-hairpin ( Δ233–240 ) ( sim2 , hairpin mutant ) , a mutant lacking the α-helix ( Δ447–460 ) ( sim3 , helix mutant ) , and a mutant lacking both ( Δ233–240 and Δ447–460 ) ( sim4 , helix-hairpin mutant ) . Each of the systems was oriented with respect to the membrane normal ( the Z axis by definition ) using the database ( Lomize et al . , 2006 ) . For the native model ( sim1 ) , a mixed lipid bilayer ( POPE/POPG 3:1 ) was generated around the protein using the replacement method ( Jo et al . , 2007 ) . To obtain a mixed lipid bilayer that reproduces an estimated surface area per lipid of 61 . 5 ± 0 . 2 Å2 ( Murzyn et al . , 2005 ) , we used the InflateGRO method ( Kandt et al . , 2007 ) . All MD simulations were performed using Gromacs 4 . 0 . 5 ( Van Der Spoel et al . , 2005 ) . TIP3P parameters were used for water molecules ( Jorgensen et al . , 1983 ) , the OPLSA-AA force-field ( Kaminski et al . , 2001 ) was applied to the protein and ions , and the Berger force-field ( Berger et al . , 1997 ) to the lipids . All four systems were solvated in water and ions were added to neutralize the total charge ( 0 . 15 M NaCl ) , resulting in more than 75 , 000 atoms in total . Next , each system was energy-minimized using a steepest descent algorithm in the presence of different position restraints on the protein and the lipid bilayer head-groups , which were gradually removed . In the simulations of the mutant systems ( sim2 to sim4 ) , the protein models were embedded in the pre-equilibrated membrane obtained after 15 ns of unrestrained equilibration of the native TD ( sim1 ) . The assembled systems were equilibrated in a multistage process using periodic boundary conditions and a 2 fs time step . Short-range interactions were used with a cut-off of 1 nm . The PME algorithm ( Darden et al . , 1993 ) was used for long-range electrostatic interactions . All bonds were constrained using the LINCS algorithm ( Hess et al . , 1997; Hess , 2008 ) . The first equilibration step was performed in the NVT ensemble , using a restraining force of 1000 kJ/ ( mol nm2 ) for 0 . 1 ns on the protein and lipids . The V-rescale thermostat ( Bussi et al . , 2007 ) was employed to couple the temperature of the system to 310 K with a time constant of tT = 0 . 1 ps . All the following equilibration steps were performed in the NPT ensemble . During the next three steps , different parts of the system were restrained using a force constant of 1000 kJ/ ( mol nm2 ) : the protein and lipids , the protein atoms only , and the protein backbone atoms . The resulting model of each system was then simulated without restraints . Constant temperature of 310 K was maintained using the Nose–Hoover thermostat ( Hoover , 1985; Nose , 1984 ) with a time constant of tT = 0 . 1 ps . Using semi-isotropic coupling with a Parrinello–Rahman barostat ( Parrinello , 1981 ) , a constant pressure of 1 bar was applied with a coupling constant ( tP ) of 1 ps and a compressibility 4 . 5 e−5 bar−1 . Each unrestrained simulation was performed for ∼70–72 ns . The last 50 ns of simulation was used for analysis . Hydrogen bonds were defined using a cut-off of 30° for the acceptor–donor–hydrogen angle and a cut-off of 3 . 5 Å for the hydrogen-acceptor distance . The definition of salt-bridges was based on a 4 Å distance cut-off between any oxygen atoms of acidic residues and nitrogen atoms of basic residues . The non-covalent interaction score ( NCI score ) of the identified bonds was defined as the percentage of simulation time during which a bond occurs between two amino acids normalised by the number of bonds . Using the normalized score , a non-covalent residue interaction network ( RIN ) was built for each of the simulated systems as a weighted undirected graph , in which each node represents a residue and each edge is weighted by the normalized score . The difference in non-covalent interaction score ( ΔNCI score ) between the native TD system and each of the mutant systems was then calculated and added as a weighted undirected edge to the pre-existing non-covalent RIN . The sequence corresponding to the X-ray structures of PapC TD ( PDB id: 2vqi; Uniprot id: P07110 ) was used as input to psiBlast resulting in a set of unique related sequences from the non-redundant NCBI data set ( Altschul et al . , 1997 ) . The E-value threshold was set as 10−3 and sequences with id >90% and <30% sequence identity were excluded . A structure-based multiple sequence alignment was calculated using Expresso ( 3DCoffee ) ( Armougom et al . , 2006 ) . Finally , an evolutionary conservation score was calculated for each residue an empirical Baysian inference method ( Mayrose et al . , 2004 ) as implemented in the ConSurf web server ( Ashkenazy et al . , 2010 ) . To estimate the coevolution within the residues in the usher TD , we used the normalized mutual information ( NMI ) ( Martin et al . , 2005 ) over all position pairs in the multiple sequence alignment obtained as described above . NMI calculations were performed using PyCogent ( Knight et al . , 2007; Caporaso et al . , 2008 ) and a Z-score was calculated for each residue pair based on the standard deviation from the mean NMI values . Only residue pairs that had Z-score > 4 were identified as coevolved pairs ( Gloor et al . , 2005; Martin et al . , 2005 ) . Next , a coevolutionary RIN was built as a weighted undirected graph where each node represents a residue ( as in the non-covalent RIN ) and an edge connecting two nodes is the NMI score . The network was visualized and analszed in Cytoscape 2 . 8 . 2 ( Smoot et al . , 2011 ) using NetworkAnalyzer plug-in ( Assenov et al . , 2008 ) for calculating degrees of connectivity and RINalyzer plug-in ( Doncheva et al . , 2011 ) for mapping the network on the PapC structure . To store the entire information , we combined the coevolutionary RIN , the non-covalent RIN , and the conservation analysis into one network . In this hybrid network each node represents each PapC TD residue and is associated with the corresponding conservation score . Two nodes can have multiple edges , each weighted according to the information it carries ( NMI score , NCI score , or ΔNCI ) . Using the hybrid network and starting from the α-helix region ( residues 445–468 ) and β-hairpin residues ( residues 230–240 ) , we generated a sub-network of first neighbours residues based only on the NCI score higher than 0 . 3 in the native RIN . This sub-network was expanded by again adding only neighbouring residues connected by NCI score higher than 0 . 3 . The procedure was repeated until no new residues could be added to the sub-network . A first set was generated by filtering the sub-network based on the evolutionary information . The filtering was done by selecting nodes with a conservation score of 9 ( i . e . , highly conserved ) or nodes that are connected by an NMI edge ( i . e . , coevolved significantly ) . A second set was generated by filtering out the nodes that are not connected by a weakened interaction ( ΔNCI > 0 ) in each of the mutant systems . Intersecting the identified sets resulted in a ‘hot spots’ sub-network . The ‘hot spots’ sub-network was then decomposed into communities using the edge-betweenness clustering algorithm ( GLay ) as implemented in clusterMaker ( Girvan and Newman , 2002; Morris et al . , 2010 ) . Cytoscape 2 . 8 . 2 ( Smoot et al . , 2011 ) , RINalyzer plug-in ( Doncheva et al . , 2011 ) , and Chimera ( Pettersen et al . , 2004 ) were used for mapping the network on the PapC structure . The PapC alanine substitution mutants were derived from plasmid pDG2 using the QuikChange Site-Directed Mutagenesis Kit ( Stratagene/Agilent Technologies , Santa Clara , CA ) and the primers listed in Supplementary file 1 . Plasmid pDG2 encodes wild-type papC with a C-terminal , thrombin-cleavable His-tag ( Li et al . , 2004 ) . All mutants were sequenced to verify the intended mutation . Each of the PapC mutants was compared with wild-type PapC for expression levels and ability to fold into a stable β-barrel in the OM . OM isolation , analysis of usher protein levels , and the heat-modifiable mobility assay for β-barrel stability were done as previously described ( Henderson et al . , 2011 ) . HA assays were performed to test the ability of each of the PapC substitution mutants to assemble functional P pili on the bacterial surface . HA assays were performed by serial dilution in microtiter plates as previously described ( Henderson et al . , 2011 ) . HA titers were determined visually as the highest fold dilution of bacteria still able to agglutinate human red blood cells . Each assay was performed in triplicate; each mutant was analszed twice and the values were averaged . Bacteria were grown in LB medium supplemented with 100 µg/ml ampicillin ( Amp ) to an OD600 of 0 . 6 and then induced for PapC expression with 0 . 1% arabinose for 1 hr . Aliquots of 0 . 1 ml bacteria were added to 3 ml melted soft top agar ( 0 . 75% LB agar ) cooled to 45°C and supplemented with 100 µg/ml Amp and 0 . 1% arabinose . The bacteria and melted agar were mixed well and poured on top of 1 . 5% solid LB agar plates containing 100 µg/ml Amp and 0 . 1% arabinose . Once the top agar solidified , sterile 6-mm filter discs were placed on top and 10 μl of the following antibiotics were added: 75 mg/ml SDS , 2 mg/ml vancomycin , or 1 . 5 mg/ml erythromycin . The diameter of the growth inhibition zone around the antibiotics , including the filter disc , was measured after overnight growth at 37°C . Each PapC mutant strain was tested twice and the values were averaged . PapC mutants ( R237A , V327A , T331A , K427A , T437A , F438A , and S444A ) were purified according to published protocols ( Henderson and Thanassi , 2013 ) and investigated by planar lipid bilayer electrophysiology . Planar bilayers were made from a preparation of L-α-phosphatidylcholine Type II-S from Sigma ( also known as asolectin ) according to the Montal and Mueller technique ( Montal and Mueller , 1972 ) following a published protocol ( Mapingire et al . , 2013 ) . Protein aliquots were diluted 1:1 in buffer T ( 1 M KCl , 5 mM Hepes , pH 7 . 2 ) containing either 1 or 2% N-Octyl-oligo-oxyethylene ( octyl-POE , Axxora ) . Eight micrograms of protein from the diluted sample was added to the cis side of a planar lipid bilayer chamber containing ∼1 . 5 ml of buffer T . Gentle stirring was applied to promote spontaneous insertions of the protein into the bilayer . Channel activity was monitored by measuring current under voltage clamp conditions using an Axopatch 1D amplifier with a CV4B headstage or an Axopatch 200B amplifier ( Axon Instruments ) . The current was digitized ( ITC-18; Instrutech ) and stored on a PC computer using the Acquire software ( Bruxton ) . 10-min long traces were sampled at 1 . 25 ms intervals and filtered at 500 Hz . Both chambers contained buffer T and Ag/AgCl electrodes with pellet . The trans side of the bilayer was set as ground . Insertions were typically performed at −90 mV . Data display and analysis were done with pCLAMP software ( Axon Instruments ) .	Escherichia coli is a bacterium that commonly lives in the intestines of mammals , including humans , where it is usually harmless and can even be beneficial to its host . However , some types of E . coli produce hair-like filaments called P pili that allow the bacteria to attach to the human urinary tract and cause disease . To pass through the outer membrane of the E . coli cell , the filaments have to travel through a protein in the membrane called PapC usher . The PapC usher protein—which is also involved in the assembly of the P pili filaments—contains a tube-like part called a β-barrel that is usually blocked by another part of the protein called the ‘plug domain’ . For the P pili to pass through the β-barrel , the plug domain has to move . This movement is controlled by two parts of the PapC protein , known as the α-helix and the β-hairpin , but it is not clear how . To address this question , Farabella et al . made computer models of the normal PapC protein and versions that lacked the α-helix and/or the β-hairpin . Looking at these structural models and analyzing the evolution of PapC proteins helped to predict that certain regions of the β-barrel may be involved in controlling the movement of the plug domain , and this was then confirmed experimentally . Farabella et al . propose that these regions—together with the α-helix and β-hairpin—control the opening and closing of the β-barrel . Further work is needed to investigate how other parts of the PapC protein are involved in P pili formation . These new insights could prove useful in the development of alternative treatments to fight bacterial infection .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "structural", "biology", "and", "molecular", "biophysics" ]	2014	Allosteric signalling in the outer membrane translocation domain of PapC usher
The brainstem plays a crucial role in sleep-wake regulation . However , the ensemble dynamics underlying sleep regulation remain poorly understood . Here , we show slow , state-predictive brainstem ensemble dynamics and state-dependent interactions between the brainstem and the hippocampus in mice . On a timescale of seconds to minutes , brainstem populations can predict pupil dilation and vigilance states and exhibit longer prediction power than hippocampal CA1 neurons . On a timescale of sub-seconds , pontine waves ( P-waves ) are accompanied by synchronous firing of brainstem neurons during both rapid eye movement ( REM ) and non-REM ( NREM ) sleep . Crucially , P-waves functionally interact with CA1 activity in a state-dependent manner: during NREM sleep , hippocampal sharp wave-ripples ( SWRs ) precede P-waves . On the other hand , P-waves during REM sleep are phase-locked with ongoing theta oscillations and are followed by burst firing of CA1 neurons . This state-dependent global coordination between the brainstem and hippocampus implicates distinct functional roles of sleep . The sleep-wake cycle is a fundamental homeostatic process across animal species ( Siegel , 2005; Anafi et al . , 2019; Aulsebrook et al . , 2016 ) . In addition to the physiological functions of sleep ( Boyce et al . , 2017; Brown et al . , 2012; Sara , 2017; Siegel , 2005; Stickgold et al . , 2001; Imeri and Opp , 2009; Liu and Dan , 2019; Rasch and Born , 2013; Tononi and Cirelli , 2014 ) , the abnormalities in the sleep-wake cycle are associated with various diseases and disorders ( Mander et al . , 2017; Musiek and Holtzman , 2016; Irwin , 2015; Brown et al . , 2012 ) . Sleep states are typically classified into two major states , non-rapid eye movement ( NREM ) sleep and REM sleep . While numerous brain regions and cell-types have been identified as part of sleep-regulating circuits ( Brown et al . , 2012; Héricé et al . , 2019; Jouvet , 1962; Moruzzi , 1963; Peever and Fuller , 2017; Scammell et al . , 2017; Weber and Dan , 2016; Luppi et al . , 2017; Adamantidis et al . , 2007; Tsunematsu et al . , 2014; Weber et al . , 2015; Zhang et al . , 2019 ) , sleep-related neural firing and oscillations have also been described across cortical and subcortical regions ( McCarley and Hobson , 1971; Hobson et al . , 1975; Sakai , 1985; Steriade , 2006; Brown et al . , 2012; Rasch and Born , 2013; Buzsáki , 2015; Weber et al . , 2015; Scammell et al . , 2017; Weber et al . , 2018; Héricé et al . , 2019; Liu and Dan , 2019 ) . For example , cortical slow oscillations , sleep spindles and hippocampal sharp wave-ripples ( SWRs ) are prominent neural events during NREM sleep , whereas theta oscillations and ponto-geniculo-occipital ( PGO ) or pontine ( P ) waves are seen during REM sleep ( Steriade , 2006; Montgomery et al . , 2008; Buzsáki , 2015; Buzsáki , 2002; Jouvet , 1969; Steriade et al . , 1993b; Callaway et al . , 1987; Datta , 1997; Rasch and Born , 2013; Bizzi and Brooks , 1963 ) . Although neural ensemble dynamics underlying these sleep-related neural events in the thalamus and the cortex including the hippocampus have been well described ( Steriade , 2006; Buzsáki , 2015; Buzsáki , 2002; Steriade et al . , 1993a ) , little is known about population activity within the brainstem . To achieve a better understanding of the functional roles of sleep states , it is essential to characterize state-dependent changes in brainstem network activity and their functional interactions with cortical regions across sleep states . The brainstem , including the midbrain , pons and medulla has long been implicated in the sleep-wake cycle ( Jouvet , 1962; Saper et al . , 2010; Brown et al . , 2012; Rasch and Born , 2013; Weber et al . , 2015; Weber and Dan , 2016; Luppi et al . , 2017; Scammell et al . , 2017; Héricé et al . , 2019; Liu and Dan , 2019 ) . It contains various nuclei , each of which consists of diverse cell-types and exhibits state-dependent firing ( Brown et al . , 2012; Rasch and Born , 2013; Weber et al . , 2015; Weber and Dan , 2016; Luppi et al . , 2017; Scammell et al . , 2017; Héricé et al . , 2019; Liu and Dan , 2019; Weber et al . , 2018; Zhang et al . , 2019 ) . However , how brainstem populations act in concert is a question that remains poorly explored . For example , the extent to which their activity exhibits anticipatory dynamics for ongoing vigilant states is still unclear . In addition , it is also unclear whether and how brainstem populations functionally interact with various neural oscillations or events in the cortex across sleep states . Characterizing these physiological properties is crucial to uncover the roles of brainstem populations in sleep regulation and ultimately in the functions of sleep states . In the present study , we adopt several in vivo electrophysiological approaches in mice to investigate state-dependent ensemble dynamics in the brainstem , mainly the pons . We show that , on a timescale of seconds to minutes , brainstem neurons show state-dependent firing with cell type-specificity . They also have a longer predictive power for vigilance states than hippocampal CA1 neurons . On a timescale of sub-seconds , we find state-dependent functional interactions between the brainstem and the hippocampus , with a focus on P-waves . During NREM sleep , the timing of P-waves is phase-locked with various cortical oscillations and hippocampal SWRs precede P-waves . During REM sleep , P-waves co-occur with hippocampal theta oscillations and precede burst firing of hippocampal neurons . These results imply that brainstem populations not only play a regulatory role in the sleep-wake cycle , but also contribute to global state-dependent dynamics across brain regions . To investigate the state-dependency of brainstem population activity , we inserted a silicon probe into the mouse brainstem in a head-fixed condition , and performed simultaneous monitoring of cortical electroencephalograms ( EEGs ) , electromyograms ( EMGs ) and pupil dilation ( Figure 1 ) . Recorded regions spanned across multiple nuclei , including the sublaterodorsal nucleus , pontine reticular nucleus , medial preoptic nucleus , parabrachial nucleus , pontine central gray , laterodorsal tegmental nucleus and other surrounding areas according to post-mortem histological analysis ( Figure 1—figure supplement 1 ) . Although a majority of neurons were recorded from the pons , we refer to recorded populations as ‘brainstem’ neurons because some cells were located in the midbrain and medulla , but not the hypothalamus . The sleep-wake cycle was classified on the basis of cortical EEGs and EMGs in every 4-s window . On the basis of the classified states , we observed clear state-dependency across measurements ( Figure 1C–F ) : wakefulness was characterized by high muscle tone ( Figure 1D ) and pupil dilation ( Figure 1F ) , whereas NREM sleep was characterized by higher power of slow oscillations ( Figure 1C ) and a wider dynamic range of pupil diameter ( Figure 1F ) . REM sleep was distinct from the other states , with respect to prominent theta oscillations ( Figure 1C ) , low muscle tone ( Figure 1D ) , higher brainstem LFPs power ( Figure 1E ) and fully constricted pupil ( Figure 1F ) . The higher power of brainstem LFPs during REM sleep was preserved across all animals ( seven animals , nine recordings ) ( Figure 1—figure supplement 2 ) . Although most of our recordings were performed under a head-fixed condition , the sleep architecture , cortical EEGs and EMGs were generally comparable with those in a tethered unfixed condition ( Figure 1—figure supplement 3 ) . Neuronal spiking activity in the brainstem also demonstrated rich state-dependent properties ( Figure 1B ) . For example , a subset of neurons fired exclusively during REM sleep , indicating state-dependent population firing on a timescale of second-to-minute . In addition , we also observed frequent burst firing across neurons on a sub-second timescale during REM sleep . In the following analysis , we investigate state-dependent brainstem neural ensembles on two distinct timescales: a long timescale of seconds to minutes ( Figures 2–4 ) and a sub-second timescale ( Figures 5–7 ) . To assess the state-dependent firing of individual neurons in the brainstem on a timescale of seconds to minutes , we performed in vivo silicon probe recording ( Figure 2A ) from seven head-fixed mice ( nine recording sessions ) and examined how individual neurons change their firing across behavioral states . Figure 2B shows representative examples of state-dependent firing from four simultaneously recorded neurons . Even within a particular state in the same animal , brainstem neurons show highly diverse and dynamic firing . To classify neurons according to their state-dependent firing , we computed mean firing rate in each state across neurons ( n = 76 ) and applied a hierarchical clustering algorithm ( Figure 2C ) . We identified four functional classes: awake ( AW ) -on neurons ( 23 . 7% ) were more active during wakefulness than during sleep states; REM-off neurons ( 17 . 1% ) reduced their firing during REM sleep; REM/AW-on neurons ( 6 . 6% ) were quiet during NREM sleep; and the largest class ( 52 . 6% ) was REM-on neurons , which showed the highest firing rate during REM sleep . Thus , we confirmed highly diverse state-dependent firing in the brainstem . Because the recorded neurons were distributed across various nuclei in the brainstem , it was difficult to determine their state-dependency in each nucleus . However , a subset of neurons was probably recorded from the cholinergic system , namely the pedunculopontine tegmental nucleus and the laterodorsal tegmental nucleus , which showed AW-on or REM-on activity ( Figure 2—figure supplement 1 ) . To verify this , we performed in vivo fiber photometry of Ca2+ signals from pontine cholinergic neurons by expressing GCaMP6s in freely behaving mice ( four animals , 12 recording sessions ) ( Figure 2D ) . Consistent with the data from in vivo electrophysiology , cholinergic populations showed larger activity during REM sleep and wakefulness than during NREM sleep ( F2 , 32 = 5 . 12 , p=0 . 012 , one-way ANOVA ) ( Figure 2E–G ) . Therefore , although the state-dependency of individual neuronal firing in the brainstem is diverse , we also confirmed state-dependent and cell-type-specific firing . Pupil diameter is an excellent biomarker of global brain state or arousal level ( Aston-Jones and Cohen , 2005; Yüzgeç et al . , 2018; Larsen and Waters , 2018; McGinley et al . , 2015 ) and activity in brainstem neurons , especially in locus coeruleus norepinephrine neurons , correlates with pupil diameter ( Aston-Jones and Cohen , 2005 ) . However , it is still unclear how pupil dilation changes around the transition of sleep-wake states and to what extent the activities of brainstem neurons as a population can predict pupil dilation quantitatively . To address these issues , we analyzed datasets from head-fixed mice either with silicon probe recordings from the brainstem ( six animals , six recording sessions ) or the hippocampus ( two animals , three recording sessions ) , or with field potential recording from the brainstem with a bipolar electrode ( six animals , nine recording sessions ) . As previously reported ( Yüzgeç et al . , 2018 ) , head-fixed mice kept their eyes open , allowing us to monitor pupil dilation across states along with cortical EEG and EMG . The effects of behavioral states on pupil diameter was statistically significant ( Figure 3A , F2 , 53 = 220 . 33 , p<0 . 0001 , one-way ANOVA ) , with pupil diameter being constricted during REM sleep and dilated during wakefulness . With respect to pupil dilation dynamics across states ( Figure 3B and Figure 3—figure supplement 1 ) , pupil diameter dynamically fluctuated during wakefulness and gradually constricted during NREM sleep . Typically , 10–20 s before REM sleep , the pupil diameter would further decrease and was fully constricted during REM sleep ( Figure 3—figure supplement 1 ) . Taking advantage of the simultaneous recording of neural population activity and pupil monitoring , we examined how brainstem neurons can predict pupil dilation . First , we predicted pupil dilation on the basis of the activity of individual neurons ( Figure 3C ) by applying a linear regression analysis . Because it was expected that the preceding neural activity can better predict pupil dilation , we systematically shifted the temporal relationship between spike trains and pupil diameter ( see 'Materials and methods' ) . As expected , most of the neurons showed asymmetric profiles of R2 values ( Figure 3C ) . Although individual profiles were diverse , the average profile showed brainstem neuron activities that were predictive of pupil diameter around 10 s in advance . We also predicted pupil diameter on the basis of simultaneously recorded brainstem neurons ( Figure 3D ) by applying a multiple linear regression analysis . As with individual neurons , we observed an asymmetric profile of predictability . Thus , changes in brainstem neural activity preceded those in pupil diameter . Thereby , brainstem populations have predictive power for pupil diameter . Next , we examined whether and to what extent brainstem neurons can predict sleep-wake states . To address this , first , we extracted the features of neural population activity by applying non-negative matrix factorization ( NMF ) , a technique that finds compact and easily interpretable representations of neural population activity in the form of parts-based activity patterns ( Lee and Seung , 1999; Onken et al . , 2016 ) ( Figure 4A and B ) ( see 'Materials and methods' ) . Overall firing rate reflected state changes ( Figure 4A ) , whereas NMF was able to extract several modules that captured state-dependent firing patterns across neurons in an unsupervised fashion . For example , module 1 represented REM-on activity , whereas module 2 was activated at the end of NREM sleep and module 3 was most active during wakefulness . Indeed , the weights in each module were consistent with the state-dependency of individual neural firing ( Figure 4—figure supplement 1 ) . Besides modules capturing firing patterns across neurons , NMF also yielded the activation coefficients of these modules . We noticed that the dynamics of these activation coefficients show predictive activity: in the case of Figure 4A , the activation coefficients of modules 1 and 2 gradually built up during REM and NREM sleep , respectively . Therefore , we hypothesized that brainstem population activity exhibits not just state-dependency , but also predictive power for behavioral states ( i . e . , wakefulness , NREM sleep and REM sleep ) . To test this , we took the activation coefficient profiles from the three modules and classified behavioral states by training a linear classifier , with systematic time shifting ( Figure 4C ) . Brainstem populations showed predictive activity tens of seconds before transitions into all three behavioral states . Does this predictive activity last longer in brainstem populations than in other brain regions ? Of many brain regions , hippocampal neurons are well known to exhibit state-dependent firing ( Buzsáki , 2002; Buzsáki , 2015; Mizuseki and Miyawaki , 2017 ) , but they are less likely to contribute to the regulation of the sleep-wake cycle directly . Hence , hippocampal neurons could serve as a reference to assess the ability of brainstem populations to predict behavioral states . To examine whether brainstem populations exhibit predictive activity over a longer period than do hippocampal populations , we performed the same analysis for neural activity in the hippocampal CA1 ( Figure 4C ) . Although CA1 neurons also had predictive power for several tens of seconds , the profile was relatively short-lasting compared to that of brainstem neurons . Thus , brainstem neurons have longer-lasting predictive power for behavioral states than hippocampal CA1 neurons . On a timescale of seconds to minutes , brainstem neurons show diverse but specific state-dependent firing and have predictive power for pupil dilation and behavioral states . To investigate brainstem neural firing on a sub-second timescale , we focused on P-waves ( Callaway et al . , 1987; Datta , 1997 ) . Although these sub-second neural events in the brainstem have long been recognized , the underlying neural ensembles and relations to other sleep-related oscillations are not fully understood . Taking advantage of our dataset , we first examined whether the mouse brainstem exhibits P-waves like those observed in other mammalian species . We implanted a bipolar electrode in the pons ( n = 16 recordings ) ( Figure 5A ) and monitored LFPs by subtracting signals . During REM sleep , we observed large-amplitude irregular neural events , which often appeared bursts ( Figure 5B right ) . We also observed similar , but isolated , neural events during NREM sleep ( Figure 5B left ) . These neural events appeared more often during REM sleep ( p<0 . 0001 , two-tailed t-test ) ( Figure 5C ) . Intriguingly , the frequency of these events gradually increased during NREM to REM sleep transitions and decreased during REM sleep to wakefulness transitions ( Figure 5D ) . Because these characteristics generally resemble those in other species ( Callaway et al . , 1987; Datta , 1997 ) , we concluded that these neural events are P-waves in mice . P-waves can also be seen in silicon probe recordings ( Figure 5E ) . Similar large-amplitude , irregular neural events were observed in subtracted and filtered LFPs ( Figure 5F ) . Many of the simultaneously recorded brainstem neurons fired during P-waves . To assess this tendency , we pooled the peri-event firing profiles of all recorded brainstem neurons around P-waves ( Figure 5G , H ) . The firing profiles were aligned at the trough timing of P-waves . A subset of neurons showed peak firing at the falling phase of P-waves . This tendency was consistent between NREM and REM sleep with respect to neural firing within the brainstem , suggesting that P-waves during NREM sleep ( P-wavesNREM ) are equivalent to P-waves during REM sleep ( P-wavesREM ) . The co-firing of a subset of brainstem neurons underlies P-waves during both NREM and REM sleep . What are the impacts of such neural events on other brain regions ? Are P-waves associated with any other sleep-related neural events ? Addressing these questions would provide insight into the functions of P-waves . To this end , first , we investigated the relationship between P-waves and cortical EEGs ( Figure 6 ) . During NREM sleep , P-waves were associated with multiple oscillatory components . Averaged P-wave-triggered cortical EEGs exhibited multiple phasic components ( Figure 6B ) , which consisted of delta ( 1–4 Hz ) , theta ( ~7 Hz ) and beta ( 15 ~ 30 Hz ) frequencies . To examine this trend further , we assessed the phase relationship between P-wave timing and cortical oscillations ( Figure 6C ) . We found significant phase preferences of P-wave timing ( p<0 . 01 , Rayleigh test ) . We further assessed this phase-locking activity by computing resultant vector length ( Figure 6D ) , which is a descriptive circular statistic and represents the length of the mean resultant vector ( Berens , 2009 ) : the closer this value is to one , the more concentrated is the phase coupling . We found larger phase modulation at delta and beta ranges . On the other hand , P-wavesREM exhibited distinct associations with cortical oscillations ( Figure 6B–D ) . We observed significant phase modulation at theta range ( p<0 . 01 ) ( Figure 6C ) , indicating that two prominent neural markers in REM sleep , that is , theta oscillations and P-waves , are temporally organized . We found that the effect of sleep states on these phase-locking activities was statistically significant ( F1 , 179 = 4 . 90 , p<0 . 05 , two-way ANOVA ) . We also computed effect size ( Figure 6E ) . The effect was larger at the delta range during NREM sleep . Hence , the temporal coupling between P-waves and cortical oscillations was modified by sleep states . Next , we investigated the relationship between P-waves and hippocampal CA1 activity ( Figure 7 ) . We started by assessing the phase relationship between hippocampal LFPs and P-waves across frequency bands ( Figure 7B ) . We found that the effect of sleep states on the resultant vector length was significant ( F1 , 79 = 4 . 00 , p<0 . 05 , two-way ANOVA ) . The timing of P-waves was strongly phase-locked at the theta range ( ~7 Hz ) in both sleep states and there was no significant difference in the resultant vector length across frequencies , but we observed stronger phase modulations in high frequency ( ≥30 Hz ) components during REM sleep based on the effect size . We further examined underlying spiking activity in the hippocampus CA1 ( Figure 7C ) . Intriguingly , although a subset of CA1 neurons fired most strongly around the timing of P-waves during both NREM and REM sleep , the temporal order between CA1 neural firing and P-waves was state-dependent ( Figure 7C ) : during NREM sleep , co-firing of hippocampal neurons was followed by P-waves , whereas P-waves were followed by burst firing in a subset of CA1 neurons during REM sleep . To test the hypothesis that co-firing of CA1 neurons during NREM sleep may reflect SWRs , we detected high-frequency ripple events based on hippocampal LFPs to assess the temporal relationship between ripples and P-wavesNREM ( Figure 7D ) . We found that ripple events preceded P-waves during NREM sleep across all of four recordings ( Figure 7D inset ) ( p<0 . 05 , t-test ) . Thus , P-waves are strongly associated with hippocampal activity in both sleep states . However , these associations are state-dependent . State-dependent activity in the brainstem has been described over the past several decades by using various methods ( Pace-Schott and Hobson , 2002; Datta and Maclean , 2007; Steriade and McCarley , 1990 ) . The present study utilized a silicon probe to monitor activity from multiple neurons simultaneously with high temporal resolution . This approach allowed us ( 1 ) to quantify the state dependency of brainstem neural ensemble dynamics on a timescale of seconds to minutes , ( 2 ) to decode vigilance states on the basis of population activity , and ( 3 ) to characterize neural population activity underlying P-waves for the first time . However , because silicon probe recording alone has a limitation in identifying cell types , additional approaches , such as Ca2+ imaging ( Figure 2 ) or electrophysiology with optogenetic tagging ( Weber et al . , 2015; Yague et al . , 2017; Zhang et al . , 2019 ) , can complement this approach in order to determine how specific types of neurons contribute to state-dependent neural ensembles in the brainstem . Our results in Figures 3 and 4 are consistent with the notion that brainstem populations play a regulatory role in pupil dilation and constriction ( Aston-Jones and Cohen , 2005; Larsen and Waters , 2018 ) , as well as in global brain states ( Brown et al . , 2012; Héricé et al . , 2019; Luppi et al . , 2012; Steriade et al . , 1984; Weber and Dan , 2016 ) . Crucially , the asymmetric profile of the predictability of pupil diameter suggests that the modulation of brainstem activity precedes pupil dilation , rather than being simply correlated to it . The long-lasting predictability of brainstem populations for behavioral states is not trivial . Intriguingly , the slow ( 30–60 s ) timescale recalls us the timescale observed in some of optogenetic experiments: although optogenetic stimulation can modulate neural firing at a millisecond resolution , the effect of optical stimulation on state transitions typically emerges tens of seconds after stimulus onset ( Adamantidis et al . , 2007; Van Dort et al . , 2015; Zhang et al . , 2019 ) . The exact mechanism is still unknown , but we hypothesize that the modulation of neural activity in the brainstem occurs tens of seconds before global brain state transitions from one state to another . In other words , each state emerges from complex interactions across various regions of the brain . Although PGO or P-waves have been studied since the 1960s in several mammalian species ( Callaway et al . , 1987; Datta , 1997; Bizzi and Brooks , 1963 ) , to the best of our knowledge , we are the first to characterize P-waves in mice . Given the growing importance of mice as an animal model for sleep research ( Héricé et al . , 2019 ) , the confirmation of P-waves in mice is important for further interrogation with respect to the generation mechanism and function of P-waves . We have noticed several similarities and differences in P-waves between mice and other species . First , the waveform of P-waves in mice is generally consistent with those in other species , such as cats ( Callaway et al . , 1987; Jeannerod et al . , 1965 ) and rats ( Datta , 1997; Farber et al . , 1980 ) , suggesting that underlying neural ensembles may be similar across species . Indeed , the phasic firing just before ( <25 ms before ) the trough of P-waves ( Figure 5 ) resembles the observations in cats ( McCarley et al . , 1978; Steriade et al . , 1990; Sakai and Jouvet , 1980; Nelson et al . , 1983 ) . In the near future , it would be interesting to extend our approach further to explore how activity propagates across brainstem neurons during P-waves by identifying cell-types . Second , the frequency of P-waves during REM sleep is generally consistent across species ( Datta , 1997 ) . However , we have also noticed that the frequency of detected P-waves varied across our experiments . This may be explained by variation in either the REM sleep quality or the electrode positions . Further analysis of P-waves across brainstem nuclei will provide insights into their relationship with sleep homeostasis and the mechanism of P-wave genesis in mice . Datta and his colleagues performed a series of pioneering studies and reported species differences in the site of P-wave genesis between cats and rats ( Datta et al . , 1998; Datta and Hobson , 1995; Datta et al . , 1992 ) : the caudolateral peribrachial area was identified as the induction site of P-waves ( PGO waves ) in cats ( Datta and Hobson , 1995; Datta et al . , 1992 ) , whereas the subcoeruleus nucleus was identified in rats ( Datta et al . , 1998 ) . It will be important to revisit this issue in mice by adopting modern technologies . Third , the temporal evolution of P-wave frequency is generally similar in mice and cats: the frequency of PGO-waves gradually increases before the transition of NREM to REM sleep in cats ( Steriade et al . , 1989 ) . Although it was weak , a similar tendency was observed in our recordings ( Figure 5D ) . Rather , the frequency of P-waves increases during REM sleep . This subtle difference may be explained by anatomical differences between species ( Datta , 2012 ) . These species differences may also imply differences in the function of P-waves between species . Finally , although P-waves appear more frequently during REM sleep , it is important to note that similar neural events also occur during NREM sleep . Given state-dependent interactions between P-waves and cortical oscillations ( Figures 6 and 7 ) , the mechanisms of P-wave genesis are probably distinct . The temporal correlation between P-waves and hippocampal theta rhythms during REM sleep is consistent with previous studies in cats and rats ( Karashima et al . , 2002; Karashima et al . , 2004; Karashima et al . , 2005; Sakai et al . , 1973 ) . The phase-locked activity with fast gamma ( 80–110 Hz ) in the hippocampus may relate to the recent observation that demonstrated the close association between local hippocampal theta and fast gamma events and brain-wide hyperemic events ( Bergel et al . , 2018 ) . Because a number of hippocampal CA1 neurons fire immediately after P-waves ( Figure 7C ) , P-waves may play a role in the regulation of hippocampal ensemble dynamics as well as in brain-wide events during REM sleep . On the other hand , the picture during NREM sleep seems to be distinct . Because SWRs precede P-waves ( Figure 7D ) , and because SWRs are known to be generated within hippocampal circuits ( Buzsáki , 2015 ) , SWRs play a leading role in brain-wide sub-second neural events , including P-waves . These state-dependent brain-wide neural ensembles imply that different sleep states have distinct functional roles . A total of 21 mice were used in this study ( Supplementary file 1 ) and were housed individually with a 12 hr:12 hr light/dark cycle ( light on hours: 7:00–19:00 ) . Mice had ad libitum access to food and water . All experiments were performed during the light period . Their genotypes consisted of wild-type , ChAT-IRES-Cre ( JAX006410 ) , or ChAT-IRES-Cre::Ai32 ( JAX012569 ) on a C57BL/6 background . ChAT-IRES-Cre::Ai32 mice were used to identify pontine cholinergic neurons in post-hoc histological analysis . For brainstem silicon probe recordings , six animals were used ( nine recordings ) . For hippocampal silicon probe recordings , two animals were used ( four recordings ) . For P-wave recordings , ten animals were used , but four were excluded because of electrode mispositioning or lack of histological data . 16 datasets were used for further analysis . For pupil monitoring , 14 animals were used , but one animal was excluded because it closed its eyes during recording . 18 datasets were used for further analysis . For fiber photometry , four animals were used ( 12 recordings ) . The detailed information of genotypes , age , sex , body weight and the number of recordings is provided in Supplementary file 1 . For all in vivo electrophysiological experiments , mice were anesthetized with isoflurane ( 5% for induction , 1–2% for maintenance ) and placed in a stereotaxic apparatus ( SR-5M-HT , Narishige ) . Body temperature was maintained at 37°C with a feedback temperature controller ( 40–90–8C , FHC ) . Lidocaine ( 2% , 0 . 1–0 . 3 mg ) was administered subcutaneously at the site of incision . Two bone screws were implanted onto the skull as electrodes for cortical EEGs , and twisted wires were inserted into the neck muscle as electrodes for EMG . Another bone screw was implanted onto the cerebellum as a ground/reference . For pontine EEG recording , bipolar electrodes were bilaterally implanted in the medial parabrachial nucleus of the pons ( 5 . 1 mm posterior , 1 . 2 mm lateral from bregma , 3 . 2 mm depth from brain surface ) . The bipolar electrodes consisted of 75 or 100 µm diameter stainless wires ( FE631309 , Advent Research Materials and FE205850 , Goodfellow , respectively ) . The tip of two glued wires were separated by 0 . 5–1 . 0 mm vertically to differentiate EEG signals . All electrodes were connected to connectors ( SS-132-T-2-N , Semtec ) and securely attached onto the skull with dental cement . A pair of nuts was also attached onto the skull with dental cement as a head-post . After the surgery , Carprofen ( Rimadyl , 5 mg/kg ) was administered intraperitoneously . For brainstem or hippocampal silicon probe recording , in addition to bone screws for cortical EEGs and a ground/reference , a pair of nuts was attached onto the skull with dental cement as a head-post . After the head-post surgery , the animals were left to recover for at least 5 days . During the habituation period , the animals were placed in a head-fixed apparatus , by securing them by the head-post and placing the animal into an acrylic tube . This procedure was continued for at least 5 days , during which the duration of head-fixing was gradually extended from 10 to 120 min . A day after the habituation period , the animals were anesthetized with isoflurane and a craniotomy to insert the silicon probe into the brainstem and hippocampus was performed . Two craniotomies were performed , one on the left hemisphere ( 4 . 0 mm to 5 . 5 mm posterior , 1 . 0 to 1 . 3 mm lateral from bregma ) for the brainstem recording and a second also on the left hemisphere ( 2 . 0 mm posterior , 1 . 5 mm lateral from bregma ) for the hippocampus recording . To protect and prevent the brain from drying , the surface was covered with biocompatible sealants ( Kwik-Sil and Kwik-Cast , WPI ) . The following day , the animals were placed in the head-fixed apparatus for electrophysiological recording as described below . For fiber photometry experiments , cortical EEG and EMG electrodes were implanted as described above and connected to a 2-by-3 piece connector ( SLD-112-T-12 , Semtec ) . Two additional anchor screws were implanted bilaterally over the parietal bone to provide stability and a small portion of a drinking straw was placed horizontally between the anchor screws and the connector . The viral vector ( AAV5-CAG-flex-GCaMP6s-WPRE-SV40 , Penn Vector Core; titer 8 . 3 × 1012 GC/ml ) was microinjected ( 500 nl at 30 ml/min ) ( Nanoliter2010 , WPI ) to target the PPT/LDT area ( −4 . 5 mm posterior , 1 mm lateral from bregma and 3 . 25 mm depth from brain surface ) . The micropipette was left in the brain for an additional 10 min and then slowly raised up . An optic fiber cannula ( CFM14L05 , Thorlabs ) was then implanted 3 mm deep from the surface of the brain and all components were secured to each other and to the skull with dental cement . Experimental procedures were as described previously ( Lyngholm and Sakata , 2019; Yague et al . , 2017 ) . Briefly , all electrophysiological recordings were performed in a single-walled acoustic chamber ( MAC-3 , IAC Acoustics ) with the interior covered with 3 inches ( ~ 7 . 5 cm ) of acoustic absorption foam . The animals were placed in the same head-fixed apparatus , by securing them by the head-post and placing the animal into an acrylic tube . During the entire recording , the animals were not required to do anything actively . For pontine EEG recording , cortical EEG , EMG and pontine EEG signals were amplified ( HST/32 V-G20 and PBX3 , Plexon ) , filtered ( 0 . 1 Hz low cut ) , digitized at a sampling rate of 1 kHz and recorded using LabVIEW software ( National Instruments ) . Recording was performed for 5 hr from 9:00 to 14:00 . For brainstem or hippocampal silicon probe recording , a 32-channel four shank silicon probe ( A4 × 8–5 mm-100-400-177 for brainstem recording or Buzsaki32 for hippocampus recording ) was inserted slowly with a manual micromanipulator ( SM-25A , Narishige ) into the brainstem ( 3 . 75 mm – 4 . 3 mm from the brain surface ) or the hippocampus ( 1 . 55 mm – 1 . 85 mm from the brain surface ) . Probes were inserted perpendicularly with respect to the brain surface . Broadband signals were amplified ( HST/32 V-G20 and PBX3 , Plexon ) relative to the screw on the cerebellum , filtered ( 0 . 1 Hz low cut ) , digitized at 20 kHz and recorded using LabVIEW software ( National Instruments ) . The recording session was initiated >1 hr after the probe was inserted to its target depth , to stabilize neural signals . Recording preparation started from 8:00 and terminated by 15:00 . For verification of silicon probe tracks , the rear of the probes was painted with DiI ( ∼10% in ethanol , D282 , Invitrogen ) before insertion . In a subset of in vivo electrophysiological experiments under a head-fixed condition , pupils were also monitored with an off-axis infrared ( IR ) light source ( 860 nm IR LED , RS Components ) . A camera ( acA1920-25µm , Basler Ace ) with a zoom lens ( M0814-MP2 , Computar ) and an IR filter ( FGL780 , Thorlabs ) was placed ~10 cm from the animal’s left eye . Images were collected at 25 Hz using a custom-written LabVIEW program and a National Instruments image grabber ( PCIe-8242 ) . The fiber photometry system consisted of two excitation channels . A 470 nm LED ( M470L3 , Thorlabs ) was used to extract a Ca2+-dependent signal and a 405 nm LED ( M405L3 , Thorlabs ) was used to obtain a Ca2+-independent isosbestic signal . Light from the LEDs was directed through excitation filters ( FB470-10 , FB405-10 , Thorlabs ) and a dichroic mirror to the fiber launch ( DMLP425R and KT110/M , respectively ) . The fiber launch was connected to a multimode patch cable ( M82L01 , Thorlabs ) , which attached to an implantable optic fiber on the mouse via a ceramic mating sleeve ( CFM14L05 and ADAF1 , respectively ) . Light emissions from GCaMP6s expressing neurons were then collected back through the optic fiber and directed through a detection path , passing a dichroic mirror ( MD498 ) to reach a photodetector ( NewFocus 2151 , Newport ) . A National Instruments DAQ ( NI USB-6211 ) and custom-written LabVIEW software were used to control the LEDs and to acquire fluorescence data at 1 kHz . LEDs were alternately turned on and off at 40 Hz in a square pulse pattern . Electrophysiology signals were recorded at 1 KHz using an interface board ( RHD2000 , Intan Technologies ) and connected to the mouse via an amplifier ( RHD2132 , Intan Technologies ) . Mice were habituated to being handled and tethered to the freely behaving system over several consecutive days . Mice were scruffed and the straw on the headcap slotted into a custom-made clamp , to keep the head still and to absorb any vertical forces when connecting the electrophysiology and fiber photometry tethers to the headcap . Once connected , mice were placed in an open top Perspex box ( 21 . 5 cm x 47 cm x 20 cm depth ) lined with absorbent paper , bedding and some baby food . Recordings lasted 4–5 hr to allow for multiple sleep–wake transitions . After electrophysiological experiments , animals were deeply anesthetized with a mixture of pentobarbital and lidocaine , and perfused transcardially with 20 mL saline followed by 20 mL 4% paraformaldehyde/0 . 1 M phosphate buffer , pH 7 . 4 . The brains were removed and immersed in the above fixative solution overnight at 4°C and then immersed in a 30% sucrose in phosphate buffer saline ( PBS ) for at least 2 days . The brains were quickly frozen and were cut into coronal or sagittal sections with a sliding microtome ( SM2010R , Leica ) or with a cryostat ( CM3050 , Leica ) , each with a thickness of 50 or 100 µm . The brain sections were incubated with a NeuroTrace 500/525 Green-Fluorescence ( 1/350 , Invitrogen ) or NeuroTrace 435/455 Blue-Fluorescence ( 1/100 , Invitrogen ) in PBS for 20 min at room temperature ( RT ) , followed by incubation with a blocking solution ( 10% normal goat serum , NGS , in 0 . 3% Triton X in PBS , PBST ) for 1 hr at RT . For ChAT-IRES-Cre::Ai32 mice , to confirm the position of ChAT-expressing neurons , we performed GFP and ChAT double staining . These brain sections were incubated with mouse anti-GFP antiserum ( 1/2000 , ABCAM ) and goat anti-ChAT antiserum ( 1/1000 , Millipore ) in 3% NGS in PBST overnight at 4°C . After washing , sections were incubated with DyLight 488-labeled donkey anti-mouse IgG ( 1/500 , Invitrogen ) and Alexa 568-labeled donkey anti-goat IgG ( 1/500 , Invitrogen ) for 2 hr at RT . After staining , sections were mounted on gelatin-coated or MAS-coated slides and cover-slipped with 50% glycerol in PBS . The sections were examined with a fluorescence microscope ( BZ-9000 , Keyence ) . Data were presented as mean ± SEM unless otherwise stated . Statistical analyses were performed with MATLAB . In Figures 2G and 3A , one-way ANOVA with post-hoc Tukey’s Honest Significant Difference ( HSD ) criterion was performed . In Figure 4C , repeated measures ANOVA was performed . In Figure 5C , a two-tailed t-test was performed . In Figure 6C , Rayleigh test for non-uniformity was performed . In Figures 6B and 7B , two-way ANOVA with post-hoc HSD criterion was performed . In Figure 7D , a two-tailed t-test was performed . To estimate effect size , Hedges’ g was computed using the Measures of Effect Size Toolbox ( Hentschke and Stüttgen , 2011 ) . The code is available on GitHub ( https://github . com/Sakata-Lab; copies archived at https://github . com/elifesciences-publications ) .	Though almost all animals sleep , its exact purpose remains an enigma . This is particularly true for the period of sleep where people dream most vividly , which is known as rapid eye movement sleep or REM sleep for short . In addition to the eye movements that give it its name , during this phase of sleep , the pupils of the eyes become smaller , muscles relax and neurons in part of the brain activate in a regular , repeating way known as pontine waves or P-waves . The brainstem is a key brain region that helps the body determine when it is time to sleep and when it is time to be awake . It is found at the back of the brain , and connects the brain to the spinal cord , serving as a conduit for nerve signals to and from the rest of the body . However , it was not clear how the brainstem’s activity during sleep interacts with other brain regions that are important in the sleep process , such as the hippocampus . REM sleep is not unique to humans; in fact , it occurs in all mammals . Tsunematsu et al . studied mice to better understand the role of the brainstem during sleep . In the experiments , the brain waves , muscle tone and pupil sizes of the mice were monitored , while a probe inserted into the brainstem of the mice measured the activity of the neurons . Analysis of the probe data could predict changes in pupil size ten seconds beforehand and transitions between wakefulness , REM sleep and non-REM sleep up to sixty seconds in advance . This long timescale suggests that there are a number of complex interactions following brainstem activity that lead to the changes in sleep state . Tsunematsu et al . were also able to detect P-waves for the first time in mice and found that they are timed with activity from the hippocampus depending on the sleep state . During REM sleep , the P-waves precede the hippocampal activity , while during non-REM sleep , they follow it . These results further imply that the two sleep states serve different purposes . The detection of P-waves in mice shows that they are similar to other mammals that have previously been studied . Further studies in mice could help to provide more insight into the mechanisms of sleep and the purpose of the different stages .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2020	State-dependent brainstem ensemble dynamics and their interactions with hippocampus across sleep states
Understanding how the brain captures transient experience and converts it into long lasting changes in neural circuits requires the identification and investigation of the specific ensembles of neurons that are responsible for the encoding of each experience . We have developed a Robust Activity Marking ( RAM ) system that allows for the identification and interrogation of ensembles of neurons . The RAM system provides unprecedented high sensitivity and selectivity through the use of an optimized synthetic activity-regulated promoter that is strongly induced by neuronal activity and a modified Tet-Off system that achieves improved temporal control . Due to its compact design , RAM can be packaged into a single adeno-associated virus ( AAV ) , providing great versatility and ease of use , including application to mice , rats , flies , and potentially many other species . Cre-dependent RAM , CRAM , allows for the study of active ensembles of a specific cell type and anatomical connectivity , further expanding the RAM system’s versatility . Neurons form ensembles that encode experiences . This has been demonstrated in the past several decades by in vivo electrophysiological and calcium imaging experiments in which the activity of neuronal ensembles has been correlated with behavior in active animals ( Buzsáki , 2004; Grewe and Helmchen , 2009 ) . Understanding the process whereby experience is converted to long-term memory and consequent behavioral modification requires that relevant ensembles of neurons be defined precisely and genetically to allow functional interrogation and manipulation . Transcription events triggered within neurons by neuronal activity are key to neural circuit plasticity , ensemble formation , and ultimately information storage ( Alberini , 2009; Cole et al . , 1989; Guzowski et al . , 2001; Johansen et al . , 2011 ) . Experience-dependent transcription events thus present a promising way to genetically identify neurons responsible for encoding learned experiences in vivo . However , the transcriptional profile must fit the following two criteria: ( 1 ) very low basal expression in the absence of salient experience and ( 2 ) strong induction by neuronal activity associated with experience and behavior . Immediate early genes ( IEGs ) such as Fos , Arc and Egr1 meet these criteria quite well ( Guzowski et al . , 2001 ) , and their promoters have been used to control the expression of effector genes such as fluorescent proteins and opsins in genetically engineered mouse lines , allowing active ensemble labeling and functional perturbation , respectively ( Barth et al . , 2004; Guenthner et al . , 2013; Koya et al . , 2009; Reijmers et al . , 2007; Smeyne et al . , 1992; Wang et al . , 2006; Eguchi and Yamaguchi , 2009; Denny et al . , 2014 ) . However , significant technical obstacles greatly limit the usability of these systems . The biggest challenge is to improve the sensitivity and selectivity of neuronal ensemble identification . Existing systems suffer from high background , i . e . labeling of neurons unrelated to the experimental stimulus of interest , which confounds precise identification of the relevant active ensemble . The level of background labeling is determined by the characteristics of the IEG promoter used and the method of temporal control used to isolate events happening within a desired experimental time window . Therefore , to address the problem of background labeling , we wanted to develop an IEG-sensitive promoter with an optimized activity-dependent induction profile and incorporate it into a platform with improved temporal control of effector gene expression . In addition , the use of transgenic reporter lines in existing systems requires laborious breeding and is experimentally inflexible . Therefore , we also aimed to develop a system in which both the activity-dependent transcription component and the effector genes for neural circuit interrogation are delivered using a single adeno-associated virus ( AAV ) . In addition to bypassing the requirement for multiple transgenic mouse lines , an entirely viral system can also be used in species other than the mouse . Here we present a virus-based platform for the analysis of active neuronal ensembles , which we call the Robust Activity Marking ( RAM ) system . The following features of the RAM system make it highly selective , sensitive , and versatile: ( 1 ) a synthetic neuronal activity-dependent promoter with very low expression in basal conditions prior to a designated experience and strong induction by neural activity during the experience for robust ensemble labeling; ( 2 ) a modified Tet-Off system that provides improved temporal control; ( 3 ) small size , well within the packaging limit of a single AAV; ( 4 ) modular design so that the promoter and effector genes can be easily substituted to address different experimental questions; and ( 5 ) proven transferability to species other than the mouse , making it a valuable tool for the wider neuroscience community . We demonstrate the use of the RAM system to interrogate active neuronal ensembles in several different regions of the murine and drosophila brain . To find a small promoter broadly responsive to neuronal activity , we searched for highly enriched DNA elements among 11 , 830 previously identified neuronal activity-regulated enhancers ( Kim et al . , 2010 ) . The Activator Protein 1 ( AP-1 ) site ( TGANTCA ) , a consensus sequence for the FOS/JUN family transcription factors , was initially identified as the most highly enriched motif ( Figure 1—figure supplement 1 ) . We further considered that multiple transcription factors often bind enhancers co-operatively or competitively to tightly regulate gene expression in a combinatorial manner ( Hermsen et al . , 2006 ) . We therefore tried combining the AP-1 site with the binding motif of the neuronal-specific activity-dependent gene Npas4 ( Ramamoorthi et al . , 2011 ) ( NRE: TCGTG ) to increase the sensitivity and specificity of our promoter . We inserted the core NRE/AP-1 DNA motifs into the central midline element ( CME ) , a characterized transcriptional regulatory sequence whose secondary structure is favorable for transcription activation ( Wharton et al . , 1994 ) , resulting in a 24 bp 'enhancer module . ' Unless otherwise stated , we assembled promoters by placing four tandem repeats of an enhancer module upstream of the human FOS minimal promoter , resulting in a 199 bp synthetic promoter ( Figure 1a ) . We assayed both the absolute transcriptional strength , reflected by the relative luciferase value , and the activity dependence and sensitivity , represented by the fold induction between stimulated and unstimulated conditions , of the synthetic promoters using dual luciferase assay in cultured neurons ( See ‘Methods’ for details ) . 10 . 7554/eLife . 13918 . 003Figure 1 . Design and characterization of the RAM promoter ( PRAM ) and AAV-based RAM system in vitro . Unless indicated otherwise , cultured mouse hippocampal neurons were transfected with luciferase reporter constructs on DIV5 and stimulated with 35 mM KCl for 6 hr on DIV 7 or 8 ( See Supplementary file 2 for details ) . The relative luciferase value is the absolute luciferase value normalized against the internal control . The fold induction is the ratio of the relative luciferase values in stimulated and unstimulated conditions . ( a ) PRAM has higher fold induction than promoters in which the NRE/AP-1 element is replaced by other elements found enriched in the 11 , 830 activity-regulated enhancers ( See ‘Methods’ ) . CME is not regulated by activity . Each construct contains four enhancer modules ( EM ) inserted upstream of the FOS minimal promoter ( left ) . The relative luciferase values are shown in Figure 1—figure supplement 2b . n = 5–7 separate experiments per condition , one-way ANOVA , Tukey’s post-hoc test . ( b ) The robust activity response of PRAM is a result of the combination of the four repeated RAM EM and FOS minimal promoter , which alone are respectively weakly responsive or non-responsive to neuronal activity . n = 5–10 separate experiments per condition , one-way ANOVA , Tukey’s post-hoc test . ( c ) Comparison of PRAM to various activity-dependent promoters . Promoter size ( Kb ) is shown in brackets . The relative luciferase values are shown in Figure 1—figure supplement 3a . n = 4–10 separate experiments per condition , Student’s t-test . ( d ) Comparison of PRAM and ESARE . n = 6–8 separate experiments per condition , Student’s t-test . ( e ) PRAM can be driven by overexpression of FOS and NPAS4 individually or in combination . n = 9–10 separate experiments per condition , one-way ANOVA , Dunnett’s post-hoc test . ( f ) Top , schematic outline of Tet-OFF system with PRAM driving tTA expression . Binding of tTA protein to the TRE promoter is prevented by Dox administration ( +Dox ) ; withdrawal of Dox ( -Dox ) allows downstream effector gene transcription . Bottom , schematic diagram of the AAV-RAM construct with critical genetic elements outlined . ( g ) Comparison of PRAM-d2tTA and PRAM-tTA by assaying pTRE-luciferase activity with and without Dox ( +Dox , −Dox ) . The relative luciferase values are shown in Figure 1—figure supplement 3e . n = 3 separate experiments per condition , Student’s t-test . ( h ) Representative images of hippocampal neurons grown on a glia monolayer and infected with AAV-RAM-tdTomato ( red ) . Neurons are identified by MAP2 staining ( green ) . Cultures were either left undisturbed ( No Stim ) or stimulated with bicuculline and 4AP ( +Bic/4AP ) , either with ( +Dox ) or without ( -Dox ) doxycycline added . The scale bar is 150 μm and applies to all images . ( i ) Quantification of h . Percentages of neurons ( MAP2+ ) that are RAM+ ( %RAM+ ) are plotted . n = 3 separate experiments per condition , one-way ANOVA , Tukey’s post-hoc test . All data in a–e , g and i are mean ± SEM . p<0 . 05 , p<0 . 01 , *p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 00310 . 7554/eLife . 13918 . 004Figure 1—figure supplement 1 . Position-weight matrix of the top-ranking motif ( AP-1 , TGANTCA ) identified by a Weeder de novo motif search . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 00410 . 7554/eLife . 13918 . 005Figure 1—figure supplement 2 . Characterization of enhancers by luciferase assay . ( a ) Fold induction with PRAM and PNRE+AP-1 constructs following 6 hr of KCl stimulation . n = 4–10 separate experiments per condition , Student’s t-test , p<0 . 05 . ( b ) Relative luciferase activity with PRAM , enriched enhancers E1-E3 and CME constructs following 6 hr of KCl stimulation . Note log10 scale . Baseline activity is similar for all constructs ( non-significant difference ) . For KCl conditions PRAM displays significantly higher activity than CME , E2 and E3 ( p<0 . 05 , *p<0 . 001 ) and E1 displays significant higher activity than CME and E2 ( ♯p<0 . 05 , ♯♯p<0 . 01 ) . CME construct expression is not induced by depolarization . n = 5–7 separate experiments per condition , one-way ANOVA , Tukey’s post-hoc test . The fold induction is shown in Figure 1c . ( c ) Fold induction with constructs containing four RAM enhancer modules attached to the following minimal promoters: CMV: cytomegalovirus; hBG: human beta-globin; Arc: activity-dependent cytoskeleton associated-protein . n = 5–12 separate experiments per condition , one-way ANOVA , Dunnett’s post-hoc test . ( d ) Fold induction with PRAM constructs containing different numbers of RAM enhancer modules . n = 5–12 separate experiments per condition , one-way ANOVA , Dunnett’s post-hoc test . Data in a–d are mean ± SEM . p<0 . 05 , p<0 . 01 , p<0 . 001 , n . s . , non-significant . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 00510 . 7554/eLife . 13918 . 006Figure 1—figure supplement 3 . Characterization of the PRAM promoter in vitro . ( a ) Relative luciferase activity of activity-dependent promoters . Note log10 scale . Statistically significant differences between baseline conditions ( No Stim ) are indicated by and between KCl conditions by # . All other comparisons are non-significant . n = 4–10 separate experiments per condition , Student’s t-test . The fold induction is shown in Figure 1c . ( b ) Relative luciferase activity of PRAM following bicuculline ( Bic ) stimulation and blocking with Nimodipine and/or APV . n = 4 separate experiments per condition , one-way ANOVA , Tukey’s post-hoc test . ( c ) Relative luciferase activity of PRAM after 6 hr of stimulation with various growth factors and pharmacological agents . n = 4 separate experiments per condition , Student’s t-test . ( d ) Relative luciferase activity of PRAM in pure glial cultures following 4 hr of KCl stimulation . n = 2 separate experiments per condition . ( e ) Relative luciferase activity of PRAM-tTA- and PRAM-d2tTA-mediated pTRE-luciferase transcription with and without KCl stimulation , and with and without Dox ( +Dox , -Dox ) . The fold induction is shown in Figure 1g . n = 3 separate experiments per condition , Student’s t-test . Data in a–e are mean ± SEM . p<0 . 05 , p<0 . 01 , p<0 . 001 , n . s . , non-significant . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 00610 . 7554/eLife . 13918 . 007Figure 1—figure supplement 4 . Quantification of AAV-RAM-tdTomato infection of neuron-glia co-cultures determined by immunocytochemistry . ( a ) Percentage of MAP2+ cells expressing tdTomato ( RAM+ ) at various amounts of virus applied per well . Experiments in b and Figure 1h–i used 0 . 1 μl/well ( red bar ) . n = 3 separate experiments per condition . ( b ) Co-labeling of MAP2 or GFAP with tdTomato ( RAM+ ) . GFAP did not co-label with tdTomato . N = 3 separate experiments per condition . All data in a–b are mean ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 007 The relative position of the NRE and AP-1 sites turned out to be critical . When these motifs partially overlapped by two base-pairs to form a 10mer TCGTGANTCA , the resulting synthetic promoter had the strongest activity-dependent induction profile ( Figure 1—figure supplement 2a ) . We named this promoter PRAM . The CME alone was not responsive to activity ( Figure 1a ) . Furthermore , PRAM performance was not improved by replacing the NRE/AP-1 element with other highly enriched AP-1 containing 10mers found in the 11 , 830 neuronal activity-regulated enhancers , indicating the significance of the NRE and AP-1 combination in our design ( Figure 1a , Figure 1—figure supplement 2b ) . In general , we found no correlation between 10mer enrichment and fold induction in the luciferase assay ( Figure 1a , Supplementary file 1 ) . We proceeded with further characterization of PRAM and found that removing or substituting the minimal promoter ( Figure 1b; Figure 1—figure supplement 2c ) , or changing the number of enhancer modules substantially reduced the fold induction ( Figure 1—figure supplement 2d ) . When compared to several other activity-regulated promoters , PRAM had among the highest fold induction ( Figure 1c ) and the highest relative luciferase activity in response to neuronal stimulation ( Figure 1—figure supplement 3a ) . We compared PRAM to another recently developed synthetic activity-dependent promoter , ESARE , which was based on the promoter of the IEG Arc ( Kawashima et al . , 2013 ) . ESARE had a significantly lower fold induction , due to its high basal activity ( Figure 1d ) . Notably , PRAM has by far the largest fold induction of any activity-dependent promoter small enough to be packaged into an AAV . We next verified that PRAM has the characteristics expected from the presence of the FOS and NPAS4 binding motifs . Indeed , over-expression of FOS , NPAS4 , or both proteins in combination was sufficient to drive transcription through PRAM ( Figure 1e ) . Expression from PRAM was strongly induced by Ca2+ influx ( Figure 1—figure supplement 3b ) and by various growth factors known to induce Fos expression ( Lin et al . , 2008 ) ( Figure 1—figure supplement 3c ) . When expressed in glia , PRAM had minimal basal activity and no activity-dependent induction ( Figure 1—figure supplement 3d ) . We concluded that PRAM is a neuron-specific and highly activity-dependent promoter . We next constructed the RAM system , incorporating PRAM into the doxycycline-dependent Tet-Off system ( Gossen et al . , 1995 ) . Specifically , PRAM drives the expression of the tetracycline transactivator ( tTA ) , which efficiently activates the tTA-responsive element ( TRE ) promoter driving the effector gene cassette . Binding of tTA to TRE can be blocked by administration of the antibiotic doxycycline ( Dox ) , preventing expression of the effector gene ( Figure 1f ) and allowing temporal control of gene expression . Our preliminary evaluation of the conventional Tet-Off system showed that the accumulation of tTA outside of the designated experimental window contributed to the undesirable background expression of effector genes . We therefore created a destabilized version of tTA , d2tTA , by fusing the degradation domain of mouse ornithine decarboxylase ( MODC ) to the N-terminus of tTA . Using a luciferase assay after co-transfecting a TRE-luciferase plasmid with either PRAM-tTA or PRAM-d2tTA into cultured neurons , we found that using d2tTA led to significantly lower basal expression ( Figure 1—figure supplement 3e ) , tighter Dox regulation ( Figure 1g ) , and improved fold induction by a factor of 7–8 compared with conventional tTA ( Figure 1g ) . Figure 1f shows the final design of the RAM system AAV vector . Due to the compact design and small size of PRAM , all the necessary components fit within the packaging limit of a single AAV vector ( 4 . 9 Kb ) with room for an effector gene of up to 1 . 8 Kb in size . We validated the RAM system first in cultured neurons , using the red fluorophore tdTomato ( tdT ) as the effector gene . Using an optimized titer of AAV-RAM-tdT ( Figure 1—figure supplement 4a ) , close to 100% of cultured mouse hippocampal neurons were infected at DIV7 . With Dox absent throughout , at DIV14 approximately 5% of neurons ( MAP2-positive cells ) were labeled with tdT under unstimulated conditions , while nearly 90% of neurons were labeled following bicuculline ( Bic ) and 4-aminopyridine ( 4AP ) stimulation ( Figure 1h–i ) . RAM labeling was tightly controlled by Dox , with no labeling cells detected when Dox was present after infection ( Figure 1h–i ) . No glial cells were tdT positive ( Figure 1—figure supplement 4b ) . Thus , our AAV-based RAM system showed strong induction and low background , as desired . We carried out a series of experiments to determine optimal working parameters for our AAV-based RAM system in vivo . For these experiments , we chose the nuclear localized , red-shifted fluorophore NLS-mKate2 as the effector gene . For all in vivo experiments in mice , AAV-Ef1α-EGFP , which constitutively expresses EGFP in infected neurons , was co-injected as an infection control . The titer of the EGFP control virus was adjusted so that nearly 100% of neurons at the injection site were infected . To optimize the RAM virus , animals were placed on a Dox diet 24 hr before viral injection into the hippocampus , and allowed to recover for at least 7 days before administration of kainic acid to induce seizures , which robustly stimulates the limbic system and fully activated PRAM in the majority of AAV-RAM infected neurons . With an optimized titer , more than 90% of EGFP+ neurons were also RAM+ after seizure . Using this setup , we found that animals should optimally be off Dox for 24–48 hr before behavioral manipulation ( in this case the manipulation was seizure , Figure 2a–d ) , and that effector gene expression peaked within 24 hr following behavioral manipulation ( Figure 2a , e–g ) . In addition , a further expression of the effector gene was completely blocked 24 hr after the animals were placed back on Dox chow ( Figure 2—figure supplement 1a–d ) . 10 . 7554/eLife . 13918 . 008Figure 2 . Optimizing the working parameters for the AAV-RAM system in vivo . ( a ) Schematic of the hippocampus showing the CA1 , CA3 and DG regions with the injection site ( DG ) highlighted in blue . ( b–d ) Experiments to determine the time required for Dox clearance to allow maximal effector gene expression . ( b ) Experimental scheme . Animals were co-infected with AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP and kept on Dox diet ( +Dox ) for a minimum of 7 days . The animals were taken off Dox diet ( −Dox ) 12 , 24 , or 48 hr before kainic acid treatment to induce seizures and sacrificed 24 hr after this treatment . ( c ) Percentage of RAM+ cells among total EGFP+ cells in the DG stratum granulosum when seized 12 , 24 or 48 hr after Dox removal . n = 3–4 animals per group , one-way ANOVA , Tukey’s post-hoc test . ( d ) Representative images from the data quantified in c . The scale bar is 150 μm and applies to all images . ( e–g ) Experiments to determine the maturation of effector gene expression . ( e ) Experimental scheme . Animals were treated similarly to a , and 48 hr following Dox removal ( -Dox ) , animals were seized then sacrificed 3 , 6 , 12 , or 24 hr later . ( f ) Percentage of RAM+ cells among total EGFP+ cells in the DG stratum granulosum 3 , 6 , 12 , or 24 hr after seizure . RAM+ cells are detected by fluorescence ( mKate2 , red ) or immuno-staining ( α-mKate2 , cyan ) . n = 2–4 animals per group , two-way ANOVA , Bonferroni post-hoc test . ( g ) Representative images from the data quantified in f . Upper row: AAV-mKate2 ( red ) , middle row: mKate2 detected by antibody ( α-mKate2 , cyan ) , lower row: EGFP ( green ) . The scale bar is 150 μm and applies to all images . Data in c and f are mean ± SEM . p<0 . 01 , *p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 00810 . 7554/eLife . 13918 . 009Figure 2—figure supplement 1 . Rapid suppression of RAM expression on returning to Dox diet . ( a ) Schematic timeline of the experimental procedure . Animals were co-injected with AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP into DG and kept on Dox diet ( +Dox ) for a minimum of 7 days . Dox was withdrawn ( −Dox ) for 48 hr then resumed for 0 , 24 or 48 hr prior to kainic acid treatment to induce seizures . Animals were sacrificed 24 hr after seizure induction . ( b ) Schematic drawing of the hippocampus showing the viral injection site . ( c ) Percentage of RAM+ cells among total EGFP+ cells in the DG with 0 , 24 or 48 hr Dox blockade before seizure . n = 3–4 animals per condition , one-way ANOVA , Tukey’s post-hoc test . ( d ) Representative images from the data quantified in c showing mKate2 ( red ) and EGFP ( green ) labeling . The scale bar is 150 μm and applies to all images . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 009 We next tested the ability of RAM to label neuronal ensembles activated by salient experience , using contextual fear conditioning ( CFC ) as a model behavioral paradigm . Animals were kept on Dox chow for at least 1 week after viral injection , switched to Dox-free chow 48 hr before CFC , and sacrificed 24 hr after CFC ( Figure 3a ) . First we virally targeted the dentate gyrus ( DG ) sub-region ( Figure 3b ) , which is known to express IEGs after CFC ( Liu et al . , 2012 ) . Each cohort of animals was divided into the following five treatment groups: ( 1 ) left unperturbed in the home cage ( HC ) , ( 2 ) CFC , ( 3 ) exposed to the novel context only without shock ( Context Only ) and ( 4 ) exposed to immediate shocks ( Shock Only ) and ( 5 ) kainic acid-induced seizure ( Figure 3c–d ) . We quantified the percentage of EGFP+ cells that were RAM+ ( labeled with NLS-mKate2 ) in each treatment group . We found a significant increase in RAM+ cells in both the CFC and Context Only group , compared to the HC animals ( Figure 3c ) . Importantly , RAM labeled a very similar number of DG neurons in animals exposed to Context Only as in those that underwent CFC ( Figure 3c ) , which is consistent with published studies showing that the hippocampus forms contextual representations independent of shock delivery ( Rudy and O'Reilly , 1999; Fanselow , 2000 ) and that similar levels of IEG induction occur for these two conditions ( Ramamoorthi et al . , 2011 ) . Furthermore , the Shock Only condition did not induce RAM activation at all ( Figure 3c ) , suggesting that RAM labeling was specific to neuronal ensembles involved in contextual learning . Interestingly , such learning-specific induction is one of the unique properties of Npas4 ( Ramamoorthi et al . , 2011; Sun and Lin , 2016 ) . These results suggest that the RAM system labels an active population of cells in an experience-dependent manner . Importantly , mice that were kept on Dox chow throughout CFC treatment showed a complete lack of RAM+ cells in the DG ( Figure 3—figure supplement 1a–b ) . 10 . 7554/eLife . 13918 . 010Figure 3 . The RAM system labels active neuronal ensembles in the DG of the hippocampus . ( a ) Schematic timeline of the experimental procedure . Animals were kept on Dox diet ( +Dox ) from 24 hr before injection of AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP into DG to a minimum of 7 days after the injection . Dox was then withdrawn ( −Dox ) 48 hr before either CFC , exposure to the novel context without shock ( Context Only ) , receiving immediate shocks ( Shock Only ) or kainic acid treatment to induce seizures . Animals were sacrificed 24 hr later . A control cohort of similarly injected and treated animals was left undisturbed in their home cages ( HC ) for the entire period before sacrifice . ( b ) Schematic drawing of the hippocampus with the viral injection site ( DG ) highlighted in blue . ( c ) Percentage of RAM+ cells among total EGFP+ cells in the DG stratum granulosum for HC , CFC , Context Only , Shock Only and seized animals . The data for seized animals are replotted from Figure 2c ( 48 hr group ) . n = 6–9 animals per condition ( with the exception for the Shock Only group , which consisted of 3 animals ) , one-way ANOVA , Tukey’s post-hoc test . ( d ) Representative images of the DG region showing mKate2 ( red ) and EGFP ( green ) labeling for each of the HC , CFC , Context Only , Shock Only and seizure conditions . The scale bar is 300 μm for the left images and 50 μm for the three right columns of images . These images are enlarged from the areas marked by purple squares . All data in c are mean ± SEM . p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 01010 . 7554/eLife . 13918 . 011Figure 3—figure supplement 1 . RAM labeling in the hippocampus following sensory experience is prevented by Dox diet . ( a ) Schematic timeline of the experimental procedure . Following injection of AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP into DG , animals were left on Dox diet ( +Dox ) throughout . Animals were subjected to CFC and sacrificed 24 hr later . ( b ) Representative images of the DG from animals given CFC treatment . Left: mKate2 ( red ) . Right: EGFP ( green ) , DAPI ( blue ) and mKate ( red ) merged . No RAM+ positive cells were detected . The scale bar is 150 μm and applies to both images . n = 4 animals . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 01110 . 7554/eLife . 13918 . 012Figure 3—figure supplement 2 . RAM labeling of CA3 pyramidal cells following contextual fear conditioning ( CFC ) . ( a ) Schematic timeline of the experimental procedure . Animals were kept on Dox diet ( +Dox ) from 24 hr before injection of AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP vectors into CA3 for a minimum of 7 days after the injection . Dox was then withdrawn ( −Dox ) 48 hr before CFC or kainic acid treatment to induce seizures . Animals were sacrificed 24 hr after CFC or seizure induction . A control cohort of similarly injected and treated animals was left undisturbed in their home cages ( HC ) for the entire period before sacrifice . ( b ) Schematic drawing of the hippocampus with the injection site ( CA3 ) highlighted in blue . ( c ) Representative images of the CA3 region showing mKate2 ( red ) and EGFP ( green ) and DAPI ( blue ) labeling for HC , CFC and seizure conditions . Red arrows indicate RAM and EGFP double-labeled cells in CA3 . The scale bar is 75 μm for the upper and middle ( small ) images and 50 μm for the lower images . The lower images are enlarged from the areas marked by purple squares . ( d ) Percentage of RAM+ cells among total EGFP+ cells in the CA3 stratum pyramidale for HC , CFC and seizure conditions . n = 3–4 animals per condition , Student’s t-test . All data are mean ± SEM . *p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 01210 . 7554/eLife . 13918 . 013Figure 3—figure supplement 3 . RAM labeling following CFC persists for at least a week . ( a ) Schematic timeline of the experimental procedure . Following infection of AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP into DG , animals were kept on Dox diet ( +Dox ) for a minimum of 7 days . Dox was withdrawn ( −Dox ) 48 hr before CFC . Animals were either sacrificed 24 hr later , or placed back on Dox diet for 1 , 2 or 4 weeks before sacrifice . ( b ) Schematic diagram of the hippocampus showing the viral injection site . ( c ) Percentage of RAM+ cells among total EGFP+ cells in the DG stratum granulosum for CFC animals sacrificed 24 hr or 1 , 2 or 4 weeks after CFC . Data are mean ± SEM . n = 3–4 animals per condition , one-way ANOVA , Tukey’s post-hoc test , p<0 . 05 , p<0 . 01 , p<0 . 001 , n . s . , non-significant . ( d ) Representative images of mKate2 ( red ) and EGFP ( green ) expressing cells 24 hr and 1 , 2 and 4 weeks after CFC . The scale bar is 150 μm and applies to all images . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 01310 . 7554/eLife . 13918 . 014Figure 3—figure supplement 4 . Infection with RAM-channelrhodopsin ( ChR2 ) or -archaerhodopsin ( ArchT ) prior to CFC enables light-activation or silencing , respectively , of repeated action potentials in RAM+ hippocampal DG granule cells . ( a ) Schematic timeline of the experimental procedure . Animals were co-injected with AAV-RAM-ChR2:EYFP and AAV-TRE-mCherry or AAV-RAM-ArchT:EGFP and AAV-TRE-mCherry into the DG . mCherry was used for visual location of RAM+ cells during ex vivo electrophysiology . Animals were kept on Dox diet ( +Dox ) for a minimum of 7 days . Dox was withdrawn ( −Dox ) 48 hr before CFC , and animals were sacrificed 24 hr after CFC for electrophysiology . ( b ) Schematic diagram of the experimental procedure . 455 nm blue light for ChR2 activation was applied through an optical lens ( 40x objective ) . Only cells in the DG stratum granulosum were used for recording . ( c ) Photocurrent generated in a representative ChR2-RAM+ cell held at −70 mV in a voltage clamp when illuminated for 1s with blue light . ( d ) Quantification of the instant maximum ( max ) and constant photocurrents generated in ChR2-RAM+ cells . n = 9 cells from 3 animals . Data are mean ± SEM . ( e ) Repeated action potential evoked by 10 ms light pulses applied at 5 Hz to a ChR2-RAM+ cell held at its resting membrane potential , −75 mV , in current clamp mode . ( f ) Examples of RAM+ cells expressing ChR2-EYFP ( green ) and mCherry ( red ) in the granule cells layer and their merged image ( together with DAPI , blue ) as seen in a 300 μm slice used for electrophysiology . The scale bar is 20 μm and applies to all images . ( g ) As for b , but with 565 nm green light for ArchT activation . ( h ) Example of a photocurrent generated in ArchT-RAM+ cells held at −70 mV . ( i ) Quantification of constant photocurrents generated in ArchT-RAM+ cells . Data are mean ± SEM . n = 10 cells from 3 animals . ( j ) Light-mediated silencing of action potentials in an RAM-ArchT+ cell depolarized by an 80pA square current pulse from its resting membrane potential of −71 mV . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 014 We repeated these experiments in the CA3 sub-region of the hippocampus and saw robust activity-dependent induction of RAM labeling after CFC along with low HC expression , consistent with a previous report of IEG expression in this region ( Guenthner et al . , 2013 ) ( Figure 3—figure supplement 2a–d ) . Interestingly , CFC treatment resulted in RAM+ labeling of 11 . 4% of infected CA3 pyramidal neurons , compared with 4 . 4% of infected DG granule cells , similar to published percentages of cells labeled by IEGs in response to CFC in these regions ( Kubik et al . , 2007; Leutgeb et al . , 2007 ) . To determine how long expression of the effector gene persists , animals were placed back on Dox 24 hr following CFC , and mKate2 labeling was measured in the following weeks . Labeling persisted for at least 1 week with no noticeable signal loss , began to decay at 2 weeks and was undetectable after 4 weeks ( Figure 3—figure supplement 3a–d ) . To test whether the RAM system drives effector gene expression sufficiently for functional manipulation the following day , we injected AAV-RAM-ChR2 or AAV-RAM-ArchT , which expressed activating channelrhodopsin and inhibiting archeorhodopsin , respectively , into the DG . Ex vivo slice recordings of sparsely RAM+ labeled dentate granule cells 24 hr after CFC revealed that active ensembles could be activated or inhibited by light ( Figure 3—figure supplement 4a–j ) , paving the way for future functional manipulation of neuronal ensembles labeled by the RAM system . We next designed experiments to test whether the active ensembles of neurons labeled by RAM are tightly associated with specific experiences . Because ensembles activated by different contexts can be distinguished within the DG region ( Liu et al . , 2012; Garner et al . , 2012 ) , and ensembles activated by a particular context are preferentially re-activated following re-exposure to the same context ( Barth et al . , 2004; Reijmers et al . , 2007; Liu et al . , 2012; Cruz et al . , 2013; Kawashima et al . , 2014; Xiu et al . , 2014 ) , we chose to focus on active ensembles in the DG following CFC . We co-injected AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP into the DG and kept animals on Dox chow until 48 hr before CFC . The animals were then subjected to CFC in an initial context , context A , and the neuronal ensemble activated by this first exposure was labeled by RAM . Twenty-four hours later , animals were re-exposed to context A or to a novel context B . Animals were sacrificed 1 . 5 hr later and brain sections were stained for the IEGs FOS and NPAS4 to label ensembles triggered by the second context exposure ( Figure 4a–b ) . Mice re-exposed to the conditioning context A froze significantly more than those exposed to the novel context B , suggesting that mice could distinguish between the two contexts ( Figure 4g ) . 10 . 7554/eLife . 13918 . 015Figure 4 . Contextual memory recall preferentially reactivates cells initially labeled with RAM during memory encoding . ( a ) Schematic drawing of the hippocampus with the viral injection site ( DG ) highlighted in blue . ( b ) Timeline of the experimental procedure . Animals were injected with AAV-RAM-NLS-mKate2 and kept on a Dox diet ( +Dox ) for at least 7 days after surgery . Two days after Dox removal ( −Dox ) , animals were exposed to context A ( for RAM labeling ) and shocked , and then exposed to either context A again or a new context B 24 hr later ( for IEG labeling ) . Animals were sacrificed 1 . 5 hr after the second context exposure . ( c , d ) Representative images of DG after A−A ( c ) and A−B ( d ) exposure showing DAPI staining ( cyan ) , FOS staining ( green ) , RAM labeling ( red ) , and the merged image . ( e , f ) As panels c and d , except staining with NPAS4 ( white ) instead of FOS . ( g ) Freezing behavior observed during re-exposure to context A or B . n = 4–5 animals per condition , Student’s t-test . ( h ) Percentage of all DAPI-labeled cells ( i . e . all neurons ) in the DG stratum granulosum labeled with RAM ( red ) and FOS ( green; n = 9 animals per condition ) , and the percentage overlap between the RAM and FOS labeled cells following A-A exposure ( n = 4 animals ) and A-B exposure ( n = 5 animals ) . Student’s t-test . ( i ) As h , except with NPAS4 ( white ) instead of FOS . The scale bar is 50 μm for all images . Data in g–i are mean ± SEM . p<0 . 05 , **p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 015 We quantified the degree of overlap between the RAM-labeled and IEG-labeled ensembles . If RAM specifically labels the neuronal ensemble encoding a particular context , we would expect to see more overlap after repeat exposure to the same context than after subsequent exposure to a new context . Indeed , the percentage overlap between RAM+ and either FOS+ or NPAS4+ granule cells ( GCs ) was significantly higher in animals re-exposed to the same context A than in those subsequently exposed to the new context B ( Figure 4c–f , h–i ) . This suggests that RAM accurately labels neurons that are involved in processing specific context information . Additionally , the absolute percentage of RAM+ or IEG+ GCs was in the 2–4% range ( Figure 4h–i ) , similar to previous results for this brain region following context exposure ( Garner et al . , 2012; Ramirez et al . , 2013 ) . These results indicate that the RAM system labels an active neuronal population similar to that identified by traditional IEG staining . Furthermore , the overlap percentage between RAM+ and IEG+ GCs for context A-A exposed animals was either on par with or significantly higher than overlap percentages shown in equivalent experiments from previously published studies . Furthermore , the roughly two-fold increase in overlap percentage between context A−A and context A−B exposed animals is also on par with previously published studies ( Denny et al . , 2014; Ramirez et al . , 2013; Deng et al . , 2013 ) . This suggests that the RAM system can also be used for functional perturbation studies dependent on context discrimination ( Liu et al . , 2012; Garner et al . , 2012; Ramirez et al . , 2013; Koya et al . , 2012; Bossert et al . , 2011 ) . We next tested the versatility of our RAM system by applying it to other brain regions . We injected AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP into the lateral section ( LA ) of the basolateral amygdala ( Figure 5a–b ) , which is the main input locus of the amygdala for auditory afferents . We examined RAM labeling in this region in animals subjected to tone-fear conditioning ( TFC ) , a form of cued associative learning known to require the amygdala ( Pare and Duvarci , 2012 ) . Each cohort of animals was divided into the following four treatment groups: ( 1 ) left unperturbed in the home cage ( HC ) , ( 2 ) TFC , ( 3 ) exposed to the tone alone without shock ( Tone ) and ( 4 ) received the immediate shock alone and removed from the testing chamber right away ( Shock ) . Compared to HC controls , TFC led to a dramatic increase in the number of RAM+ neurons in the LA ( Figure 5c , f ) . Interestingly , neither Tone nor Shock treatment alone resulted in RAM labeling significantly above the HC level ( Figure 5c , f ) . The relative levels of RAM labeling in LA under each condition were very similar to those of IEG staining reported in published studies ( Senn et al . , 2014; Liu et al . , 2003; Radulovic et al . , 1998 ) . Furthermore , long-term synaptic plasticity is known to occur in the LA following TFC to allow the formation of the tone-associated fear memory , while tone or shock alone does not result in long-term behavioral or synaptic changes ( Rogan et al . , 1997; Rogan and LeDoux , 1995 ) . Our result ( Figure 5c , f ) therefore suggests that the neuronal ensembles labeled by RAM are likely involved in fear memory formation . 10 . 7554/eLife . 13918 . 016Figure 5 . The RAM system labels active neuronal ensembles in the amygdala . ( a ) Schematic timeline of the experimental procedure . While on a Dox diet ( +Dox ) , AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP vectors were injected into the lateral amygdala ( LA , b ) , basal nucleus ( BA , d ) , or central amygdala ( CeA , g ) . After at least 7 days , Dox was removed ( -Dox ) for 48 hr , the animals were exposed to tone-fear conditioning ( TFC; consisting of Tone and Shock , T+S ) , Tone only , or Shock only and sacrificed 24 hr later . A control cohort of similarly injected and treated animals was left undisturbed in their home cages ( HC ) for the entire period before being sacrificed . ( b ) Schematic drawing of the amygdala with the injection and the quantification site ( LA ) highlighted in blue . ( c ) Percentage of RAM+ cells among total EGFP+ cells in LA for HC , T+S , Tone , and Shock animals . n = 3–6 animals per condition , one-way ANOVA , Tukey’s post-hoc test . ( d ) As panel b , but for the BA region . ( e ) Percentage of RAM+ cells among total EGFP+ cells in BA for HC , T+S , Tone and Shock animals . n = 3–4 animals per condition , one-way ANOVA , Tukey’s post-hoc test . ( f ) Representative images of neurons labeled in LA and BA for HC , T+S , Tone and Shock conditions . Neurons labeled by AAV-RAM-mKate2 are red and cells labeled by AAV-Ef1α-EGFP are green . The merged left image is enlarged in the two right images . Red arrows indicate RAM and EGFP double-labeled cells . The scale bar is 300 μm and 50 μm for left and right images , respectively . ( g ) As panel b , but for the CeA region . ( h ) Percentage of RAM+ cells among total EGFP+ cells in CeA for HC and T+S animals . n = 3 animals per condition , Student’s t-test . ( i ) Representative images of neurons labeled in CeA region . The scale bar is 150 μm for all images . All data in c , e and h are mean ± SEM . p<0 . 05 , p<0 . 01 , p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 016 In the same set of the TFC experiment , we also targeted the basal nucleus ( BA ) of the BLA ( Figures 5a , 6d–f ) , which is a main target of LA and relays information from LA to the central amygdala ( CeA ) . Like in the LA , TFC greatly increased the number of RAM labeled neurons in the BA , compared to HC ( Figure 5e–f ) . In addition , the numbers of RAM labeled neurons in both Tone and Shock groups , although significantly lower than in the TFC group , were much greater than in HC ( Figure 5e–f ) . Again , the relative levels of RAM labeling in BA were in agreement with those of IEG expression previously reported under similar conditions ( Senn et al . , 2014; Liu et al . , 2003; Radulovic et al . , 1998 ) . Because our TFC condition setup also contained the novel context component , as animals were not habituated to the testing chamber , we also examined RAM labeling in the CeA , a region that has been shown to have increased IEG expression following fear conditioning ( Radulovic et al . , 1998; Hall et al . , 2001; Milanovic et al . , 1998; Day et al . , 2008 ) . In animals subjected to TFC , the neuronal ensembles labeled by RAM in the CeA were significantly larger compared to HC ( Figure 5g–i ) . Taken together , these results indicated RAM can label neuronal ensembles that are important for fear memory formation in the amygdala . One major advantage of the RAM system is its applicability to model organisms other than the mouse . We therefore tested our AAV-RAM system and PRAM in rats and flies , respectively . AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP was injected into the infragranular layers ( V and VI ) of the rat medial prefrontal cortex ( mPFC ) and animals were placed on Dox ( Figure 6a–b ) . Five days after injection , rats were taken off Dox for four days before being subjected to the inescapable stress ( IS ) paradigm , which is known to engage the mPFC ( Wang et al . , 2014 ) . A significantly higher percentage of RAM+ labeling was observed in animals subjected to IS compared to HC controls ( Figure 6c–d ) . These results indicate that the RAM system can be used successfully in species other than mouse , and also in an additional brain region ( the mPFC ) . 10 . 7554/eLife . 13918 . 017Figure 6 . The RAM system in rats and flies . ( a–d ) RAM labels active neuronal ensembles in rats exposed to inescapable stress ( IS ) . ( a ) Schematic timeline of the experimental procedure . Rats were injected with AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP vectors in the medial prefrontal cortex ( mPFC ) . After 10 days they were either subjected to IS or left undisturbed in their home cages ( HC ) . ( b ) Schematic drawing of the rat brain with the red region indicating the target area ( PL: prelimbic cortex of the mPFC ) for virus infection and quantification of RAM+ cells . ( c ) Percentage of RAM+ cells among total EGFP+ cells in the prelimbic cortex in HC and IS animals . Data are mean ± SEM , n = 3–4 animals per group , Student’s t-test , p<0 . 01 . ( d ) Representative images of prefrontal cortex showing mKate2 ( red ) and EGFP ( green ) fluorescence in rats subjected to IS or HC conditions . Areas in purple squares are enlarged in the right image for each condition . CC: corpus callosum . Scale bars are 500 and 100 μm for the left and right images , respectively . ( e–h ) The Drosophila RAM reporter system . ( e ) Schematic diagram of the Drosophila RAM reporter system . The RAM-luc transgene can be turned on in specific cell types by the targeted expression of Flp recombinase using the GAL4-UAS system ( left ) or a cell-type specific driver ( right ) . ( f ) Drosophila RAM reporter activity has low baseline levels and high fold induction . The specificity of the RAM-luc to Flp recombinase was tested using a UAS-Gal4 system , in which Flp recombinase expression is under the control of a heat-shock HS promoter . Flies in the no heat-shock ( No HS ) condition were maintained at 20°C throughout development and experimental conditions . For the heat-shock ( HS ) condition , flies were exposed to a 37°C heat shock for 30 min and allowed to recover for a full day at 20°C before measuring reporter expression . To ensure that results were not due to insertional effects , the UAS-flp transgene was combined with fly lines with the reporter transgene on either chromosome II ( RAM-luc;UAS-flp ) or chromosome III ( UAS-flp;RAM-luc ) . n = 40–47 flies per group , Student’s t-test , p<0 . 001 . ( g ) Pan-neuronal RAM-luc reporter expression displays circadian rhythm . The RAM-luc reporter transgene was combined with a transgene expressing FLP recombinase in all adult neurons and luciferase activity measured in live flies over time . Bars under plots indicate day ( light ) and night ( dark ) . ( h ) Pan-neuronal RAM-luc reporter expression is sensitive to memory formation in Drosophila . Flies as described in g were trained in an olfactory memory task . 24 hr after training , flies exposed to Forward Spaced ( FS ) training showed significantly higher RAM-luc expression than control flies exposed to Backward Spaced ( BS ) training . Bars under plots indicate day ( light ) and night ( dark ) . n = 23–24 flies per group , Student’s t-test , p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 017 Since Drosophila contains both Fos− and Npas4-like transcription factors ( Perkins et al . , 1988; Jiang and Crews , 2007 ) , we tested PRAM in flies using a strategy that has been successfully implemented for CRE-based reporters ( Zhang et al . , 2015; Tanenhaus et al . , 2012 ) . We generated a transgenic PRAM-luciferase ( RAM-luc ) reporter fly in which luciferase expression is tightly controlled by both the PRAM promoter and FLP-recombinase ( Figure 6e–f , and see ‘Methods’ ) . To test RAM-luc expression and its dependence on neuronal activity , the RAM-luc reporter transgene was combined with a transgene expressing Flp recombinase in all adult neurons ( using the pan-neuronal 232B GAL4 driver ) . Luciferase activity measured in live flies over time showed high expression in the adult Drosophila brain . Most importantly , RAM-luc reporter activity was circadian ( Figure 6g ) , suggesting that PRAM functions as a robust activity-dependent promoter in Drosophila . To assess whether learning experience can alter RAM-luc activity , pan-neuronal RAM-luc flies were trained in an olfactory memory task ( Forward Spaced , or FS , training , in which an odor was paired with shock presentation ) that is known to produce robust long-term memory that lasts up to a week ( Zhang et al . , 2015 ) . Two days after training , RAM-luc activity in FS-trained flies was significantly higher than in those in the control group exposed to un-paired odor and shock presentation ( Backward Spaced , or BS , training; Figure 6h ) . Although further work is needed to completely validate the RAM-luc reporter fly in behavioral studies , these results suggest that it can be used to identify memory-specific cellular populations in Drosophila . We next examined the ability of our RAM system to specifically label cell types other than principal neurons that are transcriptionally induced by experience . We first sought to confirm that the RAM system works well in GABAergic neurons , which was implied by RAM labeling of neurons in CeA ( Figure 5g–i ) , a region comprised of 95% GABAergic neurons . We injected rats with AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP into mPFC and subjected the animals to the IS paradigm ( Figure 7a–b ) . Indeed , we found that the number of GAD67-labeled GABAergic neurons that were RAM+ was significantly higher in animals subjected to IS than the HC controls ( Figure 7c–d ) . A small fraction of these induced RAM+ GABAergic neurons were parvalbumin-expressing ( PV+ ) interneurons ( Figure 7—figure supplement 1a–d ) . In mice , CFC treatment resulted in RAM labeling in a substantial number of somatostatin-positive GABAergic neurons in the hilus of the DG ( Figure 7—figure supplement 2a–f ) . Additionally , using the Cre-dependent FLEX system and luciferase assay , we confirmed that PRAM was induced by KCl stimulation in cultured cortical neurons derived from Gad2-Cre mice wherein Cre is expressed exclusively in neurons expressing the GABAergic neuron marker Gad65 ( Figure 7e–g ) . These results confirm that the RAM system achieves robust activity-dependent labeling of GABAergic neurons . 10 . 7554/eLife . 13918 . 018Figure 7 . RAM labeling of transcriptionally active interneurons and application of Cre-dependent RAM ( CRAM ) . ( a–d ) RAM labels active GAD67+ neurons in rats exposed to inescapable stress ( IS ) . ( a ) Schematic timeline of the experimental procedure . Rats were injected with AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP in the mPFC . After 10 days they were either subjected to IS or left undisturbed in their home cage ( HC ) . ( b ) Schematic drawing of the rat brain with the red region indicating the target area ( PL: prelimbic cortex of the mPFC ) for viral infection and quantification of RAM+ cells . ( c ) Representative images of prefrontal cortex showing EGFP ( green ) , mKate2 ( red ) and GAD67 ( blue ) fluorescence in rats subjected to IS or HC conditions . White arrows indicate RAM+ and GAD67+ double-labeled cells . The scale bar is 100 μm and applies to all images . ( d ) Percentage of RAM+ cells co-labeled with GAD67+ in the prelimbic cortex of HC and IS animals . Data are mean ± SEM , n = 3–4 animals per group , Student’s t-test . ( e–g ) Neuronal activity-dependence of the PRAM promoter in GABAergic neurons . ( e ) Design of AAV vectors used for luciferase assays in dissociated neuronal cultures from Gad2-Cre transgenic mice . The reading frames of luciferase ( luc2p ) and renilla are double inverted and flanked by double loxP sites ( FLEX ) and inserted downstream of the PRAM and thymidine kinase promoter ( PTK ) respectively . ( f ) Relative luciferase activity of PRAM in Gad2-Cre cells following KCl stimulation , with application of Nimodipine and/or APV . n = 3 separate experiments per condition , one-way ANOVA , Tukey’s post-hoc test . ( g ) Relative luciferase activity of PRAM in Gad2-Cre cells after application of various neurotrophic factors and drugs . n = 3 separate experiments per condition , Student’s t-test . ( h ) Schematic diagram of the AAV-CRAM construct . The effector gene is flanked by double loxP sites ( FLEX ) . ( i–l ) CRAM labels IS-activated mPFC neurons projecting to the dorsomedial striatum ( DMS ) . ( i ) Experimental procedure . Rats were injected with CAV2-Cre in the DMS and AAV-CRAM-tdT in the PL ( j ) , then either subjected to IS or left undisturbed in their home cages ( HC ) . ( k ) Number of CRAM+ cells in the prelimbic cortex of HC and IS animals . Data are mean ± SEM , n = 2 animals per group . ( l ) Representative images of the prefrontal cortex showing tdT fluorescence in rats subjected to IS or HC . Areas in purple squares are enlarged in the lower images . CC: corpus callosum . Scale bars are 500 and 100 μm for the upper and lower images , respectively . Data in d , f , g and k are mean ± SEM . p<0 . 01 , p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 01810 . 7554/eLife . 13918 . 019Figure 7—figure supplement 1 . RAM labeling of transcriptionally active parvalbumin ( PV ) cells after inescapable stress ( IS ) . ( a ) Schematic timeline of the experimental procedure . Rats were injected with AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP vectors in the mPFC . After 10 days they were either subjected to IS or left undisturbed in their home cage ( HC ) . ( b ) Schematic drawing of the rat brain with the red region indicating the target area ( PL: prelimbic cortex of the mPFC ) for virus infection and quantification of RAM+ . ( c ) Percentage of RAM+ cells co-labeled with PV+ in the prelimbic cortex in HC and IS animals . Data are mean ± SEM , n = 3–4 animals per group , Student’s t-test , p<0 . 05 . ( d ) Representative images of prefrontal cortex showing EGFP ( green ) , mKate2 ( red ) and PV ( blue ) fluorescence in rats subjected to IS or HC conditions . White arrow shows a RAM+ and PV+ double-labeled cell . The scale bar is 100 μm and applies to all images . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 01910 . 7554/eLife . 13918 . 020Figure 7—figure supplement 2 . RAM labeling in the DG hilus . ( a ) Schematic timeline of the experimental procedure . Following injection of AAV-RAM-NLS-mKate2 and AAV-Ef1α-EGFP into DG , animals were left on Dox diet ( +Dox ) for at least 7 days . Dox diet was withdrawn ( -Dox ) 48 hr before CFC and animals were sacrificed 24 hr afterwards . A control cohort of animals was left undisturbed in their home cages ( HC ) and received the same Dox treatment . ( b ) Schematic diagram of the hippocampus showing the targeted viral injection site ( blue ) . RAM+ and EGFP+ double-labeled cells located exclusively in the hilus of the DG ( red area ) are quantified in panel c . ( c ) Percentage of RAM+ cells among total EGFP+ cells in the DG hilus for HC and CFC conditions . Data are mean ± SEM . n = 4 animals per condition , Student’s t-test , n . s . , non-significant . ( d ) Percentage of RAM+ cells in the DG hilus region that are mossy cells ( GluR2/3+ , 49 out of 86 cells ) and somatostatin positive ( SST+ , 101 out of 254 cells ) after CFC . n = 3 animals per condition . ( e ) Representative image of RAM+/GluR2/3+ cells ( indicated by arrows ) in the DG hilus after CFC . The scale bar is 50 μm . ( f ) Representative image of RAM+/SST+ cells ( indicated by arrows ) in the DG hilus after CFC . The scale bar is 20 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 02010 . 7554/eLife . 13918 . 021Figure 7—figure supplement 3 . Validation of the AAV-CRAM system . ( a ) CRAM-tdT and Ef1α-EGFP viruses were injected into wild-type or Gad2-Cre animals , and after at least 7 days kainic acid treatment was given to induce seizures . Dox was not administered throughout the experiment . ( b ) Schematic drawing of the hippocampus with the injection site ( DG ) highlighted in blue . ( c ) In wild-type animals , CRAM-labeled cells ( tdT ) were not detectable 24 hr after kainic acid treatment . The scale bar is 200 μm for the left image and 50 μm for the zoomed-in images on the right . ( d ) In Gad2-Cre animals , 24 hr after kainic acid treatment , CRAM labeled only GABAergic ( predominately somatostatin –expressing ) neurons while sparing the granule cells . The scale bar is 200 μm for the image on the top and 50 μm for the zoomed-in images on the bottom . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 02110 . 7554/eLife . 13918 . 022Figure 7—figure supplement 4 . FOS expression in parvalbumin ( PV ) and somatostatin ( SST ) positive cells in primary visual cortex ( V1 ) and dentate gyrus ( DG ) of the hippocampus . ( a ) Percentage of FOS+ cells among total PV+ and SST+ cells in the V1 region and DG for saline and seized conditions . Behavioral seizures were induced by PTZ or KA for maximal FOS expression . Data are mean ± SEM . n = 4–8 animals per condition , two-way ANOVA , Tukey’s post-hoc test . ( b , c ) Representative images of V1 ( b ) and DG ( c ) showing FOS ( green ) , SST ( red ) , and PV ( red ) immuno-staining . The scale bar is 100 μm for all large images . For the zoomed-in images the scale bar is 50 μm for Saline and PTZ conditions in V1 , and 25 μm and 50 μm for PV and SST conditions in DG , respectively . These images are enlarged from the areas marked by purple squares . Red arrows indicate FOS+ and PV+ or SST+ double-labeled cells . DOI: http://dx . doi . org/10 . 7554/eLife . 13918 . 022 With a view to future investigation of ensembles involving specific cell types , we developed a Cre-dependent RAM ( CRAM ) system , in which the effector gene can only be expressed in cells that express Cre ( Figure 7h ) . When CRAM-tdT virus was injected into the DG of Gad2-Cre mice , only GABAergic neurons ( most prominently somatostatin-expressing neurons in the hilus ) and none of the glutamatergic granule cells were labeled with tdT after kainic acid-induced seizure ( Figure 7—figure supplement 3a–d ) . While IEGs are known to be induced by experience in GABAergic neurons ( Spiegel et al . , 2014 ) , active ensembles of GABAergic neurons have not been explored . Using Cre driver mouse lines for various types of neurons , we envision the CRAM system as a tool well suited to investigating active ensembles of GABAergic neurons and other specific neuronal cell types . Like the RAM system , CRAM also worked well in the rat , in this case to identify neuronal ensembles with specific anatomical connections . Since a major projection of the rat PFC is the dorsal striatum ( Gabbott et al . , 2005 ) , we injected a retrograde canine adenovirus that expresses Cre recombinase into the dorsomedial striatum ( DMS ) to deliver Cre to DMS-projecting mPFC neurons . AAV-CRAM-tdT was subsequently injected into the mPFC and the animals were subjected to IS treatment ( Figure 7i–j ) . We observed significantly more tdT-labeled neurons in animals subjected to IS compared to HC controls ( Figure 7k–l ) , suggesting CRAM successfully labeled IS-activated mPFC neuronal ensembles that project to the DMS . Here we present the RAM ( Robust Activity Marking ) system , which makes possible the sensitive and specific labeling and manipulation of the active ensembles of neurons associated with a designated sensory and behavioral experience . Compared to existing methods used to gain access to recently active neuronal ensembles ( Guenthner et al . , 2013; Reijmers et al . , 2007; Smeyne et al . , 1992; Eguchi and Yamaguchi , 2009; Kawashima et al . , 2013 ) , the RAM system delivers unprecedented versatility and much improved selectivity . Additionally , the system showcases a novel , informatics-based approach to constructing small synthetic IEG-sensitive promoters . We believe that this approach could be used to generate other small yet highly sensitive promoters that , when combined with the RAM platform , would allow in vivo interrogation of specific activity-dependent genetic programs . The most important features of a neuronal-activity reporter system are high sensitivity and selectivity , resulting from a combination of low background labeling and robust effector gene expression in the active ensemble associated with a designated experience . RAM achieves exceptionally high selectivity and sensitivity through the use of an optimized activity-dependent promoter with the highest fold induction we have observed among existing small activity-regulated promoters , paired with an improved version of the Tet-Off system . In vivo RAM achieved 37-fold and five-fold increases in labeling after CFC , compared to HC controls , in CA3 and DG , respectively . This is significantly greater than the two–three-fold induction achieved using existing technologies ( Guenthner et al . , 2013; Ramirez et al . , 2013 ) . The five–37-fold induction achieved with the RAM system implies that 80–97% of the labeled neurons are truly associated with the specific experience , compared to 50–67% with existing technologies , representing a dramatic improvement in signal-to-noise ratio . This improvement alone means that the RAM system can be used in many brain regions and behavioral applications where existing methods will not work . With its high sensitivity and selectivity , the RAM system will be ideal for functional perturbation experiments in which the activity of ensemble neurons , identified by RAM , can be manipulated through the targeted expression of effector molecules such as opsins . We show the expression of opsins in CFC-induced , RAM-labeled ensemble neurons can be activated or inhibited 24 hr after CFC ( Figure 3—figure supplement 4a–j ) , demonstrating the feasibility of such an approach using RAM . Based on the robust results we obtained by applying RAM to label ensembles in several brain regions ( DG , CA3 , amygdala and mPFC ) using appropriate behavioral paradigms ( CFC , TFC and IS ) , we anticipate that RAM can be readily used to investigate neuronal ensembles in other brain regions , such as CA1 , striatum , nucleus accumbens , hypothalamus , etc . , for a variety of behavioral manipulations in the future . Like all other existing activity reporters currently being used in the neuroscience field , the sensitivity of the RAM system has not been systematically measured . For example , we do not know the activation threshold for RAM . More specifically , what is the minimal amount of neuronal activity required to activate RAM ? What type of neuronal activity is preferentially captured by RAM ? How well can RAM distinguish information-carrying neuronal firing from fluctuating noise ? Future experiments aimed to obtain these basic operational parameters for RAM , and for any activity reporter , will be critical for us to be able to use these reporters to gain mechanistic understanding of how sensory and behavioral information is processed in the brain . As for any IEG-based activity reporter , the efficiency of RAM labeling in particular brain regions or of particular cell types depends on their propensity to express IEGs . For example , activated PV+ neurons appear to be less likely to express IEGs . Even after pentylenetetrazol ( PTZ ) -induced seizure , only about 20% of the PV+ neurons , compared to over 80% of somatostatin expressing ( SST+ ) neurons , in the cortex were transcriptionally activated to express FOS ( Figure 7—figure supplement 4a–b ) . A similar trend was observed in DG after KA-induced seizure ( Figure 7—figure supplement 4a , c ) , suggesting either PV+ neurons may be less likely to be recruited to active ensembles or they may require non-IEG-based approaches to be effectively labeled . The compact design of the RAM system allows it to be packaged into a single AAV while still accommodating an effector gene up to 1 . 8 Kb in size . The RAM system can readily accommodate most of the effector genes commonly used to label or manipulate neural circuits . We envision future experiments using effector genes for genetic profiling ( e . g . RIBO-TRAP [Sanz et al . , 2009] ) , rabies-mediated monosynaptic anatomical tracing ( Callaway , 2008 ) , and functional perturbation of behavior ( Liu et al . , 2012 ) ( see Figure 3—figure supplement 4e , j ) . In cases where other , larger promoters and/or larger effector genes are needed , the RAM system can split between two AAVs , one containing the d2tTA expression cassette and the other the TRE-effector cassette . Cre-dependent RAM ( CRAM ) extends the versatility of the RAM system even further . For example , CRAM can be used to study active neuronal ensembles of a particular cell type when Cre expression is controlled by a cell type-specific promoter in a mouse line or virus . Also , by delivering Cre via retrograde and anterograde viruses , neuronal ensembles with specific anatomical connectivities can be explored . Finally , a Flp-dependent RAM system could easily be developed , allowing additional applications of the system . Since the PRAM 10mer is highly enriched in activity-dependent enhancers ( Supplementary file 1 ) and enhancers are mostly conserved between species ( Kim et al . , 2010 ) , PRAM is likely to be active in many different species . Here we demonstrate that PRAM works in mice , rats and flies , but it may be applicable to other species as well . Given the flexible design of the RAM system and the relative ease of AAV production , the system makes possible many new and powerful experiments in a field with relatively few viable tools ( Cruz et al . , 2013; Kawashima et al . , 2014 ) . The coordinates on the mouse chromosome mm9 assembly for the 11 , 830 activity regulated enhancers identified by Kim et al . ( 2010 ) were downloaded and the sequences from a 2 Kb window centered at each enhancer were extracted . We first looked for DNA elements enriched among activity-regulated enhancers . Sequences in a 160 bp window centered on each enhancer were extracted and then used as inputs for the de novo motif finding software Weeder ( Pavesi et al . , 2004 ) ( http://159 . 149 . 160 . 51/modtools/ ) . The top motif identified strongly resembles the consensus for AP-1 ( Figure 1—figure supplement 1 ) . To calculate the enrichment of all AP-1-containing 10mers ( 128 in total , see Supplementary file 1 ) , we searched for perfect matches on both strands for each 10mer and counted the number of hits on each 40 bp non-overlapping bin . Enrichment for each 10mer was calculated as the total number of occurrences in the four central bins ( i . e . the −80 to +80 region ) divided by the total number of occurrences in the eight furthest bins on each flank ( −1000 to −681 and +681 to +1000 ) . The larger flanking regions were used to obtain a more accurate estimate of the fold enrichment at the enhancer center relative to the flanks . The enrichment factor , E , adjusted for the larger size of the flanking bins , was defined as E = 4C/F , where C is the number of hits in the central bins and F is the number of hits in the flanks . The significance of finding each motif was calculated using a binomial test ( in MATLAB ) under the null hypothesis that the hits should be equally distributed between the center and flanks . The p-value is given by p=1-CDF ( 4C , 4*C + F , 0 . 5 ) , where CDF ( n , N , 0 . 5 ) is the cumulative distribution function of finding n elements from a binomial distribution with parameters N and 0 . 5 . If there were no hits in either the center bins or the flanks , the p-value was set to 0 . 5 , and the motif was not considered further . The motifs were ranked by their enrichment factors . Motifs that did not have a Bonferroni corrected p-value<0 . 05 were excluded from further analysis . A full list of 10mers containing the AP-1 consensus site , ranked by their enrichment factors , can be found in Supplementary file 1 . Several enriched 10mers ( E1-E3 ) were selected to construct synthetic promoters for further testing based on their enrichment factor and the associated p-value . All luciferase-encoding plasmids were based on the pGL4 . 11 backbone ( luc2p , Promega , Madison , WI ) . Complementary strands of enhancer modules ( EM ) , PNRE+AP1 , PRAM , enriched 10mer-containing enhancer module variants ( E1-E3 ) and CME sequences , each containing four identical repeats , were ordered as DNA oligos ( Sigma Aldrich , Natick , MA ) with the following sequences and annealed: PNRE+AP-1: 5’– CTTCGTGACTAGTCTTGACTCAGA –3’ PRAM: 5’– CTAGAAGTTTGTTCGTGACTCAGA –3’ E1: 5’– CTAGAAGTTTGTTGACTCACCCGA –3’ E2: 5’– CTAGAAGTTTGTTGACTCATTAGA –3’ E3: 5’– CTAGAAGTTTGTGTATGACTCAGA –3’ CME: 5’– CTAGAAATTTGTACGTGCCACAGA –3’ Nucleotides underlined ( in bold ) denote putative binding motif and/or variable flanking regions . One annealed PRAM EM served as a template for making various enhancer repeats ( 1 , 2 , 3 , 4 and 8 enhancer repeats ) by In-Fusion cloning ( Clontech ) . The minimal promoters were PCR cloned from other constructs: the FOS minimal promoter ( 99 bp ) from pOF-luc plasmid ( Selvaraj and Prywes , 2003 ) ; the Arc minimal promoter ( 421 bp ) from ESARE plasmid , a gift kindly provided by Haruhiko Bito ( Kawashima et al . , 2013 ) ; the human beta-globin ( hBG ) minimal promoter ( 49 bp , a gift kindly provided by Guoping Feng ) , the cytomegalovirus ( CMV ) minimal promoter ( 226 bp ) from pcDNA3 plasmid ( Invitrogen ) . Annealed enhancers were inserted upstream of the minimal promoter ( s ) and then inserted into pGL4 . 11 using Kpn1 and Nhe1 sites . Other activity-dependent promoters , inserted into pGL4 . 11 to drive luciferase expression , were derived from other studies: Npas4 ( Ramamoorthi et al . , 2011 ) , p1BDNF ( Ramamoorthi et al . , 2011 ) , ESARE ( kindly provided by Haruhiko Bito [Kawashima et al . , 2013] ) , Fos ( Barth et al . , 2004 ) , CREB reporter from CRE-luc2p ( Promega ) , and MEF2 reporter ( Selvaraj and Prywes , 2003 ) . Overexpression plasmids encoding Npas4 and Fos were constructed by PCR cloning as follows: the coding regions of Npas4 and Fos were inserted into the pEF1/myc-His ( A ) backbone ( Life Technologies , Carlsbad , CA ) using Spe1 and Pme1 sites . The empty pEF1/myc-His ( A ) backbone served as a transfection control . Vectors used to engineer and characterize the RAM driven Tet-OFF system were derived from pTet-Off-Advanced and pTRE-Tight vectors ( Clontech , Mountain View , CA ) . The CMV promoter in pTet-Off-Advanced was replaced by inserting PRAM within the Spe1 and Sac1 sites ( PRAM-tTA ) . Decreased stability tTA was made by fusing the degradation domain of mouse ornithine decarboxylase ( MODC , derived from pD2EGFP; Clontech ) to the N-terminus of tTA , making PRAM-d2tTA . The MODC contains a PEST sequence that enhances tTA degradation ( Li et al . , 1998 ) . Transferring luciferase ( luc2p ) from pGL4 . 11 into pTRE-Tight using HindIII and Xba1 sites resulted in the TRE luciferase ( pTRE-luc2p ) vector . Preparation of cultures with a confluent glial monolayer has been described elsewhere ( Paradis et al . , 2007 ) . Briefly , astrocytes derived from P1-P2 rat cortices were plated at low density in DMEM + 10% FBS on 12 mm glass coverslips coated with poly-D-lysine and laminin in 24-well plates , and stored and maintained at 37°C in a humidified incubator with 10% CO2 . Once the plated glia cells had formed a confluent monolayer , typically after 7 days , dissociated hippocampal neurons from mouse P1 pups ( C57Bl/6 , Charles River Laboratory ) were plated at a density of 40 , 000 cells per well , and the media was changed to NBA supplemented with B27 ( Invitrogen , Carlsbad , CA ) and GlutaMAX ( Life Technologies ) . AraC ( 5 μM , Sigma ) was added the next day and conditioned media was supplemented with fresh media every fifth day . Pure glia cultures were used for a set of luciferase experiments , whereas neuron-glia co-cultures were used for immunocytochemistry ( ICC ) experiments . For ICC experiments , cultures were stimulated with bicuculline ( 50 μM , Sigma Aldrich ) and 4AP ( 250 μM , Tocris , UK ) , and doxycycline was applied at 40 ng/mL final concentration . Cultures were washed once with PBS ( pH 7 . 4 ) , then fixed for 10 min with 4% paraformaldehyde in PBS containing 0 . 1% Triton X-100 . Subsequently , cultures were washed 3 times with 10 mM glycine in PBS followed by a final rinse with PBS . The primary antibody was added to a solution of 0 . 1% porcine gelatin ( Sigma ) , 0 . 25% Triton X-100 , 0 . 23 M NaCl , and 0 . 015 M phosphate buffer pH 7 . 4 , and coverslips were then transferred to a humidifying chamber . Antibody solution was dropped ( 50 μL ) onto each coverslip and allowed to incubate at 4°C overnight . The next day , the coverslips were washed 4 times with PBS , and incubated in 50 μL of secondary antibody solution for 1 hr at room temperature . Coverslips were washed twice with PBS and mounted on Superfrost slides using DAPI Flouromount-G ( SouthernBiotech , Birmingham , AL ) before imaging and analysis . The primary antibodies were MAP2 ( mouse , 1:1000 , Sigma , M9942 ) and GFAP ( mouse , 1:1000 Sigma , G3893 ) . The secondary antibody was Alexa Fluor 488 ( Goat anti-mouse IgG , 1:1000 , Life Technologies ) . Mouse pups , C57Bl/6 ( Charles River Laboratory ) or Gad2-Cre ( Gad2tm2 ( cre ) Zjh/J , Jackson Laboratory ) , at P1 were used to prepare dissociated hippocampal cultures as previously described ( Lin et al . , 2008 ) . Cultures were plated at 100 , 000 cells per well on 24-well plates coated with poly-D-lysine . Cultures were incubated in Neurobasal A Medium ( NBA , Life Technologies ) with horse serum and glutamine added . The cultures were maintained at 37°C in a humidified incubator with 5% CO2 . After three hours of incubation , the medium was changed to NBA supplemented with B27 ( Invitrogen ) and GlutaMAX ( Life Technologies ) . Neurons were transfected using lipofectamine 2000 ( Life Technologies ) on DIV5 . Reporter plasmids ( in pGL4 . 11 ) expressing firefly luciferase and a plasmid expressing renilla luciferase under control of the constitutive thymidine kinase promoter ( TK ) -Renilla ( Promega ) were co-transfected in every experiment . For a subset of luciferase experiments , cultures were co-infected with AAV vectors ( 0 . 1 μL per well ) expressing firefly luciferase and TK-Renilla , respectively . On the day of stimulation , TTX and APV were typically added 1 hr prior to stimulation , and cells were stimulated for 6 hr with 35 mM KCl ( but see Supplementary file 2 for precise details ) before being rinsed briefly in PBS and lysed in passive lysis buffer ( Promega ) . The Dual-Glo Luciferase Assay System reagents were used according to the manufacturer’s instructions ( Promega ) . Firefly luciferase levels were measured and expressed relative to renilla luciferase levels . Data were compiled from separate experiments each conducted in triplicate , and repeated a minimum of three times . Fold induction of luciferase was calculated as the ratio between stimulated and unstimulated conditions . The primary criterion we used to evaluate the synthetic promoters for use as neuronal activity reporters was the fold change in luciferase expression . This is the ratio of the relative luciferase values between stimulated and unstimulated conditions and represents the activity-dependence and selectivity of the promoter . A secondary criterion was the absolute expression level after stimulation , which is the absolute luciferase value normalized against an internal control and reflects the absolute transcriptional strength of the promoter . It is possible for a weak promoter to have high fold induction , due to extremely low activity under basal unstimulated conditions , but to only drive transcription modestly , which will limit its ability to drive the expression of an effector gene to sufficient levels to achieve the required effects . Drugs and neurotrophic factors , typically applied together with 10 μL NBA into the wells , were used at the following final concentrations: TTX ( 1 μM , Tocris ) , APV ( 100 μM , Tocris ) , Nimodipine ( 5 μM , Tocris ) , Bicuculline ( 50 μM , Sigma Aldrich ) , 4AP ( 250 μM , Tocris ) , recombinant human BDNF ( 50 ng/μL , PeproTech , Rocky Hill , NJ ) , recombinant human NT3 ( 50 ng/μL , PeproTech ) , recombinant human NT4 ( 50 ng/μL , PeproTech ) , recombinant human IGF1 ( 50 ng/μL , PeproTech ) , recombinant human EGF ( 50 ng/μL , PeproTech ) , Forskolin ( 10 μM , Tocris ) and PMA ( 100 ng/μL , Tocris ) . The AAV vector V032 ( pFB-AAV-CMV-WPRE-SV40pA , kindly provided by Rachael Neve ) served as the backbone for constructing the AAV-RAM vector . All components within the 5’-ITR and 3’-ITR defined by the Mlu1 and Kpn1 sites , respectively , were first removed from V032 . At the 5’-ITR site , the TRE promoter followed by a multiple cloning site ( MCS ) , WPRE and bGH poly ( A ) signal was inserted as one expression cassette in the 5’ to 3’ direction . In the same orientation , the second expression cassette , consisting of PRAM driving d2tTA followed by a SV40 poly ( A ) signal , was inserted downstream of the first cassette and terminated by the 3’-ITR . The two expression cassettes were insulated by incorporation of synthetic poly ( A ) and RNA polymerase II transcriptional pause signals ( both derived from pGL4 . 11 ) between the two cassettes . Fluorescent proteins , including tdTomato and mKate2 , and the two opsins , Channelrhodopsin ( ChR2 , a kind gift from Gloria Choi ) and Archaerhodopsin ( ArchT , a kind gift from Ed Boyden ) , were inserted into the MCS . The plasmids AAV-RAM and AAV-CRAM vectors ( as depicted in Figure 1f , pAAV-RAM-d2TTA-pA::TRE-MCS-WPRE-pA; and Figure 7h , pAAV-RAM-d2TTA-pA::TRE-FLEX-MCS-WPRE-pA ) with an empty MCS at the effector gene position have been deposited at Addgene . The transfection control plasmid , pAAV-Ef1α-EGFP-WPRE-pA , was made by replacing mVenus in pAAV-Ef1α-mVenus-WPRE-pA ( kindly provided by Jonathan Ting ) using BamHI and BsrGI sites . For ICC , we used AAV-RAM-tdTomato ( AAV-RAM-d2tTA-pA::TRE-TdTomato-WPRE-pA , AAV8 serotype , Virovek , Hayward , CA , 2 . 21E13 vg/mL ) . For rat studies , we used AAV-RAM-NLS-mKate2 ( AAV-RAM-d2tTA-pA::TRE-NLS-mKate2-WPRE-pA , AAV1 serotype , Virovek , 2 . 18E13 vg/mL ) and transfection control AAV-Ef1α-EGFP ( AAV-Ef1α-EGFP-WPRE-pA , AAV1 serotype , UPenn Vector Core , 5 . 51E13 vg/mL ) . We also generated homemade AAVs using a FuGene6 ( Promega ) mediated triple plasmid co-transfection method in HEK293t cells . Three days after transfection , cells were harvested and virus was purified using an adapted Iodixanol gradient purification protocol ( Matsui et al . , 2012 ) . These AAVs were a mixture of AAV2/2 ( rep/cap ) and AAV2/8 serotypes at a 1:1 ratio . Additionally , our AAV2/2 capsid incorporated three mutations ( Y444F , Y500F and Y730F ) ( Mowat et al . , 2014 ) while our AAV2/8 capsid had a double mutation ( Y447F and Y733F ) ( Qiao et al . , 2012 ) . To determine the properties of AAV-RAM expression and for quantification of behaviorally labeled cells and IEG overlap experiments in vivo , we used homemade AAV-RAM-mKate2 ( AAV-RAM-d2tTA-pA::TRE-NLS-mKate2-WPRE-pA , mixed AAV2/8 serotype , 1 . 28E13 vg/mL ) and transfection control AAV-Ef1α-EGFP ( AAV-Ef1α-EGFP-WPRE-pA , mixed AAV2/8 serotype , 2 . 07E13 vg/mL ) vectors . These were applied together at a 1:1 ratio and diluted 10 times in DPBS before use . For the RAM-ChR2 and RAM-ArchT experiments , we used homemade AAV-RAM-ChR2:EYFP ( AAV-RAM-d2TTA-pA::TRE-ChR2:EYFP-WPRE-pA , serotype 9 , 4 . 33E13 vg/mL ) , AAV-RAM-ArchT:EGFP ( AAV-RAM-d2TTA-pA::TRE-ArchT:EGFP-WPRE-pA , serotype 9 , 2 . 19E13 vg/mL ) and AAV-TRE-mCherry ( AAV-TRE-mCherry-pA , serotype 9 , 2 . 24E13 vg/mL ) . For a subset of luciferase experiments , we produced AAV viruses ( mixed AAV2/8 serotype ) of pAAV-TK-FLEX-Renilla-WPRE-pA ( 7 . 36E12 vg/mL ) and pAAV-RAM-FLEX-luc2p-WPRE-pA ( 5 . 13E12 vg/mL ) . Both these constructs were cloned in our laboratory . For Cre-dependent RAM ( CRAM ) expression , we produced AAV-CRAM-tdTomato virus ( AAV-RAM-d2TTA::TRE-FLEX-tdTomato-WPREpA , serotype 2/8 , 1 . 22E13 vg/mL ) . Canine adenovirus serotype 2 ( CAV2 ) ( Hnasko et al . , 2006 ) was engineered to express Cre recombinase , CAV2-Cre ( 10 . 3E12 pp/mL; kindly provided by Dr . Eric J . Kremer ) . Genomic AAV titer was determined by a PicoGreen-based method as described elsewhere ( Piedra et al . , 2015 ) . Before use , all viruses were carefully examined in pilot experiments and , if needed , diluted in DPBS for optimized titer . Adult male C57Bl/6 mice ( Charles River Laboratory ) were 7–10 weeks of age for all surgeries and behavioral manipulations . Gad2-Cre ( Gad2tm2 ( cre ) Zjh/J , Jackson Laboratory ) mice were used for CRAM experiments . Following stereotaxic viral vector injections mice were single-housed for 7–14 days . All mice were housed in a temperature-controlled facility with a 12 hr light/dark schedule and provided with food and water ad libitum . All mouse protocols were performed in accordance with NIH guidelines and approved by the Massachusetts Institute of Technology Committee on Animal Care ( protocol #1014-105-14 ) . Adult male Sprague-Dawley rats ( 275–300 g; Harlan , Indianapolis ) were pair housed in a temperature- and humidity-controlled room on a 12 hr light/dark cycle ( lights on at 7:00 A . M . ) . Standard lab chow and water were available ad libitum . Rats were allowed to acclimate to colony conditions for 7–10 days prior to surgery . All rat procedures were performed in accordance with NIH standard ethical guidelines and were approved by the Institutional Animal Care and Use Committee at the University of Colorado Boulder ( protocol #1505 . 07 ) . Mice were placed on doxycycline chow 24 hr prior to surgery ( 40 mg/kg , Bio-Serv , Flemington , NJ ) . On the day of surgery , mice were anaesthetized using isoflurane ( 3% induction , 1 . 5% maintenance during surgery ) and secured to a stereotactic frame ( Kopf ) with a heating pad to maintain body temperature . Mice were injected sub-dermally with Buprenex ( 1 mg/kg ) and given topical Lidocaine ( 20 mg/mL ) for analgesia ( Hi-Tech Pharmacal , Amityville , NY ) . Following exposure of the skull , a small craniotomy was made overlying the target brain structure . Using a glass pipette ( 50 µm tip ) connected to a Nanoject II ( Drummond Scientific , Broomall , PA ) injector , AAVs were delivered ( 50 nL/infusion , 30 sec between infusions ) to the brain , and allowed to diffuse for 10 min before withdrawing the pipette . The coordinates of the target brain structures in reference to bregma ( mm ) and injection volumes ( nL ) were as follows: dentate gyrus ( AP: −1 . 9 , ML: ± 1 . 4 , DV: −2 . 05; 600 ) , dorsal CA3 ( AP: −1 . 9 , ML: ± 1 . 85 , DV: −2 . 1; 500 ) , basolateral amygdala ( AP: −1 . 6 , ML: ± 3 . 25 , DV: −3 . 6 from pial surface; 1000 ) , and central amygdala ( AP: −1 . 4 , ML: ± 2 . 85 , DV: −3 . 7 from pial surface; 300 ) . All injections were performed bilaterally . Mice were kept on doxycycline chow and allowed to recover for 7–14 days following surgery . Rats were anesthetized with isoflurane . A midline incision was made to expose the skull , and a small craniotomy was made unilaterally above the medial prefrontal cortex ( mPFC ) . Recombinant AAV was injected into the prelimbic cortex ( PL ) of the mPFC . A stainless steel needle with the beveled tip facing laterally ( 31 gauge; Hamilton Company , Reno , Nevada ) was directed to the PL ( AP: +2 . 5 , ML: ±0 . 5 , DV: −2 . 0 from pial surface ) . For the CRAM experiment in rats , CAV2-Cre was bilaterally injected to the dorsal medial striatum ( AP: +0 . 1 , ML: ± 2 . 0 , DV: −3 . 5 from pial surface ) . Virus ( 1 . 0 μL/hemisphere ) was infused over 10 min ( 0 . 1 μL/min ) , followed by an additional 10 min to allow diffusion of the virus from the needle tip . The skin was sealed with Vetbond tissue adhesive . Doxycycline chow was typically withdrawn 48 hr prior to behavioral manipulation and replaced with regular feed , but see figure legends for precise experimental setup . To assess behavior-specific induction of RAM in the hippocampus , we trained mice in a context-dependent classical conditioning paradigm following our previously established protocol ( Ramamoorthi et al . , 2011 ) . Briefly , mice were placed in a box with salient visual cues , and allowed to explore for 58 s terminated by a 2 s , 0 . 55 mA foot-shock . This was repeated three times at 58 s intervals . Mice were kept in the chamber for an additional 60 s before being placed back into their home cages . Mice experiencing the conditional stimuli only ( Context Only ) were left for 240 s in the box , whereas mice experiencing the unconditional ( Shock Only ) stimuli only received the foot-shocks immediately once placed in the chamber and were then returned back to their home cage . To examine behavior-specific induction of RAM in the amygdala , we utilized an associative tone-fear conditioning paradigm , once again following an established protocol ( Ramamoorthi et al . , 2011 ) . Briefly , mice were placed in a chamber and allowed to acclimate for 40 s . A tone ( 2 . 5 kHz , 85 dB , 20 sec ) co-terminating with a foot-shock ( 0 . 55 mA , 2 s ) was presented four times at 40 s intervals . The mice were given an additional 60 s in the chamber following the final tone presentation before being placed back into their home cages . A subset of mice received the conditional ( Tone ) stimuli only , but was otherwise handled similarly . Mice experiencing the unconditional ( Shock ) stimuli only received the foot-shocks immediately once placed in the chamber and were then returned back to their home cage . In order to characterize the extent of IEG protein expression in the same brain sections as RAM expressing cells , animals were run through the contextual fear conditioning paradigm ( context A ) listed above . The following day , mice were placed either back into the box from the original conditioning ( context A ) or into a novel context ( context B ) for 4 min and manually scored for freezing behavior every 5 s . 1 . 5 hr after the recall testing , animals were sacrificed and their brains were processed for immunohistochemical analysis . Pharmacologically induced seizures were used to drive maximal RAM or IEG expression in the hippocampus or cortex . Mice were given intraperitoneal injections of 15 mg/kg kainic acid ( Sigma ) , pH 7 . 4 , or 50 mg/kg pentylenetetrazole ( Sigma ) , and were selected for further analysis only if they exhibited full motor seizures . Rats were exposed to a single session of inescapable stress ( IS ) in Plexiglas boxes ( 14 × 11 × 17 cm ) with a Plexiglas rod protruding from the rear . The rat’s tail was secured to the rod with tape and affixed with copper electrodes . Each session consisted of 100 trials of tail shock ( 5 sec duration , 33 × 1 . 0 mA , 33 × 1 . 3 mA , and 34 × 1 . 6 mA ) on a random-interval 1 min schedule . Subjects were returned to the colony immediately following the tail shock procedure . All histological analysis of RAM expression was performed 24 hr following relevant stimulus presentation . Mice and rats were deeply anesthetized with isoflourane and the brains were removed and drop-fixed in 4% paraformaldehyde in PBS . Brains were fixed for 24 hr at 4°C and then cryoprotected in 30% sucrose in PBS at 4°C until they sank . Subsequently , brains were sectioned on a cryostat at 50 µm thickness . All sections were blocked for 2 hr at room temperature in a TBS ( pH 8 . 0 ) solution containing 0 . 5% tritonX-100 , 0 . 2% BSA , 10% normal goat serum and then incubated with primary antibody in blocking solution , typically overnight at 4°C . The next day , sections were rinsed in PBS and incubated in secondary antibody for 2 hr at room temperature . Sections were mounted on Superfrost slides and coverslipped in DAPI Flouromount-G ( SouthernBiotech ) . Primary antibodies used: FOS ( rabbit , 1:1000 , Santa Cruz sc-52 ) , NPAS4 ( rabbit , 1:10 , 000 ) ( Lin et al . , 2008 ) , Glutamate Receptor 2&3 ( GLUR2/3 , rabbit , 1:200 , Millipore AB1506 ) , Somatostatin ( SST , rat , 1:200 , Millipore MAB354 ) , Parvalbumin ( PV , mouse , 1:1000 , Millipore MAB1572 ) , α-mKate2 ( tRFP antibody recognizing mKate2 , rabbit , 1:200 , Evrogen AB223 ) and GAD67 ( mouse , 1:1000 or 1:500 Millipore MAB5406 ) . Secondary antibodies used: Alexa Fluor 405 ( 1:500 , Thermo Fisher ) and Alexa Fluor 647 ( goat anti-rabbit IgG , goat anti-rat IgG , or goat anti-mouse IgG , 1:1000 , Life Technologies ) . For mouse brains , low magnification images were acquired from coronal sections using an Olympus BX51 fluorescent microscope using a 10X or 20X objective and MetaMorph software ( Molecular Devices , Sunnyvale , CA ) . High magnification images were acquired from coronal sections using an Olympus Fluoview FV1000 with a 60X oil-immersion objective and Fluoview imaging software . All images were pseudo colored , merged and quantified using ImageJ software . Cell counts were performed using the Cell Counter plug-in . Briefly , all infected cells were confirmed by using the DAPI channel , and individual counts were taken for EGFP and RAM positive cells in order to provide a percentage of RAM+ cells among the infected population . Images were only taken in regions of maximum viral infectivity as determined by a near 100% neuronal expression of the EGFP infection marker . Counts were quantified from 3–5 separate sections from each injected hemisphere from 3–9 animals per condition . For rat tissue , image acquisition was performed with a confocal system ( ZeissLSM510; Carl Zeiss ) using 5X and 20X objectives . Confocal images were acquired using identical pinhole , gain and laser settings . For each subject , the number of fluorescence mKate2 and tdT positive cells were quantified and averaged from 2–4 tissue sections of the PL . Mice were exposed to a contextual fear conditioning paradigm and were then placed back into their home cages to allow for hippocampal RAM expression . Twenty-four hours later , the mice were divided into two cohorts and were either re-exposed to the initial conditioning context , or exposed to a novel context , and were then sacrificed 1 . 5 hr later . Alternating 50 µm brain slices were stained for NPAS4 or FOS . Coronal sections were imaged using an Olympus Fluoview FV1000 confocal microscope with a 60X oil objective and Fluoview imaging software . Images were only taken in regions of maximum viral infectivity as mentioned previously . For each animal ( n = 4–5 per condition ) , 7–10 images were taken of the granule cell ( GC ) layer of the dorsal dentate gyrus , focusing on the apex of stratum granulosum . Each image was acquired as a Z-stack 20–35 µm thick , with a step size of 2 . 5 µm ( slice thickness = 0 . 884 µm ) . After acquisition , RAM+ , IEG+ and RAM+/IEG+ cells were confirmed to be DAPI positive and were manually counted for each stack in Fluoview . Overlap was defined as the percentage of RAM+ cells that were also IEG+ . We estimated the total number of GCs per image as follows . High-resolution cubes ( n = 18 ) of 35 × 35 × 10 µm were imaged in the DAPI channel with a 1 µm step size . We estimated the diameter of each DAPI labeled GC nuclei to be 7 . 5 µm . For the entire cube , DAPI cells were counted , excluding those touching 3 of the 6 image faces , and the count was divided by the total cube volume to get a GC density estimate . Our overall GC density was calculated to be 1 . 35 ± 0 . 06 cells/1000 µm3 , slightly higher than previously published estimates ( Deng et al . , 2013; Kempermann et al . , 1998 ) . For each slice , total DG granule cell layer area was measured in Fluoview and multiplied by the stack thickness to get a GC volume . The calculated GC density was then used to give a total GC estimate per slice . Mice were decapitated 24 hr following CFC . The brains were removed from the skulls and immediately immersed in carbogenated ice-cold cutting solution containing ( in mM ) sucrose 75 , NaCl 67 , NaHCO3 26 , glucose 25 , KCl 2 . 5 , NaH2PO4 1 . 25 , CaCl2 0 . 5 , MgCl2 7 , 310 mOsm , pH 7 . 35 . In the same solution , 300 µm horizontal slices were cut with a vibrating blade microtome ( VT1200 , Leica , Germany ) . Slices containing the dorsal hippocampus were transferred to an incubation chamber filled with carbogenated warm ( 32˚C ) cutting solution for 10–20 min and then at room temperature ( ~23˚C ) for at least 1 hr before being used . For recording , slices were transferred to a recording chamber perfused at a flow rate of 2 ml/min with artificial cerebrospinal fluid ( ACSF ) containing ( in mM ) 119 NaCl , 2 . 5 KCl , 1 . 24 NaH2PO4 , 1 . 3 MgCl2 , 2 . 5 CaCl2 , 26 NaHCO3 , and 10 glucose , carbogenated with 95% O2 and 5% CO2 gas mixture and kept at room temperature . Borosilicate glass pipettes ( 4–6 MΩ tip resistance ) were used for whole-cell patch-clamp recordings and filled with internal solution as follows ( in mM ) : 130 K-gluconate , 10 KCl , 1 MgCl2 , 10 HEPES , 0 . 2 EGTA , 4 Mg-ATP , 0 . 5 Na-GTP , pH 7 . 25 , 290 mOsm osmolarity . RAM+ neurons expressing ChR2 or ArchT were recorded in a mixture of 50 µM picrotoxin , 50 µM APV and 20 µM DNQX to block fast GABAergic and glutamatergic synaptic transmission . A square pulse of −5 mV was delivered at the end of every sweep to monitor access resistance . Recordings with access resistance greater than 25 MΩ or with changes in access resistance greater than 15% were discarded . ChR2 and ArchT expressing RAM+ cells were illuminated with 455 nm blue and 565 nm green light , respectively , through a 40X optical lens , and the light sources ( LEDs , Thorlabs , Newton , NJ ) were controlled by the pClamp10 interface . Data were collected with a Multiclamp 700 b amplifier ( Molecular Devices ) , Bessel filtered at 2 kHz . Data were digitized at 10 kHz using a Digidata 1440A ( Molecular Devices ) and were acquired using pClamp10 software . Photocurrents were measured using pClamp10 . Three RAM binding sites were placed upstream of a minimal promoter and CaSpeR TATAA sequence , followed by flippase recognition target ( FRT ) -flanked stop codons and the luciferase open reading frame . A short sequence coding for a poly-glycine run was placed downstream of the second FRT site and arranged so that it was in the same reading frame as the ATG start codon , regardless of which FRT site remained after site-specific recombination . The luciferase-coding region ( minus its normal ATG start codon ) was placed downstream and in frame with the poly-glycine run . In the absence of the flippase ( FLP ) , the transgene would produce no luciferase protein . After FLP-mediated recombination , a fusion protein would be expressed that contains amino acids from the FRT sequence and a poly-glycine run , all fused to luciferase . Reporter constructs were inserted at the NotI/XhoI sites of pCaSper5 . Standard methods were used to generate transgenic flies ( BestGene ) . The specificity of the RAM-luc to Flp recombinase was tested using a UAS-Gal4 system , in which Flp recombinase expression is under the control of a heat-shock ( hs ) promoter ( see Figure 6e–f ) . Flies were trained in the olfactory avoidance-training paradigm developed by Tully and Quinn and modified to allow for automated training sessions ( Fropf et al . , 2013 ) . A single cycle of training consists of 90 s exposure to ambient air; 60 s of electric shock ( the unconditioned stimulus ) ; 70 olt pulses lasting 1 . 5 s and administered every 5 s ( 12 total ) accompanied by simultaneous exposure to 1 odor ( the conditioned stimulus condition , CS+ ) ; 45 sec of ambient air exposure to clear the first odor; 60 s of exposure to the second odor , with no shock ( the CS- condition ) , 45 s of ambient air to clear the second odor . Spaced training consists of ten single cycles separated by 15 min rest intervals . We used 3-octanol and 4-methylcyclohexanol as odors . One group of flies was exposed to Forward Spaced ( FS ) training , where the odor was paired with shock presentation . The control group of files was exposed to the same number of training trials , but the non-overlapping odor and shock presentations did not overlap in time ( Backward Spaced , or BS training ) , which does not produce associative memory . Flies were housed at 50 flies per vial and entrained on a 12 hr light/dark cycle for 3–5 days before beginning experiments . 24 to 48 flies were loaded into a black 96-well microplate containing luciferin media: 1% agar , 5% sucrose , 5 mM D-Luciferin ( Gold BioTechnology , Olivette , MO ) . Plates were maintained at 22°C under controlled light conditions and cycled approximately once per hour through a Packard TopCount Scintillation and Luminescence counter . To display oscillatory activity of pan-neuronal RAM-luc reporter ( Figure 6g ) , a smoothing function was applied such that each data point represented the average of three measurements . To compare overall activity between groups ( Figure 6h ) , the mean hourly reading was calculated for each fly and compared . All statistical information is provided in the figure legends and Supplementary file 2 . Data obtained from the 10mer screen were analyzed using a binomial test ( see ‘Screening for Enriched Enhancer’ for details ) . All other data were compiled and analyzed using GraphPad Prism 5 . 0 software . Unpaired Student’s t-test , one-way ANOVA with Tukey or Dunnet post-hoc test , and two-way ANOVA with Bonferroni post-hoc test were used whenever appropriate . The level of significance was set at p<0 . 05 . All data are presented as mean ± SEM . No estimates of power were performed before experiments , but sample size numbers were similar to those generally employed in the field .	Every experience – be it a sight , a sound or a memorable event – activates a unique set of neurons within the brain that together are known as a neuronal ensemble . Identifying these ensembles is key to deciphering how the brain represents experiences and stores them in memory . The most commonly used method for doing so at present relies upon a class of genes called immediate early genes ( or IEGs for short ) . Whenever a neuron becomes active , it switches on its IEGs . By genetically modifying animals to use this mechanism to drive the production of protein markers – such as a fluorescent protein – it is possible to visualize and control the neurons that become activated in response to a stimulus . However , existing IEG-based systems for detecting neuronal activity are not ideal . In particular , these systems could be made more sensitive ( so that they are more likely to respond to neuronal activity ) and more specific ( so that they are more likely to respond only to relevant neuronal activity ) . Sørensen , Cooper et al . have now developed a new system for tagging recently activated neurons that offers a number of advantages over its predecessors . Known as Robust Activity Marking ( RAM ) , the new system consists of a specially designed DNA sequence that is switched on by neuronal activity . Compared with currently existing systems , the RAM system has low levels of background activity , meaning that it only becomes active in actively firing neurons . It is also extremely sensitive and gives a robust signal . An additional advantage of the RAM system is that the timing of its activation can be precisely controlled . This is useful for identifying those neurons that become active in response to one particular sensory stimulus . The DNA elements in the RAM system that respond to neuronal activity are conserved , which means it could be used in a variety of species , from fruit flies to primates . The relatively small size of the RAM system means that , in contrast to other IEG-based systems , it can be introduced into brains by packaging the entire DNA sequence inside a virus particle that can infect a wide range of experimental species . Finally , the design of the RAM system allows it to be targeted to specific subtypes of neurons and to cells that are connected in particular ways . Together , the multiple advantages of the RAM system over traditional IEG-based systems should make it possible for neuroscientists from many different fields to explore how the brain stores experiences in patterns of neuronal activity .	[ "Abstract", "Introduction", "Results", "Discussion", "Methods" ]	[ "tools", "and", "resources", "neuroscience" ]	2016	A robust activity marking system for exploring active neuronal ensembles
Molecular tension sensors have contributed to a growing understanding of mechanobiology . However , the limited dynamic range and inability to specify the mechanical sensitivity of these sensors has hindered their widespread use in diverse contexts . Here , we systematically examine the components of tension sensors that can be altered to improve their functionality . Guided by the development of a first principles model describing the mechanical behavior of these sensors , we create a collection of sensors that exhibit predictable sensitivities and significantly improved performance in cellulo . Utilized in the context of vinculin mechanobiology , a trio of these new biosensors with distinct force- and extension-sensitivities reveal that an extension-based control paradigm regulates vinculin loading in a variety of mechanical contexts . To enable the rational design of molecular tension sensors appropriate for diverse applications , we predict the mechanical behavior , in terms of force and extension , of additional 1020 distinct designs . The ability of cells to generate and respond to mechanical loads is increasingly recognized as a critical driver in many fundamentally important biological processes , including migration ( Doyle et al . , 2009; Lo et al . , 2000; Pelham and Wang , 1997 ) , proliferation ( Chen et al . , 1997; Provenzano and Keely , 2011 ) , differentiation ( Engler et al . , 2006; Heo et al . , 2016; McBeath et al . , 2004 ) , and morphogenesis ( Heisenberg and Bellaïche , 2013; Wozniak and Chen , 2009 ) . While the mechanosensitive signaling pathways enabling these responses are poorly understood , most are thought to have a common basis: the mechanical deformation of load-bearing proteins ( Cost et al . , 2015; Hoffman et al . , 2011; Ju et al . , 2016 ) . As such , several technologies for measuring the loads borne by specific proteins in living cells have emerged ( Freikamp et al . , 2016; Hoffman , 2014; LaCroix et al . , 2015b; Liu et al . , 2017; Polacheck and Chen , 2016 ) . These biosensors , collectively referred to as molecular tension sensors , leverage the distance-dependence of Förster Resonance Energy Transfer ( FRET ) to measure the extension of and , if properly calibrated , the forces across a specific protein of interest ( Austen et al . , 2013; Freikamp et al . , 2017; Hoffman , 2014; LaCroix et al . , 2015a ) . For example , using this approach , the tension across vinculin was shown to regulate a mechanosensitive switch governing the assembly/disassembly dynamics of focal adhesions ( FAs ) ( Grashoff et al . , 2010 ) . While this and several other FRET-based molecular tension sensors provide a critical view into mechanosensitive processes ( Cost et al . , 2015; Jurchenko and Salaita , 2015 ) , fundamental questions regarding the nature and the degree of the mechanical loading of proteins remain . A key limitation has been the inability to create tension sensors with diverse mechanical sensitivities suitable for a wide variety of biological applications ( Freikamp et al . , 2017 ) . To date , genetically-encoded molecular tension sensor modules ( TSMods ) , which are incorporated into various proteins to form distinct tension sensors ( Figure 1A ) , have been created without a priori knowledge of their mechanical sensitivity . TSMod development has largely relied on a biologically-inspired ‘guess-and-check’ design approach using naturally-occurring extensible polypeptides or protein domains as deformable elements in the FRET-based tension sensors . Furthermore , despite the use of these sensors to study intracellular processes , calibration measurements of their mechanical sensitivity are typically performed in vitro using highly precise single molecule techniques . Reported force sensitivities of several in vitro calibrated TSMods are 1–6 pN ( Grashoff et al . , 2010 ) , 2–11 pN ( Brenner et al . , 2016 ) , 3–5 pN ( Ringer et al . , 2017 ) , 6–8 pN ( Austen et al . , 2015 ) , or 9–11 pN ( Austen et al . , 2015 ) . However , it is unclear if these ranges are sufficient for diverse mechanobiological studies , and the applicability of these in vitro calibrations to sensors that are utilized in cellulo has not been verified . We sought to overcome these limitations by creating new TSMods that do not rely on naturally occurring extensible domains or in vitro calibration schemes . These new TSMods consist of a Clover-mRuby2 FRET pair connected by unstructured polypeptide extensible domains of various lengths . As the entropy-driven mechanical resistance of unstructured polypeptides can be accurately predicted by established models of polymer extension ( Becker et al . , 2010 ) , the force- and extension-sensitivities can be determined independently of in vitro calibration experiments . Using these advancements , we generate a variety of new tension sensors for the FA protein vinculin . These include a version optimized for sensitivity , which shows a nearly 3-fold increase in performance , as well as a suite of sensors with distinct mechanical sensitivities capable of determining if vinculin loading is subject to extension-based or force-based control . Lastly , we computationally predict the mechanical behavior expected for a variety of unstructured polypeptide-based tension sensors for several common FRET pairs . This resource should allow for the expedited creation and rational design of molecular tension sensors suited for use in diverse contexts , alleviating a significant limitation in the study of mechanobiology . TSMods for intracellular use consist of two fluorescent proteins ( FPs ) connected by an extensible domain ( Figure 1A ) . To enable the creation of tension sensors with diverse mechanical sensitivities , we constructed a variety of TSMods using FPs with distinct photophysical properties connected by unstructured polypeptides of various lengths and mechanical properties , as each of these characteristics critically determine the behavior of these sensors ( Figure 1B–D ) . We based our designs on the first calibrated TSMod ( Grashoff et al . , 2010 ) , which is comprised of the mTFP1-Venus FRET pair connected by a flagelliform silk-based polypeptide with the repeated sequence ( GPGGA ) 8 , and has been used in a variety of tension sensors ( Cost et al . , 2015; Jurchenko and Salaita , 2015 ) . First , we evaluated the role of the FPs in TSMod function . Reasoning that increases in the unloaded FRET efficiency could potentially increase the dynamic range of the sensor as well as alleviate technical issues with measuring small FRET signals , we sought to increase the FRET efficiency in this state ( Figure 1B ) . To do so , we replaced mTFP1-Venus with the green-red FRET pair Clover-mRuby2 ( Lam et al . , 2012 ) , which exhibits stronger FRET at a given separation distance ( Förster radius ( R0 ) of 5 . 7 and 6 . 3 nm , respectively ) . This simple substitution yielded a 12% higher baseline ( unloaded ) FRET efficiency that was observed in fixed ( Figure 1E , F ) and live cells ( Figure 1—figure supplement 1 ) , as well as cell lysates ( Figure 1—figure supplement 2 ) . While the benefits of the improved photophysical properties of Clover-mRuby2 are established ( Lam et al . , 2012 ) , we probed the effect of the physical structure of the FPs on their performance in TSMods . Although commonly identified by a characteristic beta-barrel structure , FPs also contain short unstructured regions at their termini that likely contribute to the effective mechanical properties of the extensible domains used in TSMods ( Figure 1—figure supplement 1 ) ( Ohashi et al . , 2007 ) . Previous work , and our data ( Figure 1—figure supplement 3 ) , have shown that ‘minimal’ Clover ( residues 1 – 227 ) and mRuby2 ( residues 3 – 236 ) exhibit absorbance and emission spectra indistinguishable from their full-length counterparts ( Austen et al . , 2015; Li et al . , 1997; Ohashi et al . , 2007; Ouyang et al . , 2008; Shimozono et al . , 2006 ) . Therefore , to mitigate concerns about FPs affecting the mechanical properties of the extensible domains and further increase the unloaded FRET efficiency , minimal versions of Clover and mRuby2 were used in the construction of all TSMods . Recent evidence suggests that both the mechanical properties ( Austen et al . , 2013; Ringer et al . , 2017 ) and the length ( Brenner et al . , 2016 ) of the extensible domain provide viable means by which to tune the mechanical sensitivity of TSMods ( Figure 1C , D ) . Towards this end , we created a variety of TSMods containing extensible domains comprised of either the flagelliform-based ( GPGGA ) n , which is thought to be relatively stiff ( Becker et al . , 2003 ) , or the synthetic ( GGSGGS ) n which has been characterized as an unstructured polypeptide and has previously been employed as a tunable linker in biochemical sensors ( Evers et al . , 2006 ) . Analysis of TSMods in cell lysates showed that those with ( GGSGGS ) n extensible domains exhibit higher FRET efficiencies than those with ( GPGGA ) n extensible domains of the same length ( Figure 1G ) , suggesting that ( GPGGA ) n-based polypeptides are indeed stiffer , and thus force the FPs apart more readily , than ( GGSGGS ) n-based polypeptides . However , when ( GPGGA ) n and ( GGSGGS ) n TSMods were evaluated in cellulo , the FRET efficiency versus length relationships were indistinguishable , suggesting the polypeptides are exhibiting identical mechanical properties ( Figure 1H ) . Together , these data demonstrate that factors dictating sensor functionality in the absence of applied load can be environmentally sensitive , and that the behavior of TSMods observed in vitro may not reflect their behavior in cellulo . As such , these results raise concerns about the applicability of calibrations of FRET-based tension sensors performed in vitro to sensors that are used in intracellular environments ( further discussed in Appendix 1 ) . As an alternative to TSMod calibration through in vitro approaches , we pursued a modeling-based approach for describing the mechanical sensitivities of TSMods . Given that FPs linked by ( GGSGGS ) n polypeptides are well-described by established models of polymer physics in unloaded conditions ( Evers et al . , 2006 ) , we developed an analogous model to predict TSMod behavior under load . Briefly , the proposed calibration model incorporates three main aspects of TSMods: ( 1 ) the photophysical properties of the FRET pair ( Förster radius , R0 ) , ( 2 ) the radii of the FPs ( RFP ) , and ( 3 ) the mechanical response of the extensible domain , which is well-described as a semi-flexible polymer by a persistence length ( LP ) and a contour length ( LC ) in the framework of the worm-like chain model ( Becker et al . , 2010 ) . This modeling-based approach enables the prediction of the in cellulo mechanical response of FRET-based tension sensors by leveraging separate measurements of the in cellulo LP of the unstructured polypeptide used as the extensible domain . A detailed description of the development and implementation of the model , as well as comparison to other estimates of TSMod behavior are presented in Appendix 1 , which refers to data presented in Figure 2—figure supplement 1 , 2 and Supplementary file 1 . To validate this model , we first investigated its ability to describe the behavior of several types of TSMods in terms of the relationship between FRET and the length of the extensible domain in unloaded conditions . These measurements are critical in that they are used to estimate the mechanics of the extensible domain in terms of its persistence length LP . To do so , estimates of R0 and RFP were obtained from the literature , LC was directly calculated from the number of amino acids comprising the extensible domain , and LP was used as the single adjustable parameter . With only LP left unconstrained , the model accurately describes the behavior of TSMods containing ( GPGGA ) n and ( GGSGGS ) n extensible domains in unloaded conditions in in vitro ( Figure 1G ) and in cellulo ( Figure 1H ) environments with physically reasonable estimates of LP . Model fits and 95% confidence intervals confirm that LP estimates for ( GPGGA ) n and ( GGSGGS ) n polypeptides are significantly different in vitro ( 0 . 74±0 . 05 and 0 . 33±0 . 05 nm , respectively ) , and collapse to one intermediate value in cellulo ( 0 . 50±0 . 02 and 0 . 48±0 . 05 nm , respectively ) . Also , to demonstrate that the literature estimates of R0 and RFP were appropriate , we performed a sensitivity analysis , leaving either RFP or R0 unconstrained . We observe negligible improvement in fit quality and achieve similar estimates of LP ( Figure 2—figure supplement 3 and 4 ) , validating our approach . Overall , these results demonstrate the functionality of the model to measure the LP of TSMod extensible domains in unloaded conditions and also suggest that the observed mechanics of the extensible domain can change in different environments , although less-so for ( GGSGGS ) n polypeptides . Next , we sought to investigate the generalizability of the model as well as validate the ability of the model to describe the behavior of TSMods subject to tensile loads . Therefore , we examined model fits to published fluorescence-force spectroscopy measurements of Cy3-Cy5 dyes linked by ( GPGGA ) n extensible domains ( Brenner et al . , 2016 ) . Again , with only LP unconstrained , the model accurately describes the behavior of these TSMod-like constructs in both unloaded conditions ( Figure 2A ) and under tensile loads ( Figure 2B ) . Importantly , each of these datasets is well-described by the same persistence length ( LP=1 . 05 nm ) indicating that the same mechanical model is appropriate for describing the behavior of unstructured polypeptides in both unloaded and loaded conditions when both measurements are determined in the same environment . For comparison , we show fits for a range of LP values from 1 . 0 to 1 . 15 nm ( lines in Figure 2A , shaded region in Figure 2B ) . The robustness of these fits to various parameter constraints was also verified ( Figure 2—figure supplement 5 ) . It is important to note that these differences in the LP of ( GPGGA ) n polypeptides in various environments ( Figure 2G , H , Figure 2A ) support the idea that in vitro calibrations should be applied to sensors used in living cells , or in different in vitro environments , with caution . Together these results suggest a simple model-based calibration scheme by which measurements of extensible domain mechanics ( LP ) in unloaded conditions are utilized to predict TSMod behavior under tensile loading . While our modeling efforts indicate that both ( GPGGA ) n and ( GGSGGS ) n polypeptide mechanics are consistent with unstructured polypeptides ( Figure 2—figure supplement 6 ) , we only generate calibration predictions for TSMods containing ( GGSGGS ) n extensible domains because they are also less sensitive to environmental changes . In the context of the model , the in cellulo persistence length of the ( GGSGGS ) n extensible domain ( LP=0 . 48 nm , Figure 1H ) is combined with literature estimates of the radii ( Hink et al . , 2000 ) and photophysical properties ( Lam et al . , 2012 ) of Clover and mRuby2 to predict the response of ( GGSGGS ) n-based TSMods under applied loads ( Figure 2C ) . This model-based calibration scheme uniquely overcomes the environmental sensitivity of the extensible domain ( compare Figure 1G and H ) by allowing for in cellulo measurements of LP to be used to estimate the mechanical sensitivity of TSMods . To determine which extensible domain length will be optimal for measuring tension across vinculin , we evaluated TSMod mechanical sensitivity across different force regimes by calculating the derivative along the FRET-force curve ( Freikamp et al . , 2017 ) ( Figure 2C , Figure 3—figure supplement 1 ) . Given the original vinculin tension sensor ( VinTS ) reported average loads of ~2 . 5 pN across vinculin that varied from 1 to 6 pN ( Grashoff et al . , 2010 ) , we choose to further investigate the performance of the TSMod containing the nine-repeat extensible domain , as it exhibits the highest sensitivity in this force regime and is capable of capturing the distribution of the loads on vinculin ( Figure 3—figure supplement 1A ) . This nine-repeat linker also provides a good balance between FRET dynamic range and peak sensitivity ( Figure 3—figure supplement 1B , C ) . An optimized VinTS ( opt-VinTS ) was created by genetically inserting this TSMod into vinculin at same site , after amino acid 883 , as in the original VinTS design ( Grashoff et al . , 2010 ) . We assessed the performance of opt-VinTS by evaluating its ability to detect changes in vinculin loading across both subcellular and FA length scales . Vin-/- MEFs expressing either VinTS or opt-VinTS showed indistinguishable cell and FA morphologies ( Figure 3A , A’ , C , C’ , Figure 3—figure supplement 2 ) . Furthermore , line scans of acceptor intensity across single FAs indicated similar localization of each sensor ( Figure 4—figure supplement 3A’’ , A’’’ , C’’ , C’’’ ) . These findings indicate that the two sensors exhibit identical biologically functionality . At a subcellular length scale , consistent with our previous findings ( Rothenberg et al . , 2015 ) , both VinTS and opt-VinTS report highest loads ( lowest FRET efficiency ) in the cell periphery , and no appreciable tensile loading of vinculin in the cell center ( Figure 3B , D ) . Based on previous reports of gradients of vinculin loading within individual FAs ( Sarangi et al . , 2017 ) and a skewed distribution of mechanical stresses at the cell-substrate interface ( Blakely et al . , 2014; Legant et al . , 2013; Morimatsu et al . , 2015; Plotnikov et al . , 2012 ) , we expected to see similar distally-skewed vinculin tensions . Such gradients are difficult to discern in peripheral FAs of cells expressing the original tension sensor ( Figure 3B’ , B’’ , B’’’ ) . However , striking gradients of vinculin tension across single FAs were clearly visible in peripheral FAs of cells expressing opt-VinTS ( Figure 3D’ , D’’ , D’’’ ) . To quantitatively gauge performance , we quantified the FRET efficiency change across length-normalized FAs ( slope , Figure 3B’’’ , D’’’ ) . This analysis revealed an almost 3-fold improvement in the performance opt-VinTS ( slope = 15 . 0%/FA ) when compare the original design ( slope = 5 . 5%/FA ) . In total , these results show that , as predicted by the model , opt-VinTS is significantly more sensitive than the original VinTS . A central premise of mechanotransduction , the process by which cells sense and respond to mechanical stimuli , is that applied loads induce conformational changes in mechanosensitive proteins , leading to biochemically distinct functions . However , it is unknown whether the forces or the extensions experienced by proteins mediate the activation of mechanosensitive signaling pathways . Experimental evaluation of this molecular-scale question has been challenging because force and extension are inherently linked . For example , in the case of molecular tension sensors , the force-extension relationship for the extensible domain is monotonic , so any given force corresponds to a unique extension ( Figure 4—figure supplement 1 ) . Note that extension refers to the change in the average length ( rz , Appendix 1 , Equation 6 ) , not the separation distance of the FPs ( rc ) in the construct . Importantly , rz is independent of the size of the extensible domain . To determine whether conserved forces or extensions mediate vinculin loading , we created a trio of vinculin tension sensors with extensible domains comprised of five , seven , or nine repeats of ( GGSGGS ) n . As each sensor has a unique force-extension curve , the application of equivalent forces to the three constructs will result in three distinct extensions , and vice versa ( Figure 4—figure supplement 1 ) . Cells expressing equivalent amounts of each sensor spread and formed FAs normally ( Figure 4A–D , Figure 3—figure supplement 2 ) . Using the in cellulo calibration predictions described above ( shown in Figure 2C ) , measured FRET efficiencies ( Figure 4E–H ) were converted to sensor forces ( Figure 4I–L ) and extensions ( Figure 4M–P ) . Intriguingly , we observed similar distributions of extension ( Figure 4P ) , and distinct distributions of tensile forces ( Figure 4L ) in FAs formed in cells expressing the various sensors . Furthermore , highly loaded FAs in the cell periphery exhibit conserved gradients in extension rather than force ( Figure 4—figure supplement 2 ) . To test whether vinculin is exclusively regulated by an extension-based paradigm , we conducted three additional control paradigm experiments . First , treatment of cells with the Y-27632 showed that vinculin extension-control is robust to short-term inhibition of ROCK-mediated cytoskeletal contractility ( Figure 4—figure supplements 3 and 4 ) . Secondly , vinculin extension-control does not require vinculin-talin interactions , as assessed through the introduction of a point mutation ( A50I ) in the three versions of VinTS ( Figure 4—figure supplements 5 and 6 ) . Finally , we observed that extension-based control still occurs on substrates of physiologically-relevant 10 kPa stiffness ( Figure 4—figure supplements 7 and 8 ) . Together , these results strongly suggest that loads across vinculin are exclusively governed by an extension-based control rather than the more commonly assumed force-based control paradigm . To gain insight into the physical origins of force- versus extension-controlled loading of proteins within FAs , we examined how forces and extensions propagate through a simple structural model of a FA ( see Appendix 2 for details and more comprehensive discussion of model results ) . Briefly , the structural model is comprised of various numbers of two distinct elements , which can be thought of as mechanically-dominant proteins or protein complexes within FAs . A sensor element ( subscript ‘S’ ) and an alternative linker element ( subscript ‘L’ ) are arranged in two layers ( Figure 4—figure supplement 9A ) meant to simulate the stratified organization of FAs ( Kanchanawong et al . , 2010 ) . By comparing the relative variances in forces and extensions observed across sensor elements within various arrangements ( Figure 4—figure supplement 9B ) , we examined whether a force-controlled or extension-controlled loading of the sensor element would be observed following a bulk force or extension input to the entire structure , and whether this depended on either the relative molecular abundance or the relative stiffness of each element ( Figure 4—figure supplement 9C ) . Regardless of the relative abundance of the elements or their respective stiffness , a force input to the entire structure always resulted in force-based control within the sensor elements . In contrast , an extension input to the entire structure , as might arise due to defined myosin motor step size ( Murphy et al . , 2001 ) or actin polymerization ( Peskin et al . , 1993 ) , gave rise to both extension-controlled and force-controlled regimes in the sensor elements . The extension-controlled loading of the sensor element is more strongly observed when the sensor element is relatively soft and/or in low abundance , otherwise a force-controlled system is predicted ( Figure 4—figure supplement 9D ) . Furthermore , in the extension-controlled regime , this simple model also predicts the linear relationship between sensor element stiffness and the force borne by the three sensor elements that was observed in all control paradigm experiments ( Figure 4—figure supplement 9E ) . Together , these results demonstrate that protein extension , instead of applied force , might be a key mechanical variable in some mechanosensitive processes . By expanding the simulated parameter space , the calibration model can also be used to predict the in cellulo mechanical sensitivity of a wide variety of potential TSMod designs . Specifically , as each model parameter corresponds to an alterable variable in sensor design ( R0= FRET pair , LP= composition of extensible domain , N= length of extensible domain ) , we can bypass the need to iteratively ‘guess and check’ the performance of new sensors , and , instead , predict the performance of unstructured polypeptide-based tension sensors in silico . Since our measurements and modeling efforts indicate that both force and extension might be key mechanical variables in different contexts , we report the predicted mechanical responses for simulated sensors in terms of both force and extension . The predicted relationships between force , extension , and FRET for a single sensor can be concisely described by three metrics as depicted in Figure 5A–C: ( 1 ) a FRET dynamic range ( ΔFRET ) , which is defined as the change in FRET efficiency from an unloaded state to an experimentally-determined 5% noise floor; ( 2 ) a target force ( Ftarget ) , which indicates the midpoint of force range a sensor is functional , and is defined as Ftarget=ΔF/2; and ( 3 ) a target extension ( rz , target ) , which is analogous to target force . Examining the predicted ΔFRET , Ftarget , and rz , target for a variety of Clover-mRuby2 TSMods containing extensible domains of various lengths and compositions , we generate a ‘roadmap’ for future Clover-mRuby2 sensor design ( Figure 5D–F , see Supplementary file 1 for a list of reported polypeptide mechanical properties justifying the range over which simulations were performed ) . Additional roadmaps were generated for other commonly used FRET pairs ( Figure 5—figure supplement 1 ) . With these roadmaps as a guide , the rational design and implementation of future tension sensors with diverse and a priori specified properties is now possible . Molecular tension sensors provide insight into the mechanical loading of individual proteins inside cells but have been limited by small dynamic ranges and an inability to tune their mechanical sensitivities . In this work , we leveraged the predictable mechanical responses of unstructured polypeptides to create and characterize a suite of TSMods with improved , specified , and tunable mechanical properties . These new modules were used to create a sensor optimized for the detection of loads across vinculin , as well as a suite of sensors that revealed an extension-control paradigm mediating vinculin loading . Additionally , we used the model to predict the mechanical response of over 1000 distinct sensors , enabling the rational design of future molecular tension sensors for diverse applications . Through a systematic examination of the individual components of TSMods , we identified increased Förster distance , the use of FPs that lack unstructured residues , and extensible domains comprised of tunable unstructured polypeptides as key to the rational design of the next generation of tension sensors . Surprisingly , we observed context-dependent mechanical behaviors in TSMods , as responses for the same constructs were mechanically distinct in in vitro and in cellulo experiments . Differences in many aspects of these environments , including ionic strength , pH , and crowding , could explain the variety of reported mechanics . However , we surmise that crowding effects are less likely for two reasons . First , previous work has shown that crowding effects are more prominent at longer linker lengths ( Ohashi et al . , 2007 ) . Additionally , in our in vitro and in cellulo systems , we observed that ( GPGGA ) n-based polypeptides exhibit higher FRET in vitro , while ( GGSGGS ) n polypeptides exhibited lower FRET in these conditions . These divergent behaviors are not readily explained by crowding effects , which should increase FRET in both constructs ( Ohashi et al . , 2007 ) . While we do not establish the origin of these environmental factors in this work , these results raise potential concerns about the applicability of in vitro ( single molecule-based ) calibrations to sensors that are almost exclusively utilized in cellulo . To circumvent the need for in vitro calibrations , we developed and validated a first-principles model which predicts TSMod mechanical sensitivity using in cellulo measurements of the mechanical properties of unstructured polypeptides used as the extensible domain . As quantitatively identical and physically meaningful parameters described the behavior of TSMods in both unloaded and loaded conditions , measurements of the in cellulo behavior of soluble TSMods can be used to predict the in cellulo behavior of tension sensors under load . In future tension sensor studies , this model could be paired with in cellulo measurements of LP to ( 1 ) help identify in vitro conditions that suitably match in cellulo systems , ( 2 ) re-calibrate sensors in new systems with distinct environmental properties ( cell types , subcellular compartments ) , or ( 3 ) detect , and account for , changes in TSMod function following biological treatments or through time . To demonstrate both the predictive power of the model and the improvements to these new in cellulo calibrated TSMods , we generated a variety of new vinculin tension sensors , the first of which was optimized to detect the 1–6 pN forces thought to be borne by vinculin ( Grashoff et al . , 2010 ) . This optimized sensor exhibited a marked increase in the ability to detect asymmetric distributions of molecular tension within single FAs . Similar tension asymmetries have been observed external to the cell using both high resolution traction force microscopy ( Legant et al . , 2013; Plotnikov et al . , 2012 ) and extracellular tension sensors ( Blakely et al . , 2014; Morimatsu et al . , 2015 ) . Intracellularly , gradients in vinculin tension have been reported in cells adhered to micropost arrays , although tension asymmetries were mainly attributed to the presence of discontinuous substrates ( Sarangi et al . , 2017 ) . We show that these asymmetric molecular loads are detectable without the need for super-resolution imaging and are transmitted through vinculin even on continuous substrates . Leveraging a suite of in cellulo calibrated vinculin tension sensors with distinct mechanical properties , we investigated a question that was previously technically inaccessible: are the forces across or extensions of proteins subject to cellular control ? We observed exclusively extension-control in vinculin , and showed it is robust to a reduction in cell contractility , ablation of vinculin-talin interactions , and plating cells on substrates with physiologically-relevant stiffness . Simulations of FA structures suggested extension-control paradigms were likely due to discrete extensions from the cytoskeleton . Such discrete displacements could arise due to the step-like activities of molecular motors ( myosin II ) or actin polymerization , suggesting a critical role for the cytoskeleton in these processes . Furthermore , these structural simulations indicate that force-control and extension-control are sensitive to the relative abundance and stiffness of various proteins within the bulk structure . Thus , in addition to extension-controlled protein loading , it is possible that other load-bearing proteins might be subject to either type of control or that a specific protein in distinct cellular contexts could switch control modalities . A key question for future study is how proteins with multiple repeated domains that exhibit non-monotonic force-extension curves , such as talin ( Yao et al . , 2016 ) and filamin ( Furuike et al . , 2001 ) , are mechanically regulated in cells . The results of this study provide an updated picture of vinculin function at FAs . Specifically , once vinculin is activated through interactions with talin and/or actin ( Bakolitsa et al . , 2004; Bois et al . , 2006; Chen et al . , 2006a ) , it is pulled until a given extension is achieved , not a set force . Thus , the load-dependent maintenance of vinculin activation ( Dumbauld et al . , 2013 ) may be determined by vinculin extension , rather than force . More broadly , extension may be the most pertinent biophysical variable governing the initiation of vinculin-dependent and possibly other mechanosensitive signaling pathways . We note this picture is consistent with recent results that show VinTS FRET does not change on gels ( Kumar et al . , 2016 ) and that vinculin is still subject to mechanical load following the ablation of vinculin-talin interactions or partial ROCK-inhibition ( Rothenberg et al . , 2018c ) . Extrapolated to longer length scales , this extension-control paradigm agrees well with reports that cells induce similar strains within extracellular environments of differing stiffness ( Lin et al . , 2018; Saez et al . , 2005 ) and that strain determines the activation of touch-sensitive channels in in vivo models ( Eastwood et al . , 2015 ) . To enable the rational design of future unstructured polypeptide-based sensors , we simulated the parameter space defined by the previously reported polypeptide mechanics ( 0 . 1<LP<10 nm ) and lengths ( <100 residues ) not likely to adversely affect protein function for several commonly used FRET-pairs . The prediction of more than 1000 possible tension sensor designs should allow for the creation of tension sensors suitable for most experiments in either extension- or force-based control paradigms . However , we note that measurements of force in an extension-control regime will be dependent on the stiffness of the sensor design . We speculate that this could be one possible reason for a variety of forces reported by different sensor designs for the same protein ( Erickson , 2017 ) . In total , this work provides the biophysical foundation for understanding molecular tension sensor function and delivers a suite of in cellulo-calibrated sensors whose distinct and predictable mechanical sensitivities can be leveraged to gain unique molecular understanding of the role of mechanical forces and extensions in biological systems . These advancements should expedite deployment of molecular tension sensors in diverse biological contexts where mechanical cues and cellular force generation have long been thought to play critical , but unexplored , roles . Vinculin -/- MEFs ( kindly provided by Dr . Ben Fabry and Dr . Wolfgang H . Goldmann ( Mierke et al . , 2010 ) , Friedrich-Alexander-Universitat Erlangen-Nurnberg ) were maintained in high-glucose DMEM with sodium pyruvate ( D6429 , Sigma Aldrich , St . Louis , MO ) supplemented with 10% FBS ( HyClone , Logan , UT ) , 1% v/v non-essential amino acids ( Invitrogen , Carlsbad , CA ) , and 1% v/v antibiotic-antimycotic solution ( Sigma Aldrich ) . Vinculin knockout was confirmed by western blot and immunofluorescent staining with mouse anti-vinculin antibody ( V9131 , Sigma Aldrich , dil . 1:5000 , 1:500 , respectively ) . Mycoplasma testing of this cell line by Duke Cell Culture Facility was negative . HEK293 cells were maintained in high-glucose DMEM ( D5796 , Sigma Aldrich ) supplemented with 10% FBS ( HyClone ) and 1% v/v antibiotic-antimycotic solution ( Sigma Aldrich ) . Cells were grown at 37°C in a humidified 5% CO2 atmosphere . Cells were transfected at 50–75% confluence in 6-well tissue culture plates using Lipofectamine 2000 ( Invitrogen ) following the manufacturer’s instructions . Constructs were created from the previously generated pcDNA3 . 1 mTFP1 ( Ai et al . , 2006; Grashoff et al . , 2010 ) and pcDNA3 . 1 Venus ( A206K ) ( Grashoff et al . , 2010; Nagai et al . , 2002 ) as well as pcDNA3 . 1 Clover ( Addgene 40259 ) and pcDNA3 . 1 mRuby2 ( Addgene 40260 ) ( Lam et al . , 2012 ) . Minimal versions of single FPs were generated via Polymerase Chain Reaction ( PCR ) and inserted into pcDNA3 . 1 via EcoRI/NotI digestion and subsequent ligation ( T4 DNA Ligase , NEB , Ipswich , MA ) . Specifically , creation of minimal FPs involved deletion of the 11 C-terminal residues in mTFP1 and Clover , and the first and second N-terminal residues in Venus and mRuby2 after the start codon . Oligonucleotide primers used to generate full-length and minimal versions of mTFP1 , Venus A206K , Clover , and mRuby2 are detailed in Supplementary file 2 . The FP component fragments of the mTFP1-Venus and Clover-mRuby2 TSMods were derived from pcDNA3 . 1 TS module ( Grashoff et al . , 2010 ) and pcDNA3 . 1-Clover-mRuby2 ( Addgene 49089 ) or the minimal FP variants described above . The extensible ( GPGGA ) n and ( GGSGGS ) n extensible domains were derived from pcDNA3 . 1 TS module ( Grashoff et al . , 2010 ) and pET29CLY9 ( Addgene 21769 ) ( Evers et al . , 2006 ) , respectively . Gibson assembly was used to construct TSMods containing a given FRET pair and extensible domain from three fragments: ( 1 ) vector backbone and donor FP ( complementary regions: 5’-ampicillin gene , 3’-donor FP ) , ( 2 ) extensible domain region ( complementary regions: 5'-donor FP , 3’-acceptor FP ) , and ( 3 ) vector backbone and acceptor FP ( complementary regions: 5’-acceptor FP , 3’-ampicillin gene ) . Primers used to generate the extensible domain region in this reaction scheme were designed to nonspecifically target the repetitive extensible domain sequence , thereby generating extensible domains of various lengths . Following assembly and transformation into DH5α competent cells , single colonies were assayed for extensible domain length by DNA sequencing . Oligonucleotide primers used to generate TSMods are detailed in Supplementary file 2 . All variants of the vinculin tension sensor were derived from pcDNA3 . 1 Vinculin TS ( Grashoff et al . , 2010 ) . In a cloning strategy analogous to that described above for the TSMods , Gibson assembly techniques were used to assemble vinculin tension sensors containing various minimal Clover-mRuby2 TSMods based on three fragments: ( 1 ) vector backbone and vinculin head domain residues 1–883 ( complementary regions: 5’-ampicillin gene , 3’-Clover ) , ( 2 ) TSMod with desired ( GGSGGS ) n extensible domain ( complementary regions: 5’-Clover , 3’-mRuby2 ) , and ( 3 ) vector backbone and vinculin tail domain residues 884–1066 ( complementary regions: 5’-mRuby2 , 3’-ampicillin gene ) . Again , the assembled DNA fragments were transformed into DH5α competent cells and extensible domain length was verified for single colonies by DNA sequencing . Oligonucleotide primers used to generate vinculin tension sensors are detailed in Supplementary file 2 . To generate A50I versions of the vinculin tension sensors , PCR was used to generate a fragment of the vinculin head domain containing the A50I mutation using forward primer 5’-AAT AAG CTT GCC ATG CCC GTC TTC CAC AC-3’ , reverse primer 5’-GCC GGA TCC GCA AGC CAG TTC-3’ , and template pEGFP-C1/GgVcl 1–851 A50I mutant ( Addgene 46269 ) . The product was insert into Clover-mRuby2-based vinculin tension sensors using 5'-HindIII/3'-BamHI . Plasmids will be distributed through Addgene ( http://addgene . org ) . For cell imaging on glass , no . 1 . 5 coverslips ( Bioptechs , Butler , PA ) placed in reusable metal dishes ( Bioptechs ) were coated overnight at 4°C with 10 μg/mL fibronectin ( Fisher Scientific , Pittsburgh , PA ) . Transfected vinculin -/- MEFs expressing a given tension sensor construct were then trypsinized , transferred to the prepared glass-bottom dishes at a density 50 , 000 cells per dish , and allowed to spread to 4 hr in growth media . For fixed experiments , samples were then rinsed quickly with PBS , and fixed for 10 min with 3 . 7% methanol-free paraformaldehyde ( Electron Microscopy Sciences , Hatfield , PA ) . For live experiments , growth media was exchanged , at least 1 hr before imaging , for imaging media - Medium 199 ( Life Technologies , 11043 ) supplemented with 10% FBS ( HyClone ) , 1% v/v non-essential amino acids ( Invitrogen ) , and 1% v/v antibiotic-antimytotic solution ( Sigma Aldrich ) . Live cell imaging was performed for up to 30 min at 37°C ( Stable Z system , Bioptechs ) . Polyacrylamide gels with elastic moduli of approximately 10 kPa ( Tse and Engler , 2010 ) were created by mixing 10% acrylamide , 0 . 1% bis-acrylamide ( BioRad , Hercules , CA ) and 0 . 1% acrylic acid-NHS ( Sigma Aldrich , to permit ECM functionalization ) , with polymerization initiated via addition of 0 . 1% ammonium persulfate and 0 . 05% N , N , N´ , N´-tetramethylethylenediamine ( Sigma Aldrich ) . Gels were cast between amino-silanated ( Tse and Engler , 2010 ) and hydrophobic ( Rain-X treated ) coverslips ( 18 mm diameter , 40 μL gel solution per coverslip ) . Following gel polymerization ( 15 min ) , the top ( hydrophobic ) coverslip was removed , gels were rinsed thoroughly in HEPES buffer ( 50 mM , pH 8 . 5 ) , then incubated overnight with fibronectin ( 10 μg/mL in HEPES buffer ) at 4°C . ECM-coated gels were rinsed thoroughly with PBS prior to cell seeding . Transfected vinculin -/- MEFs expressing a given tension sensor construct were trypsinized , transferred to the ECM-coated gels at a density 50 , 000 cells per dish , and allowed to spread to 4 hr in growth media . Samples were then fixed for 10 min with 3 . 7% methanol-free paraformaldehyde ( Electron Microscopy Sciences ) , then rinsed thoroughly in PBS . Finally , cells on gels were inverted onto bare no . 1 . 5 coverslips ( Bioptechs ) in reusable dishes ( Bioptechs ) and imaged . To inhibit Rho kinase ( ROCK ) -mediated myosin activity , cells were allowed to spread for 4 hr and treated with 25 μM Y-27632 ( Sigma Aldrich ) , diluted from a 10 mM stock solution in deionized H2O , 20 min before fixation . This treatment duration was the shortest capable of resulting in statistically significant loss of loading across vinculin ( Figure 4—figure supplement 3Q ) , as has been shown in previous work ( Rothenberg et al . , 2018c ) . All imaging was performed on an Olympus IX83 inverted epifluorescent microscope ( Olympus , Center Valley , PA ) equipped with a LambdaLS 300W ozone-free xenon bulb ( Sutter Instruments , Novato , CA ) , a sCMOS ORCA-Flash4 . 0 V2 camera ( Hamamatsu , Japan ) , motorized filter wheels ( Sutter Instruments 10–3 ) , and automated stage ( H117EIX3 , Prior Scientific , Rockland , MA ) . Image acquisition was controlled by MetaMorph Advanced software ( Olympus ) . Samples were imaged at 60X magnification ( Olympus , UPlanSApo 60X/NA1 . 35 objective , 108 nm/pix ) , using a three-image sensitized emission acquisition sequence ( Chen et al . , 2006b ) . The filter set for FRET imaging of mTFP1-Venus sensors includes mTFP1 excitation ( ET450/30x , Chroma , Bellows Falls , VT ) , mTFP1 emission ( Chroma , ET485/20 m ) , Venus excitation ( Chroma , ET514/10x ) , and Venus emission ( FF01-571/72 , Semrock , Rochester , NY ) filters , and a dichroic mirror ( Chroma T450/514rpc ) . Images of mTFP1-Venus sensors were acquired in , sequentially , the acceptor channel ( Venus excitation , Venus emission , 1000 ms exposure ) , FRET channel ( mTFP1 excitation , Venus emission , 1500 ms exposure ) , and donor channel ( mTFP1 excitation , mTFP1 emission , 1500 ms exposure ) . For Clover-mRuby2 sensors , we utilized the FITC and TRITC filters from the DA/FI/TR/Cy5−4 × 4 M-C Brightline Sedat filter set ( Semrock ) , which provided efficient Clover excitation ( FF02-485/20 ) , Clover emission ( FF01-525/30 ) , mRuby2 excitation ( FF01-560/25 ) , and mRuby2 emission ( FF01-607/36 ) filters , and appropriate dichroic mirror ( FF410/504/582/669-Di01 ) for FRET imaging . Images of Clover-mRuby2 sensors were acquired in , sequentially , the acceptor channel ( mRuby2 excitation , mRuby2 emission , 1500 ms exposure ) , FRET channel ( Clover excitation , mRuby2 emission , 1500 ms exposure ) , and donor channel ( Clover excitation , Clover emission , 1500 ms exposure ) . FRET was detected through measurement of sensitized emission ( Chen et al . , 2006b ) and subsequent calculations were performed on a pixel-by-pixel basis using custom written code in MATLAB ( Mathworks , Natick , MA ) ( https://gitlab . oit . duke . edu/HoffmanLab-Public/image-preprocessing ) ( Rothenberg et al . , 2015; copy archived at https://github . com/elifesciences-publications/HoffmanLab-image-preprocessing ) . Prior to FRET calculations , all images were first corrected for uneven illumination , registered , and background-subtracted . Spectral bleed-through coefficients were determined through FRET-imaging of donor-only and acceptor-only samples ( i . e . cells expressing a single donor or acceptor FP ) . Donor bleed-through coefficients ( dbt ) were calculated for mTFP1 and Clover as:dbt=IfId where If is the intensity in the FRET-channel , Id is the intensity in the donor-channel , and data were binned by donor-channel intensity . Similarly , acceptor bleed-through coefficients ( abt ) were calculated for Venus and mRuby2 as:abt=IfIawhere Ia is the intensity in the acceptor-channel , and data were binned by acceptor-channel intensity . To correct for spectral bleed-through in experimental data , corrected FRET images ( Fc ) were generated following the equation:Fc=If-dbtId-abtIa After bleed-through correction , FRET efficiency was calculated following the equation:E=Id+FcGIawhere G is a proportionality constant that describes the increase in acceptor intensity ( due to sensitized emission ) relative to the decrease in donor intensity ( due to quenching ) ( Chen et al . , 2006b ) . This constant depends on the specific FRET pair used , imaging system , and image acquisition settings , and was calculated for both mTFP1-Venus and Clover-mRuby2 biosensors through imaging donor-acceptor fusion constructs of differing but constant FRET efficiencies . See Supplementary file 3 for bleed-through and G coefficients . Wherever possible , image analysis was standardized using custom-written Matlab software . Analysis parameters ( Supplementary file 3 ) and thresholds for image segmentation were maintained across multiple days of experiments of the same type . For all TSMod and VinTS constructs , only cells with an average acceptor intensity within a pre-specified range were analyzed . This range was set to [1000 40000] for mTFP1-Venus-based sensors or [600 24000] for Clover-mRuby2-based sensors , resulting in exclusion of <10% of cells . Finally , for VinTS constructs , cells that were not fully spread were also excluded from analysis . Post-processing of FRET images to segment and quantify the characteristics of individual FAs was performed using custom-written code in MATLAB ( Mathworks ) . Briefly , FAs were identified and segmented on the acceptor channel using the water algorithm ( Zamir et al . , 1999 ) . The resultant mask was applied across all images for ease of data visualization and quantification . For each identified FA , parameters describing its brightness in the acceptor channel , morphology , and molecular loading ( FRET ) were determined . To identify single cells , closed boundaries were drawn by the user based on the unmasked acceptor-channel image . From these cell outlines , parameters describing cell morphology and FA subcellular location were also determined . Line scans of single FAs were performed using ImageJ software ( US National Institutes of Health , Bethesda , MD; http://imagej . nih . gov/ij/ ) . Specifically , the line tool was used to visualize the acceptor channel intensity profile across single , large FAs in the cell periphery . The coordinates of these lines , drawn axially starting from the tip of FAs distal to the cell body , were then transferred to masked FRET efficiency images . Acceptor intensity and FRET efficiency profiles from single FAs were saved as text files for subsequent analysis . Hypotonic lysates were prepared from HEK293 cells as previously described ( Chen et al . , 2005 ) . In addition to experimental samples , lysates from an equal number of untransfected cells were harvested and used as a reference background . Spectrofluorometric measurements were made with a Fluorolog-3 ( FL3-22 , HORIBA Scientific Jobin Yvon , Edison , NJ ) spectrofluorometer with 1 nm step size , 0 . 2 s integration time , and 3 nm excitation and emission slit widths for all samples . For FRET measurements of mTFP1-Venus sensors , spectra were traced from 472 to 650 nm following donor excitation ( λDex ) at 458 nm , and from 520 to 650 nm following acceptor excitation ( λAex ) at 505 nm . For Clover-mRuby2 sensors , spectra were traced from 520 to 700 nm following donor excitation at 505 nm , and from 590 to 700 nm following acceptor excitation at 575 nm . The same settings were used to measure the emission spectra of full length and minimal FPs to confirm their spectral properties individually . Custom-written code in MATLAB ( Mathworks ) was used to calculate FRET efficiency via the ( ratio ) A method ( Majumdar et al . , 2005 ) as:E=εAλAexεDλDex ( IfλAemIaλAem−εAλDexεAλAex ) where If and Ia are the intensities , at peak acceptor emission wavelength ( λAem , 530 nm for Venus , 610 nm for mRuby2 ) , of the sample excited at donor and acceptor wavelengths , respectively . Donor and acceptor molar extinction coefficients ( εD and εA , respectively ) were calculated from absorbance spectra measured on the same Fluorolog-3 spectrofluorometer in absorbance mode ( 1 nm step size , 0 . 1 s integration time , 2 nm excitation and emission slit widths ) using previously-measured maximal extinction coefficients: 64 , 000 M−1cm−1 for mTFP1 ( Ai et al . , 2006 ) , 93 , 000 M−1cm−1 for Venus ( Nagai et al . , 2002 ) , 111 , 000 M−1cm−1 for Clover ( ) , and 113 , 000 M−1cm−1 for mRuby2 ( Lam et al . , 2012 ) . This approach was also used to confirm the absorbance spectra of the minimal FPs were unaltered as compared to the parent version . All statistical analyses , except numerical bootstrapping , were performed using JMP Pro 12 software ( SAS , Cary , NC ) . ANOVAs were used to determine if statistically significant differences ( p<0 . 05 ) were present between groups . If statistical differences were detected , Tukey’s HSD post-hoc testing was used to perform multiple comparisons and assess statistical differences between individual groups ( see Supplementary file 4 for exact p-values and multiple comparisons test details ) . Box-and-whisker diagrams ( Figure 1F , Figure 1—figure supplement 2C ) display the following elements: center line , median; box limits , upper and lower quartiles; whiskers , 1 . 5x interquartile range; red filled circle , mean; open circles , outliers . Numerical bootstrapping using the built-in Matlab ( Mathworks ) function bootstrp . m was used to calculate 95% confidence intervals for measurements of LP . Specifically , for each of 200 bootstrapped samples , drawn with replacement from the pertinent dataset , the LP that best reflected that sample was calculated by chi-squared error minimization . Fluorescence-force spectroscopy data ( Brenner et al . , 2016 ) was digitized using the digitize2 . m function in Matlab ( Mathworks ) . To recapitulate the uncertainty in these published unloaded FRET and FRET-force datasets , random sets of 100 data points obeying a Gaussian distribution with the reported mean and standard deviation were used . For FRET efficiency measurements , numerical bootstrapping of pilot data was used to determine the sample size required to estimate FRET efficiency to within 1% of the true population mean . This was determined to be 10–20 cells from three independent experiments for in cellulo measurements , or five independent samples for in vitro spectral FRET characterization . For fluorescent protein absorbance/emission spectra characterization , sample size was not pre-determined . Rather , the reproduction of data from independent experiments was deemed sufficient to draw conclusions about changes in the fluorescent protein spectral properties . All code developed and utilized in this study is publically available at https://gitlab . oit . duke . edu/HoffmanLab-Public . Code used to measure and correct for chromatic aberration , uneven illumination , darkfield noise , and background intensities can be found in the ‘image-preprocessing’ repository https://gitlab . oit . duke . edu/HoffmanLab-Public/image-preprocessing ( copy archived at https://github . com/elifesciences-publications/HoffmanLab-image-preprocessing ) . Code used to analyze FRET-based tension sensor data , including FRET corrections , object segmentation , object analysis , and cell segmentation can be found in the ‘fret-analysis’ repository https://gitlab . oit . duke . edu/HoffmanLab-Public/fret-analysis ( copy archived at https://github . com/elifesciences-publications/HoffmanLab-fret-analysis ) . Code to perform spectral FRET analysis on in vitro fluorometric FRET experimental data can be found in the ‘fluorimetry-fret’ repository https://gitlab . oit . duke . edu/HoffmanLab-Public/fluorimetry-fret ( copy archived at https://github . com/elifesciences-publications/HoffmanLab-fluorimetry-fret ) . Source code for the computational TSMod calibration model , which allows the user to simulate the mechanical response of molecular tension sensor modules , can be found in the ‘tsmod-calibration-model’ repository https://gitlab . oit . duke . edu/HoffmanLab-Public/tsmod-calibration-model ( copy archived at https://github . com/elifesciences-publications/HoffmanLab-tsmod-calibration-model ) . Source code for the structural model of FA molecules , used to explore the physical limits of extension-control , can be found in the ‘FA-structural-model’ repository https://gitlab . oit . duke . edu/HoffmanLab-Public/FA-structural-model ( copy archived at https://github . com/elifesciences-publications/HoffmanLab-FA-structural-model ) .	Cells must sense signals from their surroundings to play their roles within the body . These signals can be biochemical , such as growth-promoting substances , or mechanical , for example the stiffness or softness of the environment . Mechanical signals can be detected by load-bearing proteins , which stretch like tiny springs in response to forces . In animals , these proteins span the membrane separating the interior of the cell from the exterior . Externally , the proteins attach to structures around the cell; internally , they connect to the machinery that both generates forces and allows cells to respond to signals from outside . As such , load-bearing proteins form a direct mechanical link between cell and environment . Scientists use tools called molecular tension sensors to measure how much a load-bearing protein stretches in response to changes , and the force that is being applied to it . However , just like any other type of scale , these sensors only work over a certain range , which happens to be limited . This means that , for example , they cannot measure forces in tissues that are too soft ( like the brain ) , or too stiff ( such as bones ) . New sensors that can assess forces in these contexts are therefore needed , but so far research in this area has been slow due to a reliance on ‘trial-and-error’ approaches . Here , LaCroix et al . developed a new method to predict the sensitivity of molecular tension sensors inside cells . This was accomplished by examining several existing sensors , and identifying which components could be altered to change the properties of the sensors . Then , this information was used to create a computer model that could predict how new sensors would behave , and which range of forces they could measure . Finally , the sensors designed following this method were tested in mouse cells grown in the laboratory , and they worked better than their predecessors . The next step was for LaCroix et al . to use a trio of new sensors with different sensitivities to study the load-bearing protein vinculin in mouse cells . The goal was to figure out exactly how cells manage their load-bearing proteins . Indeed , it was widely assumed that a cell acts on a load-bearing protein by applying a force on it . In response , the protein would stretch by a certain amount , which can change depending on its properties – a ‘stiffer’ protein would stretch less . Unexpectedly , the new sensors showed that cells instead manipulate how much vinculin stretches , applying varying forces to achieve the same length of the protein in different environments . Improved molecular tension sensors will give scientists a better insight into how cells respond to their mechanical environment , which could help to direct cell behavior in tissues engineered in the laboratory . This knowledge is also directly relevant to human health , as the mechanical properties of many tissues change during disease , such as tumors stiffening during cancer .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology", "structural", "biology", "and", "molecular", "biophysics" ]	2018	Tunable molecular tension sensors reveal extension-based control of vinculin loading
A great deal of interest has been focused recently on the habenula and its critical role in aversion , negative-reward and drug dependence . Using a conditional mouse model of the ACh-synthesizing enzyme choline acetyltransferase ( Chat ) , we report that local elimination of acetylcholine ( ACh ) in medial habenula ( MHb ) neurons alters glutamate corelease and presynaptic facilitation . Electron microscopy and immuno-isolation analyses revealed colocalization of ACh and glutamate vesicular transporters in synaptic vesicles ( SVs ) in the central IPN . Glutamate reuptake in SVs prepared from the IPN was increased by ACh , indicating vesicular synergy . Mice lacking CHAT in habenular neurons were insensitive to nicotine-conditioned reward and withdrawal . These data demonstrate that ACh controls the quantal size and release frequency of glutamate at habenular synapses , and suggest that the synergistic functions of ACh and glutamate may be generally important for modulation of cholinergic circuit function and behavior . Acetylcholine ( ACh ) was first described in the heart muscle ( Loewi , 1921 ) , and later in the peripheral nervous system , as a fast acting neurotransmitter at the neuromuscular junction ( Bennett , 2000 ) . In the central nervous system ( CNS ) , however , evidence supports the hypothesis that ACh acts by volume transmission and that slowly changing levels of extracellular ACh mediate arousal states contributing to attention , sleep , learning and memory ( Dani and Bertrand , 2007; Everitt and Robbins , 1997; Mesulam et al . , 1983; Picciotto et al . , 2012; Ren et al . , 2011; Sarter et al . , 2009 ) . Extracellular ACh levels are limited by acetylcholinesterase ( AChE ) , which cleaves ACh into choline and acetyl-coA ( Rosenberry , 1975 ) . Cholinergic projection neurons in the medial habenula ( MHb ) synapse in the interpeduncular nucleus ( IPN ) , which contains extremely high levels of AChE ( Flumerfelt and Contestabile , 1982 ) . At synapses with a high concentration of AChE , ACh is so quickly degraded that a single molecule cannot activate a second receptor ( Kuffler and Yoshikami , 1975 ) . Since both nicotinic ( nAChRs ) and muscarinic ( mAChRs ) acetylcholine receptors are often localized extrasynaptically on dendrites and somata , and presynaptically at axonal terminals ( De-Miguel and Fuxe , 2012; Descarries et al . , 1997; Role and Berg , 1996 ) , it is thought that ACh volume transmission in the IPN requires high frequency stimulation of habenular neurons ( Ren et al . , 2011 ) . The release of ACh from habenular terminals is consistent with genetic studies demonstrating altered responses to nicotine addiction as a consequence of mutations in nicotinic receptors that are enriched in the MHb-IPN ( Antolin-Fontes et al . , 2015; Fowler et al . , 2011; Jackson et al . , 2010; Salas et al . , 2009 ) . Although a clear role for ACh in volume transmission and for nAChRs in nicotine dependence has been established , several observations suggest that ACh may have additional functions in cholinergic neurons . For example , it has been demonstrated in several systems that neurotransmitters can be coreleased ( El Mestikawy et al . , 2011; Gras et al . , 2008; Guzman et al . , 2011; Hnasko et al . , 2010; Hnasko and Edwards , 2012; Ren et al . , 2011; Shabel et al . , 2014 ) , and that cooperation between vesicular neurotransmitter transporters located in the same synaptic vesicle ( SV ) can reciprocally increase the packaging of their respective neurotransmitters into SVs , a process termed vesicular synergy ( El Mestikawy et al . , 2011; Gras et al . , 2008; Hnasko et al . , 2010 ) . Furthermore , infusion of glutamate receptor antagonists into the IPN results in decreased nicotine intake ( Fowler et al . , 2011 ) and withdrawal ( Zhao-Shea et al . , 2013 ) suggesting that glutamate-mediated fast synaptic transmission is also important in nicotine addiction . Given evidence that both glutamate and ACh play important roles at habenular-IPN synapses , and that vesicular synergy contributes to the physiology and function of other critical CNS circuits , we were interested in examining the interactions between these transmitter systems in habenular neurons and in determining their contributions to nicotine dependence . To investigate the interactions between glutamate and ACh in the MHb-IPN circuit , we locally eliminated ACh at this synapse by genetic deletion of the ACh-synthesizing enzyme choline acetyltransferase ( Chat ) in MHb neurons in conditional knockout ( cKO ) mice . Immunogold electron microscopy revealed that ACh and glutamate vesicular transporters colocalize in a significant fraction of SVs in the central IPN ( IPC ) . Patch clamp recordings showed that glutamatergic miniature excitatory postsynaptic currents ( mEPSCs ) were smaller in IPC neurons of ChAT-cKO , but unchanged in neurons of the lateral IPN ( IPL ) which receives non-cholinergic input , indicating that glutamate release is reduced in the absence of ACh . Upon ACh or nicotine superfusion , wild-type ( wt ) IPC neurons displayed an increased frequency of mEPSC . In ChAT-cKO slices this response was not observed , indicating that presynaptic facilitation of glutamate release is impaired . Direct measurements of glutamate reuptake into IPN SVs in the presence and absence of ACh demonstrated vesicular synergy . Behaviorally , ChAT-cKO mice were insensitive to the rewarding properties of nicotine and displayed no withdrawal signs after cessation of chronic nicotine treatment . Our results , therefore , establish an essential role for ACh corelease with glutamate in habenular neuron function and reveal an additional mechanism that may play an important role in nicotine dependence . CHAT is the only enzyme that synthesizes ACh , and it is expressed at the neuromuscular junction and in several brain areas including the MHb , basal forebrain ( BF ) , laterodorsal tegmental nucleus ( LDTg ) , third cranial nerve ( 3N ) and nucleus of the solitary tract ( NTS ) ( Figure 1A ) . Null mutant mice for Chat obtained by crossing a floxed allele of the Chat gene ( Chatflox/flox ) to β-actin-Cre transgenic mice die at birth ( Misgeld et al . , 2002 ) . Therefore to elucidate the contribution of cholinergic transmission to the function of the MHb-IPN pathway , we sought to conditionally delete Chat in habenular neurons . To drive Cre-recombinase activity specifically to MHb cholinergic neurons we analyzed their translational profile ( Gorlich et al . , 2013 ) and selected the Kiaa1107-Cre mouse BAC transgenic line for its specific pattern of expression in the MHb ( GENSAT , www . gensat . org; Figure 1A ) . Kiaa1107-Cre mice were crossed to reporter Gt ( ROSA ) 26Sortm1 ( EYFP ) Cos mice ( Figure 1B ) to verify that EYFP expression resulting from Cre-recombinase activity was achieved in MHb neuron somata and habenular axonal projections in the IPN ( Figure 1C ) . Double immunostaining with CHAT and EYFP antibodies in Kiaa1107-Cre mice crossed to reporter Gt ( ROSA ) 26Sortm1 ( EYFP ) Cos mice ( Figure 1D ) demonstrated that 99% ( 1912 of 1933 ) of CHAT positive neurons in the MHb are positive for the EYFP reporter . In contrast , CHAT populations in striatum , PPTg and LDTg show extremely low expression of the EYFP reporter ( 0 . 5 to 1 . 3% of CHAT cells ) ( Figure 1E ) . These results establish that the Kiaa1107-Cre line specifically targets the cholinergic population of habenular neurons without affecting other cholinergic neurons . 10 . 7554/eLife . 11396 . 003Figure 1 . Analysis of the Cre driver line Kiaa1107-Cre in cholinergic neurons . ( A ) Sagittal images from GENSAT corresponding to mouse BAC transgenic lines: Chat-EGFP founder GH293 and Kiaa1107-Cre founder KJ227 . Chat-EGFP mice show EGFP expression in cholinergic areas including MHb , habenular projections to the interpeduncular nucleus ( IPN ) , the laterodorsal tegmentum ( LDTg ) , third cranial nerve ( 3N ) , basal forebrain ( BF ) , and nucleus of the solitary tract ( NTS ) . Kiaa1107-Cre mice show Cre-recombinase expression in the MHb and axonal projections in the IPN . ( B ) Mouse breeding scheme of the Cre-recombinase Kiaa1107-Cre transgenic line crossed with the Cre-dependent reporter line Gt ( ROSA ) 26Sortm1 ( EYFP ) Cos to visualize Cre-recombinase activity . ( C ) Cre-dependent EYFP-expression driven by Kiaa1107-Cre was observed in the ventral two-thirds of the MHb and in the axonal habenular projections to the central IPN . Scale bars: 200 µm . ( D ) Double immunostaining analyses with CHAT ( red ) and EYFP ( green ) antibodies in cholinergic brain areas of Kiaa1107-Cre crossed to Gt ( ROSA ) 26Sortm1 ( EYFP ) Cos mice . High magnifications of the indicated square areas are shown on the right column . Neurons in the ventral part of the MHb are double positive , while CHAT and EYFP label different cells in striatum and LDTg . Scale bars: MHb 100 µm , inset 50 µm and striatum , LDTg 200 µm , insets 100 µm . ( E ) Quantification of CHAT positive neurons expressing EYFP in MHb , striatum and tegmental cholinergic areas ( LDTg , laterodorsal tegmentum; PPTg , pedunculopontine nucleus ) . The number of double positive cells per brain area is indicated above each bar ( n=8 to 16 sections from three different mice ) . MHb: Medial habenula . DOI: http://dx . doi . org/10 . 7554/eLife . 11396 . 003 Previous studies have demonstrated that a conditional allele of the Chat gene ( Chatflox/flox ) , where exons 3 and 4 are flanked by loxP sites , generates a Chat-/- allele when these exons are excised by Cre-recombinase ( Misgeld et al . , 2002 ) ( Figure 2A ) . Thus , to remove the enzyme from habenular neurons , we employed Kiaa1107-Cre mice to drive conditional deletion of the CHAT enzyme in habenular neurons ( Figure 2A ) . Western blot analyses of habenular and IPN brain extracts revealed absence of CHAT in double positive mice for Kiaa1107-Cre and Chatflox/flox ( Figure 2B ) , hereafter referred to as ChAT-cKO mice . Immunohistochemical analyses of brain sections clearly showed that CHAT immunoreactivity was absent in the MHb , fasciculus retroflexus ( fr ) and IPN in ChAT-cKO mice ( Figure 2C–D ) . To assess the penetrance of the driver Cre-line we quantified the number of neurons that remained positive for CHAT in ChAT-cKO mice across different cholinergic areas ( Figure 2E , F ) . This analysis showed that only 0 . 3% habenular neurons in ChAT-cKO mice retained their immunoreactivity to CHAT , while the number of CHAT positive neurons in striatum , PPTg and LDTg were comparable in wt and ChAT-cKO mice ( Figure 2F ) . ChAT-cKO and wt mice also displayed comparable immunoreactivity for CHAT in other cholinergic brain areas including BF and third cranial nerve ( 3N ) ( Figure 2G ) . These data show that the Kiaa1107-Cre strain drives Cre-recombination of the Chat conditional allele in 99 . 7% of habenular cholinergic neurons , and that it can be used to specifically delete Chat only from habenular neurons without perturbing other cholinergic sources in the brain . To determine whether Chat excision occurred during the early stages of habenular development ( Quina et al . , 2009 ) , we analyzed the expression of CHAT in wt and ChAT-cKO at early postnatal ages and detected the onset of Chat Cre-mediated excision by Kiaa1107-Cre between postnatal days P6 and P7 ( Figure 3 ) . Taken together , these data show that this genetic manipulation efficiently and selectively eliminates Chat in cholinergic habenular neurons , and that it does so after formation of the MHb/IPN circuitry . The ChAT-cKO mouse , therefore , is a useful model in which to test the consequences of selectively removing one neurotransmitter in a specific axonal tract . 10 . 7554/eLife . 11396 . 004Figure 2 . Conditional gene deletion of Chat in cholinergic neurons of the MHb . ( A ) Mouse breeding scheme of the Cre-recombinase Kiaa1107 line crossed to Chatflox/flox mice to generate ChAT-cKO mice with conditional gene deletion of Chat in habenular neurons . ( B ) Western blot analysis with CHAT and α-tubulin antibodies in MHb and IPN extracts from wt and ChAT-cKO mouse brains . ( C ) Angled sections of the midbrain immunostained for CHAT ( red ) . In wt mice ( left panel ) , CHAT is highly expressed in MHb neurons , along their axons in the fasciculus retroflexus ( fr ) and in their axonal terminals in the IPN . In ChAT-cKO mice ( right panel ) , CHAT immunoreactivity is no longer detected in the MHb-fr-IPN tract . Scale bars: 300 µm . ( D ) Sagittal sections of the midbrain immunostained for CHAT ( red ) and neurofilament ( green ) . CHAT immunoreactivity is strong in the IPN ( arrowhead ) of wt mice ( upper panel ) , and absent in ChAT-cKO brains ( lower panel ) while the laterodorsal tegmental nucleus ( LDTg ) - an efferent target of the IPN - and adjacent third cranial nerves ( 3N ) show similar CHAT expression in wt and ChAT-cKO mice . Scale bars: 300 µm . ( E ) Immunostaining analyses in wt and ChAT-cKO show that CHAT immunoreactivity ( red ) is no longer detected in the MHb of ChAT-cKO mice . Wt and ChAT-cKO mice show similar CHAT immunoreactivity in striatum and LDTg . Scale bars: MHb and striatum 100 µm , LDTg 200 µm . ( F ) Quantification of CHAT positive neurons in the indicated cholinergic areas . ChAT-cKO mice do not express CHAT in the MHb , while striatum , PPTg and LDTg show comparable number of neurons positive for CHAT ( percentage of the number of cells counted per section normalized to wt is shown in bars . MHb wt: 100 . 1 ± 3 . 676 , number of sections=22; MHb ChAT-cKO: 0 . 3326 ± 0 . 1380 , n=22; unpaired t-test p<0 . 0001; Striatum wt: 100 . 0 ± 7 . 233 , n=22; Striatum ChAT-cKO: 92 . 27 ± 7 . 112 , n=22; unpaired t-test p=0 . 45; PPTg wt: 100 . 1 ± 11 . 70 , n=14; PPTg ChAT-cKO: 115 . 8 ± 10 . 78 , n=14; unpaired t-test=0 . 33; LDTg wt: 100 . 0 ± 11 . 50 , n=13; LDTg ChAT-cKO: 122 . 0 ± 16 . 86 , n=13; unpaired t-test=0 . 29 . The total number of neurons counted per brain area is shown above the bars; n=3 mice per genotype ) . ( G ) Sagittal brain sections immunostained for CHAT ( red ) and neurofilament ( green ) . The axonal projections and general anatomy of the fr , IPN and surrounding tegmental areas are comparable between wt ( left panels ) and ChAT-cKO ( right panels ) . ChAT-cKO mice lack CHAT-immunoreactivity in the MHb-fr-IPN projection but show similar CHAT expression as wt mice in other brain areas , such as the LDTg , third cranial nerve ( 3N ) , striatum ( Str ) and BF . Scale bar: 800 µm . BF: Basal forebrain; cKO: Conditional knockout; IPN: Interpeduncular nucleus; LDTg: Laterodorsal tegmental nucleus; MHb: Medial habenula . DOI: http://dx . doi . org/10 . 7554/eLife . 11396 . 00410 . 7554/eLife . 11396 . 005Figure 3 . Conditional gene deletion of Chat driven by Kiaa1107 Cre-recombinase line is specific for the MHb-IPN tract and occurs during the first postnatal week . ( A–D ) Immunostaining with CHAT ( red ) and counterstaining with DAPI ( blue ) shows progressive loss of CHAT signal in MHb neurons and their axonal terminals in the IPN at postnatal day 6 ( P6 ) and postnatal day 7 ( P7 ) . IPN: Interpeduncular nucleus; MHb: Medial habenula . DOI: http://dx . doi . org/10 . 7554/eLife . 11396 . 005 The major input to the IPN is glutamatergic and originates in the habenula , but there are several other glutamatergic afferents to the IPN . These include projections from the laterodorsal tegmentum ( LDTg ) , raphe nuclei , locus coeruleus , periaqueductal grey ( PAG ) and nucleus of the diagonal band . Studies on the neurotransmitter content of these other inputs indicate that there are only three sources that send cholinergic and glutamatergic projections to the IPN: the MHb , the LDTg and the nucleus of the diagonal band . Importantly , in both the LDTg and the nucleus of the diagonal band , none of the glutamatergic cells overlap with cholinergic markers ( Henderson et al . , 2010; Wang and Morales , 2009 ) . This implies that axonal terminals in the IPN that are colabeled with glutamatergic and cholinergic markers originate in the MHb . However , within the MHb there are two distinct populations: a cholinergic population in the ventral MHb that projects to the rostral ( IPR ) , intermediate ( IPI ) and central ( IPC ) subnuclei of the IPN and a peptidergic ( Substance P positive ) population in the dorsal MHb that projects to the lateral part of the IPN ( IPL ) ( Figure 4A ) . Both types of projections release glutamate . To quantify the degree of overlap of glutamatergic and cholinergic synapses in different nuclei of the IPN , we performed double immunostainings in wt and ChAT-cKO mice and calculated the Manders’ colocalization coefficient which ranges from 0 for no colocalization to 1 for complete colocalization ( Manders et al . , 1993 ) . These analyses indicated an extremely high colocalization of VGLUT1 and VACHT in the IPC of both wt ( M1=0 . 80 ) and ChAT-CKO ( M1=0 . 79 ) , less overlap in the IPI ( wt , M1=0 . 78 , ChAT-CKO , M1=0 . 77 ) and IPR ( wt , M1=0 . 68 , ChAT-CKO , M1=0 . 69 ) , and no colocalization in the IPL ( wt , M1=0 . 005 , ChAT-CKO , M1=0 . 01 ) ( Figure 4 ) . Taken together with the published literature , our data demonstrate that the vast majority of cholinergic+glutamatergic input to the IPC originates in the ventral MHb . 10 . 7554/eLife . 11396 . 006Figure 4 . Distribution of cholinergic and glutamatergic axonal terminals in IPN subnuclei . ( A ) Schematic representation of the segregated projections of medial habenular neurons to IPN subnuclei . The dorsal part of the MHb ( dMHb , blue ) consists of peptidergic neurons ( positive for substance P , SP ) that project to the lateral part of the IPN ( IPL , blue ) . The ventral part of the MHb ( vMHb , red ) contains cholinergic neurons ( positive for CHAT , red ) that project to the rostral ( IPR ) , intermediate ( IPI ) and central ( IPC ) subnuclei of the IPN ( red ) . Both types of projections co-release glutamate ( VGLUT , green ) . ( B–M ) Double immunostaining and Manders’ colocalization coefficient analyses of glutamatergic and cholinergic markers in different subnuclei of the IPN . M1: index of colocalization and P: significance of correlation were measured in the indicated dotted squares which are located: 1 in IPR , 2 in IPI , 3 in IPC and 4 in IPL . ( B–G ) In wt mice , VGLUT1 ( green ) and CHAT ( red ) show the strongest index of colocalization in the IPC and no colocalization in the IPL . CHAT immunoreactivity signal is absent in the IPN of ChAT-cKO mice . ( H–M ) Analyses of VGLUT1 ( green ) and VACHT ( violet ) show the highest colocalization index also in the IPC of both wt and ChAT-cKO , and no colocalization in the IPL . The M1 and the P values shown below panels 1–4 were calculated from one image for each IPN subnucleus . cKO: Conditional knockout; IPN: Interpeduncular nucleus; MHb: Medial habenula; wt: Wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 11396 . 006 We next wanted to evaluate whether the vesicular transporters for ACh and glutamate , VACHT and VGLUT1 , which colocalize in the IPC by light microscopy ( Figure 4H–M ) , actually label the same individual SV . Single immunogold electron microscopic experiments showed specific immunoreactivity for either VACHT or VGLUT1 at synaptic vesicular membranes in habenular presynaptic terminals ( Figure 5B–C ) . Quantification analyses showed that both antibodies have comparable penetration and labeling immunoreactivities , including the number of VACHT and VGLUT1-labeled vesicles per synaptic terminal ( Figure 5D ) , the labeling density ( Figure 5E ) and the percentage of vesicles labeled within a terminal ( Figure 5F ) . In addition , the area of the presynaptic terminal and the length of the active zone labeled by each antibody were similar in both groups ( Figure 5G , H ) indicating that the localization of VACHT and VGLUT1 in synaptic terminals is comparable . Given that the extent of labeling of SVs at habenular synapses is similar and high using these antibodies , these reagents can be used to study in detail the distribution , density and colocalization of VACHT and VGLUT1 transporters in SVs . 10 . 7554/eLife . 11396 . 007Figure 5 . VACHT and VGLUT1 are located in synaptic vesicles of axonal terminals in the central IPN . ( A ) Scheme of the brain area dissected for Electron Microscopy ( EM ) analyses . The red square over the IPC indicates the region analyzed by EM . ( B–C ) Representative micrographs of single pre-embedding immunogold EM analyses of VACHT ( B ) and VGLUT1 ( C ) showing immunogold particles in synaptic vesicles ( SVs ) grouped together at presynaptic terminals identifiable by the dense active zone ( AZ ) . The micrographs on the right column ( no primary control ) correspond to control experiments in which sections were treated following the same procedure while omitting the primary antibody . Scale bar: 100 nm . ( D–H ) Quantitative analysis of single pre-embedding immunogold electron micrographs . ( D ) The number of gold particles labeled either by VACHT or VGLUT1 antibodies per terminal is not statistical different between antibodies ( VACHT=23 . 05 ± 4 . 37 , n=19 terminals; VGLUT1= 23 . 86 ± 2 . 51 , n=21 terminals; unpaired t-test p=0 . 87 ) . ( E ) The labeling density ( number of gold particles per terminal/area of the terminal ) is not significantly different between VACHT and VGLUT1 positive terminals ( VACHT= 38 . 31 ± 5 . 95 , n=19; VGLUT1= 46 . 48 ± 5 . 175 , n=21; unpaired t-test p=0 . 3 ) . ( F ) No difference is observed in the percentage of VACHT or VGLUT1 immunolabeled vesicles ( labeled vesicles/ total number of vesicles100 ) per terminal ( VACHT= 39 . 73 ± 4 . 30 , n=19; VGLUT1= 31 . 91 ± 2 . 97 , n=21; unpaired t-test p=0 . 13 ) . ( G ) The area of the presynaptic terminals labeled with either VACHT or VGLUT1 gold particles is not significantly different ( VACHT= 0 . 58 ± 0 . 04 , n=19; VGLUT1= 0 . 61 ± 0 . 07 , n=21; unpaired t-test p=0 . 84 ) . ( H ) No difference is observed in the length of the active zone of presynaptic terminals labeled with either VACHT or VGLUT1 gold particles ( VACHT= 0 . 22 ± 0 . 02 , n=28; VGLUT1= 0 . 24 ± 0 . 02 , n=25; unpaired t-test p=0 . 52 ) . ( I–L ) For quantification of the distances of immunogold labeled SVs to the AZ , the area of each presynaptic terminal was delineated ( blue ) and the distance of each gold particle within a terminal to the center of the active zone ( yellow lines ) was measured for VACHT ( I ) and VGLUT1 ( J ) . Scale bar: 100 nm . ( K ) Density plot of the distances of VACHT ( black bars ) and VGLUT1 ( red bars ) labeled particles to the active zone . ( L ) Scatter plot of the distances of VACHT and VGLUT1 gold particles to the active zone . Vesicles labeled by either VACHT or VGLUT1 located beyond 350 nm from the active zone are differentially distributed ( F ( 1 , 935 ) = 4 . 634; p<0 . 05; one-way ANOVA ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11396 . 007 To determine whether they were differentially distributed across pools of synaptic vesicles , we measured the distance between each VACHT and VGLUT1-labeled vesicle and the center of the closest active zone ( Figure 5I , J ) . There were no significant differences in the density of VACHT and VGLUT-1-labeled vesicles within 350 nm from the active zone ( Figure 5K–L ) . However the scatter plot distribution showed that VACHT and VGLUT1-labeled vesicles located farther than 350 nm from the active zone were differentially distributed ( F ( 1 , 936 ) = 4 . 954; p<0 . 05; one-way ANOVA ) ( Figure 5L ) . It has been proposed that SVs might be organized in three functionally distinct pools at increasing distances from the active zone: the readily releasable pool ( RRP 0–60 nm ) , the recycling pool ( 60–200 nm ) , and the reserve pool ( beyond 200 nm ) ( Rizzoli and Betz , 2005 ) ; however it is still debated whether the recycling and reserve pools are intermingled ( Marra et al . , 2012; Rizzoli , 2014 ) . Our results show similar distribution of vesicles containing ACh and glutamate within the two closer SV pools . However the reserve pool appears to contain more glutamatergic than cholinergic SVs . Finally , to determine whether VGLUT1 and VACHT are present in the same synapses and synaptic vesicles , we performed double post-embedding immunogold electron microscopic analyses ( Figure 6A , B ) . We observed colabeling of VACHT-6 nm and VGLUT1-12 nm gold particles in 72 of 90 labeled synaptic terminals , 8 terminals with only VAChT-6 nm particles and 10 terminals with only VGLUT1-12 nm particles indicating that both transporters are present in the vast majority ( 80% ) of habenular terminals in the central IPN ( Figure 6C ) . To assess whether VACHT and VGLUT1 are present in the same SV , we measured the distance from each VGLUT1-gold particle to the nearest VACHT-gold particle ( Figure 6D ) and adopted the criterion described by ( Stensrud et al . , 2013 ) stating that 90 nm is the maximal distance between gold particles which colocalize in the same vesicle ( Figure 6E ) . This distance has been calculated based on the diameter of a synaptic vesicle of approximately 30 nm ( Gundersen et al . , 1998 ) , which we consistently found to be 35 . 18 ± 0 . 43 nm ( n=203 ) in habenular terminals , and based on the distance from an antigen epitope to the center of a secondary antibody conjugated with 10 nm gold particles which spans up to 30 nm ( Bergersen et al . , 2012 ) . Hence , two gold particles located within a distance of 90 nm could potentially label the same vesicle ( Stensrud et al . , 2013 ) . Considering this maximal theoretical distance of 90 nm , 54 . 35% of VGLUT1-gold particles ( n=75/138 ) could be located in the same SV as VACHT ( Figure 6D ) . However given that SVs are closely packed , this may be an overestimate because adjacent particles could also be labeling transporters in neighboring SVs . As control analyses , we quantified the distance from each VGLUT1-gold particle to the closest VGLUT1-particle ( Figure 6F ) , and the distance from each VACHT to the closest VACHT-gold particle ( Figure 6G ) . We found that 35 . 95% of VGLUT1 particles ( n=32/89 ) , and 61 . 08% of VACHT particles ( n=113/185 ) are within 90 nm of each other and could potentially label the same synaptic vesicle . These ultrastructural analyses indicate that ACh and glutamate vesicular transporters are coexpressed in 80% of the axonal terminals in the IPC , and that within each presynaptic terminal a significant fraction of SVs colabel with VACHT and VGLUT immunogold particles , supporting the conclusion that ACh and glutamate can be cotransported into the same synaptic vesicles in habenular cholinergic presynaptic terminals10 . 7554/eLife . 11396 . 008Figure 6 . VACHT and VGLUT1 colocalize in the same synaptic vesicles in axonal terminals in the central IPN . ( A , B ) Double post-embedding immunogold labeling of VACHT ( 6 nm gold particles ) and VGLUT1 ( 12 nm gold particles ) shows colocalization of both transporters in the same synaptic vesicles . The right panel is a higher magnification of the boxed area . Synaptic vesicles are outlined in red . Arrowheads indicate synaptic vesicles that contain both transporters . Scale bar: 100 nm . In control experiments sections were treated following the same procedure while omitting the primary antibodies . Scale bar: 100 nm . ( C ) Percentage of axonal terminals that contain VGLUT1 or/and VACHT immunolabeled SVs . The number of counted terminals is shown inside each bar of the graph: from 109 visualized terminals , 10 terminals contain only VGLUT1 positive SVs , 8 terminals contain only VACHT positive SVs , 72 terminals contain SVs positive for VGLUT1 and VACHT , and 19 terminals showed no immunogold particles . ( D ) Frequency histogram showing the percentage of VGLUT1 particles located at the indicated distances ( 30 nm bins ) from the nearest VACHT gold particle . ( E ) Scheme of an SV labeled with two primary antibodies and secondary antibodies conjugated to a gold particle . According to the indicated size of an SV and conjugated antibodies , the maximum distance between two immunogold particles to potentially label the same synaptic vesicle is 90 nm . ( D , F , G ) Frequency histograms of the distance distributions between immunogold particles showing the percentage of particles located at the indicated distances ( 30 nm bins ) for VGLUT1 to VACHT ( D ) , for VGLUT1 to VGLUT1 ( F ) and for VACHT to VACHT ( G ) . Particles located closer than 90 nm apart ( on the left side of the dashed line ) potentially label the same vesicle . SV: Synaptic vesicleDOI: http://dx . doi . org/10 . 7554/eLife . 11396 . 008 Given our finding that glutamate and ACh can be cotransported into SVs ( Figure 6 ) , and that release of glutamate and ACh activate postsynaptic receptors via wired and volume transmission , respectively ( Ren et al . , 2011 ) , we were interested in the detailed mechanisms through which the lack of ACh could affect cholinergic and glutamatergic transmission . nAChRs are highly enriched in three compartments of the MHb-IPN tract ( Perry et al . , 2002; Zoli et al . , 1995 ) : the soma of MHb neurons , presynaptic terminals of MHb neurons in the IPN , and postsynaptic IPN neurons ( Covernton and Lester , 2002; Frahm et al . , 2011; Girod et al . , 2000; Ren et al . , 2011 ) . In contrast to extrasynaptic nAChRs that are activated by ambient levels of ACh by volume transmission , glutamate receptors are present on the postsynaptic IPN membrane and they rapidly ( milliseconds ) respond to fast stimulation in a wired transmission mode ( Ren et al . , 2011 ) . To distinguish cholinergic and glutamatergic currents in wt and ChAT-cKO mice we performed electrophysiological whole-cell recordings using different pharmacological conditions . We first measured evoked excitatory postsynaptic currents ( eEPSCs ) by local puff application of nicotine ( 100 ms , 100 µM ) . In MHb neurons , nicotine elicits large eEPSCs ( >2000 pA ) that are blocked by nAChR blockers ( Frahm et al . , 2011 ) and are thus exclusively cholinergic . As expected , eEPSCs did not differ between wt and ChAT-cKO ( wt: 2265 . 3 ± 210 . 4 pA , n=18; cKO: 2467 ± 175 . 2 pA , n=18; p=0 . 465 ) ( Figure 7B , C ) , since conditional deletion of Chat in the MHb would not directly affect expression of any cholinergic genes in the stria medullaris , which gives rise to the presynaptic cholinergic input to the MHb ( Contestabile and Fonnum , 1983; Gottesfeld and Jacobowitz , 1979 ) . We next asked whether IPC neurons , which receive cholinergic afferents mostly from the MHb ( Figures 4–6 ) , respond differently in wt and ChAT-cKO . It has been shown that ACh ( 1 mM ) evokes slow ( seconds ) inward currents from IPN neurons that are reversibly abolished by nAChR blockers ( Ren et al . , 2011 ) , and that optogenetic tetanic stimulation generates slow cholinergic inward currents as well as many fast glutamatergic inward currents at high-frequency , indicating that eEPSCs are mainly cholinergic but have a fast glutamatergic component . Pressure application of nicotine ( 100 µM ) evoked medium size inward currents from IPN neurons that had similar amplitudes in wt and ChAT-cKO mice IPN: wt: 254 . 3 ± 45 . 55 pA , n=52; cKO: 304 . 8 ± 51 . 03 pA , n=53; p=0 . 475 ) ( Figure 7D , E ) , demonstrating that elimination of ACh in habenular neurons does not affect nicotine-evoked responses of postsynaptic nAChRs in MHb and IPN neurons . 10 . 7554/eLife . 11396 . 009Figure 7 . Cholinergic eEPSC are unchanged whereas glutamatergic mEPSC are smaller in central IPN neurons of ChAT-cKO mice . ( A ) Schematic representation of the habenula and IPN subnuclei . Red squares over the MHb and IPC indicate the areas where the corresponding electrophysiological recordings were performed . ( B–E ) Cholinergic representative evoked excitatory postsynaptic currents ( eEPSC ) of MHb ( B ) and IPC ( D ) neurons by local puff application of 100 µM nicotine . Cholinergic , current amplitudes were comparable between wt and ChAT-cKO . ( C ) n= 18 per genotype , unpaired t-test p=0 . 465 . ( E ) n= 52 ( wt ) , n= 53 ( ChAT-cKO ) , unpaired t-test p=0 . 475 . ( F–I ) Representative recordings of glutamatergic miniature excitatory postsynaptic currents ( mEPSC ) in IPC neurons in wt ( F ) , ChAT-cKO ( G ) , and in wt neurons after addition of the AMPA-R and NMDA-R blockers NBQX and D-AP5 ( H ) . ( I ) Representative average glutamatergic mEPSC in wt and ChAT-cKO . ( J ) Cumulative probability plots of the glutamatergic mEPSC amplitudes show a left shift indicating that ChAT-cKO neurons ( red line ) have significantly smaller amplitudes than wt neurons ( black line ) . Insets represent the average amplitude of mEPSCs per neuron recorded during 1 min ( n=24 for wt and n=24 for cKO , unpaired t-test: p<0 . 01 ) . ( K ) Cumulative probability plots of the mEPSC frequency show similar curves for ChAT-cKO neurons ( red line ) and wt neurons ( black line ) . Insets show no significant differences in the average frequencies per recorded neuron ( n=27 for wt and n=24 for cKO ) . cKO: Conditional knockout; IPN: Interpeduncular nucleus . DOI: http://dx . doi . org/10 . 7554/eLife . 11396 . 009 We next recorded IPN neurons which exhibit fast spontaneous synaptic currents , mediated by AMPA-type glutamate receptors and GABAA-type receptors ( Ren et al . , 2011 ) . To isolate miniature excitatory postsynaptic potentials ( mEPSCs ) , we performed the recordings in the presence of GABAA receptor blockers and tetrodotoxin ( TTX ) ( Figure 7 F–K ) . TTX blocks action potential formation and its propagation . Thus , mEPSCs events reflect the probabilistic release of single vesicles into the synapse and their measurement can be used for the quantification of release probability and vesicular glutamate content ( Pinheiro and Mulle , 2008 ) . mEPSC were blocked by glutamate receptor inhibitors NBQX and D-AP5 ( Figure 7H ) indicating that mEPSCs in IPC neurons are glutamatergic , not cholinergic . Because IPN neurons exhibit high frequency mEPSCs , we only analyzed fast mEPSCs that exhibited an average amplitude >5 pA . This analysis revealed that the average amplitude of mEPSC in IPC neurons of wt mice is 18 . 2 ± 1 . 6 pA; whereas the average amplitude in ChAT-cKO neurons is 12 . 4 ± 0 . 9 pA ( p=0 . 005; wt: n=25; ChAT-cKO: n=24 ) ( Figure 7J ) . This corresponds to a 32% reduction in the amplitudes of glutamatergic mEPSC in ChAT-cKO animals , which might reflect a decrease of the vesicular content of glutamate or differences in the number or function of postsynaptic glutamate receptors ( Liu et al . , 1999; Watt et al . , 2000 ) . Quantitative analyses of the frequency of mEPSC showed no differences between IPC neurons of wt ( 5 . 3 ± 1 . 2 Hz for wt ) and ChAT-cKO mice ( 3 . 9 ± 0 . 8 Hz ) ( Figure 7K ) . The fact that glutamatergic mEPSCs have significantly reduced amplitudes ( but similar frequencies ) in central IPN neurons of ChAT-cKO mice , and that nicotine-evoked cholinergic eEPSCs remain unchanged in these mice , suggest that elimination of habenular ACh decreases the vesicular content of glutamate but does not impact on the excitability of postsynaptic nAChRs . It has been reported that presynaptic nAChRs facilitate glutamate release in a variety of synapses , including MHb-IPN terminals ( Girod et al . , 2000; Girod and Role , 2001; McGehee et al . , 1995 ) . To test whether local release of ACh could influence presynaptic facilitation by activation of nAChRs , we recorded mEPSC upon bath application of nicotine or ACh . Superfusion with nicotine ( Figure 8B ) or ACh ( Figure 8C ) did not change the amplitudes of mEPSCs in either wt , or increased the amplitudes of ChAT-cKO to wt levels , consistent with the hypothesis that the decrease of glutamate vesicular content in presynaptic terminals depends on the presence of intracellular ACh . Importanlty , we observed a significant increase in mEPSC frequency in wt , but not in ChAT-cKO mice upon superfusion with nicotine ( Figure 8D ) and ACh ( Figure 8E ) . This indicates that in the absence of network activity eliminated by TTX , nicotine or ACh activation of nAChRs at presynaptic terminals elicits enhanced discharge of glutamatergic mEPSCs in IPC neurons of wt mice . In contrast there is no presynaptic facilitation in ChAT-cKO neurons , suggesting that presynaptic nAChRs are downregulated in the absence of ACh released by habenular terminals . We next analyzed holding currents as a measure of the excitability of postsynaptic IPN neurons . The values are very similar between genotypes and upon perfusion with nAChR agonists ( Figure 8F ) , indicating that postsynaptic nAChRs in IPN neurons are not affected by elimination of endogenous ACh . Western blot analyses of nAChRs and GluR1 in IPN extracts did not show significant differences between wt and ChAT-cKO ( Figure 8G–H ) . However this assay cannot distinguish between presynaptic , extrasynaptic and postsynaptic receptors , and whether these are at the membrane or not . Given the nAChR-mediated presynaptic effect we have measured , it is possible that ACh might be required for the trafficking of nAChRs to the membrane without affecting the total number of nAChRs in the IPN . Taken together , these data and Figure 7 show that the function of presynaptic but not postsynaptic nAChR receptors is reduced in the IPC of ChAT-cKO , and that ACh is necessary for presynaptic nAChR-mediated facilitation and for increasing the vesicular glutamate content . 10 . 7554/eLife . 11396 . 010Figure 8 . Presynaptic facilitation of glutamate release is absent in ChAT-cKO mice . ( A ) Schematic representation of the habenula and IPN subnuclei . Red square over the IPC indicates the area of electrophysiological recordings . ( B , C ) Superfusion with 250 nM nicotine ( B ) or 100 µM ACh ( C ) did not change the glutamatergic mEPSC amplitude within each group ( n=22–24 per genotype , n=11–12 per condition ) . ChAT-cKO neurons show significantly smaller amplitudes than wt neurons in either condition ( repeated measures two-way-ANOVA: p<0 . 05 for genotype ( for nicotine and ACh ) , Bonferroni’s posttest: p<0 . 05 and unpaired t-test: # p<0 . 05 , ## p<0 . 01 ) . ( D , E ) Bath application of 250 nM nicotine ( D ) or 100 µM ACh ( E ) significantly increased the glutamatergic mEPSC frequency in wt , but not in ChAT-cKO ( n=22–24 per genotype and n=11–12 per condition , repeated measures two-way-ANOVA: p<0 . 01 for treatment and p<0 . 001 for interaction in F and p<0 . 05 for interaction in G; Bonferroni’s posttest: p<0 . 05 , **p<0 . 0001 and paired t-test: §§ p<0 . 01 ) . ( F ) The holding current is similar in wt and cKO and is not altered by bath application of 250 nM nicotine or 100 µM ACh ( n=22–24 per genotype , n=11–12 per condition ) . ( G , H ) Western blot analyses and quantification of β4 and GluR1 levels in IPN membrane extracts reveal no significant differences between wt and ChAT-cKO mice ( n= 3 per genotype ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11396 . 010 To confirm that glutamatergic inputs to the IPN that are non-cholinergic are not affected by selective elimination of ACh in habenular neurons , we performed electrophysiological recordings in the IPL ( Figure 9A ) , which receives non-cholinergic glutamatergic inputs from substance P positive neurons in the ventral MHb ( Figure 4 ) ( Antolin-Fontes et al . , 2015 ) . Cumulative and average amplitude of glutamatergic mEPSC recordings in IPL neurons identified by biocytin and SP immunostaings ( Figure 9B , C ) showed that mEPSC amplitudes and frequencies were not significantly different between wt and ChAT-cKO ( Figure 9D , E ) . It is interesting to note that in wt mice mEPSC were smaller in the IPL with respect to the IPC and similar to the amplitudes detected in the IPC of ChAT-cKO mice ( Figure 7J and 9D ) . Altogether these results support the additional conclusion that glutamatergic mEPSC are indeed of higher amplitude if ACh is present , suggesting that cotransmission with ACh might be a general mechanism to enhance glutamatergic transmission . 10 . 7554/eLife . 11396 . 011Figure 9 . Glutamatergic mEPSC are unchanged in the lateral part of the IPN . ( A ) Schematic representation of IPN subnuclei . Red square over the IPL indicates the area of electrophysiological recordings . The IPL does not receive cholinergic afferents from the ventral part of MHb , but mostly substance P afferents from the dorsal part of MHb . ( B ) Representative image of three recorded neurons filled with Biocytin ( green ) in the substance P ( SP; red ) positive IPL . Scale bar: 100 µm . ( C ) Higher magnification of neuron 1 in the IPL . Scale bar: 100 µm . ( D ) Cumulative and average amplitude of glutamatergic mEPSC recordings in the IPL . mEPSC amplitudes were not significantly different between wt ( n=12 ) and ChAT-cKO ( n=10; unpaired t-test p=0 . 556 ) . Marked in grey are the recorded cells 1 , 2 and 3 shown in B and C . ( E ) Cumulative and average frequency of glutamatergic mEPSC recordings in the IPL . There were no significant differences in mEPSC frequency between wt ( n=12 ) and ChAT-cKO ( n=10; unpaired t-test p=0 . 364 ) . Marked in grey are the recorded cells 1 , 2 and 3 shown in B and C . cKO: Conditional knockout; IPN: Interpeduncular nucleus; IPL: Lateral interpeduncular nucleus; mEPSC: miniature excitatory postsynaptic currents; MHb: Medial habenula; wt: Wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 11396 . 011 To determine if indeed ACh promotes the uptake of glutamate into SVs we assessed the levels of vesicular transporters in ChAT-cKO and wt mice by WB and coimmunoisolations , and performed glutamate uptake experiments . Western blot analyses of isolated SVs revealed no differences in the protein levels of Synaptophysin ( Syp ) , VACHT , VGLUT1 , and VGLUT2 between wt and ChAT-cKO mice ( Figure 10A: lanes 1 and 6 and Figure 10B ) indicating that loss of CHAT did not alter the expression of synaptic vesicle proteins . Immunoisolations of SVs from wt and ChAT-cKO mice using polyclonal antibodies directed against Syp , VGLUT1 , VGLUT2 , and VACHT ( Figure 10A ) and immunoisolations with IgG antibodies as background control revealed that VGLUT1-isolated SVs were positive for VACHT and that VACHT-isolated SVs were positive for VGLUT1 ( yellow squares , Figure 10A ) . In all cases , the immune-detected signals were comparable in wt and ChAT-cKO vesicles . Additionally , a portion of VGLUT2-specific immunoisolates showed coexistence of VACHT in wt and ChAT-cKO vesicles ( blue squares Figure 10A ) . Immunostaining of brain sections containing the IPN confirmed the expression of both VGLUT1 and VGLUT2 glutamate transporters in the IPN ( Figure 10C ) . Coexpression of both transporters in habenular terminals in the IPN has been reported in the rat ( Aizawa et al . , 2012 ) . Together , these results indicate the coexistence of VGLUT1/VGLUT2 , VGLUT1/VACHT and VGLUT2/VACHT in synaptic vesicles of the IPN and show that ChAT-cKO mice express normal levels of these vesicular transporters . 10 . 7554/eLife . 11396 . 012Figure 10 . ACh enhances glutamate uptake into synaptic vesicles coexpressing VACHT and VGLUT1/2 . ( A ) Western blot analyses of synaptic vesicles prepared from the IPN of wt and ChAT-cKO mice using VACHT , VGLUT1 , VGLUT2 , and Synaptophysin ( Syp ) antibodies indicated on the left . SVs from the starting material ( shown in the first row for each genotype ) were immunoisolated with the polyclonal antibodies indicated above , or without primary antibody ( IgG ) and subsequently immunodetected with the monoclonal antibodies indicated on the left . SVs immunoisolated with VGLUT1 contain VACHT and SVs immunoisolated with VACHT contain VGLUT1 and are indicated by yellow squares . Blue squares indicate VGLUT2/VAChT co-immunoisolates . ( B ) Quantification of the western blot signal of vesicular proteins in the starting fraction of the immunoisolation shows no significant differences between wt and ChAT-cKO . ( C ) Immunostaining of mouse brain sections containing the IPN and adjacent dopaminergic VTA region ( TH positive in green ) shows expression of VGLUT1 ( red , upper panel ) and VGLUT2 ( red , lower panel ) . ( D ) Simplified illustration of vesicular synergy: synaptic vesicles are acidified and positively charged by a V-type H+-ATPase ( brown circle ) that pumps protons into the SV generating a chemical gradient ( ΔpH , inward green arrow ) and an electrical gradient ( ΔΨ , inward red arrow ) . The activities of vesicular transporters depend to differing extents on ΔpH and ΔΨ due to the charge on the neurotransmitter and the stoichiometry of coupling to H+ . VACHT preferentially uses the ΔpH chemical gradient while VGLUT relies more on ΔΨ component . During each transport cycle , VACHT exchanges two protons ( H+ ) for one ACh molecule ( ACh+ positively charged ) thereby dissipating ΔpH ( outward green arrow ) twice faster than ΔΨ , a situation promoting VGLUT activity . VGLUT activity exchanges one glutamate molecule ( Glu- negatively charged ) by nH+ and causes the opposite imbalance dissipating more ΔΨ than ΔpH ( outward orange arrow ) . The transport activity of VGLUT thereby compensates for the bioenergetic imbalance produced by VACHT activity and vice versa . This reciprocal vesicular synergy between transporters allows a maximal accumulation of ACh and glutamate under a constant ΔΨ and ΔpH gradient ( see review by Hnasko et al . , 2012 ) . In the absence of ACh+ as it occurs in synaptic terminals of ChAT-cKO mice , there is no exchange of ACh+/2H+ and synaptic vesicles progressively become less positively charged and the plateau level of Glu- uptake is reduced . Adapted from ( Gras et al . , 2008 ) . ( E ) Glutamate uptake into SV prepared from rat IPN . In the presence of 10 mM ACh iodide ( 10 mM K-iodide was used for osmotic compensation in control condition ) , vesicles accumulated significantly more [3H]glutamate ( mean ± S . E . M . of 12 control and 10 ACh samples from 4 experiments; unpaired t-test: p<0 . 05 ) . Values were corrected for nonspecific uptake in the presence of FCCP ( inhibitor of vesicular transporters ) . ACh: Acetylcholine; cKO: Conditional knockout; IPN: Interpeduncular nucleus; SV: Synaptic vesicle;VTA: Ventral tegmental areaDOI: http://dx . doi . org/10 . 7554/eLife . 11396 . 012 We next analyzed glutamate uptake into SVs isolated from rat IPN . Given that VACHT and VGLUTs are coexpressed in cholinergic SVs ( Figures 4–6 and 10A–C ) and that the absence of ACh decreases mEPSC amplitudes ( Figure 7 ) , we hypothesized that the addition of ACh would increase the uptake of glutamate into SVs . Vesicular transporters use the energy generated by the v-ATPase that pumps protons into the SV to generate an acidic ( ΔpH ) and positively charged ( Δψ ) electrochemical gradient ( ΔµH+ ) ( Figure 10D ) . During each transport cycle , two protons will be replaced by one ACh thereby decreasing ΔpH without affecting the positive outwardly directed Δψ , a situation promoting VGLUT activity ( Figure 10D ) ( Hnasko and Edwards , 2012 ) . The addition of NH4+ increases vesicular glutamate uptake by increasing the intravesicular pH therefore lowering ΔpH without affecting Δψ ( Preobraschenski et al . , 2014 ) , indicating that ACh might work in the same direction . While recent evidence indicates that vesicular synergy is operated by glutamate acting as a buffering anion ( Gras et al . , 2008 ) , the reciprocal synergy has not been reported . To test this possibility , we purified SVs from IPN samples of rats and performed uptake assays . Uptake of [3H]-glutamate was measured in the presence of neostigmine , a specific inhibitor of the AChE . Values were corrected for nonspecific uptake in the presence of FCCP ( Carbonyl cyanide 4- ( trifluoromethoxy ) phenylhydrazone; inhibitor of vesicular transporters ) . Under these conditions , the accumulation of [3H]-glutamate in IPN vesicles increased and reached a plateau of 38 ± 3 pmol/ mg of protein at 10 min ( Figure 10E ) . In the presence of ACh , [3H]-glutamate accumulation increased to 51 ± 15 pmol of glutamate/mg of protein ( Figure 10E ) . Thus , addition of ACh increased vesicular glutamate uptake by 35 . 8 ± 13 . 6% relative to control vesicles . Addition of the positively charged NH4+ also led to an increase of glutamate uptake ( 140 . 7 ± 13 pmol ) . The increase by NH4+ was much more pronounced than with ACh possibly because NH4+ addresses all glutamatergic SVs present in the IPN preparation , while ACh can only increase glutamate uptake in those SVs equipped with VACHT and VGLUT . Altogether , our results show that ACh synergizes vesicular filling of glutamate , providing additional functional evidence for the coexistence of VACHT and glutamate vesicular transporters in a subset of IPN vesicles . Given that local elimination of ACh in habenular neurons reduces glutamate corelease ( Figure 7 ) and impairs nAChR presynaptic facilitation ( Figure 8 ) , and given the role for nAChRs in the MHb/IPN in nicotine craving and intake ( Fowler et al . , 2011; Frahm et al . , 2011; Salas et al . , 2009 ) , we examined the behavioral responses of ChAT-cKO mice to nicotine . Psychomotor responses after acute nicotine challenge reflect the sensitivity of an individual to nicotine ( Clarke and Kumar , 1983 ) . Therefore we assayed locomotor activity in response to single injections of nicotine ( 0 . 32 , 0 . 65 and 1 . 5 mg/kg ) . Baseline activity of ChAT-cKO mice was comparable to wt ( Figure 11A , B ) ; however nicotine-induced hypolocomotion was significantly less pronounced in ChAT-cKO than in wt mice ( Figure 11C ) , indicating that ChAT-cKO mice show reduced sensitivity to nicotine . 10 . 7554/eLife . 11396 . 013Figure 11 . ChAT-cKO mice are insensitive to nicotine-conditioned reward and show no signs of nicotine withdrawal . ( A ) Basal activity , measured as locomotion in a novel environment is similar in ChAT-cKO and wt mice ( n = 9 per genotype ) . ( B ) Day/night locomotor activity measured as number of beam breaks in 1 hr intervals for 72 hr ( averaged to 24 hr ) was unchanged in ChAT-cKO ( n = 6 ) compared to wt mice ( n = 10 ) . ( C ) Dose-response of acute effects of nicotine injections on hypolocomotor activity . Nicotine i . p . challenges produced significantly more hypoactivity in wt mice compared to ChAT-cKO mice across all nicotine doses tested ( n = 8–10 per genotype and dose; two–way ANOVA for genotype: p<0 . 001 ) . ( D ) Daily i . p . injection of 0 . 65 mg/kg nicotine induced tolerance behavior in wt but not in ChAT-cKO mice: hypolocomotion was significantly attenuated in wt after 5 days of treatment compared to the first day of administration ( n=6 per genotype , one–way-ANOVA days 1–9: wt F ( 2 . 446 ) = 4 . 963 , p<0 . 05 and ChAT-cKO F ( 1 , 548 ) = 0 . 988 , n . s . ; paired t-test p<0 . 05 ) but ChAT-cKO maintained similar locomotor activity at consecutive days of nicotine treatment . ( E-F ) Nicotine withdrawal was measured by quantification of ( E ) somatic signs ( including scratching , rearing , head nods , body shakes and grooming ) and ( F ) anxiety-like behavior ( as time spent in dark versus light chamber ) . Signs of nicotine withdrawal were measured in mice drinking saccharin-sweetened water ( somatic tests: n=5 per genotype , affective tests: n=8 for wt and n=14 for cKO ) or saccharin-sweetened water containing nicotine ( somatic tests: n=11 for wt , n=9 for ChAT-cKO; affective: n=11 for wt , n=13 for cKO ) for 6 weeks . Precipitation of withdrawal by injection of the nicotinic antagonist mecamylamine elicited somatic and affective signs of withdrawal in wt mice treated with nicotine compared to the saccharin-treated mice ( unpaired t-test: p < 0 . 05 ) . ChAT-cKO mice treated with saccharin spent less time in the light camber than wt mice treated with saccharin , but this difference was not statistically significant ( unpaired t-test; p=0 , 14 ) . ChAT-cKO mice showed no significant signs of either somatic or affective nicotine withdrawal since there were no differences between the nicotine and control groups ( unpaired t-test: *p < 0 . 01 , p<0 . 05 ) . ( G , H ) Nicotine place preference was measured by pairing nicotine or saline injections to different chambers for 3 consecutive days and measuring the time spent in either chamber on the test day . Wt ( G ) but not ChAT-cKO ( H ) mice showed robust conditioned place preference after pairing the environment with 1 . 5 mg/kg nicotine . Each point represents the mean ± S . E . M . of 6–10 mice per group ( paired t-test: nicotine vs . saline compartment , p<0 . 01; two–way matched ANOVA for substance: p=0 . 007 in wt ) . cKO: Conditional knockout . DOI: http://dx . doi . org/10 . 7554/eLife . 11396 . 013 Tolerance to nicotine and withdrawal severity are both critical for the development and maintenance of dependence in humans and animal models ( Changeux , 2010; West et al . , 1989 ) . Nicotine tolerance can be observed in mice as a decreased response to repetitive exposure to the same amount of nicotine ( Tapper et al . , 2007; Tapper et al . , 2004 ) . To test whether this behavior was affected , acute i . p . injections of 0 . 65 mg/kg nicotine , a concentration that produced approximately 50% activity reduction in wt ( Figure 11C ) , were given once daily and activity changes were monitored . Wt mice but not ChAT-cKO became progressively less responsive to daily injections of the drug and locomotor depression was significantly attenuated after 5 days compared to the first day of administration ( Figure 11D ) . These data show that ChAT-cKO mice do not develop tolerance induced by repetitive administration of nicotine . We next measured withdrawal responses after cessation of chronic nicotine administration via drinking water over 6 weeks , starting at a concentration of 32 . 5 µg/ml in the first week , increasing to 65 µg/ml in the second week and to 162 . 5 µg/ml in the remaining 4 weeks ( Gorlich et al . , 2013; Salas et al . , 2009 ) . Withdrawal largely prevents success in quitting smoking in humans ( Changeux , 2010; West et al . , 1989 ) and manifests as a collection of physical and affective symptoms that are also observed in mice ( reviewed in ( Antolin-Fontes et al . , 2015 ) ) . Physical signs of withdrawal ( 'somatic signs' ) include scratching , rearing , jumping , head nods , and body shakes ( Damaj et al . , 2003; Grabus et al . , 2005 ) , while affective withdrawal is measured as anxiety-like behaviors ( Damaj et al . , 2003; Kenny and Markou , 2001 ) . Wt mice treated with nicotine exhibited significantly more somatic ( Figure 11E ) and affective ( Figure 11F ) signs of withdrawal compared to wt mice drinking saccharin alone . In contrast , ChAT-cKO mice did not display altered signs of withdrawal in the treated versus untreated group ( Figure 11E , F ) . Interestingly , ChAT-cKO mice spent less time than wt in the light chamber at baseline , although this difference was not significant ( Figure 11F ) . These results demonstrate that local elimination of ACh release by MHb neurons to IPN neurons prevents the development of nicotine withdrawal behaviors . To further study the motivational properties of nicotine , we performed the conditioned place preference ( CPP ) test at different nicotine concentrations . Robust preference for the nicotine-paired environment was seen in wt mice after three conditioning sessions with 1 . 5 mg/kg nicotine compared to the saline-paired environment ( Figure 11G ) . In agreement with previous reports ( Jackson et al . , 2010 ) , a reinforcing response was not induced at very low and very high nicotine doses ( 0 . 15 and 3 mg/kg ) , resulting in a bell-shaped , dose-dependent CPP curve for wt mice . In contrast , ChAT-cKO mice did not display CPP at any nicotine concentration tested ( Figure 11H ) , demonstrating insensitivity to nicotine-mediated place preference in ChAT-cKO mice . Taken together , these results reveal that nicotine-dependent behaviors such as nicotine-conditioned reward and withdrawal are controlled by the release of ACh from MHb neurons . The cotransmission of two neurotransmitters is not uncommon in the nervous system ( for reviews see [Hnasko and Edwards , 2012; El Mestikawy et al . , 2011] ) . There is physiological evidence for glutamate corelease with dopamine , GABA , adrenaline , noradrenaline , serotonin , and ACh in specific neuronal populations in the CNS ( El Mestikawy et al . , 2011 ) . This allows a very local control of excitatory and inhibitory balance at individual synapses ( Shabel et al . , 2014 ) . While some studies have documented the presence of distinct transporters in the same SV and determined their functions ( Gras et al . , 2008; Hnasko et al . , 2010; Zander et al . , 2010 ) , it is also possible that not all corelease can be recognized by analysis of neurotransmitter enzyme synthesis and packaging transporters ( Tritsch et al . , 2014 ) . It has been shown that the different ionic properties of vesicular transporters can foster physiologically complementing functions depending on the electrical ( ΔΨ , for VGLUT ) or pH gradients ( ΔpH , for VAChT , VMAT2 , VGAT ) . We show here for the first time that in the absence of ACh , VACHT can no longer promote glutamate co-entry through VGLUT1/2 , thus demonstrating synergy between VACHT and VGLUT1/2 in these synapses . Given previous studies of striatal synapses that demonstrated that loss of VGLUT3 results in a decrease of ACh uptake by synaptic vesicles ( Gras et al . , 2008 ) , and our demonstration that loss of ACh affects glutamate uptake by VGLUT1/2 , it seems most probable that transport of glutamate and ACh into IPN vesicles occurs by a reciprocal mechanism . Besides MHb neurons and striatal interneurons , other cholinergic neurons also appear to contain a vesicular glutamate transporter or corelease glutamate , including BF neurons ( Allen et al . , 2006; Gritti et al . , 2006; Henny and Jones , 2008 ) , and spinal motor neurons ( Nishimaru et al . , 2005 ) . This is also the case in the marine ray , Torpedo californica ( Li and Harlow , 2014 ) . Cotransmission of ACh and glutamate might therefore be considered the rule rather than the exception raising a fundamental question about the role of cotransmitter release and neurotransmitter synergy at cholinergic synapses and suggesting that vesicular synergy of these transmitters must have added an adaptive advantage . Our studies agree with previous work supporting the coexistence of two modes of transmission: wired and volumetric for glutamate and ACh respectively . In particular , it has been shown that brief stimulation of MHb efferents elicits glutamatergic but not cholinergic responses , and that tetanic stimulation is required to generate slow inward currents mediated by nAChRs ( Ren et al . , 2011 ) . These differences in transmission could be attributed to the existence of a cholinergic synaptic vesicle population distant from the active zone that is released only during high frequency signaling . However , we can exclude this possibility given our demonstration that glutamate and ACh vesicular transporters colocalize in cholinergic SVs ( Figure 6 ) and that both cholinergic and glutamatergic SV are found in close proximity and far ( >350 nm ) from the active zone ( Figure 5 ) . Therefore the two modes of transmission most likely reflect the different distribution of postsynaptic glutamate and ACh receptors . Indeed , a great deal of evidence suggests that ACh diffuses to pre- and extrasynaptic sites where it modulates the release of other transmitters ( Covernton and Lester , 2002; Descarries et al . , 1997; Descarries and Mechawar , 2000; Girod et al . , 2000; Mansvelder et al . , 2009 ) . The anatomical structure of the IPN , with numerous varicosities along habenular axonal projections that converge throughout the nucleus in a topographically organized manner forming ‘en passant’ and crest synapses ( Parajuli et al . , 2012 ) , supports this hypothesis . Electron microscopy studies have revealed that these boutons are highly immunoreactive for CHAT ( Lenn et al . , 1985 ) , consistent with our studies indicating that the vesicles visualized in these synapses are cholinergic . Given the high concentration and catalytic power of AChE in the IPN ( Flumerfelt and Contestabile , 1982; Franklin and Paxinos , 2008; Quinn , 1987 ) , the pre-terminal localization of synaptic boutons in ‘en passant’ synapses along habenular axons ( Parajuli et al . , 2012 ) , and our results showing colocalization of ACh and glutamate transporters in the same SV populations , it seems plausible that high frequency stimulation of MHb terminals is necessary to release sufficient ACh to reach nAChRs in the IPN ( volume transmission ) while brief stimulation can elicit fast postsynaptic glutamatergic responses ( wired transmission ) . Several nAChR subtypes in the MHb-IPN pathway are key in the control of nicotine consumption . Receptors containing the β4 and/or α5 subunit contribute to the activation of this tract during nicotine intake and are critical for the aversive ( reward-inhibiting ) effects of high-dose nicotine ( Fowler et al . , 2011; Frahm et al . , 2011 ) , while α2 , α5 and β4-subunits play a major role in nicotine withdrawal ( Salas et al . , 2004; Salas et al . , 2009 ) . Less is known about the contribution of the MHb-IPN pathway to the reinforcing effects of nicotine . Intracerebroventricular injection of the α3β4 nAChR blocker conotoxin AuIB , resulted in attenuated reward and withdrawal from nicotine ( Jackson et al . , 2013 ) . However , although α3β4 nAChR subunits coassemble with α5 nAChRs in about 15–35% of MHb-IPN neurons ( Grady et al . , 2009 ) , the reward-enhancing properties of nicotine are not altered in α5-KO mice ( Fowler et al . , 2011; Fowler et al . , 2013; Jackson et al . , 2013 ) . Given the large variety of nAChR subtypes in this circuit and their differential distribution on glutamatergic and GABAergic pre- and postsynaptic terminals , the advantage of local removal of the endogenous ligand ACh to understand the impact of nicotine on this tract is apparent . Thus , using ChAT-cKO mice we were able to demonstrate for the first time that ACh in the MHb-IPN circuit is crucial for the establishment of dependence-related behaviors , including reward and tolerance to nicotine . Presynaptic nAChRs at habenular synapses facilitate glutamate and ACh release ( Girod et al . , 2000; McGehee et al . , 1995 ) , and postsynaptic nAChRs contribute to an increased excitatory response upon activation . However , it appears that GAD-immunoreactive cells constitute the largest population of neurons in the IPN ( Kawaja et al . , 1989 ) and activation of α4β2 nAChRs located on GABAergic interneurons can trigger spike discharge and GABA-release ( Covernton and Lester , 2002; Lena et al . , 1993 ) , suggesting that ACh and nicotine modulate both glutamatergic and GABAergic synaptic transmission in the IPN . Importantly , repeated exposure to nicotine leads to inactivation of nAChRs , thus altering the modulatory effects of the endogenous transmitter ACh . Given that nicotine increases GABA release in the IPN and that continuous nicotine exposure desensitizes α4β2 nAChRs considerably more than α7 and α3β4 nAChRs ( for review see [Giniatullin et al . , 2005] ) , it is possible that persistent desensitization of α4β2 nAChRs on GABAergic neurons in the IPN increases the net excitatory output of this tract , as previously demonstrated in the ventral tegmental area ( VTA ) ( Mansvelder et al . , 2002 ) . The lack of dynamic changes in nAChR activity on inhibitory neurons in the absence of ACh , and subsequent altered stimulation of efferent targets , could thereby contribute to the insensitivity of ChAT-cKO mice to nicotine-mediated reward and tolerance . Descending projections from the IPN include the dorsal raphe ( DR ) nucleus and LDTg . Both nuclei exchange reciprocal information and provide glutamatergic ( Geisler and Wise , 2008 ) and cholinergic ( Maskos et al . , 2005 ) input to the VTA . Therefore , loss of cholinergic communication in the MHb-IPN axis , indirectly influencing activation of the mesocorticolimbic dopaminergic system , fails to induce nicotine-dependent behaviors , including reward , tolerance and withdrawal in ChAT-cKO animals . The reinforcing properties of nicotine together with the negative withdrawal symptoms that develop upon nicotine cessation are the main reasons for the maintenance of the smoking habit in spite of its known deleterious consequences ( De Biasi and Dani , 2011 ) . Genome-wide association studies have identified single nucleotide polymorphisms ( SNPs ) as risk factors for nicotine dependence and lung cancer in genes encoding nAChR subunits ( CHRNB3-CHRNA6 and CHRNB4-CHRNA3-CHRNA5 clusters ) , and in nicotine-metabolizing enzymes ( CYP2A6 and CYP2B6 ) ( Bierut et al . , 2007; Kumasaka et al . , 2012; Saccone et al . , 2009; Thorgeirsson et al . , 2010 ) . In addition , variations in the CHAT gene have been associated with prospective smoking cessation ( Heitjan et al . , 2008; Ray et al . , 2010; Turner et al . , 2013; Wei et al . , 2010 ) . The studies presented here show that nicotine does not trigger nicotine-dependent behaviors unless the endogenous neurotransmitter ACh is released from MHb neurons to further promote glutamate corelease . In the corticostriatal circuitry , it has been proposed that glutamate homeostasis underlies drug-seeking behavior ( Kalivas , 2009 ) . After repetitive drug use , deregulation of this homeostasis increases the release of glutamate during drug relapse ( Kalivas , 2009 ) . Similarly , in the MHb , re-exposure to nicotine after abstinence increases the pacemaking of MHb neurons ( Gorlich et al . , 2013 ) and subsequent corelease of ACh and glutamate from habenular axonal terminals . Although it is known that the main action of nAChRs is presynaptic in the CNS and postsynaptic at the neuromuscular junction , no studies have addressed the role of ACh in this mechanism . The studies presented here not only agree with the conclusion that nicotine-induced behaviors act via presynaptic receptors that regulate glutamate release but also show that nicotine is not sufficient to induce dependence unless ACh is released and promotes further release of glutamate . Given that other cholinergic neurons beside MHb neurons corelease glutamate , and our findings demonstrating that ACh controls the quantal size and release frequency of glutamate , it is possible that the synergistic functions of ACh and glutamate may be generally important for modulation of cholinergic circuit function and behavior and that genetic variations in CHAT may influence several aspects of glutamatergic transmission in the addiction process . Chatflox/flox mice ( Misgeld et al . , 2002 ) were crossed to Kiaa1107-Cre mice ( founder KJ227 GENSAT ) ( Gong et al . , 2003 ) . Kiaa1107 is annotated as A830010M20Rik by GENSAT . Cre-positive mice homozygous for the floxed allele were used as ChAT-cKO . Cre-negative littermates were used as wt control animals . Gt ( ROSA ) 26Sortm1 ( EYFP ) Cos ( The Jackson Laboratory , Sacramento , CA ) reporter line was crossed to Kiaa1107-Cre line . Adult rats ( Charles River Laboratories , Wilmington , MA ) were used for synaptic vesicle isolation experiments . Mice were housed with ad libitum access to food and water in room air conditioned at 22–23°C with a standard 12 hr light/dark cycle , maximal five animals per cage . All procedures were in accordance with ethical guidelines laid down by the local governing body . ( - ) Nicotine hydrogen tartrate salt and mecamylamine hydrochloride were purchased from Sigma-Aldrich ( St . Louis , MO ) . Drugs were dissolved in 0 . 9% saline and administered intraperitoneally ( i . p . ) in a volume of 100 µl per 10 g body weight . Nicotine concentrations refer to the free base . Immunohistochemistry was performed as described by ( Frahm et al . , 2011 ) in adult mice ( 3–4 weeks ) except in Figure 3 ( p6-p7 mice ) . The primary antibodies used were goat polyclonal anti-CHAT ( 1:1000 , EMD Millipore , Germany ) , mouse monoclonal anti-Tyrosine hydroxylase ( 1:2000 , Sigma-Aldrich ) , guinea pig polyclonal anti-VGLUT1 ( 1:1000 , Synaptic Systems , Germany ) , guinea pig polyclonal anti-VGLUT2 ( 1:1000 , Synaptic Systems ) , rabbit polyclonal anti-VACHT ( 1:500 , Synaptic Systems ) and rabbit polyclonal anti-EGFP ( 1:1000 , Invitrogen , Waltham , MA ) . The sections were incubated with primary antibodies overnight at 4°C . After incubation with secondary antibodies sections were washed , mounted on slides and coverslipped in immu-mount ( Thermo Scientific , Waltham , MA ) . Heat-mediated antigen retrieval , 15 min at 95°C in citric acid ( pH 6 . 0 ) , was performed prior to incubation with the choline acetyltransferase ( CHAT ) antibody to enhance immunostaining . Fluorescent signals were detected using a confocal laser scanning microscope ( Leica SP5 or Zeiss LSM700 , Germany ) and Biorevo fluorescent microscope ( Keyence , Japan ) . Image J was used for quantification analysis . The number of CHAT and EYFP cells per section was quantified in Kiaa1107-Cre crossed to Gt ( ROSA ) 26Sortm1 ( EYFP ) Cos mice . 8 to 16 sections from three different mice were used for analysis . The number of CHAT positive neurons per section was quantified in wt and ChAT-cKO mice . 13 to 22 sections from three different mice per genotype were used for analysis . Images used for colocalization analysis were acquired with a 40× oil immersion objective on a Zeiss LSM700 confocal microscope . The analysis was done as described in ( Broms et al . , 2015 ) . Briefly , the detection pinhole was set to one Airy unit and the channels were captured in sequence . The intensity gain was adjusted for each channel before capture and the intensity range of the images was left untouched to preserve linearity . Colocalization analysis was performed with the Coloc 2 plugin of the Fiji image processing package . Background was eliminated by median subtraction ( Dunn et al . , 2011 ) . Manders' colocalization coefficients ( M1 and M2 ) , which are proportional to the number of colocalizing pixels in each channel relative to the total number of pixels , were calculated ( Manders et al . , 1993 ) . M1 or M2 > 0 . 55 indicates colocalization ( Zinchuk and Grossenbacher-Zinchuk , 2014 ) . In this study , only M1 is presented and refers to the proportion of pixels with VGLUT1 immunoreactivity that colocalize with either VACHT or CHAT . Costes' test for statistical significance was used to determine that the colocalization coefficients obtained were not due to random effects ( Costes et al . , 2004 ) . This test creates random images by shuffling blocks of pixels of one channel , measuring the correlation of this channel with the other ( unscrambled ) channel of the same image . The test was performed 100 times per image and the resulted P value indicates the proportion of random images that have better correlation than the real image . A P-value of 1 . 00 means that none of the randomized images had better correlation . The M1 and P value below panels 1–4 of Figure 4 were calculated from one individual image per IPN subnucleus . For recordings of evoked excitatory postsynaptic currents ( eEPSCs ) from MHb and IPN somata , slices were cut at 350 µm from adult mice , on a Microm HM 650 V ( Thermo Scientific ) . Slices were kept submerged at room temperature in oxygenated ( 95% O2 and 5% CO2 ) artificial cerebrospinal fluid containing ( mM ) : 125 NaCl , 2 . 5 KCl , 1 . 3 MgCl2 , 2 CaCl2 , 1 . 25 KH2PO4 , 11 glucose and 26 NaHCO3 ( pH 7 . 4; osmolarity 310 mosmol l−1 ) . The internal pipette solution contained ( mM ) : 120 potassium gluconate , 2 MgCl2 , 0 . 5 CaCl2 , 5 EGTA and 10 Hepes ( pH 7 . 3; resistance 4–5 MΩ ) . Nicotine was locally applied by pressure ( 8 – 10 psi , 100 ms ) with pipettes similar to the recording pipette using a pressure regulator ( PR-10; ALA Scientific Instruments , Farmingdale , NY ) controlled with a trigger interface ( TIB 14S; HEKA Electronics , Germany ) . The pipette was moved within 15–20 μm of the recorded cell using a motorized micromanipulator ( LN mini 25 , control system SM-5; Luigs & Neumann , Germany ) for drug application and retracted at the end of the puff to minimize desensitization . For electrophysiological recordings of mEPSCs in the IPN , adult mice were sacrificed by cervical dislocation and brains were dissected in chilled ( 4°C ) artificial cerebrospinal fluid ( ASCF ) containing ( in mM ) : 87 NaCl , 2 KCl , 0 . 5 CaCl2 , 7 MgCl2 , 26 NaHCO3 , 1 . 25 NaH2PO4 , 25 glucose , 75 sucrose , bubbled with a mixture of 95% O2/5% CO2 . 250 µm coronal IPN containing slices were cut with a VT1200S vibratome ( Leica ) , preincubated for 30 min at 37°C and then transferred to the recording solution containing ( in mM ) : 125 NaCl , 2 . 5 KCl , 2 CaCl2 , 1 . 3 MgCl2 , 26 NaHCO3 , 1 . 25 NaH2PO4 , 10 glucose , 2 sodium pyruvate , 3 myo-inositol , 0 . 44 ascorbic acid , bubbled with a mixture of 95% O2/5% CO2 . Slices rested in this recording solution at room temperature for at least one hour before they were transferred to the recording chamber and perfused with ACSF containing 1 µM TTX , 100 µM Picrotoxin , and 1 µM CGP 55 , 845 hydrochloride ( all from Tocris , UK ) . Electrophysiological responses were recorded with an EPC 10 patch-clamp amplifier and PatchMaster and FitMaster software ( HEKA Elektronik ) . Cells were patch-clamped at -70 mV for 10 min , before 100 µM ACh or 250 nM nicotine tartrate ( Sigma Aldrich ) was washed in for 5 min . Spontaneous mEPSCs were analyzed for the one minute before ACh or nicotine application ( baseline ) , and for the last minute of drug application with Mini Analysis ( Synaptosoft , Decatur , GA ) . Patch pipettes had resistances of 4–8 MΩ when filled with a cesium-based solution containing ( in mM ) : 105 CsMeSO , 30 CsCl , 10 Hepes , 10 phosphocreatine , 2 ATP-Mg2+ , 0 . 3 GTP ( pH adjusted to 7 . 2 with CsOH ) . In recordings of the IPL , 3–5 mg/ml Biocytin ( Sigma Aldrich ) was included in the intracellular recording solution . After recordings , slices were fixed with 4% PFA for 30–45 min , and incubated with a streptavidin conjugated Alexa Fluor 488 dye ( Life technologies , Guilford , CT ) to label biocytin filled neurons , and a rat primary antibody for substance P ( Santa Cruz , Dallas , TX ) to label the IPL . After incubation with secondary antibodies , fluorescent signals were detected using a confocal laser-scanning microscope ( Zeiss ) . Results are presented as means ± SEM . The MHb and IPN were dissected from adult ChAT-cKO and wt mice ( n = 3 per genotype ) , collected in 1 ml of lysis buffer ( 50 mM Na phosphate pH 7 , 4 , 1 M NaCl , 2 mM EDTA , 2 mM EGTA and protease inhibitor cocktail ) and immediately homogenized . The homogenates were processed as described in ( Frahm et al . , 2011 ) . Primary antibodies used were goat polyclonal anti-CHAT ( Millipore ) , mouse monoclonal anti-α-Tubulin ( Sigma-Aldrich ) , rabbit polyclonal anti-nAChR β4 ( gift from Dr . Cecilia Gotti ) and rabbit polyclonal anti-GluR1 ( Millipore ) . Ten mice per genotype ( ChAT-cKO and wt littermates ) were used to dissect IPN brain samples . Brain tissue was resuspended in ice-cold buffer ( in mM: 320 sucrose , 4 HEPES/KOH/pH 7 . 4 , 1 PMSF , protease inhibitor for mammalian tissue ( Pi ) 1:1000 ( Sigma-Aldrich ) and homogeneized ( 10 strokes at 900 rpm ( homogenizer clearance: 0 . 1–0 . 15 mm , Wheaton , UK ) ) . Nuclei were discarded by centrifugation and the supernatant mainly containing SVs was diluted 1:10 in lysis buffer ( ddH2O , in mM:10 HEPES/KOH/pH 7 . 4 , 1 PMSF , Pi 1:1000 ) for osmotic shock and processed with three strokes at 2000 rpm using a Wheaton homogenizer to obtain SVs in suspension . Immunoisolation of SV was performed using paramagnetic beads ( Dynabeads: for monoclonal antibodies: Pan Mouse IgG , for polyclonal rabbit antibodies: M-280 sheep anti-rabbit , Life Technologies ) were first coated with the respective primary antibodies suspended in coating buffer [PBS , pH 7 . 4 , 0 . 1% BSA ( w/v ) ] , supplemented with 0 . 5 – 1 µg of IgG per 107 Pan Mouse IgG beads or 2 x 107 M-280 sheep anti-rabbit beads and rotated for 2 hr at 4°C followed by four washing steps . Immunoisolation was performed overnight at 4°C under rotation using a suspension of the coated beads and S1shock fractions diluted with incubation buffer [PBS , pH 7 . 4 , 2 mM EDTA , and 5% BSA ( w/v ) ] to yield a ratio of 50–75 µg protein to 1 . 4 x107 beads ( Pan Mouse IgG ) or 3 . 9 x 107 beads ( M-280 ) . SV bound to beads were washed three times in incubation buffer , and three times in coating buffer . Bead-bound SV were finally dissolved in sample buffer ( Takamori et al . , 2000; Zander et al . , 2010 ) . Beads without primary antibodies ( for monoclonal immunoisolation ) or with normal rabbit IgG ( Santa Cruz , for isolation using polyclonal rabbit antibodies ) were subjected to the same procedures and served as control for non-specifically bound material . The protein pattern of isolated SV was analyzed by SDS-PAGE and Western blot . Antibodies used for Western blot were rabbit polyclonal anti VGLUT1 or VAChT ( Synaptic Systems ) . Glutamate uptake was performed using a SV-enriched fraction ( S1 hyposmotically shocked and spun down as pLP2 ) from 10 rat IPN . Briefly , membranes of S1shock fraction ( see above ) were removed ( 20 min at 29 , 000 x g ) and SV were pelleted ( 30 min at 350 , 000 x g ( TLA-100 . 4 rotor , Beckman Coulter , Jersey City , NJ ) ) resulting in pLP2 fraction . Uptake was performed in K-gluconate buffer ( in mM: 146 K-gluconate , 4 KCl , 20 1 , 4-piperazinediethanesulfonic acid , 4 EGTA , 2 . 9 MgSO4 ( corresponding to 1 mM free Mg2+ ) , 2 Na-ATP , adjusted to pH 7 . 0 with KOH ) by adding 1 μM neostigmine , 49 . 5 µM K-glutamate and 0 . 5 µM [3 , 43H]-L-glutamic acid ( Hartmann Analytic GmbH , Germany ) to the SV suspension . Non-specific uptake was performed by adding 60 µM FCCP also used for background correction . Additives were given 10 min before the uptake was started at 25°C . The reaction was stopped after 10 min by adding ice-cold buffer containing the same ionic constituents used during the uptake . Non-bound radioactivity was removed by centrifugation at 435 , 000 x g ( TLA-120 . 1 rotor , Beckman Coulter ) for 10 min , and the pellets were washed three times . Radioactivity was measured by liquid scintillation counting ( LS 6500 , Beckman Coulter ) . ( Zander et al . , 2010; Preobraschenski et al . , 2014 ) . The MoTil system ( TSE Systems , Germany ) was used to monitor open-field behavior of 2–3 month old male mice in complete darkness ( 20 x 40 x 40 cm black box ) . Single events represented disruption of two distinct infrared photobeams 3 cm apart in the cage . After injection of saline or different concentrations of nicotine , the number of beam breaks was measured for 15 min . Locomotor inhibition was calculated by normalizing to basal activity values ( beam breaks nicotine x 100% / beam breaks saline ) . For measurement of tolerance behavior , mice were given daily i . p . injections of 0 . 65 mg/kg nicotine ( total of 9 days ) , starting with saline at day 0 as control activity . Activity was monitored in the open field box every second day for 15 min immediately after injections . Home cage activity of single housed animals was measured for 3 days and averaged to 24 hr . A rectangular three-compartment box separated by removable doors ( TSE-systems ) was used . The center compartment ( 6 x 15 x 20 cm ) is grey with a polycarbonate smooth floor . The choice compartments ( 17 x 15 x 20 cm ) have different visual and tactile cues . Time spent in each compartment was measured with photobeam detectors . During the preconditioning phase ( day1 ) male mice ( 8–12 weeks old ) were placed into the center compartment with closed doors . After 2 min of habituation , doors opened automatically and mice were allowed to explore the three compartments freely for 15 min . The pre-conditioning session was used to determine baseline responses and less preferred compartments were paired with nicotine . Mice exhibiting a strong preference for one side ( >150 s ) in the pre-conditioning session were excluded . During the conditioning phase ( day 2–4 ) , mice were given a saline injection in the morning and a nicotine injection in the afternoon and were confined to either one side or the other of the conditioning box for 20 min . On the preference test ( day 5 ) , the doors between the compartments were opened after mice habituated for 2 min in the central chamber and were then allowed to move freely in the three compartments for 15 min . Preference score was measured as time spent in nicotine-paired or saline-paired compartment at day 5 – day 1 . Single housed male adult mice were treated with nicotine via the drinking water over 6 weeks , starting at a concentration of 32 , 5 µg/ml in the first week , increasing to 65 µg/ml in the second week and to 162 , 5 µg/ml in the remaining 4 weeks ( Gorlich et al . , 2013; Salas et al . , 2009 ) . To minimize taste aversion , 2% saccharin was added to both , treatment and control groups . Withdrawal was precipitated by i . p . injection of 2 mg/kg mecamylamine . To measure somatic signs of withdrawal , mice were placed into their home cage immediately after injection and videotaped for 20 min . During this time , following events were scored as somatic signs of withdrawal: body tremors , head nods , paw tremors , grooming , genital licks and scratching . To measure affective signs of withdrawal we employed a light-dark box by placing a dark enclosure with a doorway on one side of the open field boxes ( Accuscan & Omnitech Electronics , Columbus , OH ) . We employed another set of mice ( treated with saccharin and nicotine ) different from the one used for somatic withdrawal . Withdrawal was precipitated by i . p . injection of 2 mg/kg mecamylamine . Immediately after injection , mice were placed in the dark chamber and allowed to move freely between both chambers for 5 min . Time spent in each chamber was recorded . Statistical analysis was performed with GraphPad Prism 6 . 0 . Unpaired two-tailed Student t-tests were used for analyzing most of the data , except when two-way analysis of variance with ANOVA or paired two-tailed Student t-tests are indicated . Results are presented as means ± S . E . M .	Neuroscientists are making progress in understanding the brain regions and neural circuits that are involved in reward and addiction . One such region , called the habenula , is found near the center of the brain and sends nerves to another brain region called the interpeduncular nucleus ( or IPN for short ) ; this creates a neural circuit that is important for the brain’s responses to nicotine . The habenular-IPN circuit is rich in receptors for a neurotransmitter called acetylcholine . These receptors are also activated by nicotine , the addictive component of tobacco . Neurotransmitters are chemicals that transmit a signal from one nerve to another . These chemicals are packaged into small structures called vesicles , which are found at nerve endings . When a nerve impulse reaches the end of a nerve , it triggers the release of a vesicle’s contents into the gap ( or 'synapse' ) between nerve cells . The released neurotransmitters can then bind to receptors on the neighboring nerve cells before being cleared away . The nicotinic acetylcholine receptors in the habenula-IPN circuit are associated with nicotine dependence in mice and humans . Frahm , Antolin-Fontes , Görlich et al . have now investigated what happens when the gene encoding the enzyme that makes acetylcholine is removed from habenular nerves in mice . The experiments revealed that these mutant mice become insensitive to the rewarding properties of nicotine and are protected from the effects of its withdrawal , following long-term exposure . The loss of acetylcholine from habenular nerves was also found to reduce the generation of small electrical currents in the nerves of the IPN . These currents are generated by another neurotransmitter called glutamate . If brain slices from normal mice are infused with acetylcholine or nicotine , these currents become more frequent . This response is not seen in brains of the mutant mice suggesting that acetylcholine helps the release of glutamate from habenular nerve endings . Frahm , Antolin-Fontes , Görlich et al . then found that the proteins that transport glutamate and acetylcholine into synaptic vesicles are found at the same sites in nerve endings in the IPN . Further experiments showed that acetylcholine also increases the reuptake of glutamate into synaptic vesicles and controls the amount and frequency of glutamate released at habenular synapses . These results thus reveal how acetylcholine mediates the effects of nicotine on the brain , in part by regulating the uptake and release of glutamate by habenular nerve endings , identifying a new mechanism important for nicotine dependence . Since many acetylcholine-releasing nerve cells also release glutamate , a future challenge will be to investigate whether the interaction between these two neurotransmitters is important for other processes that rely on acetylcholine , such as memory and thought .	[ "Abstract", "Introduction", "Results", "Discussion", "Material", "and", "methods" ]	[ "neuroscience" ]	2015	An essential role of acetylcholine-glutamate synergy at habenular synapses in nicotine dependence
Microtubules control different aspects of cell polarization . In cells with a radial microtubule system , a pivotal role in setting up asymmetry is attributed to the relative positioning of the centrosome and the nucleus . Here , we show that centrosome loss had no effect on the ability of endothelial cells to polarize and move in 2D and 3D environments . In contrast , non-centrosomal microtubules stabilized by the microtubule minus-end-binding protein CAMSAP2 were required for directional migration on 2D substrates and for the establishment of polarized cell morphology in soft 3D matrices . CAMSAP2 was also important for persistent endothelial cell sprouting during in vivo zebrafish vessel development . In the absence of CAMSAP2 , cell polarization in 3D could be partly rescued by centrosome depletion , indicating that in these conditions the centrosome inhibited cell polarity . We propose that CAMSAP2-protected non-centrosomal microtubules are needed for establishing cell asymmetry by enabling microtubule enrichment in a single-cell protrusion . Cell polarization is a prerequisite for virtually every specialized cellular process . By analogy with the compass on a ship navigating to its destination , the centrosome has been assumed to play a central role in governing cell polarity ( Bornens , 2012 ) , including two-dimensional ( 2D ) mesenchymal migration , in which cells are organized into an extending leading edge and a contractile cell rear ( Ridley et al . , 2003; Tang and Marshall , 2012 ) . The widely accepted dogma connecting the centrosome and the direction of cell movement originated from the observation that during migration , the centrosome is typically positioned at the front of the nucleus ( Gotlieb et al . , 1981; Koonce et al . , 1984; Kupfer et al . , 1982; Malech et al . , 1977 ) . Such centrosome positioning is observed in different systems ( Elric and Etienne-Manneville , 2014; Luxton and Gundersen , 2011 ) , although it does not apply to certain specific cell types ( Luxton and Gundersen , 2011; Yvon et al . , 2002 ) or in the presence of environmental constraints ( Doyle et al . , 2009; Pouthas et al . , 2008; Schütze et al . , 1991 ) . Despite the scarcity of direct experimental evidence , currently based on laser ablation of the pericentrosomal area ( Koonce et al . , 1984; Wakida et al . , 2010 ) or indirect interference with centrosome localization ( Dujardin et al . , 2003; Etienne-Manneville and Hall , 2001; Levy and Holzbaur , 2008; Schmoranzer et al . , 2009 ) , the orientation of the nucleo-centrosomal axis is commonly regarded as a major cell polarity determinant . Microtubules ( MTs ) are thought to support cell polarity by forming an asymmetrical network ( Etienne-Manneville , 2013 ) . This asymmetry can potentially be generated by specific positioning of the centrosomal anchor of a radial MT network relative to other cell structures , such as the nucleus , but also by MTs that do not originate from the centrosome ( Akhmanova and Hoogenraad , 2015; Alieva et al . , 2015; Vinogradova et al . , 2009 ) . The minus ends of centrosome-independent MTs can associate with the members of the calmodulin-regulated spectrin-associated protein ( CAMSAP ) /Patronin/Nezha family ( Akhmanova and Hoogenraad , 2015 ) . In mammalian cells , CAMSAP2 , together with CAMSAP3 in certain cell types , binds to free , non-centrosomal MT minus ends and promotes their stabilization ( Jiang et al . , 2014; Tanaka et al . , 2012 ) . Interestingly , CAMSAP2 has recently been shown to participate in MT stabilization at the Golgi apparatus ( Wu et al . , 2016 ) , a site that can function as an alternative MT-organizing centre ( MTOC ) ( Zhu and Kaverina , 2013 ) . In mammalian cells , the Golgi is often positioned close to the centrosome ( Rios , 2014 ) , and thus locates in front of the nucleus during cell migration ( Koonce et al . , 1984; Kupfer et al . , 1982; Malech et al . , 1977 ) . As the central organizer of the secretory pathway , an anteriorly positioned Golgi is thought to support polarized transport needed to sustain directional migration ( Yadav and Linstedt , 2011 ) . Consistently , Golgi disorganization without MT disassembly ( Bisel et al . , 2008; Hurtado et al . , 2011; Yadav et al . , 2009 ) , as well as the loss of Golgi-associated MTs ( Hurtado et al . , 2011; Miller et al . , 2009 ) prevent proper polarized Golgi trafficking without affecting global secretory properties but lead to defects in directional cell movement . Notably , investigation of the contributions of centrosomal and Golgi-originating MT populations to Golgi organization indicated that the role of the centrosome was restricted to facilitating assembly of an integral Golgi apparatus ( Vinogradova et al . , 2012 ) . During angiogenesis , the process of new blood vessel development , endothelial cells ( ECs ) respond to external cues by coordinating numerous activities , including proliferation , sprouting , migration , lumen formation and anastomosis ( Geudens and Gerhardt , 2011; Potente et al . , 2011 ) . The first step of vessel formation , outward cell sprouting , requires that the tip cell , which will guide growth of a new vessel , breaks symmetry by extending protrusions toward guidance cues ( Geudens and Gerhardt , 2011; Lee and Bautch , 2011 ) . MT growth dynamics has been shown to be required for the formation and maintenance of angiogenic structures ( Bayless and Johnson , 2011; Myers et al . , 2011 ) , whereas its regional regulation has been implicated in directional EC migration ( Braun et al . , 2014 ) . Vascular sprouting and repolarization are also affected by supernumerary centrosomes , a hallmark of tumor ECs that impacts on MT nucleation and dynamics ( Kushner et al . , 2014; Kushner et al . , 2016 ) . The ability of ECs to polarize can be explored in 2D , where cells respond to a monolayer wound by developing a mesenchymal motile phenotype typified by a front-rear asymmetry and anterior centrosome positioning ( Gotlieb et al . , 1981 ) . When cultured in 3D collagen gels , ECs form branched tubular structures ( Koh et al . , 2008 ) , and when spheroids of ECs are embedded in a 3D matrix , they develop sprouts that closely reproduce the first step of formation of capillaries from pre-existing vessels ( Pfisterer and Korff , 2016 ) . Here , we used a combination of 2D and 3D endothelial models to investigate the role of MT organization in cell polarization . Challenging the prevailing view , our data showed that the centrosomal MT population is dispensable and insufficient for EC migration and sprouting . In contrast , silencing of CAMSAP2 , which results in disappearance of non-centrosomal MTs , profoundly perturbed the ability of ECs to form vascular sprouts in vitro as well as in vivo , in zebrafish embryos . Detailed analysis showed that non-centrosomal MTs are required to allow MT redistribution in a single-cell protrusion and thus enable polarized trafficking , directional stabilization of protrusions and persistent migration . Using 2D monolayer wound healing assay , we confirmed the anterior position of the centrosome and the Golgi apparatus in migrating ECs and also extended this observation to tip ECs sprouting from a spheroid in 3D ( Figure 1—figure supplement 1A ) . To assess the impact of centrosome removal on these processes , we took advantage of the Plk4 inhibitor centrinone , which prevents centriole duplication and leads to centrosome depletion ( Wong et al . , 2015 ) . Efficient centrosome elimination was confirmed by staining with different markers of centrioles or pericentriolar material ( PCM ) ( Figure 1—figure supplement 1B ) . Loss of centrosomes as focal points of MT organization was also visible by super-resolution imaging of the MT networks in fixed cells and by tracing growing EB3-GFP-positive MTs in live cells ( Figure 1A , B ) . Importantly , the centrosomal arrangement of MTs in control cells was more obvious in EB3-GFP tracings than in MT images ( Figure 1A , B ) , suggesting that the centrosome plays a more important role in nucleating new MTs than in anchoring their minus ends , and that even in control cells , a significant MT population is non-centrosomal . The perinuclear MT density was partly associated with the Golgi apparatus , which was mildly enlarged and less compact in centrinone-treated cells ( Figure 1—figure supplement 1C ) . Nocodazole washout assays showed that the centrosome was the major nucleation site during MT reassembly in control cells , while this function was taken over by the Golgi membranes in centrinone-treated cells ( Figure 1—figure supplement 1D ) . In spite of these differences , MT density , the density of growing , EB3-positive MT plus ends , parameters of MT plus end growth and the levels of different tubulin post-translational modifications were not affected ( Figure 1A , B , C , Figure 1—figure supplement 2A , B ) , and in contrast to a centrosome excess ( Godinho et al . , 2014 ) , the distribution and intensity of cell-cell adhesion markers was unchanged ( Figure 1—figure supplement 2D ) ( ) . The abundance of MT minus-end stabilizing protein CAMSAP2 was mildly but not significantly increased , and the area occupied by CAMSAP2 stretches was enlarged ( Figure 1—figure supplement 2A , C ) . This is consistent with the enlarged Golgi , to which many CAMSAP2 stretches attach ( Wu et al . , 2016 ) , and possibly with an increased stabilization of non-centrosomal MTs that compensate for the loss of the centrosomal ones . 2D migration was not perturbed in centrinone-treated ECs ( Figure 1D , Figure 1—figure supplement 2E ) . ECs in the migrating front still showed proper polarized organization with their Golgi positioned toward the direction of migration ( Figure 1D ) . In a more physiological 3D context , centrosome removal did not affect the emergence of EC sprouts out of spheroids grown in collagen matrix ( Figure 1E ) . The few remaining centrosome-containing ECs were not enriched in the sprouts , and the Golgi apparatus was properly polarized in sprouting tip ECs devoid of centrosomes ( Figure 1F ) . To confirm these results , we depleted centrosomes by knocking down CPAP , a factor essential for centriole duplication ( Kohlmaier et al . , 2009; Schmidt et al . , 2009; Tang et al . , 2009 ) . Also using this approach , we could efficiently remove centrosomes in the majority of cells without affecting EC polarization in 3D and sprouting from spheroids ( Figure 1G , H , Figure 1—figure supplement 2F , G ) . These results indicate that centrosome is dispensable for endothelial polarization and movement . If the centrosomal MTs are dispensable for cell motility in ECs , their function must be taken over by non-centrosomal MTs , which in mesenchymal cells are stabilized by CAMSAP2 ( Jiang et al . , 2014 ) . Interestingly , CAMSAP2 expression was transiently enhanced upon treatment with the angiogenic factor VEGF , and the length and number of CAMSAP2-decorated MT stretches was increased ( Figure 2A , B , Figure 2—figure supplement 1A ) , suggesting that non-centrosomal MTs might play a role in angiogenesis . To test this idea , we silenced CAMSAP2 ( Figure 2—figure supplement 1B ) , thereby generating a mostly centrosome-anchored , radial MT array ( Figure 2C , D , Figure 2—figure supplement 1D ) . This treatment had no effect on the MT density or parameters of MT plus end dynamics ( Figure 2D , Figure 2—figure supplement 1C ) . The abundance of post-translationally modified forms of tubulin was also unchanged ( Figure 2—figure supplement 1E ) , showing that ECs are in this respect different from U2OS cells , where loss of detyrosinated MTs was seen upon CAMSAP2 depletion ( Jiang et al . , 2014 ) . MT nucleation from the centrosome was unaffected , in line with fact that CAMSAP2 shows no colocalization with the centrosome ( Figure 2—figure supplement 1F , G ) . Since MT properties were largely unaltered , the motility of the motor protein kinesin-3 KIF13B , which is known to play an important role in transporting VEGF receptor in ECs ( Yamada et al . , 2014 ) , was also unchanged ( Figure 2—figure supplement 1H ) . CAMSAP2 depletion caused a moderate but significant decrease in cell migration in 2D ( Figure 2E , Figure 2—figure supplement 1I ) . Strikingly , the defect was much more pronounced during 3D sprouting: ECs depleted of CAMSAP2 were able to form only shorts sprouts and form long protruding structures ( Figure 2F , G ) . This strong impairment was not related to cell viability or cell cycle progression defects ( Figure 2—figure supplement 2A ) , and CAMSAP2 depletion in non-cycling ECs still severely affected formation of long sprouts ( Figure 2—figure supplement 2B ) . Rescue experiments using expression of a siRNA-insensitive CAMSAP2 construct confirmed the specificity of the phenotype ( Figure 2H ) , with ECs positive for the rescue construct populating growing sprouts in a CAMSAP2 knockdown background ( Figure 2I ) . We also analyzed the potential involvement of CAMSAP3 and found that it was only weakly expressed in ECs and its depletion did not aggravate the phenotype of CAMSAP2 knockdown ( Figure 2—figure supplement 2C–E ) . These data point to an important and specific role of CAMSAP2 in EC morphology in 3D . To understand the poor ability of CAMSAP2-depleted cells to form long sprouts in 3D , we set out to characterize this process in more detail . We found that early stages of sprout formation were not affected much by CAMSAP2 depletion; however , the differences gradually increased over time , as , in contrast to the control situation , spheroids silenced for CAMSAP2 were unable to increase the number and especially the length of the sprouts ( Figure 3—figure supplement 1A ) . In the absence of CAMSAP2 , endothelial spheroids are thus capable of initiating protrusions but are unable to mature them into larger and more stable , MT-populated structures ( Figure 3A ) . In line with this idea , when individual isolated ECs were cultured in a collagen matrix , where they extended protrusions in different directions and fused into a tubulogenic network , CAMSAP2 depletion did not prevent the establishment of a vascular plexus ( Figure 3—figure supplement 1B ) . Protrusive activity of isolated ECs measured by their elongation ( inverse of circularity ) and the total and average protrusion length was not affected by CAMSAP2 knockdown ( Figure 3B , Figure 3—figure supplement 1C ) . However , such CAMSAP2-depleted ECs bearing a centrosome-centered MT array ( Figure 3—figure supplement 1D ) had a different organization of protrusions . Whereas control ECs had a restricted number of protrusions , with a single predominant one , CAMSAP2-depleted ECs displayed multiple short protrusions ( Figure 3B , C ) . In contrast to control ECs that had most of their protrusions , and especially the longest ones , aligned in one direction , CAMSAP2-depleted cells displayed protrusions that were more radially dispersed , irrespective of their length ( Figure 3D , Figure 3—figure supplement 1E , F ) . To understand the origin of this phenotype , we performed live recording of protrusion formation . In contrast to control ECs , which , after having formed several small transient protrusions , stabilized and extended one or two of them , CAMSAP2-depleted ECs were unable to accomplish this transition and to elongate in a single direction ( Figure 3E ) . Quantification of protrusion dynamics using a radial reslice representation ( Figure 3F; every black signal represents the presence of a protrusion at a given time point at a given radial position ) confirmed these observations and pointed to dramatically lower protrusion persistence after CAMSAP2 inactivation ( Figure 3F ) , while the total protrusion number was not affected by CAMSAP2 depletion ( Figure 3—figure supplement 1G ) . We hypothesized that in CAMSAP2-depleted cells with a radial MT array , MTs cannot become enriched in one protrusion , and found that this indeed was the case ( Figure 3G ) , strongly suggesting that non-centrosomal MTs stabilize polarized elongated cell morphology by enabling MT enrichment in a single protrusion . The reduced protrusion persistence prompted us to examine the organization of acto-myosin cytoskeleton after CAMSAP2 silencing . In 2D-cultured CAMSAP2-depleted cells , we observed a modest increase in the density of F-actin cytoskeleton ( Figure 4A ) , due to the presence of more stress fibers ( Figure 4—figure supplement 1A ) . Beside this small difference , cells established normal polarized front-rear morphologies , as revealed by the presence of actin-enriched lamellipodia and myosin IIb-positive retracting cell edges , and the distribution patterns of active Rho and Rac1 , as well as cell adhesion markers were normal ( Figure 4A , B , Figure 4—figure supplement 1A , B , C ) . Importantly , actin cytoskeleton was unchanged in 3D environment in the absence of CAMSAP2 , displaying intense peripheral cortical accumulation as in control situation ( Figure 4C , Figure 4—figure supplement 1D ) . Myosin II-dependent cell contractility has been shown to inhibit protrusion formation in ECs ( Fischer et al . , 2009 ) . In agreement with these observations , inhibition of myosin II either directly , with blebbistatin , or indirectly , with the inhibitor of the kinase ROCK ( Y-27632 ) , led to longer 3D protrusions in each condition ( Figure 4—figure supplement 2A–D ) . However , in the absence of CAMSAP2 , these treatments did not rescue the polarized elongated morphology typical of control ECs ( Figure 4D , Figure 4—figure supplement 2A , C , E ) . Moreover , although decreased contractility facilitated protrusion persistence , CAMSAP2 depletion still severely reduced protrusion stability in the presence of the ROCK inhibitor ( Figure 4E ) . Similarly , treatment of spheroids with the ROCK inhibitor or blebbistatin induced more and longer sprouts but failed to rescue sprouting impairment in CAMSAP2-depleted cells ( Figure 4F , Figure 4—figure supplement 2F ) . Altogether , these data demonstrate that changes in acto-myosin cytoskeleton or contractility cannot explain the defects associated with CAMSAP2 silencing . While displaying normal front-rear morphologies , CAMSAP2-depleted cells were often unable to orient their lamellae in the direction of migration , suggesting polarity defects ( Figure 4B , asterisks ) . Accordingly , CAMSAP2 inactivation resulted in a substantial drop in the directionality of cell movement ( Figure 5A ) , which was associated with Golgi and centrosome mispositioning respective to the wound ( Figure 5B , Figure 5—figure supplement 1A ) . This explained the global impairment of cell migration in spite of only a minor decrease in movement velocity ( Figure 2—figure supplement 1I ) . Furthermore , the correlation between the positions of the Golgi and the centrosome , which always colocalized ( Figure 5—figure supplement 1A ) , and the lamellipodia was strongly diminished after CAMSAP2 silencing , with some ECs having their leading edge in front of the Golgi and some not ( Figure 5B ) . Forward-targeted post-Golgi vesicle trafficking , which is often considered as a key regulator of cell polarity during migration , was perturbed in the absence of CAMSAP2 . Whereas in control ECs the majority of exocytotic vesicles labeled with the small GTPase Rab6 moved towards the leading edge , Rab6 trajectories were distributed more symmetrically in CAMSAP2-depleted ECs , while the other trafficking parameters were not affected ( Figure 5C , Figure 5—figure supplement 1B ) . This likely reflected a more radial , symmetric MT array in these cells ( Figure 2C , Figure 2—figure supplement 1D ) . Together , our 2D results argue in favor of a model where Golgi positioning does not directly dictate the orientation of the extending lamellae but is needed to stabilize directional persistence by controlling polarized trafficking . Forward positioning of the Golgi , which strongly co-localized with CAMSAP2 stretches , was even more striking in 3D ( Figure 5—figure supplement 1C ) . In this 3D setting , CAMSAP2 proved to be essential for Golgi polarization in the direction of sprouting ( Figure 5D ) . In line with our 2D observations , while control cells displayed highly asymmetric trafficking of MT-dependent Rab6 positive exocytotic vesicles , little asymmetry in trafficking was observed between protrusions of CAMSAP2-depleted cells ( Figure 5E ) . Consistent with the inability of centrosome-anchored MTs to redistribute to a single protrusion ( Figure 3G ) , the failure in polarization of the secretory trafficking , which is likely necessary to generate a stable leading cell edge ( Schmoranzer et al . , 2003; Stehbens et al . , 2014; Yadav et al . , 2009 ) could explain the inability of CAMSAP2 depleted ECs to form mature sprouts in 3D . The above results suggest that by protecting Golgi-tethered MTs , CAMSAP2 regulates proper Golgi positioning important for EC polarization . To test this idea , we made use of our recent findings showing that two proteins , AKAP450 and myomegalin ( MMG ) , are needed for anchoring CAMSAP2-decorated MT minus-ends to Golgi membranes ( Wu et al . , 2016 ) . As expected , depletion of AKAP450 or MMG ( Figure 6—figure supplement 1A ) displaced CAMSAP2 stretches from the Golgi ( Figure 6A , Figure 6—figure supplement 1B ) . This redistribution had remarkably similar consequences for 2D migration compared to CAMSAP2 silencing: ECs that had lost their Golgi-attached CAMSAP2 were unable to maintain directionality and to orient their Golgi , resulting in migration deficiency ( Figure 6B , Figure 6—figure supplement 1C , D ) . Interestingly , the situation differed when ECs were placed in a 3D context: whereas displacing non-centrosomal CAMSAP2-bound MT ends from Golgi by depleting AKAP450 or MMG had a negative impact on endothelial sprouting abilities , its effect was significantly milder than that of CAMSAP2 depletion , when most MTs were attached to the centrosome ( Figure 6C ) . This suggests that in 3D sprouting , non-centrosomal MTs might have a role independent of their direct Golgi association . We reasoned that non-centrosomal MTs , which are not anchored to a single point , might redistribute more easily to create asymmetry , and therefore , centrosome removal in CAMSAP2-depleted ECs might improve their polarization potential by restoring a pool of non-centrosomal MTs . To test this idea , we silenced AKAP450 , MMG and CAMSAP2 in ECs in combination with centrinone-induced centrosome depletion . Such ECs were viable and efficiently lost their centrosome , displaying a characteristic enlarged shape filled with a dense non-centrosomal MT array , even in the absence of CAMSAP2 ( Figure 7A ) . The density of MTs and growing , EB3-positive MT ends was similar in all conditions ( Figure 7B , Figure 7—figure supplement 1A ) . As we described in our previous study ( Wu et al . , 2016 ) , acentrosomal cells showed enhanced recruitment of γ-tubulin to the Golgi , an effect that was abolished by depleting AKAP450 ( Figure 7C , D ) . In line with these results , we observed abundant MT nucleation from the Golgi membranes after nocodazole washout in control , MMG- and CAMSAP2-depleted acentrosomal cells , while in AKAP450-depleted acentrosomal cells , MTs were nucleated from the cytoplasmic sites distinct from the Golgi membranes ( Figure 7—figure supplement 1B ) . Such distribution of MT nucleation sites correlated with the recruitment of the pericentriolar material ( PCM ) marker Pericentrin to the Golgi in control , MMG- and CAMSAP2-depleted centrinone-treated cells , and its dispersion in the cytoplasm in AKAP450-depleted cells ( Figure 7—figure supplement 1C ) . In agreement with our previous work in RPE1 cells ( Wu et al . , 2016 ) , MT density was detached from the Golgi region after AKAP450 and MMG knockdown , because these proteins constitute a part of an essential link between MTs and the Golgi membranes ( Figure 7A , D ) . In CAMSAP2-depleted ECs , centrinone treatment caused some disorganization of the Golgi , and MTs were not concentrated in the Golgi area either ( Figure 7A , D ) . The absence of the centrosome caused no additional reduction in the sprouting ability of AKAP450 or MMG-depleted ECs organized in spheroids ( Figure 7E ) , indicating that CAMSAP2-stabilized non-centrosomal MTs are sufficient to support formation of elongated sprouts from spheroids to some extent even when they are not attached to the Golgi . Strikingly , the removal of centrosome in the absence of CAMSAP2 significantly rescued the sprouting potential of ECs ( Figure 7E ) . These data support the idea that non-centrosomal MTs contribute positively to EC sprouting , while the centrosome is not only dispensable , but can also play an inhibitory role when CAMSAP2 is absent . Among all the situations analyzed , CAMSAP2-depleted cells which had centrosomes were the only ones which had a symmetric , strongly radial MT system , while this property was lost when these cells were treated with centrinone ( Figure 7A , Figure 8A ) . As described above , CAMSAP2-depleted cells with centrosomes had symmetric radial protrusions in 3D ( Figure 3B–D ) , but , remarkably , centrosome depletion restored their ability to generate one long dominant protrusion ( Figure 8B , Figure 8—figure supplement 1 ) . MMG-depleted cells , in which non-centrosomal MTs are present but not anchored at the Golgi performed in these assays just as well as control cells irrespective of their centrosome content ( Figure 8B ) , and we found that although CAMSAP2-decorated minus ends were not enriched at the Golgi anymore after MMG depletion , they were still concentrated in the major protrusion ( Figure 8C ) . Therefore , Golgi attachment is not a requirement for concentrating non-centrosomal MTs in one cell protrusion in 3D . This observation likely explains why centrosome removal rescues sprouting in CAMSAP2-delpeted cells: non-centrosomal MTs can concentrate in one protrusion ( Figure 8D ) even though they are not linked to Golgi membranes . Taken together , our data demonstrate that the presence of non-centrosomal MTs is essential for generating cell asymmetry required for the emergence of long EC sprouts , while attachment of these MTs to the Golgi , and the likely more efficient secretion associated with such an arrangement is beneficial but not essential . Finally , we addressed the role of CAMSAP2 in vivo using zebrafish as a model . Two CAMSAP2-encoding gene orthologues are present in zebrafish , camsap2a ( ENSDARG00000062173 ) and camsap2b ( ENSDARG00000059965 ) . To analyze the role of CAMSAP2 in zebrafish vascular development , we designed splice-blocking antisense morpholinos ( Figure 9—figure supplement 1A ) to generate CAMSAP2-silenced embryos in the endothelial reporter line Tg ( fli1a:eGFP ) . Embryos inactivated for CAMSAP2a or CAMSAP2b were viable , had no obvious morphological defects and normal somite development . We next focused on CAMSAP2b , because among CAMSAP2 orthologs , it showed the highest expression in the zebrafish endothelium and was associated with more severe vascular defects . In zebrafish , two waves of dorsal sprouting angiogenesis take place successively during vascular development ( Ellertsdóttir et al . , 2010; Isogai et al . , 2003 ) . The first one occurs at around 22 hr post fertilization ( hpf ) from the dorsal aorta and forms arterial intersegmental vessels . Another one takes place between 32 and 48 hpf from the cardinal vein and gives rise to venous intersegmental vessels and to parachordal lymphangioblasts , precursors of lymphangiogenic vessels ( Figure 9A ) . After CAMSAP2b inactivation ( Figure 9—figure supplement 1B ) , arterial intersegmental vessel formation was hardly altered , but the secondary EC sprouting was perturbed , giving rise to abnormal tortuous venous intersegmental vessels ( Figure 9—figure supplement 1C ) . In CAMSAP2b morphants , we observed fewer secondary sprouts emerging from the cardinal vein at 34 hpf ( Figure 9—figure supplement 1D ) . In addition , the fraction of venous sprouts that had fused with the neighboring arterial intersegmental vessel at 41 hpf was strongly reduced ( Figure 9—figure supplement 1D ) , suggesting a defect in directional migration . Venous intersegmental vessels form after cardinal vein-derived secondary sprouts connect to primary arterial intersegmental vessels . Because secondary sprouts had difficulties to fuse with primary intersegmental vessels , we also found that the proportion of venous intersegmental vessels at 48hpf was reduced in CAMSAP2b morphants: whereas control embryos displayed a typical 50–50 arterial/venous intersegmental vessel ratio , fewer intersegmental vessels were connected to the cardinal veinand scored as venous intersegmental vessels in CAMSAP2b morphant embryos ( Figure 9A , B ) . In addition , the alternative outcome of venous sprouting , that is parachordal lymphangioblasts formation , was alsoimpaired in the absence of CAMSAP2b , with less or aberrant parachordal lymphangioblasts in the morphants ( Figure 9A , Figure 9—figure supplement 1E , asterisks ) . Importantly , injection of mRNA coding for human CAMSAP2 restored normal phenotypes in the majority of the morphants ( Figure 9B , Figure 9—figure supplement 1F ) . To confirm these results , we performed live imaging of venous sprouting in Tg ( Fli1ep:Lifeact-EGFP ) , a zebrafish line with F-actin labeled in the endothelium ( Phng et al . , 2013 ) . In control conditions , venous ECs migrated in a highly directed manner to either fuse to the neighboring arterial intersegmental vessel or start assembling horizontal parachordal lymphangioblasts ( Supplemental Videos 1 and 2 , Figure 9C ) . In contrast , in CAMSAP2b morphants venous sprouts were very unstable and showed less directional persistence ( Supplemental Videos 3 and 4 , Figure 9C ) , sometimes resulting in the atypical fusion of two distinct sprouts with the same arterial intersegmental vessel ( Supplemental Video 5 ) . These observations were quantitatively validated by tracking the length and orientation of venous sprouts over time . In contrast to their regular extension in control animals , venous sprouts depleted of CAMSAP2b displayed a more erratic and less efficient growth ( Figure 9D ) . This aberrant behavior is also illustrated in Figure 9E , where CAMSAP2b silencing is shown to be associated with a lot of shortening episodes ( negative change in length ) and a more variable sprout orientation ( higher change in angle ) , resulting in a significantly lower growth and directional persistence ( Figure 9F ) . Supporting a role for CAMSAP2 in venous angiogenesis , formation of the caudal vein plexus , a honeycomb-like structure arising from active ventral migration of venous ECs from the cardinal vein in the caudal region , was also perturbed in the morphants ( Figure 9—figure supplement 1G ) . Altogether , these observations suggest a defect in guided migration during venous sprouting in CAMSAP2b-silenced zebrafish embryos , in agreement with our in vitro findings and supporting the idea that CAMSAP2 is involved in directional angiogenic sprouting in vivo . While the acto-myosin cytoskeleton is crucial for generating protrusions , adhesions and contractile forces during cell migration , an anisotropic MT network strongly contributes to the establishment and maintenance of cell polarity . One current model explaining the generation of an asymmetric MT array involves forward positioning of its center , the centrosome , assumed to represent the main MTOC , together with the local regulation of MT plus end stability ( Etienne-Manneville , 2013; Vinogradova et al . , 2009 ) . Here , we showed that although these regulatory processes could contribute to the asymmetry of the system , a centrosomal radial MT network was both completely dispensable and insufficient for the establishment of polarized cell morphology in soft 3D matrices ( Figure 9G ) . Previous work showed that the MT minus-end binding protein CAMSAP2 is a key player in the regulation of non-centrosomal MT minus ends in mammalian cells ( Akhmanova and Hoogenraad , 2015; Jiang et al . , 2014; Tanaka et al . , 2012; Yau et al . , 2014 ) . Here , we uncovered the crucial role of CAMSAP2 in regulating cell polarity during endothelial sprout formation in 3D and persistent directional migration in 2D ( Figure 9G ) . We think that this demonstrates the important role of non-centrosomal MTs in these processes , because , although we cannot exclude that this protein has alternative functions , for example in controlling motor-based transport or signaling , we did not find any direct evidence supporting such functions in our experiments . Furthermore , although MTs are known to regulate actin organization , and MT destabilization promotes myosin II-dependent contractility , which affects protrusion formation ( Etienne-Manneville , 2013 ) , the protrusions formed in the absence of CAMSAP2 exhibited unchanged F-actin network . This was in line with our observation that the loss of CAMSAP2 had no impact on the overall microtubule density , plus end dynamics or modifications . Our observation that pharmacological inhibition of contractility failed to rescue the persistency and polarized organization of cell protrusions after CAMSAP2 depletion further supports the view that the observed cell morphology and migration defects were not caused by changes in the actin cytoskeleton . Importantly , CAMSAP2-depleted ECs could still form lamellipodia and retracting cell rear on 2D surfaces and initiated protrusions when cultured in 3D . Whether this reflects a sufficient degree of asymmetry supported by the centrosomal MT network , or a lack of MT involvement in these actin-based processes , as it has been suggested in 2D models ( Siegrist and Doe , 2007 ) , deserves additional investigation . However , both in 2D and 3D , loss of non-centrosomal MTs interfered with persistence of lamellipodia and elongation of cell protrusions , explaining migration defects . CAMSAP2 played a much more prominent role in 3D than in 2D environment and even displayed different phenotypes in the two distinct 3D settings we used: the absence of CAMSAP2 severely reduced sprouting from spheroids , while the protrusive activity of single isolated ECs in collagen matrix was unaffected . It is likely that the distinct degree of polarity required in these assays could explain this difference . The inability of spheroids to maintain and extend long sprouting structures will culminate in their collapse ( as seen in Figure 3—figure supplement 1A ) , whereas isolated ECs could form protrusions in any direction . However , also in the latter case , the radial MT network in CAMSAP2-silenced ECs could not support polarized elongated morphology characteristic for control cells . In our recent study ( Bouchet et al . , 2016 ) , we showed that in mesenchymal cells , the initial formation of protrusions is MT-independent , but the extension and stabilization of long protrusions occurs only if they are filled with MTs . The data presented in the current study indicate that in order to acquire an elongated polarized morphology , 3D-cultured ECs need to be able to concentrate their MTs in one protrusion . If the intrinsic centrosome-driven symmetry of the MT network dominates , it inhibits this process , forcing the cells to acquire a non-polarized ‘starfish’-like shape , which is incompatible with efficient cell elongation and translocation in 3D matrix , as observed in the spheroid sprouting assay . While our results focus on one the very first steps of sprouting angiogenesis , that is the extension and stabilization of a protrusion that is required for effective outward migration , other important angiogenic behaviors involving polarity , as multicellular sprout growth and vessel lumenization were not addressed in this study and deserve further investigation . Non-centrosomal MTs can form intrinsically asymmetric networks through their attachment to the Golgi complex ( Vinogradova et al . , 2009; Zhu and Kaverina , 2013 ) . Indeed , confirming previous results ( Roubin et al . , 2013; Zhu and Kaverina , 2013 ) , we showed that the depletion of Golgi-originating MTs dependent on AKAP450 and MMG fully recapitulated CAMSAP2 inactivation-related defects in 2D assays . This suggests that the Golgi-anchored MT population , required for the proper Golgi positioning and polarized trafficking , is an important determinant of directional 2D migration . However , in the 3D spheroid assays , the impact of AKAP450 or MMG knockdown was far less severe than that of CAMSAP2 depletion . In fact , an elongated morphology in isolated ECs in 3D could be established when MMG was depleted and Golgi-attached MTs were absent . This suggests that non-centrosomal MTs can function independently of the Golgi anchoring . This idea is supported by the observation that the phenotype caused by the loss of CAMSAP2 could be partially rescued by suppressing formation of a symmetric centrosome-associated MT aster and thus reverting to a non-centrosomal MT array , albeit one lacking Golgi-MT attachments . In fact , the remarkable similarity between the phenotypes associated with CAMSAP2 and MMG depletion after centrinone treatment , leading to similar MT organization , strengthens the idea that the presence of non-centrosomal MTs per se , rather than their Golgi attachment might be crucial to support at least some degree of 3D sprout formation . The redistribution of MT nucleating and anchoring PCM complexes to the Golgi and cytoplasm likely contributes to the generation of such arrays in centrinone-treated cells . It is possible that asymmetrical cortical stabilization of MTs , regulated by extrinsic signals , can be sufficient to polarize the MT network independently of Golgi anchoring , if this polarity is not perturbed by the presence of a potent symmetric MT-anchoring structure such as the centrosome . We recently showed that a single Golgi apparatus can assemble in the absence of centrosomal and Golgi-derived MTs ( Wu et al . , 2016 ) , suggesting that non-centrosomal MTs that are detached from the Golgi membranes can still regulate Golgi organization . It is therefore possible that non-centrosomal MTs that are not anchored at the Golgi can still control Golgi positioning and ensure polarized secretion , as the Golgi is typically oriented toward the main protrusion in these conditions ( Figure 8C ) . In this situation , the Golgi apparatus and non-centrosomal MTs , although not permanently connected , likely exert a positive feedback on each other . The Golgi , which in the absence of the centrosome concentrates γ-tubulin and some other PCM components , serves as the major MT nucleation site , albeit a one which cannot tether the MTs it nucleates . MTs , in turn , serve as directional tracks for localizing Golgi membranes , and increasing MT density in one protrusion will help to maintain the Golgi at the base of this protrusion . The mechanism of MT minus end stabilization in the absence of both the centrosome and CAMSAP2 is still an open question . An interesting candidate is ninein , a protein involved in MT minus-end organization in epithelial cells ( Moss et al . , 2007 ) , whose worm homolog can act on the same pathway as CAMSAP homologs ( Wang et al . , 2015 ) . Interestingly , ninein has been described to relocalize from the centrosome to the cytoplasm upon triggering angiogenesis and to participate in endothelial morphogenesis ( Matsumoto et al . , 2008 ) . In line with our in vitro results , CAMSAP2 inactivation in zebrafish perturbed the directional and persistent migration of ECs sprouting dorsally from the cardinal vein . Interestingly , loss of CAMSAP2 had no effect on EC sprouting form the dorsal aorta , thus suggesting that venous sprouting might be more sensitive to the lack of CAMSAP2 . Recent evidence suggests that ECs from different vascular beds are differentially regulated and use different mechanisms ( Franco et al . , 2016; Rocha and Adams , 2009 ) . It is possible that the secondary wave of sprouting from the cardinal vein is more dependent on intrinsic polarity mechanisms than the arterial sprouting , for instance , due to differential participation of supportive cells and therefore different need for cytoskeleton-based processes . One of the most surprising findings of this study is the lack of importance of the centrosome in endothelial polarization . Although we do not exclude that centrosomal MTs are participating in polarity establishment in control cells , where the centrosome is in fact the major nucleating factor , we provide evidence that its MT anchoring activity is dispensable , not sufficient and should be counterbalanced by a non-centrosomal MT population . As it was already described for other organisms ( Bornens , 2012 ) , our results support the view that the centrosome has no crucial function in many types of animal tissues . Whereas the role of centrosome localization in determining neuronal polarity in vivo has been heavily debated ( Kuijpers and Hoogenraad , 2011 ) , an inhibitory role for a radial centrosomal MT organization has recently been suggested in epithelia ( Noordstra et al . , 2016 ) . Altogether , our findings support the concept that polarity induction requires a switch to an asymmetric MT network , which might involve participation of centrosome-independent MT minus end stabilizing factors and centrosome inactivation . We used rabbit polyclonal antibodies against CAMSAP2 ( Novus , Littleton , CO , NBP1-21402 ) , CEP135 , acetylated tubulin , polyglutamylated tubulin and γ-tubulin ( Sigma-Aldrich , St Louis , MO , SAB4503685 , T7451 , T9822 and T3559 ) , CDK5RAP2 ( BethylLaboratories , Montgomery , TX , A300-554A ) , detyrosinated tubulin ( Abcam , UK , ab48389 ) , EB3 ( Stepanova et al . , 2003 ) and myomegalin isoform 8 ( MMG8 ) ( Wang et al . , 2014 ) , goat polyclonal antibodies against MYOSIN IIb and PCM1 ( Santa-Cruz biotechnology , Dallas , TX , SC-47205 and SC-50164 ) , mouse monoclonal antibodies against GM130 , Pericentrin , EB1 , VE-Cadherin , ZO-1 , AKAP450 and KU80 ( BD Biosciences , San Jose , CA , 610823 , 611815 , 610535 , 610252 , 610966 , 611518 and 611360 ) , CAMSAP3 , α-tubulin and γ-tubulin ( Sigma-Aldrich , SAB4200415 , T5168 and T6557 ) , NEDD1 ( Abnova , Taiwan , H00121441-M05 ) and rat monoclonal antibodies against α-tubulin YL1/2 ( Pierce , Waltham , MA , MA1-80017 ) . Rabbit polyclonal antibody against CPAP was a kind gift of Dr . P . Gönczy ( Swiss Institute for Experimental Cancer Research , EPFL , Lausanne , Switzerland ) . For western blots , we used the following secondary antibodies: IRDye 800CW/680 LT Goat anti-rabbit and anti-mouse ( Li-Cor Biosciences , Lincoln , LE ) . For immunofluorescence , Alexa Fluor 488 , –594 and −647 conjugated goat antibodies against rabbit , rat and mouse IgG were used as secondary antibody ( Molecular Probes , Eugene , OR ) , together with Alexa Fluor 488/594 phalloidin and NucRed Live 647 ( Molecular Probes ) and DAPI ( Sigma-Aldrich ) . For STED imaging , Atto 647N Phalloidin and Abberior STAR 635P anti-mouse antibodies ( Sigma-Aldrich ) were used . High-concentration rat tail Collagen I was from Corning ( Corning , NY ) , Phorbol 12-myristate 13-acetate ( PMA ) , nocodazole , Y-27632 and DAPI were from Sigma-Aldrich and human recombinant Fibroblast Growth Factor ( FGF ) and Vascular Endothelial Growth Factor ( VEGF ) were from Peprotech ( UK ) and blebbistatin was from Enzo Life Sciences ( Belgium ) . Centrinone was a kind gift of Dr . A . Shiau and Dr . T . Gahman ( Small Molecule Discovery Program , Ludwig Institute for Cancer Research , San Diego ) . The CAMSAP2 siRNA insensitive construct consists of a truncation of the first 232 amino acids of human CAMSAP2 generated by a PCR-based strategy and cloned into peGFP-C1 ( Clonetech , Montain view , CA ) . The zebrafish rescue construct is described below in the zebrafish section . The constructs coding for Rab6A and EB3 in peGFP-C2 were described elsewhere ( Matanis et al . , 2002 ) and Stepanova et al . , 2003 ) , GFP-KIF13B was a gift from Dr . A . Chishti ( University of Illinois , Chicago ) . The Rho biosensor coding plasmid pLenti-RhoA2G ( Addgene plasmid # 40179 ) is a gift of Dr . O . Pertz , ( University of Basel , Switzerland ) and the pLVIN-Rac1-bs Rac1 biosensor plasmid was described elsewhere ( Bouchet et al . , 2016 ) . We used the following siRNAs purchased from Sigma siRNA CAMSAP2 #1 , 5’- GAATACTTCTTGACGAGTT-3’ ( Jiang et al . , 2014 ) siRNA CAMSAP2 #2 , 5’- GTACTGGATAAATAAGGTA-3’ ( Jiang et al . , 2014 ) siRNA CAMSAP3 , 5’-GCATTCTGGAGGAAATTGA-3’ ( Noordstra et al . , 2016 ) siRNA AKAP450 , 5’-AUAUGAACACAGCUUAUGA-3’ ( Hurtado et al . , 2011 ) siRNA MMG , 5’-AGAGCGAGATCATGACTTA-3’ ( Roubin et al . , 2013 ) siRNA CPAP , 5’- AGAAUUAGCUCGAAUAGAA-3’ ( Tang et al . , 2009 ) siRNA Luciferase control , 5’-CGTACGCGGAATACTTCGA-3’ Human Umbilical Vein Endothelial Cells ( HUVECs ) were obtained from Lonza and grown in endothelial basal medium ( EGM-2 ) supplemented with growth supplements ( SingleQuots , Lonza , Switzerland ) : 2% Fetal Bovine Serum ( FBS ) , human Epidermal Growth Factor ( hEGF ) , Vascular Endothelial Growth Factor ( VEGF ) , R3-Insulin-like Growth Factor-1 ( R3-IGF-1 ) , Ascorbic Acid , Hydrocortisone human Fibroblast Growth Factor-Beta ( hFGF-β ) , Heparin , Gentamicin/Amphotericin-B ( GA ) . HUVECs authentication was guaranteed by Lonza through identity and quality control and testing , including against mycoplasma , bacteria , yeast , and fungi . Only low passage cells ( between passages 3 and 7 ) were used . Plasmids and siRNA were , respectively , nucleofected using Amaxa technologies with the HUVEC nucleofector kit ( Lonza ) and transfected with GeneTrans II ( MoBiTec , Germany ) reagents according to the manufacturers' protocols . CPAP depletion was achieved through four successive rounds of siRNA transfection every 3 days . Amaxa was additionally used in the specific case of siRNA transfection of centrinone-treated HUVECs . HUVECs were treated with 125 nM centrinone for 9 days , including the time needed for functional assay . During treatment , non-treated and centrinone-treated HUVECs were passaged every 2–3 days to keep a 60–90% confluency . All functional assays involving centrinone were done in the presence of 10 nM Thymidine . For VEGF treatment , HUVECs were starved in 0 . 5% serum containing medium for 36 hr before addition of 50 ng/ml of VEGF . Nocodazole-induced MT complete disassembly was performed by treating HUVECs with 10 µM nocodazole for 2 hr at 37˚C , followed by 1 hr at 4˚C . Washout ( WO ) was then carried out by two washes with cold and two washes with warm medium . Y-27632 and blebbistatin were used at 10 µM and 50 µM , respectively . HUVECs were trypsinized 48 hr after transfection and counted by trypan blue staining for quantification of the doubling time . Alternatively , HUVECs were stained with phalloidin together with DAPI to identify mitotic cells and their percentage was calculated to determine the mitotic index . HUVECs total extracts were prepared in RIPA buffer ( 10 mM Tris-HCl pH 8 , 140 mM NaCl , 1 mM EDTA , 1 mM EGTA , 1% Triton X-100 , 0 , 1% SDS , protease inhibitor cocktail ( Complete - Sigma ) ) . SDS-PAGE and Western blot analysis were performed according to standard procedures and developed with the Odyssey technology ( Li-Cor Biosciences ) . Densitometric analysis was done using the ‘gel analysis’ plug-in of ImageJ . A confluent HUVEC monolayer was scratched using a sterile P200 tip to create a cell-free zone . Fields were photographed just after injury and 8 hr later . Quantification of cell migration was made by measuring the percentage of area recovery using ImageJ software . Alternatively , phase-contrast live imaging was performed . Single HUVECs were seeded into 2 . 5 mg/ml collagen pH buffered gels overlaid with complete medium supplemented with 50 ng/ml FGF , VEGF and PMA . For global vascular network assessment , 2 × 106 cells/ml were embedded for 48 hr and wide-field fluorescence imaging was done on EVOS cell imaging system ( ThermoFisher Scientific , Waltham , MA ) . For cell morphology analyses , 0 . 5 × 106 cells/ml embedding experiments were submitted to phase-contrast live imaging or processed for immunostaining after 24 hours . The spheroid sprouting assay was performed as previously described ( Martin et al . , 2013 ) : HUVEC spheroids were generated overnight by culturing endothelial cells in complete medium containing 20% methylcellulose in non-adherent 96 well plates . Harvested spheroids were then embedded into 2 mg/ml collagen pH buffered gels overlaid with complete medium supplemented with 40 ng/ml FGF and 50 ng/ml PMA . Angiogenic activity was quantified by measuring the cumulative length of the sprouts that had grown out of each spheroid , their mean number and length , 24 hr after embedding using ImageJ software on bright field images . The sprouts that were originating from secondary branching and the ones that were not in focus in the pictures were omitted from the analysis . For 2D ( /3D ) staining , HUVECs were fixed with −20°C methanol for 10 min or with 4% PFA for 12 ( /20 ) minutes at RT , permeabilized with 0 . 15% Triton X-100 in phosphate buffered saline ( PBS ) ( /PBS-glycine 0 . 1M ) for 2 ( /45 ) minutes , sequentially incubated 1 hr in blocking buffer 2% BSA , 0 . 05% Tween-20 in PBS ( /2% BSA , 0 . 05% Tween-20 , 0 . 2% Triton X-100 , 0 . 05% NaN3 in PBS ) , 1 ( /4 ) hr in primary antibody in blocking buffer , 1 ( /1 . 5 ) hour in secondary antibody , Alexa-conjugated phalloidin and DAPI in blocking buffer . After several washes , slides ( /dismounted 3D gel plugs ) were air-dried and mounted in Vectashield mounting medium ( Vector laboratories , Burlingame , CA ) . For immunostaining of HUVECs submitted to wound-healing assay , samples were fixed after 6 hr of migration . For STED imaging , HUVECs were pre-extracted 45 s in extraction buffer ( PEM80 , 0 . 3% Triton-X100 , 0 . 15% gluteraldehyde ) at 37˚C , fixed with 4% PFA for 12 min at 37˚C , permeabilized with 0 . 2% Triton X-100 for 10 min at RT and then submitted to the same 2D protocol as above except the removal of Tween-20 in the buffers . Bright-field images were collected on an EVOS cell imaging system ( ThermoFisher Scientific ) and phase-contrast live cell imaging was performed on a Nikon Ti equipped with a perfect focus system Nikon ) , a super high pressure mercury lamp ( C-SHG1 , Nikon , Japan ) , a Plan Fluor DLL 10x NA 0 . 3 ( Ph1 ) , a CoolSNAP HQ2 CCD camera ( Photometrics , Tucson , AZ ) , a motorized stage MS-2000-XYZ with Piezo Top Plate ( ASI , Eugene , OR ) and a stage top incubator ( Tokai Hit , Japan ) for 37°C/5% CO2 incubation . The microscope setup was controlled by Micro-manager software . For fluorescence imaging of 2D fixed samples and 3D fixed and live samples , including zebrafishes , Z-series images were collected with spinning disk confocal microscopy on a Nikon Eclipse Ti microscope equipped with a perfect focus system ( Nikon ) , a spinning disk-based confocal scanner unit ( CSU-X1-A1 , Yokogawa , Japan ) , an Evolve 512 EMCCD camera ( Roper Scientific , Trenton , NJ ) attached to a 2 . 0X intermediate lens ( Edmund Optics , Barrington , NJ ) , a super high pressure mercury lamp ( C-SHG1 , Nikon ) , a Roper scientific custom-ordered illuminator ( Nikon , MEY10021 ) including 405 nm ( 100 mW , Vortran ) , 491 nm ( 100 mW , Cobolt ) 561 nm ( 100 mW , Cobolt ) and 647 nm ( 100 mW , Cobolt ) excitation lasers , a set of BFP , GFP , RFP and FarRed emission filters ( Chroma , Bellows Falls , VT ) and a motorized stage MS-2000-XYZ with Piezo Top Plate ( ASI ) . The microscope setup was controlled by MetaMorph . Images were acquired using Plan Fluor 20x MI NA 0 . 75 and Plan Apo VC 60x NA 1 . 4 oil objectives and Apo LWD λS 40x water immersion objective . When necessary , a stage top incubator maintaining 37°C or 28°C and 5% CO2 was used . Live Fluorescence imaging of EB3-GFP in 2D and GFP-Rab6A in 3D was performed on the same spinning disk confocal configuration . Acquisitions were performed at five frames/s during 2 min . Alternatively , 2D samples imaging was performed using widefield fluorescence illumination on a Nikon Eclipse 80i upright microscope equipped with a CoolSNAP HQ2 CCD camera ( Photometrics ) , an Intensilight C-HGFI precentered fiber illuminator ( Nikon ) , a Plan Apo VC 100x NA 1 . 4 oil or 60x NA 1 . 4 oil and driven by Nikon NIS Br software . Live-cell TIRF imaging was performed on a Nikon Eclipse Ti-E inverted microscope equipped with perfect focus system ( Nikon ) , a CFI Apo TIRF 100X oil objective ( Nikon ) , a TI-TIRF-E motorized TIRF illuminator ( Nikon ) , a QuantEM 512SC EMCCD camera ( Photometrics , Roper Scientific ) and a stage top incubator maintaining 37°C and 5% CO2 ( Tokai hit ) . The system was controlled with MetaMorph 7 . 5 software ( Molecular Devices , San Jose , CA ) . Gated STED imaging was performed with Leica TCS SP8 STED 3X microscope driven by LAS X controlling software and using HC PL APO 100x/1 . 4 oil STED WHITE objective , 633 nm white laser for excitation and 775 nm pulsed lased for depletion . Images were acquired in 2D STED mode with vortex phase mask . Depletion laser power was equal to 90% of maximum power and an internal Leica GaAsP HyD hybrid detector with a time gate of 1 ≤ tg ≤ 8 ns was used . The activities of RhoA and Rac1 were measured using previously described Rho single chain biosensor ( Fritz et al . , 2013 ) and Rac1 single chain biosensor ( Moshfegh et al . , 2014 ) using ratiometric FRET between mTFP1/mCerulean and mVenus . Live FRET imaging was performed on Leica TCS SP8 microscope equipped with spectral detection using HC PL APO 100x/1 . 4 oil STED WHITE objective . 440 nm pulsed laser ( 40MHz ) was used for the excitation . Two channels were acquired simultaneously using hybrid detectors in the spectral ranges of donor 450–500 nm and acceptor 515–550 nm . Images were acquired with a scanning velocity of 100 Hz and eight line average scans , a pixel size of 0 , 416 μm and dimensions of 256 × 256 pixels . Donor and acceptor images were convolved with a Gaussian of 1 pixel , background subtracted and FRET-index image was calculated using ImageJ macro according to the formula: FRET=IAIA+ID , where IA and ID correspond to the pixel intensity values of acceptor and donor images . Cell outlines were determined from the thresholded acceptor image and the average FRET-index value was calculated per cell . For image preparation , we used ImageJ for adjustments of levels and contrast , maximum intensity projections , stitching ( with pairwise stitching plugin ) and thresholding to create binary mask used for circularity measurements and particle detection . Kymographs of MT plus end and Rab6 3D dynamics were made using the KymoResliceWide plugin of ImageJ software and analyzed using the same software . For MTs , only length changes ≥ 0 . 3 μm between two consecutive time points were considered as growth or shortening events , while changes < 0 . 3 μm were considered as a pause event; only the events starting and finishing within the recording were analyzed . Velocity was calculated for each growth event and then averaged . Catastrophe frequency was calculated by dividing the number of catastrophes ( transition from growth or pause to shortening ) by the sum of growth and pause durations . EB3 comets and CAMSAP2 stretches were automatically detected on thresholded pictures using the Particle Analysis plugin of ImageJ software . Their number ( EB3 ) and surface ( CAMSAP2 ) were quantified and reported to the cell surface area . EB3 enrichment at the Golgi ( /centrosome ) was calculated as the ratio between the average EB3 intensity in a 2 µm diameter-circle drawn around Golgi mini-stacks ( /centrosome ) and the average EB3 intensity in the cytoplasm . Golgi dispersion was calculated as SDii in which SDi is the standard deviation of intensity and i the mean intensity . For cell-cell junctions analysis ( VE-Cadherin and ZO-1 ) , ImageJ was used to plot intensity profiles along a manually drawn line across junctions . These profiles were then analyzed using the ‘area under curve’ function of GraphPad prism five and the maximum value of , as well as the area under the peaks were averaged . F-actin staining in 3D was analyzed similarly by plotting intensity profiles along a 10 µm long rectangle drawn 5 µm away from the cell body using ImageJ and measuring the maximum intensity and the peak area using GraphPad prism 5 . For stress fiber analysis , a customized ImageJ macro was used to trace the stress fibers and measure their length and width ( Teeuw and Katrukha , 2015 ) ( available at https://github . com/jalmar/CurveTracing ) . All cell protrusions were manually traced with ImageJ software to quantify their number , length and spatial distribution . Polarity index was calculated as ∑1-sin⁡αi . Li∑Li in which αi is the angle between the protrusioni and the longest protrusion and Li is the length of the protrusion . Cell masks were analyzed using the Particle Analysis plugin of ImageJ software to measure cell circularity , calculated as 4πAP2 in which A and P are the area and the perimeter of the cell mask , respectively . Time-lapse imaging of monolayer wound healing assays was analyzed using Manual Tracking and Chemotaxis Tool plugin of ImageJ software to measure the velocity and directionality ( the ratio between the Euclidian and accumulated distance ) of cell movement . Time-lapse imaging of Rab6 and KIF13B was analyzed using ImageJ software with a recently developed plugin ( Yao et al . , 2017 ) . The SOS plugin combined two procedures: particle detection and particle linking: SOS detector 3D module as detector and SOS linker ( NGMA ) module as linker were used . The tracking results were then processed using MTJ ( MTrackJ ) Simple Track Segment module to remove the non-directional tracks and to analyse speed , duration and length of the runs . MTJ Measure Region was used to determine the number of tracks contained in the cell front area , defined as the 45° sector facing the cell lamellipodia and originating from the center of the nucleus . Percentage of tracks in the front was calculated by dividing this value by the total number of tracks and correcting for area differences . Radial representations of time-lapse images of protrusion formation in 3D were made using successively the Radial Reslice , Reslice and Minimum Intensity projection functions of ImageJ software . ImageJ Radial Profile plugin was used to measure the distribution of CAMSAP2 and GM130 signal intensity along the radius in a 20 µm radius-circle originating from the Golgi center and each profile was normalized as x i-MAXxiMAXxi-MINxi . For Golgi enrichment index of α- or γ-tubulin , z-maximum projection of α- , γ-tubulin or GM130 channel was thresholded using ImageJ to create a binary mask to delineate the cell or the Golgi area . The difference between the average intensity within the Golgi area and in the area outside the Golgi was divided by the intensity outside the Golgi and expressed in percent . α-tubulin or CAMSAP2 enrichment in the longest protrusion was calculated as the ratio between the fluorescence intensity in a 0 . 75 µm diameter-circle drawn 8 µm away from the cell body or in the manually drawn area in the longest protrusion and in the other protrusions , within z-maximum projections and averaged per cell . Time-lapse imaging of zebrafish venous sprouting was analyzed by manually drawing the vector corresponding to a sprout for each time point and measuring sprout length and angle using the measure function of ImageJ software . When needed , 3D color-coded stacks were used to more easily isolate the venous sprout . When the geometry of a sprout did not fit a straight line , a segmented line was used for length measurement and the straight line between sprout extremities for the angle determination . Growth persistence of a sprout elongation event corresponds to the mean value of length variations ( Δ Length ) between two consecutive time frames ( every 10 min ) , whereas directional persistence was calculated for each frame as 1sin⁡∆α , where Δα represents the angle variation between two consecutive time frames , and then averaged per growing event . To analyze MT radiality , images of fluorescently labeled MTs were separated into radial and non-radial components using customized ImageJ macro ( Katrukha , 2017 ) . ( available at https://github . com/ekatrukha/radialitymap; copy archived at https://github . com/elifesciences-publications/radialitymap ) . First , local orientation angle map was calculated for each pixel using OrientationJ plugin ( Püspöki et al . , 2016 ) . We used ‘cubic spline gradient’ method and tensor sigma parameter of 6 pixels ( 0 . 4 µm ) . The new origin of coordinates was specified by selecting the centrosome position in a corresponding channel , or the brightest spot in case of centrinone treatment . Radial local orientation angle was calculated as a difference between the local orientation angle and the angle of vector drawn from the new origin of coordinates to the current pixel position . A radial map image was calculated then as an absolute value of the cosine of the radial local orientation angle at each pixel providing values between zero and one . A non-radial map image was calculated as one minus radial map . Both maps were multiplied with the original image to account for different signal intensities; the two maps illustrate separated radial and non-radial image components . The radial profile of the signal in the non-radial map image ( normalized to the maximum signal of the original picture ) was built using ImageJ and used to calculate the average non-radial proportion of the MT network . To avoid the artifacts of the cell center ( very high signal ) and border ( MT bending ) , only a circular section around the reference point was used in the averaging ( from 2 . 5 µm to 15 µm ) . All mentioned ImageJ plugins have source code available and are licensed under open-source GNU GPL v3 license . The Tg ( fli1a:eGFP ) y1 and Tg ( Fli1ep:Lifeact-EGFP ) ( Phng et al . , 2013 ) lines were raised according to EU regulations on laboratory animals . All animal experiments were approved by the animal welfare committee of the University of Liege ( protocol number 14–1556 , laboratory agreement number LA 1610002 ) . Knockdown experiments were performed by injecting embryos at the one- to two-cell stage with 6 ng of Camsap2b morpholino . The following Camsap2b-splice blocking morpholino sequence was used: ATACAGATGgcaagtcttttacatc . For rescue experiment , a Morpholino insensitive human Camsap2 was built by overlapping PCR-based strategy ( ATACAGATG transformed into ATTCAAATG ) , inserted into PSC2 + vector , linearized , in vitro transcribed and injected at 50 ng/µl . For RT-PCR , cDNA was generated from total RNA extracted from zebrafish embryos with Trizol reagent ( Thermo Fisher Scientific ) using RevertAid RT Kit ( Thermo Fisher Scientific ) with random hexamer primers . After DNAse treatment ( Thermo Fisher Scientific ) , cDNA was submitted PCR amplification followed by gel electrophoresis analysis using the following primers: ACTTCAGCAGGGCCAAGATA and TGTCACAGCCTCTTCAGCAT . Alternatively , cDNA was submitted to quantitative real-time ( q ) PCR using Sybrgreen technology ( Applied Biosystems , Foster City , CA ) on a ViiA7 apparatus ( Applied Biosystems ) . ELFA was used as reference gene to quantify the relative expression of the exon2 of Camsap2b using the ΔΔCt method with three alternative primers pairs . Primer sequences were as followed F1_CAMSAP2b , GTCATAACGCCGTCATCCAG R1/2_CAMSAP2b , TGTATAGGGGTCTTGCAGAGG F2_CAMSAP2b , GGGGAGTCTGATTCTCAGGA F3_CAMSAP2b , ACTTCAGCAGGGCCAAGATA R3_CAMSAP2b , TCACGAGTCTCTCCTGGTCA F_ELFA , CTTCTCAGGCTGACTGTGC R_ELFA , CCGCTAGCATTACCCTCC For analyses of vascular structure formation , screening was performed under a fluorescence stereomicroscope whereas confocal pictures and movies were performed on artificially dechorionated embryos between 30 and 48hpf embedded in low melting point agarose ( 0 . 8% ) . Statistical analyses were performed using GraphPad Prism five or Excel and significance was assessed using Mann-Whitney U- , Chi square with Yates correction- and Student’s t- two-tailed paired and unpaired tests . The statistical test used as well as the sample size is indicated in the figure legends . All data are shown using box plots where rectangles represent the second and third quartiles , contain a line corresponding to the median value and are extended with whiskers showing the minimum and maximum , except in Figure 6—figure supplement 1B and Figure 9D , which depict mean ± SEM . In Figure 1—figure supplement 1B and Figure 4—figure supplement 1B , the mean value ± SEM is indicated within the pictures . No explicit power analysis was used to determine sample size and no masking was used for analysis .	Networks of blood vessels grow like trees . Sprouts appear on existing vessels , stretching out to form new branches in a process called angiogenesis . The cells responsible are the same cells that line the finished vessels . These “endothelial cells” start the process by reorganizing themselves to face the direction of the new sprout , changing shape to become asymmetrical , and then they begin to migrate . Beneath the surface , a network of protein scaffolding supports each migrating cell . The scaffolding includes tube-like fibers called microtubules that extend towards the cell membrane and organize the inside of the cell . Destroying microtubules damages blood vessel formation , but their exact role remains unclear . A structure called the centrosome can organize microtubules within cells . The centrosome was generally believed to act like a compass , pointing in the direction that the cell will move . Microtubules can anchor to the centrosome , and this structure is thought to play an important role in cell migration . Yet , many microtubules organize without it; these microtubules instead are organized by a compartment of the cell called the Golgi apparatus and are stabilized by a protein named CAMSAP2 . Martin et al . now report that removing the cells’ centrosomes did not affect cell migration , but getting rid of CAMSAP2 did . Analysis of cell shape and movement in cells grown in the laboratory and in living animals revealed that cells cannot become asymmetrical , or “polarize” , and migrate without CAMSAP2 . In a two-dimensional wound-healing assay , a sheet of cells originally grown from the vessels of a human umbilical cord was scratched , and a microscope was then used to record the cell’s movement as they repaired the injury . Normally , the cells on either side move in a straight line using their microtubules , and though the process was not affected in cells without centrosomes , it was in those without CAMSAP2 . Even more striking results were seen in three-dimensional assays . When the same blood vessel cells from human umbilical cords are grown as spheres inside collagen gels , they form sprouts as they would in the body . Without CAMSAP2 , the cells could not organize their microtubules and they were unable to elongate in one direction and form stable sprouts . Lastly , depleting CAMSAP2 also prevented the normal formation of blood vessels in zebrafish embryos . Taken together , these findings change our understanding of how microtubules affect cell movement and how important the centrosome is for this process . Further work could have an impact on human health , not least in cancer research . Tumors need a good blood supply to grow , so understanding how to block blood vessel formation could lead to new treatments . Microtubules are already a target for cancer therapy , so future work could help to optimize the use of existing drugs .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology" ]	2018	Control of endothelial cell polarity and sprouting angiogenesis by non-centrosomal microtubules
The host–pathogen interactions induced by Salmonella Typhi and Salmonella Paratyphi A during enteric fever are poorly understood . This knowledge gap , and the human restricted nature of these bacteria , limit our understanding of the disease and impede the development of new diagnostic approaches . To investigate metabolite signals associated with enteric fever we performed two dimensional gas chromatography with time-of-flight mass spectrometry ( GCxGC/TOFMS ) on plasma from patients with S . Typhi and S . Paratyphi A infections and asymptomatic controls , identifying 695 individual metabolite peaks . Applying supervised pattern recognition , we found highly significant and reproducible metabolite profiles separating S . Typhi cases , S . Paratyphi A cases , and controls , calculating that a combination of six metabolites could accurately define the etiological agent . For the first time we show that reproducible and serovar specific systemic biomarkers can be detected during enteric fever . Our work defines several biologically plausible metabolites that can be used to detect enteric fever , and unlocks the potential of this method in diagnosing other systemic bacterial infections . Enteric fever is a serious bacterial infection caused by Salmonella enterica serovars Typhi ( S . Typhi ) and Paratyphi A ( S . Paratyphi A ) ( Parry et al . , 2002 ) . S . Typhi is more prevalent than S . Paratyphi A globally , with the best estimates predicting approximately 21 and 5 million new infections with each serovar per year , respectively ( Ochiai et al . , 2008; Buckle et al . , 2012 ) . Both S . Typhi and S . Paratyphi A are systemic pathogens that induce clinically indistinguishable syndromes ( Maskey et al . , 2006 ) . However , they exhibit contrary epidemiologies , different geographical distributions , and different propensities to develop resistance to antimicrobials ( Vollaard et al . , 2004; Karkey et al . , 2013 ) . Additionally , they are genetically and phenotypically distinct , having gone through a lengthy process of convergent evolution to cause an identical disease ( Didelot et al . , 2007; Holt et al . , 2009 ) . The agents of enteric fever induce their effect on the human body by invading the gastrointestinal tract and spreading in the bloodstream ( Everest et al . , 2001 ) . It is this systemic phase of the disease that induces the characteristic symptoms of enteric fever ( Glynn et al . , 1995 ) . However , the host’s reaction to this systemic spread , outside the adaptive immune response , is not well described . There is a knowledge gap related to the scope and the nature of the host–pathogen interactions that are induced during enteric fever that limit our understanding of the disease and prevent the development of new diagnostic tests ( Baker et al . , 2010 ) . An accurate diagnosis of enteric fever is important in clinical setting where febrile disease with multiple potential etiologies is common . A confirmative diagnostic ensures appropriate antimicrobial therapy to prevents serious complications and death and reduces inappropriate antimicrobial usage ( Parry et al . , 2011a; Parry et al . , 2014 ) . All currently accepted methods for enteric fever diagnosis lack reproducibility and exhibit inacceptable sensitivity and specificity under operational conditions ( Moore et al . , 2014; Parry et al . , 2011b ) . The main roadblock to developing new enteric fever diagnostics is overcoming the lack of reproducible immunological and microbiological signals found in the host during infection . Metabolomics is a comparatively new in infectious disease research , yet some initial investigations have shown that metabolite signals found in biological samples may have potential as infection ‘biomarkers’ ( Lv et al . , 2011; Antti et al . , 2013; Langley et al . , 2013 ) . As S . Typhi and S . Paratyphi A induce an phenotype via a relatively modest concentration of organisms in the blood ( Wain et al . , 1998; Nga et al . , 2010 ) , we hypothesized that the host/pathogen interactions during early enteric fever would provide unique metabolite profiles . Here we show that enteric fever induces distinct and reproducible serovar specific metabolite profiles in the plasma of enteric fever patients . To investigate systemic metabolite profiles associated with enteric fever we selected 75 plasma samples from 50 patients with blood culture confirmed enteric fever ( 25 with S . Typhi and 25 with S . Paratyphi A ) and 25 age range matched afebrile controls attending the same healthcare facility . Mass spectra were generated by an operator that was blinded to the sample group for each of the 75 plasma samples ( n = 105 including duplicates ) in a random order using performed two-dimensional gas chromatography with time-of-flight mass spectrometry ( GCxGC/TOFMS ) . This GCxGC/TOFMS data resulted in a series of 3D landscapes of preliminary metabolites ( Figure 1 ) . Following primary data filtering , 988 unique metabolite peaks were retained . 10 . 7554/eLife . 03100 . 003Figure 1 . A two-dimensional gas chromatogram mass spectrum of a plasma sample from a patient with enteric fever . Image shows a two-dimensional ion chromatogram of unprocessed GCxGC/TOFMS data of a plasma sample from a patient with enteric fever . The three-dimensional landscape depicts detected metabolites peaks in the first dimension ( seconds–x axis ) , the second dimension ( seconds–y axis ) , and the concentration intensity of the peak signal ( z axis ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03100 . 003 Comparisons to public databases resulted in 178 GCxGC/TOFMS metabolite peaks that could be assigned a structural identity , and a further 62 peaks that could be assigned to a metabolite class . We additionally highlighted 10 metabolites , via manual inspection , that were found in less than 50 of the 75 samples , which had a diagnostic compatible profile . These 10 metabolites were excluded from the initial pattern recognition modeling , but retained for later analysis . One of these metabolites was found to be significant and was latterly added to the modeling . To further refine the metabolite profiling we aimed to identify profiles that correlated with run order , reducing the risk of instrumental variation into the recognition modeling . We identified 279 metabolites that demonstrated a significant correlation with run order ( Pearson coefficient > \|0 . 5\| ) . These 279 metabolites were excluded from initial pattern recognition modeling but still manually investigated . Therefore , 695 unique metabolite peaks ( 105 samples ) , were retained for initial pattern recognition modeling . Principal components analysis ( PCA ) was used to summarize the systematic variation in the GCxGC/TOFMS data and to generate potential metabolite profiles from the 695 metabolite peaks . We first aimed to identify sample outliers that exhibited extreme metabolite profiles as a consequence of analytical error . We identified 11/105 samples as analytical outliers using PCA . These 11 samples were excluded from further analysis–leaving a total of 94 samples for pattern recognition modeling . These remaining samples were comprised of 32 controls ( including analytical replicates of seven samples ) , 29 S . Paratyphi A samples ( including analytical replicates of four samples ) , and 33 S . Typhi samples ( including analytical replicates of eight samples ) . Calculation of models excluding all analytical replicates was performed to rule out model overestimation due to replicates; no difference in terms of the model significance was observed . To investigate the potential of metabolite profiling in enteric fever diagnosis we applied an unsupervised pattern recognition analysis to the filtered metabolite dataset from the cases and controls . The resulting PCA score plot is shown in Figure 2A . The variation within the unsupervised pattern recognition model outlined obvious differences between the metabolite profiles in the plasma samples from the controls and the enteric fever patients . It was evident from these analyses that metabolite profiles in the plasma had a potential diagnostic value for enteric fever . However , the samples from patients with S . Typhi and S . Paratyphi A exhibited substantial overlap , indicating that the metabolite signatures induced by these organisms may be challenging to differentiate . 10 . 7554/eLife . 03100 . 004Figure 2 . Modeling the variation in the GCxGC/TOFMS data in plasma samples from enteric fever patients and controls . ( A ) PCA plot of the first two principal components ( t[2] vs t[1] ) . The PCA plot outlines a separation between the control plasma samples ( N = 32; including 7 analytical replicates ) and the plasma samples from enteric fever cases ( S . Typhi; N = 33 - including 8 analytical replicates , and S . Paratyphi A; N = 29–including 4 analytical replicates ) . PCA model incorporates 695 metabolites with eight significant principal components ( R2X = 0 . 437 , Q2 = 0 . 255 ) . ( B ) OPLS-DA scores plot of the two predictive components ( tp[2] vs tp[1]; x axis and y axis , respectively ) outlining a separation between the control plasma samples ( N = 32; including 7 analytical replicates ) and the plasma samples from enteric fever cases ( S . Typhi; N = 33 - including 8 analytical replicates , and S . Paratyphi A; N = 29 - including 4 analytical replicates ) . OPLS-DA model includes 695 metabolites with two predictive and two orthogonal components ( R2X = 0 . 269 , R2Y = 0 . 837 , Q2 = 0 . 451 , p=1 . 7 × 10−6 [CV-ANOVA] ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03100 . 004 To obtain a more comprehensive view of the differences between the plasma metabolite profiles between agents of enteric fever we applied a supervised pattern recognition approach . We fitted an extension orthogonal partial least squares with discriminant analysis ( OPLS-DA ) model to differentiate the GCxGC/TOFMS metabolite profiles in relation to the three sample groups ( Table 1 ) . The OPLS-DA model generated a Q2 value of 0 . 45 , suggesting reliable differences between the metabolite profiles in relation to the three sample groups . Further validation indicated that the OPLS-DA model provided excellent predictive power for distinguishing between the sample groups ( p=1 . 7 × 10−6; control vs S . Typhi vs S . Paratyphi A ) . The OPLS-DA method is interpreted through the scores plot ( Figure 2B ) ; the largest between group differences is found along the first component ( t[1] ) ( x-axis ) of the model , while less profound differences are found along the second component ( t[2] ) ( y-axis ) . 10 . 7554/eLife . 03100 . 005Table 1 . Multivariate modeling of enteric fever metabolitesDOI: http://dx . doi . org/10 . 7554/eLife . 03100 . 005Model Number of metabolites includedNumber of model components †R2X ‡R2Y ‡Q2 ‡CV-ANOVA §AUC scores #AUC CV scores ¶PCA69580 . 437–0 . 255–––S . Paratyphi A , S . Typhi , control6952 + 20 . 2690 . 8370 . 4511 . 7 × 10−6––S . Paratyphi A vs control6951 + 20 . 2610 . 9610 . 8154 . 2 × 10−181 . 00 . 997S . Typhi vs control6951 + 20 . 2510 . 9650 . 8244 . 1x10−201 . 01 . 0S . Paratyphi A vs S . Typhi6951 + 10 . 1600 . 7140 . 1406 . 7 × 10−20 . 9960 . 735S . Paratyphi A vs control461 + 10 . 4160 . 7940 . 7188 . 8 × 10−151 . 00 . 999S . Typhi vs control461 + 10 . 3050 . 8230 . 7492 . 2 × 10−171 . 00 . 996S . Paratyphi A vs S . Typhi461 + 10 . 3850 . 5650 . 4202 . 3 × 10−60 . 9510 . 898S . Paratyphi A vs control61 + 10 . 5430 . 6270 . 5671 . 2 × 10−90 . 9640 . 948S . Typhi vs control61 + 00 . 2990 . 5290 . 4927 . 6 × 10−100 . 9340 . 923S . Paratyphi A vs S . Typhi61 + 00 . 3180 . 3000 . 2531 . 8 × 10−40 . 8010 . 796All OPLS-DA models apart from the highlighted PCA . †The number of predictive components followed by the number of orthogonal model components . ‡R2X: The amount of variation in X explained by the model , R2Y: The amount of variation in Y explained by the model , Q2: The amount of variation in Y predicted by the model . §p-value based on cross-validated scores showing the degree of significance for the separation . #Area under the curve values from receiver operating curves ( ROC ) calculated from model scores ( t ) . ¶Area under the curve values from receiver operating curves ( ROC ) calculated from cross-validated models scores ( tcv ) . To scrutinize the differences in plasma metabolite profiles between sample groups , new OPLS-DA models were fitted for pairwise comparisons of the sample classes . The score plots for these analyses are shown in Figure 3 and the summarized data are shown in Table 1 . As predicted , the OPLS-DA models for differentiating plasma metabolite profiles between samples from the afebrile controls and the two agents of enteric fever exhibited robust and significant separation . The models between the controls and S . Typhi infections and between the controls and S . Paratyphi A infections also had high predictive power , generating Q2 values of 0 . 82 ( p=4 . 1 × 10−20 ) and 0 . 81 ( p=4 . 2 × 10−18 ) , respectively ( Figure 3A , B ) . The model for differentiating plasma metabolite profiles between the S . Typhi infections and the S . Paratyphi A infections generated a Q2 value of 0 . 14 ( p=6 . 7 × 10−2 ) ( Figure 3C ) , indicating that the plasma metabolite profiles can also be used to discriminate between the two enteric fever agents . 10 . 7554/eLife . 03100 . 006Figure 3 . Pairwise OPLS-DA models of GCxGC/TOFMS data in plasma samples from controls , S . Typhi cases , and S . Paratyphi A cases . Cross-validated OPLS-DA scores plots of the first predictive component ( tcv[1]p ) showing the separation between; ( A ) Controls ( N = 32 , including 7 analytical replicates ) and S . Paratyphi A cases ( N = 29 , including 4 analytical replicates ) ( p=4 . 2 × 10−18 ) . ( B ) Controls and S . Typhi cases ( N = 33 , including 8 analytical replicates ) ( p=4 . 1 × 10−20 ) . ( C ) S . Typhi cases and S . Paratyphi A cases ( p=6 . 7 × 10−2 ) . Error bars represent mean score values with 95% confidence intervals . The OPLS-DA model is based on 695 metabolites with one predictive and two orthogonal ( A and B ) , or one predictive and one orthogonal ( C ) component ( s ) . Additional model information is shown in Table 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 03100 . 006 Using a combination of the OPLS-DA model variable weights ( loadings ) and univariate p-values we were able to precisely define the number of metabolite peaks separating the sample groups ( Supplementary file 1 ) . There were 306 , 324 , and 58 metabolite peaks separating the controls from the S . Typhi infections , the controls from the S . Paratyphi A infections , and the S . Typhi infections from the S . Paratyphi A infections , respectively . The presence of 46 metabolites could significantly distinguish between samples from enteric fever cases and control samples , and could also distinguish between samples from S . Typhi infected cases and S . Paratyphi A infected cases ( p≤0 . 05; two-tailed Student's t test ) ( Table 2 ) . Of these 46 informative metabolites , 12 could be annotated . Three metabolites that were found to be significant in all three pairwise OPLS-DA models and annotated ( phenylalanine , pipecolic acid , and 2-phenyl-2-hydroxybutanoic acid ) were selected for confirmation . The chromatographic profiles of these peaks were compared using the ‘raw’ GCxGC chromatographic data from one sample in each sample group ( Figure 4 ) . Phenylalanine and phenyl-2-hydroxybutanoic acid were confirmed to have the highest concentration in the S . Typhi sample and the lowest concentration in control sample , while pipecolic acid had the highest concentration in S . Paratyphi A samples and the lowest concentration in control samples ( Table 2 ) . In total , seven metabolites ( 2 , 4-dihydroxybutanoic acid , 2-phenyl-2-hydroxypropanoic acid , cysteine , gluconic acid , glucose-6-phosphate/mannose-6-phosphate , pentitol-3-desoxy and phenylalanine ) exhibited a higher concentration in the plasma from S . Typhi infected patients and five ( 4-methyl-pentanoic acid , ethanolamine , isoleucine , pipecolic acid , and serine ) exhibited a higher concentration in the plasma of S . Paratyphi A infected patients ( Table 2 ) . Of the 34 remaining unidentified metabolites , two were classified as saccharides and exhibited a higher concentration in the plasma of S . Typhi patients . We could not assign a structural identity/class to the remaining 32 metabolites ( all metabolites summarized in Supplementary file 1 ) . 10 . 7554/eLife . 03100 . 007Table 2 . Metabolites with discriminatory power for diagnosing enteric feverDOI: http://dx . doi . org/10 . 7554/eLife . 03100 . 007Metabolite RT1 †RT2 †RI1 †p-value P vs Cp-value T vs Cp-value P vs TChange ‡ P vs CChange ‡ T vs CChange ‡ P vs T2 , 4-dihydroxybutanoic acid1256 . 43 . 221429 . 66 . 6 × 10−34 . 9 × 10−44 . 7 × 10−2PTT2-phenyl-2-hydroxypropanoic acid1724 . 92 . 611692 . 63 . 7 × 10−21 . 5 × 10−41 . 6 × 10−2PTT4-methyl-pentanoic acid627 . 62 . 401092 . 83 . 1 × 10−25 . 9 × 10−11 . 1 × 10−2P–PCysteine1580 . 02 . 961607 . 6–1 . 7 × 10−23 . 8 × 10−2-TTEthanolamine880 . 03 . 881233 . 61 . 2 × 10−3–7 . 8 × 10−3P–PGluconic acid1985 . 00 . 161851 . 73 . 3 × 10−21 . 4 × 10−41 . 2 × 10−2PTTGlucose-6-phosphate/Mannose-6-phosphate2615 . 33 . 652303 . 16 . 7 × 10−45 . 9 × 10−54 . 1 × 10−2PTTIsoleucine1012 . 23 . 321302 . 91 . 1 × 10−2–4 . 3 × 10−2P–PMonosaccharide_1371622 . 54 . 871633 . 86 . 0 × 10−3–6 . 1 × 10−3C–TPentitol-3-desoxy1490 . 04 . 221557 . 94 . 4 × 10−95 . 5 × 10−131 . 1 × 10−2PTTPhenylalanine1784 . 12 . 681728 . 43 . 0 × 10−71 . 3 × 10−102 . 4 × 10−2PTTPipecolic acid1130 . 03 . 101363 . 12 . 4 × 10−52 . 5 × 10−33 . 0 × 10−2PTPSaccharide_1812529 . 13 . 9922371 . 6 × 0−54 . 3 × 10−22 . 7 × 10−2CCTSerine1070 . 02 . 601332 . 11 . 7 × 10−2–4 . 8 × 10−2P–PUnknown_230549 . 22 . 321036 . 81 . 7 × 10−3–9 . 5 × 10−3P–PUnknown_2311090 . 02 . 421342 . 32 . 8 × 10−3–4 . 3 × 10−2P–PUnknown_2421550 . 02 . 941590 . 54 . 0 × 10−51 . 9 × 10−24 . 4 × 10−2CCTUnknown_2681895 . 03 . 641796 . 1–1 . 1 × 10−22 . 2 × 10−2–TTUnknown_270626 . 43 . 901093 . 11 . 7 × 10−2–3 . 2 × 10−3P–PUnknown_281680 . 03 . 381124 . 12 . 7 × 10−3–3 . 1 × 10−2P–PUnknown_294725 . 12 . 181148 . 52 . 1 × 10−3–1 . 7 × 10−2P–PUnknown_3031900 . 02 . 571798 . 59 . 1 × 10−31 . 5 × 10−42 . 0 × 10−2PTTUnknown_3342790 . 02 . 152443 . 51 . 9 × 10−52 . 8 × 10−87 . 8 × 10−3PTTUnknown_341523 . 52 . 211018 . 42 . 5 × 10−3–2 . 7 × 10−2P–PUnknown_364775 . 12 . 251176 . 36 . 8 × 10−3–2 . 3 × 10−2P–PUnknown_377961 . 12 . 431275 . 61 . 9 × 10−3–3 . 1 × 10−4P–PUnknown_3841010 . 12 . 481301 . 44 . 9 × 10−3–2 . × 10−2P–PUnknown_3971144 . 92 . 751370 . 62 . 1 × 10−2–2 . 8 × 10−2P–PUnknown_4671550 . 42 . 921590 . 71 . 6 × 10−42 . 4 × 10−24 . 7 × 10−2CCTUnknown_4701570 . 04 . 021602 . 42 . 3 × 10−2–1 . 9 × 10−2C–TUnknown_4901660 . 62 . 271654 . 6–1 . 1 × 10−32 . 1 × 10−2–TTUnknown_4951695 . 03 . 261675 . 42 . 9 × 10−52 . 3 × 10−62 . 0 × 10−2PTTUnknown_5471995 . 02 . 331859 . 6–1 . 2 × 10−23 . 1 × 10−2–TTUnknown_6042349 . 53 . 272102 . 01 . 9 × 10−2–3 . 6 × 10−2P–PUnknown_6372560 . 73 . 992261 . 38 . 9 × 10−64 . 5 × 10−34 . 0 × 10−2CCTUnknown_6382561 . 32 . 672260 . 7–3 . 2 × 10−37 . 7 × 10−3–TTUnknown_6762870 . 03 . 282511 . 61 . 9 × 10−72 . 5 × 10−34 . 0 × 10−2CCTUnknown_6812938 . 12 . 752570 . 36 . 6 × 10−4–5 . 3 × 10−3C–TUnknown_745770 . 03 . 171174 . 01 . 6 × 10−3–3 . 1 × 10−2P–PUnknown_798855 . 02 . 361219 . 74 . 6 × 10−3–1 . 0 × 10−2P–PUnknown_8111445 . 02 . 931532 . 23 . 0 × 10−23 . 6 × 10−42 . 5 × 10−2PTTUnknown_9143194 . 92 . 612802 . 5–1 . 1 × 10−33 . 2 × 10−2–CPUnknown_9492661 . 82 . 072339 . 17 . 1 × 10−61 . 9× 10−91 . 3× 10−2PTTUnknown_9612065 . 42 . 711905 . 42 . 8× 10−26 . 6× 10−42 . 2× 10−2PTTUnknown_9631045 . 12 . 321319 . 11 . 3× 10−53 . 6× 10−54 . 0× 10−2PTPUnknown_9812748 . 22 . 052408 . 51 . 8× 10−44 . 9× 10−82 . 1× 10−2PTTMetabolites with statistically significant differences in two or three pairwise comparisons according to univariate p-values ( ≤0 . 05 ) and covariance loadings w* ( <\|0 . 03\| ) . T vs C; S . Typhi vs control , P vs T; S . Paratyphi A vs S . Typhi and P vs C; S . Paratyphi A vs controls . †RT1; 1st dimension retention time ( s ) , RT2; 2nd dimension retention time ( s ) , RI1; 1st dimension retention index . ‡Change in metabolite concentration for each of the pairwise comparisons where P indicates higher concentration in S . Paratyphi A samples , T indicates a higher concentration in S . Typhi samples , and C indicates a higher concentration in control samples . 10 . 7554/eLife . 03100 . 008Figure 4 . Verification of metabolite signals in plasma samples from a control and patients with S . Typhi and S . Paratyphi A infections . Three metabolites , in three samples from each sample group that were statistically significant in differentiating between sample classes using pattern recognition modelling , were selected for confirmation using unprocessed chromatographic data . ( A ) OPLS-DA scores plot ( tp[2] vs tp[1] ) highlighting the three selected samples ( S . Typhi: 45 , S . Paratyphi A: 19 , and control: 60 ) . Panel B–D show one dimensional chromatographic peaks representing each metabolite from the three unprocessed plasma samples ( coloured by sample group ) . Second dimension retention times ( s ) are shown along the x-axes and the peak intensities along the y-axes . ( B ) Phenylalanine ( mass: 218 , 1st retention time: 1785 s ) . ( C ) Pipecolic acid ( mass: 156 , 1st retention time: 1130 s ) . ( D ) 2-phenyl-2-hydroxybutanioc acid ( mass: 193 , 1st retention time: 1725 s ) . Panel E–M show the corresponding two dimensional chromatographic peaks with one peak for each sample and metabolite . First and second dimension retention times ( s ) are shown along the x and y-axes , respectively , and the peak area is shown along the z-axes . The peaks are coloured according to area ( colour scale is shown to the right ) and the top colour for the two lowest peaks for each metabolite is determined according to the colour scale of the highest peak for the same metabolite . ( E , H , K ) Phenylalanine for sample 45 , 19 , and 60 , respectively . ( F , I , L ) Pipecolic acid for sample 19 , 4 , 5 and 60 , respectively . ( G , J , M ) 2-phenyl-2-hydroxybutanioc acid for sample 45 , 19 , and 60 , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 03100 . 008 To investigate the diagnostic potential of the informative metabolites we fitted an OPLS-DA model using the 46 metabolites contributing to the differences between control and infected samples , and between the samples from S . Typhi and S . Paratyphi A infections ( Table 1 ) . The model was highly statistically significant for all pairwise comparisons , ( p<2 . 6 × 10−6; between S . Typhi and S . Paratyphi A ) . Furthermore , receiver-operating characteristic ( ROC ) curves for the fitted and cross-validated OPLS-DA scores for each of the pairwise models verified the diagnostic capabilities of the extracted metabolite profiles ( 46 metabolites ) ( area under the curve ( AUC ) values >0 . 9 for all comparisons ) ( Figure 5 ) . 10 . 7554/eLife . 03100 . 009Figure 5 . The discriminatory power of 46 metabolites to distinguish between plasma samples from controls , S . Typhi cases , and S . Paratyphi A cases . Panels on the left show the ROC-curves based on scores ( red lines ) and cross-validated scores ( black lines ) from OPLS-DA models using the 46 most statistically significant ( S . Typhi against controls and/or S . Paratyphi A against controls ) metabolites separating enteric fever samples from control samples and separating S . Typhi samples from S . Paratyphi A samples . The ROC curve showing the best individual discriminating metabolite is shown by the grey line . The scatterplots show pairwise class differences based on scores ( t[1]p ) ( left ) , cross-validated scores ( tcv[1]p ) ( centre ) from OPLS-DA models using the 46 most statistically significant metabolites ( as above ) , and the relative concentration of the best individual discriminating metabolite ( right ) . Data presented for; ( A ) S . Paratyphi A vs Controls , ( AUC scores: 1 . 0 , AUC CV scores: 0 . 999 , AUC best metabolite: 0 . 884 ) . ( B ) S . Typhi vs Controls ( AUC scores: 1 . 0 , AUC CV scores: 0 . 996 , AUC best metabolite: 0 . 925 ) . ( C ) S . Paratyphi A vs S . Typhi ( AUC scores: 0 . 951 , AUC CV scores: 0 . 898 , AUC best metabolite: 0 . 693 . Error bars represent mean score values with 95% confidence intervals . DOI: http://dx . doi . org/10 . 7554/eLife . 03100 . 009 The best identifiable metabolite differentiating S . Typhi from S . Paratyphi A was 2-phenyl-2-hydroxypropanoic acid , which gave an AUC of 0 . 693 ( Figure 5 ) , and the best unidentified metabolite differentiating S . Typhi from S . Paratyphi A gave an AUC value of 0 . 746 . The AUC values for the best individual metabolites differentiating controls from S . Typhi infections were 0 . 884 ( phenylalanine ) ( Figure 5 ) and 0 . 889 ( unidentified ) , and the AUC values for the individual metabolites best differentiating controls from S . Paratyphi A infections were 0 . 925 ( phenylalanine ) ( Figure 5 ) and 0 . 926 ( unidentified ) . Finally , we investigated the number of metabolites with confirmed identity or metabolite class required to retain diagnostic power . We found that a metabolite pattern consisting of six identified/classified metabolites ( ethanolamine , gluconic acid , monosaccharide , phenylalanine , pipecolic acid and saccharide ) gave ROC values >0 . 8 for all pairwise comparisons ( Figure 6 ) . 10 . 7554/eLife . 03100 . 010Figure 6 . The discriminatory power of six metabolites to distinguish between plasma samples from controls , S . Typhi cases , and S . Paratyphi A cases . The panels on the left show the ROC-curves based on scores ( red lines ) and cross-validated scores ( black lines ) from OPLS-DA models using the six most statistically significant ( S . Typhi against controls and/or S . Paratyphi A against controls ) metabolites separating enteric fever samples from control samples and separating S . Typhi samples from S . Paratyphi A samples . The scatterplots show pairwise class differences based on scores ( t[1]p ) ( left ) , cross-validated scores ( tcv[1]p ) ( right ) from OPLS-DA models using the 6 most statistically significant metabolites ( as above ) . Data presented for; ( A ) S . Paratyphi A vs Controls , ( AUC scores: 0 . 964 , AUC CV scores: 0 . 948 ) . ( B ) S . Typhi vs Controls ( AUC scores: 0 . 934 , AUC CV scores: 0 . 923 ) and ( C ) S . Paratyphi A vs S . Typhi ( AUC scores: 0 . 801 , AUC CV scores: 0 . 796 ) . Error bars represent mean score values with 95% confidence intervals . DOI: http://dx . doi . org/10 . 7554/eLife . 03100 . 010 Our work represents the first application of metabolomics to study enteric fever . The potential utility of this method can be observed by the capacity of the metabolite data to successfully identify those with this infection . Currently , the ability to accurately diagnose enteric fever is restricted to a positive microbiological culture result or PCR amplification ( Nga et al . , 2010; Parry et al . , 2011b ) . However , blood culture for suspected enteric fever is commonly only positive in up to 50% of cases only , and PCR amplification on blood samples performs less well ( Gilman et al . , 1975 ) . In reality , the fundamental complications of enteric fever diagnostics are the low number of organisms in the blood ( Wain et al . , 1998 ) , and a lack of a generic systemic signal . If one combines these limitations with antimicrobial pretreatment and the spectrum of other potential etiological agents circulating in endemic locations , then a substantial technological advance is required to solve the problem of diagnosing enteric fever . It is worth stating that this is a problem worth solving , as enteric fever remains rampant in many low to middle-income countries . Some may argue that the use of broad-spectrum antimicrobials without diagnosis may be prudent . However , this actually compounds the problem , as individuals are often treated with inadequate drugs , inducing treatment failure and facilitating local transmission through fecal shedding ( Parry et al . , 2011a ) . Furthermore , antimicrobial resistance rates are rising in invasive Salmonella , which is associated with treatment failure and complications ( Koirala et al . , 2012; Walters et al . , 2014 ) . We found that 306 , 324 , and 58 metabolites separated the controls from the S . Typhi infections , the controls from the S . Paratyphi A infections , and the S . Typhi infections from the S . Paratyphi A infections , respectively . The statistical analyses found that differentiating cases from controls could be performed with considerable power; this was reduced , but still significant , between S . Typhi and S . Paratyphi A . The majority of distinguishing metabolites among the three groups were unknown , however , some were annotated and had a credible explanation . For example , elevated metabolites distinguishing cases from controls included , 2 , 4-dihydroxybutanoic acid , phenylalanine , and pipecolic acid . 2 , 4-dihydroxybutanoic acid is a hydroxyl acid that can be found in low amounts in the blood and urine of healthy individuals , but is also related to hypoxia . Many pathogenic bacteria have the ability to induce the activation of hypoxia inducible factor ( HIF ) -1 and we surmise that invasive Salmonella also play a role in HIF-1 modulation during the inflammatory response induced during early infection ( Werth et al . , 2010 ) . Phenylalanine is an essential amino acid , and higher phenylalanine to tyrosine ratios have been described in the blood of patients with various diseases including sepsis , Hepatitis C ( Herndon et al . , 1978; Zoller et al . , 2012 ) , and in rats challenged with a number of pathogens ( Wannemacher et al . , 1976 ) . Notably , elevated phenylalanine was also found in during a recent metabolite investigation of primary dengue patients and is intrinsically linked to nitric oxide synthase during infection ( Cui et al . , 2013 ) . Lastly , and most intriguingly , pipecolic acid is a non-protein amino acid and is an essential part of the inducible immunity of plants during challenge from bacterial pathogen and is elevated in the urine of malaria patients ( Sengupta et al . , 2011; Vogel-Adghough et al . , 2013 ) . These metabolites , which were all elevated in the plasma of enteric fever patients , may be generic markers of systemic disease and may prove to be vital in determining other bacterial bloodstream infections . Our data also allowed us to determine different metabolite profiles between those with enteric fever caused by S . Typhi and S . Paratyphi A . These organisms have a modified physiology in comparison to other Salmonella and enter human tissue with limited intestinal replication and by potentially suppressing gastrointestinal inflammation ( Jones and Falkow , 1996 ) . Consequently , one of the key features of enteric fever is a lack of gastrointestinal involvement as seen with other , non-invasive , Salmonella serovars . The majority of the metabolites distinguishing S . Typhi from S . Paratyphi A may be explained by these subtle biological differences between these organisms and partly by the presence of the virulence ( Vi ) capsule on the surface of S . Typhi , which is absent from S . Paratyphi A . Vi is a polysaccharide that has anti-inflammatory properties , limiting complement deposition and restricting immune activation ( Jansen et al . , 2011 ) . The presence and functionality of Vi can be observed in the metabolites differentiating S . Typhi from S . Paratyphi A as the concentrations of monosaccharide and saccharide were significantly higher in the plasma samples from S . Typhi patients than from the S . Paratyphi A infections . Conversely , ethanolamine was in significantly higher concentrations in the plasma from the S . Paratyphi A patients than in S . Typhi patients' plasma . Ethanolamine is released by host tissue during inflammation and experimental work in mice has shown that Salmonella S . Typhimurium has a growth advantage in an inflamed gut ( Thiennimitr et al . , 2011 ) . Therefore , the differential detection of ethanolamine in plasma samples from enteric fever patients with different infecting serovars , may be explained by Vi negative S . Paratyphi A not having the capacity to control gastrointestinal inflammation to the same extent as S . Typhi . The main limitation of our work was that the samples were restricted to one set of enteric fever cases only . The reason we restricted analysis to enteric fever , rather than a range of bloodstream infections , we because we felt that this was the most robust test for the methodology . Furthermore , as the samples in the study we collected as part of an enteric fever clinical trial we had a range of clinical data and observations on which to link the metabolite profile with . We suggest that future studies in this area are designed to address this limitation , both for validation in different enteric fever cohort and for comparison to other bloodstream infections . The methodology present here should be applied to future ‘fever studies’ on which there may be a wide array of pathogens . The results from this study leads us to hypothesize that this method could be applied to study the differential metabolite signals between enteric fever and multiple invasive infections and could potentially differentiate between an extensive spectrum of causes of systemic disease or both bacterial , viral , and parasitic etiology . Our work strongly supports this notion , as the metabolite profiles were able to distinguish between those infected with S . Typhi and S . Paratyphi A , which until now , with the exception of microbial culture has never been a feasible goal . S . Typhi and S . Paratyphi A have subtle biochemical differences but cause an identical disease syndrome and therefore theoretically induce similar host–pathogen interactions via the adaptive immune response . Consequently , we argue , that whilst our study was limited to enteric fever , the methodology should have the power to distinguish between Salmonella and other common bacterial causes of bloodstream infections with more disparate epidemiology , biochemical structure , and pathogenicity ( Nga et al . , 2012 ) . The science of metabolomics is relatively new , yet this method has previously shown some utility in human disease . In fact , similar methodology has shown potential in generating diagnostic markers for cancer , Dengue fever , Malaria , and Mycobacterium tuberculosis ( du Preez and Loots , 2013; Sengupta et al . , 2011; Cui et al . , 2013 ) . This study is the first where the technique has been applied specifically to enteric fever and also , to the best of our knowledge , the first to use two-dimensional gas chromatography/mass spectrometry GC/MS to interrogate plasma for potential biomarkers of infection in human blood . GCxGC/TOFMS offers an exquisite degree of resolution and sensitivity for metabolomics profiling ( Baumgarner and Cooper , 2012; Hartonen et al . , 2013 ) . This technique has a substantial methodological advantage over standard GC/MS as it has the ability to span a more expansive proportion of the metabolome , but the resulting data remains compatible with existing mass spectral libraries for metabolite identification . By combining this high-level sensitivity and metabolite identification rate with a multivariate pattern recognition approach we have generated a robust tool for extracting metabolite patterns comprised of structurally identifiable metabolites with diagnostic potential . The extracted metabolite patterns exploit a correlation between relevant metabolites to define a signature that have a greater degree of diagnostic power than any individual metabolite in isolation . The fact that some of the metabolites in the patterns were structurally identifiable , and relatively few , is advantageous in that their biological relevance can be examined and validated as well and their conversion into a practical diagnostic test may be straightforward both in verification and clinical application . We suggest that the method outlined here could be applied to other diseases with an indistinguishable syndrome of questionable etiology and the validation of these findings and the identification of metabolite signatures induced by other bacterial infections would provide greater confidence and utility . The potential drawbacks of this methodology are cost and portability; we do not advocate that every laboratory in an endemic enteric fever location should invest in a system to support this method . However , a combination of these markers may be suitable for miniaturization into a point-of-care test to measure blood concentrations in suspected enteric fever patients . The format of this diagnostic testing system is currently unclear , but simple lateral flow assays are currently able to detect small concentrations of antigens and other chemicals in whole blood . This approach requires substantial validation and development , yet we predict that the procedure has enough sensitivity to be used on small blood volumes . As an intermediate step we aim to develop this method using small blood volumes and dried blood spots on a range of febrile disease to increase utility in research investigations . A future commercial possibility would be the development of a portable system that associates metabolites in biological samples to a database of metabolites detected during known infections . Indeed , this may not be far away as similar systems are in use for bacterial identification in diagnostic microbiology laboratories ( Marko et al . , 2012 ) . In summary , we show that reproducible and serovar specific metabolite biomarkers can be detected in plasma during enteric fever . Our work outlines several novel and biologically plausible metabolites that can be used to diagnose enteric fever , and unlocks the potential of this method in understanding and diagnosing other systemic infections . The institutional ethical review boards of Patan Hospital and The Nepal Health Research Council and the Oxford Tropical Research Ethics Committee in the United Kingdom approved this study . All adult participants provided written informed consent for the collection and storage of all samples and subsequent data analysis , written informed consent was given for all those under 18 years of age by a parent or guardian ( Arjyal et al . , 2011 ) . This study was conducted at Patan Hospital in Kathmandu , Nepal . Patan Hospital is a 318-bed government hospital providing emergency and elective outpatient and inpatient services located in Lalitpur Sub-metropolitan City ( LSMC ) within the Kathmandu Valley . Enteric fever is common at the outpatient clinic at Patan Hospital ( Karkey et al . , 2010; Baker et al . , 2011 ) , which has approximately 200 , 000 outpatient visits annually . The population of LSMC is generally poor , with most living in overcrowded conditions and obtaining their water from stone spouts or sunken wells . The samples used for this study were collected from patients enrolled in a randomized controlled trial comparing gatifloxacin against ofloxacin for the treatment of uncomplicated enteric fever ( ISRCTN 53258327 ) ( Arjyal et al . , 2011 ) . The enrolment criteria were as previously described ( Pandit et al . , 2007 ) . Briefly , patients who presented to the outpatient or emergency department of Patan Hospital , Lalitpur , Nepal from May 2009 , to August 2011 with fever for more than 3 days who were clinically diagnosed to have enteric fever ( undifferentiated fever with no clear focus of infection on preliminary physical exam and laboratory tests ) whose residence was in a predesigned area of 20 km2 in urban Lalitpur and who gave fully informed written consent were eligible for the study . Exclusion criteria were pregnancy or lactation , age under 2 years or weight less than 10 kg , shock , jaundice , gastrointestinal bleeding , or any other signs of severe typhoid fever , previous history of hypersensitivity to either of the trial drugs , or known previous treatment with chloramphenicol , a quinolone , a third generation cephalosporin , or a macrolide within 1 week of hospital admission . Anti-coagulated blood samples were collected from all febrile patients upon arrival in the outpatient department . For those over the age of 12 years , 10 ml of blood sample was collected; 5 ml was collected from those aged 12 years or less . The blood samples were inoculated into tryptone soya broth and sodium polyethanol sulphonate up to 50 ml . The inoculated media was incubated at 37°C and examined daily for bacterial growth over seven days . On observation of turbidity , the media was sub-cultured onto MacConkey agar . Any bacterial growth presumptive of S . Typhi or Paratyphi was identified using serogroup specific antisera ( 02 , 09 , Vi ) ( Murex Biotech , Dartford , UK ) . Two ml of peripheral blood was collected from all participants in sodium citrate tubes and were mixed well before being separated by centrifugation at 1000×g relative centrifugal force ( RCF ) for 15 min . The plasma and cells were separated before immediate storage at −80°C . Prior to metabolite analysis , 50 culture positive ( 25 S . Typhi and 25 S . Paratyphi A ) plasma samples ( with available patient metadata ) were randomly selected from individual patients between the age of 12 and 22 years to in cooperate the median ages of both S . Typhi and S . Paratyphi A infections ( Karkey et al . , 2010 ) . Additionally , 25 plasma samples from an age-stratified plasma bank gathered from patients attending Emergency Department of the Patan Hospital for reasons other than febrile illness throughout the same period and within the same 10-year age range as previously described were randomly selected for comparison ( Karkey et al . , 2013 ) . The blood samples from these patients were collected , separated and stored as outlined above . The 75 plasma samples were divided into two batches that were maintained throughout the analysis process ( in a random order but taking the sample parameters into consideration ) . The sample containers were labeled with numbers to avoid awareness of sample group allocation during the sample preparation . All investigators were blinded to the source group of the plasma samples . The plasma samples were extracted and processed according to the plasma protocol for metabolomics at the Swedish Metabolomics Centre ( SMC ) ( Jiye et al . , 2005 ) . Frozen 100 μl aliquots of plasma , in micro centrifuge tubes ( Sarstedt Ref: 72 . 690 ) , were thawed at room temperature and then kept on ice . Metabolite extraction was performed by addition of 900 μl methanol/water extraction mix ( 90:10 vol/vol ) ( including 11 isotopically labeled internal standards [7 ng/μl] ) followed by rigorous agitation at 30 Hz for 2 min in a bead mill ( MM 400; Retsch GmbH , Haan , Germany ) and storage on ice for 120 min before centrifugation at 14 , 000 rpm for 10 min at 4°C ( Centrifuge 5417R , Eppendorf , Hamburg , Germany ) . 200 microliters of each supernatant were transferred to gas chromatography ( GC ) vials and evaporated until dry in a speedvac ( miVac; Quattro concentrator , Barnstead Genevac , Ipswich , UK ) . After evaporation the samples were stored in −80°C until derivatization . Prior to derivatization the extracted plasma samples were again dried briefly in a speedvac . Methoxyamination , by the addition of 30 μl methoxyamine in pyridine ( 15 μg/μl ) , 10 min of shaking and 60 min heating at 70°C , was carried out over 16 hr ( at ambient temperature ) . Trimethylsilylation , with addition of 30 μl MSFTA ( N-methyl-N-trimethylsilyl-trifluoroacetamide ) + 1% TMCS ( Trimethylchlorosilane ) , was performed for 1 hr ( at ambient temperature ) . Finally , 30 μl heptane , including methyl stearate ( 15 ng/μl ) , was added as an injection standard . The two dimensional chromatography provides an output which can be seen as a metabolite landscape where each detected potential metabolite is defined by a three-dimensional peak in this landscape ( retention time 1 × retention time 2 × peak height ) ( as shown in Figure 1 ) . Extracted and derivatized plasma samples were analyzed , in a random order ( within the analytical batches ) , on a Pegasus 4D ( Leco Corp . , St Joseph , MI , USA ) equipped with an Agilent 6890 gas chromatograph ( Agilent Technologies , Palo Alto , GA , USA ) , a secondary gas chromatograph oven , a quad-jet thermal modulator , and a time-of-flight mass spectrometer . Leco´s ChromaTOF software was used for setup and data acquisition . The column set used for the GCxGC separation was a polar BPX-50 ( 30 m × 0 . 25 mm × 0 . 25 µm; SGE , Ringwood , Australia ) as first-dimension column and a non-polar VF-1MS ( 1 . 5 m × 0 . 15 mm × 0 . 15 µm; J&W Scientific Inc . , Folsom , CA , USA ) for the second-dimension column . Splitless injection of 1 μl sample aliquots was performed with an Agilent 7683B auto sampler at an injection temperature of 270°C ( 2 respectively 5 pre/post-wash cycles were used with hexane ) . The purge time was 60 s with a rate of 20 ml/min and helium was used as carrier gas with a flow rate of 1 ml/min . The temperature program for the primary oven started with an initial temperature of 60°C for 2 min , followed by a temperature increase of 4°C/min up to 300°C and where the temperature was held for 2 min . The secondary oven maintained the same temperature program but with an offset of +15°C compared to the primary oven . The modulation time was 5 s with a hot pulse time of 0 . 8 s and a 1 . 7 s cooling time between the stages . The MS transfer line had a temperature of 300°C and the ion source 250°C . 70 eV electron beams were used for the ionization and masses were recorded from 50 to 550 m/z at a rate of 100 spectra/sec with the detector voltage set at 1780 V . 15 randomly selected plasma samples were unblended and run in triplicate as analytical replicates ( Control: N = 4 , S . Paratyphi A: N = 5 , S . Typhi: N = 6 ) . In addition to the plasma samples , several samples of methyl stearate in heptane ( 5 ng/μl ) were run to check the sensitivity of the instrument and three n-alkane series ( C8-C40 ) were also run to allow calculation of retention indexes , RI . The analysis time was approximately 70 min/sample . All chemicals and compounds were of analytical grade unless stated otherwise . The isotopically labeled internal standards ( IS ) [2H7]-cholesterol , [13C4]-disodium α-ketoglutarate , [13C5 , 15N]-glutamic acid , [1 , 2 , 3-13C3]-myristic acid , [13C5]-proline , and [2H4]-succinic acid were purchased from Cambridge Isotope Laboratories ( Andover , MA , USA ) ; [13C4]-palmitic acid ( Hexadecanoic acid ) , [2H4]-butanediamine·2HCl ( Putrescine ) , and [13C12]-sucrose from Campro ( Veenendaal; [13C6]-glucose from Aldrich [Steinheim , Germany] , The Netherlands ) ; and [2H6]-salicylic acid from Icon ( Summit , NJ , USA ) . Silylation grade pyridine and N-Methyl-N-trimethylsilyltrifluoroacetamide ( MSTFA ) with 1% trimethylchlorosilane ( TMCS ) were purchased from Pierce Chemical Co ( Rockford , IL , USA ) . The stock solutions for reference compounds and IS were all prepared in 0 . 5 μg/μl concentrations in either Milli-Q water or methanol . Leco's ChromaTOF software was used for baseline correction , peak detection , mass spectrum deconvolution , mass spectra library search for identification and calculation of peak height/area . A signal-to-noise ratio of 10 was used for peak picking . The library search was performed against publicly available mass spectral libraries from US National Institute of Science and Technology ( NIST ) and from the Max Planck Institute in Golm ( http://csbdb . mpimp-golm . mpg . de/csbdb/gmd/gmd . html ) together with in-house libraries established at SMC . Peak information for each of the samples was exported as individual csv-files ( comma-separated values ) . All csv-files were imported into the data processing software Guineu ( 1 . 0 . 3 VTT; Espoo , Finland ) ( Castillo et al . , 2011 ) for alignment , normalization ( with internal standards ) , filtering and functional group identification . After processing in Guineu all peaks were manually investigated by using the average spectra information , obtained from Guineu , in NIST MS Search 2 . 0 to search against the same libraries as previously used . This manual comparison was performed to additionally confirm the putative annotations of the metabolites and detect possible split peaks , which , if having comparable mass spectra and retention indices , were summed and compared to the individual peaks in the following multivariate statistical analysis to make decision about inclusion . During manual investigation , peaks were excluded from further analysis if detected in less than 50 samples , being an internal standard or silyl artifact , having few mass fragments in spectra , having mass spectra similar to another peak with a better identity match or being part of a sum . Metabolites found in less than 50 samples but still showing interesting profiles as diagnostic markers were interpreted separately . Pattern recognition is based on the concept of multivariate projection methods . In metabolomics pattern recognition is used to reduce the high dimensionality of acquired analytical data for facilitated interpretation of biochemical profile alterations and detection of patterns among characterized samples based on similarities in these biochemical profiles ( Holmes and Antti , 2002; Madsen et al . , 2010 ) . Among multivariate projection methods principal components analysis ( PCA ) ( Wold et al . , 1987 ) and partial least squares ( PLS ) with its extension orthogonal-PLS ( OPLS ) are the most commonly applied for pattern recognition in metabolomics studies . Here PCA was used initially to obtain an overview of main variations in the acquired GCxGC/TOFMS data and to detect and remove outliers . To reduce confounding from analytical drift over the time of analysis PLS was used to fit a model with run order as the response , metabolites showing a strong correlation with run order ( i . e . , Pearson product moment correlation coefficient > \|0 . 5\| ) were excluded from further modeling . OPLS with class information ( for example if a plasma sample has been sampled from a non-infected control or an infected patient ) as the response was then performed to detect metabolite patterns that best discriminate between the pre-defined sample classes . This type of pattern recognition modeling is referred to as discriminant analysis ( DA ) , thus the method used is OPLS-DA ( Bylesjö et al . , 2006 ) . OPLS-DA models were calculated in turn for ( i ) separation between the three sample classes ( control , S . Typhi infected , and S . Paratyphi A infected ) , and ( ii ) for pairwise comparisons ( control vs S . Typhi , control vs S . Paratyphi A , and S . Typhi vs S . Paratyphi A ) . For each model a Q2 value was calculated to reflect the predictive power of the OPLS model . In the case of a DA model the Q2 value , which can vary on a continuous scale between 0 and 1 , will indicate if the classification ( or metabolite pattern ) is robust . A Q2 of 1 refers to a perfect classification , while a Q2 of 0 or below refers to a poor or random classification . In addition , a p-value was calculated for each OPLS-DA model using ANOVA ( Eriksson and Trygg , 2008 ) . To define which metabolites that contribute significantly to the detected metabolite patterns the OPLS-DA variable weights ( covariance loadings; w* ) and univariate p-values ( two-tailed Student's t test ) were used in combination . A metabolite was considered significant if it had a univariate p-value≤0 . 05 and was important for class separation in the OPLS-DA model , according to the variable weight or covariance loading ( w* ) ( here the significance limit was w* >\|0 . 03\| for the models separating non-infected controls and enteric fever samples and w* >\|0 . 07\| for models between S . Typhi and S . Paratyphi A ) . All pattern recognition analysis was performed in SIMCA ( version SIMCA-P+ 13 . 0; Umetrics , Umeå , Sweden ) . Model plots were created using SIMCA or GraphPad Prism ( 5 . 04; GraphPad Software Inc . , La Jolla , CA , USA ) in combination with Adobe Illustrator CS5 ( 15 . 0 . 0; Adobe Systems Inc . , San Jose , CA , USA ) . Receiver operating curves ( ROC ) were constructed and compared for individual metabolites as well as for OPLS-DA model scores ( metabolite profiles ) to additionally investigate the usefulness of the obtained results . The area under the curve ( AUC ) can be used as an output of the ROC analysis , which can range from 0 . 5 to 1 . 0 . The higher AUC value a biomarker obtains the higher is the diagnostic potential . Here the web-based online tool ROCCET ( http://www . roccet . ca/ROCCET/ ) was used to perform univariate ROC analyses . For the individual metabolites the relative concentrations for all samples were used as input , while for the models ( metabolite profiles ) model scores ( t ) and cross-validated scores ( tcv ) ( Stone , 1974 ) were used after recalculation by subtracting the lowest score value from all other score values to avoid negative values .	Enteric fever is estimated to affect over 37 million people every year . Although treatable with antimicrobial drugs , a slow and/or incorrect diagnosis can result in serious and often life-threatening complications . Enteric fever is the combined name for typhoid fever and paratyphoid fever . While the symptoms of these diseases are indistinguishable , the strains of Salmonella bacteria that cause them are genetically distinct . Moreover , the two organisms that cause the disease exhibit different propensities to develop resistance to antimicrobials . It is important , therefore , to be able to distinguish between typhoid fever and paratyphoid fever so that the correct treatment can be prescribed . However , the diagnostic tools available today struggle to discriminate between Salmonella Typhi ( which causes typhoid fever ) and Salmonella Paratyphi A ( which causes paratyphoid fever ) . Now , Näsström et al . have developed a methodology that can determine if an individual is infected by Salmonella Typhi or Salmonella Paratyphi A , or neither . Rather than trying to detect the bacteria themselves , the test relies on measuring the levels of various metabolites—molecules produced during metabolism—in the blood . Näsström et al . discovered a set of six metabolites that are affected in different ways by typhoid and paratyphoid fever . The next challenge is to develop this approach so it can be used in endemic settings .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "epidemiology", "and", "global", "health", "microbiology", "and", "infectious", "disease" ]	2014	Salmonella Typhi and Salmonella Paratyphi A elaborate distinct systemic metabolite signatures during enteric fever
There is a continuing need for driver strains to enable cell-type-specific manipulation in the nervous system . Each cell type expresses a unique set of genes , and recapitulating expression of marker genes by BAC transgenesis or knock-in has generated useful transgenic mouse lines . However , since genes are often expressed in many cell types , many of these lines have relatively broad expression patterns . We report an alternative transgenic approach capturing distal enhancers for more focused expression . We identified an enhancer trap probe often producing restricted reporter expression and developed efficient enhancer trap screening with the PiggyBac transposon . We established more than 200 lines and found many lines that label small subsets of neurons in brain substructures , including known and novel cell types . Images and other information about each line are available online ( enhancertrap . bio . brandeis . edu ) . The mammalian brain is likely comprised of thousands of distinct neuronal cell types . The ability to distinguish these cell types and to understand their roles in circuit activity and behavior is enhanced by an increasing variety of new genetic technologies in mice . Conditional transgenes like fluorescent reporters or alleles that sense or modify neuronal activity can be turned on in cells of interest through the use of 'driver' strains selectively expressing Cre recombinase or the tet transactivator ( Huang and Zeng , 2013; Luo et al . , 2008 ) . Most techniques for producing these driver strains rely on recapitulating endogenous patterns of gene expression . However , selective expression patterns often depend both on elements within the proximal promoter , and on enhancers and other regulatory elements that can be located quite distally ( Visel et al . , 2009 ) . Recapitulating endogenous expression requires either a knock-in approach ( Taniguchi et al . , 2011 ) , or making transgenics from very large genomic fragments containing both the promoter and distal control elements ( e . g . BAC transgenics ( Gong et al . , 2003; 2007; Yang et al . , 1997 ) . One limitation of recapitulating endogenous expression patterns is that they are often broader than would be optimal for selective control . For example , the Pvalb-cre driver strain ( Hippenmeyer et al . , 2005 ) can be used to target Pvalb-positive fast-spiking interneurons in the neocortex; however , Pvalb is also expressed in cerebellum ( Purkinje cells ) , dorsal root ganglia , thalamus , and many other brain structures , as well as in skeletal muscle . Even in the neocortex , Pvalb-positive cells consist of at least two distinct interneuron subtypes ( basket cells and chandelier cells ) and some layer 5 pyramidal neurons . Limitations on cell type specificity are common , since most genes are expressed in many different cell types throughout many different brain regions and tissues . Although combinatorial approaches can enhance specificity ( Madisen et al . , 2015 ) , this comes at the cost of increasing the number of alleles that must be created and bred . Furthermore , this approach requires initial knowledge about co-expression patterns that may be lacking for some cell types . Here , we take an alternative approach that relies on the fact that some minimal promoters can , when randomly inserted into the genome , interact with local enhancers and regulatory elements to produce patterns of expression that can be more restricted . This approach , termed enhancer detection or enhancer trapping , has a long history in Drosophila where it has been pursued primarily using the Gal4-UAS system ( Bellen et al . , 1989; Brand and Perrimon , 1993 ) . More recently , this system and others have been used for enhancer trapping in zebrafish ( Balciunas et al . , 2004; Scott et al . , 2007; Urasaki et al . , 2008 ) , but the approach has been less widely used in mice ( though see Gossler et al . , 1989; Kothary et al . , 1988; Soininen et al . , 1992; Stanford et al . , 2001 ) . A large-scale enhancer trap screen was performed using the SleepingBeauty transposon system ( Ruf et al . , 2011 ) but was focused on enhancers active during embryonic development , rather than those that regulate cell-type-specific expression in the adult . Kelsch et al . ( Kelsch et al . , 2012 ) conducted a mouse enhancer trap screen for transgenic animals with specific patterns of neural expression . Their lentiviral enhancer probe successfully generated transgenic lines with expression in neuronal subsets , however , the number of lines generated was small and most lines had expression in many cell types . Thus , this approach , while promising , has not yet reached its full potential , both in terms of specificity and in terms of the efficiency with which new lines can be generated . Here , we report on an efficient enhancer trap screen to generate lines with specific expression patterns in the brain . First , using lentiviral transgenesis ( Lois et al . , 2002 ) , we discovered a tet-transactivator-dependent enhancer probe capable of generating transgenic lines with highly restricted expression patterns . Next , we incorporated this tet-enhancer probe into the PiggyBac transposon system and developed a simple and efficient system for producing mouse lines with different PiggyBac insertion sites . The majority of these lines have brain expression and many have highly restricted expression patterns in known or novel neuronal cell types . Finally , a critical consideration in using the enhancer trap approach in the CNS of any species is the question of whether trapped neurons represent specific cell types or more random subsets of largely unrelated cells . To address this , we performed more detailed anatomical and physiological characterization in a subset of lines . These experiments revealed that the neuronal populations are not random assortments of unrelated cells , but represent highly specific , previously recognized , as well as novel , neuronal cell types . In addition , quantitative comparison with a recently annotated collection of knock-in and BAC-cre driver strains revealed that expression is , on average , far more restricted in the enhancer trap lines . Hence enhancer trapping is a viable strategy for producing driver strains that complement those generated through other genetic approaches . This resource provides a platform for genetic control of a wide variety of neuronal cell types , as well as for discovering new subtypes of known neuronal cell types . Our initial enhancer trap screen employed lentiviral vectors because their highly efficient transduction of transgenes to the germ line minimized the number of injections needed to sample enough founders and their random single copy insertion permitted a broad survey of genomic sites ( Lois et al . , 2002 ) ( see Figure 1—figure supplement 1A for transgenesis scheme ) . Our enhancer probe constructs employed the tet-off genetic driver system and incorporated a tet-responsive element ( TRE; we used TREtight , the second-generation TRE ) driving the fluorescent reporter mCitrine , so that we could examine expression patterns in driver lines without crossing to separate reporter lines . We initially tried constructs with the minimal promoter from the mouse heat-shock protein 1A ( Hspa1a ) gene ( Bevilacqua et al . , 1995 , Figure 1—figure supplement 2 ) . We also incorporated other promoter sequences that had been used to generate transgenic animals with neuronal subset expression and enhancer candidate sequence from evolutionally conserved elements ( Visel et al . , 2007 ) . We found a construct containing the minimal HSP promoter most efficiently generated lines with specific expression patterns in brain ( 28 . 8% , see Table 1 ) and see supplemental note and Figure 1—figure supplement 2 for details of other constructs tried . 10 . 7554/eLife . 13503 . 003Table 1 . Efficiency of transgenesis . The numbers of lines dissected and the number of lines with brain expression are shown separately for each construct used . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 003construct analyzed with expression lentivirus HSP-tet HSP-tet132 38 hsp-tet27 4 hsp-tet312 0 promoter-tet CamKII-tet18 4 minCamKN-te17 7 Thy1-tet15 0 minThy1-tet11 0 Gad1-tet9 0 Slc-tet6 0 enhancer-tet 119-tet13 1 121-tet2 0 122-tet15 2 170-tet2 1 PiggyBac HSP-tet tet101 81 tet-Cre57 43 Throughout the rest of the paper , we use the admittedly imperfect term 'cell type' to refer to cell populations defined operationally as the group of neurons labeled in a particular brain region of a transgenic line . We imagine neuronal cell types as nodes in a hierarchical tree-like structure with the terminal branches ( 'leaves' ) corresponding to 'atomic' cell types which are homogeneous and cannot be further divided based on projections , morphology , gene expression etc . The 'operational' cell types defined here are not necessarily 'atomic' in that further characterization may reveal that they are composed of subtypes , but they offer a useful starting point for subsequent identification of 'atomic cell types' based on uniformity of morphology , connections , physiology , and gene expression . Although only a minority of lentiviral tet lines had reporter expression , the majority of lines with brain expression had highly restricted expression patterns . Some lines had expression only in restricted cell types , including medial prefrontal cortex layer 5 neurons ( Figure 1A ) , retinal ganglion cells projecting axons to superior colliculus ( Figure 1B ) , and Cajal-Retzius cells in cerebral cortex and dentate gyrus ( Figure 1C ) . We had two lines with distinctive expression in cortical layer 4 neurons; TCGS in primary sensory cortices ( including primary visual , somatosensory and auditory cortices; Figure 1D ) and TCFQ which was devoid of expression in primary sensory cortices but expressed in associative cortices ( Figure 1E ) . We also obtained lines labeling specific cell types , such as thalamocortical projection neurons in the dorsal part of the lateral geniculate complex ( LGd; Figure 1F ) , anatomically clustered subsets of cerebellar granule cells , semilunar cells and a subset of superficial pyramidal neurons in piriform cortex , and a subtype of cortico-thalamic pyramidal neurons in layer 6 of neocortex ( see below ) . Tet reporter expression could be turned off and on by administration of doxycycline ( Figure 1—figure supplement 3 ) . For a summary of expression patterns in all lines , see Supplementary file 1 . 10 . 7554/eLife . 13503 . 004Figure 1 . Example Lentiviral lines . ( A ) 48L has expression in limbic cortex ( A1 , coronal section ) layer 5 pyramidal cells ( A2 , magnified image in limbic cortex ) . ( B ) Superior colliculus ( SC ) of TCBV has columnar axons from retina . B1: sagittal section , B2: magnified image of superior colliculus . ( C ) 52L has expression in piriform cortex ( see Figure 9 ) and Cajal-Retzius cells in dentate gyrus ( DG , C2 ) and cerebral cortex ( C3 , inset: magnified image of a Cajal-Retzius cell ) . ( D ) TCGS has expression in layer 4 neurons of primary sensory cortices ( primary somatosensory area: SSp and primary visual area:VISp in D3 ) . ( E ) TCFQ has nearly complimentary layer 4 expression excluding primary sensory cortices . D1 and E1: sagittal sections , D2 and E2: confocal images of cortex , D3 and E3: dorsal view of whole brains . ( F ) TCJD has expression in dorsal part of lateral geniculate nucleus ( LGd , F1 ) , which projects to primary visual cortex ( VISp ) . F1: sagittal section , F2: higher magnification of LGd , F3: higher magnification of axons in layers 1 , 4 , and 6 of VISp . Scale bars are 50 μm in A2 , B2 , C2 , F2 and 500 μm in others . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 00410 . 7554/eLife . 13503 . 005Figure 1—figure supplement 1 . Transgenesis . ( A ) Lentiviral transgenesis . Lentivirus encoding an enhancer probe is injected into the perivitelline space between the single cell embryo and the zona pellucida . Infected embryos are transferred to foster mothers . Founders are genotyped by PCR and transgene copy number is estimated by southern blot or quantitative PCR and additional rounds of breeding and quantitative genotyping are carried out ( not shown ) to produce single copy founders . ( B ) PiggyBac ( PB ) transgenesis . Plasmid DNA for a PB enhancer probe and PB transposase ( PBase ) mRNA are injected into the cytosol of single cell embryos . Copy numbers of PB probes are examined as for lentiviral founders . Animals with single copy PB are selected as seed lines for PB transgenesis . Seed lines ( P ) are crossed with PBase animals , and their children ( F1 ) carrying both PB and PBase are mated with wild-type ( WT ) animals . PB hops only in F1 PB;PBase mice , and animals with new PB insertion sites are generated in the following generation ( F2 ) . Among F2 animals , animals with hopped PB but without PBase are founders of new transgenic lines . PB; prm-PBase females can also be founders since prm-PBase will not be expressed in the female germ line . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 00510 . 7554/eLife . 13503 . 006Figure 1—figure supplement 2 . Constructs for transgenesis . ( A ) Lentiviral constructs . Viral sequences were inserted into the lentiviral backbone plasmid . The five variants listed are described in the text . ( B ) PiggyBac constructs containing tTA or tTA and Cre . Except for hsp-tet3 , transcripts from lentiviral constructs use 3’ long terminal repeat ( △U3-R–U5 in the backbone plasmid ) as poly adenylation signal . In all constructs , tTA and mCitrine share poly adenylation signal sequences . HSPmp: minimal promoter from Hspa1a , tTA: tet transactivator , TRE: tet response element , WPRE: woodchuck hepatitis virus post-transcriptional regulatory element , 2A: FMDV-2A sequence , BGHpA: poly-adenylation signal from bovine growth hormone , HS4ins: insulator sequence from DNase hyper sensitive site in the chicken β-globin gene , PB- 5’ITR and PB-3’ITR: PiggyBac inverted terminal repeat . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 00610 . 7554/eLife . 13503 . 007Figure 1—figure supplement 3 . Transgene regulation by Doxycycline ( Dox ) . Pregnant 48L females received water with Dox ( 0 . 2 mg/ml ) or regular water ( control ) ( A–B ) P21 ( A ) and P42 ( B ) images from 48L animals receiving water lacking Dox . ( C–I ) Images from 48L animals receiving Dox . Regular water ( D–F , second row ) and doxycycline water ( G–I , third row ) were used for 3 weeks from when pups were weaned at P21 . Siblings are dissected at P21 ( C ) , P28 ( D and G ) , P35 ( E and H ) and P42 ( F and I ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 007 Although lentiviral transgenesis successfully generated lines with highly restricted expression patterns , screening was difficult to scale up effectively . Generation of each new founder requires viral injection into single cell embryos and transfer of that embryo to a foster mother ( Figure 1—figure supplement 1A ) . Tracking and segregating multiple alleles in order to identify the allele responsible for reporter expression in the case of founders having multiple insertions was especially time consuming . Moreover , we found four lines in which expression in the founder and early progeny was lost in later generations , implying possible silencing of lentiviral transgenes over generations ( Hofmann et al . , 2006 ) . We tried to incorporate insulator sequences ( see below ) to prevent silencing , but viruses with insulator sequences had 100 times lower titer ( about 1 x 107 infection unit/ml ) and were not usable for transgenesis . In order to develop a more efficient and scalable transgenesis platform , we made use of the PiggyBac ( PB ) transposon system as a means of delivering tet enhancer trap probes . The PB system has been widely used in mammalian genetics ( Ding et al . , 2005 ) for insertional mutagenesis ( Rad et al . , 2010 ) and stable transgene expression ( Woodard and Wilson , 2015 ) . Unlike the SleepingBeauty transposon , PiggyBac has a weaker tendency to undergo local hops ( Liang et al . , 2009 , but see supplemental note ) , making it more suitable for screens that target the whole genome . To simplify the process of establishing and tracking new transgenic alleles , we established lines of animals carrying a single-copy PB integration and additional lines expressing the PiggyBac transposase ( PBase ) . PB hops only in animals with both the PB and PBase alleles , allowing us to generate transgenic animals with different PB insertion sites simply by mating wild type and PB;PBase animals ( see the mating scheme in Figure 1—figure supplement 1B ) . We used the same tTA-reporter system used in the lentiviral probes ( PB tet , Figure 1—figure supplement 2B ) . We also created a probe designed to produce both tTA and Cre expression ( PB tet-cre , Figure 1—figure supplement 2B ) . In order to prevent the silencing seen in some lentiviral lines , we incorporated barrier insulator sequences from the chicken β-globin gene ( see 'Materials and methods' for detail ) . DNA plasmids encoding our PB enhancer probe and mRNA encoding a hyperactive PBase ( Yusa et al . , 2011 ) were co-injected into single cell embryos . Among 28 PB-positive animals , five had single-copy insertions confirmed by quantitative PCR and ligation-mediated PCR . These five served as seed lines for subsequent rounds of piggyBac transgenesis . We used two PBase lines: 1 ) a Rosa-PBase line generated by Allan Bradley and colleagues ( Rad et al . , 2010 ) having nearly ubiquitous expression of PBase from the Rosa-26 locus and 2 ) a Prm-PBase line that we generated having spermatid-specific expression of hyper-active PBase ( Yusa et al . , 2011 ) under the protamine-1 promoter ( Zambrowicz et al . , 1993 ) . PiggyBac seed lines were crossed with PBase mice , and PB;PBase double hemizygous animals ( P1 generation ) were selected and crossed with wild-type animals ( see Figure 1—figure supplement 1B ) . P2 generation animals were genotyped for PB alleles ( Table 2 ) . 10 . 7554/eLife . 13503 . 008Table 2 . Transposition efficiency PB;PBase double hemizygous animals ( PB/+; PBase/+ ) were crossed with wild type animals and genotypes of pups from the mating were examined ( see the mating scheme in Figure 1—figure supplement 1B ) . Numbers of animals are shown in parentheses . PB transmission rate: number of PB+ animals / total number of animals , PB transposition rate: number of PB in new sites / number of animals tested for transposition . ( Note: we did not test transposition for PB/+;Rosa-PBase/+ and PB/+;Prm1-PBase/+ males because transgenes might not be stably transmitted to the next generation in these animals ) . New line production efficiency: number of animals with new insertion site / total number of animals born . : All PB+ animals were female . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 008Seed line PBase line PB transmission rate Transposition efficiency Efficiency of new line production PBAG Rosa 28 . 6% ( 54/189 ) 41 . 4 % ( 12/29 ) 6 . 4 % ( 12/189 ) Prm1 29 . 2% ( 21/72 ) 56 . 3 % ( 9/16 ) 12 . 5% ( 9/72 ) PBAW Rosa 21 . 56% ( 80/371 ) 62 . 2 % ( 23/37 ) 6 . 2 % ( 23/371 ) Prm1 33 . 0 % ( 97/294 ) 67 . 4 % ( 62/92 ) 21 . 1% ( 62/294 ) PBAS Rosa 30 . 8 % ( 33/107 ) 25 . 0 % ( 3/12 ) 3 . 3 % ( 3/97 ) Prm1 35 . 6 % ( 130/365 ) 41 . 9 % ( 39/93 ) 10 . 7 % ( 39/365 ) PBAU Rosa 22 . 2 % ( 30/135 ) 38 . 9 % ( 7/18 ) 5 . 2 % ( 7/135 ) Prm1 34 . 3 % ( 46/134 ) 57 . 1 % ( 20/35 ) 14 . 9 % ( 20/134 ) PBAQ Rosa 37 . 5 % ( 6/16 ) 0 % ( 0/3 ) 0 % ( 0/16 ) Prm1 60 . 0 % ( 9/15 ) 12 . 5 % ( 1/8 ) 6 . 6 % ( 1/15 ) Animals carrying the PB allele were further tested to ensure transposition had occurred . We found that the PB allele transmission rate was significantly lower than the expected Mendelian ratio , implying that a substantial fraction of excised PB failed to re-insert into the host genome ( Table 2 ) . The PB alleles derived from each of the single-copy seed lines jumped at similar transposition rates , except for those from the PBAQ seed line that rarely translocated ( Table 2 ) . We found that Prm-PBase produced founders more efficiently than Rosa-PBase ( Table 2 ) . See supplemental note and Tables 3 and 4 for further details of transposition frequency . 10 . 7554/eLife . 13503 . 009Table 3 . Numbers of insertion events occurring in genes and intergenic regions . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 009Insertion Sites Number of lines gene 60 coding exon1 3'UTR4 intron55 intergenic 81 repetitive sequence 26 10 . 7554/eLife . 13503 . 010Table 4 . Rates of inter-chromosomal , intrachromosomal and local ( within 2 Mb ) transposition events . Some insertions were not located due to insertion in repetitive sequences . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 010line number of lines inter-chromosomal hop intra-chromosomal hop local ( <2 Mb ) hop not located PBAW 69 46 ( 66 . 7 % ) 11 ( 15 . 9 % ) 8 ( 11 . 6 % ) 12 ( 17 . 4 % ) PBAS 46 26 ( 56 . 5 % ) 18 ( 39 . 1% ) 9 ( 19 . 6% ) 2 ( 4 . 3 % ) PBAU 26 13 ( 50 . 0 % ) 8 ( 30 . 8 % ) 2 ( 7 . 7% ) 5 ( 19 . 2 % ) Total 141 85 ( 60 . 2 % ) 37 ( 26 . 2 % ) 19 ( 13 . 4% ) 19 ( 13 . 4 % ) We established more than 200 independent lines and examined expression patterns from more than 130 lines ( 210 and 135 as of October 2015 , respectively; see Supplementary file 1 for line expression summary and insertion sites ) . We occasionally encountered termination of lines for infertility ( 20 lines; cf . the productive mating rate of the mouse strain C57Bl6/j is 84% [Silver , 1995] ) or death mainly due to maternal complications at birth ( 5 lines ) . Because of the difficulties associated with managing a large number of colonies , some ( 4 lines ) were accidentally lost before cryopreservation . The rate of obtaining lines with brain expression in the PB screen ( 78 . 5% ) was more than twice that obtained with lentiviral transgenesis ( Table 1 ) . Lines generated by local hop of PB ( within ~100 Kb ) had similar expression patterns to that of the original line ( Figure 2—figure supplement 1 ) , probably because shared local enhancers regulated expression of the reporter . In most of lines , we did not find clear resemblance between reporter expression patterns and those of genes near insertion sites ( see supplemental note ) . Some lines had dominant expression in a single anatomical structure , such as deep entorhinal cortex ( P038 , Figure 2A ) , subiculum ( P141 , Figure 2B ) , retrosplenial cortex ( P099 , Figure 2C ) , or dorsal hindbrain ( P108 , Figure 2D ) . Many lines had expression in multiple regions but with unique cell types in each area . For example , P008 has broad expression in striatum ( Figure 2E1 ) but has restricted expression in the most medial part of the hippocampus ( fasciola cinereum , Figure 2E2 ) . P057 had cortical layer 5 expression and restricted expression in anterior-lateral caudate putamen ( CP; Figure 2H ) . Interestingly , the mCitrine-positive CP cells appeared to be part of the direct pathway; the cells projected axons to a limited area in substantia nigra pars reticulata ( Figure 2H2 inset ) but not to the globus pallidus ( Figure 2H1 arrow; compare the GP projection of P008 in Figure 2E1 ) . Lines with broad expression , ( Figure 2J ) , those labeling few cells , and those closely resembling existing lines were terminated ( 48 lines ) . Most lines with 'broad expression' had strong mCitrine expression restricted to forebrain and founders carrying multiple PB copies also had strong forebrain expression . 10 . 7554/eLife . 13503 . 011Figure 2 . Example PiggyBac lines . ( A–D ) Examples of lines that appear to label a single cell type . ( d ) P038 has expression in entorhinal cortex medial part ( ENTm ) layer 6 neurons ( A1: sagittal ) that send axons to lateral dorsal nucleus of thalamus ( LD in A2: coronal ) . ( B ) P141 has expression in a restricted area in subiculum ( SUB , B1: sagittal , B2: coronal ) . ( C ) Retrosplenial cortex ( RSP ) expression in P099 ( C1: sagittal , C2: coronal ) . ( D ) Dorsal hindbrain expression in P108 ( D1: sagittal , D2: coronal at hindbrain ) . ( E–H ) Examples of lines with regionally distinctive cell type labeling . ( E ) P008 has expression in striatum ( STR ) broadly ( E1: sagittal ) but its hippocampal expression is restricted to the most medial part ( fasciola cinereum: FC , E2 inset ) ( F ) P122 has scattered expression in hippocampus and strong expression in cortical amygdalar area . F1: sagittal , F2: coronal sections . ( G ) P134 has broad expression in cortical interneurons and cerebellar Lugaro cells ( G1: sagittal ) . Its expression in midbrain is restricted to subnuclei ( G2 , superior olivary complex: SOC and presumably pedunculopontine nucleus: PPN ) . ( H ) P057 ( H1:coronal , H2 , sagittal section ) has expression in layer 5 pyramidal cells in the cortex . Expression in caudate putamen ( CP ) is restricted to lateral-most areas ( arrows in H1 ) . H2 inset: coronal section at the level of the dotted line . The striatal neurons project to a small area in the reticular part of the substantia nigra , reticular part ( SNr , dotted area in H2 inset ) but not to globus pallidus ( H2 arrow ) . ( J ) Lines with broad expressions . Scale bar: 500 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 01110 . 7554/eLife . 13503 . 012Figure 2—source data 1 . Viral reporter expression counting data One or two animals per line were injected with TRE3G –myristorylsted mCherry HA . The numbers of cells ( mCitirne+ , mCherry+ , mCitrine+;mCherry+ , and mCitrine-:mCherry+ ) infection rate ( mCitrine+;mCherry+/ mCitirne + ) were counted from confocal image stacks from sections near injection sites ( 5 - 9 sections/line ) . Infection rates ( mCherry+;mCitrine+ /mCitrine ) and 'off-target' expression rate ( mCherry+;mCitirne-/mCherry+ ) are shown in average ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 01210 . 7554/eLife . 13503 . 013Figure 2—figure supplement 1 . Similar expression patterns in lines with nearby insertions . Insertion sites and expression patterns of a founder PBAS and lines generated from PBAS by local hop are shown . Lines inserted near original PBAS site have scattered expression in Purkinje cells in cerebellum . Many lines have axonal projections in dentate gyrus from entorhinal cortex . P103 and P136 have insertion sites more than 300 kb away from the origin and their expression patterns are quite different from PBAS . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 01310 . 7554/eLife . 13503 . 014Figure 2—figure supplement 2 . Developmental dynamics in P162 expression patterns . ( A and B ) sections from P10 ( A ) and mature ( B ) animals . Parafascicular nucleus of thalamus had expression at P10 but not in mature animal ( arrowheads ) . ( C and D ) P10 ( C ) animal expressed reporter in pontine gray ( arrowhead ) but matured animal ( D ) did not . ( E and F ) Subiculum expression was not seen at P10 ( E ) but was present in mature ( F ) animals ( arrowheads ) . ( G ) Higher magnification of parafascicular nucleus in A . Asterisk: fasciculus retroflexus . ( H ) Higher magnification of pontine gray . ( I ) Cerebellum receives axons from pontine gray . ( J ) High magnification of cerebellum . Mossy terminals were labeled . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 01410 . 7554/eLife . 13503 . 015Figure 2—figure supplement 3 . Examples of virus injection . AAV–TRE3G- myristoylated mCherry-HA was injected to the brain . Wide field images ( 1 ) and confocal images ( 2 , rectangle areas in 1 ) of injection sites . Note that myristoylated mCherry strongly labels axons and dendrites . ( A–C ) Injection to retrosplenial cortex . P160 labels layer 2/3 ( A ) , P136 in layer 5 ( B ) , and P160 in layer 6 ( C ) . In P160 , virus spread to entire cortex ( see infected cells in deep layer ( arrows in A2 ) but viral reporter expression is restricted to mCtirine positive cells . ( D ) Hippocampal CA1 injection to P160 . ( E–F ) Examples of 'off-target' expression . Primary somatosensory cortex injection in P057 ( E ) and subiculum injection in P113 ( F ) . Arrowheads: cells with viral reporter without visible mCitrine expression . Blue: DAPI , Green: anti-GFP , Red: anti-HA . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 01510 . 7554/eLife . 13503 . 016Figure 2—figure supplement 4 . tet reporter expression in cultured cell lines . ( A–I ) Induction of tet reporter constructs was tested with 293T cells . Cells were transfected without ( A–I , first panels ) or with ( A–I , second panels ) CMV-tTA plasmid . Some constructs had strong tTA-independent 'leak' expression ( ex . A1 , E1 , and F1 ) . GBP-split Cre had the strongest expression of Cre reporter in the presence of GFP ( I3 ) but could activate the reporter expression without GFP expression ( I2 ) . See Supplemental note for further details . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 01610 . 7554/eLife . 13503 . 017Figure 2—figure supplement 5 . Specificity of tet reporter expression in vivo . ( A ) AAV–TREtight-myrmCherry expression in 56L . mCherry expression was restricted to mCitrine positive cells ( mCitrine+ cells/ mCherry+ cells: 152/152 ) . ( A2 ) higher magnification of injection site . ( B ) Co-infection of AAV–TRE3G–Cre and AAV–CAG–Flex-myrmCherry HAnsulator sequence from . There was strong non-specific mCherry expression near injection site ( B2 ) . ( C ) TRE3G–Split Cre had specific expression of reporter without apparent leak . ( D ) TRE3G–Flpe had non-specific expression in a few cells ( D2 , arrows ) ( E ) TRE3G split Cre had non-specific expression from Ai14 reporter allele in P113 subiculum . ( F ) TRE3G-nanobody Split Cre had specific expression ( mCitrine+ cells/mCherry + cells: 64/64 ) . ( F2 ) higher magnification of the injection site . ( G ) Specific expression of AAV Cre repoter by TRE3G-nanobody split Cre in 56L . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 017 We compared reporter expression patterns with those observed in BAC-Cre and knock in –Cre lines . Harris and her colleagues ( Harris et al . , 2014 ) manually evaluated the density of reporter-positive cells in 295 brain structures for each of 135 BAC- and knock in-Cre lines into six categories ( widespread , scattered , sparse , enriched , restricted/Laminar , and restricted but sparse ) . We employed the same expression categories to annotate expression patterns in PB lines ( see 'Materials and methods' and Figure 3—figure supplement 1; annotation data is summarized in Supplementary file 2 ) . We found that , on average , more than three times fewer structures were labeled in PB lines ( 33 ) than in the Cre lines ( 107 ) ( Figure 3A and B ) . The numbers of structures with enriched/restricted expressions were also lower in PB lines ( Figure 3C ) . We count the number of lines with expression in 12 major subclass of brain structures ( Harris et al . , 2014 ) . Cre lines had relatively homogenous expression rates ( 70–83% ) in any brain regions , whereas PB lines had expression bias to forebrain structures such as the isocortex , olfactory bulb , and hippocampus ( Figure 3D ) . 10 . 7554/eLife . 13503 . 018Figure 3 . PB lines have more restricted expression than Cre lines . ( A , B ) Histograms of the number of brain regions ( x axis ) with expression per line . Bac-Cre/Cre knock in –lines ( A ) have expression in more areas than PB enhancer trap lines ( B ) . Arrows: averages . ( C ) Histogram of number of brain structures with enriched or restricted expression . Red: Cre lines , Green: PB lines . ( D ) Fraction of lines with expression in brain subregions . Iso: Isocortex , Olf: olfactory areas , Hip: hippocampal formation , Cor: cortical subplate , Str: striatum , Pal: pallidum , Tha: thal amus , Hyp: hypothalamus , Mid: midbrain , Pon: Pons , Med: medulla , Cer: cerebellum . Red: Cre lines , Green: PB lines . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 01810 . 7554/eLife . 13503 . 019Figure 3—figure supplement 1 . Categories of regional expression patterns . ( A ) Criteria used to delineate expression categories . ( B–H ) Examples of expression categories ( B: widespread , C: scattered , D: sparse , E: enriched , F: restricted , G: restricted sparse ) . Areas with less than 10 mCitrine-expressing cells/mm2 were ignored ( H ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 019 Although our screen was focused on brain expression , we also performed a brief screen of the rest of the body and found that some lines ( 24/135 ) also had expression in tissues other than the brain , including skin ( 8 ) , bone ( 5 ) , viscera ( 9 ) , and brown ( 1 ) and white ( 3 ) adipocytes . Although we occasionally observed expression , in retinal ganglion cells ( Figure 1B ) , and spinal cord ( P032 , P105 ) , we did not systematically examine the retina , spinal cord , or peripheral nervous system . Non-brain expression patterns are summarized at the enhancer trap web site ( enhancertrap . bio . brandeis . edu/data/ ) . Except for lines that were lost or terminated early , we examined expression patterns of multiple animals from each transgenic line ( 73 lines ) . Nearly all had consistent expression patterns over multiple generations . A few lines showed variable expression patterns in individual animals . P039 had stable expression in subiculum , but its expression in cortex varied , and two lines ( P027 and P197 ) had expression that was left-right asymmetric . P139 heterozygous animals had consistent expression in cortico-thalamic L6 cells in lateral cerebral cortex ( see Figure 10 ) , but the number of labeled cells varied across animals and sometimes across hemispheres ( data not shown ) . Since P139 homozygous animals had stable expression patterns , subthreshold-level tTA expression might have caused stochastic reporter expression . In the brain , most enhancers display developmental dynamics visible , for example , in the state of an active enhancer marker H3K27Ace ( Nord et al . , 2013; 2015 ) . Many lines showed different reporter expression patterns at different ages , likely reflecting the developmental dynamics of the trapped enhancers . Screening was primarily carried out in young adults ( P20-30 ) . We examined adult ( P50 or later ) expression in 34 lines . Mature expression was reduced in 12 of these lines , but was retained in the remaining 22 lines . Some lines showed complex spatiotemporal expression patterns . For example , young postnatal animals ( P10-12 ) from line P162 had expression in primary sensory cortices , the parafascicular nucleus of the thalamus and pontine grey , but more mature ( 3 weeks or older ) animals lost thalamic and pontine expression and gained subiculum expression ( Figure 2—figure supplement 2 ) , suggesting the probe trapped enhancer ( s ) activated in different structures at different developmental stages . Unlike some lentiviral lines , silencing of PB transgenes over generations was not observed; even lines losing expression late in adulthood had pups that regained reporter expression . We examined whether our tet lines could drive transgenes other than the mCitrine encoded in the probes . TRE promoters are known to have weak , tTA-independent ( 'leak' ) expression that can be substantial when many copies of the TRE-Cre constructs are delivered virally ( Mizuno et al . , 2014; Zhu et al . , 2007 ) . We injected adenoassociated virus ( AAV ) encoding TRE-driven transgenes into multiple areas in different lines ( Figure 2—figure supplement 3 and Figure 10 . See Figure 2—source data 1 for counts ) . We found mCherry reporter was expressed specifically expressed in mCitrine-positive cells in most of cases . For example , three lines with expression in different layers of retrosplenial cortex had specific virus expression in different layers ( Figure 2—figure supplement 3A–C ) . Infection efficiency varied from 36 . 5% ( CA1 pyramidal cells in P066 , Figure 2—figure supplement 4D ) to nearly 100% ( layer 6 pyramidal cells in 56L , Figure 10R and T ) , probably due to AAV serotype preference . In some lines , as reported ( Choy , 2015 ) , we found a few viral reporter-positive but mCitrine-negative cells in the same layer/positions with those of mCitrine-positive cells ( arrowheads in Figure 2—figure supplement 3E and F; 0 . 7% +/- 0 . 7% of infected cells , n=6 injections in 5 strains ) , but never in ectopic positions lacking mCitrine positive cells . We speculate this 'off-target' expression might be a result of competition over tTA proteins between single copy TRE of mCitrine in the genome and many copies of TRE from virus . Myristoylated mCherry driven by a second-generation TRE ( TREtight-myrmCherry-HA ) was expressed only in mCitrine-positive cells and could be used to map their axonal projections ( see below ) . Similarly , tet-dependent channelrhodopsin virus ( TREtight-ChR2H134R-mCherry ) had specific expression only in mCitrine-positive cells and could drive action potentials upon blue light stimulation ( Choy , 2015 ) . Because of the widespread utility of recombinase systems such as Cre and Flp , we made significant efforts to make reagents allowing either a ) TRE-dependent recombinase expression or b ) expression of Cre directly from the enhancer probe . Nearly all these attempts were unsuccessful ( see Figure 2—figure supplement 4 and 5 ) due first to low level leak of all versions of the TRE promoter tried , combined with the high sensitivity of cre-dependent recombination . Expression of Cre from the enhancer probe may have suffered from this problem in some cases as well as the additional problem of more widespread developmental expression . We were able to obtain specific Cre-reporter expression restricted to mCitrine-positive cells , using an implementation of the GFP nanobody-split Cre virus ( developed independently from Tang et al . , 2015 ) . The GFP nanobody-split Cre also had specific reporter expression from Ai14 ( Madisen et al . , 2010 ) TdTomato Cre reporter allele ( supplemental note , Figure 2—figure supplement 4 and 5 ) . By screening a large number of lines , we were able to identify strains that target both classically distinguished neuronal cell types and subtypes of these cell types including some previously unrecognized subtypes . In this section , we focus on seven major brain structures . Our anatomical and physiological characterizations are necessarily incomplete , but we expect that others with scientific interests in the relevant structures will contribute to more detailed characterization . We found three lines ( 48L , 52L , P113 ) with distinctive expression patterns in the cell-dense layer ( layer II ) of the piriform cortex ( Figure 9A–C ) . Two broad categories of layer II glutamatergic neurons have previously been described; semilunar ( SL ) cells , which lack well-defined basal dendrites and are located in the upper sublayer of layer II , and superficial pyramidal ( SP ) cells , which , like most pyramids , possess distinct basal and apical dendrites and are located deeper in layer II ( Suzuki and Bekkers , 2006; 2011 ) . 48L cells were recently shown to be a subset of SL cells ( Choy , 2015 ) . Based on their cell body positions ( Figure 9D ) and dendritic morphology , the labeled cells in P113 appear to be typical SP cells . 52L cells were GABA-negative ( data not shown ) but do not clearly match the anatomical and physiological properties of either subtype of previously described pyramidal neurons . 10 . 7554/eLife . 13503 . 027Figure 9 . Piriform cortex cell types . ( A–C ) Expression in three distinct populations within piriform cortex . ( D ) Cell body distributions in layer 2 . ( E–K ) 52L labels a previously undistinguished cell type . Firing patterns ( E and G ) and morphologies ( F and H ) of labeled ( E and F ) and non-labeled ( G and H ) cells in 52L piriform cortex . Arrows: initial burst present in labeled , but not unlabeled cells’ arrowheads: AHP at the end of train present in unlabeled but not labeled cells . Average F–I curves ( I ) , AHP amplitude ( J ) , and instantaneous firing frequency ( K ) for labeled cells ( red ) and non-labeled cells ( black ) were significantly different ( asterisks ) : mean firing frequencies ( averaged over 400–500 pA current injection , 11 ± 5 Hz and 28 ± 5 Hz , p = 0 . 025 ) , AHP amplitude ( -1 . 2 ± 0 . 3 mV and -3 . 4 ± 0 . 6 mV , p=0 . 0073 , labeled and non-labeled cells , respectively ) , and in instantaneous firing frequencies ( 131 ± 12 Hz and 58 ± 10 Hz , p = 0 . 00019 ) . n= 10 for each; line . Scale bars: 500 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 027 We recorded the physiological properties of mCitrine-labeled cells and non-labeled cells in 52L piriform cortex . Like SP cells , they responded to depolarizing current with an initial high frequency burst of action potentials ( Suzuki and Bekkers , 2006 ) as did nearby non-labeled cells ( arrows in Figure 9E and 8G ) . However , labeled cells ( but not unlabeled cells ) differed from the previously described SP neurons in that they exhibit a stuttering firing pattern and their firing inactivates at higher currents ( Figure 9I ) . Labeled and non-labeled neurons also differ in their afterhyperpolarizations ( arrowheads in Figure 9E and G; Figure 9J ) . While the labeled neurons could not sustain prolonged firing at high-current injections , their instantaneous firing frequency was higher in the beginning of spike train ( Figure 9E and G; Figure 9K ) . Morphologically , the non-labeled neurons resemble previously described SP cells since they possessed distinct basal and apical dendrites ( Figure 9H ) . Labeled neurons also possessed basal dendrites ( unlike SL cells ) but do not have a distinct apical dendrite ( Figure 9F ) . Taken together , our anatomical and physiological results suggest that 52L cells are a distinct subset of SP cells that differ phenotypically from other , unlabeled SP cells . Cerebral cortex and thalamus have dense reciprocal connections and layer 6 of the cortex is the major source of the cortico-thalamic ( CT ) projection . Single cell tracing has shown that there are two types of L6 CT projecting pyramidal neurons ( Thomson , 2010; Zhang and Deschenes , 1997 ) ; primary sensory CT pyramidal neurons send axons to primary sensory thalamic nuclei ( ex . ventro-posterior medial:VPm , lateral geniculate dorsal nucleus: LGd ) . Non-primary CT neurons send weaker projections to primary sensory nuclei , but also project to secondary sensory nuclei; for example , in primary somatosensory cortex , they send axons to VPM , posterior thalamic nuclei ( Po ) , and intralaminar thalamic nucleus . Primary CT neurons are located in upper layer 6 and non-primary CT neurons in lower layer 6 . Primary CT neurons also project to the thalamic reticular nucleus ( RTN ) , whereas non-primary CT neurons do not . Primary CT neurons and non-primary CT neurons also have different morphologies . Primary CT neurons extend apical dendrites to layer 4 while dendrites of non-primary CT neurons do not reach to layer 4 . Layer 6 also contains corticocortical pyramidal cells , which have long collateral projections within layer 6 . We obtained three lines with expression in layer 6 cortical pyramidal neurons ( Figure 10 ) . P162 has mCitrine-positive cells in primary somatosensory area ( SSp ) , primary visual area ( VISp ) , and retrosplenical cortex and axonal projection in VPm and LGd ( Figure 10G–J ) . P139 has expression in lateral cortex including supplemental somatosensory area ( SSs ) and gustatory cortex and projection in Po ( Figure 10M–P ) . There are topological projection patterns in RTN; dorsally located P162 cells project to dorsal RTN and laterally located P139 cells project to ventral RTN ( Figure 10—figure supplement 1B and C ) . The third line , 56L , has broad expression across neocortex ( Figure 10A–D ) . 56L neurons have projection to both primary and secondary nuclei but not to RTN ( Figure 10—figure supplement 1A ) . 10 . 7554/eLife . 13503 . 028Figure 10 . Projections of layer 6 corticothalamic ( CT ) neurons . ( A–D ) Coronal images from 56L . ( E and F ) confocal images from SSp ( E ) and VISp ( F ) from 56L . ( G–J ) Coronal sections from P162 . ( K and L ) Confocal images from SSp ( K ) and VISp ( L ) from P162 . ( M–P ) Coronal images from P139 . ( Q ) Confocal image from P139 SSs . Sections were taken from 0 . 7 mm ( A , G , and M ) , 1 . 7 mm ( B , D , H , J , N , and P ) , 2 . 3 mm ( C , I , and O ) caudal from bregma . ( R–W ) tet-reporter virus injection into 56L SSp ( R ) , 56L SSs ( T ) , P162 SSp ( V ) , and P139 SSs ( X ) and their projection to thalamus ( S , U , W , and Y , respectively ) . ( Z ) Schematic view of projections in layer 6 lines . ILM: interlaminar nucleus , Po: posterior complex , VPM: ventral posteomedial nucleus . Scale bars: 500 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 02810 . 7554/eLife . 13503 . 029Figure 10—figure supplement 1 . Projections to the reticular nucleus of the thalamus ( RT ) ( A–C ) DAPI ( blue ) , anti-GFP ( green ) , and anti-Parvalbumin ( PV , red ) staining for thalamus of 56L ( A ) , P162 ( B ) , and P139 ( C ) . Few or no mCitrine-positive axons from 56L ( A ) project to the PV-positive RT . P162 ( B ) axons project only to the dorsal ( d ) part of RT , whereas the ventral ( v ) part receives axons from P139 ( C ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 02910 . 7554/eLife . 13503 . 030Figure 10—figure supplement 2 . Sublaminar location and intrinsic physiology of layer 6 neurons . ( A and B ) Positions of mCitrine-positive cell bodies in Layer 5–6 are plotted . ( A ) P162 ( green ) and 56L ( blue ) in SSp . ( B ) P139 ( green ) and 56L ( blue ) in SSs . Dotted lines: averaged borders between layers 5 and 6 . ( C ) Current clamp responses of P162 , 56L SSp , P139 , 56L SSs to 100 pA current injections . Input resistance ( D ) whole cell capacitance ( E ) of layer 6 cells . Asterisks: p<0 . 05 with Turkey-Kremer’s post hoc test . ( F and G ) Current clamp responses of labeled ( F ) and nearby non-labeled ( G ) neurons in 56L layer 6 during current injection . ( H ) Firing frequency – current injection plot for labeled and non-labeled neurons in 56L layer 6 . n = 16–20 . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 03010 . 7554/eLife . 13503 . 031Figure 10—figure supplement 3 . 56L axonal projection from VISp to thalamus . ( A ) Injection site . ( B ) High magnification of injection site . ( C ) Axonal projections to thalamus avoid the dorsal leteral geniculate nuceus ( LGd ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 03110 . 7554/eLife . 13503 . 032Figure 10—figure supplement 4 . Long lateral projections in 56L and P139 AAV–TRE3GmCherryHA was injected to 56L . ( A–C ) and P139 ( D–F ) . B and C high magnification of designated area in A . E and F high magnification of designated area in D . 56L had callosal projections ( arrowhead in A ) but these were not seen in P139 ( arrowhead in C ) . Red: anti-HA , Green: anti-GFP , Blue: DAPI . Images in D–F and Figure 10X were taken from the same section . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 032 The labeled CT neurons also differ morphologically and in their laminar locations . Cell bodies of P162 and P139 are located in upper layer 6 , but those of 56L are in lower layer 6 ( Figure 10—figure supplement 2A and B ) . P162 and P139 have apical dendrites reaching to layer 4 ( Figure 10K , L and Q ) ; some apical dendrites even extended to layer 1 ( Figure 10K ) . Apical dendrites to layer 1 were more frequently seen in P162 VISp and P139 ( Figure 10L and Q ) . On the other hand , 56L dendrites in SSp did not step in to layer 4 . In VISp , some neurons extended dendrites to layer 1 . From their cell body locations and projection patterns , P162 and P139 appeared to be primary-CT type pyramidal neurons in different cortical areas . We also recorded the physiological properties of layer 6 cells . 56L cells have larger whole cell capacitances than P162 and P139 cells and tended to have correspondingly lower input resistances . All mCitrine-positive cells fired tonically ( Figure 10—figure supplement 2C and D ) . In the 56L recordings , we found that most mCitrine-negative cells recorded near mCitrine-positive cells fired more phasically and at lower rates ( Figure 10—figure supplement 2F–H ) reminiscent of the firing previously described for CC cells ( Mercer et al . , 2005 ) . We compared axonal projection patterns to thalamus by injecting AAV-TRE3GmyrCherryHA virus in layer 6 . P162 SSp cells projected to the dorsal part of VPM and P139 SSs neurons projected to Po ( Figure 10V–Y ) . In contrast , labeled axons from 56L SSp cells ( Figure 10R and S ) were found in Po , VPM , and the intralaminar nucleus ( ILM ) , consistent with previously described projections of non-primary CT cells ( Zhang and Deschenes , 1997 ) . Axons from 56L SSs were enriched in ventral VPM and Po ( Figure 10T and U ) and those from VISp mainly projected to the lateral posterior nucleus , not to LGd ( Figure 10—figure supplement 3 ) . We also found that 56L had long lateral axonal projections that even reached to the contralateral hemisphere , whereas P162 and P139 cells had local lateral axonal projections within the cerebral cortex ( Figure 10—figure supplement 4 ) . In order to complement our phenotypic analyses of differences between subtypes of L6 corticothalamic neurons , we also analyzed their RNAseq profiles and compared them to VISp layer 6 pyramidal neurons from the Ntsr1-Cre line , which is also known to have layer 6 specific expression in the cortex ( Gong et al . , 2007 ) . Ntsr1-Cre labels virtually all primary CT neurons in layer 6 and also has projection to RTN ( Bortone et al . , 2014; Kim et al . , 2014; Olsen et al . , 2012 ) . Clustering samples by correlations between gene expression vectors revealed two main clusters: those from 56L and all others ( Figure 11A ) . Samples from P162 and P139 are intermingled in the cluster , implying they have quite similar RNA expression profiles . Analysis of differentially expressed genes also showed clear differences between the two main groups . There were 1869 genes differentially expressed among all sample groups ( false discovery rate ( FDR ) < 0 . 01 ) , and most differentially expressed genes showed bimodal patterns; high expressions in one group and low expressions in the other ( Figure 11B and C ) . We also examined the expression of previously identified layer 6 marker genes ( Molyneaux et al . , 2007; Zeisel et al . , 2015 ) and of genes used to generate BAC-Cre lines having layer 6 expression ( Harris et al . , 2014 ) . Most of these known layer 6 markers are expressed both in the Ntsr1-cre group and in 56L ( including the Ntsr1 gene itself ) or were present only in the Ntsr1-cre lines . None were reliable markers for the 56L population ( see Figure 11—figure supplement 1 and supplemental Note ) . We also examined expression profile of entorhinal cortical layer 6 cells from P038 in addition to isocortical layer 6 cells . Based on RNAseq expression profiles , P038 cells belonged to the Ntsr1-cre group but expressed unique set of genes ( see Figure 11—figure supplement 2 ) . 10 . 7554/eLife . 13503 . 033Figure 11 . Two main subtypes of L6 CT neurons distinguished by gene expression . ( A ) Clustering of L6 CT neuron samples based on correlations ( color scale ) between expression profiles . ( B ) Heat map of normalized gene expression ( TPM ) of 50 genes with lowest ANOVA p-values . Except for Plcxd2 ( asterisk ) , the genes had dominant expression in either Ntsr1/P162/P139 or 56L . ( C ) Coverage histograms of differentially expressed genes . Examples of genes expressed in P162/P139 ( Tle4 and Rgs4 ) , 56L ( Nptxr and Cacna1g ) , P139 ( Atp1b2 ) , and P162 ( Ifitm2 ) . Scale bars: 100 counts . ( D–F ) In situ hybridization for Tle4 ( red ) and Bmp3 ( green ) in wild type P10 animal SSp . ( E ) high-magnification image . ( F ) Proportion of cells expressing Tle4 and Bmp3 in SSp layer 6 . ( G–O ) In situ hybridization for mCitrine and Tle4 ( G , J , and M ) or Bmp3 ( H , K and N ) in P162 SSp ( G and H ) , P139 SSs ( J and K ) and P56 SSp ( M and N ) . ( I , L , O ) Proportions of mCitrine+ cells that expressTle4 or Bmp3 and converse proportions of cells expressing the dominant marker ( Tle4 for I , L Bmp3 for O ) that are mCitrine+ from P162 ( I ) , P139 ( L ) and 56L ( O ) . Colors in bar graphs represent in situ signal patterns ( Red: cells with marker gene but not mCitrine , Green: cells with mCitrine signal but not marker gene , and Yellow: cells with both marker and mCitrine signals ) . Scale bar in D: 500 μm , in E: 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 03310 . 7554/eLife . 13503 . 034Figure 11—figure supplement 1 . Expression of known L6 marker genes . ( A ) Expression levels of known layer 6 marker genes ( Molyneaux et al . , 2007 ) . ( B ) Expression levels of genes used to make BAC transgenic lines with layer 6 expression ( Harris et al . , 2014 ) . ( C ) Layer 6 marker genes found by single cell RNAseq ( Zeisel et al . , 2015 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 03410 . 7554/eLife . 13503 . 035Figure 11—figure supplement 2 . P038 entorhinal cortex layer 6 neurons are a distinct population . ( A ) Sample clustering ( B ) Heat map for top 100 genes with lowest ANOVA p-values . Arrows: genes shown in C . ( C ) Example of genes uniquely expressed in P038 ( Nr4a2 and Parm1 ) , Ntsr1 group and 56L markers ( Tle4 and Bmp3 ) , and selectively not expressed in P038 ( Pcdh7 and Mef2c ) . y-axes: TPM . DOI: http://dx . doi . org/10 . 7554/eLife . 13503 . 035 We confirmed the expression of Tle4 ( which is expressed strongly in Ntsr1-cre ) and Bmp3 ( expressed in 56L ) by dual-color in situ hybridization . Tle4 and Bmp3 have essentially non-overlapping expression ( Figure 11D–F ) . mCitrine-positive cells in P162 and P139 dominantly express Tle4 with only a few Bmp3-positive cells , whereas 56L cells are mostly Bmp3-positive but Tle4-negative ( Figure 11G–O ) . In all lines , the majority of marker ( Tle4 or Bmp3 ) -positive cells do not express mCitrine , suggesting the three lines label subsets of these marker-positive cells . We also analyzed Tle4 expression in the Ntsr1-Cre animal ( Nstr1-Cre;Ai14 Rosa-TdTomato ) from dual-fluorescent in situ images ( http://connectivity . brain-map . org/transgenic/experiment/100147520 ) and found Ntsr1-Cre cells co-expressed Tle4; 100% ( 333/333 ) of Tdtomato+ cells were Tle4+ and 77 . 8% ( 333/428 ) of Tle4+ cells were TdTomato+ . These results support the view that P162 and P139 are subsets of the Ntsr1-cre population and that 56L cells are a distinct population of L6 CT neurons . We have developed a highly efficient method of enhancer trapping in the mouse and have used it to generate a resource of lines that allow targeting of a wide range of known and novel neuronal cell types . The enhancer trap approach produces more focused labeling than commonly used approaches that attempt to recapitulate known patterns of endogenous gene expression . Using this approach , we have identified dozens of new subtypes of previously identified neuronal cell types and have clarified the classification of pyramidal neurons within the piriform cortex and within layer 6 of neocortex . The approach is readily scalable since new lines can be generated simply by additional rounds of breeding . We also develop the enhancer trap line web browser to search lines of interests and to share images and detailed information about lines . The web site can serve as a useful open resource for wide range of researcher in mouse genetics and neuroscience . Cell-type-specific patterns of gene expression are thought to reflect interactions between regulatory sequences within the proximal promoter , and at other far more distal sites ( Nathanson et al . , 2009; Pennacchio et al . , 2006 ) . Viral reporter was mostly expressed only in mCitrine-positive cells , and we did not see major ectopic reporter expression , which supports that cell-type specific tTA expression , but not regional TRE silencing , mainly contributes to highly restricted expression patterns . Single genes often have multiple enhancer modules each of which regulates expression in different regional or developmental contexts ( Dickel et al . , 2013; Visel et al . , 2009 ) . By harnessing these distal enhancers , BAC-transgenic ( Gong et al . , 2003; 2007 ) and knock-in ( Taniguchi et al . , 2011 ) approaches have been used to generate lines that copy expression patterns of targeted genes with high-fidelity , but many of these are quite broadly expressed ( Harris et al . , 2014; Madisen et al . , 2015 ) . On the other hand , lines with more limited expression patterns have been generated by unintentional positional effects arising from transgene insertion sites . Restricted expression patterns in the series of Thy1 lines ( Feng et al . , 2000 ) and CA1-specific Cre mouse with a CamKII-promoter ( Tsien et al . , 1996 ) are notable examples . Because of these positional effects , even BAC transgenic lines occasionally have more restricted expression patterns that differ from those of the targeted genes ( Huang and Zeng , 2013 ) . Although positional effects can restrict expression to specific populations , the population targeted is not predictable because the enhancer code that directs expression to specific cell types is not well understood . To circumvent this limitation , a useful enhancer trap requires screening a large number of individual strains . This has previously been done using transposable element mobilization in flies and fish ( Balciunas et al . , 2004; Bellen et al . , 1989; Brand and Perrimon , 1993; Scott et al . , 2007; Urasaki et al . , 2008 ) . However , since pronuclear-injection produces a rather low efficiency of transgenesis , this approach has not frequently been used in mouse genetics . Hopping from single copy PB enabled fast identification of insertion sites without the laborious and time-consuming steps of tracking and segregating multiple transgene alleles . In fact , since we have made both single copy PB insertion lines and a line carrying PBase in the male germ line freely available , other laboratories can now screen for additional lines of interest without needing to isolate or inject embryos . Our strategy of including a mCitrine reporter on the probe enables fast expression screening without crossing to a separate reporter line , and including tTA enables inducible genetic manipulation in specifically labeled populations . Although our enhancer trap lines were generated by random insertion , most lines maintain consistent expression over generations . In addition , lines generated by local hopping have similar expression patterns . These facts support the conclusion that the expression patterns are not generated randomly , but instead are tightly linked to transgenes’ insertion sites . PB translocation sites were widely distributed over the genome and lines with insertions far away from known genes often exhibited specific expression presumably by trapping distal enhancers . Distal enhancers are known to regulate tissue specific expression of genes , especially in the forebrain ( Nord et al . , 2013; 2015; Visel et al . , 2009; 2013 ) and to regulate activity-dependent gene expression ( Kim et al . , 2010 ) . Our results suggest that enhancers are also involved in the fine-grained specification of cell types , and that trapping them can cause very restricted expression patterns . Indeed , drivers restricted to major cell types and layers in laminated structures are already available , but drivers that pick out cell types in specific cortical regions , or thalamic nuclei are quite rare with gene-based strategies but were more common with our enhancer trap strategy . The cell types accessed genetically in these and other driver strains are best thought of as operational cell types defined by the intersection of a driver strain and an anatomical region . This permits reproducibility but does not define the full set of 'atomic' cell types that comprise the nervous system . Like brain regions , cell types may be arranged hierarchically into tree-like taxonomies . Most operational cell types represent branches or nodes that can be further subdivided . This subdivision occurs when properties such as morphology , physiology , projections and gene expression are found to vary discontinuously . Eventually , those that cannot be further subdivided may be thought as terminal branches or 'atomic cell types' . For a few of the cell types identified in our enhancer trap strains ( e . g . subsets of corticothalamic neurons or piriform cortex neurons ) further characterization demonstrates functional distinctions between closely related subtypes . In other cases , the trapped neurons correspond to well-characterized subtypes of a larger class ( e . g . LGN thalamic projection neurons ) , while in many other cases additional characterization will be needed to determine how trapped subtypes differ functionally from other cells in the same class . The anatomical distinctiveness of , for example , Purkinje cells restricted to particular folia or granule cells sending their axons to particular sublaminae are suggestive , but whether these neurons differ from other Purkinje cells and granule cells in other aspects of their anatomy , physiology and gene expression remains to be seen . Efforts to enhance the aggregation of such phenotypic data are needed to better refine the definition of cell types within the vertebrate nervous system . Hopefully , additional iterations of our enhancer trap database will benefit from enhanced informatic efforts to improve usability and interoperability with other databases and to make it easier for the community to contribute data that will help parse operational cell types into atomic cell types . In addition to positional effects , the nature of our enhancer probe might have contributed to producing restricted expression patterns . We obtained lines with specific expression more frequently than those of previous enhancer screening with minimal HSP promoter-lacZ and thy1 promoter-Cre lines , although they employed the same random insertion method ( Kelsch et al . , 2012 ) . Some PB lines with intronic insertions expressed the reporter in neurons in which the inserted genes were strongly expressed . From these facts , we speculate our enhancer probes may require a certain level of transcriptional activity to drive the reporter expression , and this thresholding effect may limit the expression of the reporter . The use of a transactivator system , rather than a recombinase system , may also have contributed to generating specific reporter expression . Cre-dependent reporter expression will be present regardless of whether cre continues to be expressed , or whether it was only expressed in the labeled cells or their progenitors earlier in development ( Harris et al . , 2014 ) , while tet reporters will be expressed only when tTA protein continues to be expressed . Our screen revealed two major subtypes of corticothalamic pyramidal neurons in layer 6: primary CT neurons and non-primary CT neurons . The two cell types differ in distribution within layer 6 and have distinctive axonal projection patterns in the thalamus . They also have distinct RNA expression profiles identifying marker genes that display almost non-overlapping patterns in layer 6 . Genetic labeling of primary CT cells by Ntsrt1-Cre ( Gong et al . , 2007 ) has greatly advanced understandings of functions of layer 6 primary CT in cortical ( Bortone et al . , 2014; Kim et al . , 2014; Olsen et al . , 2012 ) and thalamic ( Crandall et al . , 2015 ) circuits . Perhaps since Ntsr1-Cre labels nearly all ( 92 . 7% in SSp and 95–100% in VISp :Bortone et al . , 2014; Kim et al . , 2014 ) primary thalamic nuclei projecting CT neurons , and since a selective driver for non-primary CT layer 6 neurons was not previously available , the role of this second CT pathway from L6 has not been taken into consideration in previous functional studies ( Bortone et al . , 2014; Kim et al . , 2014; Watakabe et al . , 2014; Yamawaki and Shepherd , 2015 ) . We found that 56L cells in SSp had projection to multiple nuclei ( VPM , Po and ILM ) as originally described by Zhang and Deschenes ( Zhang and Deschenes , 1997 ) . 56L in SSs has strong projections to VPM , which implies there are previously unidentified sources of CT feedback from higher order cortical regions to primary sensory thalamic nuclei ( Sherman , 2012; Thomson , 2010 ) . We also found that 56L cells have long collateral projections like those previously described for CC neurons ( Zhang and Deschenes , 1997 ) . Thomson and colleagues found that the most of cells with CC-like morphologies fired phasically ( Mercer et al . , 2005 ) . We speculate that 56L-like cells are the minor population of layer 6 pyramidal neurons which fire tonically like primary CT neurons but which possess large collateral projections and other morphological features associated with CC cells . Since mCitrine-negative cells in 56L were phasically firing , we speculate that these are CC cells labeled by neither Ntsr1-Cre nor 56L . For example , many of the Bmp3-positive cells not labeled by 56L could be CC-cells lacking a projection to thalamus . Although we have shown the ability of the tet enhancer trap system to label highly restricted specific cell types , further technical improvements may enhance the utility of the approach . Replacing the HSP promoter with other ( minimal ) promoters may change the forebrain bias ( see PB line expression patterns above ) to permit better exploration of cell types in other major brain regions . Developments of additional molecular genetic tools , such as optogenetic tools ( Choy et al . , 2015 is an example ) , voltage- or calcium sensors , and viral vectors targeted to synapses or other subcellular structures , or functionalized for retrograde or transynaptic transport may enhance analysis of the connectivity of trapped cell types . Finally , enhancer trap lines may be useful for analyzing the function of candidate enhancers near the insertion sites in order to better understand how distal enhancers contribute to the specification and maintenance of cell-type-specific gene expression in the mammalian nervous system . Lentiral constructs were made using a backbone from pSico ( Addgene , Cambridge , MA #11578 ) . Lentiviruses were prepared and injected into single cell embryos as described previously ( Lois et al . , 2002 ) using virus solutions at 109 infection unit/ml . Candidate forebrain enhancer sequences were chosen using the VISTA enhancer browser ( http://enhancer . lbl . gov ) . The four selected sequences ( hs119 , hs121 , hs122 , and hs170 ) were amplified from C57Bl6/J genomic DNA . pPB-UbC . eGFP ( Yusa et al . , 2009 ) was used as the backbone for PB plasmids . Prior work has distinguished two functional types of insulators: ‘barrier’ insulators , which prevent the spread of DNA methylation and silencing , and ‘blocking’ insulators , which limit promoter-enhancer interactions ( Gaszner and Felsenfeld , 2006 ) . Most vertebrate insulators with barrier activity also have blocking activity ( West et al . , 2002 ) . The cHS4 site from the chicken β-globin locus is a well-characterized insulator known to have separate sequences that mediate its blocking and barrier insulator functions ( Dickson et al . , 2010 ) . To prevent silencing but not enhancer effects , we synthesized cHS4 sequence without the region responsible for the blocking activity ( the CTCF-binding site ) . Tandem copies of the insulator sequences were inserted into each 5’ and 3’ ends of PB constructs ( HS4 ins , Figure 1—figure supplement 2B ) . The Rosa-PBase line was provided by Ronald Rad and Allan Bradley ( Rad et al . , 2010 ) . In order to establish the Protamine1 promoter-hyPBase line , the 848 bp mouse Protamine1 promoter ( Zambrowicz et al . , 1993 ) was amplified by PCR from C57Bl6/J genomic DNA and fused with a hyperactive PiggyBac transposase ( Yusa et al . , 2011 ) and the SV40 polyadenylation signal . The linearized DNA was injected to pronuclei of single-cell embryos . PiggyBac seed lines were generated by pronuclear or cytosolic injection of a PiggyBac plasmid ( 2 ng/μl ) and hyPBase mRNA ( 50 ng/μl ) that was synthesized with mMESSAGE mMACHINE T7 Ultra Kit ( Life Technologies ) and purified with MEGAClear ( Life Technologies ) . was performed as described by Wu et al . ( Wu et al . , 2003 ) . We used the same adaptors and primers to amplify lentiviral and PB insertion sites . In addition to the adaptors , the following primers were used for PiggyBac lines; PB5'LMPCR: 5’-CGGATTCGCGCTATTTAGAA-3’ , PB5'LMPCRnested: 5’-TCAAGAATGCATGCGTCAAT-3’ , PB3'LMPCR: 5’-CCGATAAAACACATGCGTCA-3’ , PB3'LMPCRnested: 5’-CGTCAATTTTACGCATGATTATCT-3’ . After nested PCR , amplified products were isolated by agarose gel electrophoresis , and then reamplified by PCR to remove non-specific products . The final PCR products were used as templates for direct sequencing with the nested primers . Insertion sites were mapped on C57Bl6/j genome ( GRCm38/mm10 ) with blat ( http://genome . ucsc . edu/cgi-bin/hgBlat ) . For nanobody-split Cre construction , GBP1 and GBP6 ( Addgene #50791 and #50796 , Tang et al . , 2013 ) were fused with NCre and CCre from split Cre ( gifts from Hirrlinger et al . , 2009 ) . AAV purification was performed as described previously ( Zolotukhin et al . , 1999 ) . Since AAV serotypes can show tropism for specific cell types , we used a cocktail of 4 serotypes ( 2/1 , 2/5 , 2/8 , 2/9 ) . After iodixanol step gradient , the virus solution was dialyzed and concentrated with Amicon Ultra 100k Da filters ( EMD Millipore , Billerica , MA ) with lactated Ringer . Virus copy number was quantified with real-time PCR . Virus titers were in the range of 1012-14 gene counts/ml . We followed surgical procedures previously described in ( Cetin et al . , 2006 ) . For each injection , 30-50 nl virus solutions were injected to the target sites with a custom-made injector . Whole cell recordings from visually identified neurons were obtained as previously described ( Miller et al . , 2008 ) . We recorded from four or more animals for each condition . We used t-test for statistical analyses if not stated . After being deeply anesthetized with Ketamine and Xylazine , mice were perfused with phosphate buffer saline ( PBS ) and 4% paraformaldehyde in PBS . Brains were post-fixed overnight with 4%A PFA/PBS , embedded in 2% agarose /PBS , and then sectioned at 50 μm with a vibratome ( Leica , Buffalo Grove , IL VT1000S ) . The following antibodies were used for immunohistochemistry: anti-GFP rabbit ( Life Technologies , Thermo Fisher , Waltham , MA A-11122 ) , anti-GFP chicken ( Aves labs , Tigard , OR GFP-1020 ) , anti-HA rat ( Roche diagnostics , Indianapolis , IN clone 3F10 ) . Whole slide images were taken with a microscope with 5x objective and XY-stage controlled by μManger ( https://micro-manager . org ) . Grid/Collection stitching Fiji plugin ( Preibisch et al . , 2009 ) was used for image assembly . We followed the dual-color in situ protocol described in BraInSitu web site ( http://www . nibb . ac . jp/brish/indexE . html ) ( Watakabe et al . , 2006 ) . The anatomy structure model in Allen reference mouse atlas ( http://mouse . brain-map . org/static/atlas ) was used to annotate expression areas . To compare the expression areas with Cre lines , 228 structures used in annotation commonly with sagittal and coronal sections in Harris et al . , 2014 were applied . All lines were annotated by three observers independently and the unions of annotations were used . Expression levels were determined by the level of localization in anatomical structures and density of mCitrine-positive cells ( Figure 3—figure supplement 1 ) . Cell densities were determined by counting cells in most zoomed images in the web viewer ( the window size is 600 x 400 px , 768 x 500 μm with images taken with a x5 objective ) . Structures with more than 10 cells/mm2 ( 4 or more cells in the window ) were annotated . Manual sorting of fluorescent-labeled cells from transgenic animals was performed as described previously ( Sugino et al . , 2006 ) . Total RNA was extracted from manually sorted cells ( <200 ) with Picopure RNA isolation kit ( Thermo Fisher ) , and RNA-seq libraries were made with Ovation RNASeq System V2 and Encore kit ( NuGEN , San Carlos , CA ) . Three or four biological duplicates were made for each sample . Illumina ( San Diego , CA ) HiSeq2500 was used for sequencing . rna-STAR ( Dobin et al . , 2012 ) and cufflinks 2 . 1 ( Trapnell et al . , 2010 ) were used for mapping reads to reference mouse genome GRCm38 and for transcriptome assembly and quantification , respectively . Gene counts data generated with HT-seq {Anders , 2015 #103} was used for differentially expressed gene analysis by edgeR ( Zhou et al . , 2014 ) . Custom-written Python programs using numpy and scipy were used for analysis . The accession number of RNAseq data is GSE75229 . Following programs were used to build the web site; Python 3 . 4 ( programming language ) , Django 1 . 8 ( web application framework ) , mySQL 5 . 6 ( relational database ) , haystack-2 . 3 . 1 ( search module for Django ) , elasticsearch-1 . 4 . 4 ( search engine ) , uwsgi-2 . 0 . 10 ( WSGI application server ) , and nginx-1 . 8 ( web server ) .	Scientists can track and even alter the activity of different kinds of neurons , as well as the connections between neurons , by manipulating their genes . However , most genes are active in many different kinds of cells in many different places in the brain , making it difficult to track or target only a particular neuron or brain area . Enhancers are sections of DNA that can regulate the activity of nearby genes so that they are only active in very specific cell types , and an “enhancer trap” is a genetic approach that essentially hijacks enhancers to express artificial genes in those same cell types . The technique relies on inserting a genetic marker , which can be easily tracked , into random locations in the genome . If this marker then interacts with an enhancer , it is activated and the effect of the enhancer on gene expression can be assessed . This method has been used in fruit flies and fish to identify enhancers that specifically restrict gene expression to a small subset of cells . Now , Shima et al . show that enhancer traps can be used successfully in mammals too . The experiments produced over 200 different strains of mice , many with the fluorescent marker only in specific brain areas or in specific kinds of brain cells . Some of the types of brain cells uncovered by these experiments are new , and the labelling of specific brain cells and brain areas in different strains makes these mice a useful resource for future work . Furthermore , it will be relatively straightforward to produce many more strains of these mice , because it would simply involve crossbreeding mice . It is likely that some of these to-be-discovered strains will be useful tools for research as well .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "tools", "and", "resources", "neuroscience" ]	2016	A Mammalian enhancer trap resource for discovering and manipulating neuronal cell types
Neurons of the cerebellar nuclei ( CbN ) transmit cerebellar signals to premotor areas . The cerebellum expresses several autism-linked genes , including GABRB3 , which encodes GABAA receptor β3 subunits and is among the maternal alleles deleted in Angelman syndrome . We tested how this Gabrb3 m-/p+ mutation affects CbN physiology in mice , separating responses of males and females . Wild-type mice showed sex differences in synaptic excitation , inhibition , and intrinsic properties . Relative to females , CbN cells of males had smaller synaptically evoked mGluR1/5-dependent currents , slower Purkinje-mediated IPSCs , and lower spontaneous firing rates , but rotarod performances were indistinguishable . In mutant CbN cells , IPSC kinetics were unchanged , but mutant males , unlike females , showed enlarged mGluR1/5 responses and accelerated spontaneous firing . These changes appear compensatory , since mutant males but not females performed indistinguishably from wild-type siblings on the rotarod task . Thus , sex differences in cerebellar physiology produce similar behavioral output , but provide distinct baselines for responses to mutations . Neurons in the cerebellar nuclei ( CbN ) form the final stage of cerebellar processing . They integrate synaptic inhibition from Purkinje neurons of the cerebellar cortex with synaptic excitation from mossy fibers and inferior olivary fibers , thereby generating the sole output of the non-vestibulocerebellum . Consequently , disruptions in intrinsic or synaptic signaling anywhere in the cerebellar circuit are likely to manifest themselves in CbN cell activity , either as altered or compensatory responses . Recent work has drawn attention to the fact that many genes disrupted in autism spectrum disorders ( ASD ) are expressed in the cerebellum ( Fatemi et al . , 2012; Wang et al . , 2014 ) , suggesting that , regardless of the etiology of the phenotypes associated with the condition , cerebellar processing may be affected . Indeed , cerebellar abnormalities are consistently found in post-mortem examinations of autistic brains , which reveal decreases in size and number of Purkinje and/or CbN cells ( Bauman and Kemper , 1985; Arin et al . , 1991; Bailey et al . , 1998; Kemper and Bauman , 1998; Whitney et al . , 2008 ) . Mice with the 15q11-13 duplication , a model for one form of autism , show defects in cerebellar learning ( Piochon et al . , 2014 ) , and mutation of Tsc1 in Purkinje cells alone can recapitulate several autism-like behaviors in mice ( Tsai et al . , 2012 ) . Studying the cerebellum in mouse models of autism may therefore yield insight into how cerebellar output is generated and to what extent cerebellar processing is plastic or disrupted in the face of genetic abnormalities . GABRB3 , which encodes the β3 subunit of the GABAA receptor ( GABAAR ) , is an autism-linked gene with cerebellar expression . It is among the genes affected in Angelman syndrome , a condition in which patients show developmental delay , motor stereotypy , and movement disorders , among other symptoms ( Williams et al . , 2006 ) . These patients have a microdeletion of the 15q11-13 region of the maternal chromosome , which spans multiple genes ( Knoll et al . , 1989 ) , of which UBE3A and GABRB3 have been the most extensively studied . Mice lacking maternal Ube3a recapitulate many but not all phenotypes expected for an animal model of Angelman syndrome ( Jiang et al . , 1998; Allensworth et al . , 2011 ) . Mice lacking only the maternal copy of Gabrb3 ( m-/p+ ) , however , also show many of these phenotypes . Regarding cerebellar function , adult Gabrb3 m-/p+ mice display deficits in motor learning as measured by the accelerating rotarod task ( DeLorey et al . , 2011 ) . In the cerebellum , β3 is expressed in Purkinje and granule cells , with low expression in the cerebellar nuclei ( Laurie et al . , 1992; Fritschy and Mohler , 1995; Hörtnagl et al . , 2013 ) . Since β3 slows the kinetics of GABAAR currents in expression systems ( Hinkle and Macdonald , 2003 ) , IPSC kinetics of neurons that express GABAAR β3 are predicted to be accelerated in Gabrb3 m-/p+ mice , which should disinhibit those cells and alter signaling through the cerebellum . Since all such changes must ultimately be funneled through the cerebellar nuclei , we examined synaptic properties of large , likely glutamatergic , premotor CbN neurons in cerebellar slices from wild-type and Gabrb3 m-/p+ mice . Because of a reported sex difference in motor behavior of m-/p+ mice ( DeLorey et al . , 2011 ) , we also compared electrophysiological responses in males and females . These comparisons revealed that , even in wild-type mice , a wide range of basal synaptic and intrinsic properties of CbN cells differ between the sexes . Males and females also respond differently to the Gabrb3 m-/p+ mutation , which triggers an augmentation of mGluR1/5 responses and intrinsic firing rates in male but not female mutant mice , which is apparently compensatory on a motor learning task . Since expression of the β3 subunit is expected to be reduced in Gabrb3 m-/p+ mice , we first tested for direct effects of the mutation on synaptically evoked GABAAR responses . Male and female Gabrb3 m-/p+ mice and littermate controls were considered separately because of sex differences reported in behavioral responses to the mutation ( DeLorey et al . , 2011 ) . Whole-cell recordings were made from large CbN cells in cerebellar slices from P17-P24 mice , and voltage-clamped synaptic currents were evoked with 100-Hz , 200-ms trains of electrical stimuli to evoke Purkinje cell-mediated IPSCs ( Telgkamp and Raman , 2002 ) . Because these stimulus trains necessarily also elicit glutamate release from excitatory mossy fibers , AMPA and NMDA receptors were blocked by 5 µM DNQX and 10 µM CPP . The trains evoked outward synaptic currents dominated by GABAAR-mediated current from Purkinje cell stimulation ( Telgkamp and Raman , 2002; Figure 1A ) . During trains , the synaptic current did not decay fully , so that with the onset of the next stimulus , the additional 'phasic' current , i . e . , synaptic current evoked by release elicited by a single stimulation , summed with the preceding 'tonic' current , i . e . , residual or accumulating synaptic current from previous release events . The tonic component is of interest because it is largely responsible for suppressing intrinsic firing by CbN cells ( Telgkamp and Raman , 2002; Person and Raman , 2012 ) . In wild-type males and females , the phasic components largely overlaid one another ( Figure 1A , B; repeated-measures ANOVA p=0 . 78 ) . In contrast , the synaptic current decayed to a greater extent in females , leading to a tonic component that was more than 3-fold larger in wild-type males than females ( Figure 1A , B , +/+ males n=11 , +/+ females n=10 , p=0 . 005 ) . This result was replicated in C57BL/6 males and females ( Figure 1—figure supplement 1A , B ) , demonstrating that elements of basal synaptic transmission in the CbN of mice differ between the sexes , even in pre-pubertal animals . 10 . 7554/eLife . 07596 . 003Figure 1 . Sex differences in CbN synaptic currents and sex-specific responses to the Gabrb3 m-/p+ mutation . ( A ) 100-Hz trains of synaptic currents evoked in CbN cells from male and female wild-type mice , normalized to the first peak . Dotted line , baseline holding current . ( B ) Mean amplitudes of phasic ( upper panel ) and tonic ( lower panel ) synaptic currents as a percentage of the first peak synaptic current vs . stimulus number . Dotted line , 0 current . ( C , D ) As in A , B , but for cells from male and female m-/p+ mice . ( E ) Top: example IPSCs evoked by a single stimulus , normalized to the peak current . Bottom: Solid symbols: weighted τdecay for IPSCs from a single stimulus . Open symbols: weighted τdecay for the last IPSC in the train . ( F ) Representative blot ( top ) and quantification ( bottom ) for β3 subunit expression in the cerebellar cortex vs . the cerebellar nuclei in C57BL/6 mice . Each symbol represents the normalized value for one lane . ( G ) Representative blot ( top ) and quantification ( bottom ) for normalized β3 subunit protein expression in the cerebellar cortex of Gabrb3 mice . ( H ) Solid symbols: measured tonic current , re-plotted from ( B ) and ( D ) . Open symbols: predicted tonic current , calculated from the weighted τdecay of the first and last IPSCs . Symbol color code as in ( E ) . In all figures , data are plotted as mean ± SEM . Asterisks indicates statistically significant differences . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 00310 . 7554/eLife . 07596 . 004Figure 1—figure supplement 1 . Sex differences in tonic current and IPSC decay kinetics in C57BL/6 mice . ( A ) Phasic current , as a percentage of initial IPSC amplitude , over 20 stimuli for C57BL/6 males ( n=16 ) and females ( n=16 ) . The curves do not differ between males and females ( p=0 . 9 ) . ( B ) Tonic current , as a percentage of initial IPSC amplitude , over 20 stimuli for C57BL/6 males and females . Females have significantly less tonic current than males ( p=0 . 015 ) . ( C ) Weighted decay time constants for IPSCs recorded from CbN cells of males and females for C57BL/6 mice ( closed symbols ) . Males tended to have slower decay times than females ( males 2 . 5 ± 0 . 4 ms , n=11; females 1 . 7 ± 0 . 2 ms , n=9; p=0 . 088 ) . Decay times for C57 males and Gabrb3 +/+ males were not significantly different ( p=0 . 7 ) , nor were decay times for C57 females and Gabrb3 +/+ females ( p=0 . 8 ) . Combining the datasets for sex-matched wild-type cells ( open symbols ) demonstrates that IPSC decay times are significantly faster in females than in males ( males 2 . 4 ± 0 . 1 , n=33; females 1 . 8 ± 0 . 2 , n=21; p=0 . 008 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 00410 . 7554/eLife . 07596 . 005Figure 1—figure supplement 2 . Tonic synaptic current , likely mediated by mGluR1/5 , may be modulated by environmental conditions such as chronic stress . Tonic current , as a percentage of initial IPSC amplitude , for 20 stimuli at 100 Hz for Gabrb3 +/+ male mice before ( control ) and during construction ( construction ) , and C57BL/6 control male mice . Nearby construction caused daily intense vibrations in the facility where mice were housed . Tonic currents from CbN cells of mice born after construction began ( open green triangles , 'construction' ) were significantly smaller than tonic currents from mice born and recorded from before construction began ( black triangles , control , re-plotted from Figure 1 , p=0 . 040 vs . 'construction' ) . C57BL/6J male mice were not bred and housed on site ( black squares ) and had similar tonic currents to control Gabrb3 +/+ mice ( p=0 . 26 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 005 When the experiment was repeated in m-/p+ mice , mutant males showed altered synaptic responses , but mutant females did not , such that both sexes had small tonic currents that resembled those of wild-type females ( Figure 1C , D ) . Consequently , cells from mutant males had a smaller tonic component than wild-type males ( m-/p+ males n=11 , p=0 . 029 ) , but wild-type and mutant females did not differ ( m-/p+ females n=11 , p=0 . 94 ) . Thus , the Gabrb3 m-/p+ mutation leads to a change in tonic synaptic current in males but not in females , eliminating the sex difference present in wild-type animals . To test whether the differences in tonic current arose directly from differences in GABAAR kinetics , we measured the decay kinetics of synaptic currents evoked by single stimuli . The weighted τdecay was 2 . 3 ± 0 . 1 ms in wild-type males ( n=22 ) and 1 . 8 ± 0 . 2 ms in wild-type females ( n=9 , p=0 . 07 ) . Similar values were seen in cells from C57BL/6 mice , and combining the datasets demonstrated that cells from females indeed had significantly slower IPSC decay times than males ( Figure 1—figure supplement 1C , males 2 . 4 ± 0 . 1 , n=33; females 1 . 8 ± 0 . 2 , n=21; p=0 . 008 ) . Despite the fact that GABAAR β3 expression is expected to slow the deactivation of GABAAR currents ( Hinkle and MacDonald 2003 ) , the mean time constants in cells from m-/p+ mice were indistinguishable from those of wild-type , for both males and females ( Figure 1E , solid symbols; m-/p+ males 2 . 5 ± 0 . 2 ms , n=16 , p=0 . 5 vs . +/+; m-/p+ females 1 . 8 ± 0 . 2 ms , n=6 , p=1 . 0 vs . +/+ ) . This similarity suggests that the kinetics of evoked synaptic currents in large CbN cells do not depend strongly on GABAAR β3 , possibly because Gabrb3 m-/p+ does not actually result in reduced cerebellar expression , or because the GABAAR β3 subunit is not strongly expressed in the cerebellar nuclei even in wild-type mice . We therefore measured β3 subunit expression in CbN tissue in C57BL/6 male and female mice , and compared it to β3 subunit expression in the cerebellar cortex . Indeed , β3 subunit expression was relatively low in the CbN; β3 expression in the cerebellar cortex was about twice that in the cerebellar nuclei , in both males and females ( Figure 1F , males , n=2 , p=0 . 02 , females , n=2 , p=0 . 004 ) . These data corroborate previous results from immunostaining and in situ hybridization that report cerebellar GABAAR β3 expression to be primarily in granule cells and Purkinje cells ( Laurie et al . , 1992; Fritschy and Mohler , 1995; Hörtnagl et al . , 2013 ) . Next , to test whether GABAAR β3 levels are detectably reduced in Gabrb3 m-/p+ mice , we compared expression in the cerebellar cortex across all four groups of mutants and wild-type siblings of both sexes . Consistent with a gene dosage effect , loss of the maternal Gabrb3 allele significantly reduced GABAAR β3 expression to about half in both sexes ( Figure 1G , males , n=3 , p=0 . 001; females , n=3 , p=0 . 005 ) . In wild-type animals , expression in the cerebellar cortex was indistinguishable in males and females , in both Gabrb3 sibling controls ( n=3 , p=0 . 21 ) and in C57BL/6 mice ( n=6 , p=0 . 37 ) . GABAAR β3 expression in the cerebellar nuclei was also significantly reduced in mutant males and females ( p≤0 . 001 , both sexes ) . Thus , the lack of changes in IPSC decay times in CbN cells of Gabrb3 m-/p+ mice is more likely to result from sparse GABAAR β3 expression in wild-type mice , rather than a failure of the mutation to reduce expression . Moreover , the similarity of IPSC decay times for sex-matched wild-type and mutant CbN cells suggests that factors other than GABAAR decay kinetics must account for differences in tonic currents between wild-type and mutant males . To test the extent to which IPSC decay times were predictive of tonic currents , we calculated the expected tonic current based on IPSC decay times and compared it to the measured tonic current . The decay time constants , however , lengthened during the train , such that the last IPSC was significantly longer than the first for wild-type males ( Figure 1E , open symbols; weighted τdecay for last IPSC , +/+ males , 4 . 4 ± 0 . 7 ms , n=10 , p=0 . 013 vs . 1st IPSC; m-/p+ males , 3 . 2 ± 0 . 8 ms , n=11 , p=0 . 36; +/+ females , 2 . 3 ± 0 . 3 ms , n=10 , p=0 . 21; m-/p+ females , 3 . 1 ± 0 . 8 ms , n=11 , p=0 . 17 ) . Although the basis for this change is unknown , it is consistent with a decreasing synchrony of neurotransmitter release time as the train progresses , e . g . , associated with changing action potential waveforms ( Bischofberger et al . , 2002 ) . We therefore incorporated this gradual change in τdecay into our calculations ( Materials and Methods ) and estimated the tonic current for each group . As shown in Figure 1H , for wild-type males , the predicted and measured currents were closely matched ( solid and open black triangles ) , indicating that IPSC kinetics are sufficient to account for the tonic synaptic current . For mutant males and both female groups , however , the IPSC decay times consistently predicted a tonic current that is smaller than in wild-type males but still larger than the measured values ( open vs . solid symbols ) , suggesting an additional factor either reducing the total outward tonic current or contributing a tonic inward current . One possibility arose from the fact that in the experiments of Figure 1 , only fast excitatory transmission was blocked . Previous work , however , shows that glutamate release from mossy fibers can activate group I metabotropic glutamate receptors ( mGluR1/5 ) in CbN neurons , which in turn can increase tonic inward currents through multiple mechanisms ( Zhang and Linden , 2006; Zheng and Raman , 2011 ) . We therefore tested the effect of CPCCOEt ( 100 µM ) , which effectively blocks group I mGluRs in CbN cells in a manner that is mimicked by a combination of the selective mGluR1 antagonist JNJ16259685 and mGluR5 antagonist MPEP ( Zheng and Raman , 2011 ) . When trains of IPSCs were evoked , CPCCOEt increased the net outward tonic current , consistent with activation of group I mGluRs in CbN cells of wild-type and mutant mice of both sexes ( +/+ males n=11 , females n=11; m-/p+ males n=12 , females n=9 , p<0 . 001 ) . Importantly , CPCCOEt also eliminated the sex difference between wild-type male and female tonic currents ( Figure 2A , B , p=0 . 93 ) . Additionally , the tonic currents in mutant cells were indistinguishable from those of wild-type cells ( Figure 2C , D , males , p=1 . 0; females , p=0 . 93 ) , suggesting that the observed sex difference in wild-type synaptic currents results from a larger mGluR1/5 response in females than in males . The difference in tonic current in control and CPCCOEt ( Figure 2E ) provides a measure of the differential contribution of group I mGluRs . Thus , the Gabrb3 m-/p+ mutation increases the mGluR1/5 response in males , but not in females . 10 . 7554/eLife . 07596 . 006Figure 2 . Group I mGluRs account for differences in synaptic currents and responses to the Gabrb3 m-/p+ mutation . ( A ) Synaptic currents in the presence of CPCCOEt evoked in CbN cells from male and female wild-type mice . Dotted line , baseline holding current . ( B ) Mean amplitudes of phasic ( upper panel ) and tonic ( lower panel ) synaptic currents recorded in CPCCOEt as a percentage of the peak current evoked by the first stimulus in each train vs . stimulus number . Control data from Figure 1 are superimposed for comparison . Dotted line , 0% current . ( C , D ) As in B , but for cells from male and female m-/p+ mice . ( E ) Percent difference in tonic current ± CPCCOEt for each group , calculated from the difference between the mean tonic current for stimuli 5–10 in control and CPCCOEt solutions . ( F ) Solid symbols: weighted τdecay for each group in CPCCOEt . Open symbols: weighted τdecay from the last IPSC of the train , in CPCCOEt . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 006 Phasic current , in contrast , was statistically indistinguishable in CPCCOEt relative to control solutions ( Figure 2D , p=0 . 12 ) . CPCCOEt , however , did consistently prolong the time course of decay of a single IPSC for all four groups ( p<0 . 001 , in CPCCOEt , +/+ males n=13 , +/+ females n=6 , m-/p+ males n=10 , m-/p+ females n=6 , Figure 2F ) . This effect on even a single stimulus suggests that group I mGluRs may be basally activated in a manner that shortens synaptic currents , e . g . , by inhibition of presynaptic Ca currents ( Xu-Friedman and Regehr , 2000 ) . Next , we examined the mGluR1/5 current directly . In these experiments synaptic inhibition was blocked by SR95531 ( 10 µM ) and strychnine ( 10 µM ) , and NMDA receptors were blocked by CPP ( 10 µM ) . After a recording was established , the excitatory amino acid transporter blocker dl-TBOA ( 50 µM ) was added to the bath . TBOA increases glutamate spillover ( Brasnjo and Otis , 2001; Huang et al . , 2004 ) and augments the evoked responses of group I mGluRs in the CbN of ~2-week-old rats ( Zhang and Linden , 2006 ) . In the present experiments , however , application of TBOA had a striking effect even in the absence of synaptic stimulation: it evoked a large standing current of a few hundred pA at -40 mV , which was largely reversed by group I mGluR antagonists . The remaining current was not further altered by a return to control solutions ( Figure 3A ) . Consistent with an action at mGluR1/5 , the magnitude of the effect of CPCCOEt ( n=10 ) was indistinguishable from that of combined JNJ16259685 ( 0 . 2 µM ) and MPEP ( 40 µM , n=29 , p=0 . 63 ) and the data were pooled . To test whether the effect of TBOA resulted from glutamate released in an action-potential-dependent manner , 1 µM TTX was added to the bath in a subset of experiments . The TBOA-induced current was indeed reversed by TTX ( Figure 3B , C57BL/6 male mice; control -305 ± 99 pA; TBOA -570 ± 139 pA , p=0 . 033 vs . control; TBOA+TTX -338 ± 43 pA , p=0 . 2 vs . control ) , and the variance of the standing current was reduced . The data are consistent with the idea that spontaneously firing nearby cells continuously release glutamate . In the presence of TBOA , the released glutamate reaches and activates group I mGluRs on CbN cells , which in turn couple to other channels; at this voltage , the inward current is likely carried primarily through L-type Ca channels ( Zheng and Raman , 2011 ) . 10 . 7554/eLife . 07596 . 007Figure 3 . Differences in mGluR1/5-dependent currents depend on glutamate access to receptors rather than receptor expression . ( A ) Increase in holding current in a +/+ male mouse by TBOA reversal by group I mGluR antagonists . ( B ) Increase in holding current by TBOA reversal by TTX . Top: sample traces from a C57BL/6 male mouse . Bottom: summary data for current amplitude ( left ) and variance ( right ) . ( C ) Voltage ramp ( top ) and current ( bottom ) for a +/+ male in control , TBOA , and TBOA + mGluR1/5 antagonist ( s ) . Each current is the mean of three traces . ( D ) Transporter-induced current ( control current minus current in TBOA ) vs . voltage . ( E ) TBOA-induced current sensitive to mGluR1/5 antagonist ( s ) ( current in TBOA minus current in TBOA + mGluR1/5 antagonist ) , vs . voltage . In all figures , 'm1/5 ant . ' indicates either CPCCOEt or JNJ16259685 + MPEP . ( F ) Representative blot ( top ) and summary ( bottom ) for normalized mGluR1 protein expression in the CbN of Gabrb3 mice . ( G ) As in ( F ) , for mGluR5 . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 007 Unlike the evoked currents measured in Figures 1 and 2 , however , blocking glutamate transport revealed an increase in mGluR1/5-dependent current amplitude at -40 mV that was , on average , larger in wild-type males than in wild-type females ( Figure 3A , +/+ males -297 ± 67 pA , n=9; +/+ females -184 ± 64 pA , n=9 , p=0 . 08 ) . Furthermore , there was no difference between sex-matched mutant vs . wild-type groups ( m-/p+ males -419 ± 99 pA , n=11 , p=0 . 5 vs . +/+; m-/p+ females -184 ± 65 , n=9 , p=0 . 9 vs . +/+ ) . These results suggest that the differences in evoked mGluR1/5-dependent currents do not necessarily result from fewer receptors or diminished properties of downstream targets in wild-type males . Instead , the reduced response to evoked release in wild-type males may stem from the accessibility of glutamate to mGluRs when transport is intact , i . e . , owing to differences in the efficacy of uptake or proximity of receptors . Moreover , the Gabrb3 m-/p+ mutation does not apparently alter the maximal mGluR1/5 response , as measured with transporter blockers , in males or females . To examine the voltage-dependence of the mGluR1/5-dependent current , we applied a voltage ramp to CbN cells from all four groups of Gabrb3 mice . From an initial holding potential of -40 mV , the voltage was stepped to -20 mV for 100 ms and then ramped down to -80 mV over 250 ms . The ramp revealed a standing inward current that increased in amplitude until about -35 mV , decreased from -35 mV to -50 mV , and then increased monotonically until the end of the ramp ( Figure 3C ) . This current profile reflects what has previously been identified as an L-type voltage gated Ca current ( Zheng and Raman , 2011 ) and a cationic leak current ( Zhang and Linden , 2006; see also Raman et al . , 2000 ) . Both currents are active during the initial depolarization , and both increase in amplitude as the voltage is ramped down and the driving force increases . With further hyperpolarization , however , the L-type current deactivates , decreasing the current amplitude , until only the leak current remains . Application of TBOA increases the current amplitude between -80 and -35 mV in all four groups , consistent with both currents being potentiated by mGluR1/5 activation ( Figure 3D ) . The magnitude of the current increase varied widely across cells , and differences were not statistically significant despite different mean values ( repeated-measures ANOVA p=0 . 66 , males n=10 , females n=8 ) . Likewise , among Gabrb3 mutants , neither male nor female mutants were statistically different from wild-type mice ( males p=0 . 92 , n=11; females p=0 . 92 , n=9 ) . Group I mGluR antagonists partly blocked the TBOA-induced current increase in each group . Wild-type males and females showed a similar response to application of mGluR1/5 antagonists ( Figure 3E , +/+ males vs . females p=0 . 99 ) . In fact , mGluR1/5 antagonists blocked little TBOA-induced current evoked below -50 mV , a voltage-dependence consistent with that of L-type Ca channels ( Zheng and Raman , 2011 ) . Mutant females were indistinguishable from wild-type females ( p=0 . 96 ) , but mutant males showed a trend toward more current that was sensitive to mGluR1/5 antagonists ( p=0 . 086 vs . +/+ males ) . In mutant males , the TBOA-induced current was blocked by mGluR1/5 antagonists at a wider range of voltages than the other groups , suggesting that in these mice transporter blockade also increased the leak current in an mGluR1/5-dependent manner . Since the sex- and mutation-specific differences in mGluR1/5-dependent current are largely eliminated by blockade of glutamate uptake , then they may be independent of differences in group I mGluR expression . We tested this idea with Western blots for both mGluR1 and mGluR5 in the cerebellar nuclei of wild-type mice ( both Gabrb3 sibling +/+ and C57BL/6 ) and Gabrb3 m-/p+ mice of both sexes . Indeed , neither mGluR1 nor mGluR5 protein levels differed significantly between wild-type males and females , or between wild-type males and mutant males ( Figure 3F , G ) . Expression of mGluR1 did not differ between any of the four Gabrb3 groups ( n=3 , one-way ANOVA p=0 . 61 ) ; the lack of a detectable sex difference was confirmed in C57BL/6 wild-type mice ( n=6 , p=0 . 61 ) . Likewise , expression of mGluR5 did not differ among any of the Gabrb3 groups , n=3 , p=0 . 82 ) or between C57BL/6 males and females ( n=6 , p=0 . 19 ) . In conjunction with the electrophysiological data in TBOA , these results support the idea that the smaller evoked mGluR1/5-dependent currents observed in wild-type males , relative to wild-type females or mutant males , arise largely from access of glutamate to group I mGluRs . In the same cells , we examined evoked excitatory synaptic transmission and its alteration by TBOA . Afferents to CbN cells were stimulated at 100 Hz , in SR95531 , strychnine , and CPP , as above . AMPARs were left unblocked so that AMPAR-mediated EPSCs could serve as evidence that glutamate release was evoked . The initial peak EPSC was decreased in TBOA by about 50% in all groups but wild-type females ( +/+ males p=0 . 008 , n=7; m-/p+ males p=0 . 047 , n=7; +/+ females p=0 . 65 , n=6; m-/p+ females p=0 . 006 , n=8 ) . The EPSC amplitude was not further changed by application of group I mGluR antagonists , however ( Figure 4A , B , vs . TBOA , +/+ males p=0 . 18 , m-/p+ males p=0 . 97 , +/+ females p=0 . 27 , m-/p+ females p=0 . 66 ) , suggesting that glutamate accumulation induced by TBOA may decrease EPSC amplitude by acting at other receptors , e . g . , group II/III mGluRs , or by directly desensitizing AMPA receptors . Additionally , attributes of these targets differ between the sexes , as well as between wild-type and mutant females . 10 . 7554/eLife . 07596 . 008Figure 4 . Group I mGluR-dependent and -independent effects of TBOA on evoked EPSCs . ( A ) Sample traces from a CbN cell from a m-/p+ male of EPSCs elicited by 100 Hz stimulus trains , in control , TBOA , and TBOA + mGluR1/5 antagonist ( s ) , normalized to the peak of the first EPSC . ( B ) First EPSC amplitude in control , TBOA , and TBOA + mGluR1/5 antagonist ( s ) . Color code indicates statistical differences , 'ns , ' non-significant . ( C ) EPSC amplitude for EPSCs evoked at 100 Hz in control , TBOA , and TBOA + mGluR1/5 antagonist ( s ) . ( D ) The steady-state amplitude , measured as the mean of the last 5 EPSCs normalized to the first EPSC , for males ( left ) and females ( right ) in control , TBOA , and TBOA + mGluR1/5 antagonist ( s ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 008 The total amplitude of train-evoked EPSCs in these experiments was composed of summed AMPAR and mGluR responses , and increases in amplitude relative to the first EPSC could result from changes in either current . When normalized to the initial amplitude , EPSCs from all four groups depressed by about 60% ( Figure 4C , D ) by the end of the train . In wild-type males , however , TBOA reduced synaptic depression ( +/+ males , control 38 ± 7% , TBOA 52 ± 7% , p=0 . 012 ) , and , unlike the change in initial EPSC amplitude , the effect of TBOA on synaptic depression was blocked by group I mGluR antagonists ( +/+ males with antagonist 42 ± 6% , p=0 . 002 ) . This observation lends support to the idea that the lack of detectable evoked mGluR1/5-dependent current in wild-type males depends on intact glutamate transport . The results in mutant males resembled those of wild-type males , in that mean synaptic depression was reduced in TBOA , but responses were more variable across cells , occasionally even showing facilitation ( m-/p+ males , control 49 ± 7% , TBOA 70 ± 16% , p=0 . 14 ) . In mGluR1/5 antagonists , currents were restored to control values ( m-/p+ males , with antagonist 55 ± 7% p=0 . 20 vs . control ) . In contrast , in both female groups , TBOA had no detectable effect on synaptic depression ( +/+ females p=0 . 81 , m-/p+ p=0 . 22 ) . Thus , although many variables interact to generate the profiles of currents , the data are consistent with sex-specific differences in mGluR currents activated when glutamate transporters are blocked: ( 1 ) an mGluR1/5-independent decrease in initial evoked EPSC amplitude , evident in both male groups as well as in mutant females , and ( 2 ) an mGluR1/5-dependent , sex-specific decrease in synaptic depression in males only , which is relatively unaffected by the Gabrb3 mutation . The observation of sex differences in basal synaptic properties , as well as the unanticipated link between the loss of an allele encoding a GABAAR subunit and changes in group I mGluR-mediated responses , raises the possibility that other aspects of excitability also differ between the sexes and/or are differentially regulated by the mutation . We therefore measured spontaneous firing rates in CbN cells , with fast excitatory transmission blocked by 5 µM DNQX and 10 µM CPP . Indeed , CbN cells fired more slowly in wild-type males ( 65 ± 7 spikes/s , n=14 ) than in females ( 98 ± 9 spikes/s , n=16; p=0 . 02 ) , again reflecting a sex difference in basic CbN cell properties ( Figure 5A , B ) . CbN cells from mutant males tended to fire somewhat faster than from wild-type males ( 92 ± 9 spikes/s , n=11 , p=0 . 08 ) , whereas cells from mutant females had the same firing rates as those from wild-type females ( 96 ± 10 spikes/s , n=20 , p=0 . 9 ) . Action potential half-widths , however , were indistinguishable among all four groups ( Figure 5B , p=0 . 3 ) . Thus , the sex differences in basal cerebellar physiology and in response to the Gabrb3 m-/p+ mutation may extend beyond synaptic activation of mGluRs . These differences in CbN cell spontaneous rates mirror the differences in synaptic responses , raising the possibility that chronic activation of group I mGluRs may contribute a depolarizing current that affects firing . The experiments of Figure 3 , however , demonstrated that holding currents at -40 mV were not different across the four groups of mice ( +/+ males -565 ± 40 pA; m-/p+ males -491 ± 59 pA; +/+ females -406 ± 83 pA; m-/p+ females -424 ± 49 pA , p=0 . 26 ) . It therefore seems unlikely that the slower firing rates in wild-type males arose from mGluR1/5 activation , and instead are more likely to reflect differences in intrinsic currents . 10 . 7554/eLife . 07596 . 009Figure 5 . Sex differences in spontaneous firing rates and evoked rebound firing , and sex-specific responses to the Gabrb3 m-/p+ mutation . ( A ) Sample traces of spontaneous action potentials in CbN cells . ( B ) Action potential half-width vs . spontaneous firing rate for all cells from all groups . Open symbols , individual cells; closed symbols , mean values . ( C ) Action potentials in a CbN cell , interrupted by a 500-ms , 100-Hz net inhibitory stimulus train , before and after application of CPCCOEt , for wild-type and m-/p+ CbN cells from males . To facilitate comparison , pre-stimulus firing was kept near 40 Hz ( below the spontaneous rate ) with constant holding current . ( D ) Difference between post- and pre-stimulus rates as a percentage of the pre-stimulus rate , before and after application of CPCCOEt for CbN cells from males ( left ) and females ( right ) . ( E ) The percent increase in rate that depends on mGluR1/5 , calculated from cell-by-cell differences between rate increases ± CPCCOEt in C , for males ( left ) and females ( right ) . Dotted lines , 0% . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 009 In addition to reducing the time course of IPSCs , counteracting tonic inhibition , and altering excitatory synaptic depression , group I mGluRs may influence the output spiking of CbN cells directly . Specifically , after periods of spike suppression either by hyperpolarization or by trains of IPSCs , CbN neurons have been shown to increase their firing above their intrinsic rates for a few hundred milliseconds . This phenomenon of 'prolonged rebound firing' depends partly on recovery of intrinsic conductances ( Aman and Raman , 2007; Sangrey and Jaeger , 2010; Tadayonnejad et al . , 2010 ) and partly on mGluR1/5 potentiation of L-type Ca currents ( Zheng and Raman , 2011 ) . If the increase in train-evoked mGluR1/5-dependent current observed in neurons from male mutants and all female mice leads to a greater potentiation of L-type currents , the extent of CPCCOEt-sensitive prolonged rebound firing should be greater in neurons from these animals than from wild-type male mice . We therefore evoked prolonged rebound firing before and after application of CPCCOEt in CbN cells from wild-type and Gabrb3 m-/p+ mice . In CPP and DNQX , CbN cells were current-clamped and enough hyperpolarizing holding current was applied to reduce regular intrinsic firing to a frequency near 40 Hz . When 100-Hz , 500-ms stimulus trains were applied , firing either slowed or ceased , consistent with the outward currents seen in voltage clamp . In all cells , firing rates for the 300 ms just after the stimulus were faster than firing rates during the 500 ms just before the stimulus ( Figure 5C ) . To quantify these changes and account for small differences in the pre-stimulus baseline firing rates across cells , we calculated the percent change in rate from the ratio of the post-stimulus to the pre-stimulus rate ( Figure 5D ) . Consistent with an upregulation of mGluR1/5 responses , the firing rate acceleration in CbN cells was greater in m-/p+ than wild-type males . Moreover , in the same cells , this difference disappeared when group I mGluRs were blocked . In females , cells from both wild-type and mutant mice showed prolonged rebound firing that was reduced on average by CPCCOEt . To quantify the contribution of group I mGluRs , the difference in the percent change in control and CPCCOEt solutions was calculated ( Figure 5E ) . This mGluR1/5-dependent increase in prolonged rebound firing did not differ between female wild-type and m-/p+ mice ( +/+ 39 ± 35% , m-/p+ 25 ± 22% , p=0 . 74 ) . In contrast , in CbN cells from wild-type males , only 7 ± 14% of the firing rate increase after inhibition can be attributed to group I mGluRs , while for cells from m-/p+ males , 53 ± 13% is CPCCOEt-dependent ( p=0 . 03 ) . This small effect of CPCCOEt on prolonged rebound firing in wild-type males correlates with the low magnitude of evoked mGluR1/5-dependent current in these animals . The larger CPCCOEt effect in mutant males is consistent with the idea that the upregulation of the mGluR1/5 response indeed generates a larger potentiation of L-type currents . In females , a CPCCOEt effect was evident in both wild-type and mutant mice , consistent with the voltage-clamp measurements , but responses were generally more variable than in males , suggesting that factors in addition to mGluR1/5 activation may affect prolonged rebound firing in females . The sex differences both in basal synaptic physiology of cerebellar output neurons and in their responses to the Gabrb3 mutation raise the question of whether and how these differences are manifested in cerebellar behavior . Since several genetic disruptions that alter cerebellar output have been demonstrated to change performance on the accelerating rotarod ( Caston et al . , 1995; Lalonde et al . , 1995; Gerlai et al . , 1996; Levin et al . , 2006; Galliano et al . , 2013 ) , we tested mice from all four groups on this motor task . Experiments were done on P22 mice ( on day 1 of training ) to permit valid correlations of behavior with electrophysiological data . Notably , despite the differences in basal physiology , wild-type males and females performed the task equivalently ( p=0 . 21 ) . This observation demonstrates that the larger mGluR1/5 responses and faster firing rates seen in CbN cells of females do not directly translate to enhanced rotarod performance per se , instead illustrating that wild-type brains of different sexes may use different synaptic and cellular mechanisms to achieve a common behavioral output ( De Vries , 2004 ) . Comparing the responses of mutant mice to sex-matched controls showed that mutant males performed indistinguishably from wild-type male siblings: both groups remained on the rotarod for a similar duration on Day 1 ( +/+ 102 ± 14; m-/p+ 101 ± 10 s , p=0 . 9 , for all groups n=8 ) and improved their performance by Day 7 ( +/+ 142 ± 11; m-/p+ 133 ± 12 s; change over training assessed by repeated measures ANOVA , +/+ , p=0 . 02; m-/p+ , p=0 . 03 ) . In contrast , female m-/p+ mice tended to remain on the rod for a longer time on Day 1 than wild-type siblings ( +/+ 78 ± 6; m-/p+ 135 ± 11 s , p=0 . 001 ) , and their performance remained relatively constant over 7 days , unlike wild-type females , whose latency to fall increased ( Day 7 latency , +/+ 130 ± 13; m-/p+ 116 ± 13 s; repeated measures ANOVA , +/+ , p<0 . 00; m-/p+ , p=0 . 3 , Figure 6A ) . Given the long but replicable latency to fall on Day 1 of female mutants , the lack of increase in fall latency over training indicates an enhanced initial ability coupled with either a failure to improve , or a saturation of performance that precludes further improvement . A similarly enhanced performance by naïve mice has been reported previously for other mutations ( Vitali and Clarke , 2004 ) . 10 . 7554/eLife . 07596 . 010Figure 6 . Performance on the accelerating rotarod varies with sex and Gabrb3 and depends on GABAAR β3 expression only in Purkinje cells . ( A ) Latency to fall vs . training day for Gabrb3 P22 males ( left ) and females ( right ) . ( B ) Change in latency , calculated as the difference between the Day-1 fall latency and mean Day 5–7 fall for all four groups . Symbol color code as in ( A ) . ( C ) Latency to fall vs . training day for P22 Purkinje-specific Gabrb3 P22 males ( left ) and females ( right ) . ( D ) Change in latency , calculated as the difference between the Day-1 fall latency and mean Day 5–7 fall for Purkinje-specific Gabrb3 mice . Symbol color code as in ( C ) . ( E ) Change in the rotation rate ( in rpm ) at which the mouse fell on Day-1 vs . mean Day 5–7 for global Gabrb3 mice ( closed symbols ) and Purkinje-specific Gabrb3 mice ( open symbols ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 01010 . 7554/eLife . 07596 . 011Figure 6—figure supplement 1 . Mouse weight does not account for differences in motor learning . Weight of Gabrb3 mice ( closed symbols ) and Purkinje-specific Gabrb3 mice ( open symbols ) on or immediately after Day 7 of rotarod testing . For Gabrb3 mice , females were lighter than males ( +/+ males 16 . 5 ± 0 . 7 g , +/+ females 11 . 6 ± 0 . 6 g , p<0 . 001 ) , but mutant males weighed the same as wild-type males ( m/p+ males 17 . 5 ± 0 . 5 g , p=0 . 61 vs . +/+ males ) , and mutant females weighed the same as wild-type females ( m/p+ females 13 . 2 ± 0 . 4 g , p=0 . 23 vs . +/+ females ) . No differences in weight were found between Purkinje-specific Gabrb3 groups ( p=0 . 49 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 01110 . 7554/eLife . 07596 . 012Figure 6—figure supplement 2 . Differences in performance on rotarod change over development . ( Top: ) Average latency to fall on Day 1 for all four age groups for males ( left ) and females ( right ) . Only m-/p+ females showed variable day 1 latencies throughout development . ( Bottom: ) Average Δ latency for all four age groups for males ( left ) and females ( right ) . Mutant males showed a decreasing Δ latency over development , whereas mutant females showed a increasing Δ latency over development . For all groups ( sex , mutation , age ) , n=8 . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 01210 . 7554/eLife . 07596 . 013Figure 6—figure supplement 3 . Selection of rotarod protocols that permit an increase in fall latency over training for transgenic mice on a 129S background . ( A ) Change latency to fall on the accelerating rotarod for Purkinje-specific Gabrb3 mice , age P22 , subjected to 2 daily trials with the rod accelerating to 30 rpm over 4 min . None of the four groups increased their fall latency over the course of 7 days . ( B ) Latency to fall for individual Purkinje-specific Gabrb3 +/+ male and female mice on the accelerated rotarod , where the rate of acceleration was reduced to 25 rpm over 4 min , and the number of daily trials was increased to 5 . This alteration in the protocol did not result in an increased fall latency on the accelerated rotarod task . ( C ) Latency to fall on the accelerating rotarod on Day 1 across various acceleration protocols . Mice from all groups are pooled . The rate of acceleration for each protocol is listed in the table ( right ) . The highest acceleration gave the shortest initial latency to fall , but made it possible for mice to improve with training . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 013 To assess differences between mutant and wild-type mice of the same sex , we quantified the change of each group over the course of training ( Δ latency ) by subtracting the initial latency to fall on Day 1 from that of the final 'steady-state' latency , given as the mean latency on Days 5–7 , and compared this value between the relevant groups . Wild-type and mutant males performed similarly , with Δ latencies of 46 ± 17 s and 43 ± 12 s , respectively ( p=0 . 9 ) . In females , however , wild-type mice increased their latency by 71 ± 13 s , while the latency of mutant mice decreased by 8 ± 11 s ( Figure 6B , p<0 . 001 ) . The differences observed in rotarod performance was independent of body mass , as mutant females weighed the same as wild-type females ( Figure 6—figure supplement 1 , p=0 . 23 ) . In weanling mice , therefore , the Gabrb3 m-/p+ mutation had no discernible effect on rotarod performance in males , but altered it in females . Previous work ( DeLorey et al . , 2011 ) has shown the opposite results in adult mice ( 10–35 weeks old ) : m-/p+ males , but not females , fail to increase their latency to fall . We therefore tested older mice , which confirmed that the effect of the mutation varies with age as well as sex , with mutant male performance degrading in adult animals , and adult mutant female performance resembling that of wild-type ( Figure 6—figure supplement 2 ) . The present behavioral data therefore suggest that the mutation-dependent electrophysiological changes in weanling males were likely to be part of an effective compensation , preventing them from displaying behavioral abnormalities . Interestingly , however , the same mutation , which led to fewer , more variable changes in the measured electrophysiological parameters , altered rotarod behavior in females , suggesting that changes in other cerebellar signals or effects elsewhere in the brain ultimately produced a deviation from normal . Nevertheless , with a global mutation , the observed changes in behavior cannot be directly attributed to changes in synaptic input to the cerebellar nuclei . Therefore , based on the observation that GABAAR β3 is expressed more strongly in the cortex than the nuclei ( Figure 1G ) , we repeated the tests of rotarod performance in mice lacking maternal Gabrb3 in Purkinje cells only ( referred to as 'Purkinje-specific mutant' or 'Pkj m-/p+' , and wild-type siblings as 'Pkj +/+' ) . These mice , however , were of a different strain than the global mutants; the former were bred on a 129S background whereas the latter were on a C57BL/6 background . Subjecting Pkj +/+ mice to the acceleration protocol used previously ( 30 rpm over 4 min ) did not result in a prolonged latency to fall ( Figure 6—figure supplement 3A ) , consistent with reported observations that 129S mice are generally less capable of motor learning than C57BL/6 ( Kelly et al . , 1998; Homanics et al . , 1999; Contet et al . , 2001; Võikar et al . , 2001 ) . We therefore tested a series of acceleration protocols to identify one in which wild-type mice on a 129S background improved their performance over the duration of training ( Figure 6—figure supplement 3B ) . Like the global mutants , Purkinje-specific mutant males did not differ from wild-type males ( Δ latency for Pkj +/+ males 12 ± 4 s , n=10; Pkj m-/p+ males 8 ± 3 s , n=11; p=0 . 40 ) , while Purkinje-specific mutant females had a significantly reduced Δ latency when compared to wild-type females ( Δ latency Pkj +/+ females 16 ± 4 s , n=10; Pkj m-/p+ females -1 . 4 ± 4 s , n=9; p=0 . 004 , Figure 6C , D ) . To directly compare the performance of each group in the Purkinje-specific Gabrb3 experiment with the corresponding group in the global Gabrb3 experiment , despite the different acceleration speeds of the two protocols , we plotted the change in performance ( days 5–7 minus day 1 ) as the change in revolutions per minute ( Δ rpm ) at the time of fall ( Figure 6E ) . The results indicate that the Δ rpm across all four groups ( wild-type and mutant males and females ) for Purkinje-specific Gabrb3 mice strongly resembles that of the global Gabrb3 mutation . Thus , disrupting Gabrb3 expression in Purkinje cells alone is sufficient to cause the behavioral alterations observed in mutant females , but not males . The question remains , however , whether loss of the maternal allele only in Purkinje cells is sufficient to elicit the electrophysiological changes observed in the global mutants . We therefore , repeated the experiment of Figure 1A–D in the Purkinje-specific Gabrb3 mice . As before , a basal sex difference was evident , as the tonic outward current produced by a 100-Hz train stimuli was higher in wild-type males than in wild-type females ( Figure 7A-7D , tonic current for last IPSC , Pkj +/+ males 7 . 3 ± 2 . 5% , n=8; Pkj +/+ females 1 . 5 ± 2 . 1% , n=9; repeated measures ANOVA p=0 . 06 ) . In addition , the Purkinje-specific mutant males had less tonic current than wild-type males ( Pkj m-/p+ males 0 . 04 ± 1 . 5% , n=8 , p=0 . 02 vs . Pkj +/+ ) , consistent with an upregulation of mGluR1/5-dependent current . In contrast , wild-type and mutant females had similar levels of tonic current ( Pkj m-/p+ females -2 . 8 ± 2 . 0% , n=7 , p=0 . 14 vs . Pkj +/+ ) . In addition , measurements of IPSC decay kinetics were similar in the Purkinje-specific and global mutants . IPSCs in CbN cells on the 129S background were again slower in wild-type males than in wild-type females ( Figure 7E , Pkj +/+ males τdecay = 3 . 1 ± 0 . 6 ms , n=6; Pkj +/+ females 1 . 8 ± 0 . 2 ms , n=7 , p=0 . 02 ) , and neither mutant males nor mutant females differed from their sex-matched controls ( Pkj m-/p+ males 2 . 2 ± 0 . 6 ms , n=3 , p=0 . 19 vs . Pkj +/+; Pkj m-/p+ females 1 . 5 ± 0 . 2 ms , n=5 , p=0 . 61 vs . Pkj +/+ ) . Together , the data illustrate that the Purkinje-specific mutation reproduces the enhanced tonic inward current in mutant males but not females , as well as the cerebellar behavioral alteration in mutant females but not males . These results support the hypothesis that the upregulation of evoked mGluR1/5-dependent current in CbN cells of mutant males arises indirectly from the reduced GABAAR β3 subunit in the cerebellar cortex , and strongly suggest that this upregulation serves a compensatory role in cerebellar motor learning tasks . 10 . 7554/eLife . 07596 . 014Figure 7 . Differences in CbN cell tonic current depend on GABAAR β3 expression only in Purkinje cells . ( A ) 100-Hz trains of synaptic currents evoked as in Figure 1 in CbN cells from Purkinje-specific Gabrb3 +/+ and m-/p+ male mice , normalized to the first peak . Dotted line , baseline holding current . ( B ) Mean amplitudes of tonic synaptic currents as a percentage of the first peak evoked current vs . stimulus number for cells from Pkj +/+ and Pkj m-/p+ male mice . Dotted line , 0% current . ( C , D ) As in A , B , but for cells from +/+ and m-/p+ female mice . ( E ) Solid symbols: weighted τdecay for IPSCs from a single stimulus for each group of CbN cells from Purkinje-specific Gabrb3 mice . Open symbols: weighted τdecay for the last IPSC in the train . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 014 The reduced expression of GABAAR β3 leads to an upregulation of evoked mGluR1/5-dependent inward currents in large premotor cells of the CbN in male but not female mutant mice . Such indirect effects of reduced GABAAR β3 expression could be an exacerbation of or a compensation for disrupted synaptic physiology elsewhere in the circuit . Since the GABAAR β3 subunit is expected to prolong IPSCs ( Hinkle and Macdonald , 2003 ) in cells that express it , i . e . , Purkinje and cerebellar granule cells ( Figure 1F; Laurie et al . , 1992; Fritschy and Mohler , 1995; Hörtnagl et al . , 2013 ) , reduced GABAAR β3 expression in m-/p+ mice is predicted to disinhibit granule and Purkinje cells during behaviors that normally engage cerebellar cortical interneurons . The simplest mode of counterbalancing the resulting elevation of inhibition to CbN cells would be by increasing the net excitatory drive and/or raising intrinsic excitability . Indeed , evidence for both changes were observed in male m-/p+ mice . The larger mGluR1/5-dependent currents evoked by stimulation of excitatory afferents serve to counteract much of the tonic outward current elicited by concurrent stimulation of inhibitory afferents , and the increased activation of group I mGluRs is sufficient to raise excitability , as evident by the facilitation of prolonged rebound firing relative to wild-type cells . In addition , the increase in spontaneous firing rates in mutant males , which occurs without a measurable increase in holding current when cells are voltage-clamped , suggests that one or more intrinsic ion channels may also be modulated by the mutation . Independently of mechanism , the observed increase in mGluR1/5-dependent current and intrinsic excitability in male Gabrb3 m-/p+ CbN cells is well suited to compensate for the predicted increase in inhibitory drive ( Figure 8 , top ) . Indeed , the results from the rotarod experiments suggest that this compensation successfully prevents mutant males from displaying altered motor behavior . 10 . 7554/eLife . 07596 . 015Figure 8 . Diagram of sex differences in mGluR1/5 and compensatory changes in males with loss of GABAAR β3 . ( Top: ) Expected relative levels of GABAAR β3 expression ( red asterisks ) in the cerebellar cortex , and mGluR1/5-dependent current amplitude ( blue arrows ) in large CbN cells . For simplicity , only Purkinje cells ( Pkj ) , granule cells ( gc ) , and large CbN cells are depicted . The m-/p+ mutation reduces β3 expression , predicting a disinhibition of granule and Purkinje cells . The resulting increase in Purkinje cell firing rates should increase inhibition of CbN cells . ( Top: ) Mutant males counteract the predicted increase in inhibition with increased via mGluR1/5-dependent inward currents . ( Bottom: ) Wild-type females have more mGluR1/5-dependent current than wild-type males and presumably balance excitation and inhibition through other means . Mutant females do not up-regulate mGluR1/5-dependent current , and apparently do not compensate for the increased inhibition . DOI: http://dx . doi . org/10 . 7554/eLife . 07596 . 015 Males and females respond differently to the m-/p+ mutation; although GABAAR β3 protein levels are reduced in m-/p+ females , CbN cells from wild-type females already have elevated spontaneous firing rates and relatively large evoked mGluR1/5 responses , and no further changes in these parameters are seen with the mutation ( Figure 8 , bottom ) . The difference in rotarod behavior between wild-type and mutant weanling females in the global and Purkinje-specific Gabrb3 mice suggests that this absence of an indirect response has behavioral consequences . The disruption in rotarod performance in mutant females is unusual: naïve mutant females show prolonged latency to fall on Day 1 , and fall latency is not prolonged by subsequent training . Interestingly , previous studies have reported enhanced initial performance on accelerated rotarod by naïve mice with a mutation in a protein repair methyltransferase; these mice were also hyperactive ( Vitali and Clarke , 2004 ) . Hyperactivity has been observed in homozygous mutant Gabrb3 mice ( DeLorey et al . , 1998; Liljelund et al . , 2005 ) , but not in m-/p+ ( Liljelund et al . , 2005 ) or mixed paternal/maternal heterozygotes ( DeLorey et al . , 1998 ) . Neither of these studies separated mice by sex , however , leaving open the possibility that m-/p+ females are more hyperactive than m-/p+ males , which may contribute to their prolonged fall latency early in the task without improvement over the training period . The change in evoked mGluR1/5 responses upon loss of GABAAR β3 illustrates the extensive plasticity of the cerebellar circuit , which often appears homeostatic in response to physiological perturbations and can be at least partly compensatory in response to genetic disruptions ( Zheng and Raman , 2010 ) . For example , in the pcd and lurcher mutations , loss of inhibitory input to CbN neurons from degeneration of Purkinje cells triggers structural and physiological changes that increase the efficacy of the residual inhibition ( Garin et al . , 2002; Sultan et al . , 2002; Linneman et al . , 2004 ) . Similarly , in Purkinje neurons , deletion of Scn8a , which encodes the voltage-gated Na channel α subunit NaV1 . 6 , slows Purkinje cell firing but also increases facilitation of afferent parallel fibers , elevating excitatory drive to Purkinje cells ( Levin et al . , 2006 ) . Finally , eliminating large conductance Ca-activated K channels , expected to reduce Purkinje cell firing , results in a reduction of climbing fiber activity , which should potentiate excitatory inputs to Purkinje cells ( Chen et al . , 2010 ) . Sex differences are evident in three dimensions of CbN cell physiology , including synaptic excitation , synaptic inhibition , and intrinsic properties . Specifically , males and females have ( 1 ) different magnitudes of evoked mGluR1/5-dependent currents , possibly owing to differential access of glutamate to receptors , ( 2 ) different IPSC decay kinetics , and ( 3 ) different spontaneous firing rates . Although the etiology of these differences has yet to be fully examined , they seem likely to arise from a sex difference in neuronal properties rather than from modulation by circulating gonadal hormones , since they are present in pre-pubertal mice . This idea is compatible with other reports of sexually dimorphic molecular and cell biological attributes of cerebellar neurons in juvenile rodents ( Dean and McCarthy , 2008 ) . For instance , calbindin expression in Purkinje neurons is higher in weanling females than males; this difference is determined by sex chromosome complement , regardless of gonadal sex ( Abel et al . , 2011 ) . Another example of pre-pubertal cerebellar sex differences comes from rat pups , in which the inflammation-associated prostaglandin PGE2 can activate aromatase , and the resulting estradiol synthesis within the cerebellum leads to reduced Purkinje cell dendritic arborization ( Sakamoto et al . , 2001 ) . A decrease in Purkinje cell capacitance , as a proxy for arborization , can be mimicked by exogenous estradiol in males but not females , even in juvenile rodent brains ( Dean et al . , 2012 ) . Precedent also exists for the idea that genetic mutations in the cerebellum are differentially manifested between the sexes; the reeler and staggerer mutations , which both lead to Purkinje cell degeneration , have earlier and/or more extreme effects in heterozygous males than females , with differences evident in animals as young as one month old ( Hadj-Sahraoui et al . , 1996; Doulazmi et al . , 1999 ) . Metabotropic glutamate receptors and their downstream targets have varied effects in the cerebellar nuclei , such that changes in mGluR1/5-dependent current may alter cerebellar output . Potentiation of L-type Ca channels by group I mGluRs can accelerate prolonged rebound firing ( Zheng and Raman , 2011 ) , and group I mGluRs can also generate a slow EPSC ( Zhang and Linden , 2006 ) . In the present study , the relative amplitudes of inward and outward synaptic currents reported are specific to the experimental conditions . In vivo , processed sensory signals from mossy fibers are expected to excite CbN cells and activate the granule cell-Purkinje cell pathway that concurrently releases GABA onto the same CbN neurons . Thus , the amplitude of mGluR1/5-dependent currents relative to IPSCs is likely to regulate CbN cell firing in vivo . Moreover , the role of group I mGluRs in the cerebellum may differ between the sexes , as has been shown in other brain regions ( Boulware et al . , 2005; Tabatadze et al . , 2015 ) . Group I mGluRs are also implicated in long term synaptic plasticity in the cerebellar nuclei . Specifically , induction of long-term depression ( LTD ) of mossy fiber EPSCs in CbN cells requires activation of mGluR1 ( Zhang and Linden , 2006 ) . Conversely , long-term plasticity ( LTP ) of mossy fiber EPSCs in the cerebellar nuclei is inhibited by flux through L-type Ca channels ( Pugh and Raman , 2006; 2008; Person and Raman , 2010 ) , which are regulated by group I mGluRs ( Zheng and Raman , 2011 ) . In all these studies of cerebellar physiology in juvenile mice , however , data were gathered from both sexes and analyzed together . Considered with the present work , previous findings raise the possibility that LTD and LTP may be differentially induced in CbN cells of Gabrb3 m-/p+ males relative to wild-type males , or even in male vs . female wild-type mice . Accumulating evidence suggests a role of mGluRs in several neurodevelopmental disorders . For instance , mGluR5 has been implicated in multiple models of intellectual disability and autism , including fragile X syndrome , tuberous sclerosis complex ( TSC ) , and Phelan McDermid syndrome ( D’Antoni et al . , 2014 ) . Male mice deficient in TSC show deficits in mGluR-mediated CA1-LTD ( Ehninger et al . , 2009; Auerbach et al . , 2011 ) . Tsc1 and Tsc2 mice ( male and female , pooled ) both have impaired hippocampal learning and memory ( Goorden et al . , 2007; Ehninger et al . , 2008 ) ; interestingly , loss of Tsc1 from Purkinje cells alone in male mice mimics many phenotypes of the disorder ( Tsai et al . , 2012 ) . Additionally , male mice deficient in the ( X-linked ) fragile X mental retardation gene ( Fmr1 ) , whose protein product FMRP binds mRNA and regulates translation ( Feng et al . , 1997; Laggerbauer et al . , 2001; Li et al . , 2001 ) , display exaggerated mGluR-mediated hippocampal CA1-LTD ( Huber et al . , 2002 ) . Both global and Purkinje cell-specific Fmr1 knockout mice show enhanced parallel fiber LTD in Purkinje cells , as well as deficits in delay eyelid conditioning , a cerebellar behavior ( Koekkoek et al . , 2005 ) . The present data from Gabrb3 m-/p+ mice provide further support for the general principle of modulation of mGluRs affecting phenotypes associated with ASD and intellectual disability , while raising the possibility of differential effects in males and females . All procedures conformed to institutional guidelines and were approved by the Institutional Animal Care and Use Committee of Northwestern University . Mice were housed with a 14:10 light:dark cycle with access to food and water ad libitum . Female B6;129-Gabrb3tm1Geh/J ( 'Gabrb3 mice' , Jackson Laboratories , Bar Harbor , ME ) heterozygotes were crossed with C57BL/6J ( Jackson Laboratories , Bar Harbor , ME ) males to create +/+ and m-/p+ offspring . Due to limited availability of Gabrb3 mice , male and female C57BL/6J mice were used where indicated for control experiments or to expand data sets on wild-type male and female mice . Purkinje-specific Gabrb3 mice were created by crossing B6;129-Gabrb3tm2 . 1Geh/J ( Gabrb3fl/+ ) females ( a kind gift of Dr . Theo Palmer , Stanford University ) with B6 . 129-Tg ( Pcp2-cre ) 2Mpin/J ( L7cre/cre ) males ( Jackson Laboratories , Bar Harbor , ME ) , since L7 ( pcp2 ) is strongly expressed almost exclusively in Purkinje neurons ( Oberdick et al . , 1998; Barski et al . , 2000 ) . Purkinje-specific Gabrb3 mice were genotyped by Transnetyx , Inc . ( Cordova , TN ) . Gabrb3 mice were genotyped either by Transnetyx , Inc or in house with primers recommended by Jackson Laboratories ( GCA TCG ACA TGG TTT CTG AAG TC , GGG CTA CTG ATC TCC TCT TTC CAC , and CAG AAA GCG AAG GAA CAA AGC TG , from Integrated DNA Technologies , Coralville , IA ) . P17-24 mice were anesthetized with isoflurane and transcardially perfused with artificial cerebrospinal fluid ( ACSF ) at 35–37°C containing ( mM ) 123 . 8 NaCl , 0 . 35 KCl , 2 . 6 NaH2CO3 , 0 . 125 NaH2PO4 , 1 . 0 glucose , 1 . 5 CaCl2 , 1 . 0 MgCl2 , and bubbled with 95 O2/5% CO2 . Mice were decapitated , and cerebella were transferred into 35–37°C oxygenated ACSF . Parasagittal cerebellar slices ( 300 µm ) were cut on a vibratome ( Leica VT1200 , Leica Microsystems Inc . , Buffalo Grove , IL ) , incubated for 30 min at 35–37°C in oxygenated ( 95 O2/5% CO2 ) ACSF , and then maintained at room temperature . Recordings were made from large cells in the interpositus and the medial portion of the lateral nucleus at 35–37 . 5°C ( Zheng and Raman , 2009 ) . Patch pipettes ( 3–6 MΩ ) were pulled from borosilicate glass on a Sutter Instruments ( Novato , CA ) P97 puller and filled with internal solution containing ( mM ) 132 K-gluconate , 5 . 5 Na-gluconate , 3 . 3 NaCl , 2 . 2 MgCl2 , 10 sucrose , 11 HEPES , 1 . 1 EGTA , 14 Tris creatine phosphate , 4 MgATP , and 0 . 3 TrisGTP , buffered to pH 7 . 3 with KOH . Voltage- and current-clamp recordings were made with a Multiclamp 700B amplifier and pClamp acquisition software . During recordings , slices were perfused with oxygenated ACSF solution with the following compounds , as noted: 5 µM DNQX ( dinitrofiquinoxaline-2 , 3-dione ) to block AMPA receptors , 10 µM CPP [ ( RS ) -3- ( 2-carboxy-piperazin-4-yl ) -propyl-1-phosphonic acid] to block NMDA receptors , 10 µM SR-95531 to block GABAA receptors , 10 µM strychnine to block glycine receptors , 1 µM tetrodotoxin ( TTX ) to block voltage-gated sodium channels , 50 µM dl-TBOA ( DL-threo-β-benzyloxyaspartic acid ) to block excitatory amino acid transporters , 100 µM CPCCOEt ( 7- ( hydroxyimino ) cyclopropa[b]-chromen-1a-carboxylate ethyl ester ) to block group I mGluRs , 0 . 2 µM JNJ16259685 ( ( 3 , 4-dihydro-2H-pyrano[2 , 3-b]quinolin-7-yl ) - ( cis-4-methoxycyclohexyl ) -methanone ) to block mGluR1 , and 40 µM MPEP hydrochloride ( 2-methyl-6- ( phenylethynyl ) pyridine hydrochloride ) to block mGluR5 . Drugs were from Tocris Cookson ( Bristol , UK ) except strychnine ( Sigma-Aldrich , St . Louis , MO ) and TTX ( Alomone Labs , Jerusalem , Israel ) . Other chemicals were from Sigma-Aldrich ( St . Louis , MO ) . In experiments where either CPCCOEt or a combination of JNJ16259685 and MPEP were used to block group I mGluRs , JNJ16259685 and MPEP reversed the TBOA-induced current slightly more quickly , so ~75% of cells were treated with JNJ16259685 and MPEP , and 25% with CPCCOEt . Synaptic currents were evoked by 250 µs current pulses at 0 . 01–10 mA delivered to the white matter surrounding the cerebellar nuclei , with either a concentric bipolar electrode ( FHC ) or a theta glass pipette filled with HEPES-buffered saline . For all voltage clamp recordings , cells were held at −40 mV , which is near the resting potential of CbN cells silenced by in TTX ( Raman et al . , 2000 ) . For current clamp recordings , where indicated , steady hyperpolarizing current was applied to maintain baseline firing at a desired rate . Voltages are not corrected for a measured junction potential of 10 mV . For pharmacological studies , recordings were made 5–10 min after perfusion of a drug was initiated to ensure complete equilibration . For experiments in Figures 3 and 4 , cells were switched to current clamp during drug perfusions and allowed to fire action potentials , which prevented both the treatment-independent increase of leak current and the rundown of L-type Ca current during the experiment . For four months during the course of the experiments , construction near the animal facility produced extreme vibrations ( >5000 µinches/s ) that correlated with changes in wild-type synaptic physiology in wild-type males ( Figure 1—figure supplement 2 ) , much as the m-/p+ mutation did , indicating that cerebellar synaptic properties can be sensitive to environmental stimuli . Data obtained from mice during the period of construction were not included other analyses . The colony was re-established from newly ordered breeder pairs after vibration-inducing construction was completed . Mice were perfused with ACSF , and cerebellar slices were cut as above in ice-cold ( 0–4°C ) ACSF . Cerebellar nuclei were punched out of slices with a 200 µl pipette tip cut to ~1 mm diameter , and cerebellar cortex was separated from the brainstem . Cerebellar nuclei ( 7 slices per mouse ) and cortex ( 11 slices per mouse ) were separated and placed in ice cold HEPES homogenizing buffer containing ( mM ) : 5 HEPES-KOH , pH 7 . 2 , 320 sucrose , 5 EDTA , 1 Na orthovanadate , 50 NaF , 10 Na pyrophosphate , 20 Na glycerophosphate , 0 . 1 phenylmethyl-sulfonyl fluoride , plus protease inhibitor cocktail ( Roche Diagnostics , Burgess Hill , UK ) . Each sample , for nuclei and cortex , contained tissue from two mice of the same age , sex , and genotype . Tissue homogenates were centrifuged at 1000 × g for 10 min to remove unbroken cells and nuclei . Membrane fractions were prepared by ultracentrifugation of postnuclear supernatant at 100 , 000 × g for 1 hr at 4°C with a fixed angle rotor ( TLA100 . 2 , Beckman Coulter , Brea , CA ) . Protein concentration was determined with the Bradford protein assay . Equal amounts of protein were separated on 7–10% SDS-PAGE gels and Western blotting was carried out as in Tabatadze et al . ( 2013 ) . Briefly , membranes were blocked with 5% nonfat milk and then incubated with one of the following primary antibodies overnight at 4°C: mouse monoclonal anti-mGluR1 ( 1:1000 , BD Biosciences , San Jose , CA ) , rabbit-polyclonal anti-mGluR5 ( 1:1000 , EMD Millipore , Darmstadt , Germany ) , rabbit-polyclonal anti-GABAAR β3 ( 1:1000 , Novus Biologicals , Littleton , CO ) and goat polyclonal anti-β-actin ( 1:2000 , Santa Cruz Biotechnology , Dallas , TX ) . Membranes were washed in TBS and then in 0 . 1% Tween 20 in TBS and incubated at room temperature for 1 hr with horseradish peroxidase-conjugated anti-mouse , anti-rabbit or anti-goat IgG secondary antibodies ( 1:1000 , Vector Laboratories , Burlingame , CA ) . Immunoreactivity was visualized using an enhanced chemiluminescence kit ( ECL Plus , Thermo scientific , Rockford , IL ) and analyzed with Image J ( NIH , Bethesda , MD ) . After visualization of mGluRs and GABAAR β3 , blots were stripped and probed for β-actin as a loading control . For each group , protein levels were expressed relative to β-actin in the same sample . To compare results across blots , the protein ratio for each band was normalized to the sum of the protein ratios for all samples on the blot , divided by the total number of lanes ( Degasperi et al . , 2014 ) . Three independent experiments were run with tissue from Gabrb3 mice ( biological replicates ) , each with one +/+ male , one m-/p+ male , one +/+ female , and one m-/p+ female sample , and each sample was run in duplicate . Three independent experiments were run with tissue from C57BL/6 mice , each with two male and two female samples ( biological replicates ) to total 6 samples per sex , and each sample was run in duplicate . For comparison of cortex vs . nuclei in C57BL/6 mice , two independent samples were run for each sex and region , with no duplicates . Five cohorts of mice underwent rotarod testing ( SDI rotor-rod , San Diego Instruments , San Diego , CA ) , consisting of four age groups: postnatal day 22 days old ( 'P22' groups , for both Gabrb3 and Purkinje-specific Gabrb3 mice ) , P43-47 ( Gabrb3 'P45' group ) , P60-68 ( Gabrb3 'P65' group ) , and P147-160 ( Gabrb3 'P155' group ) . Mice were placed on the rod and allowed to acclimate for at least 30 s before the rod was accelerated at varying speeds as noted . Latency to fall was measured automatically , but was also monitored by the experimentalist . If mice grasped the rod and rotated completely , latency to fall was counted when the mouse completed its rotation . After one trial , mice were allowed to remain in the chamber for at least 5 min , followed by another trial . The two trials were averaged for each day , and this process was repeated for a total of seven days . Data were analyzed and plotted with IGOR-Pro ( Wavemetrics , Lake Oswego , OR ) and are presented as mean ± SEM . Stimulus artifacts have been digitally reduced or removed in all figures . IPSCs were fit with the sum of two exponentials . Weighted time constants were calculated by fitting the sum of two exponentials to the decay phase of current , and finding the average of the time constants τfast and τslow , each scaled by the fractional contribution to the total amplitude Ffast and Fslow . Tonic currents were predicted from the measured double exponential fits to IPSCs at the beginning and end of 100-Hz trains with the following equations , which account for the gradual change in decay time over the train:Tn=PnFfne− ( 8 . 4/τfn ) +Fsne− ( 8 . 4/τsn ) +Tn−1Ffne− ( 8 . 4/τfn ) +Fsne− ( 8 . 4/τsn ) , withτfn=1−n−120·τf1+n−120·τf20 , and Ffn=1−n−120·Ff1+n−120·Ff20 , τsn=1−n−120·τf1+n−120·τs20 , and Fsn=1−n−120·Fs1+n−120·Fs20 , where , for the nth stimulus , Tn is the tonic current , Pn is the measured peak phasic current , τfn and τsn are the fast and slow decay time constants in ms , Ffn and Fsn are the fractional contributions of the fast and slow components of decay ( which sum to 1 ) , and 8 . 4 ms denotes the time after the stimulus when the tonic current is calculated for a 100 Hz train , i . e . , 10 ms , less the stimulus artifact time and IPSC rise time . Data were analyzed for statistical significance using SPSS software ( IBM Corp . , Armonk , NY ) with a value of p<0 . 05 considered statistically significant and indicated by an asterisk where appropriate . For all ANOVAs , significant differences were only reported if there was a significant main effect . Repeated-measures ANOVAs with Tukey post-hoc comparisons were used to assess significant differences between the wild-type sexes , and between mutants and their sex-matched controls for phasic and tonic currents , changes in holding currents upon application of various drugs , and changes in initial EPSC amplitude and steady-state amplitude upon application of various drugs . For voltage-ramp-evoked currents , data points at at 4-ms intervals were analyzed with a repeated-measures ANOVA . One-way ANOVAs with contrasts were use to assess differences in IPSC kinetics , CPCCOEt-dependent increases in prolonged post-inhibitory rebound firing , spontaneous rates , action potential half-width , normalized protein expression for Western blots , and rotarod Day 1 latency and Δ latency . One-way ANOVAs were used to compare tonic and phasic currents , as well as IPSC kinetics , between control and CPCCOEt-containing solutions across all four groups . Mixed ANOVAs , followed by post-hoc one-way ANOVAs or repeated-measure ANOVAs as appropriate , were used in the rotarod experiments to compare latency to fall across days for all four groups . Two-tailed Student’s t-tests were used to compare C57BL/6 males and females . For plotting differences in tonic currents with and without CPCCOEt , the mean ( μ ) and SEM were calculated from unpaired measurements as μ= μcontrol-μCPCCOEt and SEM = √ ( ( SEMcontrol ) 2- ( SEMCPCCOEt ) 2 ) .	The cerebellum is a part of the brain that plays a role in controlling movement , coordination and balance . It also contributes to cognitive processes that do not relate to movement . Changes in the cerebellum are often seen in individuals with autism spectrum disorders , and many genes implicated in autism are active in the cerebellum . One of these genes encodes part of a receptor for the signaling molecule GABA , and is called GABRB3 in humans . Each person usually inherits one copy of the GABRB3 gene from each parent . However , if a mother does not pass on this gene , her child may develop Angelman syndrome . Those affected by this disorder show impaired movement and cognitive abilities , and often show signs of autism . To explore how neural signals in the cerebellum might change in Angelman syndrome , Mercer et al . compared mice that lack a copy of the mouse equivalent of the gene ( called Gabrb3 ) from their mothers to “control” mice with two intact copies of the gene . The experiments unexpectedly revealed that key brain cells in the cerebellum of male control mice were different from the same cells in female mice . The cells in males had lower baseline levels of electrical activity and responded differently to signals from other cells . The differences arose partly because a group of receptors – called metabotropic glutamate receptors – were more easily activated in the brain cells in females than in males . Nevertheless , both male mice and female mice did equally well at learning to balance on a rotating rod , which is a skill that is controlled by the cerebellum . In other words , the cerebellum works differently in male and female mice but produces the same output . Mutant male mice performed just as well as non-mutant , control males at learning to balance on the rotating rod . By contrast , female mutants did not improve during training on the same task . Measuring the activity of cells in the cerebellum showed that the metabotropic glutamate receptors in cells from mutant male mice had changed so that they responded more like those of females . However , the responses of mutant female mice did not change compared to control female mice . This result suggests that the changes in the brain cells of male mutant mice helped compensate for the Gabrb3 mutation . It also shows that baseline differences in the brains of male and female animals can make them respond differently to mutations associated with genetic disorders .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2016	Sex differences in cerebellar synaptic transmission and sex-specific responses to autism-linked Gabrb3 mutations in mice
The nematodes C . elegans and P . pacificus populate diverse habitats and display distinct patterns of behavior . To understand how their nervous systems have diverged , we undertook a detailed examination of the neuroanatomy of the chemosensory system of P . pacificus . Using independent features such as cell body position , axon projections and lipophilic dye uptake , we have assigned homologies between the amphid neurons , their first-layer interneurons , and several internal receptor neurons of P . pacificus and C . elegans . We found that neuronal number and soma position are highly conserved . However , the morphological elaborations of several amphid cilia are different between them , most notably in the absence of ‘winged’ cilia morphology in P . pacificus . We established a synaptic wiring diagram of amphid sensory neurons and amphid interneurons in P . pacificus and found striking patterns of conservation and divergence in connectivity relative to C . elegans , but very little changes in relative neighborhood of neuronal processes . These findings demonstrate the existence of several constraints in patterning the nervous system and suggest that major substrates for evolutionary novelty lie in the alterations of dendritic structures and synaptic connectivity . Comparative studies on nervous system anatomy have a long tradition of offering fundamental insights into the evolution of nervous systems and , consequently , the evolution of behavior ( Schmidt-Rhaesa , 2007 ) . Traditionally , such comparative studies have relied on relatively coarse anatomical and morphometric comparisons . The relative simplicity of nematode nervous systems ( Schafer , 2016 ) facilitates the determination and subsequent comparison of neuroanatomical features of distinct nematode species , thereby enabling an understanding of how members of the same phylum , sharing a common body plan , can engage in very distinct behaviors . We examine here specific neuroanatomical features , from subcellular specializations to synaptic connectivity , of the nematode Pristionchus pacificus and compare these features with those of the nematode Caenorhabditis elegans . The species shared their last common ancestor around 100 million years ago , which is longer than the human-mouse separation ( Nei et al . , 2001; Prabh et al . , 2018; Rota-Stabelli et al . , 2013; Werner et al . , 2018 ) , and have since diverged to populate very discrete habitats and engage in distinct sets of behaviors . C . elegans is a free-living nematode that can mainly be found in rotten fruit while members of the genus Pristionchus are regularly found in association with several species of scarab beetles , depending on geography ( Herrmann et al . , 2006; Herrmann et al . , 2007; Koneru et al . , 2016; Ragsdale , 2015 ) . Additionally , Pristionchus nematodes are found in association with other soil arthropods , such as millipedes ( Yoshida et al . , 2018 ) and insect baits for soil-dwelling entomopathogenic nematodes ( Campos-Herrera et al . , 2019; Kanzaki et al . , 2018 ) , as well as other vegetal substrates ( Félix et al . , 2018 ) . Species-specific entomophilic beetle association in Pristionchus is corroborated by several adaptations . First , Pristionchus nematodes exhibit chemosensory responses towards insect pheromones and volatile plant compounds ( Hong and Sommer , 2006; Hong et al . , 2008; Cinkornpumin et al . , 2014 ) . Second , P . pacificus dauer larvae secrete a high molecular weight wax ester that promotes collective host finding ( Penkov et al . , 2014 ) . Finally , Pristionchus species show predatory behavior towards C . elegans and other nematodes ( Bento et al . , 2010; Liu et al . , 2018 ) . All these behavioral features likely require substantial modifications in the nervous system . One obvious potential substrate for evolutionary adaptions to distinct ecological habitats and interactions with other species is the perception and processing of sensory information . The amphid sensilla , comprised of a pair of bilaterally symmetrical sensilla in the head that are open to the external environment , are the largest nematode chemosensory organs ( Bargmann , 2006; Bargmann and Horvitz , 1991; Bargmann et al . , 1993 ) . In the model organism C . elegans , the anterior sensilla include the amphid sensilla , twelve inner and six outer labial neurons , and four cephalic neurons , along with their associated sheath and socket support cells ( Ward et al . , 1975; Ware et al . , 1975 ) . There are also additional sensory receptors without glia broadly grouped into non-ciliated ( URX , URY , URA , URB ) and ciliated ( BAG , FLP ) neurons that are involved in gas sensing , mechanoreception , and male mate-searching behavior ( Barrios et al . , 2012; Chatzigeorgiou and Schafer , 2011; Doroquez et al . , 2014; Gray et al . , 2004; Hallem and Sternberg , 2008; Hallem et al . , 2011; Perkins et al . , 1986 ) . Comparisons of the C . elegans amphid neuroanatomy to those of free-living and parasitic nematodes such as Acrobeles complexus ( Bumbarger et al . , 2009 ) , Haemonchus contortus ( Li et al . , 2000; Li et al . , 2001 ) , and Parastrongyloides trichosuri ( Zhu et al . , 2011 ) have shown that the number and arrangement of the amphid neurons are broadly conserved . However , a more fine-grained comparative analysis of distinct sensory structures as well as their connection to downstream circuits has been largely lacking so far . Using the 3D reconstructions of serial thin section transmission electron microscopy ( TEM ) , we describe here detailed features of the sensory anatomy of P . pacificus as well as the synaptic wiring of sensory neurons to their main , postsynaptic interneurons . Comparing anatomical features of sensory circuitry , from ciliated sensory endings to soma and axon position to synaptic connectivity , we reveal striking patterns of similarities and dissimilarities . The most striking similarity is the overall conservation of neuronal soma and process positioning while the most striking patterns of divergences lie in fine structural details of sensory anatomy as well as synaptic connectivity . Using a combination of 3D reconstructions from TEM sections of two young adult hermaphrodites , as well as live dye uptake and transgene reporter analysis , we set out to characterize the amphid sensory circuitry of P . pacificus in order to undertake a comparative analysis with the amphid sensory circuitry of C . elegans . For comparison with C . elegans , we used electron micrographs and findings from both legacy ( White et al . , 1986 ) and modern EM methodologies ( Doroquez et al . , 2014 ) . Despite being approximately 40 years old , the EMs used by John White and colleagues to create The Mind of a Worm remain the most complete publicly available data of the adult hermaphrodite C . elegans nervous system . While the methods used to create these legacy EM series are technologically inferior to current practices ( chemical fixation , analog microscopy , thicker sections ) , the overall staining and elucidation of synaptic zones remain useful and valuable for anatomical comparisons as has been shown in a recent publication on whole-animal connectomes of both C . elegans sexes ( Cook et al . , 2019 ) . Recent studies of the amphid dendritic endings in C . elegans using the modern High Pressure Freezing ( HPF ) method have validated the original studies , resulting in a richer description of ultrastructural anatomy ( Doroquez et al . , 2014 ) . The numerous anatomical similarities and differences we observed across species are both reproducible and share equivalences to previous nematode comparative anatomical studies . To account for all the amphid neurons in P . pacificus , we identified and traced every amphid neuron from the tip of its cilium in the channel near the mouth to its cell body posterior to the nerve ring in two young adult hermaphrodites . Like C . elegans , P . pacificus possesses 12 neurons per amphid sensillum . All amphid neurons and the amphid sheath cell ( AMsh ) in P . pacificus have their cell bodies in the lateral ganglion posterior to the nerve ring , which resembles the condition found in C . elegans and other nematodes . At the anterior end , the tips of the dendrites are housed by the processes of a pair of amphid sheath cells , which are glial cells that connect to the amphidial pore in the cuticle via a pair of amphid socket cells ( AMso ) that expose the amphid neuronal cilia to the environment ( Figure 1A–D ) . All amphid neurons have ciliated endings with a circle of varying numbers of doublet microtubules surrounding a few inner singlet microtubules in their transition zones and middle segments ( Figure 1F ) . We counted 13 cilia ( from 11 neurons ) in each amphid channel in P . pacificus , compared to 10 cilia ( from eight neurons ) in the channel in C . elegans ( Figure 1D–F , Table 1 ) . The greater number of ciliated endings in P . pacificus coincides with a conspicuous lack of neurons with winged dendritic morphology found in the C . elegans wing cells ( AWx ) , whose elaborate ciliary endings terminate as invaginations inside the distal amphid sheath cell cytoplasm rather than in the amphid channel . This indicates that the AWA , AWB , AWC cellular homologs in P . pacificus are among the 11 single or double ciliated neurons in the channel and , therefore , do not display a characteristic ‘wing’-shaped cilia morphology . The only other known amphid neurons with elaborate dendritic processes resembling the AWA cells are found in the swine parasite , Oesophagostomum dentatum ( Hoholm et al . , 2005 ) . Because the wing neurons are the most morphologically distinct and best studied amphid neurons in C . elegans , their absence in most other nematodes contributes to the difficulty in assigning homology in the amphid neurons in these other nematode species ( Ashton et al . , 1995; Bumbarger et al . , 2007a; Bumbarger et al . , 2009; Li et al . , 2001; Ward et al . , 1975 ) . We designated provisional names for the P . pacificus amphid neurons utilizing ‘AM’ for amphid , followed by numbers 1 to 12 ( Table 2 ) . Of the 11 dendrites in the channel , nine have single ciliated endings and only two of them , AM3 and AM9 , possess dual-ciliated endings . The lack of neurons with winged morphology compelled us to consider several other criteria for assigning amphid neuron homology between P . pacificus and C . elegans , such as i ) relative cell body positions , ii ) axon projections , iii ) manner of dendrite entry into the amphid sheath cell , iv ) number of cilia in channel , v ) DiI dye filling properties , and vi ) connections to first layer interneurons . While no single criterion allows homology assignment for all amphid neurons , our analysis results in high confidence homology assignments for most neurons . Using these provisional homologies , we proceeded to evaluate possible evolving features and found conservation and divergence in synaptic connectivity and orthologous transcriptional reporters . At the end of this analysis , we will revisit homology assignments and will discuss potential strategies to overcome current limitations . The most unambiguous amphid neuron homolog found in P . pacificus is AM12 , with finger-like dendritic endings akin to the finger-neuron in most other nematode species: C . elegans , Ancylostoma caninum , Oesophagostomum dentatum , Haemonchus contortus , Acrobeles complexus and Parastrongyloides trichosuri L1 ( Bhopale et al . , 2001; Bumbarger et al . , 2009; Hoholm et al . , 2005; Li et al . , 2001; Ward et al . , 1975; Zhu et al . , 2011 ) ( Figures 2A , C and 3A , Figure 2—figure supplements 1 and 2 ) . The sensory ending of the AM12 dendrite is formed by a short cilium of about 1 µm length ( in C . elegans about 500 nm , see Figure 12A in Doroquez et al . , 2014 ) and a complex of 30–40 microvilli-like projections branching off from the periciliary membrane compartment ( PCMC ) , which strongly resembles the morphology of the AFD neurons responsible for thermosensation in C . elegans , A . caninum , and H . contortus ( Bhopale et al . , 2001; Li et al . , 2001; Mori and Ohshima , 1995 ) . Similar to AFD endings in other species , the AM12 cilium is fully embedded in the amphid sheath cell process , does not enter the lumen of the sheath cell or the amphid channel and thus has no contact to the outside environment . We also observed that like in other species , the cell body of AM12 ( AFD ) is the most anterior among the amphid neurons , located just ventral of the lateral mid-line ( Figure 3C ) . Nevertheless , there is a small difference compared to other species – the AM12 sensory ending is located in a part of the AMsh that is clearly posterior to the lumen into which the other amphid dendrites enter . Taken altogether , we conclude that AM12 is the P . pacificus homolog of AFD . The confirmation of the AM12 ( AFD ) neurons’ role in thermosensation , however , will ultimately depend on cell ablation experiments followed by behavioral assays . Thus , of the six nematode species with detailed descriptions of their amphidial sensory neuroanatomy ( P . pacificus , C . elegans , H . contortus , S . stercoralis , A . complexus , Parastrongyloides trichosuri L1 ) the only nematode species known so far not to share finger-like dendritic endings is the mammalian parasite Strongyloides stercoralis , which has instead evolved a lamellar morphology for its putative thermosensory neuron ALD ( Ashton et al . , 1995; Bumbarger et al . , 2007a; Bumbarger et al . , 2009; Li et al . , 2001; Ward et al . , 1975; Zhu et al . , 2011 ) . Without other signature dendritic endings to nominate possible amphid homologs , we turned to likely conservations in cell body position as well as in the projection trajectories of individual axon processes that enter and terminate in the nerve ring . Using the 3D reconstructions of amphid neurons from EM sections ( specimen 107 , 148; Figure 3A; Figure 3—videos 1–15 ) , we first looked for P . pacificus neurons that might share another defining feature of the C . elegans AWC neurons: the AWC axons cross the dorsal midline , overlap each other and terminate just before reaching the lateral midline . We identified only one pair of single ciliated amphid neurons that shares this feature , the AM7 ( Table 2 ) . The AM7 ( AWC ) cell body is located between the AM1 and AM8 neurons , which are likely the respective cellular homologs of ASH and ASJ based on conservation in DiI uptake and cell body positions along the ventral edge of the amphid neuron cluster ( discussed below ) . As the AM7 and AWC axons both cross the dorsal midline and terminate above the lateral midline , we regard them as homologs based on this singular feature , although the AM7 ( AWC ) cell body appears to have shifted from a position ventral of ASH in C . elegans to a position between AM1 ( ASH ) and AM8 ( ASJ ) in P . pacificus , such that the three cell types are just ventral and parallel to the lateral midline on each side . Two other amphid neurons also show strong resemblance to their C . elegans counterparts according to their unique axon projections . The P . pacificus AM6 is likely the homolog of the C . elegans ASG neuron because these counterparts have the unique property of short axon projections that do not project into the nerve ring much further than the lateral midline , terminating before the dorsal midline . The P . pacificus AM2 is the likely homolog of the C . elegans ADL neuron because like in ADL , the AM2 axons are the only amphid sensory neurons that do not run through the amphidial commissure but enter the nerve ring directly from an anterior projection before branching in the dorsal-ventral direction , which is unique among all amphid neurons in both species ( Table 2 ) . If axon projection is more highly conserved than cilia branching , then the P . pacificus cellular homolog for ADL is single ciliated and not double ciliated . The nomination of AM2 as the ADL homolog is corroborated by its cell body position just posterior to AM4 and its ability to take up DiI . Altogether , the conserved unique axon trajectories of AM2 , AM6 , and AM7 argue they are the likely homologs of the C . elegans ADL , ASG , and AWC neurons , respectively . In C . elegans , the axons of the ASEL and ASER neuron pair cross the dorsal midline around the nerve ring until they end ventrally close to the entry point of the contralateral axon ( White et al . , 1986 ) . In P . pacificus , the cell bodies of AM5 are located just ventral to those of the AM2 ( ADL ) neurons at the second-most posterior position of the amphid cluster in the lateral ganglion [AM8 ( ASJ ) is the most posterior pair] . The conservation of cell body position and the expression of the Ppa-che-1p::rfp transgene reporter ( see below ) provide independent support to nominate the AM5 neurons as the ASE homologs . However , the P . pacificus AM5 ( ASE ) axons do not cross each other at the dorsal midline , but rather terminate at the dorsal midline . At their respective termination points P . pacificus , ASEL and ASER make a small gap junction with each other , like all but two amphid sensory neuron pairs do ( Table 2 ) . This apparent electrical coupling between the P . pacificus ASE homologs is notable because in C . elegans no such gap junctions are formed between the ASEs ( White et al . , 1986 ) . The consequent lack of electrical coupling between the C . elegans ASEL and ASER neurons has been found to be necessary to produce a physiological asymmetry between these neurons , such that both neurons are differentially activated by distinct sensory cues , that is their function is ‘lateralized’ ( ‘left/right asymmetric’ ) ( Pierce-Shimomura et al . , 1999; Ortiz et al . , 2009; Suzuki et al . , 2008 ) . Moreover , it has been shown that artificial establishment of gap junctions between the left and right C . elegans ASEs leads to loss of functional lateralization and changes in chemotaxis behavior ( Rabinowitch et al . , 2014 ) . The apparent coupling of ASEL and ASER in P . pacificus suggests that these two neurons are not functionally lateralized in P . pacificus . Two additional genetic observations are consistent with a lack of functional lateralization in P . pacificus: First , the key regulatory factor that triggers the asymmetry of the ASEL/R neurons in C . elegans , the miRNA lsy-6 ( Cochella and Hobert , 2012; Johnston and Hobert , 2003 ) , does not exist in the P . pacificus genome and is apparently specific to the Caenorhabditis crown clade ( Ahmed et al . , 2013 ) . Second , the ASEL and ASER neurons in C . elegans ( as well as closely related Caenorhabditis species ) each express a different subfamily of duplicated and chromosomally-linked , receptor-type guanylyl cyclases ( rGCYs ) , the ASER-rGCYs ( e . g . gcy-1 , gcy-2 , gcy-3 , gcy-4 , gcy-5 ) and the ASEL-rGCYs ( gcy-6 , gcy-7 , gcy-14 , gcy-20 ) ( Ortiz et al . , 2006; Yu et al . , 1997 ) , several members of which are thought to be salt chemoreceptors ( Ortiz et al . , 2009 ) . In contrast , the P . pacificus genome contains no C . elegans- ASEL-type or ASER-type rGCYs ( Figure 4—figure supplements 1 and 2 ) . Together with the changes in electrical coupling of the ASE neurons in C . elegans versus P . pacificus , the differences in the existence of molecular regulators ( lsy-6 ) and molecular effectors ( gcy genes ) of C . elegans ASE laterality suggest that the ASE neurons of P . pacificus may not be functionally lateralized . To further explore homologous features of P . pacificus and C . elegans amphid neurons , we visualized the organization and location of the neuronal cell bodies and their dendritic processes in live wild-type animals with the lypophilic dyes DiI and DiO . In C . elegans non-dauer hermaphrodites , DiI , DiO , and FITC routinely stain five specific pairs of amphid neurons with open sensory endings to the environment - ASK , ADL , ASI , ASH , ASJ ( Table 3; Figure 4B and I ) - along with two pairs of tail phasmid sensory neurons - PHA and PHB . Additionally , ADF is stained weakly by FITC but not by DiI , while AWB is stained weakly by DiI but more strongly by DiO ( Hedgecock et al . , 1985; Perkins et al . , 1986; Starich et al . , 1995 ) . It is unclear why only certain amphid neurons exposed to the environment take up certain dyes . In C . elegans , the cell bodies of the three dorsal-most amphid neurons just below the amphid sheath cell form a distinctive DiI-stained trio ( ASK , ADL , ASI ) , while ASH and ASJ are visible at different focal planes ventral or posterior to this trio , respectively ( Figure 4 ) . The cell ablations of the DiI positive ASH neurons show that its polymodal function is strongly conserved across various nematode species ( Srinivasan et al . , 2008 ) . While ciliated channel neurons take up DiI differentially but are superficially conserved in diverse free-living nematode species examined , including other Caenorhabditis species , Panagrellus redivivus , and P . pacificus , DiI uptake patterns are less similar in the insect parasites such as Steinernema carpocapse and Heterorhabditis bacteriophora . Thus , DiI uptake is an important but not singular criterion for defining homologous sensory neurons ( Han et al . , 2016; Srinivasan et al . , 2008 ) . In P . pacificus young adult hermaphrodites , DiI stains five pairs of amphid neurons in a pattern similar to the one in C . elegans [AM9 , AM4 , AM2 ( ADL ) , AM1 , and AM8] ( n > 50; Figure 4A–B ) but DiO stains only the AM2 ( ADL ) and AM8 neurons ( Figures 4E , G and H; Table 3 ) . In contrast to C . elegans , the P . pacificus phasmid neurons only dye fill in the dauer larvae ( data not shown ) , while the AM11 ( AWB ) neurons rarely dye fill in any developmental stage with either dye ( AWB is stained more robustly with DiO than with DiI in C . elegans ) . The similarity in cell body positions for AM8 and morphology for both AM2 as well as AM8 , suggest they are likely ADL and ASJ homologs , respectively . As mentioned earlier , AM2 axons are also the only neurons that resemble ADL for not passing through the commissure and branching in the dorsal-ventral direction , so that the AS2 ( ADL ) homology can be considered well supported . Thus , DiI staining in the three dorsal-most cells in the same focal plane as the AMsh cell body has only superficial resemblance to the three dorsal-most neurons in C . elegans ( Figure 4C; Figure 4—video 1 ) . If the posterior AM2 cell in the trio is the ADL homolog , rather than the ASI as in C . elegans , then the middle cell of this trio , AM4 , is likely the ASK homolog , while the anterior cell of this trio , AM9 , is the ADF . In addition to the conservation in DiI staining , we have nominated AM9 as the ADF homolog primarily due to the conservation of a distinctively bulbous PCMC in the ultrastructure of the P . pacificus AM9 similar to the C . elegans ADF ( Figure 1G–H ) ( Doroquez et al . , 2014 ) . Thus we assume that AM9 ( ADF ) , AM4 ( ASK ) , and AM2 ( ADL ) together make up the dorsal trio of neurons that take up DiI . In this proposed homology , the putative ASI homolog , AM10 , does not take up DiI . In C . elegans , the ASI neurons are important for regulating dauer development and are remodeled in the dauer larvae such that the dauer ASI neurons no longer take up DiI due to cilia retraction from the amphid pore ( Albert and Riddle , 1983; Peckol et al . , 2001 ) . Given that in P . pacificus the phasmid neurons only take up DiI as dauer larvae , it is possible that differential DiI uptake in homologous neurons between the two species recapitulates certain remodeling events in amphid neurons during dauer entry and exit that is lost in one lineage . Our cell homology nominations are consistent with the ASH and ASJ identified by position and DiI staining in a previous study by Srinivasan et al . ( 2008 ) , but are different for the DiI stained dorsal trio ( ADF , ASK , ADL ) . Table 3 summarizes the dye filling properties of the individual amphid neurons . While the positioning of the neurons taking up DiI appears mostly unaltered at first glance , cell physiology or the chemical environment for the live dye may have diverged modestly during evolution . An overview of the reconstructed cilia in one-to-one comparisons to their C . elegans counterparts is shown in Figure 5 . To complement the characterization of the neuronal composition of the amphid sensillum , we set out to visualize the morphology of the glia-like amphid sheath cells ( AMsh ) in more detail and in vivo . To this end , we constructed an AMsh reporter using a 2 . 4 kb region of the P . pacificus daf-6 promoter to drive the Red Fluorescent Protein ( RFP ) . DAF-6 is a Patch-related protein required for proper tubule formation in C . elegans , including the morphogenesis of the amphid sheath channel ( Oikonomou et al . , 2011 ) . Indeed , P . pacificus daf-6 expression shows strong conservation not only in the AMsh , but also in cells of the excretory duct and pore , seam cells , as well as in the VulE epidermal cell that contributes to vulva formation during the mid-J4 larval stage ( Perens and Shaham , 2005 ) ( Figure 6A; Figure 6—video 1; data not shown ) . We found a similar daf-6 expression profile in dauer larvae ( Figure 6B ) . The cell bodies of the amphid sheath cells are distinctively large and sit dorso-anteriorly to the terminal bulb of the pharynx . The AMsh cells extend thick processes anteriorly that swell to form vesicle-filled paddle-like endings near the nose of the worm ( Figure 2A–B , Figure 3A–B ) , unlike their C . elegans counterparts , whose endings fan out widely into the head , forming large sheets to accommodate the expanded ciliated endings of the winged AWC neurons and the finger cells . The amphid socket cells distally surround the ciliated ends of the sensory dendrites , each forming autocellular junctions onto themselves to create a pore in the cuticle of the lateral lip that is in direct contact with the environment ( Figure 1C–D; Figure 2A–B ) . The sensory dendrites enter the matrix-filled lumen of the amphid sheath cell process quite distal to their respective cell bodies . The lumen , or channel , is an extracellular space formed by the merging of numerous large matrix-filled vesicles ( Figure 1G–H; Figure 2A–B ) . Accordingly , the cytoplasm of the posterior process and the cell body is rich in mitochondria , ribosomes , rough endoplasmic reticulum , Golgi complexes and vesicles of different sizes , distinguishing the AMsh as an active secretory cell ( Figure 1I–K ) . We speculate that some of the secreted factors act as accessory to chemosensory function to modify odors or water soluble molecules ( Bacaj et al . , 2008; Cinkornpumin et al . , 2014 ) , or to protect the neurons against reactive oxygen species ( Liu et al . , 2017; Liu et al . , 2015 ) . In contrast to C . elegans , all the non-finger amphid neurons in P . pacificus have dendritic processes that terminate in the amphid channel and are thus in direct contact to the external environment ( Figure 1A–F , Figure 2A–B ) . Because the C . elegans AMsh cells accommodate the prominently large AWC winged neurons , the left and the right AMsh can fuse to each other in the dauer larvae , which undergo radial constriction during dauer entry ( Procko et al . , 2011 ) . Given the absence of any amphid neurons with winged morphology in P . pacificus , not surprisingly , Ppa-daf-6p::rfp expression in the pair of anterior AMsh endings remains distinctively separated in P . pacificus dauer larvae ( Figure 6B ) . While the C . elegans amphid sheath processes expand into a large sheet at their anterior ends to accommodate the wing-shaped cilia , the amphid sheath processes in P . pacificus maintain a tube-like form , becoming slightly wider in diameter towards the anterior tip , where they take up the 12 neurons , and thinner posteriorly near the nerve ring in an apparent kink anterior to the cell body ( n = 3 ) . As a result , the P . pacificus amphid sheath morphology is tubular , lacking the distal enlargement of the C . elegans amphid sheath . Next , we examined the interaction of the neuronal dendrites with sheath cells in more detail . During C . elegans development , the nascent dendrites of the amphid neurons and the processes of the sheath glial cells attach to the tip of the nose and from there elongate posteriorly by retrograde growth when the cell bodies start migrating posteriorly to their final position . This elongation process happens in a precisely coordinated way in each bundle ( Heiman and Shaham , 2009 ) . Reconstruction of transverse TEM sections from four C . elegans specimens show invariant arrangement of the amphid neurons in the sensory channel , such that each amphid neuron can be reliably identified by its position in the channel ( Ward et al . , 1975 ) . However , recent studies using live imaging on a much larger number of C . elegans samples reveal some variability in dendrite entry during larval development ( Yip and Heiman , 2018 ) . In our data set of only two specimens , we saw a variation in the channel position of one cilium even between the left and right side in one animal . To determine the degree in which the order and position of each dendrite entry is stereotypical in P . pacificus , we determined the individual entry points of amphid dendrites into the sheath in the reference sample ( Specimen 107 , similar data from specimen 148 not shown; Figure 2—figure supplements 1 and 2 ) . Over most of their length the dendrite bundles are located on the ventral side of the AMsh but from 36 . 4 µm and 39 . 5 µm from the tip of the head on the left and right sides , respectively , shortly before the tips of the AMsh processes start to become wider , the bundles loosen and dendrites distribute around the sheath processes , finally arranging themselves into a dorsal and a ventral group . The first dendrites to enter are those of the finger neurons AM12 ( AFD ) at approximately 25 µm from the tip of the head , or 1/6 the remaining distance between the posterior end of the pharynx ( section 3000 ) and the tip of the ‘nose’ at the anterior end ( Section 0 ) . Anterior to the region occupied by AM12 ( AFD ) , the dendritic bundle splits into two or three groups consisting of one or more dendrites that enter the sheath in variable order from two or three sides . On the left side , for example , AM5 ( ASE , orange ) , AM4 ( ASK , bright green ) , AM6 ( ASG , light blue ) and AM3 ( AWA , cream ) form a group which enter laterally whereas on the right side there is no lateral group . Instead , AM5 ( ASE , orange ) and AM4 ( ASK , bright green ) enter from the ventro-lateral side , while AM6 ( ASG , light blue ) and AM3 ( AWA , cream ) are part of the dorsal entry group . The entry of the finger neuron AM12 ( AFD , yellow ) is also variable , it can be dorsal as on the left side or ventral as on the right side . In contrast , the site of entry for AM8 ( ASJ , middle blue ) is always ventral and for the remaining seven other neurons consistently dorsal on both sides . The dendrites of AM11 ( AWB , magenta red ) are the last to enter the sheath ( dorsally ) 13 . 5 and 13 . 8 µm from the tip of the head . Thus , the sequence and site of entry for many neurons relative to the amphid sheath are variable in P . pacificus , but higher sample size involving semi-automated live imaging would be necessary to determine if the left-right variability is consistent and if the degree of variability is different between P . pacificus and C . elegans . In addition to the sensory sensilla , both P . pacificus and C . elegans also possess five types of sensory receptors that terminate in the nose region of the animal but are not accompanied by sheath or socket cells . Of these , the BAG neurons ( named for bag-like sensory ending ) stand out for being required for CO2 and O2 sensing in C . elegans ( Hallem and Sternberg , 2008; Zimmer et al . , 2009 ) , as well as CO2 avoidance in P . pacificus ( Hallem et al . , 2011 ) , both of which are presumably important for host detection in parasitic nematodes . In P . pacificus , the two BAG neuron cell bodies are located just anterior of the nerve ring in subventral position close to the isthmus ( Figure 3A–B , Figure 7A–B ) , with their dendritic processes running in the ventral-most position of the amphid process and lateral labial bundle . The ciliated distal ends of these processes form branched lamellae around the ventral halves of the lateral inner labial socket cells ( ILsoL ) , opposite to those of another pair of internal receptors , the URX neurons ( Figure 1L , Figure 7A–B , Figure 1—figure supplement 1 and Figure 1—video 1 ) . In C . elegans , the pair of URX neurons is known to be important for sensing oxygen levels and to control carbon dioxide response ( Carrillo et al . , 2013; Gray et al . , 2004 ) . The cell body of the URX neuron in both nematode species is located posterior to the nerve ring , and directly anterior to the anterior-most amphid neuron cell body in dorsal position [AM9 ( ADF ) in P . pacificus and ASK in C . elegans] ( Figure 3B ) . Interestingly , the dendritic endings of the homologous pair of P . pacificus URX neurons appear to be ciliated with eight microtubules , which are associated with the dorsal half of the lateral IL socket cells ( Figure 1L , Figure 7A–B ) . In contrast , the C . elegans URX neurons are non-ciliated and unaffiliated with any sensilla ( Doroquez et al . , 2014 ) . Ciliated URX neurons are also found in other nematode species , for example , in one of two dendritic endings of the URX neurons in the soil-dwelling nematode Acrobeles complexus , as well as in all of the URX endings of the mycophagus nematode Aphelenchus avenae ( Bumbarger et al . , 2007b; Ragsdale et al . , 2009 ) . Similarly , the four putative URY neurons have dendritic endings with numerous ( 6-20 ) singlet microtubules but they do not appear to be ciliated . These extensions are split into several overlapping branches extending membranous elaborations towards all six ILsh and ILso , resembling their C . elegans counterparts ( Figure 1—figure supplement 2 and Figure 1—video 2 ) ( Doroquez et al . , 2014 ) . Lastly , the four putative URA neurons of unknown function also resemble the morphology of their homologs in C . elegans ( Figure 1—video 3 ) . A comparison of C . elegans , P . pacificus , and A . complexus suggests that wing neurons ( AWx ) and unciliated URX neurons are derived characters in C . elegans , whereas ciliated BAG and unciliated URY are conserved ( Figure 1—figure supplement 3 ) . The C . elegans FLP neurons have elaborate multi-dendritic structures throughout the head region , and are polymodal receptors for thermosensation and mechanosensation ( Albeg et al . , 2011; Chatzigeorgiou and Schafer , 2011 ) . Like in C . elegans , the cell bodies of the likely FLP homologs in P . pacificus are located posterior to all of the amphid neuron cell bodies near the terminal pharyngeal bulb and have dendrites with extensive branching , whereas their axon projections do not enter the nerve ring . Unlike the FLP neurons in C . elegans however , the P . pacificus FLP neurons do not appear to be associated with the lateral IL socket cell ( Doroquez et al . , 2014 ) , such that the most anterior process we traced on the left side of Specimen 107 terminates in close proximity to URX and BAG in a region where the BAG neurons start to form their lamellae around the IL socket ( Figure 7A–B ) . In C . elegans , the BAG and FLP neurons are unusual among the anterior sensory neurons for not possessing their own set of glial cells , but instead associate with the socket cells of the lateral IL neurons ( Doroquez et al . , 2014 ) . The socket cells could interact with any nearby neuron or provide functional support , since it is not clear if the C . elegans BAG and FLP association with the IL socket cells is primarily for structural stability . It also remains to be determined what functional significance is there , if any , for P . pacificus URX neurons rather than the FLP neurons to associate with the lateral IL sensilla . Another unusual neuron pair , called AUA in C . elegans ( for Amphid Unknown Type A ) , has a clear homolog in P . pacificus . Although not a true amphid sensory neuron , the C . elegans AUA also sends its axon through the amphid commissure and possesses a distinctive dendrite-like process ( White et al . , 1986 ) . While the axons of the AUA neuron pair are similar to many other amphid sensory neurons , also terminating by making a gap junction between the left and right partners at the dorsal midline , their dendrites terminate just anterior of the nerve ring rather than projecting to the anterior end of the animal . We observed a similar neuron in P . pacificus , AMU1 ( AMphid Unknown 1 ) , whose dendritic-like process terminates just anterior of the nerve ring ( Figure 3A–B ) . Given this strong dendritic structural similarity , as well as a similar axonal projection , AMU1 is the likely homolog of AUA . The conservation of the AMU1 ( AUA ) neurons , which mediate oxygen sensing and social feeding behavior in C . elegans , implies that they share conserved functions in several nematode species ( Bumbarger et al . , 2009; Chang et al . , 2006; Coates and de Bono , 2002 ) . We next explored amphid neuron axon process placement within the nerve ring . The original characterization of the C . elegans nervous system identified and used reproducible placement of axons within ganglia as criteria to identify individual neurons ( Ware et al . , 1975; White et al . , 1986 ) . We used a similar approach and compared axonal placement in P . pacificus to C . elegans ( Figure 8A ) . Specifically , we evaluated the ultrastructure of the ventral ganglion ( Figure 8B and D ) , which is proximal to where the amphid commissure enters the main axonal neuropil , as well as the dorsal midline ( Figure 8C and E ) , where most amphid axons terminate . Using the P . pacificus color codes , we labeled the location of each amphid axon in both species . Despite differences in sample preparation ( HPF for P . pacificus and chemical fixation for C . elegans ) and EM section thickness ( 50 nm for P . pacificus and ~80 nm for C . elegans ) , we observed striking similarities in axonal placement in both species , providing further strong support for our homology assignment of neurons . For example , we observed similar amphid sensory neuron axonal fasciculation along the ventrodorsal and mediolateral axes of the ventral ganglion , where the ASJ and ASG neurons are most lateral , and the AFD and AUA neurons are the most ventral in both species ( Figure 8B and D ) . The most posterior segment of the dorsal nerve cord , where most bilaterally symmetric neurons meet , is occupied by a similar set of neurons in both species ( Figure 8C and E ) . Also , of note are the ASJ neurons , which are most ventral , and ADL , which are most lateral in both P . pacificus and C . elegans . We then examined the next processing layer of amphid sensory information in the worm , the first-layer amphid interneurons . In C . elegans there are four such amphid interneuron classes , each composed of a bilaterally symmetric pair of neurons ( termed AIA , AIB , AIY , AIZ , for Amphid Interneuron A , B , Y and Z ) . All four neurons display highly distinctive features and an examination of the P . pacificus nervous system revealed a remarkable extent of conservation of these features , thereby easily identifying these neurons ( the ease of identification prompted us to skip the tentative numerical naming scheme that we initially applied to sensory neurons , and thus we assigned these P . pacificus neurons with the same name as in C . elegans straight away ) . In both species , the AIY interneuron cell bodies are located in the same relative position in the ventral ganglion and their axons display a characteristic ‘humped’ morphology ( White et al . , 1986 ) . This ‘humped’ morphology corresponds to a large synaptic output onto AIZ , which forms a sheet-like cross-section immediately dorsal to AIY ( Figure 8B and D ) . The AIB neurons are identifiable in both species by a characteristic switch of their process into two distinct neighborhoods of the anterior portion of the ventral ganglion , where its proximal axon is ventral and its distal axon is more dorsal . The somas of the P . pacificus and C . elegans AIA interneurons occupy a stereotypic mediodorsal location and , like the AIY and AIZ neurons , form a dorsal midline gap junction . We rendered the complete structure of the amphid interneurons in 3D , further illustrating that cell body locations and axon projections are nearly identical in both species ( Figure 8F ) . Having identified the first-layer amphid interneurons in P . pacificus , we next explored the degree to which synaptic connectivity in the amphid circuit is conserved across species . We annotated all chemical synapses and gap junctions between the P . pacificus amphid sensory neurons , AUA , AIA , AIB , AIY , and AIZ , recording both the number of individual synapses as well as the number of serial section electron micrographs where ultrastructural synaptic anatomy was present as a proxy for anatomical connection strength . We identified 138 chemical connections ( directed edges in a graph of connectivity ) and 98 gap junction connections ( undirected edges ) in P . pacificus , compared to 73 chemical edges and 96 gap junction edges in a recent re-evaluation of connectivity in C . elegans ( Cook et al . , 2019 ) . As small synapses are more difficult to annotate reliably ( Xu et al . , 2013 ) , we limited our analysis to only include connections that are ≥10 EM sections in strength in both species , which represents the strongest 50% of synaptic connections . Such thresholding also should minimize any potential concerns about comparing synaptic annotations between distinct datasets of different provenance . Of these strong chemical synaptic connections , 32/53 connections were present in both species while 15 and 6 were specific to P . pacificus and C . elegans , respectively . Four of the strong gap junction connections were present in both species while 6 and 2 were specific to P . pacificus and C . elegans , respectively ( Figure 9A ) . To better contextualize similarities and differences in connectivity , we created a circuit diagram that shows a layered output from sensory neurons ( triangles ) onto interneurons ( hexagons ) . We found that , on average , conserved edges are larger and more frequently made by amphid interneurons . Moreover , neurons whose structure is qualitatively most similar between species made more similar synaptic outputs ( Figure 9B ) . Examples include the synaptic output of AFD , whose output is almost exclusively onto AIY , or the patterns of interconnectivity among the amphid interneurons . In contrast , the amphid wing neurons ( AWA , AWB , AWC ) showed multiple connections present in only one species . For example , AWA makes strong synaptic connections to the AIB interneurons exclusively in P . pacificus . Similarly , the P . pacificus polymodal ASH neuron class makes P . pacificus-specific outputs to AIY and is innervated by AIA . Similarly , the AUA is presynaptic to the RIA and RIB interneurons in both species . Like in C . elegans , the AUA neuron is the exclusive target of the ADF neurons . While many of the P . pacificus-specific synaptic contacts concern the connection of sensory neurons to first layer interneurons , many of the C . elegans-specific synaptic contacts concern the connections between sensory neurons . This may indicate an increase in cross-communication of sensory modalities in C . elegans as well as distinctions in which sensory outputs are processed in P . pacificus . Synaptic connections present in only one species could be due to either differences in the structure of neuronal neighborhoods or synaptic specificity among adjacent processes . To distinguish between these possibilities , we computationally determined all axon-axon adjacencies of the amphid circuit in both species as previously described ( Brittin et al . , 2018 ) . To compare across species , and to reduce section thickness and fixation artifacts , we calculated the proportion of adjacency ( total number of EM sections where two processes are adjacent divided by the number of sections in the region of interest ) , similar to previous analyses of neuronal adjacency ( White et al . , 1983 ) . Of the 29 species-specifc connections , there are only 21 pairs of possible neuron-neuron adjacencies due to reciprocal connectivity . Of these 21 neuron-neuron pairs , we found that all make physical contact in both species . The amount of contact did , however , vary across a wide range in both species . Among species-specific neuronal partnerships , we found the percent of axonal-axonal contact ranged from 0 . 95% to 142% ( median contact 26 . 7% ) ( Figure 9C ) . We evaluated whether the amount of neuron-neuron adjacency could predict the strength of a synaptic connection . We compared the correlation between synaptic connectivity and neuron-neuron adjacency and found a very weak , non-significant , correlation in both species ( Spearman’s R = 0 . 2042 , p=0 . 3747 in P . pacificus; Spearman’s R = 0 . 1248 , p=0 . 5898 in C . elegans ) . Together , these results suggest that species-specific differences in connectivity are largely determined by synaptic partnership choice or recognition rather than large differences in axonal fasciculation and neuronal neighborhood changes . Moving beyond anatomy , we assessed the evolutionary divergence of molecular features of individual neurons . Divergences in molecular features between homologous neurons in different species can be expected to be manifested via the species-specific loss or gain of genes that control certain neuronal phenotypes or via the modifications of expression patterns of genes . There appears to be ample evidence for both . For example , several censuses taken for the number of GPCR-type sensory receptor-encoding genes indicated a much smaller number in P . pacificus compared to C . elegans ( Dieterich et al . , 2008; Krishnan et al . , 2014 ) . Similarly , there are species-specific gene losses and gains in the complement of specific subfamilies of taste receptor-type guanylyl cyclases , as discussed above ( Figure 4- figure supplement 1 and 2 ) . On the level of regulatory factors , we noted above the absence of the lsy-6 locus outside the Rhabditidae . In regard to genes that control structural features to neurons , we considered a gene , oig-8 , which was recently identified as controlling the morphological elaborations of the winged cilia of the AWA/B/C neurons in C . elegans ( Howell and Hobert , 2017 ) . oig-8 encodes a single transmembrane , immunoglobulin domain protein that not encoded in the P . pacificus genome . To assess whether this gene loss is responsible for the lack of winged cilia in P . pacificus , we mis-expressed Cel-oig-8 under the Ppa-odr-7 promoter ( described below ) along with the Ppa-odr-7p::rfp marker but found no difference in the terminal dendritic cilia of RFP positive neurons that were co-injected with the Ppa-odr-7p::Cel-oig-8 transgene compared to those that were injected only with the Ppa-odr-7p::RFP marker ( Figure 4—figure supplement 3 ) . This result suggests the two Ppa-odr-7-expressing amphid neurons may lack other factors ( DiTirro et al . , 2019 ) , such as factors involved in ciliary membrane morphogenesis in wing neurons , to realize the branching function of Cel-oig-8 . To assess potential divergences in the expression patterns of conserved genes , we considered three genes that encode regulatory and signaling factors . For the regulatory factors , we chose two transcription factors that are expressed exclusively in a single neuron class in C . elegans , odr-7 , an orphan nuclear hormone receptor uniquely expressed in AWA ( Sengupta et al . , 1994 ) and che-1 , a zinc-finger transcription factor uniquely expressed in the ASE neurons ( Tursun et al . , 2009 ) . Both transcription factors control the differentiated state of the respective neuron class in C . elegans ( Etchberger et al . , 2007; Sengupta et al . , 1994; Uchida et al . , 2003 ) and 1–1 orthologs could be identified in P . pacificus by reciprocal best BLASTP hits as well as protein sequence phylogeny ( Figure 4—figure supplement 4 ) . We generated a reporter gene fusion for the Ppa-che-1 locus and found that transgenic P . pacificus expressed the reporter in two rather than one neuronal pairs in the head region of the worm ( Figure 4E , Figure 4—video 2 ) . The same expression can be observed by smFISH against the endogenous locus ( Figure 4—figure supplement 5 ) . By cell positions , as well as by the axons meeting at the dorsal midline using fluorescence microscopy , we provisionally identified these neurons as the AM5 ( ASE ) and , tentatively , the AM6 ( ASG ) neurons . Expression in AM5 ( ASE ) matches the expression observed in C . elegans and expression in AM6 ( ASG ) is notable because the ASG neuron is also a water-soluble chemical taste receptor ( Bargmann and Horvitz , 1991 ) that can compensate for loss of ASE function under hypoxic conditions ( Pocock and Hobert , 2010 ) . Since the AM5 ( ASE ) neurons of P . pacificus may not be functionally lateralized and , therefore , may not be able to discriminate between cues that are sensed by ASEL and ASER in C . elegans , we speculate that che-1 may endow AM6 ( ASG ) neurons of P . pacificus with chemosensory abilities that allow for the discrimination of ASE- and ASG-sensed water-soluble cues . The expression pattern of the odr-7 transcription factor seems to have diverged more substantially between P . pacificus and C . elegans . Based on cell body position and axonal projections , a Ppa-odr-7p::rfp reporter is expressed in the proposed AM7 ( AWC ) and AM9 ( ADF ) neuron pairs ( Figure 4G–H , Figure 4—video 3 , Figure 4—figure supplement 4 ) , but not in AM3 ( AWA ) , the exclusive expression site of odr-7 in C . elegans . This divergence of expression is particularly notable if one considers that C . elegans odr-7 contributes to the specification of the C . elegans--specific elaborations of the AWA cilia ( Howell and Hobert , 2017 ) . In contrast , P . pacificus AM3 ( AWA ) does not express odr-7 and its cilia do not display the winged elaborations , as discussed above . Since we have not confirmed endogenous odr-7 expression with smFISH , unlike with che-1 , it is conceivable that our reporter construct lacks relevant cis-regulatory elements . In contrast to the reduction of gcy genes important for C . elegans ASE laterality , the P . pacificus genome contains almost all of the downstream G-protein subunit signaling proteins found to be encoded in the C . elegans genome ( Figure 4—figure supplement 4 ) . We therefore established a transgenic reporter strain using the promoter of Ppa-odr-3 , a G-protein subunit homolog known to be expressed in a number of C . elegans sensory neurons , most strongly in AWC , weaker in AWB , and faintly in AWA , ADF , ASH neurons in C . elegans ( Roayaie et al . , 1998 ) . We found consistent Ppa-odr-3p::rfp expression in AM3 ( AWA ) and less robust expression in AM4 ( ASK ) ( Figure 4C–D ) . Hence , like odr-7 expression , the conservation of cell-type specific expression is limited to the amphid neurons but not to the proposed cellular homologs when soma position and axon projection patterns are also considered . In the absence of morphological characteristics such as the winged endings of AWx neurons in C . elegans , we proposed neuronal homologies between P . pacificus and C . elegans amphid neurons based on their relative cell body positions , axon projections , the manner of dendrite entry into the amphid sheath cell , the number of cilia in channel , dye filling properties , and the connections to first layer interneurons . Using specific features present only in one of the neuron types in both species , we have high confidence in identifying the homologs of ASH , ADL , ASJ , and AFD . We have lower confidence for other neuronal counterparts , particularly AWA and ASI , which lack most of these discriminating features . However , by the process of elimination , we are nevertheless confident about the homology assignments for the remaining neurons . Taking the analysis of all 12 amphid neurons together , we found modest conservation in synaptic connectivity , as well as both conservation and divergence in orthologous transcriptional reporters , Ppa-che-1 and Ppa-odr-7 , respectively . Although we have weighted each feature type equally , it is not likely that the same number of genetic changes separates each difference , resulting in the limitation and uncertainty of individual homology assignments as it is often noticed in comparisons of distantly related species . In the future , functional studies targeting the individual P . pacificus amphid neurons will provide the necessary evidence for determining their sensory modalities , such as the AM5 ( ASE ) in salt chemotaxis , the AM12 ( AFD ) in thermotaxis , and the AM3 ( AWA ) and AM7 ( AWC ) neurons in olfaction . While such functional studies are of unique importance to elucidate the neuronal properties of the P . pacificus amphid system , any functional investigation can never fully overcome uncertainties in structural homology assignments because ‘form and function’ represent one of the three crucial antitheses of comparative biology ( Rieppel , 1988 ) . Additionally , homology at different hierarchical levels of biological organization can be independent from each other ( Riedl , 1978; Raff , 1996 ) . We found little overlap between the olfactory profiles of P . pacificus and C . elegans , also because the cell-specific markers , such as GPCRs , are themselves fast evolving ( e . g . C . elegans str-2 as a marker for lateral asymmetry ) . Ultimately , one criterion that might help solving some of the existing homology uncertainties is cell lineage reconstruction . Given that the pattern of cell divisions is largely invariant during development within individual nematode species ( Schierenberg and Sommer , 2014; Memar et al . , 2019 ) , the 1-to-1 homology assignments in the amphid neurons may be resolvable by cell lineage analysis in a way that is currently only known from mollusks ( Katz , 2016 ) . The genomes of C . elegans and P . pacificus are remarkably distinct . Detailed recent analyses using phylotranscriptomics , Illumina , and single molecule sequencing revealed striking differences in gene content and genome organization , which allow to more accurately date their divergence to ~100 million year ago ( Prabh et al . , 2018; Roedelsperger , 2018; Rödelsperger et al . , 2017 ) . In light of this divergence , the extent of similarities of nervous system patterning is remarkable . Neuron number appears invariant , neuronal soma position is restricted , and perhaps most remarkable are the similarities in process outgrowth and relative process position . Relative neighborhoods of processes are retained , including a number of remarkably subtle aspects of process morphology , such as the highly unique and characteristic neighborhood change of the AIB processes or the humped axon morphology of AIY at a specific location . These findings strongly suggest the existence of constraints in patterning of the nervous system , particularly during the phase of axonal and dendritic outgrowth , that is relative placement of processes into specific neighborhoods . Such placement is a critical pre-requisite for proper synaptic targeting choice by constraining which possible targets any given neuron can innervate ( White , 1985 ) . In light of these constraints , it is enlightening to observe a number of striking differences in synaptic connectivity . These changes are likely to generate very distinct avenues of information flow . Whether such changes in synaptic connectivity produce different types of behavior will require further investigation , since fascinating work in mollusks has shown that distinct wiring patterns of homologous neurons in distinct nudibranch species can also produce similar behaviors ( Katz , 2016 ) . In any case , our results argue that alterations in en passant synaptic connectivity between adjacent neurons , rather than initial patterning of neuronal fascicles , are a key substrate of evolutionary change . This is in striking contrast to evolutionary novelties in the olfactory systems of insects , where species-specific differences in olfactory behavior are achieved ( among other mechanisms ) by differences in the axonal targeting properties of olfactory sensory and projection neurons ( Ramdya and Benton , 2010 ) . Our work rather demonstrates striking constraints in axonal patterning , appearing invariant between C . elegans and P . pacificus . These constraints are even more notable if one considers the vast differences in chemotaxis behavior observed in C . elegans and P . pacificus ( Hong and Sommer , 2006 ) . Perhaps the main drivers of these chemotaxis differences are changes in olfactory perception , that is sensory anatomy and sensory receptors , rather than the means by which chemosensory signals are processed . A previous comparative analysis of the pharyngeal nervous system , composed of an isolated circuit of 20 neurons , also found that homologous neurons types display differences in synaptic connectivity between C . elegans and P . pacificus ( Bumbarger et al . , 2013 ) . However , in contrast to our present study , this past analysis has not taken process adjacency into account , thereby leaving it unclear whether pharyngeal synaptic connectivity differences are the result of distinct process placement or distinct synaptic target choice within invariant process neighborhoods . Currently unpublished adjacency analysis of the C . elegans pharyngeal connectome suggests that pharyngeal neurons synapse onto almost all neighboring processes ( S . J . C . , unpubl . data ) , which is in striking contrast to the somatic nervous system where synapses are made only onto a fraction of neighboring processes ( White , 1985 ) . Differences in pharyngeal synaptic connectivity between C . elegans and P . pacificus may therefore be driven by distinct local process placements , a mechanism different from the one described here for the amphid sensory circuit . There are genomic differences between P . pacificus and C . elegans that display tantalizing correlates to some of the specific neuroanatomical diversities . The three main olfactory neurons of C . elegans display elaborated cilia morphology ( winged cilia ) where olfactory receptors are known to localize . The C . elegans genome encodes more than 1300 olfactory-type GPCRs ( Robertson , 2006; Troemel et al . , 1995 ) , many known to be co-expressed in AWA , AWB and AWC ( Troemel et al . , 1995; Vidal et al . , 2018 ) . In contrast , the P . pacificus genome contains significantly fewer olfactory-type GPCRs than C . elegans ( Prabh et al . , 2018 ) , coinciding with less morphological complexity of olfactory cilia . It is tempting to speculate that the expansion of the olfactory receptor repertoire in C . elegans , relative to P . pacificus , and the concomitant expansion of the morphological elaborations in C . elegans are functionally coordinated . Yet despite the smaller GPCR repertoire , P . pacificus exhibits an odor preference profile that has scant overlap with C . elegans ( Cinkornpumin et al . , 2014; Hong and Sommer , 2006 ) , hence one priority for future studies is to identify the amphid neurons that express odor receptors in P . pacificus . Other genome sequence changes reveal intriguing correlates to a specific synaptic alteration that we observed . Like many other neuron pairs , the AM5 ( ASE ) neurons of P . pacificus are electrically coupled . In C . elegans , the left and right ASE neurons are not electrically coupled ( even though the processes are in contact to each other ) , thereby allowing both sensory neurons to discriminate between distinct sensory cues sensed by the left and right ASE neurons ( Ortiz et al . , 2009; Suzuki et al . , 2008 ) . Concomitant with the lack of electrical coupling of ASEL and ASER , a specific subset of ASEL and ASER-expressed rGC-type receptor proteins have expanded in the C . elegans genome ( Ortiz et al . , 2006 ) , thereby expanding the spectrum of chemosensory cues that can be differentially sensed by ASEL vs . ASER . The genetic mechanisms to express this expanded set of C . elegans-specific receptor protein subfamilies in the left versus right ASE protein is triggered by the lsy-6 miRNA , the most upstream regulator of ASEL/R asymmetry ( Cochella and Hobert , 2012; Johnston and Hobert , 2003 ) . Remarkably , this miRNA is a Caenorhabditis genus-specific ‘invention’ , that is it does not exist in P . pacificus ( Ahmed et al . , 2013 ) . In accordance with earlier suggestions ( Etchberger et al . , 2009; Ortiz et al . , 2006 ) , these findings indicate that ASE laterality arose from a bilaterally symmetric ground state . One obvious question for the future is about the functional consequences of the observed differences , especially with regard to the different life styles of P . pacificus compared to C . elegans . While C . elegans is often found in rotten fruits ( Félix and Duveau , 2012 ) and in association with slugs ( Petersen et al . , 2015 ) , P . pacificus was first described from soil samples ( Sommer et al . , 1996 ) and subsequently found in reliable associations with several beetle species around the world ( Herrmann et al . , 2007; Koneru et al . , 2016; Ragsdale , 2015 ) . Additionally , P . pacificus nematodes have been found in insect baits for entomopathogenic associations ( Campos-Herrera et al . , 2019 ) and other vegetal substrates ( Félix et al . , 2018 ) . However , none of these associations allows conclusions about the functional relevance of the divergent morphologies . Instead , they might represent examples of drift . We speculate therefore that ‘network systems drift’ could result in the observed patterns , similar to the developmental systems drift observed in vulva development between C . elegans and P . pacificus ( Wang and Sommer , 2011 ) . However , much work at the interface between neurobiology and ecology is necessary to confirm such hypotheses . The TEM data used in the current study are derived from two sets of slightly different preparations of young adult P . pacificus strain PS312 ( Sommer et al . , 1996 ) hermaphrodites . All of the 3D reconstructions and most descriptions of the morphological details originate from two datasets of roughly 3000 serial sections of 50 nm thickness , covering the anterior parts of two high-pressure-frozen and freeze-substituted adult hermaphrodite ( specimen 107 and specimen 148 ) , which were generated by Daniel Bumbarger . For a detailed methods description see Bumbarger and coworkers ( Bumbarger et al . , 2013 ) . Alignment and manual segmentation was done in TrakEM2 ( Cardona et al . , 2012; Kremer et al . , 1996 ) . Segmentation and 3D reconstruction of the sensory head neurons were initially performed by Tahmineh Sarpolaki ( 2011 ) in the course of her Diploma thesis . The datasets can be accessed freely at https://wklink . org/7348 . Analysis of neuronal adjacencies was performed by using a modified python script written by Christopher Brittin ( Brittin et al . , 2018 ) Circuit diagrams were generated using Cytoscape ( Shannon et al . , 2003 ) and graphs were generated using the ggplot2 package for R ( Wickham , 2016 ) . In this study , as well as other comparisons of nematode electron micrographs , we find that smaller synapses show more variation . Thus , small synapses can be ‘noisy’ – this is likely due to biological noise but also some technical issues with evaluating small synapses . We additionally used sections of four older TEM specimens , three transversely sectioned and one sagittally , which were prepared as follows: worms were placed in 100 µm deep specimen carriers half-filled with thick E . coli OP50 suspension , covered with the flat side of another carrier and high-pressure-frozen with a Bal-tec HPM-10 high-pressure freezer ( Balzers , Liechtenstein ) . Freeze substitution was carried out in a freeze-substitution unit ( Balzers FSU 010 , Bal-Tec , Balzers , Liechtenstein ) according to the following protocol: fix in 2% OsO4 , 0 . 5% UA , 0 . 5% GA in 97 . 5% acetone , 2 . 5% Methanol for 24 hr at −90°C , raise temperature to −60°C in 3 hr , hold for 6 hr , raise to −40°C in 2 hr , hold for 12 hr , keep on ice for 1 hr , wash with 100% acetone , embed in Epon/acetone . Blocks were sectioned with an LKB 2128 Ultratome . Ultrathin sections were viewed in a Philips CM10 or in a Fei Tecnai G2 Spirit T12 transmission electron microscope , images were acquired on photo plates or with a Morada TEM CCD camera , respectively . Clean specimens of adult P . pacificus strain PS312 ( Sommer et al . , 1996 ) were fixed in 2 . 5% glutaraldehyde in PBS , post-fixed with 1% osmium tetroxide in PBS , dehydrated in a graded series of 30% , 50% , 70% , 95% and 100% ethanol , critical-point dried in liquid CO2 and sputter-coated with 10 nm Au/Pd . Inspection was carried out at 15 kV in a Hitachi S-800 field emission scanning electron microscope . We viewed ten processed P . pacificus PS312 hermaphrodite adults ‘face on’ and did not notice any differences in the external structures of the mouth regions among them . P . pacificus California PS312 and transgenic strains were raised on OP50 E . coli seeded NGM plates at 20°C . We used P . pacificus gene names ( PPAxxxxx ) and putative homology from www . wormbase . org based on the most recent P . pacificus Genome Assembly El_Paco ( Rödelsperger et al . , 2017 ) and C . elegans WS269 . To confirm the assigned gene orthology , we looked for best reciprocal BLASTP hits between P . pacificus and C . elegans genomes , as well as 1–1 orthology in gene phylogeny trees . To resolve ambiguities , we also performed BLASTP search against AUGUSTUS gene predictions in the previous P . pacificus Genome Assembly Hybrid1 ( www . pristionchus . org >Genome > Hybrid1 , select track AUGUSTUS2013 ) and the amino acid sequences downloaded under ‘Sequences’ . To make a Ppa-odr-3 reporter plasmid , a ~ 1 . 7 kb long region upstream of the first ATG codon of Ppa-odr-3 ( PPA14189 ) ( FP: GAGCGAGTGAAATGAGCTCAGTCC , RP: GGGTGATCGATACGAGGAGTGTTC ) and the coding sequence of TurboRFP fused to the 3’ UTR of the ribosomal gene Ppa-rpl-23 ( Schlager et al . , 2009 ) were cloned into the pUC19 plasmid using Golden Gate Assembly Mix ( New England BioLabs , E1600S ) following the manufacturer’s instructions . To make the Ppa-daf-6 reporter , a 2 . 4 kb promoter sequence upstream of the start codon Ppa-daf-6 ( PPA15978 ) ( FP: CTCGCCCGTGGATCATGTG , RP: TGCAAATCATTGATTGAATCATGG ) was fused with rfp and Venus by fusion PCR ( Hobert , 2002; Kieninger et al . , 2016 ) . Because the homology assignment for odr-7 on Wormbase . org was Ppa-nhr-66 , we looked for more likely orthology candidates using the AUGUSTUS genome assembly . The best BLASTP hit of Cel-odr-7 was Contig1-aug1055 . t1 ( Contig1:2723982–2725788 ) and the Ppa-odr-7 promoter was amplified and fused to rfp ( FP: AACCAATGCATTGGCTTAGTTGGTTTCACTAATCACTACTG , RP: CCCTTGTCATTCAGATGAGCGAGCTGATCAAGGAG ) . To test if Cel-oig-8 expression in the Ppa-odr-7-expressing neurons is sufficient to induce branching , we fused the 1 . 8 kb Ppa-odr-7 promoter region to the genomic region of Cel-oig-8 ( FP: CCCTTGTCATTCAGATGAGCCTCCTTTCCAATATT , RP: TTACAGGGAGAAAGAGCATGTAG ) and injected it with the Ppa-odr-7p::rfp . Each fusion junction site was verified by Sanger sequencing . For the construction of Ppa-che-1p::rfp , a 3 . 1 kb upstream fragment containing the first exon was amplified and fused with rfp . Although Ppa-che-1 ( PPA01143 ) was not the best hit when using the Cel-che-1 as query , Cel-che-1 was the best hit when using PPA01143 as query . The best hit homolog on Wormbase . org , Ppa-blmp-1 ( PPA04978 ) , is predicted to encode for a much larger protein than both che-1 and Ppa-che-1 ( PPA01143 ) . Curiously , no RFP fluorescence was visible when the reporter did not include the endogenous first exon and intron fused to rfp . The expression pattern observed with this transgene recapitulated the pattern of expression of the endogenous Ppa-che-1 determined by single-molecule fluorescence in situ hybridization ( smFISH ) . The probe set used to stain for Ppa-che-1 was obtained from Stellaris , Biosearch Technologies ( Middlesex , UK ) and targets the validated , full-length Ppa-che-1 sequence . The smFISH staining was performed as previously described ( Ji and van Oudenaarden , 2012 ) . To create complex arrays for transgenesis , wildtype PS312 genomic DNA , the PCR product or the plasmid carrying the target reporter , and the Ppa-egl-20::rfp reporter ( co-injection marker expressed in the tail ) were digested with the FastDigest PstI restriction enzyme ( Thermo Fisher Scientific , FD0615 ) and then mixed at the final concentration of 60 ng/µl for genomic DNA and 10 ng/µl for each plasmid . Prepared mix was injected in the gonad rachis in hermaphrodites ( Cinkornpumin and Hong , 2011; Schlager et al . , 2009 ) . The F1 progeny of injected animals were examined under a fluorescent dissecting microscope and animals that expressed the Ppa-egl-20p::rfp co-injection marker were isolated . All reporters were maintained as extrachromosomal arrays: Ppa-daf-6p::rfp ( tuEx231 ) , Ppa-daf-6p::venus ( tuEx250 ) , Ppa-che-1p::rfp ( lucEx367 ) , Ppa-odr-3p::rfp ( tuEx265 ) and Ppa-odr-7p::rfp ( tuEx296 and tuEx297 ) . Lipophilic dyes DiI and DiO ( Molecular Probes , V22889 and V22886 ) were used as neuronal tracers for a stereotypical subset of amphid neurons to facilitate cell identification either by relative cell position or overlap of red or green fluorescence in combination with transgenic reporters . DiI ( red ) and DiO ( green ) specifically stain five head amphid neurons in C . elegans , but DiO stains only two ( AM2 ( ADL ) and AM8 ( ASJ ) ) of the five ( AM1 ( ASH ) , AM2 ( ADL ) , AM4 ( ASK ) , AM8 ( ASJ ) , and AM9 ( ADF ) ) possible stereotypical amphid neurons in P . pacificus . Well-fed nematodes were washed once in M9 buffer and then incubated for 2 hr at ~23°C with 300 µl of fresh M9 containing 1:150 dilution of DiI or DiO ( 6 . 7 µM ) . The nematodes were subsequently washed twice in 800 µl of fresh M9 buffer and placed onto OP50 E . coli seeded NGM plates to let the worms crawl freely for ~30 min to remove excess dye . For images of DiI-filling in Figure 3A–B , we used AutoDeblur and Autovisualize v9 . 3 ( AutoQuant Imaging , Inc , New York ) to reduce fluorescence background , and MetaMorph v6 . 2r5 ( Universal Imaging Corp . Pennsylvania ) to 3D reconstruct or stack images from multiple planes . Adobe Photoshop and Image J were used to process other images . The amino acid sequences of potential homologs were first identified by BLASTX searches on WormBase . The phylogeny trees were built using the following workflow: alignment and removal of positions with gap with T-COFFEE , Maximum Likelihood phylogeny by PhyML , and tree rendering by TreeDyn ( www . phylogeny . fr ) ( Dereeper et al . , 2008 ) . Midpoint rooting was used and branch support ≥30% is shown . For the nuclear hormone receptor tree building , only the DNA binding C4 domain was used .	Nerve cells , also called neurons , are responsible both for sensing signals from the environment and for determining how organisms react . This means that the unique features of an animal’s nervous system underpin its characteristic behaviors . Comparing the anatomy of the nervous systems in different animals could therefore yield valuable insights into how structural and behavioral differences emerge over time . Behavioral variation often occurs even in similar-looking animals . One example is a group of microscopic worms , called nematodes . Although many nematode species exist , their overall body plans are the same , and the worms of each species contain a fixed number of cells . Despite these apparent similarities , different species of nematodes inhabit a variety of environments and may respond differently to the same signals . The main sensory organs in nematodes are called the amphid sensilla . They are used to detect chemicals , as well as other inputs from the environment such as temperature and pheromones from other nematodes . Although researchers have often speculated that the number of cells in these organs and their arrangement are broadly the same across species , their anatomy had not been studied in detail . Hong , Riebesell et al . compared the detailed structure and genetic features of the sensory systems in two distantly related species of nematode worms , Pristionchus pacificus and Caenorhabditis elegans . These two species behave in different ways , for example , P . pacificus is usually found in association with different species of beetles , while C . elegans is free-living and usually found on rotting fruit . By comparing the two , Hong , Riebesell et al . wanted to determine whether the diverse behaviors observed in the two species could be determined by differences between their sensory systems . Experiments using electron microscopy yielded several thousand high resolution images spanning the entire sensory organ . These images were then used to create detailed reconstructions of the sensory nervous system in each worm species , demonstrating that both species had the same number of sensory nerve cells , allowing one-to-one comparisons between them . Further analysis showed that while the overall structure of the neuronal connections remains the same between the two species , the neurons in P . pacificus made more diverse connections than those in C . elegans . Detailed studies of gene activity also revealed that neurons in each species switched on a slightly different group of genes , possibly indicating that each type of worm processes sensory signals in different ways . These results shed new light on how nervous systems in related species can change over time without any change in neuron count . In the future , a better understanding of these changes could link the evolution of the nervous system to the emergence of different behaviors , in both simple and more complex organisms .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2019	Evolution of neuronal anatomy and circuitry in two highly divergent nematode species
Polarized epithelial morphogenesis is an essential process in animal development . While this process is mostly attributed to directional cell intercalation , it can also be induced by other mechanisms . Using live-imaging analysis and a three-dimensional vertex model , we identified ‘cell sliding , ’ a novel mechanism driving epithelial morphogenesis , in which cells directionally change their position relative to their subjacent ( posterior ) neighbors by sliding in one direction . In Drosophila embryonic hindgut , an initial left-right ( LR ) asymmetry of the cell shape ( cell chirality in three dimensions ) , which occurs intrinsically before tissue deformation , is converted through LR asymmetric cell sliding into a directional axial twisting of the epithelial tube . In a Drosophila inversion mutant showing inverted cell chirality and hindgut rotation , cell sliding occurs in the opposite direction to that in wild-type . Unlike directional cell intercalation , cell sliding does not require junctional remodeling . Cell sliding may also be involved in other cases of LR-polarized epithelial morphogenesis . Left-right ( LR ) asymmetry is a basic feature of metazoan development . Although the outer structures of bilaterians appear bilaterally symmetric , their internal organs are often LR asymmetric in morphology and positioning . The mechanisms for LR asymmetry formation have been studied extensively in vertebrates ( Hirokawa et al . , 2012; Nakamura and Hamada , 2012; Vandenberg and Levin , 2013; Yoshiba and Hamada , 2014 ) . In mice , a leftward flow of extraembryonic fluid , called nodal flow , is generated in the node by the clockwise rotation of cilia . The nodal flow breaks the LR symmetry and eventually induces left-side-specific gene expressions , including Nodal , Lefty , and Pitx-2 , the so-called nodal cassette genes ( Nonaka et al . , 2002; Nonaka et al . , 1998; Okada and Hirokawa , 1999 ) . In addition to nodal flow , various other mechanisms are used in different vertebrates , but they also ultimately lead to the left-side-specific expressions of nodal cassette genes ( Levin , 2005 ) , which influence the LR asymmetric morphology and positioning of internal organs ( Huang et al . , 2014; Yoshiba and Hamada , 2014 ) . However , it was recently reported that the LR asymmetric looping of the zebrafish heart develops by a tissue-intrinsic mechanism , independent of nodal ( Noël et al . , 2013 ) . Therefore , parallel mechanisms are involved in the LR asymmetric development of vertebrates . LR asymmetry has been reported at the cellular level , as well as in organs ( Chen et al . , 2012; Wan et al . , 2011; Xu et al . , 2007 ) . Many mammalian cell lines adopt an LR asymmetric shape when cultured on a micropattern ( Chen et al . , 2012; Raymond et al . , 2016; Wan et al . , 2011; Worley et al . , 2015 ) . The LR asymmetric cell shape is termed ‘cell chirality’ because the cell shape cannot be superimposed on its mirror image . Cell chirality is observed in both the shape and behavior of cells . Cultured zebrafish melanophores show chirality in cellular locomotion and in cytoplasm swirling ( Yamanaka and Kondo , 2015 ) . Fibroblasts from human foreskin seeded on a micropattern exhibit a chiral swirling of actin fibers ( Tee et al . , 2015 ) , and cultured neutrophils show LR-biased movement in the absence of positional cues ( Xu et al . , 2007 ) . However , the physiological roles of cell chirality in vertebrates remain unknown . An in vivo function of cell chirality was first discovered in the Drosophila embryonic hindgut ( Taniguchi et al . , 2011 ) , which first forms as a bilaterally symmetric structure and then rotates 90° counterclockwise as viewed from the posterior , showing dextral looping ( Hozumi et al . , 2006 ) . The posterior end of the hindgut does not rotate , and thus the hindgut twists as a whole . The hindgut epithelial cells are probably responsible for this rotation , since the LR defect in hindgut rotation in mutants is fully rescued when the responsible genes are expressed specifically in hindgut epithelial cells ( Hozumi et al . , 2006; Taniguchi et al . , 2011 ) . Before the directional rotation begins , the anterior-posterior axis of the hindgut can be defined , because its simple tubular structure extends in the anterior-posterior direction , and the hindgut epithelial cells exhibit an LR asymmetric shape of their apical surface with respect to the anterior-posterior axis ( Taniguchi et al . , 2011 ) . Because hindgut epithelial cells have apical-basal polarity , like other epithelial cells , their LR asymmetric shape can be regarded as chiral . The LR asymmetric shape eventually disappears and the cells become symmetric after the rotation ( Taniguchi et al . , 2011 ) . A previous computer simulation showed that the introduction and subsequent dissolution of cell chirality are sufficient to induce the rotation of a model epithelial tube ( Taniguchi et al . , 2011 ) . During the rotation , neither cell proliferation nor cell death occurs in the hindgut ( Lengyel and Iwaki , 2002; Wells et al . , 2013 ) , indicating that cell-shape changes and/or cell rearrangements are involved in this process . Together , these observations indicate that cell chirality drives the counterclockwise rotation of the hindgut . However , the cellular dynamic mechanism by which cellular chirality is converted into axial rotation of the hindgut remains unknown . In addition to cell chirality , various other cellular dynamic mechanisms contribute to the morphological changes of epithelial tissues , such as cell intercalation and cell deformation . Cell intercalation involves anisotropic cell-boundary remodeling ( Bertet et al . , 2004 ) . For example , if cells intercalate in a medial direction , the tissue becomes narrower and elongates along the axis perpendicular to the medial direction ( Honda et al . , 2008; Tada and Heisenberg , 2012; Uriu et al . , 2014 ) . Polarized cell intercalation is important in convergent extension , which induces morphological changes in early embryogenesis , such as the germband extension in Drosophila and the dorsal mesoderm extension in zebrafish and Xenopus ( Bertet et al . , 2004; Shih and Keller , 1992 ) . Convergent extension is also required for organogenesis . For example , tubular structures , such as the Drosophila trachea and hindgut and the vertebrate kidney and cochlea , elongate by convergent extension ( Chen et al . , 1998; Iwaki and Lengyel , 2002; Karner et al . , 2009; Wang et al . , 2005 ) . Cell intercalation also contributes to LR asymmetric morphogenesis . For example , LR biased junctional remodeling induces the directional rotation of the Drosophila male genitalia ( Sato et al . , 2015a ) . Cell deformation is another mechanism that plays important roles in epithelial morphogenesis . During gastrulation and neurulation , the apical constriction of epithelial cells is important for invagination and tubular structure formation ( Inoue et al . , 2016; Munjal and Lecuit , 2014 ) . Thus , one of these cellular dynamic mechanisms or an as-yet undescribed mechanism might be involved in the cell-chirality-driven hindgut rotation . Here , to investigate the cell dynamics underlying the counterclockwise rotation of the Drosophila hindgut , we first performed live imaging and revealed a novel cellular behavior we call ‘chiral cell sliding , ’ in which cells directionally change their position relative to their subjacent ( posterior ) neighbors by sliding in one direction . We found that this cell sliding did not require junctional remodeling . We then performed an improved 3D computer simulation analysis of the cell-chirality-driven hindgut rotation , which confirmed that chiral cell sliding is a dynamic cellular process induced by the dissolution of cell chirality . Our findings collectively showed that chiral cell sliding is a novel mechanism that induces anisotropic changes in tissue morphology . Our previous genetic analyses suggested that the hindgut itself generates the mechanical force that drives its rotation ( Taniguchi et al . , 2011 ) . Here , we found that an explanted wild-type hindgut rotated counterclockwise in vitro , confirming that the hindgut itself generates an active force to accomplish the rotation ( Figure 1—figure supplement 1 , Figure 1—video 1 ) . To investigate what kind of cellular behaviors contribute to the hindgut rotation in vivo , we observed the movement of hindgut epithelial cells by live imaging . We used a Gal4-UAS system ( Brand and Perrimon , 1993 ) . The expression of UAS-redstinger ( a nuclear marker ) and UAS-myrGFP ( a membrane marker ) was driven by a hindgut-specific driver , byn-gal4 ( Barolo et al . , 2004; Iwaki and Lengyel , 2002; Pfeiffer et al . , 2012 ) . We aligned embryos in the anterior-posterior direction , observed them from the dorsal side , and took movies for 2 hr from the onset of hindgut rotation . The hindgut started to rotate at late embryonic stage 12 , when the germ band was almost completely retracted ( Figure 1A1 ) . Before rotation , the hindgut is LR symmetric and has a hook-like shape pointing to the ventral side of the embryo ( Lengyel and Iwaki , 2002 ) . The hindgut twisted 90° counterclockwise , and this rotation was fully complete by 2 hr ( Figures 1A1–3 , Figure 1—video 2 ) . After completion of the rotation , the hindgut had a hook shape that pointed rightward ( Figure 1A3 ) . We analyzed the cell displacement by observing the positions of nuclei . This analysis was done in 2D , because the z resolution ( 1 . 5–1 . 7 μm ) was much lower than the xy resolution ( 0 . 313 μm ) . To minimize the loss of displacement by projecting the image to 2D , we analyzed only the central three columns of cells ( Figures 1B , C1–3 ) . It is important to consider the hook shape of the hindgut when analyzing the displacement . Cells located in the hook part of the anterior region of the hindgut are affected more by the rotation of the whole hindgut than cells located in the posterior root part . Even when cells do not change their relative positions , they appear to move leftward if they are located posterior to the hook peak , whereas they appear to move rightward when located anterior to the hook peak ( Figure 1B ) . To minimize this apparent LR asymmetric displacement that is intrinsically associated with the hook-like structure , we analyzed the cell displacement in the sub-root part of the hindgut , which is about 20–40% of the hindgut in length ( red and blue boxes in Figure 1B ) . Using time-laps movies , we tracked cells located in the dorsal side of the hindgut by their nuclear position in individual time frames ( 5 min intervals ) ( red boxes in Figure 1B ) . The cells changed their position relative to the cells located below them ( posterior in the embryo ) by sliding leftward ( Figures 1C1–3 , Figure 1—video 3 ) . We next measured the relative displacement in the x direction of each cell against its subjacent neighbor [placed at ( 0 , 0 ) in xy coordinates] every 30 min ( Figure 1E ) . ‘Minus’ displacement indicates leftward movement , while ‘plus’ displacement indicates rightward movement . The displacement of wild-type epithelial cells in the dorsal side was about −0 . 5 μm , indicating that the cells moved significantly leftward in relation to their subjacent neighbors ( Figure 1F , dorsal ) . As a control , we measured the LR displacement in the root-most part of the hindgut , where our time-lapse analysis did not detect displacement ( Figure 1—figure supplement 2 ) and found that the value was negligible ( Figure 1F , ctl ) . To confirm that this movement was not caused by an apparent displacement caused by the hook-like shape of the hindgut , we also measured cell movement on the ventral side ( blue boxes in Figure 1B ) . If cell displacement contributed to the rotation , the cell displacement in the ventral side should occur in the opposite direction to that in the dorsal side ( Figure 1B ) . In contrast , an apparent displacement associated with the hook-like structure of the hindgut should be observed in the same direction , even on the ventral side . For this experiment , we used two-photon confocal microscopy , because the ventral side is more than 50 μm deep from the dorsal surface of the embryo . We visualized the cell nuclei using UAS-stinger ( a nuclear GFP ) ( Barolo et al . , 2004 ) or UAS-NLS-tdTomato with byn-Gal4 , by the same technique used for the dorsal side ( Figures 1D1 and 2 , Figure 1—video 4 ) . Although we could only obtain data from low numbers of cells because most images of the deep ventral region gave poor signals , even using two-photon microscopy , the results revealed a significant opposite directional movement of cells on the ventral side ( Figure 1F , ventral ) . These results together suggested that during the counterclockwise rotation of the hindgut , cells change their position relative to their subjacent cells ( posterior neighbors ) by sliding in the direction of rotation ( Figure 1C , D , F ) . Here , we refer to this novel cellular behavior as ‘cell sliding . ’ To investigate the contribution of cell sliding to the directional rotation of the hindgut , we also examined the dynamic hindgut cell behavior in a null mutant of the Myosin31DF ( Myo31DF ) gene , which encodes the Drosophila ortholog of Myosin ID ( Hozumi et al . , 2006; Spéder et al . , 2006 ) . In Myo31DF mutants , the LR asymmetry of various organs is reversed ( Hozumi et al . , 2006; Spéder et al . , 2006 ) . More than 80% of the Myo31DF embryos showed inverted hindgut rotation , with sinistral looping ( Figures 2A1–3 , Figure 2—video 1 ) ( Hozumi et al . , 2006 ) . In Myo31DF mutants , the cell chirality of the hindgut epithelial cells before the onset of rotation is also inverted ( Taniguchi et al . , 2011 ) . Furthermore , the chirality of Myo31DF cells is determined cell-autonomously ( Hatori et al . , 2014 ) . Using the same analysis as for wild type , we examined the behavior of hindgut epithelial cells in the dorsal side of the hindgut during its inverted rotation in Myo31DF homozygotes . We found that these cells changed their relative position by sliding rightward , which is the opposite direction to that observed in the wild-type cells ( Figures 2B1–3 , Figure 2—video 2 ) , and the ventral cells slid to the left side ( Figure 2C1 and , Figure 2—video 3 ) . Quantitative analysis confirmed that the cells on both the dorsal and ventral sides of the Myo31DF hindgut slid in a direction opposite to that of wild-type cells ( Figure 2D , dorsal and ventral , cf . Figure 1F ) . As a control , we again measured the LR displacement in the root-most part of the hindgut where the rotation was negligible , and confirmed that cell sliding was hardly observed ( Figure 2D , ctl ) . These results showed that the direction of cell sliding is consistent with the rotation direction of the wild-type and Myo31DF hindgut . In addition , the enantiomorphic status of the cell chirality before rotation also coincided with the directionality of the rotation of the wild-type and Myo31DF hindgut ( Taniguchi et al . , 2011 ) . Therefore , the initial cell chirality appeared to determine the direction of the cell sliding . It was possible that the cell sliding we observed was coupled with anisotropic cell intercalation , which plays many important roles in epithelial morphogenesis ( Bertet et al . , 2004; Sato et al . , 2015a ) . In particular , Sato et al . showed that directional cell intercalation is important in male genitalia rotation , another case of LR asymmetric morphogenesis in Drosophila ( Sato et al . , 2015a ) . To evaluate the potential contribution of LR directional cell intercalation to the cell sliding observed in the hindgut epithelium , we analyzed junctional remodeling , which is a critical feature of cell intercalation ( Bertet et al . , 2004 ) . To investigate the junctional dynamics during cell sliding , we tracked the cell boundaries using UAS-myrGFP and byn-gal4 in vivo ( Figure 3A , B ) . The results revealed that cells maintained their junctions during cell sliding in both the wild type and the Myo31DF mutant in most cases ( Figure 3A , B , Figure 3—video 1 , Figure 3—video 2 ) . The frequency of cell-intercalation events accompanied by junctional remodeling was 9 . 2 and 6 . 0% for wild type and the Myo31DF mutant , respectively ( Table 1 , Figure 3—figure supplement 1A , B ) . Therefore , more than 90% of the cell junctions were not remodeled during cell sliding , suggesting that the cell sliding does not necessarily require junctional remodeling . Although the frequency of junctional remodeling was low , it still might have been associated with LR directional cell intercalation . Therefore , we also examined the directionality of the cell intercalation during hindgut rotation . When two cells in a column had initial contact and were subsequently separated by another intervening cell , cell intercalation was said to occur , and the direction from which the intervening cell came was determined ( Figure 3—figure supplement 1A , B ) . The frequency of leftward versus rightward intercalations was similar in both the wild-type and Myo31DF hindguts ( Table 1 ) , indicating that the direction of cell intercalation during hindgut rotation does not have an LR bias . We also measured the angles of the cell boundaries undergoing junctional remodeling in the wild-type and Myo31DF hindgut as described by Sato et al . ( Sato et al . , 2015a ) . In both the wild-type and Myo31DF hindgut , all of the diminishing cell boundaries had an angle with an absolute value less than 30° , and most of them were within ±10° ( Figure 3—figure supplement 1C , D ) . These results further indicated that cell intercalation does not have a major role in the LR directional rotation of the hindgut . Instead , this LR symmetrical cell intercalation contributes to elongation of the hindgut in the anterior-posterior direction through convergent extension ( Iwaki and Lengyel , 2002 ) . However , it might also amplify the LR asymmetry by increasing the length of the tilted cell columns . In male genitalia rotation , the distribution of Myosin II ( MyoII ) to the apical cell boundary is anisotropic , which is responsible for the LR asymmetric junctional remodeling ( Sato et al . , 2015a ) . Here , we investigated the distribution of MyoII in cell boundaries of the hindgut epithelium . We performed live imaging of MyoII using UAS-sqhGFP driven by byn-gal4 to visualize the medial and junctional MyoII in the apical regions of hindgut epithelial cells . However , we failed to detect clear anisotropy in the distribution of MyoII ( Figure 3—video 3 ) . This finding was consistent with our observation that an LR bias in cell interactions was not observed in the hindgut epithelium ( Table 1 , Figure 3—figure supplement 1 ) . Our results suggest that the cell sliding is not caused by cell intercalation but by cell deformation that is probably associated with the dissolution of cell chirality during the hindgut rotation , as reported before ( Taniguchi et al . , 2011 ) . To investigate this possibility , we examined the cell-shape change during the cell sliding by measuring the angle changes in the boundaries between cells aligned in a column every 30 min during hindgut rotation ( Figure 3C , D ) . We then classified the changes in these angles as counter-clockwise ( ccw ) , unchanged ( uc ) , and clockwise ( cw ) ( Figure 3E ) . The boundaries between cells aligned in a column tended to tilt in a ccw direction in wild type and in a cw direction in Myo31DF mutants during hindgut rotation ( Figure 3F ) . These results suggested that cell sliding is accompanied by cell deformation associated with the dissolution of cell chirality . To verify that the cell sliding observed in vivo is responsible for the hindgut rotation , we performed a computer simulation analysis . We previously constructed a computer model that suggested that the dissolution of cell chirality in the epithelial cells induces rotation of the hindgut epithelial tube ( Taniguchi et al . , 2011 ) . However , this prototype model was a 2D polygonal model artificially converted to a tube in 3D space , in which the vertices moved in 2D ( Taniguchi et al . , 2011 ) . In addition , the model did not precisely recapitulate the architecture of the hindgut epithelial tube , so it could not be used to verify the cellular dynamics during hindgut rotation . Thus , in the present study , we performed a computer simulation using a 3D cell-based vertex model for tissues ( Honda and Nagai , 2015; Honda et al . , 2008; Honda et al . , 2004; Nagai and Honda , 2001 ) . A new vertex model for a cell sheet in a 3D space , in which vertices can move in 3D , was constructed ( Figure 4A ) . In this model , 452 cells ( mean polygonal area of about 1 ) were placed in a 3D space in which the relative diameter and length of the tube reflected the in vivo situation ( for the definition of the polygon area , see Materials and methods and Figure 4—figure supplement 1 ) . The initial configuration of the model tube was obtained computationally ( see Materials and methods ) . In the model , the LR asymmetry ( chirality ) of the hindgut epithelial cells in vivo was recapitulated qualitatively , which was achieved by anisotropic contraction of the edges ( see Materials and methods ) ( Taniguchi et al . , 2011 ) . The axes of the model cell polygons tended to tilt leftward before starting the twist ( t = 0 ) ( Figure 4C ) , as observed in vivo ( Figure 4—figure supplement 2 ) . In addition , the boundaries of the model cell polygons slanted leftward ( −90 to 0° ) more frequently than rightward ( 0 to 90° ) before starting the twist ( t = 0 ) ( Figure 4D ) , as previously reported in vivo ( Taniguchi et al . , 2011 ) . In the previous study , the dissolution of cell chirality was achieved by releasing the anisotropic edge contraction ( Taniguchi et al . , 2011 ) . Thus , we performed a relaxing procedure in the computer simulation , in which there was no anisotropic contraction of the edges ( wαk = 1 , everywhere ) . The process of structural changes in the model hindgut is shown as still shots at t = 1 . 0 and t = 80 . 0 ( Figure 4A ) . The tube had twisted 88 . 5° at t = 80 . 0 , forming a left-handed screw ( its tip was oriented anteriorly ) , which could be observed by coloring blue an array of cells that lined up straight along the anterior-posterior axis of the model gut tube before the twist and gradually became slanted during the twisting ( Figure 4A , Figure 4—video 1 ) . The tube then continued to twist more slowly ( 91 . 0° at t = 100 ) . To examine the cellular dynamics associated with the hindgut tube twisting using the model , we characterized the changes in the polygon structures over time ( Figure 4C–E ) . We found that most of the initial leftward tilt of the cell axes shifted to neutral as the simulation progressed ( Figure 4C ) , which recapitulated the in vivo observation ( Figure 4—figure supplement 2 ) . This simulation also demonstrated that the dominance of leftward tilted cell boundaries at the beginning became less prominent or almost disappeared after the rotation ( Figure 4D ) , which was also consistent with our previous observation in vivo ( Taniguchi et al . , 2011 ) . Thus , during the rotation , the cell chirality was gradually lost in the model hindgut . Our in vivo analyses above indicated that cell sliding is a potential dynamic cell process for driving hindgut rotation . To test this possibility using the model , we investigated the detailed changes occurring in individual polygons during the twisting of the model hindgut tube , as shown in a higher magnification image of the model ( Figure 4E ) . We found that in the early phases of the simulation , the polygons quickly became rounder or less polarized in shape , suggesting that the loss of chirality in the cell shape began even before the polygons started to prominently change their relative positions ( Figure 4E , t = 1 . 0 ) . This rapid cell deformation was also observed quantitatively , as the initial leftward biases of the cell axes ( dark-colored bars , Figure 4C ) and cell boundaries ( dark-colored bars , Figure 4D ) were largely abolished at t = 1 . 0 ( light-colored bars , Figure 4C , D ) . Subsequently ( t = 80 . 0 ) , the polygons slowly changed their relative positions in a leftward direction ( Figure 4E , t = 80 . 0 ) . Thus , the loss of cell chirality began before the changes in the relative positions of the cells ( called cell sliding in this study ) . During cell sliding , the cell shape change became less prominent but still continued , given that the LR biases observed at t = 1 . 0 were further randomized at t = 80 . 0 ( compare light-colored and white bars , Figure 4C , D ) . These results suggested that the mechanical force was not yet balanced at t = 1 . 0 , which presumably induced further events , including the cell sliding . The time lag between the initiations of cell deformation and of cell sliding suggests that these two events were mechanically distinct , probably due to the different viscoelastic properties of individual cells versus cell aggregates in our model ( Honda et al . , 2004 ) . However , it was difficult to analyze such a time lag in vivo , because we could not simultaneously obtain high-resolution time-lapse images of nuclei and cell boundaries due to the thickness of the hindgut . Nevertheless , our 3D vertex model recapitulated chiral cell sliding that was associated with a loss of cell chirality . Our simulation suggested that the cell chirality loss and the cell sliding are mechanically distinguishable processes . This idea was supported by another theoretical simulation , in which we fixed the vertices at the top and bottom of the tube , so the tube could not rotate ( Figure 4—figure supplement 3A–D ) . In this simulation , the polygons did not slide in one direction ( Figure 4—figure supplement 3D ) , while the cell chirality was still lost ( Figure 4—figure supplement 3B , C ) , indicating that the cell sliding or gut-tube rotation is not required for the loss of cell chirality . These results also showed that the loss of cell chirality and the cell sliding are not always coupled , further suggesting that they are distinct mechanical events . Therefore , this simulation further supported the idea that the mechanical force bias induced by cell chirality drives directional cell sliding and gut-tube rotation . In the in vivo studies above , we also showed that the cell chirality and chiral cell sliding in Myo31DF mutants in which the direction of hindgut rotation was reversed were mirror images of their wild-type counterparts ( Taniguchi et al . , 2011 ) . This result suggested that the initial enantiomorphism of the cell chirality determines the direction of cell sliding , which consequently defines the direction of the hindgut rotation . To verify this idea , we performed another simulation recapitulating the inverted rotation of the model gut tube . In this simulation , we introduced the enantiomorphic chirality into the 3D vertex model , and otherwise the same parameters were used as described above . We found that the inverted rotation and cell sliding were recapitulated in this simulation ( Figure 4F , Figure 4—figure supplement 4 , Figure 4—video 2 ) . These results supported the idea that the initial cell chirality determines the direction of cell sliding , as predicted in the in vivo studies above . Our in vivo studies suggested that cell intercalation does not play a major role in the LR directional rotation of the hindgut . Consistent with this idea , we found that cell intercalation rarely occurred during the simulation , although our model allowed for it [it occurred only 12 times for 452 vertices ( 2 . 7% ) by t = 80]; for example , we did not observe any cell intercalation among the array of blue colored cells ( Figure 4A ) . The frequency of cell intercalation in vivo ( 9 . 2% every 30 min for 60 min ) was much higher than that of our vertex model . This discrepancy was probably because our model did not include the anterior-posterior elongation ( convergent extension ) of the hindgut , which occurs in vivo at the same time as the hindgut rotation ( Lengyel and Iwaki , 2002 ) . Cell intercalation is known to be required for the convergent extension of the hindgut ( Johansen et al . , 2003 ) . To confirm that cell deformation rather than cell intercalation is a major driving force of the cell sliding and the hindgut rotation , we generated a computer simulation that did not allow junctional remodeling ( Figure 4B ) . In this simulation , the cells still slid and the tube rotated ( twist angle 83 . 1° at t = 80 ) . In this respect , the cell sliding might be considered a type of autonomous shear deformation at the multicellular level , although it is not directly induced by the loss of chirality in the shape of each individual cell or by a simple LR asymmetric elongation of the cell . Thus , collectively , the simulations indicated that cell sliding induced by a cell-chirality-provoked mechanical force bias is a dynamic cellular process connecting cell chirality and LR asymmetric tube twisting . In conclusion , our in vivo and in silico analyses both demonstrated that cell sliding converts the intrinsic chirality of the cell shape into the LR asymmetric epithelial morphogenesis though chiral cell deformation . The morphogenesis of epithelial cell sheets in the absence of cell number change has been explained as a consequence of cell-shape changes and/or cell rearrangements . Regarding cell rearrangements , the cell intercalation in convergent extension , in which cells intercalate in a medial direction to induce cell sheets to elongate in an anterior-posterior direction , is well described ( Bertet et al . , 2004; Honda et al . , 2008; Uriu et al . , 2014 ) . Directional cell intercalation is also important in the LR asymmetric rotation of the male genitalia in Drosophila ( Sato et al . , 2015a ) . However , in the present study , we revealed that the LR asymmetric rotation of the embryonic Drosophila hindgut is achieved by chiral cell sliding , in which cells change their relative position against their subjacent neighbors in one direction . In contrast to directional cell intercalation , this cell sliding does not necessarily require cell rearrangements ( Figure 3A , B ) . Indeed , we showed that a computer simulation in which cell-boundary remodeling was prohibited still rotated as well as our standard 3D vertex model hindgut ( Figure 4B ) . Thus , in the hindgut case , we speculate that the accumulation of small chiral cell sliding events between adjacent cells in the hindgut epithelial tube are sufficient to induce the 90° rotation of the hindgut epithelial tube , because of the large difference between the radius and the height of this organ . Considering that cell sliding and cell rearrangement may not be mutually exclusive , the definition of cell sliding may be extended . For example , theoretical models of Drosophila male genitalia rotation include the sliding motion of cells ( Sato et al . , 2015a , 2015b ) , although this mechanism also requires cell rearrangements , because a large amount of cell displacement takes place . In any event , for the dynamic cell events induced by cell chirality , cell sliding appears to drive the LR asymmetric rotation of tissues with or without cell intercalations . Thus , cell sliding may be a commonly used mechanism in LR asymmetric morphogenesis , although it has not been demonstrated to date . Directional cell intercalation often requires a biased subcellular distribution of Myosin II ( Bertet et al . , 2004; Sato et al . , 2015a ) . For example , during the LR asymmetric rotation of Drosophila genitalia , LR asymmetric cell intercalation is induced by the LR asymmetric distribution of Myosin II ( Sato et al . , 2015a ) . However , in the Drosophila hindgut , a biased Myosin II distribution was not detected in fixed ( Hatori et al . , 2014; Taniguchi et al . , 2011 ) or live tissues ( this study ) . Given that chiral cell sliding requires a very small amount of change in the relative positions of cells , a large bias in the amount of Myosin II may not be required . Indeed , our simulation revealed that the anisotropic contraction of edges before but not during the rotation was sufficient to induce whole-organ rotation ( Figure 4A ) , whereas the anisotropic contraction of edges needs to be maintained in the computer simulation to recapitulate the rotation of Drosophila male genitalia ( Sato et al . , 2015a ) . Thus , if there is any bias in the subcellular distribution of Myosin II , it might be too subtle to detect by the methods we used ( Hatori et al . , 2014; Taniguchi et al . , 2011 ) . Alternatively , force-generating mechanisms other than the biased expression of Myosin II might be used in the LR asymmetric rotation of the hindgut . In our computer simulation , we recapitulated the LR directional axial rotation of the gut tube , which is driven by a mechanical force bias induced by cell chirality . Our analyses of the model cells in the simulations suggested that the rotation of the model gut tube is achieved through two steps: the first is a deformation in the shape of each cell ( loss of cell chirality ) without a change in the relative positions of anterior-posterior neighboring cells , and the second is a change in the relative positions of anterior-posterior neighboring cells ( called cell sliding in this study ) , which presumably drives the rotation of the model gut tube . Although the cell deformation and cell sliding temporally overlapped in part ( Figure 4A , C–E ) , the following observations suggested that these two events are mechanically distinct . First , the initiation of cell chirality loss mostly preceded the onset of cell sliding ( Figure 4A , C–E ) . The timing of the model gut tube rotation coincided with that of the cell sliding ( Figure 4A , E ) , but not of the cell chirality loss ( Figure 4C–E ) , suggesting that the rotation of the model gut tube involves two cellular dynamic steps . Second , in a simulation prohibiting the tube rotation , cell sliding did not occur , although the loss of cell chirality was still observed ( Figure 4—figure supplement 3 ) , indicating that the loss of cell chirality is not always coupled with cell sliding . Based on these results , we speculated that the loss of cell chirality and the cell sliding are distinct mechanical processes . The differences between the cell chirality loss and cell sliding could be explained by their distinct mechanical properties . In our simulation , the equalization of the initial anisotropic contractile force was the only source driving subsequent events . During the balancing processes , individual cells were deformed quickly; most of the deformation was achieved by t = 1 . 0 , although the force balance had not yet been neutralized given that the deformation continued to t = 80 . 0 ( Figure 4C , D ) . In vertex models , a viscoelastic property is generally reflected in the speed of the response upon a force application ( Honda et al . , 2008 , 2004 ) . Thus , we speculate that changes in the relative positions of cells ( cell sliding ) slowly occurred after the major dissolution of cell chirality , due to the difference in viscoelastic properties of the individual cell versus the cell aggregate , until the initial anisotropy of the force was neutralized . In turn , completion of the cell sliding led to global stabilization of the model tube . Our conclusions about the mechanical properties of chiral cell sliding were based on the computer simulation . However , our ideas did not contradict the observations made in the embryonic hindgut in vivo . In this study , we also found that the hindgut itself generates the active force for driving its rotation . Considering that cell chirality was observed before the hindgut rotation and was lost during the rotation in vivo , the cancelation of cell chirality is probably a primary driving force for the subsequent events including the cell sliding and hindgut rotation , in vivo . This result also excludes the possibility that the cell sliding is a consequence of hindgut rotation driven by an external force , further supporting the validity of our computer simulation for analyzing the relationship between cell chirality loss and cell sliding . In addition , the enantiomorphism of the initial cell chirality is correlated with the LR direction of the cell sliding , based on our analyses of the LR inversion mutant , Myo31DF ( Figure 2D ) . Thus , the LR direction of the cell sliding appears to depend on the initial cell chirality . These results are consistent with the conclusions obtained from our computer simulations . In conclusion , we propose that chiral cell sliding is a cell dynamic mechanism that connects cell chirality with the LR asymmetric rotation of the hindgut tube . In vertebrates , organ laterality is thought to be determined by the LR body axis established by Nodal cassette gene expressions , which is distinct from the mechanisms reported for Drosophila ( Huang et al . , 2014; Yoshiba and Hamada , 2014 ) . However , a recent study revealed that LR asymmetry formation in the zebrafish heart does not depend on nodal gene expression ( Noël et al . , 2013 ) . Moreover , explanted linear hearts of various species develop dextral looping in culture ( Bacon , 1945; Manning and McLachlan , 1990; Noël et al . , 2013 ) , suggesting that the heart LR asymmetry formation is tissue-intrinsic . In Drosophila , which lacks the Nodal cassette gene functions , various organs use cell chirality as a common strategy to establish LR asymmetry in a tissue-intrinsic manner , in the absence of an LR body axis ( González-Morales et al . , 2015; Sato et al . , 2015a; Taniguchi et al . , 2011; Vorbrüggen et al . , 1997 ) . To date , cell chirality in tissues has been observed only in Drosophila ( González-Morales et al . , 2015; Sato et al . , 2015a; Taniguchi et al . , 2011 ) . However , cell chirality has been reported in cultured cells , including those of vertebrates ( Chen et al . , 2012; Raymond et al . , 2016; Wan et al . , 2011; Xu et al . , 2007 ) . Importantly , many cell types from various organs show a chiral cell shape ( Chen et al . , 2012; Raymond et al . , 2016; Wan et al . , 2011 ) . Therefore , cell chirality may play specific roles in the LR asymmetric organogenesis in vertebrates as well . Dechorionized Drosophila embryos were placed on grape juice agar plates . Late stage 12 embryos of the appropriate genotype were selected under florescence microscopy and mounted dorsal side up on double sticky tape on slide glasses . We added oxygen-permeable Halocarbon oil 27 ( Sigma ) , and overlaid a coverslip of regular thickness over the embryos using 0 . 17–0 . 25 mm-thick coverslips as spacers . We imaged embryos every 5 min for 2 hr with a scanning laser confocal microscope , LSM 700 ( Zeiss ) or A1 ( Nikon ) at 22–25°C . The fly strains used were byn-Gal4 ( Iwaki and Lengyel , 2002 ) , UAS-redstinger ( BL8547 ) ( Barolo et al . , 2004 ) , UAS-stinger ( BL28845 ) , UAS-myrGFP ( JFRC29 ) ( Pfeiffer et al . , 2012 ) , UAS-NLS-tdTomato , UAS-sqhGFP ( see below ) , and Myo31DFL152 ( Hozumi et al . , 2006 ) . For the cell movement analysis , we tracked the position of the cell nucleus manually using ImageJ software and measured the x , y coordinates every 30 min . We also used a particle analysis plugin for tracking , and obtained comparable results . We only analyzed the three central columns of cells in the root part of the hindgut , to minimize the influence of the tubular hook shape of the hindgut on the displacement measurements . In the analysis , we set the subjacent cell position as ( 0 , 0 ) and measured the relative displacement of the upper cell in the x direction ( Figure 1E ) . Statistical analyses were performed using Student’s t-test . For the cell deformation analysis , we measured the angle changes in the boundaries between cells aligned in a column every 30 min . Statistical analyses were performed using the χ2 test . For the cell intercalation analysis , we used both the cell nucleus and boundary images . We defined cell intercalation as two cells that had initial contact and were separated by another cell within 30 min . We calculated the frequency by dividing the number of intercalation events by the total number of examined cells . We also examined the direction from which the intervening cell came and the angles of the diminishing cell boundaries . Statistical analyses were performed using the χ2 test . Summary: The model tube consisted of 452 polygonal cells . The polygons covered the surface of the tube without overlaps or gaps . The height of the tube was 28 . 7 , its diameter was 5 . 0 , and the mean polygonal area was about 1 . The cell number and ratio of the height to diameter reflected those of the hindgut in vivo . The polygonal pattern of the surface was described by the positions of vertices ( x- , y- , z-coordinates ) of the polygons . Movements of the vertices were calculated by differential equations , such as the cell-based vertex dynamics for tissues , which has a potential energy term ( see below ) . The potential energy term mainly involved the edge energy and elastic energy of the polygonal area . According to vertex dynamics , when the vertices moved , the edge energy and the surface elastic energy were reduced . We added another process to the system of differential equations: when the edge length became less than a critical length δ , a reconnection of vertices took place causing a change in the polygon shape ( Figure 4—figure supplement 1 ) . Thus , we could obtain a stable state of the tube using computer simulations . Numerical calculations of the differential equations were performed using the Runge-Kutta method with step size h ( =0 . 005 ) . Initial model tube for computer simulations including cells with chiral properties: We made an initial tube recapitulating the in vivo hindgut , in which the cell axes tilted leftward as shown in Figure 4C , as follows . In a rectangular area ( 15 . 75 × 28 . 7 ) we distributed 452 circular dishes ( diameter = 0 . 82 ) at random with the periodic boundary condition , and performed Dirichlet ( or Voronoi ) tessellation ( Honda , 1978 ) . The rectangle was converted into a cylinder in 3D space as described by Honda et al . ( 2008 ) , resulting in a model tube consisting of 452 polygons . For each cell to have chiral properties , we introduced anisotropic contraction of the edges of the polygonal cells . Each cell had its own polarity , which was defined as a deflection angle from the anterior-posterior axis ( AP axis ) of the system at the initial stage of the simulations . That is , we considered a plane ( blue rectangle ) that was perpendicular to the polarity direction ( blue arrow ) and included the central point of the polygon ( Figure 4—figure supplement 1A ) . The plane crossed two edges of the polygon , and the two edges ( expressed by thick black lines ) were the specific edges undergoing the strongest contraction . Thus , the polarity determined two special edges that were approximately parallel to the polarity direction . The special edges had a high energy density ( wαk , wαk-fold higher than that of the other edges ) and contracted more strongly . The polygons then became elongated and assumed a chiral shape . To give chiral properties to the polygons for which the polygon axes tilted leftward , we applied vertex dynamics to the model tube , where the deflection angle was +30° and weight was applied to the strongly contracting edges , wαkk3 . 5 . The vertex dynamics stopped at t = 5 . 0 . As a result , we obtained a tube as described in Figure 4A ( t = 0 ) . For the computer simulation of inverted twisting ( Figure 4F , Figure 4—figure supplement 4 ) , we used another initial tube as shown in Figure 1E ( t = 0 ) , by similar methods except that the deflection angle was −35° and t = 10 . 0 . The parameter values were not exactly enantiomorphic between the two initial tubes , because the original tube from which the two initial tubes were made was not perfectly symmetric . For the computer simulation of stopped rotation ( Figure 4—figure supplement 3 ) , we used the same initial tube as we used for the wild-type chirality ( see Figure 4A , t = 0 ) . In this case , the rotation of the vertices at the top and bottom of the tube was fixed; that is , the x- and y-coordinates of the vertices were fixed . Vertex dynamics for a sheet in 3D space: A tube composed of multiple cells was considered as a curved sheet consisting of polygons without gaps or overlaps . The edges ( boundaries between cells ) and area of the polygons ( cell volume ) were expressed as x- , y- , and z-coordinates of the vertices ( Figure 4—figure supplement 1B ) . The spatial relationships between neighboring vertices were defined by the surrounding polygons ( Honda et al . , 2004; Nagai and Honda , 2001 ) . The vertices obeyed the equation of motion: ( 1 ) η dri/dt = −▽iU ( i=1 , . . . , nv ) where ri is a 3D-positional vector of vertex i , ▽i is the nabla differential operator , and nv is the total vertex number ( nv = 904 ) . The left side of Equation ( 1 ) represents a viscous drag force proportional to the vertex velocity dri/dt with a positive constant η ( an analog of the coefficient of viscosity ) . Vertices do not have mass ( inertia ) , so the motion of the vertices and polygons is completely damped . Differentiation of the potential U with respect to time t yields the following inequality using Equation ( 1 ) , ( 2 ) dU/dt = Σı▽i Udri/dt = −η Σı ( dri/dt ) 2≤0 . The inequality indicates that vertices move to decrease U ( strictly , not to increase U ) . We obtained a stable shape described by vertices using Equation ( 1 ) . The right side of Equation ( 1 ) represents a potential force ( driving force ) , that is , minus the gradient of the potential U . The potential U includes various terms related to the edges and surface areas of polygons and the tube volume , which are all expressed by vertex positions . Therefore , U is a function of the vertex coordinates . In the present study , the potential U contains terms for edge energy ( UL ) , elastic surface energy ( UES ) , elastic volume energy ( UEV ) , and the boundary restriction energy of the top and bottom of the tube ( UB ) : ( 3 ) U = UL + UES + UEV + UB . The potential UL denotes the total edge energy of the cells: ( 4 ) UL = σLΣn a ( Σna k wakLak ) . The term ( Snαk wαkLαk ) in Equation ( 4 ) represents the edge energy of cell α . nα is the edge number of cell α . Lαk and wαk are the edge length and weight applied to the respective edge . The weight , wαk , depends on the edge species . When an edge contracts strongly , wαk is large ( =1 . 3 or 2 . 0 ) ; otherwise , wαkk1 . 0 . αk designates the k-th edge of polygon α . n is the total polygon number except for the top and bottom polygons ( n = 452 ) . σL is the edge energy density . The potential UES denotes the total elastic energy of the polygon area: ( 5 ) UES = κSΣn a ( Sa−So ) 2 , where Sα and So are the polygon area at time t and the polygon area at the relaxed state , respectively . κS is the elastic energy density of the polygon area . The potential UEV denotes the elastic energy of the tube: ( 6 ) UEV = κV ( Va−Vo ) 2 , where Vα and Vo are the tube volume at time t and the tube volume at the relaxed state , respectively . κV is the elastic energy density of the tube volume . The potential UB denotes the boundary restriction energy of the top and bottom of the tube: ( 7 ) UB=κB{ΣjnvTop[ ( rj−rTop ) 2−RTop2]2+ΣjnvBottom[ ( rj−rBottom ) 2−RBottom2]2} . Centers of the top and bottom polygons of the tube are rTop and rBottom , respectively . The vertices of the top and bottom polygons of the tube are restricted to the circles of the top and bottom ends of the tube ( radii are RTop and RBottom , respectively ) . κB is the elastic constant of the circular array of vertices of the top and bottom polygons . Thus , Equation ( 1 ) takes the form: ( 8 ) ηdri/dt=−▽i{σLΣan ( ΣknawakLak ) +κSΣan ( Sa−So ) 2+κV ( Va−Vo ) 2+κBΣjnvTop[ ( rj−rTop ) 2−RTop2]2+κBΣjnvBottom[ ( rj−rBottom ) 2−RBottom2]2} . To reduce the parameter number without a loss of generality , we introduced a new length unit Ro and rewrote Equation ( 8 ) using new dimensionless quantities ri" , ▽i" , Sa"and V" as follows: ( 9 ) ri=ri"Ro , ▽i=▽i"/Ro , Lak=Lak"Ro , Sa=Sa"Ro2 , So=So"Ro2 , V=V"Ro3 , Vo"Ro3 , rTop=rTop"Ro , rBottom=rBottom"Ro , RTop=RTop"Ro , RBottom=RBottom"Ro . Thus , Equation ( 8 ) takes the form: ( 10 ) dri"/dt"=−▽i"{σL"Σan ( Σknawak"Lak" ) +κS"Σan ( Sa"−So" ) 2+κV" ( V"−V0" ) 2+κB"ΣjnvTop[ ( rj"−rTop" ) 2−RTop"2]2+κB"ΣjnvBottom[ ( rj"−rBottom" ) 2−RBottom"2]2} . in which the new quantities are defined as follows: ( 11 ) t"=t/ ( ηRo ) , σL"=σL , wak"=wak , κS"=κSRo3 , κV"=κVRo5 , κB"=κBRo3 . We take Ro ( =So1/2 ) =1 , so that So = 1 . Equation ( 10 ) lacks explicit parameters corresponding to η . Thus , without a loss of generality , we can describe cell behaviors using the simple parameters κS , κV , and κB . Below , the cell motions were measured in terms of the new length unit Ro = So1/2 and the new time unit 1/ ( ηRo ) , which are the characteristic length scale and time scale of the tube , respectively . Hereafter , we omit the primes ( ” ) on the rescaled quantities in Equation ( 9 ) . ( 12 ) dri/dt=−▽i{σLΣan ( ΣknawakLak ) +κSΣan ( Sa−So ) 2+κV ( V−V0 ) 2+κBΣjnvTop[ ( rj−rTop ) 2−RTop2]2+κBΣjnvBottom[ ( rj−rBottom ) 2−RBottom2]2} . We used the parameter values σL = 2 . 2 , κS = 10 . 0 , κV = 0 . 2 , κB = 1 . 0 , RTop =RTop = 2 . 5 , and Vo = 566 . 64 to obtain a stable shape of the tube . Elementary process of reconnecting neighboring vertices: In addition to the equations of motion , our model involves an elementary process of reconnecting neighboring vertices ( Honda et al . , 2004; Nagai and Honda , 2001 ) . We extended the reconnection into the 3D space as shown in Figure 4—figure supplement 1C . When the length of an edge connecting two neighboring vertices became shorter than a critical length δ ( =0 . 3 ) , the relationship of the neighboring vertices changed and the neighbors reconnected with each other . We also performed a computer simulation that did not allow the reconnection of vertices to test whether cell intercalation is required for the hindgut rotation ( Figure 4B ) . Polygons on the tube surface were projected onto a geometrical cylinder ( diameter of 5 . 0 ) , and the surface of the cylinder was extended to a flat plane . To analyze the directions of cell axes and edges statistically , projected polygons , except for polygons close to the peripheral parts of the tube , were used . To determine the polygon shape , a polygon was approximated by a momental ellipse ( ellipse of inertia ) , and the direction of the major axis of the ellipse was defined as the direction of the polygon . The degree of deviation from the circle was defined as ( dmax − dmin ) / ( dmax +dmin ) , where dmax and dmin were the lengths of the major and minor axes of the ellipse , respectively . The culture medium consisted of equal volumes of M3 medium and fly extract . Embryos expressing UAS-GFP-moesin ( Chihara et al . , 2003 ) driven by en-Gal4 ( Bloomington Stock Center ) were dechorionated using double-sided tape . In the hindgut , UAS-GFP-moesin is specifically expressed in the dorsal side , which allowed us to determine the direction of hindgut rotation . The hindgut was dissected with a tungsten needle and mounted in the medium . Images were captured using an LSM 5 PASCAL laser-scanning microscope ( Zeiss ) . We generated transgenic lines carrying UAS-NLS-tdTomato and UAS-sqhGFP . For UAS-NLS-tdTomato , we first made a vector with 20 × UAS by inserting a 10 × UAS fragment amplified from pJFRC81 ( Pfeiffer et al . , 2012 ) by PCR using the primers AAAGCTAGCTCAACGACAGGAGCACGATC and AAAGACGTCTCAACGACAGGAGCACGATC , into pJFRC81 . An NLS-tdTomato fragment ( NotI-NheI ) from pQC NLS TdTomato IX ( Addgene , #37347 ) was inserted into the NotI and XbaI sites of the vector . For UAS-sqhGFP , a sqhGFP fragment was amplified by PCR from genomic DNA isolated from a sqhGFP fly strain ( BL 57144 ) using the primers CACCGCGGCCGCATGTCATCCCGTAAGACCGC and CACCGGTACCCTATTTGTATAGTTCATCCA , and cloned into the NotI and KpnI sites of pUAST ( Brand and Perrimon , 1993 ) . To generate transgenic lines , we used the attP2 site for UAS-NLS-tdTomato and random insertion for UAS-sqhGFP . For imaging analyses , flies carrying UAS-sqhGFP insertions in the second and third chromosomes were used . To track the movement of nuclei , we adjusted the depth of the z stack or z slice at each time point to make clear time-lapse movies , because the hindgut cells change their z position over time .	Many organs arise from simple sheets and tubes of cells . During development these sheets bend and deform into the more complex shape of the final organ . This can be seen , for example , in the hindgut of fruit flies , which is an organ that is equivalent to our intestines . Initially , the hindgut is a simple tube of cells . Later the hindgut develops a twist to the left that renders its right and left sides non-symmetrical . During twisting , the cells in the hindgut also change shape . It was not known how this shape change and other behaviors of the cells cause the hindgut to twist . Inaki et al . have now filmed how the hindgut develops in live fruit flies and produced computer simulations of the development process . The results suggest that a previously unidentified type of cell behavior called ‘cell sliding’ is responsible for twisting the hindgut . During sliding , the cells stay in contact with their neighbors as they move in a single direction . Sliding is triggered by the cells in the hindgut taking on a more symmetrical shape . Cell sliding may prove to be a common way to shape organs , many of which feature non-symmetrical twisted tubes of cells . In the future , learning how to control cell sliding could help researchers to create organs and biological structures in the laboratory that could be used in organ transplants and regenerative medicine .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology" ]	2018	Chiral cell sliding drives left-right asymmetric organ twisting
The bacteriophage population is large , dynamic , ancient , and genetically diverse . Limited genomic information shows that phage genomes are mosaic , and the genetic architecture of phage populations remains ill-defined . To understand the population structure of phages infecting a single host strain , we isolated , sequenced , and compared 627 phages of Mycobacterium smegmatis . Their genetic diversity is considerable , and there are 28 distinct genomic types ( clusters ) with related nucleotide sequences . However , amino acid sequence comparisons show pervasive genomic mosaicism , and quantification of inter-cluster and intra-cluster relatedness reveals a continuum of genetic diversity , albeit with uneven representation of different phages . Furthermore , rarefaction analysis shows that the mycobacteriophage population is not closed , and there is a constant influx of genes from other sources . Phage isolation and analysis was performed by a large consortium of academic institutions , illustrating the substantial benefits of a disseminated , structured program involving large numbers of freshman undergraduates in scientific discovery . Bacteriophages are the dark matter of the biological universe , forming a vast , ancient , dynamic , and genetically diverse population , replete with genes of unknown function ( Pedulla et al . , 2003 ) . Phages are the most abundant organisms in the biosphere , and the ∼1031 tailed phage particles participate in ∼1023 infections per second on a global scale , with the entire population turning over every few days ( Suttle , 2007 ) . The population is not only vast and dynamic , but comparisons of virion structures suggest that it is also extremely old ( Krupovic and Bamford , 2010 ) . It is thus not surprising that bacteriophages are genetically highly diverse , although their comparative genomics has lagged behind that of other microbes , largely due to the lack of individual isolates for genomic analyses ( Hatfull and Hendrix , 2011 ) . To date , there are approximately 2000 completely sequenced bacteriophage genomes in the GenBank database , a small number relative to the more than 30 , 000 sequenced prokaryotic genomes ( http://www . ncbi . nlm . nih . gov/genome/browse/ ) , in spite of phage genomes being only 1–5% of the size of their host genomes . Double-stranded DNA tailed phages are proposed to have evolved with common ancestry but with different phages having differential access to a large common gene pool ( Hendrix et al . , 1999 ) . Phage genomes are typified by their mosaic architectures generated by gene loss and gain through horizontal genetic exchange; however , the parameters influencing access to the common gene pool are numerous and likely include host range , genome size , replication mode , and life style ( temperate vs lytic ) . Migration to new hosts is probably common , but is affected by local host diversity and mutation rates , as well as resistance mechanisms such as receptor availability , restriction , CRISPRs , and abortive infection systems ( Buckling and Brockhurst , 2012; Jacobs-Sera et al . , 2012; Hoskisson et al . , 2015 ) . Constraints on gene acquisition may also be imposed by synteny—particularly among virion structural genes—and by size limits of DNA packaging ( Juhala et al . , 2000; Hatfull and Hendrix , 2011 ) . We have previously described comparative analyses of modest numbers of mycobacteriophages and shown that they can be sorted by nucleotide sequence and gene content comparisons into groups of closely related genomes referred to as ‘clusters’ ( designated Cluster A , B , C , etc . ) ; phages without any close relatives are referred to as ‘singletons’ . Some of the clusters can be further divided into subclusters ( e . g . , Subcluster A1 , A2 , A3 , etc . ) according to nucleotide sequence relatedness ( Pedulla et al . , 2003; Hatfull et al . , 2006 , 2010; Pope et al . , 2011b ) . The genomes are mosaic whereby individual phages are constructed as assemblages of modules , many of which are single genes ( Pedulla et al . , 2003 ) . Each mycobacteriophage cluster has features particular to that cluster ( e . g . , regulatory systems , repeated sequences , tRNA genes , etc . [Pope et al . , 2011a , 2011b , 2013 , 2014a , 2014b] ) , but because of the pervasive mosaicism , the relationships among phages within clusters and between clusters are complex . Collections of phages have been isolated on other hosts such as Bacillus spp . , Escherichia coli , Pseudomonas spp . , Propionibacterium spp . and Staphylococcus spp . ( Kwan et al . , 2005 , 2006; Kropinski et al . , 2007; Marinelli et al . , 2012; Hatfull et al . , 2013; Grose and Casjens , 2014; Grose et al . , 2014; Lee et al . , 2014 ) and these can be similarly divided into clusters based on DNA similarity . Recent analysis of 337 phages infecting 31 bacterial species within the Enterobacteriaceae ( Grose and Casjens , 2014 ) reveals 56 clusters of phage genomes . It is thus clear that there is substantial diversity within the phage population , even when comparing phages of a common host and which are expected to be in direct genetic contact with each other in their natural environment ( Hatfull and Hendrix , 2011 ) . Nonetheless , the numbers of genomes isolated on a particular host generally are too small to define the nature and the size of the populations at large with any substantial resolution . Viral metagenomic studies provide valuable insights into phage diversity and population dynamics , but typically generate few complete genome sequences or any specific information relating viral genomes to specific bacterial hosts ( Hambly and Suttle , 2005; Rodriguez-Brito et al . , 2010; Mokili et al . , 2012 ) . A recent analysis of Synechococcus phages using metagenomic analysis coupled with viral tagging showed that there are multiple ‘populations’ of these phages ( similar to the clusters described above ) , but suggested that these represent distinct groups of related phages rather than a continuous spectrum of diversity ( Deng et al . , 2014 ) . This differs from prior predictions that the phage population as a whole likely spans a continuum of diversity—albeit with uneven representation of different groups of related phages—because of genomic mosaicism ( Hendrix , 2003; Hatfull , 2010 , 2012 ) . However , as the Synechococcus phage data are derived from a single sample using a single host , it is unclear if this extends to phages of other hosts ( Deng et al . , 2014 ) . Here we describe the comparative analysis of a large number of completely sequenced mycobacteriophage genomes and demonstrate that they represent a spectrum of diversity and do not constitute discrete populations . Rarefaction analyses of their constituent genes are consistent with populations of gene families shared among mycobacteriophages being augmented by the introduction of new gene families from outside sources . The assembling of a large and highly informative collection of bacteriophages by a consortium of students and faculty at multiple institutions demonstrates that a course-based research experience ( CRE ) can be successfully implemented at large scale without compromising the authenticity or richness of a scientific investigation imbued with discovery and project ownership . Exploring phage diversity using a genome-by-genome approach has notable advantages and some potential disadvantages . The main advantage is that complete genome sequences give information about genome length and composition , providing key insights into genome mosaicism and how genome segments are shared and exchanged . A difficulty is that there are not large extant phage collections available for most bacterial hosts , and isolation , purification , and characterization of phages can be slow and time-consuming . Because isolation typically requires plaque formation and growth in the laboratory , some naturally occurring phages may escape isolation using standard methods . Thus , although the diversity of phages isolated and propagated in the laboratory may not capture all types of phage , it represents a minimum , not a maximum , index of diversity . The 2012 report from the President's Council of Advisors on Science and Technology ( PCAST ) focused on the poor retention of undergraduate students in science , technology , engineering and mathematics ( STEM ) as an impediment to meeting US economic demands ( PCAST , 2012 ) . One of the PCAST recommendations is to replace traditional introductory laboratory courses with research-based experiences that would inspire freshman students and promote STEM retention . A powerful strategy is to engage students in scientific discovery through CREs . The successful implementation of this strategy depends on ( i ) identifying research questions that can engage students in contributing genuine advances in scientific knowledge without requiring prior expert knowledge , and ( ii ) designing the project so that large numbers of students can participate in a meaningful fashion . We have previously described the Howard Hughes Medical Institute ( HHMI ) Science Education Alliance Phage Hunters Advancing Genomics and Evolutionary Science ( SEA-PHAGES ) program , in which beginning undergraduate students isolate , purify , sequence , annotate , and compare bacteriophages , and have described its educational advantages ( Jordan et al . , 2014 ) . By taking advantage of the massive diversity of the phage population so that each student can isolate a unique phage , the program encourages student ownership of their science . And because the collective discoveries by many students generate new scientific insights , the program creates a scientific community of students engaged in authentic research . The SEA-PHAGES program has contributed to the growth of the collection of sequenced mycobacteriophages to nearly 700 individual isolates ( http://phagesdb . org ) , of which 627 were selected for a detailed analysis ( Supplementary file 1 ) . This is by far the largest collection of sequenced phage genomes for any single host and thus promises to substantially advance our understanding of phage diversity . The phages were isolated using either direct plating or by enrichment using Mycobacterium smegmatis mc2155 as a host , and sequenced using next-generation approaches ( see ‘Materials and methods’ ) . More than 5000 students—primarily freshmen—at 74 institutions have been involved since inception of the SEA-PHAGES program in 2008 , and the phages isolated represent a broad geographical distribution ( Figure 1 ) and a variety of viral morphotypes ( http://phagesdb . org ) . The new insights gained from comparative genomic analyses of these phages—as described below—demonstrate the effectiveness of viral discovery and genomics as a model for CRE development . 10 . 7554/eLife . 06416 . 003Figure 1 . Geographical distribution of sequenced mycobacteriophages . ( A ) Locations of sequenced mycobacteriophages across the globe . ( B ) Locations of sequenced mycobacteriophages across the United States . Colors and letter designations on the isolates refer to the cluster to which the genomes belong . Data from www . phagesdb . org . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 003 Using previously reported parameters based primarily on nucleotide sequence similarity spanning >50% genome length ( Hatfull et al . , 2006 ) , the 627 genomes were assembled into 20 clusters ( A–T ) and eight singletons ( with no close relatives ) ( Figure 2 , Supplementary file 1 ) ; 11 clusters were subdivided into 2 to 11 subclusters ( Table 1 ) . There is considerable variation in cluster size with substantial differences in the numbers of genomes in each cluster ( 2–232 ) , but there is relatively little variation in either genome length or the numbers of genes per genome in any given cluster ( Table 1 ) . Cluster assignment is of practical utility and is generally robust , with clustered phages typically sharing genome architectures , as noted for the Enterobacteriacea ( Grose and Casjens , 2014 ) . For example , Cluster A phages are similar in size and transcriptional organization , and share an unusual immunity system ( Brown et al . , 1997; Pope et al . , 2011b ) . Cluster M phages all contain large numbers of tRNA genes ( Pope et al . , 2014a ) , Cluster K ( Pope et al . , 2011a ) and Cluster O ( Cresawn et al . , 2015 ) phages have different but characteristic repeated sequences , and Cluster J phages have an unusual capsid with a triangulation ( T ) number of 13 ( Pope et al . , 2013 ) . Therefore , the organization of related mycobacteriophages into clusters provides a framework for identifying and interpreting gene trafficking within and among potentially distinct groups of genomes . 10 . 7554/eLife . 06416 . 004Figure 2 . Nucleotide sequence comparison of 627 mycobacteriophages displayed as a dotplot . Complete genome sequences of 627 mycobacteriophages were concatenated into a single file which was compared with itself using Gepard ( Krumsiek et al . , 2007 ) and displayed as a dotplot using default parameters ( word length , 10 ) . The order of the genomes is as listed in Supplementary file 1 . Nucleotide similarity is a primary component in assembling phages into clusters , which typically requires evident DNA similarity spanning more than 50% of the genome lengths . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 00410 . 7554/eLife . 06416 . 005Figure 2—source data 1 . Concatenated DNA sequences for 627 phage genomes . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 00510 . 7554/eLife . 06416 . 006Figure 2—figure supplement 1 . Dotplot of phages in Clusters I , N , P and the singleton Sparky . A dotplot was generated using a concatenated file of genome sequences using Gepard ( Krumsiek et al . , 2007 ) . The complexity of the genome relationships is illustrated by the Cluster I phages which share varying degrees of similarity to phages in Clusters N and P , as well as the singleton Sparky . Because inclusion of a phage in a cluster typically requires sharing a span of similarity over half of the genome lengths , these phages are not assembled into a single larger cluster . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 00610 . 7554/eLife . 06416 . 007Figure 2—figure supplement 2 . Dotplot of Carcharodon , Che9c , Kheth , and Dori . The dotplot of concatenated genome sequences illustrates the ambiguity of whether the singleton Dori warrants inclusion in Cluster B . Dori shares DNA sequence similarity with its closest relative Kheth ( Subcluster B2 ) , but it does not span 50% of the genome lengths . Dori also shares DNA sequence similarity with Che9c ( Cluster I2 ) and Carcharodon ( Cluster N ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 00710 . 7554/eLife . 06416 . 008Figure 2—figure supplement 3 . Dotplot of Corndog , Brujita , SG4 , Yoshi , and MooMoo . The dotplot of concatenated genome sequences illustrates the complex relationships between the singleton MooMoo and other phages . MooMoo shares DNA sequence similarity with SG4 ( Subcluster F1 ) and Yoshi ( Subcluster F2 ) , but also with Brujita ( Subcluster I1 ) . MooMoo has barely detectable DNA sequence similarity with Corndog ( Cluster O ) , but has a similar prolate virion morphology . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 00810 . 7554/eLife . 06416 . 009Table 1 . Diversity and genetic isolation of mycobacteriophage genome clustersDOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 009Cluster# Subclusters# GenomesAverage # genesAverage length ( bp ) Total phams†Total genesCLASP‡CAP§CCI#CII¶A1123290 ± 5 . 351 , 514108520 , 88038 . 312 . 40 . 0880 . 2B5109100 . 4 ± 4 . 568 , 65342110 , 94466 . 223 . 20 . 2481 . 0C245231 ± 5 . 9155 , 50448610 , 39589 . 329 . 40 . 4884 . 6D21089 . 3 ± 6 . 464 , 96514789388 . 164 . 30 . 6171 . 4E135141 . 9 ± 3 . 475 , 526236496787 . 263 . 80 . 6059 . 3F366105 . 3 ± 5 . 357 , 416658695054 . 44 . 90 . 1655 . 8G11461 . 5 ± 1 . 241 , 8457286196 . 091 . 10 . 8555 . 6H2598 . 4 ± 5 . 769 , 46920749261 . 631 . 50 . 4867 . 6I2478 ± 3 . 749 , 95414731258 . 935 . 00 . 5323 . 8J116239 . 8 ± 9 . 3110 , 332530377670 . 840 . 10 . 4558 . 5K53295 . 7 ± 4 . 659 , 720411306951 . 820 . 00 . 2373 . 5L313127 . 9 ± 6 . 575 , 177246166378 . 250 . 80 . 5272 . 4M23141 ± 8 . 881 , 63620142373 . 563 . 00 . 7069 . 2N1769 . 1 ± 2 . 242 , 88815248464 . 145 . 60 . 4540 . 8O15124 . 2 ± 3 . 170 , 65115162190 . 683 . 30 . 8264 . 2P2978 . 8 ± 2 . 147 , 66815970976 . 142 . 30 . 5034 . 0Q1585 . 2 ± 3 . 753 , 7559042696 . 690 . 40 . 9573 . 3R14101 . 5 ± 2 . 571 , 34811740691 . 484 . 80 . 8771 . 8S12109 ± 2 . 065 , 17211721891 . 791 . 70 . 9370 . 9T1366 . 7 ± 2 . 442 , 8338320086 . 182 . 50 . 8062 . 7Dori119464 , 6139494N/AN/AN/A35 . 8DS6A119760 , 5889697N/AN/AN/A58 . 3Gaia1119490 , 460193194N/AN/AN/A58 . 0MooMoo119855 , 1789898N/AN/AN/A31 . 6Muddy117148 , 2287071N/AN/AN/A71 . 4Patience1110970 , 506109109N/AN/AN/A57 . 8Sparky119363 , 3349393N/AN/AN/A48 . 4Wildcat1114878 , 296148148N/AN/AN/A69 . 6Average number of protein-coding genes per genome , with standard deviation . †Total phams is the sum of all phamilies ( groups of homologous mycobacteriophage genes ) in that cluster . ‡The Cluster Averaged Shared Phamilies ( CLASP ) index is the average of the percentages of phamilies shared pairwise between genomes within a cluster . §The Cluster-Associated Phamilies ( CAP ) index is the percentage of the average number of phamilies per genome within a cluster whose phamilies are present in every cluster member . #The Cluster Cohesion Index ( CCI ) is generated by dividing the average number of genes per genome by the total number of phamilies ( phams ) in that cluster . ¶The Cluster Isolation Index ( CII ) is the percentage of phams that are present only in that cluster , and not present in other mycobacteriophages . N/A: Not applicable . Genome mosaicism is more apparent from comparison of gene product amino acid sequences than nucleotide sequence comparisons because of the accumulation of genome rearrangements over a longer period of evolution , during which indications of DNA similarity are lost . To compare mycobacteriophage gene contents we grouped related genes into protein families ( ‘phamilies’ or ‘phams’ ) using Phamerator ( Cresawn et al . , 2011 ) , which we modified to use kClust ( Hauser et al . , 2013 ) so as to easily accommodate the large numbers of comparisons . The 69 , 633 genes assembled into 5205 phams of which 1613 ( 31% ) are orphams ( single-gene phamilies [Hatfull et al . , 2010] ) . Approximately 25% of phams can be assigned functions in viral structure and assembly , DNA metabolism , integration , lysis , and regulation , but the vast majority are of unknown function . Representation of gene content relationships among all 627 phages as a network phylogeny reveals relationships that are in accord with the cluster and subcluster designations derived from nucleotide sequence comparisons ( Figure 3 ) . The multiple branches between clusters/subclusters reflect the phylogenetic complexities that arise from genome mosaicism , where genes within a genome have distinct evolutionary histories . 10 . 7554/eLife . 06416 . 010Figure 3 . Network phylogeny of 627 mycobacteriophages based on gene content . Genomes of 627 mycobacteriophages were compared according to shared gene content using the Phamerator ( Cresawn et al . , 2011 ) database Mykobacteriophage_627 , and displayed using SplitsTree ( Huson and Bryant , 2006 ) . Colored circles indicate grouping of phages labeled according to their cluster designations generated by nucleotide sequence comparison ( Figure 2 ) ; singleton genomes with no close relatives are labeled but not circled . Micrographs show morphotypes of the singleton MooMoo , the Cluster F phage Mozy , and the Cluster O phage Corndog . With the exception of DS6A , all of the phages infect Mycobacterium smegmatis mc2155 . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 01010 . 7554/eLife . 06416 . 011Figure 3—source data 1 . Nexus file containing phamily assignments for 627 phage genomes . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 011 The distribution of orphams ( genes without mycobacteriophage homologues ) provides additional support for cluster/subcluster assignments; Figure 4 ) . A relatively high proportion of orphams is a characteristic of both singleton genomes and single-genome subclusters ( Figure 4 ) . At least 30% of genes in all of the singleton genomes are orphams , and the single-genome subclusters have a minimum of 15% orphams; genomes in other clusters and subclusters typically have fewer than 10% orphams ( Figure 4 ) . The presence of numerous orphams ensures that the lack of cluster inclusion did not result from sequence errors or insufficient or inappropriate gene annotation . Notable exceptions are Predator ( Subcluster H1 ) and Mendokysei ( Cluster T ) , both of which are in very small clusters/subclusters , and KayaCho ( Subcluster B4 ) . KayaCho may warrant separation into a new subcluster ( e . g . , B6 ) , but overall the orpham distribution is consistent with the cluster/subcluster designations . 10 . 7554/eLife . 06416 . 012Figure 4 . Proportions of orphams in mycobacteriophage genomes . The proportions of genes that are orphams ( i . e . , single-gene phamilies with no homologues within the mycobacteriophage dataset ) are shown for each phage . The order of the phages is as shown in Supplementary file 1 . All of the singleton genomes have >30% orphams , and most of the other genomes with relatively high proportions of orphams are the single-genome subclusters ( Table 2 ) including Hawkeye ( D2 ) , Myrna ( C2 ) , Squirty ( F3 ) , Barnyard ( H2 ) , Che9c ( I2 ) , Whirlwind ( L3 ) , Rey ( M2 ) , and Purky ( P2 ) . Three phages shown in red type are not singletons or single-genome subclusters but have relatively high proportions of orphams . Predator and Mendokysei are members of the diverse and small clusters ( five or fewer genomes ) H and T , respectively; KayaCho is a member of Subcluster B4 but has a sufficiently high proportion of orphams to arguably warrant formation of a new subcluster , B6 . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 01210 . 7554/eLife . 06416 . 013Figure 4—source data 1 . Pham table containing phamily designations for 627 phage genomes . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 01310 . 7554/eLife . 06416 . 014Figure 4—figure supplement 1 . Shared gene content between Dori , MooMoo , and other mycobacteriophages . ( A ) Average percentages of phamilies shared between Dori and other mycobacteriophages . ( B ) Average percentages of phamilies shared between MooMoo and other mycobacteriophages . Genomes on the x axis are listed in the same order as in Supplementary file 1 and the cluster designations are indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 01410 . 7554/eLife . 06416 . 015Figure 4—figure supplement 2 . Shared gene content between Gaia , Sparky , and other mycobacteriophages . ( A ) Average percentages of phamilies shared between Gaia and other mycobacteriophages . ( B ) Average percentages of phamilies shared between Sparky and other mycobacteriophages . Genomes on the x axis are listed in the same order as in Supplementary file 1 and the cluster designations are indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 015 To determine the extent to which the various clusters/subclusters represent discrete groups , we generated a heat map showing pairwise shared gene content ( Figure 5 ) and quantified the cluster/subcluster diversity ( Table 1 , Figure 6 ) . The heat map strikingly illustrates that diversity is non-uniform , with genomes in some clusters ( e . g . , Subclusters B1 , C1 ) being very closely related , whereas in others they display substantial differences ( e . g . , Subclusters A1 , F1 ) . The variation is also evident within the large Cluster A group , with some subclusters having low diversity ( e . g . , A4 , A5 , A6 ) , some being highly diverse ( e . g . , A1 , A2 ) , and some plausibly further splitting into subgroups ( A3 ) ( Figure 5 ) . 10 . 7554/eLife . 06416 . 016Figure 5 . Heat map representation of shared gene content among 627 mycobacteriophages . The percentages of pairwise shared genes was determined using a Phamerator ( Cresawn et al . , 2011 ) database ( Mykobacteriophage_627 ) populated with 627 completely sequenced phage genomes . The 69 , 574 genes were assembled into 5205 phamilies ( phams ) of related sequences using kClust , and the average proportions of shared phams calculated . Genomes are ordered on both axes according to their cluster and subcluster designations ( Supplementary file 1 ) determined by nucleotide sequence similarities ( Figure 2 ) . The values ( proportions of pairwise shared phams averaged between each partner ) are colored as indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 01610 . 7554/eLife . 06416 . 017Figure 5—source data 1 . Dataset showing percentages of pairwise shared phamilies . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 01710 . 7554/eLife . 06416 . 018Figure 6 . Cluster diversity and isolation . ( A ) The CLuster Averaged Shared Phamilies ( CLASP; blue ) , Cluster Associated Phamilies ( CAP; red ) and Cluster Cohesion Index ( CCI; green ) values are plotted for each mycobacteriophage cluster . ( B ) The Cluster Isolation Index ( CII ) and CLASP values ( both shown as percentages ) are plotted for each phage cluster . Singletons ( white circles ) are not individually labeled but correspond to the values shown in Table 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 01810 . 7554/eLife . 06416 . 019Figure 6—source data 1 . Datasets showing numbers of CLuster Average Shard Phamilies . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 01910 . 7554/eLife . 06416 . 020Figure 6—figure supplement 1 . Resampling CLASP values for cluster diversity and size . CLuster Averaged Shared Phamilies ( CLASP ) values were calculated for Clusters A , B , C , E , F , and K by resampling random subsets of the genomes . The size of the subsets is shown on the x axis and each point is the average of 20 iterations . The minimum and maximum variations among the iterations are shown . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 02010 . 7554/eLife . 06416 . 021Figure 6—figure supplement 2 . Cluster diversity shown by Cluster-Associated Phamilies ( CAP ) and Cluster Phamily Variation ( CPV ) indices . The CAP and CPV values are plotted for each cluster . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 021 We quantified the cluster diversity using three different measures , CLuster Average Shared Phamilies ( CLASP ) , Cluster Associated Phamilies ( CAP ) , and Cluster Cohesion Index ( CCI ) ( Tables 1 , 2 , Figure 6A ) . Both CAP ( the number of phams present in all genomes within a cluster divided by the average number of genes per genome ) and CCI ( the average number of genes per genome as a percentage of the total number of phams in that cluster ) show substantial variation between clusters ( Table 1 , S2 ) , and little evidence for commonly conserved ‘core genes’ , as suggested for T4-related phages ( Petrov et al . , 2010 ) . However , both of these parameters are somewhat influenced by cluster/subcluster size , which varies from cluster to cluster . In contrast , CLASP ( the percentage of phamilies shared between two genomes , then averaged across all possible pairs within a cluster or subcluster ) is relatively insensitive to cluster/subcluster size ( as seen by a resampling analysis; Figure 6—figure supplement 1 ) , but still shows substantial variation from one cluster to another ( Table 1 , Figure 6A ) . 10 . 7554/eLife . 06416 . 022Table 2 . Genometrics and Cluster Cohesion Indexes of mycobacteriophagesDOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 022ClusterSubcluster# GenomesAverage # genesAverage length ( bp ) # PhamsCLASPCAP†CCI‡A23290 . 051 , 514108538 . 312 . 48 . 0A17291 . 251 , 95441672 . 336 . 922 . 0A22893 . 452 , 80531264 . 730 . 130 . 0A33787 . 750 , 32516381 . 148 . 854 . 0A44687 . 451 , 37612592 . 770 . 670 . 0A51686 . 050 , 53115281 . 458 . 757 . 0A61197 . 851 , 67712890 . 275 . 176 . 0A7384 . 352 , 94111574 . 964 . 473 . 0A8497 . 851 , 59710793 . 586 . 891 . 0A9496 . 052 , 83810692 . 783 . 491 . 0A10780 . 049 , 17411281 . 660 . 971 . 0A11498 . 552 , 26011393 . 688 . 387 . 0B108100 . 468 , 65342166 . 223 . 224 . 0B177101 . 868 , 53214493 . 272 . 971 . 0B2889 . 967 , 26710194 . 984 . 689 . 0B312102 . 868 , 69812196 . 384 . 785 . 0B4896 . 170 , 61916679 . 945 . 858 . 0B5396 . 370 , 03310891 . 787 . 289 . 0C45231 . 0155 , 50448689 . 329 . 448 . 0C144231 . 0155 , 29734591 . 973 . 267 . 0C21229 . 0164 , 602227N/AN/AN/AD1089 . 364 , 96514788 . 164 . 361 . 0D1987 . 364 , 69710094 . 988 . 887 . 0D21107 . 067 , 383107N/AN/AN/AE35141 . 975 , 52623587 . 263 . 860 . 0F66105 . 357 , 41665854 . 44 . 916 . 0F160104 . 857 , 48657359 . 620 . 618 . 0F25110 . 855 , 99620765 . 749 . 054 . 0F31107 . 060 , 285105N/AN/AN/AG1461 . 541 , 8457296 . 091 . 185 . 0H598 . 469 , 46920761 . 631 . 548 . 0H1495 . 869 , 13713181 . 967 . 973 . 0H21109 . 070 , 797110N/AN/AN/AI478 . 049 , 95414758 . 935 . 053 . 0I1376 . 047 , 58810177 . 566 . 775 . 0I2184 . 057 , 05084N/AN/AN/AJ16239 . 8110 , 33253070 . 840 . 145 . 0K3395 . 759 , 72041151 . 820 . 023 . 0K11594 . 359 , 87716685 . 547 . 957 . 0K2496 . 356 , 59712885 . 277 . 775 . 0K3398 . 261 , 32211192 . 289 . 588 . 0K4594 . 057 , 86510693 . 787 . 289 . 0K5698 . 262 , 15414482 . 168 . 268 . 0L13127 . 975 , 17724678 . 250 . 852 . 0L13123 . 774 , 05013592 . 688 . 892 . 0L29129 . 375 , 45617090 . 172 . 276 . 0L31128 . 076 , 050126N/AN/AN/AM3141 . 081 , 63620173 . 563 . 070 . 0M12135 . 080 , 59313896 . 696 . 698 . 0M21153 . 083 , 724152N/AN/AN/AN769 . 142 , 88815264 . 145 . 645 . 0O5124 . 270 , 65115190 . 683 . 382 . 0P978 . 847 , 66815976 . 142 . 350 . 0P1878 . 447 , 31312682 . 952 . 962 . 0P2182 . 050 , 51382N/AN/AN/AQ585 . 253 , 7559096 . 690 . 495 . 0R4101 . 571 , 34811791 . 484 . 887 . 0S2109 . 065 , 17211791 . 791 . 793 . 0T366 . 742 , 8338386 . 182 . 580 . 0Cluster Averaged Shared Phamilies . †Cluster Associated Phamilies . ‡Cluster Cohesion Index . The heat map of genome comparisons ( Figure 5 ) also illustrates the degrees to which clusters and subclusters share gene content , a reflection of cluster discreteness , or how isolated discrete clusters are from each other . For example , although the Cluster A phages are highly diverse , they also appear relatively isolated and share relatively few genes with other clusters ( Figure 5 ) . In contrast , phages in Cluster E share substantial numbers of genes with other clusters , including those in Clusters F , J , L , P , and several singletons . We have quantified these relationships with the Cluster Isolation Index ( CII , the percentage of phams present within a cluster that are not present in other mycobacteriophage genomes ) , which demonstrates the considerable variation in isolation from phages of other clusters/subclusters ( Table 1 , Figure 6B ) . For example , at one extreme , 84 . 6% of Cluster C gene phamilies are found only in Cluster C and not elsewhere . At the other extreme , only 23 . 8% of Cluster I gene phamilies are constrained to that cluster , with the remainder having relatives present in genomes in other clusters . Other clusters form a spectrum of relationships between these extremes ( Table 1 , Figure 6B ) , and clusters such as I and P—which share recognizable DNA sequence similarity ( Figure 2—figure supplement 1 ) —share >60% of their genes with other phages ( low CII values; Table 1 ) . Thus , although some clusters could be considered as discrete groups—as reported for the Synechococcus phages ( Deng et al . , 2014 ) —this is far from being a universal or characteristic feature of groups of related phages . Cluster isolation analyses reveal additional complexities arising from highly mosaic genomes . For example , the singleton Dori is clearly related to Cluster B phages ( Figure 3 ) with which it shares limited DNA similarity ( Figure 2—figure supplement 2 ) with 20–26% of its genes ( Figure 4—figure supplement 1 ) , but also has nucleotide similarity and shares genes with Cluster N and I2 phages , among others ( Figure 2—figure supplement 2 , Figure 4—figure supplement 1 ) , as reflected in its low CII ( Table 1 , Figure 6B ) . Likewise , the singleton MooMoo has segments of DNA similarity and shares ∼20% of its gene content ( as determined by shared phams ) with Cluster F phages ( Figure 3 , Figure 2—figure supplement 3 , Figure 4—figure supplement 1 ) , but also has similarity to Clusters N and I , as well as a low CII ( Table 1 , Figure 6B ) . It has low DNA similarity to Cluster O ( Figure 2—figure supplement 3 ) , but has several phams in common with the Cluster O phages , and has the same unusual prolate morphology ( Figure 3 ) . Complex relationships are also seen in the singletons Gaia and Sparky ( Figure 4—figure supplement 2 ) . Taken together , the analyses of both cluster diversity and cluster isolation show that mycobacteriophage populations contain a continuum of diversity , with non-uniform abundance of different types of phages . The prevalence of isolated phages may not necessarily reflect the proportions of different types of phages in the environment , but the availability of a large collection of isolated phages enables capture and whole genome analysis of relatively rare phages that are critical to understanding the complexities of genome relationships . We recently reported genomic analysis of the singleton mycobacteriophage Patience , which has a substantially lower GC% than its host ( 50 . 3% vs 67 . 4% ) , has a different codon usage profile , but is undergoing codon selection for growth in a high GC% environment ( Pope et al . , 2014b ) . If there is a flux of phage genomes and genes entering the mycobacterial neighborhood , then we predict that the phages of a single host do not reflect a closed system with discrete populations , but one that is open with ever-expanding diversity . Both the huge diversity of phamilies in mycobacteriophages and the high frequency of orphams suggest that genes are constantly added to phage genomes from outside sources just as genes are added to the genomes of their bacterial hosts via horizontal gene transfer . Such gene influx—for example , from host-jumping phages such as Patience ( Pope et al . , 2014b ) —would provide genetic novelty and enable phages to adapt to their ever-changing hosts . To examine gene flux into the mycobacteriophage population , we performed a rarefaction analysis by re-sampling the gene phamilies within the phage population ( Figure 7 ) . Remarkably , the rarefaction curves of the entire collection—including the 95% confidence limits—do not fit a hyperbola as would be expected if the mycobacteriophages were limited to an isolated set of genes , and about 2 . 5 new gene phamilies are predicted to be identified with each newly isolated phage ( Figure 7A ) . Similar independent analyses on the phages of Cluster A or the phages of Cluster B show that this is also observed within these clusters ( Figure 7B , C ) . Thus both individual clusters and the collection as a whole are not genetically fixed , but are in constant flux . While a hyperbola can model sampling of gene phamilies from a finite pool , it does not accommodate the influx of new phamilies . The addition of a linear term ( see ‘Materials and methods’ ) , representing the introduction of new phamilies from outside sources , results in a non-asymptotic curve which predicts the continual identification of new phams even after large numbers of genomes have been sampled ( R > 0 . 999; Figure 7D ) . This linear term acts as a surrogate for the linear range of a second hyperbolic curve , one representing the resampling of a much larger set of gene phamilies available for introduction into mycobacteriophage genomes . Unfortunately , the current dataset remains insufficient to confidently extrapolate to give an estimate of the total number of viral protein families in the biosphere , which has been previously estimated to be anywhere between a half a million and 2 billion ( Rohwer , 2003; Ignacio-Espinoza et al . , 2013 ) . 10 . 7554/eLife . 06416 . 023Figure 7 . Rarefaction analysis of mycobacteriophage genomes . ( A ) The numbers of phamilies are reported for between 1 and 627 phage genomes sampled at random without replacement; the mean of 10 , 000 iterations is shown in red; gray lines indicate a confidence interval of two standard deviations . The black line shows a hyperbolic curve fit to the data from phage counts 1 to 314 . The inset shows the number of new phams encountered upon the inclusion of each phage , with the mean number for the 10 , 000 iterations shown in blue and the predicted value from the hyperbolic curve shown in black . ( B ) Rarefaction analysis of 232 Cluster A phages . The total numbers of phamilies are reported for between 1 and 232 phages sampled at random without replacement from Cluster A; the mean of 10 , 000 iterations is shown in red; gray lines indicate a confidence interval of two standard deviations . The black line shows a hyperbolic curve fit to the data from phage counts 1 to 117 . The inset shows the number of new phams encountered upon the inclusion of each phage , with the mean number for 10 , 000 iterations shown in blue and the predicted value from the hyperbolic curve shown in black . ( C ) Rarefaction analysis of 108 Cluster B phages; the hyperbolic curve was fit to the data from phage counts 1 to 54 . ( D ) Fits of the hyperbolic ( Equation 1 ) and hyperbolic with linear ( Equation 2 ) models for phamily identification within genome samples . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 02310 . 7554/eLife . 06416 . 024Figure 7—source data 1 . Datasets for determination of rarefaction curves . DOI: http://dx . doi . org/10 . 7554/eLife . 06416 . 024 We note that because of the generally slow pace of the advancement of phage genomics , we have little insight into the phage populations of other hosts . We retrieved all double-stranded DNA tailed phage genomes in GenBank that we could identify ( a total of 1781 ) , corresponding to about 120 host bacterial genera , with a median number of phages per host genus of two . Using similar parameters for pham building as described above , the 181 , 717 predicted genes assemble into 47 , 479 phamilies . The relatively low representation of each phamily ( 3 . 8 genes/phamily ) compared to the mycobacteriophages ( 13 . 4 genes/phamily ) is a further reflection of the gross under-sampling of the phage population as a whole . Bacteriophage taxonomic classification reflecting phylogeny presents substantial challenges because of genome mosaicism ( Lawrence et al . , 2002 ) . Classification by viral morphology is well established , but may not accurately reflect the genetic relationships , as illustrated for the prolate-headed MooMoo ( Figure 3 ) . We also note that the mycobacteriophage myoviruses have a high CII and form a discrete group ( Table 1 ) as do the Synechococcus myophages ( Deng et al . , 2014 ) , perhaps reflecting a virulent lifestyle that constrains productive gene exchange; T4-related phages from diverse hosts share a core set of 15–20% of their genes , and whole genome comparisons reveal extensive mosaicism ( Petrov et al . , 2010 ) . Host range mutability thus may differ in phages with different morphotypes , limiting access to the gene pool , and although grouping phages into clusters and subclusters provides analytical advantages because of the wide range in prevalence of different phages ( Table 1 ) , it is not suitable as a broadly applicable hierarchical taxonomic system . The comparative analysis of these mycobacteriophages thus supports reticulate taxonomies that more accurately reflect the phylogenetic complexities ( Lawrence et al . , 2002; Lima-Mendez et al . , 2007 ) . A research experience can be a powerful vehicle that enables a person to gain an understanding of the process of science ( Hunter et al . , 2007 ) . When the research experience occurs early and at a large scale , as described here , the focus can shift from selecting a few ‘qualified’ students to exploring the potential interests of many students . Clearly , an essential ingredient is the nature of the research project , as definitions of research may vary from an inquiry-based exercise to authentic research with the potential to contribute publishable findings . To optimize the educational benefits , the research project must be intellectually and technically accessible to beginning students ( i . e . , few prerequisites ) and scalable so that many students can simultaneously make progress in parallel , yet independently ( Hatfull et al . , 2006 ) . Importantly , each student's findings should contribute to a scientific question with integration of all students' discoveries advancing a scientific question of significance , as judged by scientific peer review . This , we believe , defines an ‘authentic’ research experience . We note that in the SEA-PHAGES platform , substantial student effort is invested in arriving at high-quality genome annotations by close manual inspection followed by expert verification , a critical component of the detailed comparative analysis of phage gene content described here . Bacteriophage genomics has progressed relatively slowly compared to that of other microbes in spite of their relatively small genome sizes . Here we have demonstrated that programmatically integrating the research and education missions at large scale provides an effective solution to expanding our knowledge of viral diversity , with a multitude of insights gained as a consequence of the scale of phage discovery . The nature of different genomic types , the variations of the diversity both within clusters and shared genome content among clusters , and the expanse of the mycobacteriophage population can be viewed at an unprecedented level of resolution . Our conclusions align well with comparative analyses of phages of Enterobacteriacea ( Grose and Casjens , 2014 ) and Bacillus spp . ( Grose et al . , 2014 ) and we predict that these are general parameters of bacteriophage diversity , at least when sampling broadly across the environment . Both the rarefaction analysis described here and preliminary analysis of phamilies of all sequenced DNA phages illustrate how little of the global phage population has been genomically sampled . With a near endless supply of diverse viruses readily accessible for isolation and analyses , integrated research/education programs will continue to play substantial roles in defining the nature of the virosphere . In addition to extant GenBank sequence information , mycobacteriophages were isolated , sequenced , and annotated in the Phage Hunters Integrating Research and Education ( PHIRE ) or SEA-PHAGES programs . Phage genomes were shotgun sequenced using either 454 , Ion Torrent , or Illumina platforms to at least 20-fold coverage . Shotgun reads were assembled de novo with Newbler versions 2 . 1 to 2 . 9 . Assemblies were checked for low coverage or discrepant areas , and targeted Sanger reads were used to resolve weak areas and identify genome ends . All genome sequences are publically available at phagesDB . org or in GenBank . Nucleotide comparisons used BLASTN or Gepard ( Krumsiek et al . , 2007 ) . To create Phamerator database Mykobacteriophage_627 , phamilies were constructed by first clustering the entire database of 69 , 574 genes using strict kClust parameters ( 70% clustering threshold and 0 . 25 alignment coverage of the longer sequence ) . This was followed by multiple sequence alignment of each preliminary cluster using Kalign ( Lassmann and Sonnhammer , 2005 ) . Consensus sequences were then extracted using HHmake and HHconsensus ( Remmert et al . , 2012 ) . The resulting list of sequences was subjected to a second—and less strict—round of clustering via kClust ( 30% clustering threshold and 0 . 5 alignment coverage of the longer sequence ) to obtain the final phamily assignments . Network phylogeny constructions were made using the NeighborNet function with default parameters in SplitsTree ( Huson , 1998; Huson and Bryant , 2006 ) . Four parameters were used to evaluate cluster diversity . The first is the CLASP index that calculates the percentage of phamilies shared between two genomes , then averages across all possible pairs within a cluster or subcluster . Because the pairwise similarities are averaged , CLASP is relatively insensitive to either the overall size of the cluster , or the heterogeneity of its diversity ( such as in Cluster C in which of the 45 genomes in total , 44 are in Cluster C1 , and only one is in Cluster C2 ) . CLASP robustness with respect to cluster size was demonstrated through a resampling analysis . For each cluster with more than 30 members , a random subset ( of 5 , 10 , 20 , or 30 genomes ) was selected and CLASP was calculated . For each sample size , 20 iterations were performed with replacement . As expected , there is substantial deviation among the iterations , especially at smaller sizes . However , there is little change in the average CLASP values with different sample sizes ( Figure 4—figure supplement 1 ) , showing that cluster size is not a primary driver of diversity . The resampling analyses also suggest that while a greater number of genomes helps refine the CLASP value , there is still predictive power when only 10 genomes are compared . On average , the maximum and minimum iteration values at a sample size of 10 genomes were within 8% of the whole-cluster CLASP value . This implies that , for example , increasing Cluster D from 10 to 50 or 100 genomes may raise or lower its current CLASP value of 88 . 1 , but that value is likely to remain between ∼80 and ∼96 . The second measure used is the CAP , which is calculated as the number of phamilies present in all genomes within a cluster divided by the average number of phamilies per genome . These cluster-conserved genes could correspond to core genes that define a particular phage group such as cluster or subcluster . However , for those clusters with sufficient diversity to detect such core genes , these values are low . For example , among the 66 Cluster F genomes , only five phamilies are present in all genomes . None are virion structural genes , one is a glycosyltransferase whose role is unknown , one is a putative regulator , and the others are small proteins of unknown function . For the Cluster A genomes , 11 phamilies are conserved , seven of which are virion structural proteins , three are involved in DNA metabolism ( DNA Pol , Helicase , Rec-Like protein ) , and one is of unknown function . The third parameter is the Cluster Phamily Variation ( CPV ) index , which is the proportion of phams that are not present in all members of the cluster . CAP and CPV are inversely related but imperfectly as CPV varies with cluster size even among similarly diverse clusters; a plot of CAP values against CPV values is shown in Figure 6—figure supplement 2 . The CCI is calculated as the average number of genes per genome as a percentage of the total number of phams in that cluster . Thus if all genomes in a cluster are identical ( and if phamilies occur only once in a genome ) , CCI would be 100; the CCI for two sets of five randomly chosen genomes is ∼2 . CCI values correlate with cluster size , but similarly sized clusters as such G , J , and L , or E and K have substantially different CCI values ( Table 1 ) . The CII is the percentage of phams present within a cluster that are not present in other mycobacteriophage genomes . Rarefaction analysis was performed by randomly selecting subsets ( without replacement ) of between 1 and 627 ( all ) , 232 ( Cluster A ) or 108 ( Cluster B ) mycobacteriophages and determining the numbers of phamilies represented . This was repeated 10 , 000 times to generate a mean number of phamilies observed given a number of phage genomes selected . The means of the accumulated numbers of phams and the numbers of new phages identified are plotted as the function of the number of genomes selected at random . The observed numbers were fit to a hyperbolic function for 50% of the sample ( i . e . , 1 to 314 , 116 or 54 genomes for all , Cluster A or Cluster B phages , respectively ) ; Hanes-Woolf regression was used to estimate PhamMax and Km of the hyperbola: ( 1 ) NPhams=PhamMax×NGenomesKm+NGenomes , where NGenomes is the number of genomes sampled , NPhams is the number of total phams seen within those genomes , PhamMax is the total number of phams among all mycobacteriophage genomes , and Km is the number of genomes required to sample one half of PhamMax . The lack of fit of the observed data to the hyperbola—with the observed data reflecting infinite size—suggests that the overall population is dynamic . The lack of hyperbolic fit of the data does not result from outliers such as phages with highly deviant GC% , because removing these does not improve the fit . The fit is also not substantially improved by analysis of the two largest clusters , Cluster A and Cluster B ( Figure 7 ) , suggesting that the dynamic nature of the gene pool is not an artifact of examining independent phage clusters with separate gene pools . To model this behavior , we modified Equation 1 to include the introduction of novel phams via recombination with outside , non-mycobacteriophage genomes: ( 2 ) NPhams=NGenomes×CPhage+PhamMax×NGenomesKm+NGenomes , where CPhage is the number of outside phams seen in each phage . The value of CPhage was estimated from Figure 7B and new values for PhamMax and KPham were estimated by Hanes-Woolf regression following data normalization .	Viruses are unable to replicate independently . To generate copies of itself , a virus must instead invade a target cell and commandeer that cell's replication machinery . Different viruses are able to invade different types of cell , and a group of viruses known as bacteriophages ( or phages for short ) replicate within bacteria . The enormous number and diversity of phages in the world means that they play an important role in virtually every ecosystem . Despite their importance , relatively little is known about how different phage populations are related to each other and how they evolved . Many phages contain their genetic information in the form of strands of DNA . Using genetic sequencing to find out where and how different genes are encoded in the DNA can reveal information about how different viruses are related to each other . These relationships are particularly complicated in phages , as they can exchange genes with other viruses and microbes . Previous studies comparing the genomes—the complete DNA sequence—of reasonably small numbers of phages that infect the Mycobacterium group of bacteria have found that the phages can be sorted into ‘clusters’ based on similarities in their genes and where these are encoded in their DNA . However , the number of phages investigated so far has been too small to conclude how different clusters are related . Are the clusters separate , or do they form a ‘continuum’ with different genes and DNA sequences shared between different clusters ? Here , Pope , Bowman , Russell et al . compare the individual genomes of 627 bacteriophages that infect the bacterial species Mycobacterium smegmatis . This is by far the largest number of phage genomes analyzed from a single host species . The large number of genomes analyzed allowed a much clearer understanding of the complexity and diversity of these phages to be obtained . The isolation , sequencing and analysis of the hundreds of M . smegmatis bacteriophage genomes was performed by an integrated research and education program , called the Science Education Alliance Phage Hunters Advancing Genomics and Evolutionary Science ( SEA-PHAGES ) program . This enabled thousands of undergraduate students from different institutions to contribute to the phage discovery and sequencing project , and co-author the report . SEA-PHAGES therefore shows that it is possible to successfully incorporate genuine scientific research into an undergraduate course , and that doing so can benefit both the students and researchers involved . The results show that while the genomes could be categorized into 28 clusters , the genomes are not completely unrelated . Instead , a spread of diversity is seen , as genes and groups of genes are shared between different clusters . Pope , Bowman , Russell et al . further reveal that the phage population is in a constant state of change , and continuously acquires genes from other microorganisms and viruses .	[ "Abstract", "Introduction", "Results", "and", "discussion", "Materials", "and", "methods" ]	[ "microbiology", "and", "infectious", "disease", "genetics", "and", "genomics" ]	2015	Whole genome comparison of a large collection of mycobacteriophages reveals a continuum of phage genetic diversity
Life history strategies for optimizing individual fitness fall on a spectrum between maximizing reproductive efforts and maintaining physical health over time . Strategies across this spectrum are viable and different suites of personality traits evolved to support these strategies . Using data from 538 captive chimpanzees ( Pan troglodytes ) we tested whether any of the dimensions of chimpanzee personality – agreeableness , conscientiousness , dominance , extraversion , neuroticism , and openness – were associated with longevity , an attribute of slow life history strategies that is especially important in primates given their relatively long lives . We found that higher agreeableness was related to longevity in males , with weaker evidence suggesting that higher openness is related to longer life in females . Our results link the literature on human and nonhuman primate survival and suggest that , for males , evolution has favored the protective effects of low aggression and high quality social bonds . Life-history theory posits that strategies for increasing individual fitness lay on a continuum that describes an energetic trade-off between maximizing reproductive efforts and maintaining physical health as the organism ages ( Stearns , 1976 ) . At one end of this continuum are ‘r-selected’ populations . Individuals within these populations are characterized by early and frequent reproduction , the rapid onset of senescence , and a shorter lifespan . At the other end of this continuum are ‘K-selected’ populations . Individuals within these populations are characterized by later and less frequent reproduction , but delayed senescence , and a longer lifespan . Both ends of this continuum are viable fitness strategies , as are , depending upon ecological and social contingences , life history strategies between these extremes . These strategies are supported by behavioral adaptations ( Stearns , 1976 ) . Differences in life history strategy have been advanced as one possible explanation for why individuals within populations exhibit stable differences in behavioral , affective , and cognitive dispositions , that is , personality traits ( Dingemanse and Réale , 2005; Réale et al . , 2010 ) . A simulation study indicated that this theory is plausible ( Wolf et al . , 2007 ) , and a meta-analysis on studies of boldness , exploration , and aggression in insects , fish , birds , and mammals offered mixed empirical support ( Smith and Blumstein , 2008 ) . This meta-analysis showed that bolder animals put themselves at greater risk and die at younger ages , but enjoy greater reproductive success than their shyer counterparts , which do not enjoy as many opportunities for copulation , but live longer , and so are able to invest more in their offspring ( Smith and Blumstein , 2008 ) . Boldness therefore is associated with a ‘faster’ ( r-selected ) life-history strategy . The findings of the meta-analysis for exploration and aggression were less clear: more aggressive individuals had greater reproductive success than less aggressive individuals , but this was not offset by reduced lifespan; individuals more prone to exploring their environment lived longer than neophobic individuals , but did not experience reduced reproductive success ( Smith and Blumstein , 2008 ) . Two concurrent reviews showed that , across a range of species , greater boldness , activity , and aggressiveness , and lower sociability and exploration , were associated with a faster life history strategy ( Réale et al . , 2010; Biro and Stamps , 2008 ) . Recent research found evidence that variation in the personality traits of humans and nonhuman primates are also associated with variables related to life history strategies . Studies of humans predominate this literature and , although there are exceptions ( e . g . , Alvergne et al . , 2010; Gurven et al . , 2014 ) , this human literature grew out of personality psychology , health psychology , and epidemiology . Consequently , these studies did not set out to deliberately test whether personality variation reflected individual differences in life history . The studies of human personality described above tended to focus on one or more of five traits - extraversion , agreeableness , openness , neuroticism , and conscientiousness - known collectively as the ‘Big Five’ or ‘Five-Factor Model’ ( Digman , 1990 ) . These five traits are operationalized as dimensions onto which several related lower-order traits cluster ( Digman , 1990 ) . Four of the five human traits correspond to personality traits studied by behavioral ecologists . Extraversion and agreeableness characterize how often and how well humans navigate their social world ( Digman , 1990 ) . Among other characteristics , extraversion features sociability and activity ( Costa and McCrae , 1995 ) , which are comparable to the same-named traits studied in behavioral ecology; agreeableness is the opposite of aggressiveness ( Réale et al . , 2007 ) . Openness captures curiosity , originality , and a tendency to find novel ideas and situations appealing ( Digman , 1990 ) , and corresponds to exploration ( Réale et al . , 2007 ) . Finally , neuroticism is related to fearfulness , vigilance , and emotional reactivity ( Digman , 1990 ) , and so appears to be the opposite of boldness , that is shyness or timidity ( Réale et al . , 2007 ) . Conscientiousness describes individual differences in self-control , delay of gratification , and thoughtful planning ( Digman , 1990 ) . Animal analogues of conscientiousness have emerged in a few nonhuman primates , for example chimpanzees ( King and Figueredo , 1997 ) , and in Asian elephants ( Seltmann et al . , 2018 ) . However , conscientiousness has only recently been operationalized in ways familiar to behavioral ecologists , that is as naturally occurring behaviors or responses to behavioral tests ( Delgado and Sulloway , 2017; MacLean et al . , 2014; Altschul et al . , 2017 ) . In this literature , conscientiousness is often termed ‘self-control’ ( e . g . , MacLean et al . , 2014 ) . In addition to its focus on the Big Five traits , the life history variables most often examined in the human literature have been health outcomes , especially longevity . Meta-analyses of this extensive literature showed that people who enjoy better health and live longer tend to be higher in agreeableness , extraversion and conscientiousness , and lower in neuroticism ( Strickhouser et al . , 2017; Roberts et al . , 2007 ) . The explanatory theories emerging from this field posit that health-related behaviors , including diet , mediate relationships between personality and health ( Turiano et al . , 2015; Graham et al . , 2017 ) . The possibilities that agreeableness , extraversion and conscientiousness are related to a slower life history strategy , and that neuroticism is related to a faster life history strategy , are mostly not considered in this literature . Studies of personality and life history in nonhuman primates are often narrower in scope than studies of humans . Specifically , they mostly test whether one or more personality traits related to social interactions are associated with health and/or mortality outcomes . This narrow focus is probably attributable to two characteristics of these species . First , nonhuman primates have relatively slow life-history strategies; lifespans are comparatively long and reproductive rates are comparatively low ( Jones , 2011 ) . Consequently , health and longevity are influential fitness measures in primates , including humans . Second , most primate species live in groups and are highly social ( Napier and Napier , 1967 ) . To date , whether they use rating and/or coding measures of personality , studies of personality and survival in nonhuman primates have shown that western lowland gorillas ( Weiss et al . , 2013 ) , baboons ( Silk et al . , 2010; Archie et al . , 2014; Seyfarth et al . , 2012 ) , and female rhesus macaques ( Brent et al . , 2017 ) that are higher in sociability live longer . However , a study of female blue monkeys found that the association between sociability and mortality was only true for individuals that had consistent bonds with groupmates ( Thompson and Cords , 2018 ) . In addition to the fact that all but one of these studies focus on a narrow set of traits ( Weiss et al . , 2013 ) , studies of primate personality and longevity have focused on a small number of species . In particular , New World monkeys are not represented and only one study was of a species of great ape ( Weiss et al . , 2013 ) , the evolutionary line that includes humans . We wished to expand on what is known about the links between personality traits and life history strategy in nonhuman primates and in humans . To do so we examined these associations in chimpanzees , which are one of our closest living great ape relatives . The present study was made possible by the existence of a database containing a large sample ( n = 538 ) of captive chimpanzees living in zoological parks , research facilities , and sanctuaries located in the United States , the United Kingdom , the Netherlands , Australia , and Japan . Personality in this sample was assessed by ratings on two comparable questionnaires that assessed a wide range of traits . These ratings were made by keepers , researchers , and others who knew and worked with these chimpanzees for considerable lengths of time . Furthermore , the long follow-up times from when chimpanzees’ personalities were assessed to the present ( 7 to 24 years ) meant that there were enough deaths to provide adequate statistical power for detecting associations between personality and mortality . The sample used in this study and the means of measuring personality deserve comment . There is some disagreement as to whether chimpanzees or bonobos , which are as related to humans as chimpanzees , are the best model for ancestral humans ( Stanford , 2012; Sayers et al . , 2012 ) . However , studies using similar personality measures in captive groups of chimpanzees and bonobos have found that the dimensions along which chimpanzee personality traits align themselves ( King and Figueredo , 1997 ) are more similar to the human dimensions than are those of bonobos ( Weiss et al . , 2015 ) . Specifically , in addition to a dominance dimension , which reflects competitive prowess , social competence , and fearlessness , that is not present in humans ( King and Figueredo , 1997; Murray , 1998; Dutton et al . , 1997; Freeman et al . , 2013; Weiss et al . , 2009; Weiss et al . , 2007 ) , chimpanzee personality is defined by five dimensions that resemble the human Big Five . These dimensions have been identified in many studies , including those that measured personality with different questionnaires ( King and Figueredo , 1997; Murray , 1998; Dutton et al . , 1997; Freeman et al . , 2013; Weiss et al . , 2009; Weiss et al . , 2007; King et al . , 2005; Martin , 2005; Buirski et al . , 1978 ) and those that used coded behavioral observations instead of ratings ( Freeman et al . , 2013; Massen et al . , 2013; Koski , 2011; Vazire et al . , 2007; Pederson et al . , 2005; van Hooff , 1970 ) . In bonobos , questionnaire-based and coding-based methods revealed evidence for human- and chimpanzee-like agreeableness , conscientiousness , and openness dimensions , a dimension like the chimpanzee dominance dimension , and an additional dimension , attentiveness , which is distinct from conscientiousness ( Weiss et al . , 2015; Staes et al . , 2016 ) . However , these studies find next to no evidence for neuroticism and extraversion . Taken with findings from comparable studies of the other great apes ( Weiss et al . , 2006; Gold and Maple , 1994 ) , one plausible scenario is that bonobo personality diverged from that of chimpanzees and the other great apes , including humans . Some question the use of ratings to measure animal personality given the possibility of anthropomorphic projection ( Uher , 2013 ) . For studies of nonhuman primates , as noted in the previous paragraph , ratings and behavioral measures yield comparable personality traits . Moreover , a review and meta-analysis found evidence that different raters provide similar ratings , that these measures are heritable , and that they are repeatable ( Freeman and Gosling , 2010 ) , the latter being most recently demonstrated in ratings taken 35 years apart and made by two independent sets of raters on two different questionnaires ( Weiss et al . , 2017 ) . In addition , the effects of anthropomorphic projection by raters , if present , are minimal ( Weiss et al . , 2012 ) . These just-described findings are probably attributable to the fact that items on most questionnaires do not consist of a single word ( typically an adjective ) , but include behavioral definitions , which limit the degree of subjectivity in interpreting the traits and making ratings ( Uher and Asendorpf , 2008; Stevenson-Hinde and Zunz , 1978 ) . Another concern that some raise is the use of captive samples . Although they limit the conclusions that we can draw about ancestral humans , by using captive samples one is able to remove many extrinsic sources of mortality , for example predators and infectious diseases . Therefore , captive samples , such as that used in this study , control for potential confounds that might crop up in studies of wild samples . In addition , captive samples are uniquely suited to testing whether the associations between human personality and mortality risk reflect life history strategies followed by individuals apart from links between personality and health-related behaviors that are endemic to human personality studies . We used these data to test six hypotheses , one for each chimpanzee personality trait . We will first describe the hypotheses for the chimpanzee personality traits of extraversion , agreeableness , openness , and neuroticism , which are closely related to traits studied by behavioral ecologists . We will then describe the hypotheses for conscientiousness and dominance , which were based on literature that we will discuss . Because sociability and aggressiveness are associated with slower and faster life-history strategies , respectively ( Réale et al . , 2010; Brent et al . , 2017 ) , we expect that higher extraversion and agreeableness will be related to longer life . In nonhumans , lower boldness is related to a slower life-history strategy . In humans , although overall neuroticism is associated with poorer health and a shorter lifespan , aspects of neuroticism related to worry and vigilance , key characteristics related to lower boldness ( Réale et al . , 2007 ) , are associated with better health and a longer lifespan ( Gale et al . , 2017; Weston and Jackson , 2018 ) . We thus expect that neuroticism should be associated with a longer life-span . Exploration , in animals , is linked to some characteristics of a slower life history , and so we expect that openness in chimpanzees will be associated with longer life . We expect that conscientiousness will be related to a slower life history , and so longer life . This expectation was based on the above-described finding that humans who are higher in conscientiousness enjoy better health and live longer . If we do not find such an association , it would suggest that the association between conscientiousness and better health in humans may be attributable to human-specific health behaviors , such as exercising , that are related to higher conscientiousness and lead to individuals being healthier ( Turiano et al . , 2015 ) . Our basis for this interpretation of these results stems from the fact that captive chimpanzees do not have many ( if any ) opportunities to control their health , which is in fact maintained by humans . Finally , among primates , social standing is related to physiological stress responses ( Sapolsky , 2005 ) and high dominance is associated with higher stress , as well as faster , energetically intense growth in chimpanzees ( Pusey et al . , 1997 ) . High-ranking individuals also mate more frequently and dominate resources to support their growth and reproductive efforts ( Ellis , 1995 ) . Higher rank in chimpanzees , therefore , is associated with a faster life history strategy . Because ratings on traits such as dominance in chimpanzees and other primates are related to rank , including in the wild ( Buirski et al . , 1978 ) , we expected that dominance would be related to a shorter lifespan . During the follow-up period , 187 chimpanzees died . A Kaplan-Meier plot ( Figure 1 ) shows survival functions for our sample and a wild sample ( Bronikowski et al . , 2011 ) . Unlike wild chimpanzee populations in which infant mortality is high , captive chimpanzee populations have strikingly reduced infant mortality , live longer , and display accelerated mortality in older ages . These results show that captive chimpanzees benefit from protection against extrinsic sources of mortality , for example shelter from elements and predators , good health care , and abundant food . Inspection of the six chimpanzee personality dimensions ( Figure 2 ) , as well as prior studies ( King et al . , 2008 ) indicate that personalities change as individuals age , making it possible that an association between personality and longer life might be confounded . This is not necessarily undesirable , as it indicates that personality and lifespan are linked , but to be conservative , we modeled and therefore controlled for potential confounds between age and personality scores . We fitted generalized additive models ( GAMs ) for each personality dimension , regressing personality ratings on the age at which the individual was rated . The GAM regression lines for each model are plotted against the personality data in Figure 2—figure supplements 1 through 6 . Curvilinear associations were presented between age and personality for all dimensions except neuroticism , where only a linear relationship was present . Because personality does change over time , some of the raw personality score variance could be attributed to rating age variance . Alternative , adjusted personality scores were therefore calculated as residuals from the regression function of each GAM . In the subsequent analyses , adjusted scores were fitted as predictors in separate survival models from the raw scores . We fit decision trees to test whether sex , origin ( wild-born or other ) , or any personality dimensions were related to longevity . A conditional inference survival tree procedurally determined that among males , higher agreeableness was associated with longer survival ( Figure 3 ) . Specifically , males with agreeableness scores less than 0 . 063 standard deviations below the mean were at significantly higher risk than other males ( p<0 . 027 ) . These results held for the age-adjusted agreeableness scores as well . The association between agreeableness and survival in males was confirmed with parametric hazards modeling: in a AIC weighted model including all covariates and frailty effects , the hazard ratio for males was 0 . 66 ( 95% CI: 0 . 49 – 0 . 89 ) per standard deviation increase , and in a model where we adjusted personality scores to control for age , the hazard ratio associated with a standard deviation increase was 0 . 61 ( 95% CI: 0 . 42 – 0 . 89 ) . In the models of only females , a positive association between openness and survival was also revealed with a hazard ratio of 0 . 77 ( 95% CI: 0 . 59 – 0 . 99 ) for unadjusted scores , but the association was not significant when we used the adjusted openness scores . Higher openness in males was not related to living longer nor was higher agreeableness in females ( Table 1 presents a full description of the AIC weighted models ) . For a subset of the sample , more detailed rearing data were available , but survival analyses did not find any association between rearing conditions or origin and longevity ( Table S1 ) . A complete description of all survival analyses is available in the supporting information . We found a clear pattern of relationships between personality and longevity in these data: among males , higher agreeableness was associated with longer life , even when agreeableness was adjusted for age . In other words , long-living captive male chimpanzees are those who engage in positive social interactions characterized by cooperation , geniality , and being protective . These findings match our prediction , although we did not necessarily expect to find the association only in males . However , this finding is consistent with the literature: in wild chimpanzees , male coalitionary aggression towards conspecifics is associated with greater chances of siring offspring ( Gilby et al . , 2013 ) . Agreeableness , the opposite of aggression , ought to lie on the other end of the life-history spectrum , and be associated with longer life , as we found . More agreeable males may adopt a more cooperative dominance style ( Foster et al . , 2009 ) , ultimately allowing for fewer , but more consistent reproductive opportunities over the course of a long life . We were surprised to find no association between extraversion and longevity . Studies in monkeys ( Silk et al . , 2010; Seyfarth et al . , 2012; Brent et al . , 2017 ) have shown positive , protective relationships with extraversion . Of note , a positive association between extraversion and longevity was found in a study of gorillas that were also kept in captivity and assessed for personality by means of ratings ( Weiss et al . , 2013 ) . Like their close chimpanzee cousins , captive gorillas show evidence for strong age-related declines in extraversion ( Kuhar et al . , 2006 ) , yet extraversion was still associated with longevity . However , high sociability among primates does not support longevity in all circumstances ( Thompson and Cords , 2018 ) . The remaining difference between gorillas and chimpanzees that could explain our null findings for extraversion lies in the mating systems of these species . Specifically , gorillas have strict harems where one or two males have exclusive sexual access to multiple mature females ( Harcourt et al . , 1981 ) . Chimpanzees , on the other hand , have a promiscuous mating system ( Tutin , 1979 ) . There was no association between longevity and conscientiousness . It is possible that this finding reflects our captive sample in which the extrinsic benefits of being higher in conscientiousness have been removed . For instance , although chimpanzees are known to self-medicate using plants in the wild ( Huffman and Wrangham , 1994 ) , and while conscientious chimpanzees in captivity are more diligent ( Altschul et al . , 2017 ) , individuals have no resources to use for self-medication in captivity . Our results thus suggest that the associations commonly found between conscientiousness and longevity in human is not related to intrinsic characteristics of the organism , but to the health-related behaviors associated with this trait ( Turiano et al . , 2015 ) . Females that were higher in openness lived longer , but the effect was not present when we corrected for confounding by age of rating . This is due to the strong curvilinear relationship between age and openness ( Figure 2 ) . Younger chimpanzees were much higher in openness and there was an association between lower openness and age , a limitation we might have missed had our sample been smaller . It is therefore impossible for us to conclude whether there is a protective association between openness and longevity in females or whether lower openness was a proxy for age . Low boldness resembles one aspect of human neuroticism that is related to a longer lifespan , and so we predicted that neuroticism would be associated with greater longevity . However , we found no association in either direction . The absence of any effect of neuroticism in chimpanzees may be attributable to the fact that the health-harming and health-benefitting roles of neuroticism are , like conscientiousness , mediated by health behaviors , as well as the environment . For example , people who are higher in neuroticism tend to smoke , and this behavior explains some of the relationship between neuroticism and shorter lifespans ( Graham et al . , 2017 ) . On the other hand , after the onset of certain diseases , some high neuroticism individuals are more likely to stop smoking ( Weston and Jackson , 2018 ) . Smoking does not explain the entire association in humans , however , as high neuroticism is also associated with greater reactivity to stressors ( Chapman et al . , 2011 ) and energetically expensive physiological responses ( Réale et al . , 2010 ) , which could offset potential benefits of slow life-history benefits from neuroticism . Moreover , with the absence of predators in captivity benefits of vigilance would be reduced if not entirely eliminated , as danger and risks to health from agonistic social encounters remain . Dominance , and the degree to which captive chimpanzees are characterized by their competitive prowess and fearlessness , and , consequently , the ability to enjoy the spoils of rank , had no bearing on how long individuals lived . In chimpanzees specifically , high-ranking individuals are generally less stressed ( Goymann and Wingfield , 2004 ) , but when the hierarchy is destabilized , high-ranking individuals become more stressed , and instability and reorganization can be common in wild chimpanzee groups ( Muller and Mitani , 2005 ) . Dominance may not play a major role in influencing longevity in captive populations because fission-fusion dynamics are not in play to the same extent as in the wild , thus group stability will be greater , and stressful disruption will be reduced . Moreover , in captivity there is less need for chimpanzees to compete with one another for resources , so traits such as dominance , that are related to rank , may not be related to mortality in this sort of environment . This study had several limitations . Our data did not have measures of social variables like rank or social network , or psychological variables like intelligence . These chimpanzees lived exclusively in captive environments , which limits our ability to make evolutionary inferences regarding the associations between personality and survival . However , our captive sample was also a strength as it allowed us to identify extrinsic influencers that would be eliminated by captive environments and test novel hypotheses about the relationships between personality and life-history strategies in chimpanzees . Our study also examined only a single species . More generally , future studies that incorporate multiple primate species could utilize phylogenetic approaches , which consider the importance of species differences in social organization and ecology ( MacLean et al . , 2012; Cornwell and Nakagawa , 2017 ) . Phylogenetic analyses could allow researchers to identify which specific species differences moderate relationships between certain personality traits and measures of health and survival , as well as reproductive success and fitness more broadly . The present study is a reminder of the complex , multifaceted nature of personality and sex , social relationships and the life course in chimpanzees . It also shows how studying the personality of our biological kin reveals that , as in humans , it is not the quantity of social relationships that matters , but the quality . All research reported in this study was non-invasive . The research complied with the regulations and guidelines prescribed by The University of Edinburgh and the participating zoos , research institutes , and sanctuaries . 556 chimpanzees were assessed for personality between 1993 and 2010 . Eighteen chimpanzees had to be removed from the sample due to incompatibilities with the study design , either because personality was assessed after death or because a veterinary staff member requested the individual not be analyzed and mortality data were thus withheld . Of the 538 remaining chimpanzees , 175 came from zoos in the United States , 164 came from the Yerkes National Primate Research Center ( also in the United States ) , 156 came from zoos , a sanctuary , and two research centers in Japan , 21 came from the Taronga Zoo in Australia , 11 came from the Beekse Bergen Safaripark in the Netherlands , and 11 came from the Edinburgh Zoo in the United Kingdom . Vital status was recorded throughout 2016 and 2017 , yielding follow-up times ranging from 7 to 24 years , which is approximately equivalent to 10 to 36 human years ( Napier and Napier , 1967 ) . A total of 187 chimpanzees died during the follow-up period . As is standard in studies that seek to identify mortality risk factors , our analytic approach treated the remaining 353 chimpanzees as right-censored at the date that mortality data were gathered for that group . 336 individuals were known to be alive at the time of data collection , and 17 individuals were lost to follow-up and censored at the date of their last known record . All records were also left-truncated , beginning each record at the age at which the individual was assessed for personality . Fifty-four items comprising a trait name , for example ‘Fearful’ and a one to three sentence behavioral description , for example 'Subject reacts excessively to real or imagined threats by displaying behaviors such as screaming , grimacing , running away or other signs of anxiety or distress . ’ were developed to assess the personalities of the chimpanzees ( King and Figueredo , 1997; Weiss et al . , 2009 ) , Between 1993 and 2005 , 43 of these items were used to assess the personalities of chimpanzees in the American zoos , the Taronga Zoo , and chimpanzees living at the Yerkes National Primate Research Center ( King and Figueredo , 1997; Weiss et al . , 2007 ) . Starting in 2007 , all 54 items were used to assess the personality of the chimpanzees living in Japan ( Weiss et al . , 2009 ) , the Netherlands ( Herrelko , 2011 ) , and at the Edinburgh Zoo ( Herrelko et al . , 2012 ) . The distributions of all six chimpanzee personality dimensions split by sex are shown in Figure 2 . The personalities of the chimpanzees in this study were assessed via ratings on these items by multiple keepers and researchers who knew the individual chimpanzees , sometimes for decades ( King and Figueredo , 1997; Weiss et al . , 2009; Weiss et al . , 2007 ) . In addition to showing that the interrater reliabilities are comparable to those found in human studies of personality , previous studies have shown that chimpanzee personality , measured this way , yields measures that are more reliable than behavioral codings ( Vazire et al . , 2007 ) , that are heritable ( Weiss et al . , 2000; Wilson et al . , 2017; Latzman et al . , 2015a ) and stable over time ( King et al . , 2008 ) , and that generalize across samples ( Weiss et al . , 2009; Weiss et al . , 2007; King et al . , 2005 ) , and are not adversely affected by anthropomorphic attributions on the part of raters ( Weiss et al . , 2012 ) , Finally , these measures have been related to observed behaviors ( Pederson et al . , 2005 ) , differences in brain morphology ( Latzman et al . , 2015b; Blatchley and Hopkins , 2010 ) , and genetic polymorphisms ( Wilson et al . , 2017; Hong et al . , 2011; Hopkins et al . , 2012 ) . To adjust for confounding in the personality variables brought on by changes with age , we fit GAMs modeling the relationship between age at assessment and each personality variable ( Wood , 2006 ) . GAMs are an extension to linear models that allow the input data to ‘suggest’ non-linearities ( Hastie , 2017 ) as opposed to requiring researchers to manually specify them , by , for example , adding a quadratic term to a model formula . To avoid overfitting , non-parametric transformations penalize roughness in the transformation function creating terms aptly called ‘smooths’ ( Faraway , 2016 ) . For our smooths , we used thin plate regression splines with a basis dimension ( k ) of 20 . The basis dimension was verified as being acceptable using internal package functions; varying k did not alter any model fits . GAMs are difficult to interpret mathematically , but visually intuitive , so each GAM is described by its line of best fit , drawn in Figure 2—figure supplements 1 through 6 . GAMs generate residuals like other regression models , thus , bivariate GAMs are a powerful method for identifying and controlling for the effects of confounders ( Benedetti and Abrahamowicz , 2004 ) . To be conservative , our survival models included all six personality scores . We also included sex and origin ( whether the individual was born in the wild or not ) as controls . We used decision-tree analyses to identify associations between personality and longevity . Parametric and semi-parametric survival regression models force a specific link between variables and outcome , but decision trees do not impose any such assumptions; trees are able to automatically identify meaningful variables and even some interactions without prior specification ( Bou-Hamad et al . , 2011 ) . Survival trees in particular have advantages over other techniques . In simulation studies of left-truncated right-censored decision trees with data much like ours , that is a large sample ( N > 500 ) with many censored observations ( >50% ) , conditional inference trees identified the correct predictors 94% and 93% of the time , respectively ( Fu and Simonoff , 2016 ) . This method can handle binary and continuous variables and is robust to the effects of time-dependent covariates , such as our chimpanzees’ personality dimensions , which could be confounded with age at rating . We grew trees with both unadjusted and adjusted covariates . Adjusted covariates were residualized versions drawn from the GAMs used earlier to model the effects of age on personality . Using adjusted covariates had no meaningful effect on the conditional inference analysis; the tree grown was identical . We validated our decision-tree analyses with fully parametric hazard regression models . We followed an information theoretical approach which allowed us to pool and average model estimates across a wide-range of possible choices of error distribution and variables to include ( Burnham et al . , 2011 ) . We first built two sets of models , again , with unadjusted covariates and without adjusted covariates . Adjustment creates a different , alternative dataset which cannot be directly compared to the unadjusted data , so our evaluations of these models were necessarily kept separate . The linking distributions we used included the Weibull , log-logistic , Gompertz ( Klein and Moeschberger , 2005 ) , and semi-parametric splines survival functions ( Goodman et al . , 2011 ) . There were no convergence issues and all splines were fit with 12 knots and κ = 10 , 000 . The hazard models were fit with Gamma distributed frailty ( random ) effects to control for any influence that the different sample groups might have on survival , and estimated both jointly and separately by sex ( Table S2 and Table 1 , respectively ) . We also built models including and excluding the demographic covariates of sex and origin . No variation in specification affected our results ( Tables S3 & S4 ) .	Like humans , animals have distinct personalities . Our close evolutionary cousins chimpanzees even display the same five major personality traits that we do – extraversion , neuroticism , conscientiousness , openness , and agreeableness – as well as a distinct trait , for dominance . How did these distinct personality traits evolve and persist across different species ? Ultimately , each trait must provide some fitness benefits that help the animal to reproduce and pass on the trait to its offspring . Longevity is an important factor in promoting fitness; an animal that lives for longer will have more opportunities to reproduce . Previous work in humans and other animals suggested that some personality traits are associated with a longer life . However , few studies have been large enough to test all major personality traits in both sexes of an animal species . Altschul et al . used data from a long-term study of 538 captive chimpanzees to investigate possible associations between longevity and personality traits . The personalities of the chimpanzees started being rated between seven and 24 years ago . Since then , 187 of the chimpanzees have died . Altschul et al . found that different personality traits were associated with longer life in males and females . Male chimpanzees with higher levels of agreeableness – the personality trait characterized by low aggression and positive social interactions such as cooperation – generally lived for longer . Female chimpanzees who were more open to new experiences also appeared to live for longer , but this apparent association may be influenced by age . Like humans , chimpanzees become less open to experiences as they become older . No other personality traits appear to be linked to lifespan in chimpanzees . However , evidence suggests that conscientiousness and neuroticism can influence lifespan in humans . These two traits may therefore drive uniquely human behaviours that affect health . The results presented by Altschul et al . suggest that human and ape agreeableness evolved through individuals who were able to achieve higher fitness by living longer . They also provide insights into how important personality and life history are to the health and survival of captive animals . For a fuller understanding of how ape personality evolved , future work should explore longevity and fitness in wild chimpanzees , as well as in our other closest relatives , bonobos .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "ecology", "epidemiology", "and", "global", "health" ]	2018	Personality links with lifespan in chimpanzees
The eradication of tumor cells requires communication to and signaling by cells of the immune system . Natural killer ( NK ) cells are essential tumor-killing effector cells of the innate immune system; however , little is known about whether or how other immune cells recognize tumor cells to assist NK cells . Here , we show that the innate immune receptor Dectin-1 expressed on dendritic cells and macrophages is critical to NK-mediated killing of tumor cells that express N-glycan structures at high levels . Receptor recognition of these tumor cells causes the activation of the IRF5 transcription factor and downstream gene induction for the full-blown tumoricidal activity of NK cells . Consistent with this , we show exacerbated in vivo tumor growth in mice genetically deficient in either Dectin-1 or IRF5 . The critical contribution of Dectin-1 in the recognition of and signaling by tumor cells may offer new insight into the anti-tumor immune system with therapeutic implications . In recent years , our understanding of innate immune recognition of microbial components and its critical role in host defense against infection has grown considerably . The discovery of Toll-like receptors ( TLRs ) and other classes of signal-transducing innate immune receptors exemplifies a model of pathogen recognition by germline-encoded pattern-recognition receptors ( PRRs ) of the innate immune system that detect components of invading pathogens , termed pathogen-associated molecular patterns ( PAMPs ) ( Janeway and Medzhitov , 2002; Blasius and Beutler , 2010; Kawai and Akira , 2010 , 2011 ) . Upon recognition of PAMPs , these receptors activate NF-κB , interferon regulatory factors ( IRFs ) and other transcription factors , which then induce the transcription of cognate target genes ( Tamura et al . , 2008; Ikushima et al . , 2013 ) . This results in the evocation of innate immune responses that also instruct subsequent innate immune responses . Dead or injured cells also recruit and activate innate inflammatory cells in the absence of infection through recognition by PRRs of released self-derived molecules termed damage-associated molecular patterns ( DAMPs ) ( Bianchi , 2007; Rubartelli and Lotze , 2007 ) . To date , the role of innate immune receptors in anti-tumor innate immune response is unknown . It has been well-established that natural killer ( NK ) cells are essential effector cells of the innate arm of the immune system to control virus infections and tumor developments by exerting cytotoxicity of target cells ( Yokoyama and Plougastel , 2003 ) . NK cells are sub-classified into circulating conventional NKs ( cNKs ) and tissue-resident NKs ( trNKs ) ( Sojka et al . , 2014 ) . Thus far , most of the functional studies on NK cells have focused on cNKs , which are found to be abundant in the spleen . In view of the critical role of cNKs ( referred to as NKs hereafter unless stated otherwise ) in anti-tumor innate immunity , the interesting issue for whether PRRs expressed on NK and/or other immune cells may recognize tumor cells for the enhancement of tumoricidal activity of NK cells . It has been reported that cell-to-cell contact between dendritic cells ( DCs ) and resting NK cells causes an enhancement of NK cell-mediated cytolytic activity against tumor cells ( Fernandez et al . , 1999 ) ; however , the involvement of PRRs in this context remains unknown . Of note , some TLRs appear to have the opposite effect ( Rakoff-Nahoum and Medzhitov , 2009; Pradere et al . , 2014 ) . For instance , activation of TLR2 and TLR6 in macrophages enhances metastasis of tumor cells , wherein receptor activation is mediated by tumor cell-derived versican , an extracellular matrix proteoglycan that is up-regulated in many tumor cells ( Kim et al . , 2009 ) . During our studies on the IRF family of transcription factors in the context of regulation of PRR signaling and oncogenesis , we observed that mice genetically deficient for IRF5 ( IRF5-deficient mice ) show extensive lung metastasis of the B16F1 melanoma cells ( hereafter referred to as B16 cells ) , a consequence reported to be controlled by NK cells . Since IRF5 is known to be activated by TLRs and other classes of PRRs , we asked whether any of these PRRs is involved in this process in the context of NK cell-mediated control of tumor cells . In light of the tumor-promoting effect by TLRs ( Rakoff-Nahoum and Medzhitov , 2009; Pradere et al . , 2014 ) , we hypothesized that another class of PRRs expressed on the cell surface may be involved in the recognition of tumor cells for the NK cell-mediated anti-tumor response . Dectin-1 , a C-type lectin receptor ( CLR ) family member known to recognize β-glucans of fungal cell wall ( Brown , 2006 ) , was of particular interest since ( i ) it is highly expressed in macrophages and DCs ( Herre et al . , 2004 ) and ( ii ) IRF5 is involved in Dectin-1 signaling for anti-fungal innate immune response ( del Fresno et al . , 2013 ) . We show here that Dectin-1 expressed on DCs and macrophages critically contributes to the enhancement of NK-mediated killing of tumor cells . Further , we demonstrate that IRF5 is activated by Dectin-1 signaling in these immune cells and that this Dectin-1-IRF5 pathway constitutes a critical limb for their orchestration of NK cells . We also provide evidence that tumor cell-mediated Dectin-1 signaling is instigated by receptor recognition of N-glycan structures on the surface of some but not all tumor cells , which we propose to term tumor-associated molecular patterns ( TAMPs ) . Finally , the in vivo significance of these observations is validated by the massive growth of the TAMP-expressing tumors in mice genetically deficient for either Dectin-1 or IRF5 . We discuss our results revealing a new facet of the CLR family of receptors in the orchestration of anti-tumor innate immune responses as well as future prospects of innate recognition and control of tumor cells , which may have clinical implications . Within the IRF family of transcription factors , several members including IRF3 , IRF5 and IRF7 are particularly well-known for the pivotal roles they serve in the innate immune receptor-mediated gene induction programme ( Honda and Taniguchi , 2006 ) . Here , we challenged mice deficient in these individual transcription factors with the lung metastasis model of B16F1 melanoma cells ( hereafter referred to as B16 unless stated otherwise ) . As shown in Figure 1A , B , we observed markedly enhanced metastasis of B16 cells in the lungs of mutant mice deficient in the Irf5 gene ( hereafter IRF5-deficient mice ) . Massive tumor growth was also observed when B16 cells were subcutaneously injected into the mutant mice ( Figure 1—figure supplement 1 ) . 10 . 7554/eLife . 04177 . 003Figure 1 . Critical contribution of IRF5 to the enhancement of NK cell-mediated anti-tumor responses . ( A ) Selective contribution of IRF5 in the suppression of lung metastasis of B16F1 cells . Number of metastasized colonies in lungs from wild-type ( WT ) , Irf3−/− , Irf5−/− , or Irf7−/− mice 14 days after intravenous injection of 1 × 106 of B16F1 cells . Means are indicated as black bars . p < 0 . 05 by Student's t test . ( B ) Representative images of lungs from WT or Irf5−/− mice 14 days after intravenous injection of 2 × 106 of B16F1 cells . ( C ) In vitro killing assay of immune cells from WT or Irf5−/− mice against B16F1 cells . Whole splenocytes ( left panel ) , purified NK cells ( middle panel ) , or NK-depleted splenocytes ( right panel ) from WT or Irf5−/− mice are mixed with 51Cr-labeled target B16F1 cells at the indicated ratios . 4 hr later , 51Cr radioactivity released from target cells was monitored . E/T: effector/target cell ratio . ( D ) Purified NK cells ( WT; 1 × 105 cells ) without or with 1 × 105 , 2 × 105 , or 4 × 105 of WT splenic CD11c+ , CD11b+ , T , or B cells were subjected to in vitro killing assay for B16F1 cells . Target cell lysis was measured by co-culturing target cells and myeloid cells , with ( total values ) or without NK cells ( background values ) , and background values were subtracted from the total values . Each background lysis was less than 6% of maximum release . The calculated percentage of cytotoxicity was represented as Net lysis ( % ) . In all in vitro killing assays , 1 × 104 of 51Cr-labeled B16F1 cells were used ( C and D ) . All in vitro killing assays were performed at least three times with high reproducibility . Represented as means ± SD . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 00310 . 7554/eLife . 04177 . 004Figure 1—figure supplement 1 . Critical role of IRF5 in the suppression of tumor growth . Tumor growth in WT and Irf5−/− mice . Tumor volume of WT or Irf5−/− mice was monitored at the indicated days after subcutaneous injection of 1 × 105 of B16F1 cells . Represented as means ± SD . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 00410 . 7554/eLife . 04177 . 005Figure 1—figure supplement 2 . Requirement of IRF5 in myeloid cells for the suppression of tumor metastasis . ( A ) Tumor metastasis in bone marrow chimera mice . Chimera mice were generated using WT , Irf5−/− , and C57BL/6-Ly5 . 1 ( Ly5 . 1 ) mice . Numbers of metastasized colonies in lungs from the bone marrow chimera mice were counted 14 days after intravenous injection with 1 × 106 of B16F1 cells . Means are indicated as black bars . p < 0 . 05 by Student's t test . ( B ) Tumor metastasis in WT or Rag1−/− mice . Numbers of metastasized colonies in lungs from WT or Rag1−/− mice were counted 14 days after intravenous injection with 1 × 106 of B16F1 cells . Means are indicated as black bars . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 00510 . 7554/eLife . 04177 . 006Figure 1—figure supplement 3 . Involvement of IRF5 in CD11b+ and CD11c+ cells to the enhancement of NK cell-mediated anti-tumor responses . Requirement of IRF5 in myeloid cells for the enhancement of NK cell's in vitro killing activity . In vitro killing activities of purified NK cells ( WT; 1 × 105 cells ) against B16F1 cells were monitored in the absence or presence of 1 × 105 , 2 × 105 , or 4 × 105 of WT or Irf5−/− splenic CD11b+ ( left panel ) or CD11c+ ( right panel ) cells . The percentage of cytotoxicity was calculated as described in the legend of Figure 1D and represented as Net lysis ( % ) . Each background lysis was less than 6% of maximum release . Represented as means ± SD . p < 0 . 05 by Student’s t test . In in vitro killing assays , 1 × 104 of 51Cr-labeled B16F1 cells were used . All in vitro killing assays were performed at least three times and the results were highly reproducible . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 00610 . 7554/eLife . 04177 . 007Figure 1—figure supplement 4 . Contribution of DCs and macrophages to the NK cell-mediated tumor killing . The effect of various myeloid cells on the enhancement of NK cell killing activities . In vitro killing activities of purified NK cells ( WT; 1 × 105 cells ) were monitored in the absence or presence of 1 × 105 , 2 × 105 , or 4 × 105 of splenic CD8+CD11c+ ( left panel ) , CD8–CD11c+ ( middle panel ) , or CD11c−CD11b+ ( right panel ) cells . CD11c−CD11b+ cells were purified from diphtheria toxin-treated CD11c-DTR mice . The percentage of cytotoxicity was calculated as described in the legend of Figure 1D and represented as Net lysis ( % ) . Each background lysis was less than 5% of maximum release . Represented as means ± SD . 1 × 104 of 51Cr-labeled B16F1 cells were used as target cells . All in vitro killing assays were performed at least three times and the results were highly reproducible . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 007 To examine the contribution of immune cells , we next conducted bone marrow transplantation and found that IRF5 expression specifically in bone marrow-derived cells critically contributes to the suppression of the tumor cell metastasis ( Figure 1—figure supplement 2A ) . Since the marked B16 lung metastasis was not observed in mice deficient in T and B lymphocyte development ( Figure 1—figure supplement 2B ) , we next examined the contribution of IRF5 in anti-tumor innate immune response , wherein NK cells are best known for their direct anti-tumor cytotoxicity ( Smyth et al . , 2002 ) . When whole splenocytes , of which about 3% are resting NK cells in both wild-type ( WT ) and IRF5-deficient mice ( Takaoka et al . , 2005 ) , were subjected to an in vitro killing assay ( Brunner et al . , 1968 ) ( 51Cr release assay ) for B16 cells , a notable decrease of killing activity was observed in those from IRF5-deficient mice ( Figure 1C; left panel ) . Interestingly , however , when NK cells were purified from these splenocytes and then subjected to the killing assay , the lack of IRF5 did not affect the NK activity ( Figure 1C; middle panel ) . Since the killing activity is entirely abrogated by NK cell depletion in both WT and mutant splenocytes ( Figure 1C; right panel ) , these results indicate there is a critical contribution of non-NK cells within the total splenocyte population for which IRF5 is critical for the full-blown killing activity of NK cells . Consistent with this notion , when purified NK cells were co-cultured with CD11c+ DCs and macrophage-enriched CD11b+ cells from splenocytes ( hereafter referred to as co-culture assay ) , a dose-dependent and significant increase in NK killing activity was observed , whereas T or B lymphocytes had no such effect ( Figure 1D ) . Of note , a marked reduction of the NK killing activity was observed when CD11b+ or CD11c+ cells from IRF5-deficient mice were used in lieu of those from WT mice in the co-culture assay ( Figure 1—figure supplement 3 ) . The enhancement of NK killing activity was also made in another co-culture assay , wherein CD8+CD11c+ , CD8−CD11c+ , or CD11c−CD11b+ cells purified from splenocytes were used , indicating that both DCs and macrophages participate in the enhancement of NK cell-mediated killing activity ( Figure 1—figure supplement 4 ) . The above observations prompted us to investigate the contribution of innate immune receptor ( s ) that recognizes B16 cells and activate IRF5 for the NK-mediated killing of the tumor cells . Although IRF5 is best known as a transcription factor activated by TLR signaling , we observed that splenocytes from mice deficient in MyD88 , the common adapter for all TLRs , shows killing activity against B16 cells at a rate even slightly higher than those from WT mice ( Figure 2—figure supplement 1 ) . We then focused our attention on Dectin-1 , a member of the CLR family , which is expressed on the cell surface of innate immune cells . In fact , Dectin-1 , known to recognize β-glucans of fungal cell wall ( Brown , 2006 ) , is widely expressed by DCs and macrophages and the activation of IRF5 by the Dectin-1-Syk pathway has been implicated in the induction of type I interferon-β ( IFN-β ) gene upon fungal infection ( del Fresno et al . , 2013; Leibundgut-Landmann et al . , 2008; Backer et al . , 2008; Brown et al . , 2002; Honda and Taniguchi , 2006 ) . We first examined whether IRF5 is activated in splenocytes through recognition of and signaling induced by B16 cells in a Dectin-1-dependent manner . Interestingly , we observed nuclear translocation of IRF5 , a hallmark of its activation ( Honda and Taniguchi , 2006 ) , following incubation of WT splenocytes with B16 cells ( in 50 to 1 or 100 to 1 cell ratio ) , but not in splenocytes from mice deficient in the Clec7a gene that encodes Dectin-1 ( hereafter Dectin-1-deficient mice ) , indicating there is a Dectin-1 signal-dependent IRF5 activation by the tumor cells ( Figure 2A ) . Of note , IRF5 activation by B16 cells was not inhibited by FK506 , suggesting there is a calcineurin-independent pathway for this activation ( Figure 2—figure supplement 2 ) . As such , these data support a Dectin-1 signal-dependent IRF5 activation by tumor cells . 10 . 7554/eLife . 04177 . 008Figure 2 . Critical role of Dectin-1 signaling in DCs and macrophages for IRF5 activation by and NK cell-mediated killing against B16F1 cells . ( A ) Nuclear translocation of IRF5 in WT or Dectin-1−/− splenocytes ( 5 × 107 cells ) co-incubated with B16F1 cells ( 5 × 105 or 1 × 106 cells ) or stimulated with curdlan ( 30 µg/ml ) for 6 hr . Nuclear protein fraction from the culture was analyzed by immunoblotting for IRF5 and USF-2 . USF-2 was used as a nuclear marker protein . ( B ) In vitro killing activity of whole splenocytes from WT or Dectin-1−/− mice against B16F1 cells . ( C ) In vitro killing activity of purified NK cells ( WT; 1 × 105 cells ) against B16F1 cells was assessed in the absence or presence of 1 × 105 , 2 × 105 , or 4 × 105 of WT or Dectin-1−/− splenic CD11b+ ( left panel ) or CD11c+ ( right panel ) cells . The percentage of cytotoxicity was calculated as noted in the legend of Figure 1D and represented as Net lysis ( % ) . Each background lysis was less than 6% of maximum release . In all in vitro killing assays , 1 × 104 of 51Cr-labeled B16F1 cells were used ( B and C ) . All in vitro killing assays were performed at least three times with high reproducibility . Represented as means ± SD . p < 0 . 05 by Student's t test . Of note , we could not found mRNA induction for typical inflammatory and cytotoxic mediators in the co-culture system , wherein the ratio of B16F1 cells , DCs , and NK cells is 1:30:10 ( Figure 2—figure supplement 8 ) . As such , from these analyses , it seems unlikely that Dectin-1 signaling in DCs affects the expression of these molecules in NK cells . ( D ) Number of metastasized colonies in lungs from WT or Dectin-1−/− mice intravenously injected with 1 × 106 of B16F1 cells . Means are indicated as black bars . p < 0 . 05 by Student's t test . ( E ) Representative images of lungs from WT or Dectin-1−/− mice 14 days after intravenous injection of 2 × 106 of B16F1 cells . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 00810 . 7554/eLife . 04177 . 009Figure 2—figure supplement 1 . Dispensable role of MyD88 in anti-tumor killing activity of NK cells . In vitro killing activity of WT or Myd88−/− splenocytes against B16F1 cells . Represented as means ± SD . E/T: effector/target cell ratio . In in vitro killing assays , 1 × 104 of 51Cr-labeled B16F1 cells were used . In vitro killing assays were performed at least three times , and the results were highly reproducible . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 00910 . 7554/eLife . 04177 . 010Figure 2—figure supplement 2 . Effect of FK506 treatment on IRF5 activation in splenocytes . Nuclear translocation of IRF5 of WT splenocytes ( 5 × 107 cells ) co-incubated with B16F1 cells ( 1 × 106 cells ) for 6 hr in the absence or presence of 50 or 500 nM of FK506 . Nuclear protein extracts from the culture were analyzed by immunoblotting for IRF5 and USF-2 . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 01010 . 7554/eLife . 04177 . 011Figure 2—figure supplement 3 . Minor effects of Dectin-1 expressed in NK cells on the tumor killing activity . ( A ) In vitro killing activity of purified NK cells from WT or Dectin-1−/− mice against B16F1 cells . Represented as means ± SD . E/T: effector/target cell ratio . In in vitro killing assays , 1 × 104 of 51Cr-labeled B16F1 cells were used . In vitro killing assays were performed at least three times , and the results were highly reproducible . ( B ) qRT-PCR analysis of Dectin-1 mRNA in WT splenic CD11b+ cells , CD11c+ cells and NK cells , and MEFs . Results are presented as relative to the expression of Gapdh mRNA . Represented as means ± SD . ND , none detected . ( C ) Expression levels of Dectin-1 mRNA in myeloid cells from liver and lungs . CD11b+F4/80+ cells and CD11c+ cells from liver and lung were isolated by cell sorting . Results are presented as relative to the expression of Gapdh mRNA . Represented as means ± SD . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 01110 . 7554/eLife . 04177 . 012Figure 2—figure supplement 4 . Essential role of Dectin-1 in the suppression of tumor growth . Tumor growth in WT and Dectin-1−/− mice . Tumor volume of WT or Dectin-1−/− mice was monitored at the indicated days after subcutaneous injection of 1 × 105 of B16F1 cells . Represented as means ± SD . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 01210 . 7554/eLife . 04177 . 013Figure 2—figure supplement 5 . Contribution of Dectin-1 and IRF5 to the control of tumor metastasis . Number of metastasized colonies in the lungs from WT , Irf5−/− , or Dectin-1−/− mice . Mice were intravenously injected with 1 × 106 of B16F1 cells and , 14 days later , the number of metastasized colonies in lungs was measured . Means are indicated as black bars . p < 0 . 05 ( WT vs Irf5−/− , WT vs Dectin-1−/− , Irf5−/− vs Dectin-1−/− ) by Student's t test . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 01310 . 7554/eLife . 04177 . 014Figure 2—figure supplement 6 . Dispensable role of Dectin-1 in NK-cell-independent tumor suppression . NK-depleted WT , or NK-depleted Dectin-1−/− mice were intravenously injected with 1 × 106 of B16F1 cells . After 14 days , the numbers of metastasized colonies were counted in the lungs of each mouse . This result suggests that NK cells are indeed the effector cells and that Dectin-1 signaling in DCs and macrophages contributes to NK cell-mediated tumor killing . Means are indicated as black bars . NS , not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 01410 . 7554/eLife . 04177 . 015Figure 2—figure supplement 7 . Normal population and functions of Dectin-1-deficient NK cells . ( A ) The population of splenic NK cells in Dectin-1−/− mice . Splenocytes were isolated from WT and Dectin-1−/− mice . The NK cell population ( percentage of total cells ) was then analyzed by flow cytometry using anti-DX5 and anti-CD3ε antibodies . Represented as means ± SD . NS , not significant . ( B ) IL12-induced IFN-γ production in NK cells . Purified NK cells ( WT or Dectin-1−/−; 5 × 104 cells ) were stimulated with 10 or 100 ng/ml of IL12 . IFN-γ levels in the cultured supernatants were measured by ELISA 24 hr after IL12 stimulation . Represented as means ± SD . NS , not significant . ND , none detected . ( C ) Viral loads in the spleens of WT and Dectin-1−/− mice after MCMV infection . WT , Dectin-1−/− , and NK-depleted WT mice were intraperitoneally infected with MCMV ( 5 × 103 pfu ) . Mice were sacrificed 3 days after infection and MCMV titers were then examined by plaque forming assay . Means are indicated as black bars; NS , not significant . ( D ) Survival of the WT ( n = 4 ) , Dectin-1−/− ( n = 4 ) , and NK-depleted WT mice ( n = 4 ) after MCMV infection . Mice were intraperitoneally infected with MCMV ( 3 × 105 pfu ) and their survival was monitored at the indicated periods . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 01510 . 7554/eLife . 04177 . 016Figure 2—figure supplement 8 . Expression levels of cytotoxic mediators and inflammatory cytokines in co-culture system . NK cells ( WT; 1 × 105 cells ) and WT or Dectin-1−/− splenic CD11c+ cells ( 3 × 105 cells ) were co-cultured with B16F1 cells ( 1 × 104 cells ) . Total RNA was isolated at time zero and 4 hr after the co-culture and then mRNA expression levels for Granzyme B ( Gzmb ) , Perforin-1 ( Prf1 ) , IFN-γ ( Ifng ) , IL-6 ( Il6 ) , and TNF-α ( Tnf ) were analyzed by qRT-PCR analysis . Results are presented relative to the expression of Gapdh mRNA . Represented as means ± SD . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 016 Consistent with the above notion , a dramatic reduction of in vitro tumoricidal activity was observed with Dectin-1-deficient splenocytes ( Figure 2B ) . When NK cells were purified from WT and Dectin-1-deficient splenocytes and subjected to the in vitro killing assay for B16 cells , a slight decrease in cell killing activity of Dectin-1-defcient NK cells was seen as compared to WT NK cells , indicating an ancillary role of the B16-Dectin-1 signaling in NK cells ( Figure 2—figure supplement 3A ) . Dectin-1 mRNA expression was indeed observed in NK cells and other innate immune cells ( Figure 2—figure supplement 3B , C ) ; however , Dectin-1 expression in NK cells does not account for the above Dectin-1-IRF5 axis as IRF5-deficient NK cells show normal cell killing activity ( Figure 1C; middle panel ) . When WT-derived resting NK cells were purified and subjected to the co-culture assay , a marked reduction of NK killing activity was also observed when Dectin-1-deficient CD11b+ or CD11c+ cells were used in lieu of those from WT mice ( Figure 2C ) . It is worth noting that , consistent with the results showing the existence of IRF5-independent pathway for the Dectin-1 signaling , the reduction of NK cell killing activity was even more pronounced when CD11b+ or CD11c+ cells from Dectin-1-deficient mice were subjected to the co-culture assay compared to those from IRF5-deficient mice ( Figure 1—figure supplement 3 ) . These observations in toto underscore the involvement of Dectin-1 on DCs and/or macrophages in the full-blown NK-mediated tumoricidal activity . On the other hand , since the NK cell-enhancing activity is not totally abrogated by the absence of Dectin-1 in CD11b+ or CD11c+ cells ( Figure 2C ) , there is perhaps contribution of additional innate immune receptor ( s ) in these cells for NK cell activation ( ‘Discussion’ ) . On the basis of the above observations , we examined the in vivo contribution of Dectin-1 to anti-tumor innate immune responses by challenging WT and Dectin-1-deficient mice with the B16 cell lung metastasis model . As shown in Figure 2D , E , a marked enhancement of metastasis of B16 cells was seen in the lungs of Dectin-1-deficient mice as compared with those of WT mice . Loss of tumor growth control also occurred when Dectin-1-deficient mice were inoculated subcutaneously with B16 cells ( Figure 2—figure supplement 4 ) . Of note , the metastasis to the lung was significantly more profound than that observed in IRF5-deficient mice ( Figure 2—figure supplement 5 ) ; this observation is consistent with the above in vitro data , which shows that the IRF5 pathway contributes partly to Dectin-1 signaling ( Figure 2C , Figure 1—figure supplement 3 ) . In addition , the control of lung metastasis of B16 cells is equally lost between WT and Dectin-1-deficient mice when NK cells were depleted prior to tumor challenge ( Figure 2—figure supplement 6 ) ; this result further supports the notion that NK cells are indeed the effector cells and that they need the assistance of DCs and macrophages for which Dectin-1 signaling is critical , in the orchestration for the innate control of tumor cells in vivo . It is worth noting that no overt difference was observed between WT and Dectin-1-deficient mice in terms of ( i ) the proportion of splenic NK cells , ( ii ) production of IFN-γ by NK cells in vitro , and ( iii ) NK cell-dependent clearance of murine cytomegalovirus ( MCMV ) in vivo ( Figure 2—figure supplement 7 ) . Therefore , NK cell-dependent immune responses are not globally impaired by the absence of Dectin-1; impairment is selective to the anti-tumor response . Does Dectin-1 recognize a molecular structure ( s ) on B16 cells ? To address this question , we generated a soluble form of Dectin-1 conjugated to human IgG1 Fc ( termed sDectin-1 ) to detect binding of Dectin-1 to the cell surface . Interestingly , substantial binding of sDectin-1 to B16 cells was detected , whereas binding was almost undetectable to mouse embryonic fibroblasts ( MEFs ) and other primary , non-transformed cells ( Figure 3A , Figure 3—figure supplement 1 ) . 10 . 7554/eLife . 04177 . 017Figure 3 . Recognition of N-glycan structures on B16F1 cells by Dectin-1 and its requirement for the enhancement of NK cell-mediated killing activity . ( A ) Binding of sDectin-1 to B16F1 cells ( left panel ) and primary mouse embryonic fibroblasts ( MEFs; right panel ) . The cells ( 4 × 105 cells ) were incubated with human IgG1 Fc ( control Fc ) or sDectin-1 ( fused with the Fc ) for flow cytometric analysis . ( B ) Effect of N-glycosidase treatment ( left panel ) or O-glycosidase in combination with neuraminidase ( right panel ) on the sDectin-1 binding to B16F1 cells . The cells ( 4 × 105 cells ) were treated with either N-glycosidase ( 25 U/ml ) or O-glycosidase ( 25 mU/ml ) with neuraminidase ( 250 mU/ml ) for 1 hr and then subjected to the sDectin-1 binding assay . These enzymatic reactions were performed under the conditions wherein these cells remain alive . ( C ) The effect of N-glycosidase or O-glycosidase treatment of B16F1 cells on in vitro killing activity of WT splenocytes . B16F1 cells were treated with or without N-glycosidase ( 25 U/ml ) for 1 hr in RPMI medium ( left panel ) or treated with or without the combination of O-glycosidase ( 25 mU/ml ) and neuraminidase ( 125 mU/ml ) for 1 hr in RPMI medium ( right panel ) and then subjected to in vitro killing assay . 51Cr radioactivity released from target cells was measured . Represented as means ± SD . E/T: effector/target cell ratio . In in vitro killing assays , 1 × 104 of 51Cr-labeled B16F1 cells were used . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 01710 . 7554/eLife . 04177 . 018Figure 3—figure supplement 1 . Binding of sDectin-1 to various mouse primary cells . The sDectin-1 binding to splenic CD11b+ cells , CD11c+ cells , NK cells , liver cells , or lung cells were examined as described in Figure 3A . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 01810 . 7554/eLife . 04177 . 019Figure 3—figure supplement 2 . Effect of neuraminidase treatment on the sDectin-1 binding to B16F1 cells . B16F1 cells ( 4 × 105 cells ) were treated with or without neuraminidase ( 250 mU/ml ) for 1 hr and then subjected to the sDectin-1 binding assay . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 01910 . 7554/eLife . 04177 . 020Figure 3—figure supplement 3 . Effect of N-glycosidase treatment of B16F1 cells on in vitro killing activity of purified NK cells . B16F1 cells were treated with or without N-glycosidase ( 25 U/ml ) for 1 hr in RPMI medium and then used as target cells for in vitro killing assay with purified NK cells from WT mice . 51Cr radioactivity released from the target cells was measured . Represented as means ± SD . E/T: effector/target cell ratio . In in vitro killing assays , 1 × 104 of 51Cr-labeled B16F1 cells were used . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 02010 . 7554/eLife . 04177 . 021Figure 3—figure supplement 4 . Mass spectrometric analysis of N-glycosidase-treated B16F1 cells . The supernatant of B16F1 cells treated with N-glycosidase for 1 hr was collected and incubated with protein G-conjugated sepharose bound without ( blue peaks ) or with ( green peaks ) sDectin-1 . N-glycans remained in the supernatant was then analyzed by mass spectrometry . The intensities of the spectra were normalized to that of glycan corresponding to the peak ( m/z; 1159 . 40 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 02110 . 7554/eLife . 04177 . 022Figure 3—figure supplement 5 . Proposed N-glycan structures detected by mass spectrometric analysis . Proposed structures of N-glycans detected by the mass spectrometric analysis ( Figure 3—figure supplement 4 ) are depicted . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 02210 . 7554/eLife . 04177 . 023Figure 3—figure supplement 6 . No differences in the amount of each N-glycan structure between samples with and without sDectin-1 treatment . Effect of the incubation of sDectin-1 on the relative amount of N-glycan structures released from N-glycosidase-treated B16F1 cells . Relative amounts of each N-glycan structure to total N-glycan amount in the two samples in Figure 3—figure supplement 4 were plotted . Note that no differences are seen in the concentration of each glycan structure between the supernatants with or without treatment with sDectin-1 prior to the mass spectrometry analysis . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 023 Since β-glucans , known ligands of Dectin-1 , are not expressed by mammalian cells ( Brown , 2006 ) , we hypothesized the presence of other types of glycosylated structure ( s ) on B16 cells that are mediating the recognition by Dectin-1 . In this context , it has been shown that enhanced glycosylation levels provide growth advantages to many if not all tumor cells ( Granovsky et al . , 2000; Gu and Taniguchi , 2008 ) . Interestingly , we found that sDectin-1 binding to B16 cells was markedly reduced upon N-glycosidase treatment , while treatment by O-glycosidase or neuraminidase showed only a marginal effect , suggesting there is a major requirement for N-glycan structures to Dectin-1 binding ( Figure 3B , Figure 3—figure supplement 2 ) . Consistent with this , a marked reduction of tumoricidal activity of splenocytes was observed when B16 cells were pretreated with N-glycosidase , whereas O-glycosidase treatment did not affect this activity ( Figure 3C ) . Further , N-glycosidase treatment of B16 cells did not affect the in vitro killing activity of purified NK cells ( Figure 3—figure supplement 3 ) , indicating in toto there is a critical role of Dectin-1 recognition of and signaling by N-glycan structures on tumor cells by DCs and macrophages . We then investigated the nature of N-glycan structures on B16 cells by subjecting the supernatant generated after N-glycosidase treatment to mass spectrometric ( MS ) analysis . As reported previously , N-glycan structures , such as N-glycans with β1 , 6-GlcNAc branching ( Fernandes et al . , 1991 ) , the expression of which is increased in tumor cells , were detected . Of note , pretreatment of the supernatant by sDectin-1 did not alter the MS peak pattern ( Figure 3—figure supplements 4–6 ) . This observation suggests that N-glycan structures , highly expressed in tumor cells , need to be bound to proteins for the Dectin-1 recognition ( See below ) . To address the issue of whether Dectin-1 signaling is required for direct cell-to-cell contact or for the induction of a soluble mediator ( s ) , we examined supernatants from CD11b+ or CD11c+ cells mixed with B16 cells and found that they had little , if any , effect on NK cell activity ( Figure 4A ) . In addition , the use of antibodies against type I IFNs or IL12 , both cytokines are known to activate NK cells ( Degli-Esposti and Smyth , 2005 ) , showed no effect on the NK cell-enhancing activity by DCs ( Figure 4B ) . These results support the notion that Dectin-1 signaling is crucial for mediating cell-to-cell contact and subsequent NK activation . 10 . 7554/eLife . 04177 . 024Figure 4 . Requirement of cell-to-cell contact between NK cells and DCs for the enhancement of NK cell-mediated killing activity . ( A ) Effect of the supernatant of myeloid cells after incubation with tumor cells . NK cell killing activity against B16F1 cells was assessed in the presence of supernatants from the co-culture of B16F1 cells with splenic CD11b+ cells ( left panel ) or CD11c+ cells ( right panel ) . Represented as means ± SD . ( B ) Effect of antibodies against cytokines on NK cell-mediated killing activity . In vitro killing activity of purified NK cells ( WT; 1 × 105 cells ) against B16F1 cells in the presence of 3 × 105 of splenic CD11c+ cells was assessed without or with neutralizing antibodies for type I IFNs or IL12p40 . ( C ) Induction of mRNAs in DCs co-cultured with B16F1 cells via Dectin-1 signaling . WT or Dectin-1−/− splenic CD11c+ cells ( 3 × 105 cells ) were co-cultured with B16F1 cells ( 1 × 104 cells ) . Total RNA from those cells was then isolated at time zero and 4 hr after the co-culture and then subjected to microarray analysis . We first identified genes for which mRNA is induced more than two-fold in WT DCs co-cultured with B16F1 cells and then , of those , selected the genes whose mRNA levels are increased more than twofold in WT 4 hr sample compared to Dectin-1−/− 4 hr sample . Those selected genes are listed in the order of fold change ( WT 4 hr/Dectin-1−/− 4 hr ) ( Table 1 ) . Genes encoding a membrane-bound protein are listed . ( D ) Induction of Fam26f ( Inam ) mRNA by the Dectin-1-IRF5 pathway . The expression levels of Inam mRNA were monitored by qRT-PCR analysis of total RNA from splenic CD11c+ cells ( WT , Irf5−/− , or Dectin-1−/−; 3 × 105 cells ) co-cultured with B16F1 cells ( 1 × 104 cells ) for 4 hr as described in ‘Materials and methods’ . Results are presented relative to the expression of Gapdh mRNA . Represented as means ± SD . p < 0 . 05 by Student's t test . NS , not significant . Although the IL15 cytokine system is known to promote growth and activity of NK cells , Il15 and Il15ra mRNA expression levels were affected only marginally in the Dectin-1−/− DCs ( Figure 4—figure supplement 1 ) . The results suggest that this cytokine system will not be involved in this particular experimental setting . ( E ) Effect of INAM expression in DCs on the enhancement of NK cell killing activity . Purified NK cells ( WT; 1 × 105 cells ) were mixed with increasing amounts ( 1 × 104 , 3 × 104 , and 1 × 105 cells ) of INAM-transduced WT BMDCs ( BMDC-INAM ) or mock-transduced WT BMDCs ( control BMDC ) and killing activities against B16F1 cells were monitored . Represented as means ± SD . In all in vitro killing assays , 1 × 104 of 51Cr-labeled B16F1 cells were used . 51Cr radioactivity released from target cells was measured . The percentage of cytotoxicity was calculated as described in the legend of Figure 1D and represented as Net lysis ( % ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 02410 . 7554/eLife . 04177 . 025Figure 4—figure supplement 1 . Induction of Il15 or Il15ra mRNAs in DCs co-cultured with B16F1 cells . Il15 ( left panel ) or Il15ra ( right panel ) mRNA were monitored by qRT-PCR analysis as described in Figure 4D . Results are presented relative to the expression of Gapdh mRNA . Represented as means ± SD . p < 0 . 05 by Student's t test . NS , not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 025 We then examined the gene expression profiles of WT or Dectin-1-deficient DCs exposed to B16 cells by microarray analysis and found the induction of a substantial number of genes ( Figure 4C; Table 1 ) . The affected genes include several membrane-bound proteins amongst which Inam ( termed as Fam26f ) is known to activate NK cells via its homophilic interaction ( Ebihara et al . , 2010 ) . Interestingly , the Inam mRNA induction by B16 cells is IRF5-dependent in splenic CD11c+ cells ( Figure 4D ) and lentiviral expression of INAM cDNA in bone marrow-derived DCs resulted in the enhancement of NK cell-mediated killing of B16 cells ( Figure 4E ) . Thus , the Dectin-1-IRF5-INAM pathway may participate in the DC-mediated activation of NK cells , at least in part , in this experimental setting . Obviously , more detailed analyses will be required to gain further mechanistic insight into the enhancement of NK-mediated tumor control by DCs and macrophages . 10 . 7554/eLife . 04177 . 026Table 1 . Induction of mRNAs in DCs co-cultured with B16F1 cells via Dectin-1 signalingDOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 026UnigeneGene symbolFold changeMm . 275426Amy2a1///Amy2a2///Amy2a3///Amy2a4///Amy2a51995 . 76Mm . 45316Cela2a173 . 36Mm . 383263Try4///Try588 . 72Mm . 475541Cela3b///Gm1301161 . 79Mm . 20407Pnlip21 . 28Mm . 142731Reg116 . 65Mm . 34374Ctrb116 . 42Mm . 21160Clps14 . 85Mm . 276926Prss213 . 67Mm . 34692Cpb110 . 25Mm . 450553Tpte9 . 99Mm . 10753Pnliprp18 . 31Mm . 46360Reg26 . 86Mm . 1825Tff25 . 19Mm . 867Ccl12///LOC1008625784 . 96Mm . 464256Tcl1b33 . 97Mm . 2745Ctrl3 . 72Mm . 35088Chrnb23 . 49Mm . 14874Gzmb3 . 46Mm . 159219Batf23 . 45Mm . 439927Nol73 . 00Mm . 4769Fzd62 . 85Mm . 26760Lrrc192 . 85Mm . 34479Fam26f ( Inam ) 2 . 80Mm . 4662Irg12 . 72Mm . 4922Csf22 . 70Mm . 2319Stmn32 . 70Mm . 24375Ttpa2 . 70Mm . 41416Rilp2 . 65Mm . 159575Cdyl22 . 57Mm . 261140Iigp12 . 44Mm . 42791Crtam2 . 37Mm . 26730Hp2 . 32Mm . 203866Ahnak2 . 30Mm . 34520Gtpbp82 . 30Mm . 32368Krit12 . 29Mm . 116997Hmmr2 . 26Mm . 208125Adamts62 . 17Mm . 333734Adora2a2 . 15Mm . 484815Ly6i2 . 13Mm . 271830Dhx582 . 10Mm . 131098Golga12 . 10Mm . 131723Cxcl112 . 09Mm . 5022Mmp132 . 09Mm . 291595Klf92 . 09Mm . 33691Reg3d2 . 08Mm . 130Socs12 . 06Mm . 10948Slfn12 . 05Mm . 1062Tnfsf102 . 04Mm . 22213Glipr22 . 01The genes identified in the microarray analysis in Figure 4C are listed in the order of fold change ( WT 4 hr/Dectin-1−/− 4 hr ) . Genes encoding a membrane-bound protein are indicated in red letters . To what extent the Dectin-1-induced anti-tumor immune responses account for other types of tumor cells ? To address this issue , we first examined the binding capacity of sDectin-1 for other tumor cell lines . The binding capacity was variable in that the binding was strong for 3LL ( lung carcinoma ) , YAC-1 ( lymphoma ) , and Meth-A ( fibrosarcoma ) cell lines , but was significantly weaker for some tumor cells such as SL4 ( colon carcinoma ) and B16F10 ( melanoma ) cell lines ( Figure 5A , Figure 5—figure supplement 1 ) . It is worth noting that the latter two cell lines are known to metastasize massively even in WT mice ( Morimoto-Tomita et al . , 2005 ) ( See below ) . 10 . 7554/eLife . 04177 . 027Figure 5 . Dectin-1 binding on other tumor cells and its role in tumor suppression . ( A ) Binding of sDectin-1 to 3LL ( left panel ) or SL4 cells ( right panel ) . The sDectin-1 binding to these cell lines was examined as described in Figure 3A . ( B ) In vitro killing activity of WT or Dectin-1−/− splenocytes against 3LL ( left panel ) or SL4 cells ( right panel ) as indicated E/T ratio . Represented as means ± SD . In in vitro killing assays , 1 × 104 of 51Cr-labeled target cells were used . ( C ) Quantification of metastasis of 3LL ( left panel ) or SL4 cells ( right panel ) in WT or Dectin-1−/− mice . WT and Dectin-1−/− mice were intravenously injected with GFP-expressing 3LL ( 3LL-GFP ) or SL4 cells ( SL4-GFP ) . GFP mRNA expression levels in lungs were assessed by qRT-PCR 12 ( 3LL cells ) or 14 ( SL4 cells ) days after the injection . Means are indicated as black bars . p < 0 . 05 by Student's t test; ND , none detected; NS , not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 02710 . 7554/eLife . 04177 . 028Figure 5—figure supplement 1 . Binding of sDectin-1 to various mouse cancer cell lines . The sDectin-1 binding to YAC-1 ( left panel ) , Meth-A ( middle panel ) , or B16F10 ( right panel ) cells were examined as described in Figure 3A . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 02810 . 7554/eLife . 04177 . 029Figure 5—figure supplement 2 . Pull-down analysis of sDectin-1 binding on cancer cells . B16F1 , 3LL , B16F10 and MEF cells ( 4 × 107 cells ) treated with or without N-glycosidase ( 25 U/ml ) for 1 hr were incubated with sDectin-1 ( tagged with HA and human IgG1 Fc region ) , and then the sDectin-1 was chemically cross-linked to the cells . After that , cells were lysed and sDectin-1 was precipitated by protein G-conjugated sepharose . sDectin-1 binding was detected by immunoblot analysis using anti-HA antibody . Arrowhead indicates sDectin-1 protein . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 02910 . 7554/eLife . 04177 . 030Figure 5—figure supplement 3 . In vitro killing activity of WT or Dectin-1−/− splenocytes against YAC-1 cells . In vitro killing activity of WT or Dectin-1−/− splenocytes against YAC-1 cells as indicated E/T ratio . 51Cr radioactivity released from the target cells was measured . Represented as means ± SD . In in vitro killing assays , 1 × 104 of 51Cr-labeled YAC-1 cells were used . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 03010 . 7554/eLife . 04177 . 031Figure 5—figure supplement 4 . Contribution of Dectin-1 signaling to anti-tumor killing activity against B16F10 cells . Number of metastasized colonies in lungs from WT or Dectin-1−/− mice intravenously injected with 5 × 105 of B16F10 cells . Means are indicated as black bars . NS , not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 03110 . 7554/eLife . 04177 . 032Figure 5—figure supplement 5 . Binding of sDectin-1 to various human cancer cell lines . Binding of human sDectin-1 to HepG2 , Huh7 , HBC4 , HeLa , T98 , U251 , A549 , HCT116 , MKN45 , PC-3 , K562 , or G-361 cells was examined as described in Figure 3A . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 03210 . 7554/eLife . 04177 . 033Figure 5—figure supplement 6 . Induction of Inam mRNAs in DCs co-cultured with human cancer cells . Dectin-1-dependent induction of Inam ( Fam26f ) mRNA by a human cancer cell line . Mouse splenic CD11c+ cells ( 3 × 105 cells ) , either from WT mice or Dectin-1−/− mice , were co-cultured with HBC4 cells ( 1 × 104 cells ) for 4 hr and total RNA was subjected to qRT-PCR analysis for Inam mRNA induction . Results are presented relative to the expression of Gapdh mRNA . Represented as means ± SD . p < 0 . 05 by Student's t test . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 033 In addition , chemical cross-linking of sDectin-1 , followed by immunoblot analysis , generated multiple smear bands of similar pattern using B16F1 and 3LL cells , whereas bands were barely detectable with B16F10 cells or MEFs . Further , these bands are abolished by the pretreatment of cells with N-glycosidase ( Figure 5—figure supplement 2 ) . Thus , these data suggest that similar or identical N-glycan structures are expressed on multiple proteins that function as Dectin-1 ligands in these , and probably other , Dectin-1 binding tumor cells . To examine the co-relationship between sDectin-1 binding and NK-mediated killing activity of splenocytes , we selected the lung carcinoma 3LL and YAC-1 cells with strong sDectin-1 binding and colon carcinoma SL4 cells with weak binding , and subjected them to the in vitro killing assay described above . We observed Dectin1-dependent cell killing activity against 3LL and YAC-1 cells ( Figure 5B; left panel and Figure 5—figure supplement 3 ) , whereas the killing activity of WT splenocytes against SL4 cells was significantly weaker as compared to that for 3LL or YAC-1 cells and was reduced only marginally in Dectin-1-deficient splenocytes ( Figure 5B; right panel ) . These results support the notion that tumor cells expressing Dectin-1 ligands at high levels are more susceptible to NK cell-mediated killing in a manner dependent on Dectin-1 signaling . We further expended our in vivo analysis to 3LL and SL4 cells . As shown in Figure 5C , a marked enhancement of lung metastasis of 3LL cells was observed in Dectin-1-deficient mice , but not in WT mice ( Figure 5C; left panel ) , while SL4 cells without significant sDectin-1 binding ( Figure 5A; right panel ) underwent massive metastasis in both WT and mutant mice ( Figure 5C; right panel ) . Expectedly , massive metastasis of B16F10 cells was observed without significant difference between WT and Dectin-1-deficient mice ( Figure 5—figure supplement 4 ) , suggesting that B16F10 cells could have been selected to evade the Dectin-1-mediated anti-tumor orchestration . Thus , these in vivo observations are consistent with in vitro observations in that the recognition of and signaling by Dectin-1 in DCs and macrophages constitute a critical aspect of the NK cell-mediated anti-tumor innate immunity . Finally , Dectin-1 binding to human tumor cell lines was examined by preparing a human sDectin-1 . Interestingly , we found human sDectin-1 binds to several of these cell lines ( Figure 5—figure supplement 5 ) . We also observed the Dectin-1-dependent induction of Inam mRNA in mouse DCs by human cancer cell line HBC4 , to which human sDectin-1 strongly binds ( Figure 5—figure supplement 6 ) . Since mouse and human Dectin-1 are highly conserved , we infer the mouse DCs respond to human Dectin-1 ligands for gene induction . As such , these results suggest the presence of a similar or identical anti-tumor Dectin-1 signaling mechanism in the human immune system . The critical role of signal-transducing innate immune receptors in mediating innate and adaptive immune responses against invading pathogens is well known . However , it has been enigmatic if and how these receptors contribute to anti-tumor responses . In the present study , we demonstrated that the innate immune receptor Dectin-1 expressed on DCs and macrophages is important to NK-mediated killing of tumor cells . Our results indicate that NK cells are required to orchestrate with DCs and macrophages for cell killing , wherein activation of the IRF5 transcription factor by Dectin-1 signaling instigated by receptor recognition of N-glycan structures on tumor cells is critical . This notion is supported by an excessive growth of tumors with sDectin-1 binding in mice genetically deficient in either Dectin-1 or IRF5 in vivo . To our knowledge , this is the first demonstration that an innate immune receptor contributes to anti-tumor recognition and signaling through orchestration of innate immune cells . Our study offers new insight into the NK-mediated anti-tumor activity of the innate immune system ( model depicted in Figure 6 ) . 10 . 7554/eLife . 04177 . 034Figure 6 . Schematic view of the orchestration of innate immune cells for NK cell-mediated tumor killing . Dectin-1 expressed by DCs and macrophages recognizes N-glycan structures on tumor cells and signals to activate IRF5 pathway and other pathways , thereby activating NK cells . Thus , NK cells require tumor recognition and signaling by these innate immune cells for their effective tumoricidal action . Although INAM is depicted here , the DC-mediated activation of NK cells would involve other molecules and this Dectin-1-IRF5-INAM pathway may represent only a part of the entire picture . It remains to be further characterized as to whether and how this and/or other effector ligands induced by Dectin-1 signaling contribute to NK activation in full . It is possible that DCs/macrophages are secondarily activated by yet unknown factors from NK cells ( see dashed arrow ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04177 . 034 A more detailed structural analysis of the Dectin-1 ligand ( s ) needs to be clarified . Our sDectin-1 chemical cross-linking data suggest that similar or identical N-glycan structures are expressed on multiple protein molecules in tumor cells as Dectin-1 ligands . On the basis of the MS and chemical cross-linking data ( Figure 3—figure supplement 4–6 , Figure 5—figure supplement 2 ) , we infer that N-glycan structures need to be bound to proteins for the Dectin-1 recognition . Obviously further work will be required to discern the detailed nature of the ligands , it is possible that Dectin-1 recognition of and activation by N-glycan structures are contingent on the absolute expression levels of N-glycans and/or their associated proteins on the cell; that is , tumor cells with ‘increased self’ molecules are targets for innate recognition for activation of the immune system . However , it is also possible that Dectin-1 recognition requires particular , tumor-specific N-glycan-containing structures , which may belong to ‘altered self’ ( Medzhitov and Janeway , 2002 ) . Whichever the case , Dectin-1-binding structures may fit into the category of ‘tumor-associated molecular patterns ( TAMPs ) ’ vis-à-vis PAMPs for invading pathogens and DAMPs for normal cells subjected to stress or death ( Rubartelli and Lotze , 2007; Kawai and Akira , 2010 ) . This issue obviously merits more advanced study . Our data showing that Dectin-1-deficiency does not entirely abrogate the NK-enhancing activity of CD11b+ or CD11c+ cells suggests the interesting possibility that other CLRs are also involved in the orchestration of innate immune cells for the efficient NK cell-mediated anti-tumor immune response . It is curious that in many tumor cells glycosylation levels are generally enhanced , providing an advantage to tumor cells in migration and metastasis ( Granovsky et al . , 2000; Gu and Taniguchi , 2008 ) . If so , the host's innate system could have evolved to limit the immune evasion of tumor development through recognition of glycosylation products via Dectin-1 and other CLR family members . As such , the potential involvement of other CLR members in the recognition tumor cells , to which sDectin-1 binding is low , is also an interesting future issue to be addressed . The examination of the role of Dectin-1 and other CLRs in the control of tumors in de novo carcinogenesis models will be an exciting issue to address in future studies . It is interesting to note that Dectin-1 mRNA is also expressed in CD11b+ or CD11c+ cells from liver and lung ( Figure 2—figure supplement 3C ) ; hence , we infer that Dectin-1 signaling may contribute to the control of tumor progression in various tissues . In addition , since tumor cells are phenotypically and functionally heterogeneous even within the same tumor mass ( Meacham and Morrison , 2013 ) , it is possible that tumor cells show a differential expression profiles for Dectin-1 ligands in vivo . If so , it may be the case that the in vivo progression of a tumor is controlled via direct and indirect Dectin-1 signaling , that is , NK cells activated by CD11b+ or CD11c+ cells via Dectin-1 signaling may exert tumoricidal activities on tumor cells regardless on their Dectin-1 ligand expression . Obviously , these possibilities are speculative and need to be investigated further . Identification of critical target molecules of Dectin-1 signaling that participate to the enhancement of NK cells for anti-tumor response is also an interesting , though unanswered question . Although our study suggests the involvement of INAM , we infer that other membrane molecules identified here ( Figure 4C ) , which are without a known function at present , may also participate . This is obviously an important future issue to be rigorously addressed . Our present study may also offer a novel view on the clinical efficacy of monoclonal antibodies ( Abs ) such as cancer therapeutics , specifically for how Ab-dependent cell-mediated cytotoxicity ( ADCC ) mediates antitumor cytotoxicity via Fc receptor ( s ) on immune cells ( Alderson and Sondel , 2011; Seidel et al . , 2013 ) . Although the conventional wisdom has been that Fc receptors ( FcRs ) expressed on NK cells are responsible for ADCC , primarily based on in vitro studies using human mononuclear cells ( Alderson and Sondel , 2011; Seidel et al . , 2013 ) , it has been appreciated for some time that in the mouse , Ab mediated tumor killing is mediated primarily by myeloid cells . For example , mouse IgG2a is the most potent Ab isotype for the activation of effector cells through its intermediate–affinity interaction with FcRIV ( Nimmerjahn and Ravetch , 2005; Nimmerjahn et al . , 2010 ) . Yet , this FcR is not expressed by NK cells , but is expressed by DCs and macrophages ( Nimmerjahn et al . , 2005 ) . It is also interesting to note that FcRIV utilizes FcRγ , the FcR common γ subunit , to transmit the Ab signal to the cell interior via pathways similar , if not identical , to those of CLRs ( Honda and Taniguchi , 2006 ) . As such , Abs may activate DCs and macrophages via FcRIV-FcRγ pathway to mediate both ADCC and further contribute to NK cell activation through a mechanism similar to the pathway we describe in this study . The process may be termed ‘indirect ADCC’ . Clearly , Ab-mediated cancer therapeutics likely involves multiple mechanisms ( Simpson et al . , 2013 ) ; therefore , our speculation obviously needs further investigation , particularly in the context of human cancer therapeutics . Finally , in light of the tenet that innate immunity instructs adaptive immunity ( Janeway and Medzhitov , 2002 ) , our study may also raise an interesting issue of whether Dectin-1 signaling by tumor recognition also affects adaptive anti-tumor immune responses , and also may provide new means for the efficient immune responses for cancers such as Dectin-1 agonistic Abs . C57BL/6 mice were from CLEA Japan Inc . Clec7a ( Dectin-1 ) −/− , Irf3−/− , Irf5−/− , Irf7−/− , Rag1−/− , and Myd88−/− mice as described previously were maintained on a C57BL/6 genetic background ( Sato et al . , 2000; Honda et al . , 2005; Takaoka et al . , 2005; Honda and Taniguchi , 2006; Saijo et al . , 2007; Kano et al . , 2008 ) . CD11c-DTR and C57BL/6-Ly5 . 1 mice were obtained from The Jackson Laboratory ( Bar Harbor , ME ) . Each experiment was performed using sex- , age-matched mice . All animal care and experiments conformed to the guidelines for animal experiments of the University of Tokyo , and were approved by the animal research committee of the University of Tokyo ( Reference number: P10-122 and P10-123 ) . Curdlan was purchased from Wako Chemicals ( Japan ) . FK506 was from Enzo Life Sciences ( Farmingdale , NY ) . N-glycosidase , O-glycosidase , and neuraminidase were from Roche Diagnostics ( Switzerland ) . Anti-IFN-α ( 4E-A1 ) and anti-IFN-β ( 7F-D3 ) monoclonal antibodies were purchased from YAMASA ( Japan ) . Anti-IL12p40 antibody ( C17 . 8 ) , APC-conjugated anti-CD49b antibody ( DX5 ) , APC-conjugated anti-CD11b antibody ( M1/70 ) , APC-conjugated anti-CD11c antibody ( N418 ) , and FITC-conjugated anti-F4/80 antibody ( BM8 ) were from Biolegend ( San Diego , CA ) . FITC-conjugated anti-CD3ε antibody ( 145-2C11 ) was from BD Biosciences ( San Jose , CA ) . Mouse melanoma cell line B16F1 cells and B16F10 cells , Lewis lung carcinoma cell line 3LL cells , fibrosarcoma cell line Meth-A cells , and HEK293T cells were obtained from RIKEN BioResource Center ( Japan ) . Mouse colon carcinoma cell line SL4 cells were kindly provided by Dr T Irimura ( The University of Tokyo ) . B16F1 cells , B16F10 cells , and HEK293T cells were maintained in Dulbecco's Modified Eagle's Medium ( DMEM; Nissui , Japan ) supplemented with 10% fetal calf serum ( FCS; Hyclone , Logan , UT ) . SL4 cells were cultured in DMEM-F12 medium ( Gibco/Invitrogen , Grand Island , NY ) supplemented with 10% FCS . 3LL cells and Meth-A cells were cultured in RPMI medium ( Nacalai Tesque , Japan ) supplemented with 10% FCS . Mouse lymphoma cell line YAC-1 cells were cultured as described previously ( Sato et al . , 2001 ) . Primary mouse embryonic fibroblasts were isolated and cultured as described previously ( Yanai et al . , 2009 ) . Human hepatocellular carcinoma cell line HepG2 cells and melanoma cell line G-361 cells were obtained from National Institute of Biomedical Innovation ( Japan ) . Human cervical carcinoma cell line HeLa cells , lung adenocarcinoma cell line A549 cells , colorectal carcinoma cell line HCT116 cells , gastric carcinoma cell line MKN45 cells , prostate cancer cell line PC-3 cells , and chronic myelogenous leukemia cell line K562 cells were obtained from RIKEN BioResource Center . Human breast cancer cell line HBC4 cells were kindly provided by Dr K Yamazaki ( Cancer Institute Hospital of JFCR , Japan ) . Human hepatoma cell line Huh7 cells were kindly provided by Dr M Kohno ( Toray Co . ) . Human glioblastoma cell lines T98 cells and U251 cells were kindly provided by Dr J Yoshida ( Nagoya University ) . HepG2 cells , Huh7 cells , HeLa cells , T98 cells , A549 cells , and HCT116 cells were maintained in DMEM supplemented with 10% FCS . HBC4 cells , MKN45 cells , PC-3 cells , and K562 cells were cultured in RPMI medium supplemented with 10% FCS . U251 cells were maintained in DMEM supplemented with 10% FCS and non-essential amino acids ( Nacalai Tesque ) . G-361 cells were cultured in McCoy's 5A medium ( Gibco/Invitrogen ) supplemented with 10% FCS . Mycoplasma contaminations of cell lines were checked by MycoAlert Mycoplasma Detection Kit ( Lonza , Switzerland ) according to manufacturer's instruction; no evidence of contamination was found . To prepare splenocytes , spleens were digested with collagenase D ( Roche Diagnostics ) and DNase I ( Roche Diagnostics ) as described previously ( Negishi et al . , 2013 ) . Splenic CD11b+ , CD11c+ , B , or T cells were purified using CD11b or CD11c MicroBeads , B cell isolation kit , or pan T cell isolation kit II , respectively ( Miltenyi Biotec , Germany ) . For CD8+CD11c+ or CD8−CD11c+ cell purification , cells were negatively selected from splenocytes using anti-mouse CD90 . 2 ( 30-H12; eBioscience , San Diego , CA ) , anti-mouse CD5 ( 53-7 . 3; eBioscience ) , and anti-mouse B220 ( RA3-6B2; eBioscience ) antibodies with Dynabeads M-280 sheep anti-mouse IgG ( Invitrogen ) , and CD19 and CD49b ( DX5 ) MicroBeads ( Miltenyi Biotec ) . CD8+CD11c+ and CD8−CD11c+ cells were selected using CD8a ( Ly-2 ) and CD11c MicroBeads ( Miltenyi Biotec ) . CD11c−CD11b+ cells were purified using CD11b Microbeads from splenocytes of CD11c-DTR mice after depletion of CD11c+ cells by intraperitoneal injection of diphtheria toxin ( Jung et al . , 2002 ) ( 4 ng/g body weight; Sigma , St Louis , MO ) . For NK cells purification , splenocytes were incubated within nylon-wool column ( Wako Chemicals ) for 1 hr , and then NK cells were negatively isolated from the nylon-non-adherent cells with NK cell isolation kit II ( Miltenyi Biotec ) as described previously ( Takeda et al . , 2011 ) . For NK cell depletion , mice were injected intraperitoneally with 200 µg anti-asialo GM1 antibody ( Wako Chemicals ) . For liver cell and lung cell preparation , livers and lungs were digested with collagenase D and DNase I , passed through a sterile 40 µm pore size nylon cell strainer ( BD Biosciences ) and the cell suspensions were treated with RBC lysis buffer ( eBioscience ) . To purify CD11b+ and CD11c+ cells from livers and lungs , the cell were stained with APC-conjugated anti-CD11b antibody and FITC-conjugated anti-F4/80 antibody or APC-conjugated anti-CD11c antibody . CD11b+F4/80+ cells and CD11c+ cells were sorted using FACSAria ( BD Biosciences ) . To generate bone marrow-derived dendritic cells ( BMDCs ) , bone marrow cells were cultured with 20 ng/ml mouse GM-CSF ( BD Pharmingen , San Diego , CA ) in RPMI medium supplemented with 10% FCS for 6 days . Mice were intravenously injected with 1 × 106 or 2 × 106 of B16F1 cells , 1 × 106 of 3LL-GFP cells , 3 × 105 of SL4-GFP cells , or 5 × 105 of B16F10 cells . For B16F1 cells and B16F10 cells , the number of metastatic colonies in lungs was counted after 14 days of injection . Lungs were harvested 12 ( 3LL-GFP cells ) or 14 ( SL4-GFP cells ) days after injection . Total RNA was purified from lungs and subjected to quantitative reverse transcription PCR ( qRT-PCR ) to measure GFP mRNA expression level . GFP-expressing 3LL cells and SL4 cells ( 3LL-GFP cells and SL4-GFP cells ) were established using retrovirus-mediated gene transfer system as described previously ( Nakagawa et al . , 2005 ) . pMX-GFP retrovirus expression vector was kindly provided by Dr T Kitamura ( The University of Tokyo ) . Mice were injected subcutaneously with 1 × 105 of B16F1 cells . Tumor volume was evaluated using the equation volume = πab2/6 , where a and b are the lengths of the major and minor axes , respectively ( Ikushima et al . , 2008 ) . Bone marrow transplantation was performed as described previously ( Honda et al . , 2004 ) . Briefly , recipient mice were subjected to sublethal γ-irradiation ( 5 Gy × 2 with a 3-hr interval ) , and were injected with 5 × 106 of bone marrow cells freshly isolated from donor mice . 3 months after the transplantation mice were subjected to metastasis assay . All mice showed a high-degree chimerism ( 90–95% ) . Killing activity of NK cells was tested by 51Cr release assay ( Takeda et al . , 2011 ) . Splenocytes or NK cells were prepared from WT , Irf5−/− , or Dectin-1−/− mice and used as effector cells . Target cells suspended in RPMI medium were incubated with 100 µCi Na251CrO4 ( Perkin Elmer , Waltham , MA ) for 1 hr at 37°C and washed with PBS ( Nacalai Tesque ) three times . Target cells were then mixed with effector cells at the indicated effector/target ( E/T ) ratios in flat-bottomed 96-well plates with a total volume of 200 µl and then co-cultured for 4 hr at 37°C . In all in vitro killing assays , 1 × 104 of 51Cr-labeled target cells were used . Released 51Cr radioactivity in the supernatants was measured with a Wallac 1480 Wizard 3′′ gamma counter ( Perkin Elmer ) . The percentage of cytotoxicity was calculated as following formula: specific lysis ( % ) = ( experimental release − spontaneous release ) / ( maximum release − spontaneous release ) × 100 . Target cell lysis was also measured by co-culturing target cells with myeloid cells in the absence of NK cells . This background value was subtracted from the total values obtained in the presence of NK cells and the calculated percentage of cytotoxicity was represented as Net lysis ( % ) . All experiments were performed at least three times and the results were highly reproducible . We also confirmed there was no discernible phagocytosis by myeloid cells in our killing assays . Immunoblotting assay was performed as described previously ( Yanai et al . , 2011 ) . Antibodies for IRF5 and USF-2 were purchased from Cell Signaling ( Danvers , MA ) and Santa Cruz ( Santa Cruz , CA ) , respectively . USF-2 was used as a nuclear marker protein . Total RNA from tissues or cells was extracted using RNAiso ( TAKARA , Japan ) or NucleoSpin RNA II ( MACHEREY NAGEL , Germany ) , and was reverse-transcribed with PrimeScript RT Master Mix ( TAKARA ) . qRT-PCR was performed on Light Cycler 480 ( Roche Bioscience ) using the SYBR Green PCR Master Mix ( Roche Bioscience ) and values were normalized to the expression of Gapdh mRNA as described previously ( Yanai et al . , 2009 ) . Primer sequences are as follows: Gapdh forward 5′-ctcatgaccacagtccatgc-3′; Gapdh reverse 5′-cacattgggggtaggaacac-3′; Clec7a forward 5′-catcgtctcaccgtattaatgcat-3′; Clec7a reverse 5′-cccagaaccatggccctt-3′; Gzmb forward 5′-accaaacgtgcttcctttcg-3′; Gzmb reverse 5′-tttggtgaaagcacgtggag-3′; Prf1 forward 5′-tctccccactctggtttcca-3′; Prf1 reverse 5′-gagatggggcagacacttgg-3′; Ifng forward 5′-tggctttgcagctcttcctc-3′; Ifng reverse 5′-tccttttgccagttcctcca-3′; Il6 forward 5′-acgatgatgcacttgcagaa-3′; Il6 reverse 5′-gtagctatggtactccagaagac-3′; Tnf forward 5′-tcataccaggagaaagtcaacctc-3′; Tnf reverse 5′-gtatatgggctcataccagggttt-3′; Fam26f forward 5′-gacacagttggccgaagaga-3′; Fam26f reverse 5′-aacgctgagatttcctgcca-3′; GFP forward 5′-cttcttcaagtccgccatgc-3′; GFP reverse 5′-gtgtcgccctcgaacttcac-3′; Il15 forward 5′-catccatctcgtgctacttgtg-3′; Il15 reverse 5′-gcctctgttttagggagacct-3′; Il15ra forward 5′-gctgacatccgggtcaagaa-3′; Il15ra reverse 5′-cacttgaggctgggagttgt-3′ . All experiments were performed at least three times in triplicate . Purified NK cells ( WT or Dectin-1−/−; 5 × 104 cells ) were stimulated with 10 or 100 ng/ml of recombinant mouse IL12 ( R&D Systems , Minneapolis , MN ) . IFN-γ production in culture supernatants was evaluated by ELISA kit ( R&D Systems ) after 24-hr incubation . Mouse cytomegalovirus ( MCMV; Smith strain ) was purchased from ATCC . MCMV stocks were prepared as described ( Brune et al . , 2001 ) . Briefly , MEFs were infected with MCMV and incubated for 72 hr until the cell layer was completely infected . Then , the cells and supernatant were collected and were subjected to three freeze-and-thaw cycles to release cell-associated virus . The crude virus-containing medium was centrifuged and filtrated with a sterile 0 . 45 µm pore size filter ( Millipore , Billerica , MA ) . MCMV in the filtrated medium was concentrated by centrifugation at 26 , 000×g ( 15 , 000 rpm; Beckman SW 41 Ti rotor ) for 3 hr at 4°C . The pellet was resuspended in PBS and filtrated through a sterile 0 . 45 µm filter . MCMV titer was determined by plaque-forming cell assay . MEFs were infected with serially diluted ( log10 steps ) virus stock solution or organ homogenates and incubated for 2 hr at 37°C . Then , the cells were washed extensively and cultured in 2 . 4% methylcellulose-containing DMEM supplemented with 4% FCS for 5 days . After incubation , cells were fixed with formalin and stained with crystal violet solution . Virus plaques were then counted . WT and Dectin-1−/− mice were intraperitoneally infected with MCMV ( 5 × 103 or 3 × 105 pfu ) . For NK depletion , WT mice were given anti-asialo GM1 antibody intravenously one day before infection . Mice were sacrificed at 3 days postinfection and spleens from the mice were excised and homogenized in PBS . The DNA fragment encoding extracellular domain ( amino acid residues 73–244 ) of murine Dectin-1 was cloned using forward primer 5′-aaagatcttacccatacgatgttccagattacgctaattcagggagaaatc-3′ and reverse primer 5′-aaaaatctagacagttccttctcacagatac-3′ , and inserted into the BglII sites of pFUSE-hIgG1-Fc2 vector ( Invivogen , San Diego , CA ) , to make HA-tagged murine sDectin-1 ( msDectin-1 ) conjugated with human IgG1 Fc region ( Hino et al . , 2012 ) . The DNA fragment encoding extracellular domain ( amino acid residues 73–247 ) of human Dectin-1 was cloned using forward primer 5′-agatcttacccatacgatgttccagattacgctaattcaggaagcaacacattgg-3′ and reverse primer 5′-agatctcattgaaaacttcttctcac-3′ to make HA-tagged human sDectin-1 ( hsDectin-1 ) conjugated with human IgG1 Fc region . HEK293T cells were transfected with pFUSE-hIgG1-Fc2-HA-msDectin-1 , pFUSE-hIgG1-Fc2-HA-hsDectin-1 , or pFUSE-hIgG1-Fc2 empty vector using X-tremeGENE 9 DNA Transfection Reagent ( Roche Applied Science ) . Then , supernatants were filtrated and gently mixed with Protein A Sepharose Fast Flow ( GE Healthcare , Waukesha , WI ) . The mouse sDectin-1 , human sDectin-1 , or human IgG1 Fc ( control Fc ) was eluted with 100 mM Glycine-HCl ( pH 3 . 0 ) , dialyzed with PBS , and concentrated using Amicon Ultra centrifugal filter ( Millipore ) . Cells were incubated with sDectin-1 or human IgG1 Fc in TBS ( pH 8 . 0 ) containing 1 . 3 mM CaCl2 for 15 min at 4°C , and then labeled with anti-human IgG1 antibody ( 4E3; Abcam , Cambridge , MA ) conjugated with allophycocyanin ( APC ) . The cells were then analyzed by LSRII/Fortessa ( BD Biosciences ) . APC conjugation to anti-human IgG1 antibody was performed using APC Labeling Kit-NH2 ( Dojindo , Japan ) according to the manufacturer's protocol . After B16F1 cells ( 1 × 107 cells ) were treated with N-glycosidase ( 25 U/ml ) for 1 hr , the supernatant was collected and incubated at 75°C for 15 min to inactivate N-glycosidase . Then , the supernatant was treated with or without sDectin-1 and Protein G Sepharose 4 Fast Flow ( GE Healthcare ) . N-glycans remained in the supernatant were analyzed by GlycanMAP ( Ezose Science , Pine Brook , NJ ) ( Furukawa et al . , 2008 ) . Microarray analysis was performed as described previously ( Negishi et al . , 2012 ) . WT or Dectin-1−/− splenic CD11c+ cells ( 3 × 105 cells ) were co-cultured with B16F1 cells ( 1 × 104 cells ) for 4 hr . Total RNA was extracted from co-cultured B16F1 cells and CD11c+ cells and analyzed by GeneChip Mouse Genome 430 2 . 0 Array ( Affymetrix , Santa Clara , CA ) . pCSII-EF-MCS-IRES2-Venus ( pCSII-EF ) , pMDLg/pRRE , and pCMV-VSV-G-RSV-Rev vector were kindly provided by Dr H Miyoshi ( RIKEN ) . Mouse INAM cDNA was cloned by PCR using primers ( Forward 5′-gtcgacatggaaaagttcaaggcagtg-3′ , Reverse 5′-gcggccgctcatagttcgtgagtgttagtcat-3′ ) and inserted into pT7-Blue T-vector ( TAKARA ) . The cDNA was excised by XhoI and NotI digestion , blunted , and inserted into the blunted NotI site of pCSII-EF-MCS-IRES2-Venus vector ( pCSII-EF-INAM ) . A total of 5 × 106 HEK293T cells were seeded on 10-cm dishes 24 hr prior to transfection . pCSII-EF ( 6 µg ) or pCSII-EF-INAM ( 6 µg ) vector in combination with pMDLg/pRRE ( 6 µg ) and pCMV-VSV-G-RSV-Rev ( 6 µg ) vectors was co-transfected into HEK293T cells . The medium was replaced after 4 hr . The lentivirus-containing medium was collected after another 48 hr , cleared by low-speed centrifugation , and filtrated through 0 . 45 µm filter . Virus particles were concentrated by ultracentrifugation at 70 , 000×g ( 25 , 000 rpm; Beckman SW 41 Ti rotor ) for 90 min at 4°C . The pellet was resuspended in RPMI supplemented with 10% FCS and filtrated through a sterile 0 . 45 µm filter , and the viral solution was subdivided to store at −80°C . The transduction efficiency was assessed by fluorescence of Venus ligated in the lentivirus vector , and the frequency of positive cells determined by flow cytometry in comparison with that of non-infected cells . Briefly , serially diluted viral solution was added to BMDCs ( 3 × 105 cell/500 µl of RPMI medium supplemented with 10% FCS with 4 µg/ml polybrene [Sigma] in 24-well plate ) , centrifuged at 1200×g ( 2360 rpm; TOMY LX-141 ) for 90 min at 30°C , and incubated for 3 hr . These cells were cultured for 20 hr additionally in fresh medium after wash . Then , the fluorescence intensities of the cells were analyzed by flow cytometry . The appropriate viral titer was determined so as to reach 90% Venus expression in BMDCs and was used for the experiments . B16F1 and 3LL cells ( 4 × 107 cells ) treated with or without N-glycosidase ( 25 U/ml ) and B16F10 cells and MEFs ( 4 × 107 cells ) treated without N-glycosidase were incubated with sDectin-1 . After the unbound sDectin-1 was washed out with PBS , sDectin-1 and its ligand on these cells were cross-linked by Sulfo-NHS-LC-Diazirine ( Thermo Scientific , Waltham , MA ) according to manufacturer's instruction . Cells were lysed in T-PER Tissue Protein Extraction Reagent ( Thermo Scientific ) , followed by the immunoprecipitation with Protein G Sepharose 4 Fast Flow ( GE Healthcare ) . After elution with 100 mM Glycine-HCl ( pH 3 . 0 ) , sDectin-1-ligand complex was detected by Immunoblotting assay using HA-probe ( F-7 ) HRP ( Santa Cruz Biotechnology ) . Data are expressed as mean ± SD Student's t test was performed and difference was considered to be statistically significant when p-value < 0 . 05 .	When cells in the body grow and divide uncontrollably , cancerous tumors can form . An individual's likelihood of recovering from cancer is highly variable and often depends on the type of cancer and the extent of the disease at the start of treatment . Researchers are therefore interested in discovering how the body responds against cancerous cells . The first line of defense against infection and disease is the body's innate immune system , which includes a suite of immune cells known as white blood cells . These cells patrol the body's organs and tissues in an effort to immediately respond to pathogens and damaged , stressed or otherwise abnormal host cells . Among white blood cells , natural killer cells are involved in identifying and destroying tumor cells . However , it was unclear whether or how other immune cells might help natural killer cells to destroy tumors . In addition , although immune cells detect pathogens and injured cells by producing proteins called pattern recognition receptors , it was unknown whether these receptors also detect tumor cells . Here , Chiba et al . reveal that two other types of immune cell—dendritic cells and macrophages—play essential roles in helping natural killer cells to prevent tumors from growing in mice . The dendritic cells and macrophages produce a pattern recognition receptor called Dectin-1 that recognizes a molecule found on the surface of some—but not all—types of tumor cell . In doing so , Dectin-1 activates a critical signaling pathway and directs the activity of the natural killer cells so that they can effectively kill tumor cells . Chiba et al . found that these tumors grew faster in mice that lack the Dectin-1 protein . The findings of Chiba et al . may also help to explain the effectiveness of certain antibodies—proteins that recognize and neutralize foreign objects such as bacteria and viruses—in cancer therapy . In addition , the Dectin-1 pathway presents a new avenue of research that may offer new cancer treatments .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "immunology", "and", "inflammation" ]	2014	Recognition of tumor cells by Dectin-1 orchestrates innate immune cells for anti-tumor responses
In mammals , hepatic lipid catabolism is essential for the newborns to efficiently use milk fat as an energy source . However , it is unclear how this critical trait is acquired and regulated . We demonstrate that under the control of PPARα , the genes required for lipid catabolism are transcribed before birth so that the neonatal liver has a prompt capacity to extract energy from milk upon suckling . The mechanism involves a fetal glucocorticoid receptor ( GR ) -PPARα axis in which GR directly regulates the transcriptional activation of PPARα by binding to its promoter . Certain PPARα target genes such as Fgf21 remain repressed in the fetal liver and become PPARα responsive after birth following an epigenetic switch triggered by β-hydroxybutyrate-mediated inhibition of HDAC3 . This study identifies an endocrine developmental axis in which fetal GR primes the activity of PPARα in anticipation of the sudden shifts in postnatal nutrient source and metabolic demands . In mammals , embryonic and postnatal development depends on nutrition from placentation and lactation , respectively ( Brawand et al . , 2008 ) . Before birth , hepatic energy metabolism relies mainly on glucose catabolism . Metabolic fluxes change abruptly at birth when milk , which has a higher lipid but relatively lower glucose content , becomes the exclusive nutrient ( Girard et al . , 1992 ) . At the first few hours after birth , liver expresses the rate-limiting enzymes responsible for extracting energy from milk ( Krahling et al . , 1979; Huyghe et al . , 2001 ) . However , whether lipid catabolism at birth is developmentally programmed or an adaptive response requiring an external stimulus remains unknown . Failure to adapt to this catabolic switch results in life-threatening errors of metabolism , with serious energy imbalances that are recapitulated in mouse models of neonatal liver steatosis ( Ibdah et al . , 2001; Cherkaoui-Malki et al . , 2012 ) . Peroxisome proliferator-activated receptor α ( PPARα ) is a key transcriptional regulator of lipid metabolism owing to its activation by a lipid surge to induce lipid catabolism ( Desvergne et al . , 2006; Montagner et al . , 2011 , 2016 ) . However , the role of PPARα in the perinatal liver is not fully understood . Certain PPARα target genes ( e . g . , acyl-CoA oxidase 1 [Acox1] ) are highly expressed in fetal liver at the end of gestation , whereas the expression of others ( e . g . , fibroblast growth factor 21 [Fgf21] ) strictly depends on postnatal milk uptake ( Hondares et al . , 2010; Yubero et al . , 2004 ) . Based on these contrasting observations , we hypothesized the presence of a PPARα-triggering program before birth that does not require milk suckling for activation . Given that fetal liver metabolism mostly relies on glycolysis rather than lipid oxidation , we postulated that the liver phenotype of PPARα-deficient fetuses may not be obvious due to the absence of a lipid-rich diet challenge . By measuring gene expression in prenatal and postnatal livers , we show that PPARα activation has already occurred a few days before birth , and that glucocorticoid receptor ( GR ) -mediated control of PPARα in late gestation prepares its physiological role in the pups for harnessing milk lipids immediately after birth . However , before birth we could barely detect the expression of certain PPARα target genes such as Fgf21 , which is exclusively involved in adaptive metabolism . Therefore , we explored the mechanistic basis for the temporal regulation of PPARα target genes . We found that certain PPARα target genes , including Fgf21 and Angptl4 ( Angiopoietin-like 4 ) , are epigenetically controlled by histone deacetylase 3 ( HDAC3 ) and de-repressed in response to β-hydroxybutyrate , a by-product of fatty acid oxidation ( FAO ) . Taken together , our data provide the evidence of a major role of glucocorticoid signaling in direct hepatic regulation of PPARα and indirect HDAC3-mediated regulation of FGF21 , which controls important metabolic and thermogenic events in the early days of life ( Hondares et al . , 2010 ) . Stress at labor is associated with high glucocorticoid signaling ( Barlow et al . , 1974 ) . We previously reported that glucocorticoids stimulate PPARα expression in the adult liver , but the mechanism was not elucidated ( Lemberger et al . , 1994 ) . Interestingly , the mRNA expression of GR ( Nr3c1 ) in the fetal liver is the highest just before birth ( Speirs et al . , 2004 ) . Because GR plays a role in the preparation for birth ( Cole et al . , 1995 ) , we further tested GR implication in the regulation of fetal PPARα expression . We confirmed that Nr3c1 mRNA levels in the fetal liver peak at embryonic day E19 . 5 , similar to Ppara mRNA expression ( Figure 1A , B ) . Notably , Ppara mRNA levels were low in the liver at E13 and E15 , but markedly increased at E17 , peaking just before birth at E19 . 5 ( Figure 1B ) . This observation coincides with a maximal RNA polymerase 2 ( Pol2 ) recruitment to the PPARα transcriptional start site ( TSS ) ( Figure 1C ) and enhanced nuclear accumulation of PPARα protein similar to the levels in the postnatal pups ( Figure 5A ) . Interestingly , we also observed a significant reduction in the expression of Nr3c1 in Ppara-/- livers during development when compared with Ppara+/+ controls ( Figure 1A ) , suggesting that PPARα may also reciprocally regulate Nr3c1 expression . To investigate the regulation of PPARα expression by GR , we treated E15 fetal liver explants , when Ppara expression is relatively low , with GR agonist dexamethasone , which induced Ppara mRNA expression in a dose-dependent manner ( Figure 1D ) . We also examined the expression of Ppara in GR-null fetuses at E17 ( Figure 1E ) , when Ppara mRNA levels just begin to increase in the wild-type fetal liver ( Figure 1B ) . GR exerted a gene dosage effect on the expression of both Ppara and its target genes , including Cpt1a , Hadha , Hadhb , and Cyp4a14 ( Figure 1E , F ) . 10 . 7554/eLife . 11853 . 003Figure 1 . GR directly controls fetal PPARα expression . ( A , B ) Ontogenic expression of Nr3c1 ( A ) and Ppara ( B ) mRNA in the developing mouse liver . p<0 . 05 , p<0 . 01 vs . E13 samples; #p<0 . 05 , ##p<0 . 01 vs . WT counterparts . ( C ) Enrichment of the DNA fragments containing the PPARα TSS using anti-Pol2 antibodies or pre-immune control IgG with or without PPARα agonist ( WY-14643 ) treatment in pregnant dams . p<0 . 01 , *p<0 . 001 vs . respective WT counterparts without WY-14643 treatment . ( D ) Ppara mRNA levels in the E15 Ppara+/+ liver explants with or without dexamethasone ( Dex ) treatment for 24 hr . Dex concentrations of 0 . 1 μM , 1 μM , and 10 μM were used . The vehicle control group was treated with 0 . 1% ethanol . p<0 . 05 vs . vehicle control . ( E , F ) mRNA expression of PPARα and its target genes in the fetal livers of GR models at E17 . p<0 . 05 , * p<0 . 01 , **p<0 . 001 vs . Nr3c1+/+ liver . ( G ) Alignment of the GRE consensus sequence with the three putative GRE sequences located upstream of the PPARα promoter . ( H ) Enrichment of the DNA fragment containing the three putative GRE found within the PPARα promoter at regions spanning −1007 to −993 , −2080 to −2066 , and −2953 to −2939 in fetal liver at E17 using anti-GR antibody or pre-immune control IgG . Enrichment levels were expressed as the percentage input . GR-targeting siRNA was used to knockdown Nr3c1 expression and to determine the specificity of GR binding to this putative GRE . p<0 . 05 vs . non-targeting siRNA treatment group . ( I ) Enrichment of GRE spanning -1007 to −993 of the PPARα promoter in Ppara+/+ liver during development . p<0 . 05 , p<0 . 01 vs . E13 samples . Data are presented as mean ± SEM; n = 4–6 . Statistical analyses were performed using two-tailed Mann-Whitney tests . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 003 Next , we addressed the mechanisms involved in priming the high expression and activity of PPARα just before birth . We identified three putative GR response elements ( GRE ) in the promoter/regulatory region of Ppara and analyzed their occupancy by GR in the fetal liver at E17 ( Figure 1G ) . Chromatin immunoprecipitation ( ChIP ) using an antibody against GR revealed that the element spanning -1007 to -993 was preferentially occupied while the other two sites were not ( Figure 1H ) . Upon treatment of primary hepatocytes with GR-targeting siRNA , the occupancy of GR at this putative site was significantly decreased as compared to treatment with non-targeting siRNA , thereby indicating the specificity of GR binding to this site ( Figure 1H ) . Binding of GR to this putative GRE also followed the ontogenic pattern of Ppara expression in the fetal liver ( Compare Figure 1I with Figure 1B ) . Therefore , PPARα is a direct target of GR in the fetal liver and its expression parallels that of GR in later stages of fetal development . These findings reveal a novel endocrine GR-PPARα axis in the regulation of the fatty acid ( FA ) catabolic machinery just before birth . We next performed microarray analysis on gene expression in the fetal and neonatal Ppara+/+ and Ppara-/- livers . PPARα-dependent fold changes in gene expression were higher in the term fetuses than in suckling pups , revealing major transcriptional PPARα activities just before delivery ( Figure 2A; Figure 2— figure supplement 1A , B ) . We found 915 and 425 down-regulated genes in Ppara-/- liver at E19 . 5 and postnatal day 2 ( P2 ) , respectively ( Figure 2A ) . Many of the differentially expressed genes were preferentially regulated in either the prenatal or postnatal liver , suggesting the involvement of distinct metabolic pathways at these two time points ( Figure 2A , B; Figure 2—figure supplement 1B; Figure 2—source data 1 , SD12 ) . Genes preferentially controlled by PPARα in fetal liver ( e . g . , Pex19 , Slc25a20 , Cpt2 , and Fabp1 ) relate to peroxisome biogenesis and FA shuttling−two upstream steps essential for FA catabolism ( Figure 2B , C ) . Genes encoding the peroxisomal and mitochondrial enzymatic core of β- and ω-FAO , such as Acox1 , Acaa2 , Acadl , Acadvl , Acsl1 , and Cyp4a14 , were prenatally and/or postnatally controlled by PPARα ( Figure 2B , C ) . In contrast , genes encoding the liver-secreted adaptive effectors of lipid catabolism ( e . g . , Fgf21 ) and xenobiotic-detoxifying enzymes ( e . g . , Ugt3a1 , Ces1g , Adh4 , Ces3a , Sult2a1 ) were only stimulated by PPARα postnatally ( Figure 2B , C ) . Focused gene expression profiling further confirmed that genes encoding the rate-limiting enzymes involved in FA shuttling , mitochondrial and peroxisomal FA β-oxidation , and microsomal FA ω-oxidation were concomitantly down-regulated in Ppara-/- liver before and/or after birth ( Figure 2C ) . Lack of PPARα also resulted in fetal liver in an up-regulation of other nuclear receptors and their target genes involved in lipid metabolism ( Figure 2—figure supplement 1C , D ) . 10 . 7554/eLife . 11853 . 004Figure 2 . PPARα is a functional transcription factor in the term fetus . ( A ) Heat map of genes significantly altered at E19 . 5 ( left panel ) or P2 ( right panel ) in Ppara-/- livers ( red: up-regulation; blue: down-regulation ) . Genes were grouped by fetal ( E19 . 5 ) or postnatal ( P2 ) expression and ordered by strength of regulation based on the logarithmic fold change ( log2FC ) . ( B ) Gene ontology summarizing prenatal and postnatal PPARα-mediated regulation of metabolic pathways . ( C ) mRNA levels of representative PPARα target genes with differential fetal ( E19 . 5 ) and postnatal ( P2 ) regulation in Ppara-/- and Ppara+/+ livers . Slc25a20: carnitine translocase; Cpt2: carnitine palmitoyltransferase 2; Fabp1: fatty acid binding protein 1; Pex19: peroxisome biogenesis factor 19; Acox1: peroxisomal acyl-CoA oxidase 1; Cyp4a14: cytochrome P450 4A14; Fgf21: fibroblast growth factor 21; Angptl4: angiopoietin-like protein 4; Acaa2: acetyl-CoA acyltransferase 2; Acadl: acyl-CoA dehydrogenase , long chain; Acadvl: acyl-CoA dehydrogenase , very long chain; Acsl1: acyl-CoA synthetase , long-chain family member 1 . Data are presented as mean ± SEM; n = 6 , p<0 . 05 , p<0 . 01 , p<0 . 001 vs . wild-type controls; #p<0 . 05 , ##p<0 . 01 , ###p<0 . 001 vs . respective E19 . 5 samples . ( D ) Flow cytometric analyses of hepatoblasts ( DLK+ ) and hepatocytes ( CK18+ ) in fetal livers at E19 . 5 . ( E ) mRNA expression levels of Nr3c1 , Ppara , and PPARα target genes involved in peroxisome biogenesis ( Pex19 ) , peroxisomal and mitochondrial FAO ( Acox1 , Acaa2 , Acadl , Acadvl , and Acsl1 ) in sorted fetal hepatoblast and hepatocyte fractions . Glycolytic genes ( e . g . , Gck and Hk1 ) were also investigated in parallel with oxidative genes . Nr3c1: glucocorticoid receptor; Ppara: peroxisome proliferator-activated receptor α; ud: undetermined; Gck: glucokinase; Hk1: hexokinase 1 . Data are presented as mean ± SEM; n = 4 , p <0 . 05 , p<0 . 01 , p<0 . 001 vs . wild-type controls . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 00410 . 7554/eLife . 11853 . 005Figure 2—source data 1 . A list of PPARα-regulated genes and pathways in prenatal and neonatal livers . The dataset provides a list of PPARα-regulated genes ( A ) and pathways ( B ) in E19 . 5 and P2 mouse livers . Significant genes based on a false discovery rate < 0 . 05 were classified as regulated at both E19 . 5 and P2 , in E19 . 5 only , or P2 only . The logarithmic fold change ( logFC ) cut-off value was set at 1 . 3 . For each group , the significant enrichment of underlying KEGG , GO , and Reactome curated pathways was determined from the hypergeometric distribution and corrected for multiple comparisons . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 00510 . 7554/eLife . 11853 . 006Figure 2—figure supplement 1 . PPARα deficiency leads to compensatory up-regulation of genes . ( A ) Venn diagram depicting the number of up-regulated genes in the Ppara-/- liver with overlapping PPARα regulation ( false discovery rate <0 . 05 ) before ( E19 . 5 ) and after ( P2 ) birth . The absolute fold change cut-off value was set at 1 . 3 . ( B ) Heat map of genes significantly altered at E19 . 5 ( left panel ) or P2 ( right panel ) in Ppara-/- livers ( red: up-regulation; blue: down-regulation ) . Genes were grouped by fetal or postnatal expression and ordered by strength of PPARα regulation based on the logarithmic fold change ( logFC ) . ( C , D ) Up-regulated and down-regulated nuclear receptors and co-regulators ( C ) and nuclear receptor target genes ( D ) in Ppara-/- livers . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 006 Since there is a heterogeneous population of undifferentiated hepatoblasts and differentiated hepatocytes in the liver at E19 . 5 , we next determined whether one of these two cell populations was predominantly affected by the PPARα-dependent changes in gene expression . Using antibodies against hepatoblast- and hepatocyte-specific markers ( i . e . , Delta-Like Homolog 1 ( DLK ) and cytokeratin 18 ( CK18 ) , respectively ) , we performed flow cytometric analyses on the cells extracted from Ppara+/+ and Ppara-/- livers at E19 . 5 . Upon cell sorting , we specifically recovered the DLK+ and CK18+ fractions . Firstly , we showed that PPARα deficiency only led to marginal changes in these two hepatic cell populations ( Figure 2D ) . Secondly , we determined the mRNA expression of Nr3c1 , Ppara and the target genes of PPARα in these cells . Our results indicated that both Ppara+/+ hepatoblasts and hepatocytes similarly contributed to the expression of these genes ( Figure 2E ) . Notably , we observed a modest but not significant upward trend in the expression of oxidative genes ( e . g . , Acox1 , Acaa2 , Acadl , and Acadvl ) and a downward trend in the expression of glycolytic genes ( e . g . , Gck and Hk1 ) in the Ppara+/+ hepatocytes compared to hepatoblasts , possibly suggesting a more pronounced oxidative program in hepatocytes ( Figure 2E ) . In contrast , the expression of these genes was concomitantly down-regulated in Ppara-/- hepatoblasts and hepatocytes , thereby highlighting the pivotal role of PPARα in glycolytic and oxidative metabolism in both cell types . Consistent with our gene expression studies , Ppara-/- fetal livers had reduced numbers of peroxisomes and mitochondria compared to wild-type littermates at E19 . 5 ( Figure 3A ) . However , only the mitochondria numbers but not the peroxisome numbers varied between the two groups at P2 ( Figure 3B ) , underscoring the crucial role of mitochondrial FAO in PPARα-mediated lipid catabolism after birth . We next measured mitochondrial membrane potential ( ∆Ψm ) and intracellular reactive oxygen species ( ROS ) production levels which are two common parameters used in assessing mitochondria health and functions ( Suski et al . , 2012 ) . Interestingly , we observed a much lower ∆Ψm and a markedly increased intracellular ROS level in Ppara-/- primary hepatocytes at P2 ( Figure 3C , D ) , indicative of mitochondria dysfunction at this stage . However , there was no discernible difference in ∆Ψm between Ppara+/+ and Ppara-/- hepatocytes at E19 . 5 despite that a slight increase in the intracellular ROS level was observed in the latter . These findings suggest that an increased ROS production in cells likely precedes the loss of ∆Ψm and mitochondria dysfunction , consistent with a previous report of a similar mitochondrial disorder associated with the dysfunctions of the respiratory chain components ( Lebiedzinska et al . , 2010 ) . Indeed , respiratory chain-mediated ATP production in Ppara-/- hepatocytes was approximately 35–50% of the levels measured in both prenatal and postnatal wild-type livers ( Figure 3C ) . Substantiating these findings , a reduction of ~2 . 3-fold was observed in the basal oxygen consumption rate in Ppara-/- primary hepatocytes from both E19 and P2 livers ( Figure 3D , E ) . In the presence of FA ( i . e . , palmitate ) , the oxygen consumption rate significantly increased in Ppara+/+ hepatocytes but remained very similar in Ppara-/- hepatocytes , indicating a deficiency in the activation of FAO in the latter ( Figure 3D , E ) . The addition of etomoxir , an inhibitor of carnitine palmitoyltransferase 1 , resulted in significant inhibition of mitochondrial β-FAO in Ppara+/+ but not in Ppara-/- hepatocytes . Ppara+/+ hepatocytes exhibited a robust mitochondrial respiratory capacity of ~40% at both time points ( Figure 3E ) . In contrast , only ~25% and ~11% of the total oxygen consumption rate were attributed to mitochondrial β-FAO in Ppara-/- hepatocytes from E19 and P2 livers , respectively . Thus , these results provide evidence that PPARα-dependent respiration occurs in the term fetus . The higher expression of FA catabolic genes in fetal liver just before birth may prime the liver for the upcoming postnatal energy demand or point to an unknown role of fetal FAO . Therefore , we investigated whether the above defects in Ppara-/- hepatocytes contribute to any phenotype in the fetal and neonatal liver . 10 . 7554/eLife . 11853 . 007Figure 3 . Defective mitochondrial function , fatty acid oxidation and energy production in Ppara-/- hepatocytes . ( A , B ) Flow cytometric analyses of intracellular peroxisomes and mitochondria in primary hepatocytes isolated from Ppara-/- and Ppara+/+ livers at E19 . 5 ( A ) and P2 ( B ) using Alexa Fluor 488-labeled antibodies against peroxisome membrane protein 70 and Mitotracker Red , respectively . ( C , D ) Flow cytometric analyses of mitochondrial membrane potential ( ∆ΨM ) ( C ) and intracellular ROS ( D ) in primary hepatocytes isolated from Ppara-/- and Ppara+/+ livers at E19 . 5 and P2 using tetramethylrhodamine , ethyl ester ( TMRE ) and 2’ , 7’-dichlorofluorescin diacetate ( DCFDA ) , respectively . ( E ) ATP production in primary hepatocytes isolated from Ppara-/- and Ppara+/+ livers ( n = 4 per group ) in the presence of glucose ( +Glu ) , galactose ( +Gal ) , or rotenone ( +Rot , a mitochondrial electron transport chain complex I inhibitor ) as a negative control . Values represent arbitrary bioluminescence units normalized to the number of viable cells . ( F , G ) Oxygen consumption rates ( OCRs ) in primary hepatocytes isolated from Ppara-/- and Ppara+/+ livers in the presence or absence of palmitate and etomoxir , a mitochondrial β-oxidation inhibitor . Data are presented in time-lapse ( F ) and treatment end points ( G ) at 15 min for basal respiration , 50 min for palmitate treatment , and 80 min for palmitate cum etomoxir treatment . Data represent mean ± SEM; n = 3–9 unless otherwise stated , p<0 . 01 , *p<0 . 001 vs . wild-type controls; ###p<0 . 001 vs . no treatment group; §§§p<0 . 001 vs . palmitate treatment group ( two-tailed Mann-Whitney test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 007 Gross examination and histological analysis of the fetal Ppara-/- livers at E19 . 5 did not reveal any discernible differences when compared with Ppara+/+ controls . However , after birth , steatosis developed in Ppara-/- livers at P2 but not in Ppara+/+ livers ( Figure 4A , B ) . Compared to Ppara+/+ littermates , Ppara-/- pups displayed an enlarged and pallid liver with neutral lipid accumulation demonstrated by Oil Red O staining ( Figure 4A , B ) . Ppara-/- livers contained significantly higher levels of triglycerides and cholesterol ester at P2 with a concomitant increase in blood triglycerides ( Figure 4D–F ) . In contrast , Ppara-/- pups also exhibited markedly reduced serum β-hydroxybutyrate levels ( i . e . , hypoketonemia ) and impaired essential FA profiles ( Figure 4G , Figure 4—figure supplement 1A , B ) . All of these anomalies were absent in Ppara-/- fetuses at E19 . 5 . Interestingly , the postnatal phenotype of liver steatosis observed in Ppara-/- pups spontaneously and gradually resolved at P15 , coinciding with the suckling-to-weaning transition period when carbohydrate-rich food gradually replaces the lipid-rich milk ( Figure 4—figure supplement 1C , D ) . These findings suggest that the neonatal steatosis observed in Ppara-/- pups may be attributed to the ingestion of milk lipids . 10 . 7554/eLife . 11853 . 008Figure 4 . PPARα deficiency leads to congenital hepatic steatosis after birth . ( A ) Photographs showing dissected Ppara-/- and Ppara+/+ pups ( left ) and livers ( right ) . The pallid liver of a Ppara-/- pup is indicated by an arrow . The white content of the stomach indicates milk ingestion . ( B ) Representative Oil Red O-stained liver sections counterstained with methylene blue . Scale bars = 100 μm . ( C–G ) The Mean body weight ( C ) , triglyceride ( D ) and cholesterol ester ( E ) contents of the liver , and serum levels of triglyceride ( F ) and β-hydroxybutyrate ( β-OHB ) ( G ) . Data are presented as mean ± SEM; n = 6 , p<0 . 05 , p<0 . 01 , p<0 . 001 vs . wild-type controls; #p<0 . 05 vs . respective E19 . 5; ns , not significant ( two-way ANOVA with Bonferroni post-hoc analysis ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 00810 . 7554/eLife . 11853 . 009Figure 4—figure supplement 1 . Postnatal lipid catabolic derangements in Ppara-/- pups . ( A , B ) Boxplots showing the ω-3 ( A ) and ω-6 ( B ) indexes of the P2 livers calculated from the ratios of docosahexaenoic acid to α-linolenic acid and arachidonic acid to linoleic acid , respectively . Boxes indicate the 25th to 75th percentiles and whiskers represent the full range; n = 16 , p<0 . 001 ( two-tailed Mann-Whitney test ) . ( C ) Oil Red O staining of cryosections prepared from the PPARα-/- liver at the indicated postnatal days . Note the onset and the progressive recovery of hepatic steatosis in Ppara-/- mice from P2 to P15 . ( D ) Liver triglyceride content measured by gas chromatography-high performance liquid chromatography; n = 4–6 , p<0 . 01 , *p<0 . 001 vs . wild-type controls; ns , not significant ( two-tailed Mann-Whitney test ) . ( E–H ) Ten-day high-fat weaning challenge in 5-day-old Ppara+/+ and Ppara-/- pups . Representative images of the Oil Red O-stained liver sections after counterstaining with methylene blue are shown ( E ) . Right-most panel shows the dissected livers at P15 after the challenge . Note the pallid color of Ppara-/-liver . Scale bars = 100 μm . Wet liver weight ( F ) , liver triglyceride levels ( G ) , and serum triglyceride levels ( H ) measured in P15 pups fed the control ( Ctrl ) or high-fat diet ( HFD ) . ( I ) Oil Red O staining of cryosections prepared from the liver of hepatocyte-specific knockout of PPARα ( Pparafl/flAlbCre/+ ) and control ( Pparafl/fl ) pups at postnatal day three ( P3 ) . Scale bars = 100 μm . Bars represent means ± SEM; n = 4–6 , p<0 . 01 vs . wild-type controls under the same dietary treatment ( two-way ANOVA with Bonferroni post-hoc analysis ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 00910 . 7554/eLife . 11853 . 010Figure 4—figure supplement 2 . Anaplerotic compensation for defective oxidative metabolism and ketogenesis in suckling Ppara-/- pups . ( A , B ) Body weight ( A ) and plasma glucose levels ( B ) of Ppara+/+ and Ppara-/- pups at the indicated postnatal days . ( C ) Alanine aminotransferase ( ALT ) activity measured in liver lysates at P2 . ( D ) Gluconeogenesis pathway in Ppara-/- liver at P2 with the supply of pyruvate through the anaplerotic metabolism of alanine indicated . ( E ) The affected amino acid oxidation pathways in Ppara-/- liver at P2 . Colors indicate up-regulation ( red ) or down-regulation ( blue ) of genes identified by microarray analysis . The up-regulated genes are Hal ( 1 ) , Uroc1 ( 2 ) , Amdhd1 ( 3 ) , Ftcd ( 4 ) , Glud ( 5 ) , Pycr ( 6 ) , Oat ( 7 ) , Fah ( 8 ) , Asl ( 9 ) , Ass1 ( 10 ) , Got2 ( 11 ) , Got1 ( 12 ) , Sds ( 13 ) , Cth ( 14 ) , Cbs ( 15 ) , Cdo1 ( 16 ) , Got1 ( 17 ) , Sds ( 18 ) , Gpt ( 19 ) , Pcx ( 20 ) . The down-regulated genes are Ehhadh ( 21 , 22 , 24 ) , Hibch ( 23 ) , Hmgcs2 ( 25 ) , Hmgcl ( 26 ) . ( F , G ) Body weight ( F ) and blood glucose ( G ) of P2pups before and after daily intraperitoneal injections of 30 mg/kg L-cycloserine for two days ( P4 ) , three days ( P5 ) , and four days ( P6 ) . Bars represent means ± SEM; n = 4–6 , p<0 . 05 , p<0 . 01 vs . wild-type controls ( two-way Mann-Whitney test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 010 Pups begin to nibble solid food in their second week , before weaning . To test whether the change in food composition that occurs at weaning is responsible for the reduction in the fatty liver phenotype as observed in Ppara-/-pups after P5-6 , we performed a 10-day high-fat , low-carbohydrate diet challenge starting just before P5 and continuing into the suckling-to-weaning transition period . After the high-fat diet ( HFD ) challenge , the occurrence of liver steatosis was reenacted at P15 in Ppara-/- mice which consistently exhibited significantly higher liver weight , hepatic and serum triglyceride levels ( Figure 4—figure supplement 1E–H ) . In contrast , liver steatosis was absent in the Ppara+/+ counterparts fed the same diet as well as in mice fed the control diet ( Figure 4—figure supplement 1E– H ) . Furthermore , we also demonstrated in hepatocyte-specific Ppara-/- mice ( i . e . , Pparafl/flAlbCre/+ ) the presence of liver steatosis at P3 , thereby confirming that the phenotypes observed in Ppara-/- mice is most likely due to the hepatocyte-specific effects ( Figure 4— figure supplement 1I ) ( Montagner et al . , 2016 ) . Taken together , PPARα deficiency leads to lower mitochondria numbers , defective FA oxidative metabolism , and aberrant mitochondria functions which collectively contribute to the development of liver steatosis and hypoketonemia in Ppara-/- neonates as a result of their inability to metabolize milk lipids . An effective hepatic FAO is necessary to support hepatic de novo ketogenesis and gluconeogenesis by providing essential co-factors such as acetyl-CoA and/or NADH ( Girard , 1986 ) . This notion is consistent with the presence of hypoketonemia in Ppara-/- neonates with defective hepatic FAO . Interestingly , we found that Ppara-/- pups were able to maintain a body weight increase similar to their Ppara+/+ littermates ( Figure 4—figure supplement 2A ) , suggesting that Ppara-/- neonates harnessed energy required for normal growth from fuels other than ketone bodies and lipids . Furthermore , we did not detect any differences in the plasma glucose levels between the two genotypes during the suckling period from P2 to P15 ( Figure 4—figure supplement 2B ) . This was rather surprising since Ppara-/- neonates were previously reported to exhibit hypoglycemia at P1 due to impaired gluconeogenesis from glycerol ( Cotter et al . , 2014 ) . This finding prompted us to check whether the gluconeogenesis pathway is modified in the Ppara-/- liver at P2 . Based on our microarray data , we found that most of the genes ( e . g . , Gpt , Pck1 , and G6pc ) encoding the rate-liming enzymes involved in this pathway were up-regulated in parallel with a down-regulation of the rate-limiting genes involved in glycolysis ( e . g . , Gck , Hk1 , and Gpd2 ) ( Figure 4—figure supplement 2D ) . We also detected in the Ppara-/- liver a significantly elevated level of alanine aminotransferase ( ALT ) , which is encoded by Gpt ( Figure 4—figure supplement 2C ) . ALT is an enzyme that catalyzes the transfer of amino groups to form the hepatic metabolite oxaloacetate , an intermediate substrate in the tricarboxylic acid ( TCA ) /Krebs cycle , which can be used for gluconeogenesis . Thus , we postulated that an increased anaplerotic oxidation of amino acids may be an alternative pathway engaged to maintain gluconeogenesis and to supply glucose as an energy source for normal growth in Ppara-/- neonates when the FAO capacity was impeded in the absence of PPARα . Indeed , most of the rate-limiting enzymes involved in amino acid oxidation ( except for valine , leucine , and isoleucine ) were concomitantly up-regulated in Ppara-/- liver at P2 ( Figure 4—figure supplement 2E ) . Up-regulation of these genes would provide some of the intermediate substrates of the TCA cycle , such as α-ketoglutarate , fumarate , and oxaloacetate , which could be used for gluconeogenesis ( Figure 4—figure supplement 2E ) . Importantly , selective inhibition of ALT by intraperitoneal injection of L-cycloserine led to decoupling of amino acid oxidation from gluconeogenesis and ultimately resulted in stunted postnatal growth and hypoglycemia in Ppara-/- pups ( Figure 4—figure supplement 2F , G ) . In short , we conclude that PPARα functions both as a prenatally anticipatory and postnatally adaptive regulator of lipid catabolism , ultimately protecting the postnatal liver against a rapid insurgence of steatosis by promoting the use of milk lipids as an energy source . How PPARα target genes are controlled at different stages of metabolic ontogenesis remains unclear . For instance , the genes ( e . g . , Acox1 ) involved in the anticipatory functions of PPARα ( i . e . , FAO ) are stimulated in the fetus , but the adaptive regulators of lipid catabolism , such as the liver-secreted FGF21 , were markedly stimulated by PPARα only after birth ( Figures 2C , 5A ) . The binding of PPARα to the peroxisome proliferator response element ( PPRE ) in the promoter of Acox1 and Cyp4a14 ( both genes are indicators for PPARα activation ) ( Tugwood et al . , 1992; Anderson et al . , 2002 ) , as well as Pol2 recruitment to their respective TSS , occurs before birth and results in increased expression of these genes ( Figure 5C , D ) . This stimulation correlates with higher Ppara TSS activity and mRNA levels during hepatic ontogenesis ( Figure 1B , C ) . Despite having comparable PPARα occupancy in the Fgf21 , Acox1 , and Cyp4a14 promoters before birth , the recruitment of Pol2 to the Fgf21 TSS was markedly delayed until after birth at P2 , and then gradually increased thereafter ( Figure 5D ) . 10 . 7554/eLife . 11853 . 011Figure 5 . A temporal dichotomy in the regulation of PPARα target genes before and after birth . ( A ) Immunoblots showing the ontogenic expression of cytoplasmic and nuclear GR , HDAC3 , PPARα , and its target genes , including ACOX1 , Cyp4A14 , FGF21 , and full-length ( FL ) -ANGPTL4 , in Ppara-/- and Ppara+/+ livers . U2AF65 and β-tubulin were used as loading controls for nuclear and cytoplasmic proteins , respectively . ( B–D ) Enrichment of the DNA fragment containing the PPAR response element ( PPRE ) ( left panels ) on the ACOX1 ( B ) , Cyp4A14 ( C ) , and FGF21 ( D ) promoters or their respective TSS ( right panels ) using anti-PPARα and anti-Pol2 antibodies or pre-immune IgG . Data are presented as mean ± SEM; n = 4–6 , p<0 . 05 , p<0 . 01 , p<0 . 001 vs . untreated wild-type controls ( two-tailed Mann-Whitney test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 011 Based on these observations , we postulated that other mechanisms may be involved in the differential Pol2 recruitment and promoter transactivation of these PPARα target genes . At E19 . 5 , we found increased active histone marks ( i . e . , AcH4 and H3K4me3 ) near the TSS of Acox1 and Cyp4a14 , which correlated with a reduced enrichment of repressive histone marks ( i . e . , H3K9me3 and H3K27me3 ) ( Figure 6A , B ) . At P2 , the enrichment of active histone marks at the TSS of Acox1 and Cyp4a14 decreased with a concomitant increase in repressive histone marks , but this was not observed for Fgf21 ( Figure 6A–C ) . HDAC3 has been reported to repress hepatic Fgf21 expression ( Hondares et al . , 2010; Estall et al . , 2009; Archer et al . , 2012; Feng et al . , 2011 ) . In line with these reports , we observed a higher occupancy of HDAC3 at the Fgf21 TSS at E19 . 5 compared to P2 ( Figure 6D ) . We also found that the expression of Angptl4 was also controlled by HDAC3 ( Figure 6E ) . Interestingly , the lack of PPARα correlated with a ~10-fold increase in the HDAC3 occupancy at the Fgf21 and Angptl4 TSS ( Figure 6D , E ) . In contrast , we detected negligible levels of HDAC3 recruitment to the Cyp4a14 TSS ( Figure 6F ) . Taken together , our results suggest that HDAC3-mediated histone modifications represent an additional mechanism by which certain PPARα target genes are temporally regulated . 10 . 7554/eLife . 11853 . 012Figure 6 . PPARα-mediated regulation of Fgf21 expression is dictated by the occupancy of HDAC3 on its promoter . ( A–C ) Enrichment of the DNA fragment containing the ACOX1 ( A ) , Cyp4A14 ( B ) , or FGF21 ( C ) TSS in primary hepatocytes isolated from Ppara+/+ pups using antibodies against acetyl-histone four ( AcH4 ) , trimethylated histone three at lysine four ( H3K4me3 ) , lysine nine ( H3K9me3 ) , lysine 27 ( H3K27me3 ) , or pre-immune IgG ( Ctrl IgG ) and evaluated by real-time qPCR . ( D–F ) Enrichment of the DNA fragment containing the FGF21 ( D ) , ANGPTL4 ( E ) , or Cyp4A14 ( F ) TSS in primary hepatocytes isolated from Ppara-/- and Ppara+/+ pups with or without WY-14643 treatment in pregnant dams using anti-HDAC3 antibodies or control IgG . Data are presented as mean ± SEM; n = 4–6 , p<0 . 05 , p<0 . 01 , p<0 . 001 vs . untreated wild-type controls unless otherwise indicated ( two-tailed Mann-Whitney test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 012 Previous studies indicate that milk lipids and hydroxymethylglutaryl-coenzyme A synthase 2 ( HMGCS2 ) activity are required for Fgf21 expression ( Hondares et al . , 2010; Vilà-Brau et al . , 2011 ) . We hypothesized that PPARα-dependent production of ketone bodies , particularly β-hydroxybutyrate , which was ~seven-fold higher in neonates than fetuses ( Figure 4G ) , may activate postnatal gene expression by alleviating the repressive role of class I HDACs ( Shimazu et al . , 2013 ) . To test this possibility , we treated Ppara+/+liver explants from E19 . 5 pups with β-hydroxybutyrate , sodium butyrate ( a known HDAC3 inhibitor ) , and trichostatin A , a selective inhibitor of class I and class II HDACs . Activation of the Fgf21 transcriptional activity was dependent on both the activation of PPARα and HDAC3 inhibition through butyrate or β-hydroxybutyrate treatment ( Figure 7A ) . In particular , HDAC3 inhibition or PPARα activation alone did not cause a significant change in Fgf21 induction . However , trichostatin A alone , regardless of PPARα activation , led to significantly higher expression of Fgf21 , suggesting the involvement of a PPARα-independent pathway and other class I or class II HDACs in the regulation of Fgf21 expression in addition to PPARα ( Figure 7A ) . In contrast , the activity of AcH4 at the TSS of Cyp4a14 and Acox1 and the expression of these genes were not influenced by any of the treatments except the PPARα ligand WY-14643 , thereby excluding the involvement of HDAC3 or other class I and class II HDACs in the regulation of their transcriptional activity ( Figure 7B , C ) . These results suggest that milk lipids can affect the epigenetic status of the postnatal liver in which hepatic production of β-hydroxybutyrate acts as an inhibitor of HDAC3 that regulates Fgf21 . 10 . 7554/eLife . 11853 . 013Figure 7 . β-hydroxybutyrate acts as an endogenous inhibitor of HDAC3 to activate Fgf21 expression upon milk suckling . ( A–C ) Ex vivo liver explants isolated from Ppara+/+ fetuses at E19 . 5 were used to study the effects of butyrate , β-hydroxybutyrate , and trichostatin in the presence or absence of WY-14643 ( WY ) on PPARα target genes . Left: mRNA expression of Fgf21 ( A ) , Cyp4a14 ( B ) , and Acox1 ( C ) . Right: enrichment of the DNA fragment containing the FGF21 ( A ) , Cyp4A14 ( B ) , or ACOX1 ( C ) TSS after chromatin immunoprecipitation ( ChIP ) using antibody against acetyl-histone 4 ( AcH4 ) . Data are presented as mean ± SEM; n = 6 , p<0 . 05 , p<0 . 01 , *p<0 . 001 vs . E19 . 5 samples without WY-14643 treatment ( two-tailed Mann-Whitney test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 013 In mammals , the transition to extra-uterine life represents a sudden shift in the source of energy ( i . e . , from a carbohydrate-laden fetal diet to a high-fat , low-carbohydrate diet ) . The neonatal liver must coordinate hepatic FAO , gluconeogenesis , and ketogenesis in order to maintain bioenergetic homeostasis and to meet the metabolic demands associated with extra-uterine life which depends exclusively on milk ( Cotter et al . , 2013 ) . In contrast to the carbohydrate-replete nutrient state experienced in utero , neonatal energy is dominated by PPARα-dependent transcriptional regulation of lipid catabolism . We found that this anticipatory regulation of PPARα activity and lipid metabolism is directly controlled by GR before birth . Glucocorticoid hormones are crucial in the functional maturation of many key tissues , notably in preparation for birth ( Cottrell and Seckl , 2009 ) . The initiation of parturition is marked by short bursts of stress hormones ( Barlow et al . , 1974 ) . We demonstrate that this short burst of glucocorticoids at the initiation of birth directly stimulate the GR-dependent transcription of PPARα and its lipid catabolic target genes , which in turn prepare neonates for the sudden shift to fat-rich milk diet as the primary source of energy ( Figure 8A , B ) . Indeed , we previously demonstrated the GR-mediated hormonal induction of PPARα in adult hepatocytes ( Lemberger et al . , 1994 ) , while others showed that ligand-activated PPARα interferes with the recruitment of GR and Pol2 to the promoter of classical GRE-driven genes , inhibiting their transcription ( Bougarne et al . , 2009 ) . This is also consistent with our observation of a reduced expression of Nr3c1 in Ppara-/- livers , implicating a reciprocal regulatory relationship between GR and PPARα . 10 . 7554/eLife . 11853 . 014Figure 8 . Schematic illustrations of GR-mediated regulation of PPARα expression , HDAC3-dependent and -independent regulation of PPARα target genes before and after birth , and the affected liver metabolic processes with or without PPARα . ( A ) In fetal liver , hormone-activated GR binds to the GR response element ( GRE ) spanning -1007 to -993 within the PPARα promoter region to directly activate the transcription of Ppara . NTD: N-terminal domain , LBD: ligand-binding domain , DBD: DNA-binding domain . ( B ) At E19 . 5 , direct binding of HDAC3 near the TSS of Fgf21 leads to the repression of Fgf21 transcriptional activity . Upon milk suckling , PPARα-dependent production of β-hydroxybutyrate from the neonatal liver alleviates the HDAC3-mediated repression of Fgf21 by directly inhibiting the activity of HDAC3 , permitting PPARα-dependent Fgf21 expression . Activated GR leads to the stimulation of PPARα and its target genes in fetal liver , such as those involved in mitochondrial and peroxisomal fatty acid oxidation ( FAO ) . After birth , the expression of PPARα target genes involved in mitochondrial FAO predominates the expression of genes involved in peroxisomal FAO , probably due to the higher long-chain fatty acid ( LCFA ) content than very-long-chain fatty acid ( VLCFA ) content in the milk . ( C ) Schematic illustration depicting the affected processes of hepatic metabolism in the presence and absence of PPARα . Green boxes/arrows indicate increments; reds indicate decrements . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 014 We indicate that lipid catabolic target genes of PPARα , particularly those involved in mitochondrial , peroxisomal , and microsomal oxidation , are switched on just before birth in anticipation of a postpartum lipid-rich meal . In the neonatal liver , however , we show that mitochondrial β-oxidation prevails as the major contributor to the neonatal oxidative capacity and protects against hepatic steatosis when faced with a sudden surge in dietary fat . Our findings are consistent with the notion that mitochondrial FAO represents the dominant metabolic pathway whereas peroxisomal FAO assumes a relatively minor role ( Hashimoto et al . , 1999 ) . Accordingly , long-chain FAs constitute more than 60% of the total FAs in animals and milk ( Smith et al . , 1968 ) , and their abundance makes them the only significant source of metabolic fuel for the mitochondrial , but not peroxisomal , β-oxidation system ( Reddy and Mannaerts , 1994 ) . Moreover , decreased mitochondrial FAO has been considered one of the major contributing factors leading to liver steatosis ( Ockner et al . , 1993 ) . Thus , we conclude that the concomitant presence of lipid content of milk , a lower number of mitochondria and overt mitochondrial dysfunctions jointly contributes to the development of liver steatosis in Ppara-/-neonates . Notably , mitochondrial dysfunctions were evident in Ppara-/- hepatocytes due to reduced ∆ΨM and increased intracellular ROS levels . We postulate that an increased intracellular ROS level likely precedes the loss of ∆ΨM and mitochondria dysfunction . As this oxidative stress condition continues to worsen after birth , perhaps induced by nutritional lipid as previously indicated ( Tirosh et al . , 2009 ) , reduced ∆ΨM and ATP production ultimately ensue . This is in agreement with a previous report of a similar mitochondrial disorder associated with the dysfunctional respiratory chain components ( Lebiedzinska et al . , 2010 ) . Particularly , the increased intracellular ROS levels observed in Ppara-/- hepatocytes may be due to the reduced clearance of ROS as evidenced by the down-regulation of genes coding for mitochondrial superoxide dismutase 2 ( SOD2 ) and copper chaperone for SOD1 ( a metalloprotein responsible for delivering copper as a co-factor to SOD1 ) before and/or after birth ( Figure 2—source data 1 ) . This observation is consistent with the report that PPARα activation by agonist clofibrate stimulates SOD1 and catalase activities as part of the defense mechanisms against oxidative stress in the heart ( Ibarra-Lara et al . , 2016 ) . Many of the PPARα-regulated genes involved in FAO have previously been shown to contain a PPRE in their promoter region and hence , recognized as direct PPARα target genes ( Rakhshandehroo et al . , 2010 ) . This implicates that the reduced oxidative capacity observed in Ppara-/- mice was due to the lack of direct PPARα-mediated stimulatory effects rather than a cellular-autonomous metabolic adaptation in response to lower mitochondria numbers . As discovered by Semenkovich andRakhshandehroo et al . , 2010 co-workers ( Chakravarthy et al . , 2005 , 2009 ) , newly synthesized FA by fatty acid synthase may also regulate glucose , lipid , and cholesterol metabolism by serving as endogenous activators of a distinct physiological pool of PPARα ligands in adult liver . However , we doubt that de novo synthesis of FA plays a predominant role in the regulation of PPARα activity during early postnatal period since insulin/glucagon ratio is low , which promotes FAO rather than de novo synthesis of FA ( Girard et al . , 1977 ) . Our recent work indicates that PPARα has little impact on the expression of lipogenic genes in normal conditions ( Montagner et al . , 2016 ) . Moreover , an effective hepatic FAO is essential for the provision of acetyl-CoA and NADH to support hepatic de novo ketogenesis and gluconeogenesis ( Girard , 1986 ) . This notion is consistent with the hypoketonemia and hypoglycemia in Ppara-/-neonates reported in this work and by Crawford and co-workers ( Cotter et al . , 2014 ) . Notably , the hypoglycemic phenotype previously reported in Ppara-/-neonates by Cotter et al . happened at P1 due to a decreased hepatic gluconeogenesis from glycerol . However , we did not detect any sign of hypoglycemia in these mice starting from P2 . This apparent discrepancy is likely attributable to the rapid ( ~1 day ) and dynamic adaptive functions of Ppara-/- neonatal liver in providing glucose as the primary source of energy required for postnatal growth and in maintaining glucose homeostasis . Our finding also supports the work of others indicating a preserved gluconeogenesis from glycerol in adult Ppara-/- mice ( Xu et al . , 2002 ) . We elucidated that this cell-autonomous metabolic adaptation was enhanced in the absence of PPARα and mediated through the up-regulation of hepatic ALT and anaplerotic amino acid oxidation . Thus far , the mechanism behind the increased production and secretion of ALT in Ppara-/- neonatal liver still remains unclear . It is notable that ALT2 expression is up-regulated under metabolic stress and that plasma and tissue ALT levels are elevated in response to endoplasmic reticulum ( ER ) stress ( Salgado et al . , 2014; Josekutty et al . , 2013 ) . Interestingly , defective PPARα signaling has been reported to cause hepatic mitochondrial and ER stress in the pathogenesis of hepatic steatosis through reduced mRNA expression of the sarco/endoplasmic reticulum calcium ATPase ( SERCA ) ( Su et al . , 2014 ) . In addition , our transcriptome analysis revealed a consistent reduction by ~1 . 2-fold in the expression of Atp2a2 , which encodes SERCA , in Ppara-/- liver at E19 . 5 and P2 ( Figure 2—source data 1 ) . Moreover , we also delineated that most of the rate-limiting enzymes involved in amino acid oxidation were concomitantly up-regulated in Ppara-/- neonatal liver to provide essential intermediate substrates in TCA/Krebs cycle required for gluconeogenesis . Hence , we speculate that de novo synthesis of glucose becomes the primary energy source in sustaining normal postnatal growth in Ppara-/- neonates as evidenced by the observed growth retardation upon decoupling of amino acid oxidation from gluconeogenesis . This is in conjunction with the failure of Ppara-/- neonatal liver in harnessing energy from ketone bodies and lipids due to defective mitochondrial functions . In parallel , the compensatory up-regulation of pyruvate carboxylase ( Pcx ) and other gluconeogenic genes in Ppara-/-neonates at P2 probably corresponds with a lower hepatic acetyl-CoA level , as supported by a recent report that hepatic acetyl-CoA acts as an allosteric regulator of Pcx ( Perry et al . , 2015 ) . Importantly , these findings in Ppara-/-mice illustrate the tremendous flexibility of the neonatal liver in coordinating ketogenesis , FAO , and gluconeogenesis so as to meet the energy demand required for normal growth especially when faced with an energy crisis caused by defective oxidative metabolism and hypoketonemia . Mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase 2 ( HMGCS2 ) is one of the rate-liming enzymes involved in ketogenesis . Hmgcs2 contains a PPRE in its promoter region and is under the direct transcriptional control of PPARα ( Rodríguez et al . , 1994 ) . HMGCS2 has been shown to induce mitochondrial FAO and FGF21 expression , possibly via a SIRT1-dependent mechanism ( Vilà-Brau et al . , 2011 ) . Furthermore , Hmgcs2 mRNA expression peaks 16 hr after birth and coincides with a decline in Acox1 mRNA expression thereafter ( Yubero et al . , 2004; Serra et al . , 1993 , 1996 ) . These reports substantiate our findings of preferential activation of mitochondrial FAO at birth . Our results also support the in vivo finding that β-hydroxybutyrate is a potent physiological inhibitor of HDAC3 ( Shimazu et al . , 2013 ) , suggesting that PPARα-mediated activation of HMGCS2 and β-hydroxybutyrate production precedes the PPARα-regulated production of FGF21 . Similar to other PPARα target genes , we detected PPARα occupancy in the Fgf21 promoter before birth , but its transcription is ultimately determined by the alleviation of HDAC3-mediated inhibition ( Figure 8B ) . Thus , fatty acid catabolism provides β-hydroxybutyrate , which acts as a secondary signal to initiate the de-repression of the Fgf21 promoter by HDAC3 , allowing it to become PPARα responsive . It was previously indicated that HDAC-mediated histone deacetylation may inhibit transcription at the initiation and/or elongation step ( Wang et al . , 2009 ) . If the genes are loaded by Pol2 at the TSS , similar to what we observed at the Fgf21 TSS upon PPARα agonistic activation , HDAC-mediated inhibition of transcription most likely happens at the elongation step although abortive initiation of the TSS bound Pol2 cannot be excluded . Our results suggest that PPARα activation induces the recruitment and assembly of a transcription initiation complex at the Fgf21 TSS . However , the elongation of Fgf21 mRNA transcript only ensues after alleviation of HDAC3 inhibition by β-hydroxybutyrate . Therefore , it is consistent with our findings that PPARα agonist has no effect on Fgf21 mRNA or transcriptional activation in the absence of β-hydroxybutyrate . Both human and mouse neonates exhibit mild ketogenic conditions during early postpartum period ( 0–1 day , 0 . 2–0 . 5 mmol/L ) , which further exacerbates after 5–10 days ( 0 . 5–1 . 1 mmol/L ) ( Hamosh , 2004 ) . We believe that it could be due to the use of lipids as preferred fuel for growth while the carbohydrate parts of milk ( e . g . , lactose and oligosaccharide−two main carbohydrates in milk ) mainly contribute to brain growth ( i . e . , myelin synthesis ) and gut microbiota nourishment ( Edmond , 1992; Chichlowski et al . , 2011 ) . Further , the observed ketogenic condition may also be explained by the low insulin/glucagon ratio in early postpartum life ( Girard et al . , 1977; Ferré et al . , 1979; Girard et al . , 1992 ) . We found that ketone bodies act as a hit that is important to prime PPARα-dependent expressions of Fgf21 and Angptl4 by modulating HDAC3 activity on their promoter . Both FGF21 and ANGPTL4 function as secreted systemic effectors of PPARα ( Badman et al . , 2007; Dijk and Kersten , 2014 ) . However , unlike Fgf21 , the expression of Angptl4 has already begun in the fetal liver . This finding is congruent with the report that ANGPTL4 functions as a glucocorticoid-dependent gatekeeper of FA flux by inhibiting the activity of adipose tissue lipoprotein lipase during fasting ( Koliwad et al . , 2012; Dijk and Kersten , 2014 ) . In fact , significant overlap exists between the bioenergetic challenges experienced during fasting and at birth . Liver-derived FGF21 acts as an endocrine regulator of ketogenesis , which links the phenotypes observed during fasting and in newborns ( Badman et al . , 2007; Hondares et al . , 2010 ) . This concept is evident in Ppara-/- mice , which have reduced plasma FGF21 levels . During fasting , the switch to a lipid-dominated nutrient supply provokes hypoketonemia , hypoglycemia , and hepatic steatosis in adult Ppara-/- mice ( Kersten et al . , 1999; Montagner et al . , 2016 ) . In concordance with a recent report ( Cotter et al . , 2014 ) , the shift to a high-fat , low-carbohydrate ketogenic diet in Ppara-/- neonates results in steatosis similar to that observed in fasted adult Ppara-/- mice . Notably , FGF21 has previously been shown to stimulate hepatic FAO and to prevent hepatic steatosis following ingestion of lipid-laden milk ( Xu et al . , 2009 ) . Hence , we believe that the physiological relevance of FGF21 induction in neonates after milk suckling relates to its role in metabolic regulation by stimulating lipid catabolism rather than a response to starvation or a low calorie condition which no longer exists upon milk ingestion . It is also important to note that the early human breast milk is not initially ketogenic due to the relatively high lactose content , which gradually decreases while the fat content increases as the milk matures over time ( Jenness , 1979 ) . In contrast , mouse milk is likely a ketogenic diet as evidenced by a previous report that early mouse milk comprises ~17% of fat and ~1 . 7% of lactose at P3 ( Görs et al . , 2004 ) . Therefore , direct translation of our findings to human situations needs to be cautioned for such differences in milk composition . In summary , we provide evidence that prenatal expression of PPARα is under the direct control of GR . The GR-dependent PPARα activity is pivotal for the induction of hepatic FA catabolic genes , pointing to a novel anticipatory role of PPARα in the fetal liver in preparation for the efficient use of milk fat as an energy source . In addition , hepatic FA catabolism provides essential co-factors for the synthesis of ketone bodies such as β-hydroxybutyrate . Interestingly , ketone bodies act as a secondary signal that further activates PPARα-dependent regulation of the hepatokine FGF21 . Therefore GR-PPARα axis may represent a critical signaling pathway in late gestation for the ability of mammalian newborns to use nutrients and maintain whole-body homeostasis . We conclude that in the absence of PPARα , a vicious cycle pertaining to the ( i ) lower mitochondria numbers , ( ii ) mitochondrial dysfunctions , ( iii ) impaired mitochondrial FA β-oxidation , ( iii ) hypoketonemia either due to a lack of direct PPARα stimulation of Hmgcs2 or a lack of essential co-factors provided by functional FAO , ( iv ) lack of epigenetic activation of FGF21 by ketone bodies ( e . g . , β-hydroxybutyrate ) , and ( v ) a surge in nutritional lipids upon milk suckling , exists to concomitantly contribute to the development of hepatic steatosis in neonates ( Figure 8C ) . PPARα-null ( Ppara-/- ) and GR-null ( Nr3c1-/- ) mice were acquired from the Jackson Laboratory ( Bar , Harbor , ME ) and Nuclear Receptor Zoo ( MGI: 95824 , Strasbourg , France ) , respectively . These mice were given standard rodent chow diet ( AIN93M , Specialty Feeds , Australia ) ( Table 1 ) and water ad libitum , maintained in a C57BL/6 background , and bred in our specific pathogen-free facilities by inter-crossing Ppara+/- mice to obtain experimental wild-type and knockout pups in the same litter . This breeding strategy allowed the experimental mice to be exposed to the same gestational environment and ensured that pups received milk of the same nutritional content from the heterozygous dams . Pregnancy was timed based on vaginal plug formation . Pregnant dams approaching full-term were closely monitored and cesarean section performed to obtain fetuses at E19 . 5 or earlier ( E13 , E15 , E17 ) . Pups at P2 were timed based on the delivery day . Litters of 6–8 pups were used to minimize differences in milk availability verified by stomach inspection . All experimental protocols involving animals were reviewed and approved by the Veterinary Office of the Canton Vaud ( SCA–EXPANIM , Service de la Consommation et des Affaires Vétérinaires , Epalinges , Switzerland ) in accordance with the Federal Swiss Veterinary Office Guidelines and by the Institutional Animal Care and Use Committee ( #2013/SHS/866 ) in Singapore . The animal handling procedures were compliant with the NIH Guide for the Care and Use of Laboratory Animals . 10 . 7554/eLife . 11853 . 015Table 1 . Standard chow diet formulation . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 015Manufacturer: SPECIALITY FEEDS Product ID: AIN93MStandard AIN93M rodent diet A semi-pure diet formulation for laboratory rats and mice based on AIN-93M . This formulation satisfies the maintenance nutritional requirements of rats and mice . Some modifications have been made to the original formulation to suit locally available raw materials . We have evidence that vitamin losses and other changes to the diet can occur when irradiated at 25KGy . The diet SF08-020 has been formulated for irradiation . Please contact us for more information if the diet is to be irradiated . Calculated nutritional parametersIngredientsProtein13 . 6%Casein ( acid ) 140 g/kgTotal fat4 . 0%Sucrose100 g/kgTotal digestible carbohydrate as defined by FSANZ Standard 1 . 2 . 8 64 . 8% Canola oil40 g/kgCellulose50 g/kgCrude fiber4 . 7%Wheat starch472 g/kgAD fiber4 . 7%Dextrinised starch155 g/kgDigestible energy15 . 1 MJ/kgDL methionine1 . 8 g/kg% Total calculated digestible energy from lipids9 . 0%Calcium carbonate13 . 1 g/kg% Total calculated digestible energy from protein15 . 0%Sodium chloride2 . 6 g/kgAIN93 trace minerals1 . 4 g/kgDiet form and featuresPotassium citrate1 . 0 g/kgSemi pure diet . 12 mm diameter pellets . Pack size 5 kg . Vacuum packed in oxygen impermeable plastic bags , under nitrogen . Bags are packed into cardboard cartons for protection during transit . Smaller pack quantity on request . Diet suitable for irradiation but not suitable for autoclave . Lead time 2 weeks for non-irradiation or 4 weeks for irradiation . Potassium dihydrogen phosphate8 . 8 g/kgPotassium sulphate1 . 6 g/kgCholine chloride ( 75% ) 2 . 5 g/kgAIN93 vitamins10 g/kgCalculated amino acidsCalculated total vitaminsValine0 . 90%Vitamin A ( retinol ) 4000 IU/kgLeucine1 . 30%Vitamin D ( cholecalciferol ) 1000 IU/kgIsoleucine0 . 60%Vitamin E ( α-tocopherol acetate ) 75 mg/kgThreonine0 . 60%Vitamin K ( menadione ) 1 mg/kgMethionine0 . 60%Vitamin C ( ascorbic acid ) None addedCystine0 . 05%Vitamin B1 ( thiamine ) 6 . 1 mg/kgLysine1 . 00%Vitamin B2 ( riboflavin ) 6 . 3 mg/kgPhenylanine0 . 70%Niacin ( nicotinic acid ) 30 mg/kgTyrosine0 . 70%Vitamin B6 ( pryridoxine ) 7 mg/kgTryptophan0 . 20%Pantothenic acid16 . 5 mg/kgHistidine0 . 42%Biotin200 μg/kgCalculated total mineralsFolic acid2 mg/kgCalcium0 . 47%InositolNone addedPhosphorus0 . 35%Vitamin B12 ( cyancobalamin ) 103 μg/kgMagnesium0 . 09%Choline1670 mg/kgSodium0 . 15%Calculated fatty acid compositionChloride0 . 16%Myristic acid 14:0No dataPotassium0 . 40%Palmitic acid 16:00 . 20%Sulphur0 . 17%Stearic acid 18:00 . 10%Iron75 mg/kgPalmitoleic acid 16:1No dataCopper6 . 9 mg/kgOleic acid 18:12 . 40%Iodine0 . 2 mg/kgGadoleic acid 20:1traceManganese19 . 5 mg/kgLinoleic acid 18:2 n60 . 80%CobaltNo dataα-Linolenic acid 18:3 n30 . 40%Zinc47 mg/kgArachidonic acid 20:4 n6No dataMolybdenum0 . 15 mg/kgEPA 20:5 n3No dataSelenium0 . 3 mg/kgDHA 22:6 n3No dataCadmiumNo dataTotal n30 . 45%Chromium1 . 0 mg/kgToal n60 . 76%Fluoride1 . 0 mg/kgTotal mono-unsaturated fats2 . 46%Lithium0 . 1 mg/kgTotal polyunsaturated fats1 . 21%Boron3 . 1 mg/kgTotal saturated fats0 . 28%Nickel0 . 5 mg/kgVanadium0 . 1 mg/kgCalculated data uses information from typical raw material composition . It could be expected that individual batches of diet will vary from this figure . Diet post treatment by irradiation or autoclave could change these parameters . We are happy to provide full calculated nutritional information for all of our products , however we would like to emphasise that these diets have been specifically designed for manufacture by Specialty Feeds . Total RNA was extracted from liver samples using TRIzol reagent ( Life Technologies , Carlsbad , CA ) and purified using RNeasy Mini Kit ( Qiagen , Hilden , Germany ) according to the manufacturer’s instructions . The purified RNA was spectrophotometrically quantified and its quality assessed by measuring the absorbance ratios at 260 nm/280 nm and 260 nm/230 nm using Nanodrop Spectrophotometer ( Thermo Fisher Scientific , Wilmington , DE ) . One microgram of mRNA was reverse-transcribed to cDNA using Superscript II Reverse Transcriptase ( Life Technologies , Carlsbad , CA ) . The cDNA template was amplified by real-time PCR using iTaq SYBR Green Supermix ( Bio-Rad , Hercules , CA ) . Relative mRNA levels were calculated using the comparative 2-∆∆CT method after normalization to 36B4/RplP0 expression , which was used as an invariant control . The primer sequences used for real-time PCR were obtained from the Harvard PrimerBank ( http://pga . mgh . harvard . edu/primerbank ) and are provided in Table 2 . 10 . 7554/eLife . 11853 . 016Table 2 . Primer sequences for mouse genes used in quantitative real-time PCR . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 016GeneForward primer ( 5’-3’ ) Reverse primer ( 5’-3’ ) Acaa2ATGTGCGCTTCGGAACCAAACAAGGCGTATCTGTCACAGTCAcetyl-coenzyme A acyltransferase 2/short chain-specific 3-ketoacyl-CoA thiolase ( mitochondrial ) AcadlTGCCCTATATTGCGAATTACGGCTATGGCACCGATACACTTGCAcyl-coenzyme A dehydrogenase , long chainAcadvlTGACCTTGGTGTTAGCGTTACCTGGGCCTTTGTGCCATAGAGAcyl-coenzyme A dehydrogenase , very long chainAcox1TCGAAGCCAGCGTTACGAGATCTCCGTCTGGGCGTAGGAcyl-CoA oxidase 1 , palmitoylAcsl1ACCAGCCCTATGAGTGGATTTCAAGGCTTGAACCCCTTCTGAcyl-CoA synthetase , long chain family member 1Angptl4TCCAACGCCACCCACTTACTGAAGTCATCTCACAGTTGACCAAngiopoietin-like 4Cpt1aCTATGCGCTACTCGCTGAAGGGGCTTTCGACCCGAGAAGACarnitine palmitoyltransferase 1a ( liver ) Cpt2CAAAAGACTCATCCGCTTTGTTCCATCACGACTGGGTTTGGGTACarnitine palmitoyltransferase 2Cyp4a14TCATGGCGGACTCTGTCAATAGCAGGCGAAAGAAAGTCAGGCytochrome P450 , family 4 , subfamily a , polypeptide 14EhhadhACAGCGATACCAGAAGCCAGTGGCAATCCGATAGTGACAGCEnoyl-coenzyme A , hydratase/3-hydroxyacyl coenzyme A dehydrogenaseFabp1AAGGCAGTCGTCAAGCTGGCATTGAGTTCAGTCACGGACTTFatty acid bind protein 1 ( liver ) Fgf21CTGCTGGGGGTCTACCAAGCTGCGCCTACCACTGTTCCFibroblast growth factor 21GckAGACGAAACACCAGATGTATTCCGAAGCCCTTGGTCCAGTTGAGGlucokinaseNr3c1CCGGGTCCCCAGGTAAAGATGTCCGGTAAAATAAGAGGCTTGGlucocorticoid receptorHadhaAGCAACACGAATATCACAGGAAGAGGCACACCCACCATTTTGGHydroxyacyl-coenzyme A dehydrogenase , alpha subunitHadhbTGAATATGCACTGCGTTCTCATCCTTTCCTGGTACTTTGAAGGGHydroxyacyl-coenzyme A dehydrogenase , beta subunitHk1CGGAATGGGGAGCCTTTGGGCCTTCCTTATCCGTTTCAATGGHexokinase 1Fkbp51TTTGAAGATTCAGGCGTTATCCGGGTGGACTTTTACCGTTGCTCFK506 binding protein 51Pex19GACAGCGAGGCTACTCAGAGGCCCGACAGATTGAGAGCAPeroxisomal biogenesis factor 19PparaTCGGCGAACTATTCGGCTGGCACTTGTGAAAACGGCAGTPeroxisome proliferator activated receptor alphaSlc25a20GCGCCCATCATTGGAGTCACACACCAGATAACATCCCAGCSolute carrier family 25 ( mitochondrial carnitine/acylcarnitine translocase ) , member 2036B4/RplP0CGAGGACCGCCTGGTTCTCGTCACTGGGGAGAGAGAGGRibosomal protein P0 Eight to ten livers were pooled , homogenized , and cross-linked with 1% formalin at room temperature for 5 min . The reaction was stopped with the addition of glycine and nuclei isolated by sucrose-density ultracentrifugation . The nuclei were sonicated to produce DNA fragments of ~500–1000 base pairs . Total chromatin was incubated with 10 μg of antibody overnight at 4°C and precipitated with 10 μl of blocked protein A-agarose beads ( Life Technologies , Carlsbad , CA ) at 4°C for 2 hr . After de-crosslinking , the DNA was purified and the enrichment quantified by quantitative real-time PCR , expressing the results as the percentage of input . Antibodies against RNA polymerase II ( Pol2 , #sc-67318X ) , GR ( #sc-1004X ) , and pre-immune rabbit antibodies ( #sc-2027X ) were purchased from Santa Cruz Biotechnology ( Santa Cruz , CA ) . Antibodies against PPARα ( #AB2779 ) , histone deacetylase 3 ( HDAC3 , #AB7030 ) , acetyl-histone 4 ( AcH4 ) , and trimethylated lysine 4 ( H3K4me3 , #AB8580 ) , lysine 9 ( H3K9me3 , #AB8898 ) , and lysine 27 ( H3K27me3 , #AB6002 ) of histone 3 were from Abcam ( Cambridge , MA ) . The primer sequences used for chromatin immunoprecipitation were provided in Table 3 . 10 . 7554/eLife . 11853 . 017Table 3 . Primer sequences for mouse genes used in chromatin immunoprecipitation . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 017GeneForward primer ( 5’-3’ ) Reverse primer ( 5’-3’ ) ACOX1_TSSTCCCGGAAAGATCACGTGAACCTCCCCGAGCGGCTCCTCGCCAACOX1_PPRETAGCCAACGACAATGAACCCGGAAACCAGAAGGGAATGANGPTL4_TSSCCAGCAAGTTCATCTCGTCCTCCCTCCCACTCCCACACCCYP4A14_TSSATTCCCCCTCCCACAAGTAGCCCATGGTTAGTAGTTTCTGGACYP4A14_PPREAAGGAAAAGGCCACCGTCTATCCATCTCACTGAACTTTACCCFGF21_TSSATATCACGCGTCAGGAGTGGTCCCCAGCTGAGAAGACACTFGF21_PPREAGGGCCCGAATGCTAAGCAGCCAAGCAGGTGGAAGTCTPPARα_TSSGTTGTCATCACAGCTTAGCGCAGATAAGGGACTTTCCAGGTCPPARα_GRE ( −1007 to −993 ) GGGACTCGGGGAACAAGCTGTGCGATCTAGGGAAGGGTGCGCCTTGGCGCGCACTCCPPARα_GRE ( −2080 to −2066 ) CTTTCCTCTCAATACAGTCTGTCAAACAAAAGTTTTGTTTGTTTTGACTCTCTGTCCAGPPARα_GRE ( −2953 to −2939 ) AAGGGTGAACACACTTTGTTTCCTGGATGTCCACCAGGGCAGGGGAAGTAGGTATT E15 fetal liver explants were used for treatment with dexamethasone due to their relatively low PPARα activation status . Fresh liver sections ( n = 6 per treatment group ) were cultured in the presence of dexamethasone ( 0 . 1 μM , 1 μM , or 10 μM ) for 24 hr . After treatment , total RNA was isolated and purified , and PPARα mRNA levels were measured by quantitative real-time PCR . Fetuses or pups were sacrificed by decapitation . The liver was removed , minced into fine consistency with a pair of scissors , and digested at 37°C with agitation at 115 rpm for 1 hr in 10 ml of sterile-filtered 4- ( 2-hydroxyethyl ) -1-piperazineethanesulphonic acid ( HEPES ) :collagenase solution containing 0 . 1 M HEPES , 0 . 12 M NaCl , 50 mM KCl , 5 mM D-glucose , 1 . 5% bovine serum albumin ( BSA ) , 1 mM CaCl2 , and 10 , 000 activity units of type-I collagenase ( Life Technologies , Carlsbad , CA ) . The cell suspension was filtered through a 100-μm nylon mesh ( BD Falcon , Franklin Lakes , NJ ) and collected in a 50-ml centrifuge tube . The suspension was centrifuged at 200 × g for 10 min to pellet the cells and the supernatant removed . Red blood cell ( RBC ) lysis solution was added to re-suspend the cells , followed by incubation at room temperature for 5 min . After RBC lysis , ice-cold PBS was added and the cell suspension filtered through a 40-μm nylon mesh ( BD Falcon , Franklin Lakes , NJ ) to remove cell clumps . Finally , the cell suspension was centrifuged at 200 ×g for 5 min to pellet the cells . For primary hepatocyte culture , the isolated mouse hepatocytes ( 2 × 105 cells/per 9 . 5 cm2 well ) were then cultured in DMEM containing 10% fetal calf serum ( FCS ) and 2 mM penicillin/streptomycin in 6-well plates coated with rat tail collagen ( Corning , Tewksbury , MA ) . Primary hepatocytes were isolated and cultured as described above . Upon reaching a confluency of ~60% , the adherent cells were treated with 50 ng ON-TARGETplus NR3C1/GR-targeting SMARTpool siRNAs ( L-045970-01-0005 ) or non-targeting pool ( D-001810-10-05 ) ( GE Dharmacon , Lafayette , CO ) for 24 hr using transfection reagent DharmaFECT 1 as per the manufacturer’s protocol . Cells were harvested for α-GR ChIP assays after 48 hr of incubation with complete medium . Successful knockdown was determined by reduced Nr3c1 mRNA expression of at least 80% when compared with non-targeting siRNA treatment group . RNA samples were randomly selected from three different litters for each genotype ( n = 6 ) . Total RNA was extracted from mouse liver using TRIzol reagent ( Life Technologies , Carlsbad , CA ) and further purified using RNeasy Mini Kit ( Qiagen , Hilden , Germany ) according to the manufacturer’s instructions . RNA quality measurements were performed using an Agilent 2100 bioanalyzer ( Agilent Technologies , Waldbronn , Germany ) . Only samples with intact bands corresponding to the 18S and 28S rRNA subunits without contamination with chromosomal DNA and had an RNA integrity number > 8 . 0 were selected for array hybridization . Hybridization was performed with Affymetrix Mouse Genome MoGene1 . 0 ST arrays according to the manufacturer’s protocol and analyzed as described previously ( Leuenberger et al . , 2009 ) . Briefly , the significant enrichment of underlying KEGG , GO , and Reactome curated pathways was determined from the hypergeometric distribution and corrected for multiple comparisons using Broad Institute Molecular Signatures Database v4 . 0 ( http://www . broadinstitute . org/gsea/msigdb/ ) . Gene sets with a false discovery rate p-value<0 . 05 were considered significant . Genes that were up- or down-regulated were identified by selecting genes with logarithmic fold-change ratio ( PPARα-/-/PPARα+/+ ) > 1 . 3 ( up-regulated genes ) or < −1 . 3 ( down-regulated genes ) . The raw data have been deposited in NCBI Gene Expression Omnibus and made accessible through the GEO database ( accession number: GSE39669 and GSE39670 ) . E19 . 5 fetal hepatic cells were isolated as described above and incubated with PE- or FITC-conjugated monoclonal antibodies against DLK ( LS-C179444 ) ( LifeSpan Biosciences , Seatlle , WA ) or CK18 ( ab52459 ) ( Abcam , Cambridge , MA ) at 1:200 dilution in PBS supplemented with 0 . 1% fetal calf serum according to a protocol previously described ( Tanimizu et al . , 2003 ) . The samples were then washed with PBS and mixed with 1 μg/ml propidium iodide before cell sorting using a FACSAria cell sorter ( Becton Dickinson , San Jose , CA ) . Primary hepatocytes were isolated as described above . Mitochondria and peroxisomes were stained using MitoTracker Red and SelectFX Alexa Fluor 488 Peroxisome Labeling Kits ( Life Technologies , Carlsbad , CA ) according to the manufacturer’s protocols . Negative controls without Mitotracker Red or fluorophore-conjugated secondary antibodies were used to gate the quadrants . Levels of mitochondrial membrane potential and intracellular ROS production were determined by tetramethylrhodamine , ethyl ester ( TMRE ) ( ab113852 ) staining and 2’ , 7’ –dichlorofluorescin diacetate ( DCFDA ) ( ab113851 ) ( Abcam , Cambridge , MA ) staining , respectively , according to the manufacturer’s protocols . Treatments with carbonyl cyanide 4- ( trifluoromethoxy ) phenylhydrazone ( FCCP , an ionophore uncoupler of oxidative phosphorylation capable of eliminating mitochondrial membrane potential ) at 20 μM for 10 min or N-acetylcysteine ( NAC , an antioxidant ) at 5 mM for 2 hr served as negative controls for the respective staining . A total of 10 , 000 events were recorded . The data were analyzed using BD FACSDiva software ( version 6; Becton Dickinson , San Jose , CA ) and further processed by FlowJo software ( version 7 . 6 . 1; Tree Star , OR ) . Primary hepatocytes were isolated and cultured as described above . Cellular ATP production was measured in the presence of 25 mM glucose , 10 mM galactose , or 2 . 5 μM rotenone ( Sigma-Aldrich , Saint Louis , MO ) using the ENLITEN ATP Assay System Bioluminescence Detection Kit ( Promega , Madison , MI ) according to the manufacturer’s protocol . Bioluminescence signals were read on a GloMax 20/20 Luminometer ( Promega , Madison , MI ) and normalized to the total number of viable cells . Cell viability assay was performed in extracted primary hepatocytes using trypan blue exclusion test based on a protocol previously described ( Strober , 2001 ) . The production of pyruvate via the glycolytic metabolism of glucose yields 2 net ATP , but the same catalytic pathway yields no net ATP when galactose is used instead of glucose , thereby forcing cells to rely on oxidative phosphorylation for energy . Thus , the use of galactose in the primary hepatocyte culture acts as a positive control for the measurement of oxidative phosphorylation-dependent energy production ( Aguer et al . , 2011 ) . Primary hepatocytes were isolated as described above and 2 × 104 cells seeded per well on XF-24 cell culture plates ( Seahorse Bioscience , Billerica , MA ) coated with rat tail collagen in DMEM medium containing 5% FCS and incubated overnight at 37°C . The cellular oxygen consumption rate was measured as described previously ( Nasrin et al . , 2010 ) . The next day , cells were equilibrated with buffer ( 111 mM NaCl , 4 . 7 mM KCl , 2 mM MgSO4 , 1 . 2 mM Na2HPO4 , 2 . 5 mM glucose , 0 . 5 mM carnitine ) and incubated at 37°C for 60 min . The basal cellular respiration was measured without any treatment for 15 min ( n = 9 per group ) , followed by treatment with 200 μM palmitate conjugated with BSA in a 6:1 molar ratio or BSA alone for 40 min ( n = 6 per group ) . To measure the mitochondrial fatty acid oxidation , cells were subsequently incubated with 300 μm etomoxir , a known carnitine palmitoyltransferase I inhibitor , for another 30 min ( n = 3 per group ) . All measurements utilized the Seahorse XF24 Flux Analyzer ( Seahorse Biosciences , Billerica , MA ) . The OCR of whole hepatocytes was standardized for total protein concentration after the assay was completed . Fresh liver samples were collected and embedded in OCT tissue freezing medium ( Leica Microsystems , Wetzlar , Germany ) . Oil Red O stock solution was prepared by dissolving 0 . 5 g of Oil Red O powder ( Sigma-Aldrich , St . Louis , MO ) in 500 ml isopropanol . To constitute 50 ml of 60% Oil Red O working solution , 30 ml of the stock solution was diluted with 20 ml of water . Fresh-frozen samples were sectioned ( 6 μm thick ) and stained with 60% Oil Red O solution for 10 min . The Oil Red O-stained sections were counterstained with methylene blue . Serum glucose and triglyceride levels were measured using the Accutrend Plus meter ( Roche Diagnostics , Indianapolis , IN ) . Serum β-hydroxybutyrate and liver ALT levels were measured using the β-hydroxybutyrate Assay Kit and ALT Activity Assay Kit , respectively ( Sigma-Aldrich , Saint Louis , MO ) . Lipids were extracted from the liver samples by the Bligh/Dyer method and analyzed by gas-liquid chromatography as previously described ( Zadravec et al . , 2010 ) . Although feeding the P5 pups with milk harvested from Ppara+/- dams would be ideal for this HFD-weaning challenge , we were ethically and technically restricted to perform oral gavage in these young pups and to collect sufficient milk for this experiment . For these reasons , we resorted to the best compromise by exposing the pups to a high-fat/low-carbohydrate ( HF/LC ) diet or a control diet from P5 ( i . e . , just before tooth eruption ) to P15 ( i . e . , when liver steatosis is mostly resolved ) . The diets ( Provimi Kliba , Kaiseraugst , Switzerland ) had the following macronutrient compositions ( % w/w: fat , carbohydrates , protein ) : control , 16 . 7/64 . 3/19 . 0 ( #2222 ) ( Table 4 ) and HF/LC , 74 . 4/6 . 6/19 . 0 ( #2201 ) ( Table 5 ) . Both diets used identical macronutrient sources ( fat source: beef tallow; carbohydrate source: starch; protein source: casein ) , allowing a precise comparison of differences in macronutrients . 10 . 7554/eLife . 11853 . 018Table 4 . Control diet formulation . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 018Manufacturer: KLIBA NAFAG , SWITZERLAND Product ID: 2222Mouse and ratExperimental diet , purified dietAIN-93G Major nutrients Dry matter90 . 0% Crude protein18 . 0% Crude fat7 . 0% Crude fiber3 . 5% Crude ash3 . 0% Nitrogen-free extract ( NFE ) 58 . 5% Gross energy17 . 5 MJ/kg Metabolic energy15 . 9 MJ/kg Starch35 . 0% Amino acids Arginine0 . 65% Lysine1 . 40% Methionine0 . 50% Methionine + cystine0 . 85% Tryptophan0 . 22% Threonine0 . 70% Major mineral elements Calcium0 . 52% Phosphorus0 . 32% Magnesium0 . 08% Sodium0 . 22% Potassium0 . 36% Chlorine0 . 15% Trace elements Iron65 mg/kg Zinc45 mg/kg Copper6 mg/kg Iodine0 . 6 mg/kg Manganese12 mg/kg Selenium0 . 2 mg/kg Vitamins Vitamin A4000 IU/kg Vitamin D31000 IU/kg Vitamin E100 mg/kg Vitamin K34 mg/kg Vitamin B16 mg/kg Vitamin B26 mg/kg Vitamin B67 mg/kg Vitamin B120 . 05 mg/kg Nicotinic acid30 mg/kg Pantothenic acid16 mg/kg Folic acid2 mg/kg Biotin0 . 2 mg/kg Choline1200 mg/kg Ingredients Corn starch , casein , dextrose , sucrose , refined soybean oil , cellulose , minerals , vitamins , amino acids Remarks - Experimental diet for mice and rats - Given values are calculated averages in air-dry feed - Production on demand Delivery form Pellets 10 mm round 2222 . PH . A05: 5 kg in welded aluminium bag 2222 . MA . A05: 5 kg in welded aluminium bag KLIBA NAFAG \| PROVIMI KLIBA AG \| CH-4303 Kaiseraugst \| Tel . +41 61 816 16 16 \| Fax . +41 61 816 18 00 \| kliba-nafag@provimi-kliba . ch \| www . kliba-nafag . ch10 . 7554/eLife . 11853 . 019Table 5 . High-fat diet formulation . DOI: http://dx . doi . org/10 . 7554/eLife . 11853 . 019Manufacturer: KLIBA NAFAG , SWITZERLAND Product ID: 2201Mouse and ratExperimental diet , purified dietKetogenic diet XL75:XP10 Major nutrientsDry matter99 . 1%Crude protein9 . 9%Crude fat74 . 4%Crude fiber5 . 5%Crude ash6 . 3%Nitrogen-free extract ( NFE ) 3 . 0%Metabolic energy7208 kcal/kgStarch0 . 7%Amino acidsArginine0 . 35%Lysine0 . 79%Methionine0 . 28%Methionine + cystine0 . 90%Tryptophan0 . 13%Threonine0 . 38%Major mineral elementsCalcium0 . 98%Phosphorus0 . 61%Magnesium0 . 15%Sodium0 . 40%Potassium0 . 69%Chlorine0 . 57%Trace elementsIron151 mg/kgZinc97 mg/kgCopper16 mg/kgIodine1 . 4 mg/kgManganese31 mg/kgSelenium0 . 6 mg/kgVitaminsVitamin A8000 IU/kgVitamin D32000 IU/kgVitamin E200 mg/kgVitamin K39 mg/kgVitamin B112 mg/kgVitamin B213 mg/kgVitamin B614 mg/kgVitamin B120 . 1 mg/kgNicotinic acid66 mg/kgPantothenic acid32 mg/kgFolic acid5 mg/kgBiotin0 . 4 mg/kgCholine1975 mg/kgIngredientsBeef fat , casein , cellulose , minerals , vitamins , amino acidsRemarks- Experimental diet for mice and rats- Given values are calculated averages in air-dry feed- Production on demandDelivery formPaste2201 . MA . A05:5 kg in welded aluminium bagKLIBA NAFAG \| PROVIMI KLIBA AG \| CH-4303 Kaiseraugst \| Tel . +41 61 816 16 16 \| Fax . +41 61 816 18 00 \| kliba-nafag@provimi-kliba . ch \| www . kliba-nafag . ch Two-day-old pups were injected with L-cycloserine ( Sigma-Aldrich , Saint Louis , MO ) at 30 mg/kg/day or 0 . 9% saline ( vehicle control ) for 2 to 4 days . Blood glucose and body weight of the pups was monitored before and after treatment . Cytoplasmic and nuclear protein fractions were isolated as described above . For Western blotting , equal amounts of protein extracts ( 20 μg ) were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and electrotransferred onto a nitrocellulose membrane . The membranes were processed as recommended by the antibody suppliers . Chemiluminescence was detected using the Luminata Crescendo Western HRP Substrate ( Millipore , MA ) . Beta-tubulin and U2AF65 were used to check for equal loading and the transfer of cytoplasmic and nuclear proteins , respectively . Primary antibody against full-length ANGPTL4 was a kind gift from Prof . Andrew Tan Nguan Soon . Primary antibodies against ACOX1 ( #sc-98499 ) , FGF21 ( #sc-292879 ) , CYP4A14 ( #sc-46087 ) , β-tubulin ( #sc-9104 ) , U2AF65 ( #sc-48804 ) , GR ( #sc-1004 ) , and HDAC3 ( #sc-17795 ) , and all secondary antibodies conjugated with horseradish peroxidase were acquired from Santa Cruz Biotechnology ( Dallas , TX ) . PPARα polyclonal antibody ( #101710 ) was purchased from Cayman Chemical ( Ann Arbor , MI ) . Fetal livers ( E19 . 5 ) were sliced < 0 . 1-mm-thick using two adjacent scalpel blades and cultured on collagen-coated plates at 37°C for 24 hr in DMEM:F12 with 10% fetal bovine serum containing 5 mM butyrate , 5 mM β-hydroxybutyrate , or 0 . 3 μM trichostatin A . All of these chemicals were acquired from Sigma-Aldrich ( Saint Louis , MO ) . We performed power analysis for sample size estimation using Power And Precision software ( version 4 ) by Biostat Inc . when the study was being designed . We set the mean outcome for Ppara+/+ and Ppara-/- mice at 100 and 80 , respectively ( i . e . , 20% difference ) and estimated the standard deviation to be ± 10% based on our preliminary data . We estimated that a total of 6 mice per group is needed in order to have a power of 80% , which means there is an 80% likelihood that the study will yield a statistically significant ( i . e . , α value = 0 . 05 ) effect to allow us to detect a mean outcome difference of 20% between Ppara+/+ and Ppara-/- mice . The outcomes ( e . g . , gene expression , metabolic parameters ) are measured on a continuous scale . The null hypothesis is that the mean outcome for these two groups is identical . The computation of the sample size is based on the assumption that there would be no missing data ( i . e . , all mice will produce data ) . The data in all figure panels reflect multiple experiments performed on different days using pups ( n = 6 with triplicates ) derived from different litters . In this study , we used 6 mice per group ( i . e . , biological replicates ) for all experiments . Further , measurements and experiments were repeated in triplicates for each sample ( i . e . , technical replication ) . Values were expressed as mean ± standard error of the mean ( SEM ) . Statistical tests , including the two-tailed Mann-Whitney and two-way ANOVA with Bonferroni post-hoc analysis , were performed using GraphPad Prism software ( version 5 . 00 ) . p-values<0 . 05 were considered significant .	Birth is a highly stressful and critical event . In the womb , babies rely on the supply of oxygen and nutrients provided by the placenta . However , once they are born they need to breathe for themselves and gain all their nutrients from suckling milk . The placenta provides a sugar-rich diet , while milk is richer in fat . Failing to cope with this change in diet leads to serious complications and sometimes death . Therefore , a better understanding of how the body adapts to these changes may shed light on pathways that are important for good health in later life . The liver plays a central role in processing the nutrients absorbed by the gut . It uses fats to produce molecules called ketone bodies , such as β-hydroxybutyrate , which are then used as fuel by other tissues and organs including the heart , muscle and the brain . A protein called PPARα controls the production of ketone bodies primarily by regulating genes that are involved in the uptake and breakdown of fat in the liver . However , little is known about how this protein affects the development of the liver . Here , Rando , Tan et al . report that mice start to produce more PPARα in the liver shortly before birth . This ultimately activates several genes that encode enzymes that break down fats . The experiments show that during labor , stress hormones called glucocorticoids directly stimulate the production of PPARα in the liver of the fetus to prepare newborn mice for harnessing energy from fat-rich milk . In the absence of PPARα , mouse liver cells are less able to break down fats after birth and so start to accumulate fat , resulting in fewer ketone bodies being produced . Rando , Tan et al . show that β-hydroxybutyrate regulates some PPARα target genes , including one called Fgf21 . The activity of this gene increases only after milk suckling starts and it encodes a protein that enhances the breakdown of fats in the liver . Without PPARα , the expression levels of its target genes , including Fgf21 , do not increase after birth , which promotes the build up of fats in liver cells , a condition known as liver steatosis . Overall , the results reported by Rando , Tan et al . highlight how stress during labor plays an important role in priming the body to cope with a fat-rich diet after birth . Future studies will need to determine if stress hormones and ketone bodies could be used as therapies for babies born by caesarean section with liver steatosis .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology" ]	2016	Glucocorticoid receptor-PPARα axis in fetal mouse liver prepares neonates for milk lipid catabolism
Control of the COVID-19 pandemic will rely on SARS-CoV-2 vaccine-elicited antibodies to protect against emerging and future variants; an understanding of the unique features of the humoral responses to infection and vaccination , including different vaccine platforms , is needed to achieve this goal . The epitopes and pathways of escape for Spike-specific antibodies in individuals with diverse infection and vaccination history were profiled using Phage-DMS . Principal component analysis was performed to identify regions of antibody binding along the Spike protein that differentiate the samples from one another . Within these epitope regions , we determined potential sites of escape by comparing antibody binding of peptides containing wild-type residues versus peptides containing a mutant residue . Individuals with mild infection had antibodies that bound to epitopes in the S2 subunit within the fusion peptide and heptad-repeat regions , whereas vaccinated individuals had antibodies that additionally bound to epitopes in the N- and C-terminal domains of the S1 subunit , a pattern that was also observed in individuals with severe disease due to infection . Epitope binding appeared to change over time after vaccination , but other covariates such as mRNA vaccine dose , mRNA vaccine type , and age did not affect antibody binding to these epitopes . Vaccination induced a relatively uniform escape profile across individuals for some epitopes , whereas there was much more variation in escape pathways in mildly infected individuals . In the case of antibodies targeting the fusion peptide region , which was a common response to both infection and vaccination , the escape profile after infection was not altered by subsequent vaccination . The finding that SARS-CoV-2 mRNA vaccination resulted in binding to additional epitopes beyond what was seen after infection suggests that protection could vary depending on the route of exposure to Spike antigen . The relatively conserved escape pathways to vaccine-induced antibodies relative to infection-induced antibodies suggests that if escape variants emerge they may be readily selected for across vaccinated individuals . Given that the majority of people will be first exposed to Spike via vaccination and not infection , this work has implications for predicting the selection of immune escape variants at a population level . This work was supported by NIH grants AI138709 ( PI JMO ) and AI146028 ( PI FAM ) . JMO received support as the Endowed Chair for Graduate Education ( FHCRC ) . The research of FAM was supported in part by a Faculty Scholar grant from the Howard Hughes Medical Institute and the Simons Foundation . Scientific Computing Infrastructure at Fred Hutch was funded by ORIP grant S10OD028685 . The future of the COVID-19 pandemic will be determined in large part by the ability of vaccine-elicited immunity to protect against current and future variants of the SARS-CoV-2 virus . Several vaccines have now been approved for use in multiple countries , including two that are based on mRNA technology: BNT162b2 ( Pfizer/BioNTech ) and mRNA-1273 ( Moderna ) . In the United States , over half of adults are now vaccinated against SARS-CoV-2 , the majority of whom have received one of these mRNA vaccines . While these vaccines have been shown to effectively guard against infection , severe disease , and death related to SARS-CoV-2 ( Polack et al . , 2020; Keehner et al . , 2021; Amit et al . , 2021; Angel et al . , 2021; Thompson et al . , 2021; Haas et al . , 2021; Baden et al . , 2021 ) , less is known about how effective they will be against emerging and future variants . One example is the recent surge of the Delta variant coupled with reports of reduced potency of vaccine-elicited antibodies against this variant , highlighting this concerning ongoing dynamic ( Planas et al . , 2021; Lopez Bernal et al . , 2021 ) – a situation that is playing out in an even more significant way with the Omicron variant . Evidence from related endemic coronaviruses indicates that evolution in the Spike protein results in escape from neutralizing antibodies elicited by prior infection ( Eguia et al . , 2021 ) , potentially contributing to why endemic coronaviruses can reinfect the same host ( Edridge et al . , 2020; Hendley et al . , 1972; Schmidt et al . , 1986 ) . Without immunity that is robust in the face of antigenic drift , continual updates of the vaccine to combat new SARS-CoV-2 variants will likely be necessary to provide optimal protection against symptomatic infection . Prior infection with SARS-CoV-2 also provides some immunity against subsequent reinfection , and several studies have characterized the epitopes targeted by convalescent sera ( Hanrath et al . , 2021; Greaney et al . , 2021a; Shrock et al . , 2020; Li et al . , 2021; Stoddard et al . , 2021 ) . It is currently unknown whether SARS-CoV-2 infection and vaccination result in antibodies that bind to similar epitopes , an important point to consider given that most people have acquired antibodies through immunization and not infection . The Spike protein encoded by the mRNA in both SARS-CoV-2 vaccines is stabilized in the prefusion conformation by addition of two proline substitutions ( Corbett et al . , 2020 ) . This change in sequence and fixed conformation of the Spike protein could result in altered antibody targeting when compared to antibodies elicited during infection , where Spike undergoes several conformational changes . It is also possible that differences in antibody specificity could be due to the amount of antigen or type of immune response stimulated in the context of infection versus vaccination . We know that vaccines drive higher neutralization titers and more Spike binding IgG antibodies than infection ( Goel et al . , 2021; Prendecki et al . , 2021; Edara et al . , 2021 ) , indicating some differences in the B cell response compared to infection . A recent study showed that antibodies against the receptor binding domain ( RBD ) of Spike differ between infected and vaccinated individuals; they are generally less sensitive to mutation and bind more broadly across the domain in the context of vaccination as compared to infection ( Greaney et al . , 2021b ) . Although the majority of the serum binding response in SARS-CoV-2-infected and -vaccinated people is directed towards regions of the protein outside of the RBD epitopes ( Greaney et al . , 2021a; Greaney et al . , 2021b; Garrett et al . , 2021; Piccoli et al . , 2020 ) , few studies have examined the prevalence and escape pathways of these antibodies , especially in the setting of vaccination . Antibodies to linear epitopes in the S2 domain of Spike overlapping the fusion peptide ( FP ) , and in the stem helix region just upstream of heptad repeat 2 ( SH-H ) region are found in serum from COVID-19 patients , and some studies suggest that these antibodies may be neutralizing ( Poh et al . , 2020; Li et al . , 2020 ) . These non-RBD responses may also be important contributors to non-neutralizing antibody activities , which have been associated with protection and therapeutic benefit in experimental SARS-CoV-2 models and with vaccine protection ( Schäfer et al . , 2021; Tauzin et al . , 2021; Winkler , 2021; Ullah et al . , 2021 ) . Importantly , these epitopes lie in more conserved regions of Spike than RBD where functional constraints on variation may counter the selective pressure for viral escape . To compare antibody immunity elicited by SARS-CoV-2 infection and vaccination , we used a high-resolution Spike-specific deep mutational scanning phage display library to profile the epitopes and sites of escape for serum antibodies from people who had been infected , vaccinated , or a combination of both . This approach , called Phage-DMS , identified four non-RBD antibody binding epitopes across all samples: the FP and SH-H region in the S2 subunit , and the N-terminal domain ( NTD ) and C-terminal domain ( CTD ) in the S1 subunit of Spike . Antibodies to NTD and CTD were uniquely present in the setting of mRNA vaccination or severe infection , but mostly absent in mild COVID-19 cases . In vaccinated individuals , the magnitude of the response varied over time both to the CTD and SH-H epitopes . Other covariates , such as age , dose , and vaccine type , had no significant differences in the binding profiles observed . Of particular relevance to protection against emerging variants , infection and vaccination appear to shape the pathways of escape differently in different epitopes . In the FP epitope , which is a dominant response after infection , the escape pathway was maintained after subsequent vaccination; in the SH-H epitope , infection resulted in antibodies with diverse pathways of escape , whereas vaccination induced a highly uniform escape profile across individuals . Overall , these findings indicate that vaccination induced a broader antibody response across the Spike protein but induced a singular antibody response at the SH-H epitope , which could favor variants that emerge with these mutations . We collected serum samples from two cohorts , termed the Moderna Trial Cohort and the Hospitalized or Ambulatory Adults with Respiratory Viral Infections ( HAARVI ) Cohort ( Garrett et al . , 2021; Jackson et al . , 2020 ) . The Moderna Trial Cohort were participants in a Phase I trial and consisted of 49 individuals , 34 who received the 100 µg dose of mRNA-1273 ( Moderna ) and 15 who received the 250 µg dose . Serum samples were taken at days 36 and 119 post first dose ( 7 and 90 days post second dose , respectively; Jackson et al . , 2020 ) . The HAARVI Cohort included 64 individuals , 44 who had confirmed SARS-CoV-2 infection and 20 who had no reported infection; among this group , 44 were also vaccinated . Those with infection history were stratified by severity based on hospitalization status ( 39 nonhospitalized/mild vs . 5 hospitalized/severe ) , and serum was sampled at timepoints ranging from 8 to 309 days post symptom onset . Of these 44 individuals , 24 were also sampled after vaccination with two doses of either mRNA-1273 ( Moderna , n = 8 ) or BNT162b2 ( Pfizer/BioNTech , n = 15 ) , with 23 from the nonhospitalized group and one from the hospitalized group . All 20 SARS-CoV-2-naïve individuals were sampled post vaccination , with 18 having an additional sample taken pre vaccination ( 0–98 days ) . Post-vaccination timepoints for all naïve and convalescent individuals ranged from 23 to 65 days after the first dose ( 5–42 days after the second dose , respectively ) . Figure 1 provides an illustration of the two cohorts and their respective samples’ infection and vaccination statuses . Additional details are available in Supplementary file 1 . We used a previously described Spike Phage-DMS library to profile the epitopes bound by serum antibodies in the samples described above ( Garrett et al . , 2021 ) . This library consists of peptides displayed on the surface of T7 bacteriophage that are 31 amino acids long , tiling across the length of Spike in one amino acid increments . Peptides in the library correspond to the wild-type Wuhan Hu-1 Spike sequence as well as sequences that contain every possible single amino acid mutation at the central position of the peptide . Serum samples were screened with this library by performing immunoprecipitation ( IP ) followed by sequencing of the pool of phage enriched by the serum antibodies as previously described ( Garrett et al . , 2021; Garrett et al . , 2020; Mohan et al . , 2018 ) . We first examined the wild-type peptides in the Spike Phage-DMS library that were enriched by each serum sample to determine the epitopes bound by antibodies in each sample from these cohorts ( Figure 2A ) . The major targeted epitopes across all the cohorts were in the NTD , CTD , FP , and SH-H regions . Serum from nonvaccinated infected individuals who were not hospitalized mostly bound to immunodominant epitopes in the FP and SH-H , both of which are epitopes previously identified in infected individuals using Phage-DMS ( Garrett et al . , 2021 ) . Samples from hospitalized/severe COVID-19 cases and vaccinated individuals also bound to the FP and SH-H regions , but additionally bound to epitopes within the NTD and CTD regions . In naïve serum samples , there were antibodies that occasionally bound to the FP and SH-H peptides . These findings likely reflect that some individuals have preexisting cross-reactive antibodies that bind to these conserved regions between SARS-CoV-2 and endemic coronaviruses , as suggested by previous studies ( Shrock et al . , 2020; Stoddard et al . , 2021 ) . A principal component analysis ( PCA ) was used to further investigate differences between the infected and/or vaccinated groups ( Figure 2—figure supplement 1 ) . This analysis indicated that binding to epitopes in the NTD , CTD , FP , and SH-H regions was the driving difference between samples ( Figure 2B ) . To quantify differences in antibody binding between groups , for each sample we summed together the enrichment values within each identified epitope region and performed pairwise comparisons between nonhospitalized infected people and all other groups ( Figure 2C ) . Most strikingly , we found nontrivial group differences in the magnitude of humoral responses to these major epitopes on the Spike protein . Specifically , antibodies from both hospitalized infected and vaccinated individuals had significantly higher binding to the NTD , CTD , and SH-H regions compared to nonhospitalized infected individuals . However , antibodies from nonhospitalized infected individuals displayed significantly higher binding to the FP epitope than samples from hospitalized or vaccinated individuals . There was no significant difference in any epitope binding in these four regions between vaccinated samples with and without prior infection ( p>0 . 05 , Mann–Whitney–Wilcoxon [MWW] ) . In order to determine if there were covariates that contributed to differences in antibody binding , we examined the effect of participant age , vaccine dose and type , and timepoint post infection or vaccination on binding to the four epitopes identified above ( Figure 3 ) . For samples in the Moderna Trial Cohort , there was significantly decreased binding to the CTD epitope and SH-H epitope ( p=0 . 008 , p=0 . 011 , Wilcoxon rank-sum test with Bonferroni correction ) at the later timepoint post first dose ( day 119 ) compared to the earlier timepoint ( day 36 ) ( Figure 3A ) . To examine the effect of dosage , we compared 100 µg and 250 µg mRNA-1273 groups for those between the age of 18–55 , as that was the only age group included for the 250 µg dose ( Figure 3B ) . There was no significant difference by vaccine dosage for any of the four epitope regions ( NTD , CTD , FP , or SH-H ) . Participant age was also examined as a variable; there appeared to be a difference in epitope binding in the SH-H region , but this did not survive multiple testing correction ( Figure 3C ) . In infected individuals , the effect of time post symptom onset on epitope binding was examined using nonhospitalized infected individuals in the HAARVI Cohort , who were sampled between 26 and 309 days post symptom onset ( Figure 3—figure supplement 1 ) . Samples were binned into three groups: 0–60 , 60–180 , and 180–360 days post symptom onset . At all times post symptom onset , there was no significant difference in binding to the four identified epitopes ( p>0 . 05 , MWW ) . Individuals in the HAARVI Cohort were given either the Moderna mRNA-1273 or Pfizer/BioNTech BNT162b2 mRNA vaccine , and comparison of the epitope binding response between the two vaccine types revealed no significant differences in all epitope regions ( Figure 3—figure supplement 1B , p>0 . 05 , MWW ) . The Spike Phage-DMS library contains peptides with every possible single amino acid substitution in addition to the wild-type sequence , enabling us to assay the impact of mutations on antibody binding . The effect of site-specific substitutions in critical antibody binding regions not only provides a high-resolution picture of the likely epitope intervals , but also identifies mutations that confer escape within the binding region . The effect of each mutation on serum antibody binding was quantified by calculating its scaled differential selection value , a metric that reports log-fold change of mutant peptide binding over wild-type peptide binding at any given site ( see ‘Materials and methods’ ) ( Garrett et al . , 2020 ) . Site mutations that cause a loss of binding when compared to the wild-type peptide centered at that same site are reported as having negative differential selection values , whereas those that bind better than the wild-type peptide have positive differential selection values . In order for differential selection to be meaningful , however , we must ensure that we do not include weak or sporadic signals that may be due to nonspecific binding . Accordingly , we set a threshold of summed wild-type peptide binding in any one region . By doing so , we lose samples in the analysis but can be confident in the results presented by samples passing this curation step ( Figure 4—figure supplement 1 ) . For samples that passed this threshold , we compared the effect of prior infection and/or time post vaccination on the pathways of escape in each epitope region as follows . Plots depicting the effect of mutations for all samples are publicly explorable at https://github . com/matsengrp/vacc-dms-view-host-repo ( copy archived at swh:1:rev:6519940a17ea2489f445b897485e621d8c6b781d , Galloway , 2022a ) . We examined the sites of escape within the NTD and CTD epitope regions , focusing on vaccinated individuals from the Moderna Trial Cohort because these epitopes were notable targets of the vaccine response and not commonly found in infected individuals . Vaccination elicited antibodies with a strikingly uniform escape profile in the NTD epitope across samples ( Figure 4A ) , with the majority of samples being sensitive to mutation at sites 291 , 294–297 , 300–302 , and 304 , which are in the very C-terminal portion of NTD as well as the region between NTD and RBD . The CTD region appeared to consist of multiple epitopes , the dominant being located at the N-terminal region between positions 545–580 ( termed CTD-N ) . Antibodies that bound to this dominant CTD epitope had a less uniform escape profile , but sites 561 and 562 were common sites of escape in most samples ( Figure 4B ) . For antibodies to both the NTD and CTD-N epitopes , the pathways of escape tended to drift over time and were different at 119 days post vaccination as compared to 36 days post vaccination . Antibodies against the FP epitope are strongly stimulated after infection but are less strongly induced after subsequent vaccination ( Figure 2 ) . Thus , we investigated whether the pathways of escape for serum antibodies also changed after vaccination within samples from previously infected individuals in the HAARVI Cohort . The escape profiles of antibodies in paired samples that strongly bound to the FP epitope both after infection and after subsequent vaccination are shown as a logo plot ( Figure 5A ) . The major sites of escape within the FP epitope for these samples were sites 819 , 820 , 822 , and 823 , and these sites of escape did not appear to change after vaccination , although we noted that there was more variability in the escape profiles after vaccination . We next examined the pathways of escape for FP binding antibodies in vaccinated individuals from the Moderna Trial Cohort . In people with no prior infection , vaccination induced diverse pathways of escape in the FP region ( Figure 5B ) . For example , for participant M10 escape was focused on sites 814 , 816 , and 818 , whereas for participant M38 escape was focused on 819 , 820 , and 823 . There appeared to be some differences in the escape profile at 119 days as compared to 36 days post vaccination , as exemplified by participants M15 , M17 , and M20 . However , in general many of the major sites of escape were shared at both timepoints within each individual and as a group . In order to determine the effect of prior infection on the binding profiles of antibodies after vaccination within the SH-H epitope region , we explored the pathways of escape for paired samples from patients with prior infection in the HAARVI Cohort before and after vaccination as was done for the FP region . Samples from previously infected individuals with no vaccination history had diverse pathways of escape within the SH-H epitope ( Figure 6A ) . For example , site 1149 was only sensitive to mutation for participant 217C , and site 1157 was only sensitive to mutations for participants 120C and 146C . In contrast , the samples from vaccinated individuals , regardless of infection history , tended to have a uniform pathway of escape . The most prominent and consistent sites of escape for vaccinated individuals , both with and without prior infection , were at sites 1148 , 1152 , 1155 , and 1 , 156 . Of note , the pre-vaccination sample from an individual with prior infection requiring hospitalization ( participant 6C ) displayed an escape profile highly similar to those from vaccinated individuals , and this escape profile did not change after vaccination . To see whether the pathways of escape changed over time after vaccination , we visualized the escape mutations within the SH-H epitope for the samples in the Moderna Trial Cohort at 36 and 119 days after the first dose of vaccine ( Figure 6B ) . We saw a highly uniform pattern of escape for most samples at days 36 and 119 , again with escape mainly occurring at sites 1148 , 1152 , 1155 , and 1156 . For some participants , such as M11 , M34 , and M35 , the escape mutations appeared to drift over time , but the major sites of escape remained the same . In this study , we comprehensively profiled the antibody response to the SARS-CoV-2 Spike protein , including pathways of escape from sera in individuals with diverse infection and vaccination histories . We identified four major targets of antibody responses outside of the core RBD domains , in the NTD , CTD , FP , and stem helix-HR2 regions . Vaccinated individuals as well as individuals with severe infection requiring hospitalization both had antibodies to these four epitope regions , whereas individuals with mild infection that did not require hospitalization preferentially targeted only FP and SH-H . One explanation for why vaccination and severely infected individuals share this antibody binding profile is that it is a result of the high antigenic load experienced by both groups . In previously infected cases , the epitope binding patterns changed over time after vaccination , with decreased binding to both the CTD and SH-H epitopes . However , there was not uniform decay across all four epitopes , indicating that waning antibody titers may not occur for all epitopes equally . Other factors such as vaccine dose ( 100 µg or 250 µg ) , vaccine type ( BNT162b2 or mRNA-1273 ) , and participant age did not significantly affect the specificity of the antibody response . We explored the pathways of escape for antibodies binding to these key regions in infected and vaccinated people . We defined for the first time the escape pathways for NTD and CTD-N binding antibodies , epitopes that were commonly found in vaccinated individuals but not in infected individuals . In the case of the NTD epitope , which was located at the C-terminal end of the NTD , escape mutations were uniform and consistent amongst vaccinees , while pathways of escape were more diverse for CTD-N antibodies . Individuals with antibodies that strongly bound the FP epitope had focused escape profiles , with the majority of escape occurring at sites 819 , 820 , 822 , and 823 , although the sample size of this group is small ( N = 3 ) . Vaccination did not greatly alter the escape profile in previously infected individuals , nor did vaccination alone induce a strong or uniform response at the FP epitope . In contrast , antibodies that bind in the SH-H epitope region after infection have diverse pathways of escape , while after vaccination they appear to converge on a more uniform pathway of escape that includes mutations at sites 1148 , 1152 , 1155 , and 1156 . Interestingly , these are also the sites of contact for a cross-reactive HR2-specific antibody isolated from a mouse sequentially immunized with the MERS and SARS Spike proteins ( Sauer et al . , 2021 ) . This hints that a singular antibody clonotype could be elicited when exposed to a stabilized Spike protein , dominating the response in the SH-H region . We also observed some drift in the pathways of escape within a single person over time after vaccination . This mirrors findings from a recent study that examined sites of escape for RBD-specific antibodies in serum samples from the same Moderna Trial Cohort as used in this study ( Greaney et al . , 2021b ) . Together , these results suggest that the B cell response after vaccination with Spike mRNA continues to evolve over time . Multiple studies have demonstrated that SARS-CoV-2-specific B cells undergo continued somatic hypermutation in the months after infection , likely due to antigen persistence ( Gaebler et al . , 2021; Sakharkar et al . , 2021 ) . Spike antigen has been detected in the lymph nodes at least 3 months after vaccination with BNT162b2 , and continued maturation of germinal center B cells could be a possible explanation for the changes in epitope binding we observed ( Turner et al . , 2021 ) . Alternatively , turnover of short-term plasma cells and memory B cells could account for loss of antibody binding to certain epitopes . Our study has important limitations worth noting . Because the Spike Phage-DMS library displays 31 amino acid peptides , we are unable to detect antibodies that bind to conformational epitopes and/or glycosylated epitopes . This is demonstrated by the lack of observable binding to the RBD region , a domain with complex folding and known target of antibodies from infected and vaccinated individuals . However , prior studies of RBD epitopes have already been reported using an overlapping set of samples from the HAARVI Cohort , and together these results paint a more complete picture of epitopes across the Spike protein ( Greaney et al . , 2021a; Greaney et al . , 2021b ) . Finally , we only have five individuals within the hospitalized group and this small sample size limits our ability to make conclusions about epitope binding in those with severe infection . Our finding that vaccinated individuals have a broader response across the Spike protein than infected individuals may have important implications for immune durability against future SARS-CoV-2 variants . Evidence suggests that a polyclonal antibody response that is resistant in the face of multiple mutations is necessary for long-lasting immunity against a mutating viral pathogen ( Greaney et al . , 2021c ) . Thus , the polyclonal response to vaccination may provide greater protection from infection than the more focused response after infection . However , the number of epitopes targeted provides just one benchmark and the ability to escape at the population level could also be influenced by the diversity of individuals’ antibody responses at each epitope and thus the likelihood that a single escape mutation could be widely selected . At one S2-domain epitope region ( SH-H ) , vaccination induced uniform sites of escape that may be due to a singular type of antibody that would allow escape by the same mutations for all vaccinated people . However , epitopes in the S2 domain tend to be in highly conserved regions with important functions that constrain the virus’ ability to mutate , making escape from these antibodies less likely than for RBD , where escape is already common . Indeed , mutations in the FP and SH-H epitopes are not arising in the global population of SARS-CoV-2 ( Garrett et al . , 2021 ) , providing some suggestion that these regions may be constrained ( Walls et al . , 2020; Jaroszewski et al . , 2021 ) . Overall , further studies of the functional capacity of these vaccine-elicited antibodies targeting epitopes outside of RBD are warranted to provide a path towards a polyclonal response to epitopes across the full Spike potein . This comprehensive view may further the goal of a more universal coronavirus vaccine that elimin/ates the need for continual updates of the SARS-CoV-2 vaccine strain due to mutations in variable regions on Spike .	When SARS-CoV-2 – the virus that causes COVID-19 – infects our bodies , our immune system reacts by producing small molecules called antibodies that stick to a part of the virus called the spike protein . Vaccines are thought to work by triggering the production of similar antibodies without causing disease . Some of the most effective antibodies against SARS-CoV-2 bind a specific area of the spike protein called the ‘receptor binding domain’ or RBD . When SARS-CoV-2 evolves it creates a challenge for our immune system: mutations , which are changes in the virus’s genetic code , can alter the shape of its spike protein , meaning that existing antibodies may no longer bind to it as effectively . This lowers the protection offered by past infection or vaccination , which makes it harder to tackle the pandemic . As it stands , it is not clear which mutations to the virus’s genetic code can affect antibody binding , especially to portions outside the RBD . To complicate things further , the antibodies people produce in response to mild infection , severe infection , and vaccination , while somewhat overlapping , exhibit some differences . Studying these differences could help minimize emergence of mutations that allow the virus to ‘escape’ the antibody response . A phage display library is a laboratory technique in which phages ( viruses that infect bacteria ) are used as a ‘repository’ for DNA fragments that code for a specific protein . The phages can then produce the protein ( or fragments of it ) , and if the protein fragments bind to a target , it can be easily detected . Garrett , Galloway et al . exploited this technique to study how different portions of the SARS-CoV-2 spike protein were bound by antibodies . They made a phage library in which each phage encoded a portion of the spike protein with different mutations , and then exposed the different versions of the protein to antibodies from people who had experienced prior infection , vaccination , or both . The experiment showed that antibodies produced during severe infection or after vaccination bound to similar parts of the spike protein , while antibodies from people who had experienced mild infection targeted fewer areas . Garrett , Galloway et al . also found that mutations that affected the binding of antibodies produced after vaccination were more consistent than mutations that interfered with antibodies produced during infection . While these results show which mutations are most likely to help the virus escape existing antibodies , this does not mean that the virus will necessarily evolve in that direction . Indeed , some of the mutations may be impossible for the virus to acquire because they interfere with the virus’s ability to spread . Further studies could focus on revealing which of the mutations detected by Garrett , Galloway et al . are most likely to occur , to guide vaccine development in that direction . To help with this , Garrett , Galloway et al . have made the data accessible to other scientists and the public using a web tool .	[ "Abstract", "Introduction", "Results", "Discussion" ]	[ "microbiology", "and", "infectious", "disease", "immunology", "and", "inflammation" ]	2022	Comprehensive characterization of the antibody responses to SARS-CoV-2 Spike protein finds additional vaccine-induced epitopes beyond those for mild infection
C . elegans SET-9 and SET-26 are highly homologous paralogs that share redundant functions in germline development , but SET-26 alone plays a key role in longevity and heat stress response . Whereas SET-26 is broadly expressed , SET-9 is only detectable in the germline , which likely accounts for their different biological roles . SET-9 and SET-26 bind to H3K4me3 with adjacent acetylation marks in vitro and in vivo . In the soma , SET-26 acts through DAF-16 to modulate longevity . In the germline , SET-9 and SET-26 restrict H3K4me3 domains around SET-9 and SET-26 binding sites , and regulate the expression of specific target genes , with critical consequence on germline development . SET-9 and SET-26 are highly conserved and our findings provide new insights into the functions of these H3K4me3 readers in germline development and longevity . Dynamic regulations of histone methylation status have been linked to many biological processes ( Santos and Dean , 2004 ) . Recent studies have revealed that specific histone methyltransferases and demethylases can play key roles in regulating germline functions and/or modulating longevity ( Han and Brunet , 2012; Greer and Shi , 2012 ) . In C . elegans , loss of the COMPASS complex , which is critical for methylating histone H3 lysine 4 , results in a global decrease in H3K4 trimethylation ( H3K4me3 ) levels , and reduced brood size ( Li and Kelly , 2011; Robert et al . , 2014 ) and extended lifespan ( Greer et al . , 2010 ) phenotypes . Interestingly , deletion of spr-5 or rbr-2 , both of which encode demethylases that erase methyl marks on H3K4 , also results in fertility defects ( Alvares et al . , 2014; Katz et al . , 2009 ) and altered lifespan ( Alvares et al . , 2014; Greer et al . , 2010 ) . These findings suggest that histone methylations need to be precisely controlled to maintain longevity and germline function . It is important to note that the molecular mechanisms whereby these H3K4 modifying enzymes effect reproduction and longevity functions , including the genomic regions that they act on and how the altered H3K4 methylation levels contribute to the biological outcomes in these mutants , are largely unknown . SET ( Su ( var ) 3–9 , Enhancer of Zeste , Trithorax ) domain-containing proteins represent a major group of histone methyltransferases ( Dillon et al . , 2005 ) . We previously carried out a targeted RNAi screen to identify the SET-domain containing proteins that play a role in longevity in C . elegans . We found that RNAi knockdown of set-9 and set-26 , two closely related genes , results in significant lifespan extension ( Ni et al . , 2012 ) . SET-9 and SET-26 share 96% sequence identity and both proteins contain highly conserved PHD and SET domains . PHD domains are known to bind to specific histone modifications ( Shi et al . , 2007; 2006 ) , suggesting that SET-9 and SET-26 could be recruited to chromatin via binding to specific histone marks . Interestingly , the SET domain of SET-9 and SET-26 contains mutations in conserved residues thought to be key for methylating activities ( Ni et al . , 2012 ) , making it unclear whether SET-9 and SET-26 could be active enzymes . Nevertheless , a recent study reported that the SET domain of SET-26 exhibits H3K9me3 activity in vitro ( Greer et al . , 2014 ) . In this work , we demonstrated that , despite their high sequence identity , SET-26 , but not SET-9 , plays a key role in heat stress response and longevity . In addition , we revealed a novel redundant function of SET-9 and SET-26 in germline development . We also confirmed that SET-26 is broadly expressed , whereas SET-9 is only expressed in the germline , which likely accounts for their distinct and redundant functions in lifespan and reproduction . Indeed , genetic and transcriptomic analyses supported the notion that SET-26 acts through the FOXO transcription factor DAF-16 in the soma to modulate longevity . Furthermore , we showed that the PHD domains of SET-9 and SET-26 bind to H3K4me3 in vitro and that the genome-wide binding patterns of SET-9 and SET-26 are highly concordant with that of H3K4me3 marking in C . elegans , indicating that SET-9 and SET-26 are recruited to H3K4me3 marked regions in vivo . Although the SET domain of SET-26 was reported to methylate H3K9me3 in vitro ( Greer et al . , 2014 ) , our results indicated that loss of set-9 and set-26 does not affect global H3K9me3 levels and the genome-wide binding patterns of SET-9 and SET-26 are highly divergent from that of H3K9me3 . Instead , we found that loss of set-9 and set-26 results in expansion of H3K4me3 marking surrounding most if not all of the SET-9 and SET-26 binding sites specifically in the germline , and significant RNA expression change of a subset of germline specific genes bound by SET-9 and SET-26 . We propose that SET-9 and SET-26 are recruited to the chromatin via binding to H3K4me3 , where they function to restrict H3K4me3 spreading and to regulate the expression of specific genes , and together these activities contribute to the proper maintenance of germline development . RNAi knockdown of the highly similar paralogs set-26 and set-9 were previously shown to significantly extend lifespan in C . elegans ( Greer et al . , 2010; Ni et al . , 2012 ) . However , due to their high sequence similarity , RNAi likely knocks down both set-26 and set-9 in those experiments . We confirmed the lifespan extension phenotype with multiple available set-26 single mutants , but a set-9 single mutant was not available at the time ( Ni et al . , 2012 ) . To delineate whether set-9 , like set-26 , also plays a role in lifespan determination , we used CRISPR-cas9 to generate a set-9 mutant ( Figure 1A ) . The set-9 mutant we obtained carries a mutation that causes a premature stop codon and is expected to produce a truncated SET-9 protein lacking the conserved PHD and SET domains ( Figure 1A ) . We tested the lifespan phenotype of this set-9 single mutant along with the set-26 single and set-9 set-26 double mutants . Consistent with previous results , the set-26 single mutant lived longer than wild-type worms ( Figure 1B ) . Surprisingly , although SET-9 and SET-26 proteins share 97% identity in protein sequence , the set-9 ( rw5 ) mutation did not alter lifespan in either wild-type or the set-26 mutant background ( Figure 1B ) . Similar to the lifespan phenotype , set-26 , but not set-9 single mutant , was more resistant to heat stress compared to wild-type worms ( Figure 1C ) . These results suggested that inactivation of set-26 , but not set-9 , extends lifespan and improves heat resistance . While propagating the set-9 set-26 double mutant , we noticed a possible fertility defect . To more thoroughly assess the roles of SET-9 and SET-26 in reproduction , we assayed the brood size of set-9 , set-26 single and double mutants . We found that the progeny number produced by set-9 and set-26 single mutants was slightly smaller compared to that of wild-type worms ( Figure 2A ) . Interestingly , the homozygous set-9 set-26 double mutant derived from heterozygous parents ( first generation , i . e . F1 ) also exhibited a mild brood size defect , and this defect became significantly more severe in the second and later generations ( F2 to F6 , Figure 2A ) . Deficiency of several histone modifiers has been previously reported to exhibit a ‘mortal germline’ phenotype . We performed a classical mortal germline assay and found that the set-9 set-26 double mutant indeed displayed a mortal germline phenotype ( Figure 2B ) . Further detailed analyses indicated that the set-9 set-26 double mutant exhibited a high sterile rate and a low brood size through the F2-F6 generations that we assayed ( Figure 2A and Figure 2—figure supplement 1A ) . Since we noted a large difference between the brood size of the set-9 set-26 double mutant in the F1 and F2 generations and suspected a possible maternal influence , we therefore performed a series of crosses to test this possibility . We found that set-9 set-26 double mutants derived from homozygous set-9 set-26 hermaphrodites crossed with heterozygous fathers exhibited a significantly more severe brood size defect compared to those from heterozygous hermaphrodites crossed with homozygous mutant fathers ( Figure 2C ) . In other words , heterozygous mothers , but not heterozygous fathers , helped to maintain better germline function in the progeny . These data supported the notion of a maternal contribution in germline maintenance in the set-9 set-26 double mutant worms . We next used DAPI staining to monitor the germ cells of the set-9 set-26 double mutant at the F3 and F4 generations . For the double mutant worms that became sterile , we observed variable germline phenotypes , including a very small mitotic region with no differentiated cells , and a small mitotic region with sperms only or a largely normal mitotic region with oocytes only ( Figure 2—figure supplement 1B ) , suggesting problems with both the germline stem cells and their subsequent differentiation . For the double mutant worms that remained fertile , we observed germlines with a smaller but stable number of mitotic cells ( Figure 2D ) . The results together indicated that SET-9 and SET-26 act redundantly to maintain normal germline function and they may regulate both the proliferation and differentiation of the germline stem cells . Given the high degree of sequence identity between SET-9 and SET-26 , and given their differential roles in lifespan and heat resistance , we wondered whether these two proteins could be expressed in different tissues . In an attempt to resolve the expression patterns of SET-9 and SET-26 , we previously used RT-PCR at precise temperatures , as well as an antibody that recognized both SET-9 and SET-26 , in wild-type , set-26 single , and germlineless mutant worms , and deduced that SET-26 is likely broadly expressed and SET-9 is likely expressed in the germline ( Ni et al . , 2012 ) . To unambiguously determine the expression patterns of the SET-9 and SET-26 proteins , we used CRISPR-cas9 to knock-in a GFP tag at the C-terminus of the endogenous set-9 and set-26 loci and monitored their expression patterns . Consistent with our previous report ( Ni et al . , 2012 ) , we found that GFP-tagged SET-9 was only detected in germline cells of C . elegans ( Figure 3A ) . In contrast , the GFP-tagged SET-26 was broadly expressed in both the somatic and germline cells ( Figure 3B ) . As expected , expression of these two proteins was restricted to the nucleus , which is consistent with their possible roles in chromatin regulation . The ubiquitous expression of SET-26 , but not SET-9 , likely explains why SET-26 alone has a role in lifespan and heat resistance . We noted that the knock-in worms expressing GFP-tagged SET-26 lived slightly longer than wild-type ( but significantly shorter than the set-26 mutant ) ( Figure 3—figure supplement 1A ) and had a slight heat resistance phenotype ( Figure 3—figure supplement 1B ) , and the knock-in worms expressing both SET-9::GFP and SET-26::GFP had a slightly lower brood size compared to wild-type worms , but a significantly larger brood size than the set-9 set-26 double mutant worms ( Figure 3—figure supplement 1C ) . The data together suggested that the GFP-tags somewhat compromise the functions of SET-9 and SET-26 , but the tagged proteins remain largely functional . We next wondered whether the germline or somatic expression of SET-26 is important for lifespan modulation . We previously showed that RNAi knockdown of set-9/–26 ( RNAi targets the two genes due to high sequence identity ) in glp-1 ( e2141 ) germlineless mutant worms extended lifespan to a similar degree as in wild-type worms ( Ni et al . , 2012 ) , suggesting that somatic set-26 is important for lifespan modulation . To further test this possibility , we used the rrf-1 mutant , in which RNAi is efficient in the germline but not somatic cells ( Sijen et al . , 2001 ) , to assess whether knockdown of set-26 ( and set-9 ) in the germline alone can extend lifespan . As a control for tissue-specific RNAi , we monitored SET-26::GFP expression in wild-type or rrf-1 mutant worms treated with set-9/–26 RNAi . As expected , set-9/–26 RNAi greatly reduced SET-26::GFP expression in most tissues except neurons in wild-type worms ( Figure 3—figure supplement 1D ) , whereas set-9/–26 RNAi treatment specifically knocked down SET-26::GFP expression in the germline in rrf-1 mutant worms ( Figure 3—figure supplement 1D ) . We next assessed the lifespan of wild-type or rrf-1 mutant worms treated with set-9/–26 RNAi . We included wdr-5 . 1 RNAi as a positive control as wdr-5 . 1 is known to act in the germline to modulate lifespan ( Greer et al . , 2010 ) . As expected , RNAi knockdown of wdr-5 . 1 extended lifespan in both wild-type and rrf-1 mutant worms . In contrast , set-9/–26 RNAi knockdown extended lifespan in wild-type but not in the rrf-1 mutant background ( Figure 3C and D ) , indicating that inactivation of set-26 ( and set-9 ) in the germline is not sufficient for lifespan modulation . These results corroborated with our previous findings , and indicated that SET-26 likely acts in the somatic cells to modulate longevity and heat stress response , but SET-9 and SET-26 act redundantly in the germline to maintain reproductive function . To gain insights into the molecular changes that may contribute to the somatic SET-26 effect on lifespan , we investigated the transcriptional profiles of the long-lived germlineless glp-1; set-26 double mutant . We isolated total RNA from glp-1; set-26 double and glp-1 single mutant worms , and performed RNA sequencing after removing ribosomal RNAs ( ribo-minus RNA-seq ) . We next used edgeR , an RNA-seq analysis tool in the R package ( Robinson et al . , 2010 ) , to identify the genes that showed statistically significant expression change in the glp-1; set-26 double mutant compared to glp-1 mutant ( Figure 4A ) . We identified 887 up-regulated and 946 down-regulated genes in response to set-26 loss in the soma ( Figure 4A ) , and gene ontology ( GO ) analyses indicated that these genes were over-represented in multiple functional groups ( Figure 4—figure supplement 1A ) , with ‘collagen’ stood out as the most highly enriched GO term . We noted that collagens , as well as some of the other genes with expression change , have been implicated to be important for lifespan in C . elegans ( Ewald et al . , 2015 ) . It would be interesting to test how altered expression of collagens , and other genes identified in our RNA-seq data , might contribute to the extended lifespan of the set-26 mutant in the future . Since we previously showed that somatic set-26 largely acts through daf-16 , which encodes the Forkhead box O ( FOXO ) transcription factor , to modulate lifespan ( Ni et al . , 2012 ) , we sought to further identify the transcriptional changes in response to somatic set-26 loss that are also dependent on daf-16 . Using similar RNA-seq experiments , we investigated the transcriptional profiles of the germlineless daf-16; glp-1; set-26 triple and glp-1; set-26 double mutants ( Figure 4A ) . We identified 164 genes that were up-regulated , and 131 genes that were down-regulated in the daf-16; glp-1; set-26 triple mutant ( Figure 4A ) . By comparing these gene lists with the gene lists discussed above for the germlineless glp-1; set-26 double mutant vs . glp-1 , we deduced the somatic genes whose expression become significantly up-regulated or down-regulated when set-26 is deleted , but those expression changes were reverted when daf-16 was simultaneously lost ( down-regulated or up-regulated in the daf-16; glp-1; set-26 triple mutant , respectively ) ( Figure 4A ) . We termed these DAF-16-depednent somatic SET-26 regulated genes . Interestingly , GO term analyses revealed that the functional group ‘determination of adult lifespan’ was highly enriched in these DAF-16-dependent somatic SET-26 regulated genes ( Figure 4B ) . Therefore , the transcriptomic analysis corroborated the genetic analysis , and supported a model that DAF-16-mediated gene regulation likely contributes to the lifespan phenotype of the set-26 mutant . We additionally investigated the transcriptional profiles of the long-lived fertile set-26 single mutant and revealed that 869 genes showed significant expression change in the set-26 mutant compared with wild-type worms . As expected , there was a significant and substantial overlap between the genes that exhibited expression change in response to whole-body loss of set-26 and the somatic SET-26 regulated genes discussed above ( Figure 4—figure supplement 1B ) . Interestingly , the analysis using germlineless worms revealed far greater number of genes with expression change compared to that using reproductive worms . This could be due to technical variations between experiments , but might also suggest that some genes exhibit selective expression changes only in somatic cells , and those expression changes could be masked when germ cells were included in the analysis . To gain insights into the molecular changes that may underlie the germline phenotypes , we next compared the transcriptional profiles of the set-9 single , set-26 single , and F1 set-9 set-26 double mutants , all of which were fertile . We identified 162 , 334 , 1888 genes that were up-regulated , and 545 , 534 , 1644 genes that were down-regulated in the set-9 , set-26 , and F1 set-9 set-26 mutants respectively ( Figure 5A ) . Interestingly , although there was significant overlap among the three gene sets , a substantial number of genes appeared to only show expression changes in the F1 set-9 set-26 double mutant ( Figures 5A , 1430 down-regulated , 1781 up-regulated ) , suggesting a redundant role of SET-9 and SET-26 in regulating gene expression . Since the F1 set-9 set-26 mutant had a mild brood-size phenotype , and gave rise to progeny that exhibited severe defects in germline development ( Figure 2 ) , we speculated that many of these SET-9 and SET-26 co-regulated genes could be important for germline function . Indeed , GO analyses revealed that the genes that showed expression change in response to the simultaneous loss of set-9 and set-26 were over-represented for a number of different functional groups , including genes with annotated functions in sperm development and function ( Figure 5B–C ) . We further compared these SET-9 and SET-26 co-regulated genes with genes previously determined to be germline- , oocyte- , and sperm-specific ( Reinke et al . , 2004 ) . Interestingly , we found a significant over-representation of germline-specific genes among the genes that exhibited up-regulated expression in the F1 set-9 set-26 double mutant , but not the genes that exhibited down-regulated expression ( Figure 5D ) . Both sperm- and oocyte-specific genes ( 383 and 252 , respectively ) were among these germline-specific genes that were up-regulated in the F1 set-9 set-26 double mutant . It is possible that up-regulated expression of these germline-specific genes contribute to the reproductive defects of the set-9 set-26 double mutant ( Greer et al . , 2014; Katz et al . , 2009; Kerr et al . , 2014 ) . Considering the maternal effect of SET-9 and SET-26 on fertility ( Figure 2C ) , we also profiled the transcriptome of the F3 set-9 set-26 double mutant , which exhibited greatly compromised fertility ( Figure 2A ) , and compared that with the transcriptional profile of the F1 set-9 set-26 double mutant discussed above . We noted that the germline of the F3 set-9 set-26 was morphologically quite different from wild-type and the F1 set-9 set-26 double mutant ( Figure 2—figure supplement 1 ) . Interestingly , we found that the genes with significant expression change in the F1 set-9 set-26 and F3 set-9 set-26 double mutants not only substantially overlapped , but they were also enriched for similar functional groups based on GO term analyses ( Figure 5—figure supplement 1A , B and C ) . The fold change of gene expression , compared to wild-type , in the F1 set-9 set-26 and the F3 set-9 set-26 double mutants also positively correlated ( Figure 5—figure supplement 1D and E ) . These results together suggested that the transcriptional profiles of the F1 and the F3 set-9 set-26 double mutants are highly correlative despite that the F3 set-9 set-26 double mutant has a more severely defective germline . We note that the GO term ‘development and reproduction’ was unique for the F3 set-9 set-26 ( Figure 5—figure supplement 1B ) , which may reflect the more severe germline defects in these mutant worms . We next investigated the possible normal functions of SET-9 and SET-26 , which could inform how their inactivations lead to the gene expression changes and biological phenotypes discussed above . The SET domain of SET-26 was recently reported to show H3K9me3 methylation activity in vitro ( Greer et al . , 2014 ) . This was a somewhat surprising result , as the SET domain of SET-26 ( and SET-9 ) contains multiple mutations in the critical residues generally thought to be key for the methylating enzymatic activity of SET domain proteins ( Figure 6—figure supplement 1A ) , and we have previously speculated that SET-9 and SET-26 are likely not active enzymes ( Ni et al . , 2012 ) . Furthermore , the likely homologs of SET-9 and SET-26 in flies ( UpSET ) and mammals ( MLL5 ) have been reported to lack methylating activity ( Sebastian et al . , 2009; Rincon-Arano et al . , 2012 ) . Nevertheless , because of the reported in vitro results , we sought to monitor the global levels of H3K9me3 in the set-9 and set-26 mutants . We reasoned that if SET-9 and SET-26 are major enzymes for depositing H3K9me3 in C . elegans , then we would detect reduced H3K9me3 levels in the set-9 set-26 double mutant strain . Using Western blotting , we showed that the global levels of H3K9me3 were not detectably altered in the set-9 set-26 double mutants compared to wild-type worms at the L4 stage ( Figure 6—figure supplement 1B ) . We further investigated the genomic distribution of H3K9me3 in wild-type N2 and the F3 set-9 set-26 double mutant worms using ChIP-seq ( chromatin immunoprecipitation coupled with next generation sequencing ) . Inspection of the genome-wide H3K9me3 distribution between N2 and the F3 set-9 set-26 double mutant revealed highly similar patterns ( Figure 6—figure supplement 1C , 2A ) . Furthermore , the Diffbind data analysis pipeline ( Bardet et al . , 2011 ) identified little significant differences between the two data sets ( data not shown ) . Taken together the Western and ChIP-seq results , we concluded that SET-9 and SET-26 are likely not the major enzymes required for H3K9me3 deposition in C . elegans . However , we could not rule out the possibility that the SET-9 and SET-26 could deposit H3K9me3 in specific cells , or during a specific time . Indeed , we previously reported that in the germlineless mutant glp-1 , loss of set-26 resulted in lower levels of H3K9me3 ( Ni et al . , 2012 ) . Further investigations will be necessary to resolve whether SET-9/SET-26 could regulate H3K9me3 levels under some circumstances . We also note that SET-25 has been shown to be important for the deposition of the majority of H3K9me3 in C . elegans , although the effect could be indirect as direct H3K9 methylating activities of SET-25 have yet to be demonstrated ( Towbin et al . , 2012 ) . We next turned our attention to the PHD domains of SET-9 and SET-26 , which are 100% identical . PHD domains are known to recognize specific histone modifications , we therefore tested whether the PHD domains of SET-9 and SET-26 also bind to specific histone modifications in vitro . We first used GST-tagged PHD domain of SET-9/SET-26 to perform an in vitro pull-down experiment using histones from calf thymus . We found that the PHD domain of SET-9 and SET-26 pulled down histones , in particular histone H3 ( Figure 6A ) . A small amount of histone H4 was also recovered , which may be due to H3 and H4 associating as histone octamers in cells . We next screened for the specific histone modifications that are recognized by the PHD domain of SET-9 and SET-26 using a histone peptide array containing 95 unique modifications and 265 synthetic histone peptides . This assay revealed that the PHD domain of SET-9 and SET-26 specifically interacted with H3 peptides containing the K4me3 modification in combination with nearby acetylation ( K9ac , K14ac and/or K18ac ) , but not H3K4me3 alone ( Figure 6B ) . We further examined the genome-wide binding profiles of SET-9 and SET-26 in C . elegans using ChIP-seq . Because antibodies capable of immunoprecipitating endogenous SET-9 and SET-26 were not available , we utilized the GFP knock-in strains discussed above . We performed anti-GFP ChIP-seq using the set-9::gfp , set-26::gfp , and set-9::gfp set-26::gfp strains . The double GFP strain was used in the hope that higher levels of GFP expression would provide more robust ChIP-seq data ( Figure 6—figure supplement 3 ) . MACS2 was used to identify the genomic regions significantly enriched for SET-9 and SET-26 binding . From the analyses , 602 , 3948 , and 5903 peaks were identified as bound by SET-9 , SET-26 , and SET-9 and SET-26 together , respectively . Interestingly , 67% of the SET-9 peaks ( Figure 6—figure supplement 3B ) overlapped with the SET-26 peaks , and 87% of the SET-26 peaks overlapped with the SET-9 and SET-26 peaks ( Figure 6—figure supplement 3C ) . That SET-9 was found to bind many fewer regions than SET-26 , and that the SET-9-bound peaks largely overlapped with those bound by SET-26 , are consistent with the earlier data indicating SET-9 has a much more restricted expression pattern and a more limited function in the germline that is redundant with SET-26 . Interestingly , ChIP-seq analysis from the double GFP strain nevertheless revealed many more peaks compared to SET-26::GFP alone . We interpreted these results to suggest that the double GFP strain simply represented a better reagent for capturing the SET-9 and SET-26 binding profiles than either of the single GFP strain , because the ChIP assay worked more effectively with higher levels of GFP expression . For further analyses , we used the peak regions identified in the double GFP strain as representation of the binding sites of SET-9 and SET-26 . Given the in vitro binding of the PHD domain of SET-9 and SET-26 to H3K4me3 , we wondered whether SET-9 and SET-26 also bind to H3K4me3 in vivo . To test this , we examined the genome-wide pattern of H3K4me3 in wild-type worms using ChIP-seq . A representative genome browser view revealed that the SET-9 and SET-26 binding profile generally correlated well with the H3K4me3 profile ( Figure 6C ) . In contrast , the SET-9 and SET-26 binding profile was largely different from that of H3K9me3 ( Figure 6C ) , again consistent with our earlier conclusion that SET-9 and SET-26 likely do not modify H3K9me3 . To more rigorously assess whether the SET-9 and SET-26 profile is concordant with the H3K4me3 profile , we tested whether SET-9 and SET-26 , and H3K4me3 were enriched in overlapping genomic regions . To this end , we used MACS2 to identify 5996 genomic regions enriched for H3K4me3 marking ( peaks ) , and then compared the degree of overlap between the SET-9 and SET-26 peaks with those of H3K4me3 . In this comparison , we found that 75% of the SET-9 and SET-26 binding regions overlapped with the H3K4me3 peaks ( Figure 6D ) . The reciprocal comparison revealed that 49% of the H3K4me3 peaks overlapped with the SET-9 and SET-26 binding regions ( Figure 6E ) . We sought to further characterize the overlapping profiles between SET-9 and SET-26 and H3K4me3 using meta-analysis . We separated the H3K4me3 peaks into the group that bound by SET-9 and SET-26 and the group that did not , and we plotted the average normalized H3K4me3 levels centered around the summits of the H3K4me3 peaks and oriented at a 5’ to 3’ direction according the nearest genes associated with the peaks . We found that , on average , the H3K4me3 peaks bound by SET-9 and SET-26 had higher levels of H3K4me3 marking and their H3K4me3 marking was somewhat higher 5’ to the summit ( Figure 6—figure supplement 4A ) . This asymmetrical marking of H3K4me3 might relate to that these peaks generally localized around annotated transcriptional/translational start sites ( TSSs ) and H3K4me3 levels tend to be higher 5’ to the start sites of the genes . In contrast , the average plot of the H3K4me3 peaks that were not bound by SET-9 and SET-26 was more symmetrical , and this correlated with these H3K4me3 peaks generally localized to gene body regions ( data not shown ) . We next used a similar meta-analysis approach but plotted the normalized SET-9 and SET-26 ChIP signals for the two groups of H3K4me3 peaks , still oriented at the summits of the H3K4me3 peaks ( Figure 6—figure supplement 4B ) . This analysis allowed us to determine how far the average summit of the SET-9 and SET-26 peaks was relative to that of the H3K4me3 peaks . The results indicated that the average summit of the SET-9 and SET-26 peaks was ~100–200 bp upstream of the H3K4me3 summit ( Figure 6—figure supplement 4B ) . Interestingly , for the group of H3K4me3 peaks that were not identified to share SET-9 and SET-26 enrichment based on MACS2 , we nevertheless detected a small amount of SET-9 and SET-26 binding exactly at the summits of the H3K4me3 peaks ( Figure 6—figure supplement 4B ) . We interpreted these results to suggest that SET-9 and SET-26 likely bind to most if not all of the H3K4me3 enriched regions , but some of the binding was too weak ( or strong binding only in a subset of the C . elegans cells ) to be called by a statistical program like MACS2 . Lastly , we used scatter plot analysis to compare the H3K4me3 enriched signal vs . the SET-9 and SET-26 binding signal for each of the H3K4me3 peak region ( Figure 6—figure supplement 4C ) . The results showed that the H3K4me3 signal intensity is positively correlated with that of SET-9 and SET-26 ( Figure 6—figure supplement 4C ) . The data thus far supported a model that SET-9 and SET-26 bind to regions marked by H3K4me3 in C . elegans , and that the detectable SET-9 and SET-26 binding sites are generally marked by higher levels of H3K4me3 . Since the in vitro histone peptide array results suggested that histone acetylations are also important for the binding , we next compared the degree of overlap between the SET-9 and SET-26 peaks with those of H3K9ac using data from modENCODE . In this comparison , we found that 28% of the SET-9 and SET-26 binding regions overlapped with the H3K9ac peaks ( ***p<0 . 001 ) ( Figure 6—figure supplement 5A ) , and 98% of these shared peak regions were also marked by H3K4me3 ( data not shown ) . The significant but lower percentage of overlap between SET-9 and SET-26 binding with the H3K9ac enriched regions ( relative to the overlap with H3K4me3 enriched regions ) is consistent with the in vitro observation that SET-9 and SET-26 bind to H3K4me3 with adjacent acetylation , but the exact acetylated residues could vary ( Figure 6B ) , but is also likely partly due to technical differences , for example the different ChIP-seq data were generated using worms at different stages and acetyl specific antibodies generally have lower specificity . We additionally compared the overall correlation between H3K4me3 and H3K9ac enriched regions and whether SET-9 and SET-26 may affect their co-occurrence . As expected , we found that the genome-wide distribution of H3K4me3 correlated well with that of H3K9ac ( Figure 6—figure supplement 5B ) . Interestingly , the correlation between H3K4me3 and H3K9ac was higher for the peaks that were bound by SET-9 and SET-26 compared to those that were not ( Figure 6—figure supplement 5C and Figure 6—figure supplement 5D ) . These genomic data are consistent with the model that SET-9 and SET-26 bind to genomic regions marked by H3K4me3 with nearby acetylations in C . elegans . We next asked whether SET-9 and SET-26 binding could influence gene expression . To test this , we assigned the 5903 regions bound by SET-9 and SET-26 ( based on ChIP-seq results , Figure 6 ) with their closest genes , and then filtered them for genes that were detectably expressed in our RNA-seq data sets , which yielded 4427 potential targets of SET-9 and SET-26 that were also actively expressed in our experimental conditions . We then compared the list of SET-9 and SET-26 target genes with the lists of genes that exhibited significant gene expression change in the set-9 single , set-26 single , and the F1 set-9 set-26 double mutants compared to wild-type worms based on our RNA-seq data ( Figure 7A and Figure 7—figure supplement 1A and D ) . Through this comparison , we identified the putative SET-9 and SET-26 target genes that changed expression when set-9 and/or set-26 were inactivated ( Figure 7A and Figure 7—figure supplement 1A and D ) . Out of the 735 , 876 , 3532 differentially expressed genes in the set-9 , set-26 , and F1 set-9 set-26 double mutant strains , SET-9 and SET-26 bound to 153 , 217 , 641 of them respectively ( Figure 7A and Figure 7—figure supplement 1A and D ) . GO analyses revealed some interesting over-represented functional groups among these , especially for the SET-9 and SET-26 targets that exhibited expression change in the F1 double mutant ( Figure 7B–C and Figure 7—figure supplement 1 ) . In an attempt to identify the somatic SET-26 target genes with a role in longevity , we compared the genes bound by SET-9 and SET-26 to the genes that showed expression change in the germlineless glp-1; set-26 double mutant ( Figure 7D ) . GO analysis did not reveal functional groups that are directly linked to longevity ( Figure 7E and F ) , but ‘collagen’ was again overrepresented . Moreover , for the DAF-16-dependent somatic SET-26 regulated genes that were highly enriched for the functional group ‘determination of lifespan’ ( Figure 4 ) , only 3 out of the 134 genes were bound by SET-9 and SET-26 , a representation that is lower than expected based on random chance . Together , these data suggested that SET-26 indirectly impact DAF-16 activity and DAF-16-mediated longevity change . We next tested whether SET-9 and SET-26 , in addition to binding to H3K4me3 , can influence the marking of H3K4me3 in anyway . We first examined the global H3K4me3 levels in the set-9 single , set-26 single and F3 set-9 set-26 double mutants . We observed a subtle but significant increase in H3K4me3 levels in the F3 set-9 set-26 double mutant but not in the set-9 or set-26 single mutants using Western blotting ( Figure 8A and Figure 8—figure supplement 1A ) . This increase in H3K4me3 levels was more prominent in dissected gonads of the set-9 set-26 double mutant ( Figure 8B ) . Given our earlier data indicating that SET-9 and SET-26 bind to H3K4me3 , the elevated H3K4me3 levels in the set-9 set-26 double mutant strain could be a result of the lost recruitment of SET-9 and SET-26 at H3K4me3 sites . To test this hypothesis , we examined the genome-wide patterns of H3K4me3 in wild-type , the F1 and F3 set-9 set-26 double mutant worms . Inspection of the genome-wide H3K4me3 distribution in the three genotypes revealed a ‘spreading’ of H3K4me3 enriched regions around SET-9 and SET-26 binding sites in worms lacking set-9 and set-26 ( Figure 8—figure supplement 2A and B ) . To quantify this ‘spreading’ globally , we separated the H3K4me3 enriched regions into those bound by SET-9 and SET-26 vs . those that were not , and compared their average profiles using meta analysis ( Figure 8C and D ) . The meta analysis plots were centered around the summits of the H3K4me3 peaks and orientated in the 5’ to 3’ direction according to the closest gene associated with each of the peak ( Figure 8C and D ) . For the H3K4me3 peaks bound by SET-9 and SET-26 , we again observed a ‘spreading’ of H3K4me3 marking , especially towards the 3’ direction . Statistically significant elevated levels of H3K4me3 were detected starting at around +500 and −1000 bp beyond the summit in the F1 and F3 set-9 set-26 double mutant compared to wild-type worms , where the 95% confidence intervals of H3K4me3 marking in the F1 and F3 set-9 set-26 mutants did not overlap with that in the wild-type worms ( Figure 8C ) . Interestingly , for the H3K4me3 peaks that showed no detectable SET-9 and SET-26 binding , the average plot showed no significant ‘spreading’ of the H3K4me3 marking ( Figure 8D ) . Nevertheless , the genome-wide heatmaps revealed some slight ‘spreading’ of H3K4me3 signals in the set-9 set-26 double mutant even in regions not determined to be bound by SET-9 and SET-26 ( Figure 8—figure supplement 2B ) . This could be due to residual binding of SET-9 and SET-26 that was statistically unable to be detected by the MACS2 pipeline ( Figure 6—figure supplement 4B ) . We further examined the observed ‘spreading’ of H3K4me3 marking by producing meta plot of the H3K4me3 peaks that overlapped with SET-9 and SET-26 binding that centered around the summits of SET-9 and SET-26 binding peaks . We again observed an insignificant increase at the center of the plot , where SET-9 and SET-26 binding peaked , but a detectable increase of H3K4me3 levels in regions flanking the summit of SET-9 and SET-26 binding sites in the F1 and F3 set-9 set-26 double mutant compared to wild-type worms ( Figure 8—figure supplement 3A ) . It is interesting to note that the F3 set-9 set-26 double mutant showed a more obvious expansion of H3K4me3 compared to the F1 set-9 set-26 double mutant , even though this difference was not statistically significant , as their 95% confidence intervals overlapped ( Figure 8C and D and Figure 8—figure supplement 3A ) . Since the F3 set-9 set-26 double mutant had a more severe fertility defect ( Figure 2 ) , this observation hinted at a possible correlation between H3K4me3 expansion and germline defects . We previously reported that the levels of H3K4me3 as detected by Western blotting was not impacted by the loss of set-26 in the germlineless glp-1 mutant ( Ni et al . , 2012 ) , suggesting that inactivation of SET-26 does not impact the global levels of H3K4me3 in the soma . To further investigate this possibility , we performed similar H3K4me3 ChIP-seq analysis in the germlineless glp-1; set-26 and glp-1 mutants . Interestingly , we observed no significant difference in the genome-wide patterns of H3K4me3 in germlineless worms with or without SET-26 , even in regions bound by SET-9 and SET-26 ( Figure 8—figure supplement 3B ) . The ChIP-seq results therefore corroborated with our previous Western blotting results , and together they supported the notion that SET-9 and SET-26 binding does not detectably impact H3K4me3 marking in somatic cells . Rather , SET-9 and SET-26 binding likely normally help to restrict H3K4me3 marking specifically in the germline . H3K4me3 has been generally associated with active gene expression ( Sims et al . , 2007 ) . We wondered whether the regions with expanded H3K4me3 marking could be associated with gene expression changes in the F1 and F3 set-9 set-26 double mutants . To assess this , we used csaw , an R package for differential binding analysis of ChIP-seq data using sliding windows , to identify 3438 and 5456 regions that exhibited statistically significant different H3K4me3 marking in F1 and F3 set-9 set-26 double mutants compared with wild-type worms ( data not shown ) . Consistent with a slight global increase in H3K4me3 levels in the F1 and F3 set-9 set-26 double mutant , 92 and 99% of the differential H3K4me3 regions revealed by csaw showed elevated H3K4me3 levels in the F1 and F3 set-9 set-26 double mutants . We next assigned these differential H3K4me3 peaks to their closest genes and identified 3017 and 4517genes that are associated with altered H3K4me3 markings . We then compared these gene lists with the list of genes that exhibited significant expression change between the F1 and F3 set-9 set-26 double mutants compared to wild-type worms based on our earlier RNA-seq data . In this comparison , only 479 and 310 genes with elevated H3K4me3 markings showed expression change in the F1 and F3 set-9 set-26 double mutants ( Figure 8—figure supplement 3C and D ) , an overlap that was significantly lower than what would be expected based on random chance . These results suggested that the expanded H3K4me3 regions were not generally accompanied with detectable gene expression changes based on comparison with RNA-seq analyses from whole worms . Since our data suggested that the ‘spreading’ of H3K4me3 surrounding SET-9 and SET-26 binding sites likely occur specifically in the germline , we wondered whether the SET-9 and SET-26 target genes in the germline would show a correlation between H3K4me3 ‘spreading’ and gene expression increase . To investigate this possibility , we compared the H3K4me3 profiles for the genes that were bound by SET-9 and SET-26 and showed expression increase in F1 set-9 set-26 double mutant compared with wild-type worms ( Figure 7 ) , and were determined to be ‘germline-specific’ based on previous reports ( Reinke et al . , 2004 ) ( Figure 5 and Figure 8—figure supplement 4A ) . We found that their average H3K4me3 markings were highly upregulated in the F1 and F3 set-9 set-26 double mutants compared with wild-type worms , and the elevation was noticeable at both the TSS and the TES and throughout the gene body ( Figure 8—figure supplement 4B ) . A comparison of this list of germline-specific SET-9 and SET-26 target genes also indicated that their H3K4me3 elevation ( based on csaw ) was significantly correlated with gene expression increase ( based on RNA-seq ) ( Figure 8—figure supplement 4C ) . Therefore , it appeared that for the germline-specific genes , H3K4me3 expansion indeed correlates well with RNA expression increase . We concluded that SET-9 and SET-26 have a unique function in the germline , where they likely restrict H3K4me3 domains and maintain the expression of a subset of genes . Since many histone methyltransferases and demethylases are known to play important roles in germline function in C . elegans ( Katz et al . , 2009; Li and Kelly , 2011; Nottke et al . , 2011; Xiao et al . , 2011; Robert et al . , 2014; Kerr et al . , 2014; Greer et al . , 2014 ) , we performed an RNAi screen targeting putative histone methyltransferases and demethylases to uncover genes that potentially work with SET-9 and SET-26 to regulate germline function . We treated F1 set-9 set-26 double mutant worms , which showed a mild reproductive defect ( Figure 2 ) , with each of the RNAi and assayed their consequent brood size . Whereas most of the RNAi treatment did not substantially affect the brood size of the F1 set-9 set-26 double mutant , we found that three components of the MLL complex , which is well-established to deposit H3K4me3 , including set-2 , set-16 and wdr-5 . 1 , significantly reduced the brood size of the F1 set-9 set-26 double mutant when knocked down ( Figure 9A ) . To rule out off-target effects , we crossed the partial loss-of-function set-2 ( ok952 ) mutant with the set-9 set-26 double mutant and assayed their brood size at the F1 generation . We again observed a synergistic effect , where the set-2 ( ok952 ) single mutant had a normal brood size as previously reported , but the F1 set-2; set-9 set-26 triple mutant had a drastically reduced brood size compared with the F1 set-9 set-26 double mutant ( Figure 9B ) . This result is consistent with the model that SET-2 and SET-9 and SET-26 regulate H3K4me3 marking in different ways and their simultaneous loss results in synergistic detrimental effect . Our data above indicated that SET-9 and SET-26 bind to H3K4me3 and restrict H3K4me3 domain , likely specifically in the germline . The results with the weak loss-of-function set-2 mutation suggested that suboptimal deposition of H3K4me3 together with broadening of H3K4me3 marking could cause substantial defects in germline function . set-9 and set-26 share high sequence identity both within the coding regions and in the non-coding sequences flanking the coding regions ( near 90% identity in the ±500 bp regions ) , and even in the genes 3’ of set-9 and set-26 ( Y24D9B . 1 and Y51H4A . 13 respectively ) . Although set-9 and set-26 are highly conserved from worms to yeast to mammals , only one homolog in each of the other diverse species has been detected based on sequence and domain structure alignment ( Rincon-Arano et al . , 2012; Zhang et al . , 2017 ) . Even the closely related Caenorhabditis species , such as C . briggsae , C . remanei , C . brenneri and C . japonica , only harbor one gene that is highly homologous to set-9 and set-26 . These results suggested that set-9 and set-26 have arisen from gene duplication specifically in C . elegans . In the future , it will be interesting to explore how this duplication event allowed set-9 and set-26 to adopt unique functions . Based on the similarity in sequence and domain structure , Drosophila UpSET and mammalian SETD5 and MLL5 represent the likely homologs of SET-9 and SET-26 . SET-9 and SET-26 , UpSET , SETD5 and MLL5 all harbor centrally localized SET and PHD domains , and they share ~30–40% sequence identity in their PHD and SET domains . The SET domains of SET-9 , SET-26 , UpSET , SETD5 and MLL5 proteins are highly conserved and all share similar mutations that suggest they should lack enzymatic activities ( Figure 6—figure supplement 1A ) . Indeed , UpSET , SETD5 and MLL5 have not been found to harbor methylating activities ( Osipovich et al . , 2016; Rincon-Arano et al . , 2012; Madan et al . , 2009 ) . Whereas the SET domain of SET-26 has been suggested to methylate H3K9me3 in vitro ( Greer et al . , 2014 ) , our data indicated that SET-9 and SET-26 do not have major roles in H3K9me3 deposition in C . elegans . The exact functions of the SET domains in these proteins remain to be elucidated . The PHD domains of SET-9 , SET-26 , UpSET and MLL5 proteins are also highly conserved . The PHD domain of MLL5 was found to bind to H3K4me3 in vitro ( Ali et al . , 2013 ) , and both MLL5 and UpSET have been shown to localize at promoter regions in cultured cells ( Rincon-Arano et al . , 2012; Ali et al . , 2013 ) . Our results demonstrated that SET-9 and SET-26 also bind to H3K4me3 in vitro and in vivo . We further revealed that the PHD domains of SET-9 and SET-26 bind to H3K4me3 with adjacent acetylation ( e . g . K9 , K14 , K18 ) with much greater affinity ( Figure 6B ) . This is an important finding and potentially links SET-9 and SET-26 to the regulation of both H3K4me3 and histone acetylation . Indeed , we showed that loss of set-9 and set-26 results in a slight elevation of global H3K4me3 and H3K9Ac levels ( Figure 8A and Figure 8—figure supplement 1B ) . We additionally uncovered expansion of H3K4me3 domains surrounding SET-9 and SET-26 bound regions , and we predict that similar expansion of H3K9Ac likely occurs in the set-9 set-26 double mutant . Interestingly , loss of set-26 in somatic tissues does not result in similar H3K4me3 elevation ( Figure 8—figure supplement 1F ) and we concluded that SET-9 and SET-26 are particularly important for restricting H3K4me3 domains in the germline . These data are reminiscent to those reported for MLL5 and UpSET ( Rincon-Arano et al . , 2012; Gallo et al . , 2015 ) . In human glioblastoma cells with self-renewing potential , it was found that knockdown of MLL5 leads to increased global levels of H3K4me3 and a more open chromatin environment ( Gallo et al . , 2015 ) . Importantly , this anti-correlation between MLL5 and H3K4me3 was only detected in primary glioblastoma cells with self-renewing potential , but not in bulk glioblastoma samples , nor in non-neoplastic brain samples or colon cancer cells ( Gallo et al . , 2015 ) , indicating a regulatory process that is highly cell type specific . A possible parallel in C . elegans is that the anti-correlation between SET-9 and SET-26 status and H3K4me3 levels is particularly obvious in the germline of adult worms ( Figure 8B ) , which harbor proliferative germline stem cells . In Drosophila Kc cells , UpSET knockdown has been shown to increase the levels and spreading of H3K9Ac , H3K16Ac , and H3K4me2/3 around some TSSs , which results in a generally more accessible chromatin environment ( Rincon-Arano et al . , 2012 ) . UpSET achieves this partly through recruitment of specific histone deacetylases . Although the role of SETD5 PHD domain has not been studied , SETD5 also has been found to interact with histone deacetylase complex ( Osipovich et al . , 2016 ) . Loss of SETD5 causes elevation in histone acetylation at transcriptional start sites and near downstream regions ( Osipovich et al . , 2016 ) , suggesting that it acts in a similar manner to Drosophila UpSET . Taking together the C . elegans , Drosophila , and mammalian data , it appears that the SET-9 , SET-26 , UpSET , SETD5 , MLL5 family of factors play an important role in binding to H3K4me3 , possibly with flanking acetylation sites , and regulating local chromatin accessibility , partly through restricting the spread of histone modifications such as H3K4 methylation and H3K9 acetylation . Extending from the findings with UpSET in Drosophila and SETD5 in mammals , SET-9 and SET-26 likely also recruit demethylating and deacetylating enzymes to confine local methylation and acetylation domains . In this study , we uncovered a novel redundant function of SET-9 and SET-26 in germline function . It is interesting to note that an earlier report suggested that loss of set-26 , but not set-9 , accelerated the progressive sterility of the spr-5 mutant ( Greer et al . , 2014 ) . However , our analyses indicated that the set-9 ( n4949 ) mutant used in the study ( Greer et al . , 2014 ) represents a deletion/duplication allele , as we can PCR amplify the set-9 gene from the mutant ( data not shown ) . Our data using the newly generated set-9 ( rw5 ) deletion/frame-shift mutant supported a role of SET-9 in collaboration with SET-26 to regulate germline development . In considering the possible homologs of SET-9 and SET-26 in other species , MLL5 has been implicated in male fertility ( Heuser et al . , 2009; Madan et al . , 2009 ) , whereas UpSET is important for female fertility ( Rincon-Arano et al . , 2012 ) . Our data indicated that SET-9 and SET-26 have a more pleiotropic role in the germline , affecting both germline stem cell proliferation , and the subsequent differentiation into oocytes and sperms . This difference could be due to that C . elegans are hermaphrodites and SET-9 and SET-26 have adopted broader functions in the germline . In addition , MLL5 has been implicated in regulating cell cycle progression ( Deng et al . , 2004 ) and stem cell pluripotency ( Zhang et al . , 2009 ) , which may parallel the redundant roles of SET-9 and SET-26 in germline stem cells proliferation . Considering the highly conserved functions of SET-9 , SET-26 , UpSET , SETD5 and MLL5 at the molecular and phenotypic levels , an intriguing possibility is that UpSET , SETD5 and MLL5 have yet-to-be characterized roles in stress response and longevity . A key important question is how SET-9 and SET-26 can mediate their effects on stress response , longevity and germline development . Our data clearly supported that SET-26 acts in the soma and through DAF-16 to modulate longevity . The exact functional relationship between SET-26 and DAF-16 remains unclear . It was striking that the comparison of the SET-9 and SET-26 bound targets from whole worms and the DAF-16-mediated transcriptomic changes in germlineless mutant not only did not show a significant overlap , it in fact showed an overlap that was much lower than expected by random chance . Although it is difficult to cross-compare results from different experimental set-ups , it seems likely that SET-9 and SET-26 do not bind to candidate DAF-16 regulated genes . Therefore , SET-9 and SET-26 likely mediate their effect on DAF-16 indirectly . We also note that while we were able to identify SET-9 and SET-26 targets whose expression show significant change in response to the loss of set-9 and set-26 , the SET-9 and SET-26 binding targets were not enriched for genes that showed expression change in the set-9 set-26 double mutant . In other words , genes bound by SET-9 and SET-26 were not more likely to show expression change when set-9 and set-26 were deleted . Since our ChIP-seq data were from whole worms and could not distinguish the SET-9 and SET-26 binding targets in the soma vs the germline , our current results cannot resolve whether SET-9- and SET-26-binding has a direct consequence on gene expression regulation . We detected significant broadening of H3K4me3 marking surrounding the SET-9 and SET-26 bound regions . Although this change was observed in whole worms , it was not detectable in worms lacking germline ( Figure 8—figure supplement 1F ) . We interpreted this to mean that the expansion of H3K4me3 domains likely happen either specifically in the germline , or most noticeable in the germline . Somewhat surprisingly , such broadening of H3K4me3 domains did not appear to correlate with gene expression using whole worm analyses . This finding could partly be due to technical caveats , as the ChIP-seq and RNA-seq data were generated using whole worms , which could mask correlated changes in specific cells . Consistent with this speculation , we detected a significant and positive correlation between H3K4me3 expansion with RNA expression change of germline-specific genes . In the future , ChIP-seq and RNA-seq analyses using dissected gonads vs somatic tissues will help to further establish this prediction . In addition to gene regulation , the expanded H3K4me3 regions likely represent perturbed chromatin environment that could interfere with processes other than gene regulation , such as genome maintenance . In fact , in C . elegans , altered H3K4 methylations have been shown to predispose mutant worms to DNA damage and genome instabilities , which result in germline defects ( Nottke et al . , 2011 ) . Interestingly , inactivating UpSET in Drosophila and MLL5 in mammals also lead to increased genome instability and DNA damage ( Tasdogan et al . , 2016; Rincon-Arano et al . , 2012 ) . In C . elegans , we observed an increased number of germ cell apoptosis in the set-9 set-26 double mutant ( data not shown ) . Moreover , RNAi knockdown of mre-11 , a double-strand break repair protein , synergistically aggravated the fertility defects of the set-9 set-26 double mutant ( data not shown ) . These data together suggested that DNA damage and genome instability could be increased in the set-9 set-26 double mutant , which may contribute to their germline defects . In summary , our findings provided new mechanistic insights into the functions of SET-9 and SET-26 with important implications for their roles in longevity and germline development . We revealed that SET-9 and SET-26 are recruited to H3K4me3 marked regions and participate in confining H3K4me3 domains , particularly in the germline . Loss of set-9 and set-26 in these regions likely leads to a more open and accessible chromatin environment and RNA expression change in a subset of genes . In addition , SET-26 acts in the soma to modulate longevity and it achieves this by regulating DAF-16-mediated transcription indirectly . Our findings are consistent with the possibility that human MLL5 and SETD5 represent functional homologs of SET-9 and SET-26 , and the results discussed here provide new insights into how MLL5 and SETD5 may act and also implicate them in stress response and longevity . The N2 strain was used as the wild-type ( WT ) . The mutants used in the study are: set-2 ( ok952 ) , set-9 ( rw5 ) , set-26 ( tm2467 ) , rrf-1 ( pk1417 ) , daf-16 ( mgDf47 ) , glp-1 ( e2141 ) ; the set-9 ( rw5 ) set-26 ( tm2467 ) double mutant was maintain as balanced heterozygote set-9 ( rw5 ) set-26 ( tm2467 ) /nT1 . The GFP knock-in strains constructed in this study are: set-9::gfp ( rw24 ) , set-26::gfp ( rw25 ) . The antibodies used were anti-H3K4me3 ( Millipore , Billerica , MA 17–614 ) , anti-H3K9me3 ( abcam , Cambridge , United Kingdom ab8898 ) , anti-GFP ( abcam , Cambridge , United Kingdom ab290 ) , anti-H3 ( abcam , Cambridge , United Kingdom ab1791 ) , anti-H3K9ac ( Wako , Richmond , VA 309–32379 ) . Brood size assay was performed as described ( Li et al . , 2008 ) . Each single L4 worm was picked onto individual plate and was transferred to a new plate every 24 hr until the end of its reproductive phase . Dead eggs and alive progenies were counted as its total brood size . All experiments were repeated two to three times . Student’s t-test was used to calculate the p-values . Lifespan assay was performed as described ( Li et al . , 2008 ) . All experiments were performed at 20°C . For RNAi plates , the set-9/26 RNAi construct was taken from the Ahringer RNAi library . RNAi bacteria were grown in LB with 100 ug/ml Carbenicillin ( Carb ) and 15 ug/ml Tetracycline ( Tet ) at 37°C to OD600 around 0 . 8 . The culture was concentrated 5-fold , and seeded onto plates with Carb and Tet . Sufficient IPTG stock was added to plates so that final IPTG concentration is 4 mM . Let plates dry and induce for ~4 hr before use . For lifespan assays using NGM plates seeded with OP50 bacteria , the OP50 bacteria were grown in LB overnight at 37°C and the culture was concentrated 3-fold and seeded onto plates . For all lifespan assays , Worms were picked onto RNAi plates to lay ~40 eggs per plate and the progeny were grown on the plate until they reached adulthood . The worms were transferred to a new plate every day during their reproductive phase and then transferred to a new plate every 4 days . Worms were scored every other day , and those that failed to respond to a gentle prodding with a platinum wire were scored as dead . Animals that bagged , exploded , or crawled off the plate were considered as censored . We deﬁned the day when we transferred the young adult worms as day 0 of adult lifespan . All the lifespan experiments were repeated at least two independent times . Standard survival analysis was performed using SPSS and OASIS software ( Yang et al . , 2011 ) . The survival function was estimated using the Kaplan-Meier method , and the p-values were calculated using a log-rank test . Heat stress assay was performed as described ( Li et al . , 2008 ) . Synchronous D0 adult worms grown on OP50-NGM plates at 20°C were shifted to 35°C . Worms were scored every 3–4 hr . All the heat stress experiments were repeated at least two independent times . All experiments were repeated two to three times . Standard analysis was performed using OASIS software . The survival function was estimated using the Kaplan-Meier method , and the p-values were calculated using a log-rank test . Mortal Germline assays were performed by transferring 6 L1 larvae per plate to fresh NGM plates every generation , as previously described ( Ahmed and Hodgkin , 2000 ) . Percentage fertile lines were calculated as the number of fertile plates divided by the number of total plates . The gonads from 100 to 150 worms were dissected out using syringe needle on a poly-lysin coated slide . Gonads were permeabilized by the standard freeze-crack method and were fixed in 4% formaldehyde fixative ( PBS/4% formaldehyde ) for 30 min followed by 5 min incubation in chilled methanol at −20°C in a Coplin jar . Slide was washed twice with PBST for 5 min each and finally incubated with blocking buffer ( PBS + 5% BSA + 0 . 1% tween-20 +0 . 1% triton-100 ) for 1 hr . Blocking buffer was removed and gonads were incubated with 25 µl of 1:50 dilution of H3K4me3 antibodies for overnight at 4°C in a humidity chamber . Next day , gonads were washed twice with PBST for 10 min each and incubated with 25 µl of 1:50 dilution of secondary antibody for 1–2 hr at RT . DAPI ( final concentration 25 ng/ µl ) was also added with secondary antibody . Gonads were further washed three times with PBST for 10 min each and mounted in 10 µl vectashield mounting medium ( Vecta Laboratories ) . Experiments were repeated twice . 4 worms and 5–7 germline nuclei for each worm were used for quantification of H3K4me3 and DAPI . Student’s t-test was used to calculate the p-values . The gonads from 100 to 150 worms were dissected out using syringe needle . Gonads were fixed in 4% formaldehyde fixative ( PBS/4% formaldehyde ) for 1 hr . After the removal of fixative , gonads were washed twice with 1 ml PBST ( PBS buffer +0 . 1% Tween 20 ) each . Fixed worms were incubated with DAPI ( 25 ng/ µl in PBS ) for 30 min . Gonads were further washed three times with 1 ml PBST for 5 min each and mounted on agarose pads in 10 µl vectashield mounting medium ( Vecta Laboratories ) . DAPI-stained gonad images were taken using the Z-stacking function of the microscope . To count mitotic germ cell numbers , we marked the boundary between mitotic zone and transition zone by observing the transition zone-specific nuclear morphology ( crescent shape ) and counted the number of nuclei in each focal plane within the mitotic region . Experiments were repeated three times . 6–9 worms were used for quantification . Student’s t-test was used to calculate the p-values . GST protein pull-down assay pGEX-2TK was used for generating bacteria expression construct . SET-9/26PHD cDNA was amplified from wildtype cDNA by PCR using the primers: SET-9/26PHD-F: CTCAGGATCCGATTCCGAATCCGAGGGAA; SET-9/26PHD-R: GCGTGAATTCCCGCTCGAAGTCGATTCAAAA; and subcloned into pGEX-2TK . The expression construct was transformed into BL21 bacteria . Bacteria containing cDNA of SET-9/26PHD was culture to OD600 equal to around 0 . 6 and the SET-9/26PHD expression was induced by adding IPTG to final concentration equals to 0 . 5 mM . Bacteria were lysed by sonication and protein was purified by Glutathione Sepharose ( Sigma ) . GST protein pull-down assay was performed as described ( Tsai et al . , 2010 ) . 25 µg GST-PHD protein were incubated with 10 , 100 , 1000 µg of calf thymus total histones ( Sigma ) in 500 μl NTP overnight at 4°C . 90 μl of a 50% slurry of GST-beads were added and incubated for 2 hr at 4°C , recovered by centrifugation and washed six times ( 10 min at 4°C ) with NTP buffer ( 50 mM Tris-HCl 7 . 4 , 300 mM NaCl , 0 . 1% NP-40 ) . The protein bound beads were analyzed by SDS-PAGE and detected by Coomassie stain . The array binding assay was performed by EpiCypher . Briefly , GST-PHD were applied on an EpiTitan array that was separated by a gasket such that two chambers were delineated . After the protein incubation , a series of the anti-GST ( primary ) and the fluorescent AlexaFluor 647 ( secondary ) antibody incubation steps were carried out to detect the bound protein . Two independent experiments were performed . Immunoblotting was performed as described ( Ni et al . , 2012 ) . Synchronized embryos were put onto NGM-OP50 plates and grown at 20°C until reaching mid-L4 . Worms were harvested and washed three times using ice-cold M9 buffer . Worm pellets were lysed with boiling SDS sample and equal amount of lysates were used for SDS-PAGE ( 18% ) and transferred to nitrocellulose membranes . The membranes were incubated with primary antibodies overnight ( H3K4me3/H3K9me3/H3K9ac , 1:1 , 000; H3 , 1:2 , 000 ) . IRDye secondary antibody was used and the result was quantified using Odyssey imaging system . All experiments were repeated 3–4 times . Modified histone levels were normalized to H3 levels . Student’s t-test was used to calculate the p-values . Plasmid and homologous DNA repair template construction pU6::set-9 sgRNA was generated using pU6::unc-119 sgRNA as described ( Friedland et al . , 2013 ) . The pU6::unc-119 sgRNA was used as template to amplify two overlapping PCR fragments using the primers U6prom EcoRI F and set-9 gRNA R or set-9 gRNA F and U6prom HindIII R . These PCR products were gel-purified and then mixed together in a second PCR with primers U6prom EcoRI F and U6prom HindIII R . This final PCR product was digested with EcoRI and HindIII and ligated into a pU6::unc-119 sgRNA plasmid that had been digested with EcoRI and HindIII , creating the vector pU6::set-9 sgRNA . The pU6::set-9/set26 sgRNAs ( 1 and 2 ) that used for generating set-9::gfp and set-26::gfp strains were constructed using the same strategy . The homologous DNA repair templates that used for generating set-9::gfp and set-26::gfp strains were designed and synthesized as described ( Paix et al . , 2014 ) . Homologous arms flanking gfp DNA sequence were generated using the pPD90 plasmid that contains gfp sequence as template and the primers: armF12-GFP: CGAGACGAAGCCGaTCtACgCGaTGGAAcagtaaaggagaagaacttttcactggagttg; armR12-GFP: caagtttttcgcagattccttgCTAtttgtatagttcAtccatgccatgtgtaatccc; The DNA repair template with ~30 bp homologous arms was generated using the primer: armF1-complete: ccctcaatttttttcagCTGAAACAAACTCGAGACGAAGCCG; and armR1-complete: gggacaattttattcttcaagtttttcgcagattcc . The DNA repair template with ~60 bp homologous arms was generated using the primer: armF2-complete: ccaaaaaatctccttaaaaaccctcaatttttttcagCTGAAACAAACTCGAGACGAAGC; and armR2-complete: cgagatagaaagagatgatatgggacaattttattcttcaagtttttcgcagattcc . CRISPR-mediated genome editing was performed as described ( Paix et al . , 2014; Friedland et al . , 2013 ) . For generating set-9 mutant , day1 adult animals were injected with pDD162 ( Peft-3::Cas9::tbb-2 3’UTR ) , pCFJ90 ( pmyo-2::mCherry ) and pU6::set-9 sgRNA and grown overnight at 16°C . The survived worms were separated , transferred to 20°C and their F2 mCherry-positive animals were genotyped for set-9 mutation . For generating set-9::gfp and set-26::gfp strains , day1 adult animals were injected with pDD162 ( Peft-3::Cas9::tbb-2 3’UTR ) , pCFJ90 ( pmyo-2::mCherry ) , pU6::set-9/set26 sgRNAs ( 1 and 2 ) and homologous DNA repair templates and grown overnight at 16°C . The survived worms were separated , transferred to 20°C and their F2 mCherry-positive animals were genotyped for GFP knock-in strains . sgRNAs that target two loci and two DNA repair templates with different length of homologous arms ( one ~30 bp and the other ~60 bp ) were co-injected to increase efficiency . The set-9::gfp and set-26::gfp strains were mounted on a microscope slide and visualized using a Zeiss 710 confocal system . ChIP experiments for SET-9::GFP and SET-26::GFP were performed as described ( Zhong et al . , 2010 ) with the following modifications . Approximately 70 , 000–100 , 000 L4s were harvested and crosslinked in 2% formaldehyde-M9 solution for 25 min at room temperature with rotation . The worms were then washed with 100 mM Tris pH 7 . 5 to quench formaldehyde solution , washed two times with M9 , and once with FA buffer ( 50 mM HEPES/KOH pH 7 . 5 , 1 mM EDTA , 1% Triton X-100 , 0 . 1% sodium deoxycholate; 150 mM NaCl ) supplemented with 2X protease inhibitors ( Roche Cat#11697498001 , cOmplete Protease Inhibitor Cocktail Tablets ) . Worms were then collected in a 15 ml conical tube , snap-freezed in liquid N2 and stored at −80°C . The pellet was resuspended in 1 ml FA buffer plus protease inhibitors . Using a Bioruptor sonicator , the sample was sonicated on ice/salt water 30 times with the following settings: 30 s on , 60 s off . The chromatin was then further sheared by the covaris s2 40 times with the following settings: 20% duty factor , intensity 8 , 200 cycles per burst , 60 s on , 45 s off . The tube spun containing worm extract was then spun at 13 , 000 g for 30 min at 4°C . The supernatant was then transferred to a new tube and the protein concentration of the supernatant was then determined by Bradford assay . Extract containing approximately 2 mg of protein was incubated in a microfuge tube with 6–12 ul anti-GFP antibodies overnight at 4°C with gentle rotation . 10% of the material was removed and used as input DNA . Then 30 ul of protein A conjugated to sepharose beads ( EMD Millipore ) were added to each ChIP sample and rotated at 4°C for 4 hr . The beads were then spun at 2000 rpm for 1 min to collect and washed twice for five mins each at 4°C in 1 ml of FA buffer , once in FA with 500 mM NaCl and once in FA with 1M NaCl with gentle rotation . The beads were then washed in TEL buffer ( 0 . 25 M LiCl , 1% NP-40 , 1% sodium deoxycholate , 1 mM EDTA , 10 mM Tris-HCl , pH 8 . 0 ) for 5 min and twice in TE for 5 min . To elute the immunocomplexes , 50 ul Elution Buffer ( 1% SDS in TE with 250 mM NaCl ) was added and the tube incubated at 65°C for 15 min , with brief vortexing every 5 min . The beads were spun down at 2000 rpm for 1 min and the supernatant transferred to a new tube . The elution was repeated and supernatants combined . To each sample , RNAseA was added and incubated at 37°C for 15 min , and proteinase K was added and incubated for 1 hr at 55°C , then 65°C overnight to reverse crosslinks . The DNA was then purified with the Qiaquick PCR purification kit ( Qiagen ) , and eluted with 40 ul H2O . The immunoprecipitated DNA was either checked by qPCR or subjected to high-throughput sequencing library preparation . The protocol for library preparation for SET-9/26 ChIP–Seq is NEBNext Ultra II DNA Library Prep Kit for Illumina ( NEB ) . ChIP experiments for H3K4me3 and H3K9me3 were performed as described ( Pu et al . , 2015 ) . For worm collection , 1000–2000 F1 set-9 set-26 double mutant worms were collected from progenies of balanced heterozygote set-9 ( rw5 ) set-26 ( tm2467 ) /nT1 . F3 set-9 set-26 double mutant worms were collected from progenies of F2 set-9 set-26 double mutant worms . L4 worm pellets were ground with a mortar and pestle and cross-linked with 1% formaldehyde in PBS for 10 min at room temperature . Worm fragments were collected by spinning at 3000 g for 5 min and resuspended in FA buffer followed by sonication with Bioruptor . Chromatin extract was incubated with antibody overnight at 4°C . Precipitated DNA ( 10–15 ng ) from each sample was used for Illumina sequencing library preparation . DNA from ChIP was first end-repaired to generate a blunt end followed by adding single adenine base for adaptor ligation . The ligation product with adaptor was size-selected and amplified by PCR with primers targeting the adaptor . Up to 12 samples were multiplexed in one lane for single-end 50-nt Illumina HiSeq sequencing . All ChIP-seq experiments were repeated at least two times . The data analysis pipeline was performed as described ( Pu et al . , 2015 ) . Low quality reads were removed using the FASTX Toolkit . The sequencing reads from two independent experiments were combined and then aligned to the WS250 C . elegans genome using bowtie2 . PCR duplicates were removed and bam files were generated using SAMtools . The bam files were then used for calling peaks by MACS2 ( combined broad and narrow peaks ) . A GLM model was then applied to compute the counts of all the peaks to identify significant ones using two independent replicates . H3 ChIP data were used as a control for H3K4me3 ChIP and genomic DNA input was used as a control for SET-9 and SET-26 ChIP . ChIP signals ( z-score ) were normalized and calculated using bamCoverage software . For analyses using genes , each peak was associated with its closest gene . Overlapping peaks were determined by 1 bp overlap between two peaks using bedtools . For oriented meta plots , 3000 bp upstream and downstream of the summit were included and 50 bp windows were used for normalized counts in each window . The summits determined by MACS2 were assigned to their closest genes . If a gene is on the '−' strain , the normalized counts for −3000 to 3000 bp of that summit were counted in a reverse direction . For example , if a peak , which was assigned to a gene in the ‘−' strain , has a summit of X , the extended region for this summit in 5’ to 3’ direction is X + 3000 bp to X − 3000 bp; if a peak , which has assigned to a gene in the ‘+' strain , has a summit of Y , the extended region for this summit in 5’ to 3’ direction is Y-3000bp to Y + 3000 bp . The 95% confidence interval was calculated using bootstrap method . Briefly , 10% of the total summits were randomly selected and the mean was calculated . This random selection was repeated1000 times and the 95% confidence interval was calculated based on the estimate that these 1000 mean number follow normal distribution ( Hesterberg et al . , 2005 ) . Fishers’ exact test was used when comparing two lists of genes or peaks . H3K9ac ChIP-seq data was downloaded from: http://data . modencode . org/cgi-bin/findFiles . cgi ? download=3578 RNA isolation was performed as described ( Li et al . , 2008 ) . For worm collection , 100–200 F1 set-9 set-26 double mutant worms were collected from progenies of balanced heterozygote set-9 ( rw5 ) set-26 ( tm2467 ) /nT1 . F3 set-9 set-26 double mutant worms were collected from progenies of F2 set-9 set-26 double mutant worms . Synchronized mid/late L4 staged worms that grown at 20°C were homogenized in 1 ml Tri-reagent for 30 min at room temperature . 0 . 1 mL of BCP was added to the sample and mixed well . The sample was then spun at 12 , 000 g 15 min at 4°C and the aqueous phase was transferred to a new tube . 0 . 5 ml Isopropanol was added to the sample , incubated at room temperature for 10 min and spun at 12 , 000 g 10 min . The RNA pellet was washed twice with 75% EtOH and dissolved in water . The RNA sample was then was then purified to remove DNA using RNeasy Mini Kit ( Qiagen ) . The protocol for library preparation was using Ovation Human FFPE RNA-Seq Library Systems ( NuGEN ) . The data analysis pipeline was performed as described ( Pu et al . , 2015 ) . Low quality reads were removed using the FASTX Toolkit . Illumina primers ( adaptors ) were then removed using cutadapt . And tRNA and rRNA reads were removed using Bowtie and the remaining reads were aligned to WS250 C . elegans genome using TopHat2 with no novel junctions . Mapped reads were then input into Cufflinks to calculate raw counts for each gene , which were then used for differential expression analysis by edgeR . Genes with less than 20 reads mapped to them in all samples were removed and the remaining genes were used as input to test for differential expression . PCA analysis was performed using the built-in function in edgeR . 5% false discovery rate ( FDR ) was used to determine differential expression . Heatmaps were generated using CISTROME ( Liu et al . , 2011 ) . H3K4me3 and H3K9me3 ChIP-seq results from wild-type and the set-9 set-26 double mutant were used to generate the maximum signal ranked heatmaps . RNA constructs were obtained from the Ahringher libarary . HT115 bacteria containing RNAi constructs were grown at 37°C and seeded on nematode growth medium ( NGM ) plates containing carbenicillin and tetracycline and dry overnight . dsRNA expression was induced by adding IPTG to a final concentration 0 . 4 mM . Heterozygous adult set-9 set-26/+worms were put on plates for 1~2 hr to lay eggs and F1 homozygous set-9 set-26 worm was picked onto new plates with RNAi bacteria . Brood size of 3–4 RNAi treated worms were scored . Gene ontology ( GO ) analysis was carried out using the DAVID 6 . 8 Bioinformatics Database ( http://david . abcc . ncifcrf . gov ) ( Huang et al . , 2009 ) .	Cells keep their DNA organized by wrapping it around groups of proteins called histones . These structures not only keep the genetic code tidy , they also affect how and when a cell uses its genes . This is because small chemical groups that are added to histones , such as a methyl group added to the fourth position of histone H3 ( known as H3K4me3 ) , affect which proteins can access the surrounding genes . This in turn determines whether those genes are likely to be on or off . Many proteins help to regulate histone modifications , including proteins that add or remove the specific chemical groups . Enzymes that add a methyl group to histone usually contain a region called SET; while proteins containing a structure called a PHD finger can recognize histone modifications and help to amplify the signal to switch a gene on or off . SET-9 and SET-26 are two proteins containing both SET regions and PHD fingers . Found in the worm Caenorhabditis elegans , these proteins are 97% identical . Changes in histone modifications can affect the lifespan of these worms , and the number of offspring they produce . Recent work revealed that loss of SET-9 and SET-26 makes the worms live longer . Now , Wang et al . use gene editing to better understand how these proteins have their effects . Experiments with worms lacking the gene for SET-9 or SET-26 or both revealed that , despite looking almost identical , SET-9 and SET-26 have different roles . Every cell in the worm makes SET-26 protein and getting rid of it increases their lifespan by affecting the activity of a protein called DAF-16 . But , only the cells in the reproductive system make SET-9 , and both proteins play a role in fertility . A technique called ChIP-seq revealed where each protein attached to the genome . The PHD fingers of SET-9 and SET-26 bound to around half of the possible H3K4me3 modification sites . Not all the possible sites actually had a methyl group attached , and the pattern of binding matched the pattern of modifications . This indicates that the two proteins arrive only once the positions already have their methyl groups . Getting rid of the SET-9 and SET-26 proteins increased the number of H3K4me3 sites with methyl groups attached . This suggests that the role of SET-9 and SET-26 is to stop the spread of H3K4me3 modifications , controlling the use of certain genes . In mammals , the proteins SETD5 and MLL5 likely do the job of SET-9 and SET-26 . Understanding how they work in worms could further our understanding of fertility and ageing in humans .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "chromosomes", "and", "gene", "expression", "developmental", "biology" ]	2018	SET-9 and SET-26 are H3K4me3 readers and play critical roles in germline development and longevity
Whole-organism chemical screening can circumvent bottlenecks that impede drug discovery . However , in vivo screens have not attained throughput capacities possible with in vitro assays . We therefore developed a method enabling in vivo high-throughput screening ( HTS ) in zebrafish , termed automated reporter quantification in vivo ( ARQiv ) . In this study , ARQiv was combined with robotics to fully actualize whole-organism HTS ( ARQiv-HTS ) . In a primary screen , this platform quantified cell-specific fluorescent reporters in >500 , 000 transgenic zebrafish larvae to identify FDA-approved ( Federal Drug Administration ) drugs that increased the number of insulin-producing β cells in the pancreas . 24 drugs were confirmed as inducers of endocrine differentiation and/or stimulators of β-cell proliferation . Further , we discovered novel roles for NF-κB signaling in regulating endocrine differentiation and for serotonergic signaling in selectively stimulating β-cell proliferation . These studies demonstrate the power of ARQiv-HTS for drug discovery and provide unique insights into signaling pathways controlling β-cell mass , potential therapeutic targets for treating diabetes . Diabetes is associated with reductions in pancreatic β-cell mass , thus , curing diabetic patients will require β-cell replacement therapy . β cells can be replaced by transplantation of pancreatic islets ( Vardanyan et al . , 2010 ) . Alternatively , drugs that induce β-cell differentiation in endogenous pancreatic progenitor cells ( Chong et al . , 2006a ) or which stimulate β-cell proliferation ( Lysy et al . , 2013 ) would provide a significant step forward in the treatment of diabetes by avoiding surgical risks . Placing animal models at the start , rather than the end , of the drug discovery process has the potential to circumvent high attrition rates that have plagued in vitro high-throughput screening ( HTS ) over the past two decades . The zebrafish is an ideal vertebrate model system for whole-organism-based drug discovery ( Zon and Peterson , 2005 ) . As a key example , a chemical derivative of prostaglandin E2 ( 16 , 16 dimethyl prostaglandin E2 ) , originally identified for the capacity to induce increased hematopoietic stem cell ( HSC ) numbers in zebrafish embryos , recently completed Phase I and entered Phase II clinical trials as a means of enhancing engraftment of cord blood transplants in leukemic patients ( Cutler et al . , 2013 ) . Conserved cellular and molecular mechanisms are known to govern pancreatic development and β-cell proliferation in zebrafish and mammals ( Zorn and Wells , 2007; Kinkel and Prince , 2009 ) . We therefore hypothesized that identifying pre-existing drugs that promote increased β-cell mass during zebrafish development might provide potential new drug leads and therapeutic targets for treating diabetic patients . In embryonic zebrafish , early endocrine cells exist as a single principal islet in the head of the pancreas . At larval stages , additional endocrine cells are added by differentiation of extra-pancreatic ductal cells and proliferation within the principal islet ( Dong et al . , 2007; Pisharath et al . , 2007 ) . Around 6 days post-fertilization ( dpf ) , progenitors located in the pancreatic duct start to differentiate to form smaller secondary ( 2° ) islets . At this early stage , 2° islets consist of one or more endocrine cells that form within the tail of the pancreas ( Biemar et al . , 2001; Wang et al . , 2011; Ninov et al . , 2012 ) . These easily visualized features of zebrafish pancreatic development can be used to delineate the specific effect ( s ) of exogenous factors on β-cell biology . For instance , the appearance of ‘precocious’ 2° islets before 6 dpf is an indication of induced endocrine differentiation ( Rovira et al . , 2011 ) . Conversely , an increase in principal islet cell numbers , in the absence of effects on endocrine differentiation ( e . g . , 2° islet formation ) , suggests stimulation of endocrine cell proliferation . We previously visualized precocious 2° islet formation in a manual chemical screen of a library of largely FDA-approved drugs ( Johns Hopkins Drug Library; JHDL ) to identify six compounds that induced endocrine differentiation ( Rovira et al . , 2011 ) . Here , we developed a reporter-based strategy for identifying compounds that increase β-cell mass at high-throughput rates . By labeling β cells with a fluorescent protein and quantifying changes in fluorescence after exposure to JHDL compounds , many more drugs were identified that induced endocrine differentiation and/or stimulated proliferation of β cells . Most whole-organism drug discovery efforts to date have relied on manual assays or high-content screening ( HCS ) . These approaches attain only mid-throughput rates , thus , in vivo drug screens have typically been limited to small sample sizes and screening compounds at a single concentration ( Mathias et al . , 2012 ) . Ideally , false-call rates could be minimized by using ‘statistical power’ to establish appropriate sample sizes ( Ellis , 2010; Grissom and Kim , 2011; Halsey et al . , 2015 ) and by testing compounds at multiple concentrations , a strategy called ‘quantitative HTS’ ( qHTS; [Inglese et al . , 2006] ) . However , due to increased volume demands , applying such strategies to whole-organism drug discovery requires methods for evaluating compounds at HTS rates in vivo . Toward that end , we previously adapted existing HTS instrumentation , specifically a microplate reader , to the task of quantifying fluorescent reporters in living zebrafish , termed automated reporter quantification in vivo ( ARQiv; ( Walker et al . , 2012 ) . ARQiv provides purely quantitative data whereas HCS typically produces images , thus , more complex data . However , offsetting any comparative reduction in data complexity , ARQiv significantly increases throughput capacity . Indeed , ARQiv assays can be performed at a pace equivalent to in vitro HTS; an upper limit of 200 , 000 organisms per day , per plate reader ( Walker et al . , 2012 ) . Accordingly , ARQiv enables optimal HTS practices , such as qHTS , to be applied to whole-organism drug discovery . Here , we have combined ARQiv with a custom-designed robotics system to enable the first truly high-throughput whole-organism drug screen in a vertebrate model ( ARQiv-HTS ) . We analyzed a zebrafish transgenic line in which β cells are labeled with ( Yellow Fluorescent Protein ) YFP and neighboring delta ( δ ) cells are labeled with RFP ( Walker et al . , 2012 ) . The goal of the primary screen was to identify drugs that increased β-cell reporter activity relative to vehicle only controls , thus , compounds that potentially increased β-cell mass . Secondary confirmation screens were designed to determine whether potential hit drugs induced endocrine differentiation ( precocious secondary islet formation ) or stimulated β-cell proliferation ( increased β-cell numbers in the absence of effects on differentiation ) . Our results revealed: ( 1 ) ARQiv can be applied at HTS rates . Over 500 , 000 transgenic larvae were evaluated in the primary screen and can detect small differences in the number of fluorescently labeled cells; ( 2 ) qHTS can be effectively applied to whole-organism drug discovery . All JHDL compounds were tested at six different concentrations and a sample number of 16 per condition; ( 3 ) new purposes for FDA-approved drugs in increasing β-cell mass . We validated 11 drugs that induced endocrine differentiation and 15 drugs that stimulated β-cell proliferation ( two compounds had activity in both assays ) ; and ( 4 ) novel roles for NF-κB signaling in regulating pancreatic progenitor differentiation and for serotonergic signaling in selectively stimulating β-cell proliferation . Due to the near limitless number of reporter-based assays applicable to ARQiv-HTS―that is , anything involving a change in reporter intensity―we anticipate this approach will become a useful platform for whole-organism drug discovery and development . Our first goal was to develop a HTS-compatible reporter-based assay for identifying compounds that increase pancreatic β-cell mass in vivo . Toward that end , we established a dual-reporter transgenic line , Tg ( ins:PhiYFP-2a-nsfB , sst2:tagRFP ) lmc01 ( β/δ-reporter ) in which the insulin ( ins ) promoter drives expression of a yellow fluorescent protein ( PhiYFP ) in β cells , and the somatostatin 2 ( sst2 ) promoter drives a red fluorescent protein ( TagRFP ) in adjacent δ cells ( Figure 1A , B; ( Walker et al . , 2012 ) . We reasoned that the β/δ-reporter line would allow us to detect compounds affecting endocrine differentiation and/or proliferation of β cells or their progenitors since both would cause an increase in YFP reporter signal ( Figure 1C ) . Expressing RFP in δ cells secondarily could facilitate identification of compounds that selectively increased β-cell mass ( >YFP only ) vs expansion of endocrine tissue in general ( >YFP and >RFP ) . We tested whether a chemical inhibitor of γ-secretase ( DAPT ) , an enzyme necessary for Notch signaling , could serve as a positive control . Prior studies had shown that inhibition of Notch signaling promoted precocious 2° islet formation and thereby increased insulin reporter activity ( Parsons et al . , 2009 ) . We therefore adapted a protocol used to manually screen for precocious 2° islet formation at 5 dpf ( Rovira et al . , 2011 ) to the task of detecting increased β-cell numbers ( >YFP fluorescence ) via ARQiv . 10 . 7554/eLife . 08261 . 003Figure 1 . Screening resources , design , and controls . ( A ) Transgenic line used for the primary screen , Tg ( ins:PhiYFP-2a-nsfB , sst2:tagRFP ) lmc01 ( β/δ reporter; Walker et al . , 2012 ) , the insulin promoter drives YFP-expression in β cells ( yellow ) , the somatostatin 2 promoter drives RFP expression in neighboring δ cells ( red ) . Photomicrograph of the anterior region of a 7 dpf larva shows YFP and RFP labeling of the principal islet ( arrow ) . ( B ) Confocal z-projection of the principal islet in a β/δ-reporter fish ( scale bar: 10 µM ) , YFP labeling β cells ( yellow ) and RFP labeling δ cells ( red ) —note , apparent ‘orange’ co-labeling is an artifact of z-projection in 2D format . ( C ) Illustration of two potential mechanisms by which drug exposures could lead to increased β-cell mass: ( 1 ) enhanced endocrine differentiation , indicated by secondary ( 2° ) islet formation ( left path ) and ( 2 ) increased β-cell proliferation , indicated by supernumerary β cell numbers in the principal islet ( right path ) in the absence of effects on endocrine differentiation—that is , no effect on 2° islet formation . ( D ) Schematic of the ARQiv-HTS screening process: Day 0 , mass breeding produced 5000–10 , 000 eggs per day; Day 2 ( evening ) , JHDL compounds were serially diluted into drug plates; Day 3 , the COPAS-XL ( Union Biometrica ) was used to dispense individual 3 dpf larvae into single wells of drug plates , and plates were then maintained under standard conditions for 4 days; Day 7 , larvae were anesthetized and reporters quantified by automated reporter quantification in vivo ( ARQiv ) . ( E ) β/δ-reporter larvae were exposed to 0 . 1% DMSO ( negative control ) or the γ-secretase/Notch inhibitor DAPT ( positive control ) at six different concentrations from 3 to 7 dpf . ARQiv was then used to measure fluorescent signals from β cells ( yellow line , left y-axis ) and δ cells ( red line , right y-axis ) . The DAPT to DMSO ratio ( DAPT/DMSO ) was used to indicate signal strength for each fluorophore independently , as per the primary screen . The β-cell data show a non-monotonic dose response ( yellow dashed line , polynomial curve fit ) , with maximal signal observed at 25–50 μM DAPT . The δ-cell data show a similar trend ( red dashed line , polynomial curve fit ) , but with approximately fourfold lower signal strength due to higher autofluorescent background in the RFP emission range . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 00310 . 7554/eLife . 08261 . 004Figure 1—figure supplement 1 . ARQiv-HTS system . ( A ) Robotics-integrated ARQiv-HTS system ( all units Hudson Robotics unless otherwise indicated ) . ( 1 ) Micro10× liquid handlers , ( 2 ) SOLO automated pipettor , ( 3 ) COPAS-XL ( Complex Object Parametric Analyzer and Sorter , Union Biometrica ) , ( 4 ) TECAN Infinite M1000 PRO plate reader , ( 5 ) PlateCrane EX robotic arm , ( 6 ) Plate stacks ( arrows ) , and ( 7 ) Barcode scanner ( Zebra Technologies ) . ( B ) Table summarizing the function of each robotics unit . ( C ) Schematic of reiterative screening process , every ∼1-hr cycle 12 plates were scanned ( thus , 1152 larvae ) : one negative control plate ( 0 . 1% DMSO ) , ten drug plates and one positive control plate ( titration of DAPT , as per Figure 1E ) . In this manner , each set of ten drug plates was bounded by control plates that were used to calculate assay and compound effectiveness . On screening days , the PlateCrane EX ( 5 ) transferred plates from holding stacks ( 6 ) , to an anesthetic treatment holding location ( administered by Micro10× ) , then to the plate reader for scanning ( 4 ) , and finally to return stacks ( 6 ) . Real-time data analysis ( MATLAB and/or R ) was used to flag ‘hit call’ plates for initial visual follow-up , as described in the text . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 004 To determine an optimal dosage , DAPT was titrated across a twofold dilution series ( from 200 µM to 6 . 25 µM ) and used to treat β/δ-reporter larvae for 2 , 3 , and 4 days starting at 3 dpf . Reporter signals induced by DAPT treatment were compared to vehicle only negative controls ( 0 . 1% DMSO ) . This analysis determined that a 4-day exposure ( 3–7 dpf; Figure 1D ) achieved reporter signal levels necessary for HTS . The data also validated the utility of DAPT as a positive control for inducing increased YFP signal ( maximal DAPT/DMSO ratio of >5 . 5 ) and to a lesser extent for RFP ( maximal DAPT/DMSO ratio of >1 . 25 , see Figure 1E ) . Dose-response curves show concentration-dependent effects for both cell types , with maximal responses at 25–50 μM . To assess assay quality , establish appropriate sample sizes , and set ‘hit’ call criteria , we used statistical methods developed for HTS that account for increased signal variability attending in vivo assays ( see ‘Materials and methods’ , and [White et al . , 2015] ) . To generate large data sets for this analysis , 192 individual positive ( DAPT ) and negative ( DMSO ) control assays were performed . Strictly standardized mean difference ( SSMD ) calculations were used to determine assay quality , set a hit call cut-off , and as a means of comparing effect size across compounds ( Zhang , 2011 ) . This analysis determined that our assay was of high enough quality to pursue HTS ( robust SSMD* score of 1 . 67 ) . The sample size calculation ( Ellis , 2010; Grissom and Kim , 2011 ) , using power and significance values minimizing false-call rates ( 99 . 9% and p = 0 . 001 , respectively ) , determined that a sample number of 14 was sufficient to detect a 50% effect size ( i . e . , half as potent as the DAPT positive control ) . However , to account for occasional automation errors , and in keeping with 96-well plate layouts , we elected to screen 16 larvae per compound concentration . Due to greater background autofluorescence in the RFP emission range , a sample size of 16 was predicted to be insufficient for detecting a 50% effect size on δ cells . Thus , we limited the use of RFP data to a simple comparison between YFP and RFP dose-responses , rather than as a ratiometric standard . Bootstrapping ( random sampling with replacement ) of the positive and negative control data sets at a sample size of 16 resulted in a predicted SSMD score of 1 . 3 for an effect size of 50% relative to the positive control . Accordingly , we set the SSMD ‘hit’ selection cut-off at ≥1 . 3 . After defining the sample size and hit criterion , we initiated a full-scale screen of the JHDL ( Chong et al . , 2006b , 2006c ) using the ARQiv-HTS system ( Figure 1—figure supplement 1A , B ) . The JHDL is a collection of 3348 compounds , comprised largely of drugs approved for use in humans ( Shim and Liu , 2014 ) . Screening the JHDL served three purposes: ( 1 ) tested the value of whole-organism qHTS by screening the same library as our prior manual screening effort ( Rovira et al . , 2011 ) , ( 2 ) provided an enriched number of biologically active compounds with defined mechanisms of action , and ( 3 ) facilitated the identification of existing drugs as potential new treatments for diabetes . Moreover , drug repurposing has the potential to fast track delivery of new therapeutics to the clinic ( Shim and Liu , 2014 ) . Custom-designed mass breeding units were used to maximize egg production ( White et al . , 2015 ) . The number of viable eggs on day 1 established the number of drugs to be tested per session . The evening of day 2 , robotic plate and liquid handling systems ( Hudson Robotics ) were used to titrate all JHDL compounds across a twofold dilution series from 4 μM to 125 nM in 0 . 1% DMSO , thus , testing a total of six different concentrations ( Figure 1D ) per qHTS principles ( Inglese et al . , 2006 ) . At a sample size of 16 per condition , this equated to each drug being arrayed across an entire 96-well plate . DAPT and DMSO control plates bracketed each subset of 10 drug plates ( Figure 1—figure supplement 1C ) . On day 3 , the COPAS-XL system ( Complex Object Parametric Analyzer and Sorter , Union Biometrica ) was used to automate dispensing of individual 3 dpf β/δ-reporter larvae into single wells of 96-well plates containing pre-diluted drug solutions; all plates were then incubated under standard conditions . After a 4-day treatment regimen , 7 dpf larvae were anesthetized and fluorescent reporter levels were quantified by ARQiv ( Figure 1D ) . We developed an R script for processing and plotting ARQiv data in near real-time to flag plates containing potential hit compounds . This was done to facilitate immediate visual follow-up of larvae in potential hit plates using standard microscopy to eliminate false positives , such as increased autofluorescence due to toxicity , and as an initial assessment of effects on 2° islet formation . Three graphical outputs were plotted: ( 1 ) standard box plots , Drug over DMSO ( Drug/DMSO ) signal ratios to reveal dose-responses and variability , ( 2 ) SSMD hit scores , as a means of comparing effect size and to flag compounds of interest ( those in which at least one concentration achieved an SSMD ≥1 . 3 ) , and ( 3 ) heat maps , to guide initial visual follow-ups to assess 2° islet formation ( Figure 2A–C ) . 10 . 7554/eLife . 08261 . 005Figure 2 . ARQiv data and screen flow chart . ( A–C ) Example of MATLAB/R-generated real-time data plots provided for each drug plate; note , data for YFP are shown; however , plots were provided for both fluorophores . ( A ) Boxplots of Drug to DMSO signal ratio ( Drug/DMSO ) provided dose–response and variance data . ( B ) Strictly standardized mean difference ( SSMD ) scores were used to rank compounds according to relative strength; black line shows the 1 . 3 cut-off used to implicate compounds of interest ( i . e . , ‘hit calls’ ) . ( C ) Heat maps facilitated same day visual evaluation of each hit call plate . ( D ) Screening process—drug discovery results: the numbers of compounds tested , implicated ( Hits I and II ) , and validated ( Leads I and II ) are listed at each stage . In addition , hit calls that were eliminated from further analysis due to being either fluorescent ( 29 compounds ) or toxic ( 19 compounds ) , and others which remain to be further evaluated ( 131 compounds ) , are indicated by diagonal dashed arrows . ( E ) Screening process—assays utilized: schematic showing primary and secondary screening processes . In keeping with the high-throughput screening ( HTS ) practice of confirming implicated compounds in ‘orthogonal’ assays , different transgenic reporter lines were used for the following ( progressing from the top ) : ( 1 ) the primary screen and initial 2° islet evaluation ( β/δ-reporter ) , ( 2 ) validating effects on endocrine differentiation ( pan-endocrine GFP reporters ) , and ( 3 ) validating of effects on β-cell proliferation ( β-cell nuclear reporters ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 00510 . 7554/eLife . 08261 . 006Figure 2—figure supplement 1 . Observation of 2o islet formation in live β/δ-reporter larvae after drug treatment . ( A , B ) Representative in vivo confocal images—brightfield and fluorescence images merged—of pancreata in β/δ-reporter larvae following treatment with 0 . 1% DMSO ( A ) or a representative Hit I drug ( B , Beta-estradiol ) from 3 to 7 dpf . White arrows indicate 2° islets in the tail of the pancreas . Scale bar = 25 µm . ( C ) Percentages of larvae having 2° islets following treatment from 3 to 7 dpf with the indicated control of Hit I compounds at optimal concentrations . n > 20 . negative control: 0 . 1% DMSO . Positive control: RO2949097 ( 5 μM ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 006 After screening more than 500 , 000 β/δ-reporter larvae , 225 compounds ( 6 . 7% ) produced an SSMD ≥1 . 3 and were designated as ‘hit calls’ ( Figure 2D ) . Corresponding plates underwent an initial visual assessment . 29 hit call compounds proved to be autofluorescent , another 19 negatively impacted fish viability and/or morphology . These 48 compounds were designated as false positives and eliminated from further evaluation ( Figure 2D ) . The remaining 177 hit call plates were further examined for evidence of enhanced 2° islet formation ( Parsons et al . , 2009; Rovira et al . , 2011 ) . Increased 2° islet formation was observed in 23 plates ( Figure 2D , ‘Hit I’ subset; Figure 2—figure supplement 1 , Supplementary file 1 ) . These 23 Hit I compounds were deemed most relevant for secondary validation assays involving a more direct test of endocrine differentiation effects , precocious 2° islet induction ( Parsons et al . , 2009; Rovira et al . , 2011 ) . The other 154 hit call plates displayed no preliminary evidence of enhanced 2° islet formation . To account for other mechanisms that could result in elevated insulin reporter activity , we examined a second group of 23 strongly implicated drugs ( SSMD values ≥1 . 75 , Figure 2D , ‘Hit II’ subset ) for evidence of increased β-cell numbers in the absence of differentiation effects ( Figure 2E; Supplementary file 1 ) . A residual 131 compounds await further evaluation ( Supplementary file 2 ) . The majority of the 46 Hit I and Hit II compounds underwent a series of ‘validation assays’ to confirm effects on endocrine differentiation and/or β-cell proliferation . In keeping with common HTS practices , secondary assays were performed with complementary toolsets rather than the β/δ-reporter line used in the primary screen in order to independently confirm the findings of the primary screen . In preliminary visual evaluations to assess endocrine differentiation effects in larvae within hit call plates , considerable variability was noted . Typically , additional 2° islets were observed only in a subset of treated larvae among a given hit call condition ( Figure 2—figure supplement 1C ) . This is likely due to the β/δ-reporter being less than ideal for detecting endocrine differentiation effects; reporters are linked to late-stage differentiation of β and δ cells , which are only just beginning to appear in 2° islets at 7 dpf ( Parsons et al . , 2009 ) . The requirement of a 4-day chemical exposure to observe expression differences in the β/δ-reporter line via ARQiv reflects this issue . Conversely , we have shown that transgenic lines labeling early endocrine progenitors are useful for identifying compounds that induce endocrine differentiation as early as 5 dpf ( Rovira et al . , 2011 ) . Therefore , in keeping with the practice of using ‘orthogonal’ assays to confirm the activity of compounds implicated in primary screens ( Thorne et al . , 2010 ) , we tested Hit I compounds in transgenic backgrounds better suited to visualizing 2° islets . In particular , we used the pan-endocrine reporter line , Tg ( neurod:EGFP ) nl1 ( Obholzer et al . , 2008; Dalgin and Ward , 2011 ) , to confirm the efficacy of ‘Hit I’ drugs for inducing early endocrine differentiation . In this line , GFP is expressed in nascent endocrine cells , permitting the detection of ‘precocious’ 2° islet formation at 5 dpf after 2-day drug exposures , akin to our previous manual screen ( Rovira et al . , 2011 ) . A subset of Hit I compounds ( 20 of 23 ) was tested accordingly . An alternative Notch pathway inhibitor , RO4929097 ( 5 μM , Selleck Chemicals; Luistro et al . , 2009 ) , was used as the positive control . Our prior studies had shown that RO4929097 functions equivalently to DAPT for this assay ( Huang et al . , 2014 ) . Transgenic larvae were treated from 3 to 5 dpf with compounds across an expanded concentration range ( 0 . 5–25 μM ) to account for differences with the primary assay ( e . g . , transgenic line used , timing and duration of compound treatment ) and/or differences between compound lots . Following treatments , larvae were fixed at 5 dpf and processed for imaging by confocal microscopy . As GFP expression is widespread throughout the endoderm in the neurod:EGFP line , pancreata of treated fish were micro-dissected . High-resolution imaging afforded an increased sensitivity in scoring the induction of 2° islets ( Figure 3A , B ) . Of the 20 Hit I drugs tested , 11 were validated as inducers of endocrine differentiation ( 55%; Figure 3C , Figure 3—figure supplement 1 ) . The confirmed hits were reclassified as the ‘Lead I’ drugs ( Figure 2D; Table 1 ) . Equivalent tests using a second pan-endocrine transgenic line , Tg ( pax6b:GFP ) ulg515 ( Delporte et al . , 2008 ) , confirmed the same 11 drugs as leads ( Figure 3—figure supplement 2 ) . 10 . 7554/eLife . 08261 . 007Figure 3 . Validation of endocrine differentiation induction: precocious 2° islet assay . ( A , B ) Representative confocal images—brightfield and fluorescence images merged—of dissected pancreata ( dashed lines ) from neurod:EGFP transgenic larvae treated from 3 to 5 dpf with 0 . 1% DMSO ( A ) or a Hit I drug ( B , example shown is parthenolide ) . Early endocrine cells are labeled with GFP ( green ) allowing precocious formation of 2° islets ( white arrows ) to be visualized following drug exposures . ( C ) The number of precocious 2° islets was quantified following treatment with the indicated Hit I compounds from 3 to 5 dpf . Results obtained with the optimal concentration were plotted relative to negative ( 0 . 1% DMSO ) and positive controls ( RO2949097 , 5 μM ) . Of 20 Hit I compounds tested , 11 were confirmed as Lead I drugs for inducing endocrine differentiation ( optimal concentrations for validated leads are shown in parentheses ) . Arrows indicate drugs that inhibit NF-κB signaling . Scale bar , 25 μm . Error bars , standard error . All p-values were calculated using Dunnett's test . p < 0 . 05 , p < 0 . 01 , p < 0 . 001 , *p < 0 . 0001 . n = 5–10 larvae per condition , experiment was repeated 3 times per compound . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 00710 . 7554/eLife . 08261 . 008Figure 3—figure supplement 1 . Validation of endocrine differentiation induction: precocious 2° islet assay ( neurod reporter ) . Precocious 2° islet assays were performed as per Figure 3 . ( A–J ) Representative confocal images—brightfield and fluorescence images merged—of dissected pancreata ( dashed lines ) from neurod:EGFP transgenic larvae treated with indicated Lead I compounds ( at optimal concentrations ) from 3 to 5 dpf: ( A ) Thioctic acid ( 5 μM ) ; ( B ) N-acetylmuramic acid ( 12 . 5 μM ) ; ( C ) Maprotiline ( 5 μM ) ; ( D ) β-estradiol ( 12 . 5 μM ) ; ( E ) Biperiden HCl ( 12 . 5 μM ) ; ( F ) Benzamidine ( 20 μM ) ; ( G ) Dexetimide ( 1 μM ) ; ( H ) Oxacillin ( 5 μM ) ; ( I ) Dimethindene ( S , + ) maleate ( 1 μM ) ; ( J ) Methylthiouracil ( 12 . 5 μM ) . Secondary islets are indicated by arrows . Scale bar , 25 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 00810 . 7554/eLife . 08261 . 009Figure 3—figure supplement 2 . Validation of endocrine differentiation induction: precocious 2° islet assay ( pax6b reporter ) . Precocious 2° islet assays were performed as per Figure 3 . ( A–L ) Representative confocal images—brightfield and fluorescence images merged—of dissected pancreata ( dashed lines ) from pax6b:EGFP transgenic larvae treated with indicated Lead I compounds ( at optimal concentrations ) from 3 to 5 dpf: ( A ) Thioctic acid ( 5 μM ) ; ( B ) N-acetylmuramic acid ( 12 . 5 μM ) ; ( C ) Maprotiline ( 5 μM ) ; ( D ) β-estradiol ( 12 . 5 μM ) ; ( E ) Biperiden HCl ( 12 . 5 μM ) ; ( F ) Benzamidine ( 20 μM ) ; ( G ) Dexetimide ( 1 μM ) ; ( H ) Oxacillin ( 5 μM ) ; ( I ) Dimethindene ( S , + ) maleate ( 1 μM ) ; ( J ) Parthenolide ( 5 μM ) ; ( K ) Methylthiouracil ( 12 . 5 μM ) . Secondary islets are indicated by arrows . ( M ) The number of precocious 2° islets was quantified following treatment with the indicated Hit I compounds from 3 to 5 dpf . Results obtained with the optimal concentration were plotted relative to negative ( 0 . 1% DMSO ) and positive controls ( RO2949097 , 5 μM ) . The same 11 Lead I compounds validated with the neurod:GFP reporter line ( Figure 3 , Figure 3—figure supplement 1 , Table 1 ) showed significant results—albeit producing fewer numbers of 2° islets than the neurod:EGFP line . Scale bar , 25 μm . All p-values were calculated using Dunnett's test . p < 0 . 05 , p < 0 . 01 , p < 0 . 001 , **p < 0 . 0001 . n = 5–10 larvae per condition , experiment was repeated 3 times per compound . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 00910 . 7554/eLife . 08261 . 010Table 1 . Lead I drugs: inducers of endocrine differentiationDOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 010Drug name>2° islet #ARQiv ( µM ) >2° islet ( µM ) 1N-Acetylmuramic acid+++0 . 512 . 52Methylthiouracil++0 . 2512 . 53Dimethindene ( S , + ) maleate++114Thioctic acid+0 . 555Biperiden HCl+112 . 56Parthenolide+457Maprotiline+0 . 558Estradiol diacetate → Beta-estradiol+112 . 59Oxacillin+1510Benzamidine+0 . 1252011Dexetimide+1112Decitabine−0 . 5n/a13N-Acetylaspartic acid−4n/a14Nevirapine−0 . 25n/a15Ethynodiol diacetate−0 . 25n/a16Vitamin K3−0 . 25n/a17Fosphenytoin sodium−0 . 25n/a18Thiram−0 . 5n/a19BOC-S-acetaminomethyl-L-cysteine→N-Acetyl-L-cysteine−0 . 5n/a20Tretinoin−0 . 25n/a21Iodinend0 . 25nd22Bayberry waxnd0 . 25nd231 , 5-Bis ( succinimidooxycarbonyloxy ) pentanend0 . 5ndThe 23 Hit I drugs are listed . 20 were tested for induction of endocrine differentiation , that is , precocious 2° islet formation . Compounds are ordered according to the results of the validation screen , 11 drugs were confirmed as leads ( ++ = p < 0 . 01′; + = p < 0 . 05 ) , 9 failed ( − ) . optimal response concentration for the ARQiv and validation screens . n/a: not applicable; nd: not determined , n = 5–10 larvae per condition , experiment repeated 3 times . Of the original ARQiv Calls , 154 compounds showed no preliminary evidence of enhanced 2° islet formation . However , many of these drugs had high SSMD scores suggesting substantial biological significance . We hypothesized that increased β-cell mass in the principal islet would also have been reported as increased insulin reporter activity ( YFP ) during the primary screen . Furthermore , any increase in β-cell mass in the absence of effects on endocrine differentiation would suggest a capacity to induce β-cell proliferation ( Figures 1C , 2D , E ) . Discovery of drugs promoting β-cell proliferation would have obvious implications for treating diabetic conditions associated with β-cell paucity . To prioritize which drugs to screen for increases in β-cell number within the principal islet , we chose a threshold SSMD score of 1 . 75 , denoted as the Hit II subset ( Figure 2D ) . Of 23 drugs that met this criterion , we were able to perform validation assays on 19 . We also included the 9 Hit I drugs that failed to induce 2° islet formation , and two validated Hit I compounds with an SSMD >1 . 75; thus , a total of 30 compounds ( Figure 2D ) . In β/δ-reporter transgenic fish , YFP is expressed in β-cell cytoplasm , making it difficult to count cell numbers accurately , ( Figure 1B ) . To facilitate detailed quantification of β-cell numbers , we turned to a transgenic line in which GFP is expressed in β-cell nuclei , Tg ( ins:hmgb1-EGFP ) jh10 ( Wang et al . , 2011 ) . All compounds were tested as per the treatment regimen established for Validation assay I . The data showed that 15 compounds ( 50% ) caused a significant increase in β-cell number at the optimal tested concentration ( Figure 4A ) . Hits confirmed for the ability to stimulate increased β-cell numbers in the absence of effects on differentiation were reclassified as ‘Lead II’ drugs ( Figure 4A; Table 2 ) . 10 . 7554/eLife . 08261 . 011Figure 4 . Validation of increased β-cell proliferation: cell counts . ( A ) Quantification of β-cell numbers following incubation of ins:hmgb1-EGFP transgenic larvae from 3 to 5 dpf in one of 30 Hit compounds , 0 . 1% DMSO , or the Notch inhibitor RO4929097 ( 5 µM ) . 15 compounds were confirmed as Lead II drugs for increasing β-cell numbers . Arrows indicate drugs that enhance serotonin signaling . ( B , C ) ARQiv screen data for paroxetine: box plots of β cells ( B ) and δ cells ( C ) suggest a β cell-specific effect—that is , a dose–response in YFP but not RFP signal ( dashed line , single polynomial curve fit ) . ( D ) Numbers of δ cells ( red bars ) and β cells ( green bars ) were quantified following treatment with paroxetine , 0 . 1% DMSO , or RO4929097 . Increased β-cell numbers were seen following paroxetine and RO4929097 treatments . However , only RO4929097 increased both β and δ cells . ( E ) Ratio of the number of β cells to δ cells , which confirms that the number of β cells increases following paroxetine treatment relative to δ cells , suggesting cell-type selective effects . Error bars , standard error . n = 5–10 larvae per condition , experiment was repeated 2–3 times per compound . ( F–H ) Representative z-projection confocal images of the principal islets in dissected pancreata ( post-paraformaldehyde fixation ) fromTg ( ins:hmgb1-EGFP; β/δ-reporter ) triple transgenic lines treated with DMSO ( F ) , paroxetine ( G ) , or RO4929097 ( H ) . Shown are EGFP+ β-cell nuclei ( green ) and TagRFP+ δ cells ( red ) ; note , PhiYFP in the β/δ-reporter line does not withstand fixation , allowing ‘clean’ labeling of β-cell nuclei with EGFP . In addition , apparent overlap between the β-cell and δ-cell markers ( i . e . , occasional ‘yellow’ cells ) is an artifact of z-projection images shown in 2D format . For clarity , the inset panels show a single z-slice image of partial islet showing no co-localization of cell type specific reporters . All p-values were calculated using Dunnett's test . p < 0 . 05 , p < 0 . 01 , p < 0 . 001 , *p < 0 . 0001 . N . S . , non-significant . Scale bar , 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 01110 . 7554/eLife . 08261 . 012Table 2 . Lead II drugs: increased β-cell numberDOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 012Drug name> β-cell #ARQiv ( µM ) > β-cell ( µM ) 1Promethazine HCl++++0 . 12512Paroxetine HCl++++413Biperiden HCl++++112 . 54Decitabine++++0 . 5255Amitriptyline++++216N-Acetylaspartic acid++++417Nevirapine++++0 . 25258Tretinoin++++0 . 2519Ethynodiol diacetate+++0 . 25110Pasiniazid++2111NCS-382++0 . 25112Amcinonide+0 . 25513Thioctic acid+0 . 5514Promazine HCl+41015D-Gluconic acid calcium salt+22516Benzalkonium chloride−2n/a17Chloroacetoxyquinoline−0 . 125n/a18Acyclovir−2n/a19Diphenhydramine−0 . 5n/a20Reserpine−2n/a21Resorcinol monoacetate−2n/a22Khellin−2n/a23Butenafine HCl−0 . 5n/a24Phenothrin−0 . 25n/a25Nomifensin maleate−2n/a26Ethopropazine HCl−1n/a27Fosphenytoin sodium−0 . 25n/a28Thiram−0 . 5n/a29Vitamin K3−0 . 25n/a30BOC-S-acetaminomethyl-L-cysteine → N-Acetyl-L-cysteine−0 . 5n/a31RIAA 94nd0 . 5nd32Trientinend1nd33Beta propiolactonend2nd34Emodic acidnd0 . 5ndAll 23 Hit II drugs ( non-shaded ) , as well as 2 Hit I validated compounds with high SSMD values ( shaded light gray ) , and 9 Hit I ‘fails’ ( shaded dark gray ) , are listed . The top 30 drugs were tested for increased β-cell numbers: 15 were validated as leads ( ++++ = p < 0 . 0001; +++ = p < 0 . 001; ++ = p < 0 . 01; + = p < 0 . 05 ) , 15 failed ( − ) . optimal response concentration for the ARQiv and validation screens; substituted compound due to availability issues; n/a: not applicable; nd: not determined . n = 5–10 larvae per condition , experiment repeated 2–3 times . Of the leads that increased β-cell number , paroxetine was particularly intriguing as comparisons between β-cell and δ-cell reporter activity suggested that this drug might selectively increase β-cell numbers without affecting δ-cells , that is , potentially acting in a cell type-specific manner ( Figure 4B , C ) . To verify that the actions of paroxetine were specific to β cells , we treated double transgenic ( ins:hmgb1-EGFP; β/δ-reporter ) larvae with paroxetine at the optimal concentration ( 1 µM ) from 3 to 5 dpf and quantified β and δ cells using confocal microscopy . DMSO treated larvae had an average of 29 . 4 ± 1 . 1 β cells and 24 . 1 ± 1 . 6 δ cells . As expected , RO4929097 treatment caused a significant increase in both endocrine cell types examined ( 35 . 8 ± 1 . 3 β cells and 29 . 7 ± 1 . 5 δ cells; Figure 4D–H ) , consistent with DAPT treatment in the ARQiv assay ( Figure 1E ) and likely due to induced differentiation of progenitors contributing to the principal islet . Conversely , paroxetine significantly increased β-cell numbers ( 37 . 1 ± 1 . 4 , p < 0 . 01 ) but had no effect on δ cells ( 23 . 0 ± 1 . 1 , p = 0 . 57 ) ( Figure 4D–H ) . This result suggests that it is possible to increase β-cell mass without incurring concomitant increases in other endocrine compartments , an important finding with respect to the development of targeted therapies . One of the central advantages of screening clinically approved drugs is that molecular mechanisms of action are typically well characterized . Thus , having validated several compounds for the capacity to induce endocrine differentiation ( Table 1 ) and/or β-cell proliferation ( Table 2 ) , we next sought to investigate whether shared mechanisms of action were implicated between drugs eliciting the same effect on pancreatic biology , that is , among compounds within the Lead I or Lead II sets . Two of the 11 drugs in the ‘Lead I’ set , thioctic acid and parthenolide , are known inhibitors of the NF-κB signaling pathway ( Ying et al . , 2010; Ghantous et al . , 2013 ) . This inspired us to ask whether these drugs enhance endocrine differentiation by modulating NF-κB signaling . In quiescent cells , the NF-κB complex is sequestered in the cytoplasm and associates with inhibitory IκB proteins ( Schmitz et al . , 2004 ) . Activation of NF-κB signaling leads to kinase-dependent phosphorylation and degradation of IκB , allowing NF-κB to translocate to the nucleus and regulate target-gene transcription ( Schmitz et al . , 2004 ) . Despite having different molecular targets , thioctic acid and parthenolide both block NF-κB nuclear translocation ( Ying et al . , 2010; Ghantous et al . , 2013 ) . To further validate the NF-κB pathway as a target for stimulating endocrine differentiation , we used two other compounds , not present in our chemical library , but known to inhibit NF-κB signaling at different steps in the pathway: NF-κB inhibitor II ( NFκBi-II ) blocks the target transcription without affecting IκB degradation ( Shin et al . , 2004 ) ; and NF-κB I inhibitor III ( NFκBi-III ) inhibits cytokine-stimulated NF-κB activation ( Lee et al . , 2005 ) . Neurod:GFP larvae were used to determine effects on 2° islet appearance following incubation ( 3–5 dpf ) with either NFκBi-II ( 1 µM ) or NFκBi-III ( 10 µM ) . Initial working concentrations of these compounds were based on previous studies ( Shin et al . , 2004; Lee et al . , 2005 ) . Dosages were then decreased until concentrations that did not induce morphological defects were defined . Treated larvae showed significant increases in the 2° islet number with both NFκB inhibitors ( Figure 5A–D ) . From this work , it is clear that targeting multiple steps in the NF-κB signaling pathway results in precocious 2° islet formation , and therefore , β-cell neogenesis . Using a previously characterized NF-κB reporter transgenic line , Tg ( 6xNFκB:EGFP ) nc1 ( Kanther et al . , 2011 ) , we confirmed that 2-day treatments ( 3–5 dpf ) with thioctic acid , parthenolide , as well as NF-κB inhibitors II and III dramatically reduced NF-κB reporter activity in the pancreas and globally ( Figure 5—figure supplement 1 ) . We next sought to confirm that NF-κB inhibition could induce endocrine differentiation . Larvae from the pan-endocrine reporter line , neurod:EGFP ( Figure 5A–C ) , showed a significant increase in 2° islet number when treated with either inhibitor . This result clearly demonstrates that targeting the NF-κB signaling pathway results in precocious 2° islet formation , thus , β-cell neogenesis through induction of endocrine differentiation . 10 . 7554/eLife . 08261 . 013Figure 5 . NF-κB pathway inhibition induces endocrine differentiation . ( A , B ) Representative confocal images—brightfield and fluorescence images merged—of dissected pancreata ( dashed lines ) from neurod:EGFP transgenic larvae treated from 3 to 5 dpf with NF-κB signaling inhibitor II ( A ) or III ( B ) . Both inhibitors induced precocious secondary islet formation ( white arrows ) . ( C ) Secondary islet numbers were quantified and plotted relative to vehicle control ( 0 . 1% DMSO ) . n = 5–10 larvae per condition , experiment was repeated 3 times . Error bar , standard error . All p-values were calculated using Dunnett's test . p < 0 . 0001 . ( D , D′ ) Representative in vivo confocal z-projection of pancreas ( dashed lines ) in 6xNFκB:EGFP ;Tp1:hmgb1:mCherry double transgenic larvae at 5 dpf showing co-labeling of the NF-κB reporter ( green ) and Notch reporter ( red ) in endocrine progenitor cells ( arrows in D′ ) , suggesting endocrine progenitors respond to both Notch and NF-κB signaling . Scale bars , 25 μm ( D ) , 10 μm ( D′ ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 01310 . 7554/eLife . 08261 . 014Figure 5—figure supplement 1 . Thioctic acid and parthenolide inhibit NF-κB signaling . ( A–E ) Confocal images of 5 dpf pancreata ( dashed lines ) from 6xNFκB:EGFP/Tp1:hmgb1-mCherry larvae treated with indicated compounds or DMSO control from 3dpf to 5dpf . The NF-κB reporter showed reduced fluorescence levels in the pancreas following exposure to all tested NF-κB inhibitors ( A–D ) compared with DMSO ( E ) . Scale bar , 25 μm . ( F ) ARQiv scans were performed on individually tracked 6xNFκB:EGFP/Tp1:hmgb1-mCherry larvae prior to ( 3 dpf ) and after compound exposures ( 5 dpf ) . All compounds induced a significant reduction of NF-κB reporter activity relative to 0 . 1% DMSO controls . GFP reporter expression levels were normalized to pre-treatment levels—that is , plotted as percent change in fluorescence over time ( as per Walker et al . , 2012 ) —and showed significant signal loss for all tested compounds . Error bar = standard deviation . NFκBi-II: NF-κB inhibitor II , NFκBi-III: NF-κB inhibitor III . All p-values were calculated using Dunnett's test . p < 0 . 001 , **p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 014 Having established that NF-κB is involved in the regulation of endocrine differentiation , we sought to identify which pancreatic cell types are actively undergoing NF-κB signaling during development . To do so , pancreata from double transgenic fish carrying NF-κB and Notch pathway reporters ( 6xNFκB:EGFP; tp1:hmgb1-mCherry ) ( Parsons et al . , 2009; Kanther et al . , 2011 ) were imaged using confocal microscopy . We found that the NF-κB reporter signal ( GFP ) overlapped with the Notch-pathway dependent signal ( mCherry ) at 5 dpf ( Figure 5D , D′ ) . This indicates that NF-κB signaling is active in Notch-responsive progenitors that line the pancreatic duct , consistent with a novel role for the NF-κB pathway in endocrine differentiation . Of note , NF-κB inhibition did not appear to reduce Notch-reporter expression ( Figure 5—figure supplement 1A–E ) , a result requiring further characterization . A potential shared mechanism of action among Lead II compounds was revealed by the fact that paroxetine and amitriptyline ( Figure 4A , compounds 10 and 22 , arrows; Table 2 ) are both predicted to increase serotonergic signaling ( Dechant and Clissold , 1991; Boyer and Feighner , 1992 ) ( Sangdee and Franz , 1979 ) . Clinically , both drugs are used as antidepressants . Paroxetine is a selective serotonin reuptake inhibitor ( SSRI ) , thereby increasing extracellular serotonin concentration . Amitriptyline inhibits reuptake of both norepinephrine and serotonin . We hypothesized that these drugs regulated β cells by mediating serotonergic signaling . To test this hypothesis , we evaluated fluoxetine , another SSRI , and serotonin itself . β cells were quantified following a 2-day exposure ( 3–5 dpf ) to fluoxetine ( 25 μM ) or serotonin ( 25 μM ) . Both treatments displayed a significant increase in β-cell numbers ( fluoxetine , 42 . 6 ± 2 . 0; serotonin , 45 . 7 ± 2 . 5; DMSO , 33 . 7 ± 1 . 2 ) suggesting that elevated serotonergic signaling promotes β-cell proliferation ( Figure 6A ) . 10 . 7554/eLife . 08261 . 015Figure 6 . Serotonin signaling stimulates β-cell proliferation in a cell type-specific manner . ( A ) β-cell quantification following 25 μM serotonin or 25 μM fluoxetine treatment of ins:hmgb1-eGFP transgenic larvae from 3 to 5 dpf indicates enhanced serotonin signaling increases β-cell numbers in zebrafish larvae . ( B ) β-cell quantification in the principal islet ( All ) and the number of EdU-labeled β cells ( EdU+ ) are plotted following treatments with EdU and either DMSO , 1 μM paroxetine , or 5 μM RO4929097 . More β cells overall , and more EdU+ β cells , are observed with 1 μM paroxetine and 5 μM RO4929097 treatments , suggesting effects on β-cell proliferation . ( C ) Plot of EdU+ β cells as a percentage all β cells shows that paroxetine treatment stimulates β-cell proliferation , whereas Notch inhibition does not . Error bars , standard error . ( D–F ) Single-plane confocal fluorescence images of ins:hmgb1-eGFP islets ( dashed lines ) treated with EdU and either DMSO ( D ) , 1 μM paroxetine ( E ) , or 5 μM RO4929097 ( F ) —β cell nuclei ( green ) ; EdU+ cells ( red ) ; double-labled EdU+ β cells ( yellow ) . Scale bar , 10 μm . n = 5–10 larvae per condition , experiment was repeated 3 times . ( G , G′ ) Confocal images of immunostained adult zebrafish pancreas indicate that serotonin signaling is active in islets ( white arrows , islets indicated by dashed lines ) . aTub: acetylated tubulin ( red ) ; 5HT ( 5-hydoxytryptamine ) : serotonin ( green ) ; insulin ( magenta ) . Scale bar , 10 μm . ( H , I ) Confocal images of adult zebrafish pancreas following injections with EdU and either DMSO ( H ) or 1 mM paroxetine ( I ) , and immunostained as indicated . ( J ) Plot of EdU+ β cells as a percentage all β cells shows that paroxetine treatment stimulates β-cell proliferation in adult zebrafish . Error bars , standard deviation . n = 3–5 adult fish per condition , experiment was repeated 3 times . All p-values were calculated using Dunnett's test . p < 0 . 05 , p < 0 . 01 , p < 0 . 001 , **p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 01510 . 7554/eLife . 08261 . 016Figure 6—figure supplement 1 . Amcinonide increases β-cell mass by inducing hyperglycemia . Absolute glucose values in zebrafish larvae treated with selected lead compounds . For each drug , 3 treatments ( 20 embryos each treatment ) were measured . The absolute glucose levels per embryo were calculated based on standard controls . Error bars = standard error , All p-values were calculated using Dunnett's test . p < 0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 01610 . 7554/eLife . 08261 . 017Figure 6—figure supplement 2 . Paroxetine injection stimulates β-cell proliferation in mice . Paroxetine effects on β-cell proliferation in postnatal mice assayed at postnatal day 14 ( P14; A–C ) : EdU and DMSO ( A ) , or EdU and 1 mM paroxetine ( B ) , were injected into mice daily from P7 to P14 . ( A , B ) Representative confocal images of a single islet in DMSO ( A ) and paroxetine ( B ) injected animals immunostained for Nkx6 . 1 ( red ) to mark β cells and EdU ( green ) . ( C ) The number of EdU-positive β cells ( arrows in A and B ) plotted as the percentage of all β cells shows a significant increase in β-cell proliferation in paroxetine-injected mice . *p < 0 . 0001 , n = 4 animals , 150 islets ( DMSO ) ; 5 animals , 190 islets ( paroxetine ) . ( D–F ) Paroxetine effects on β-cell proliferation in postnatal mice assayed at postnatal day 21 ( P21 ) ; assay performed as above except that injections were performed from 7 to 21 days postnatal ( P7–P21 ) . ( D , E ) Representative confocal images of a single islet in DMSO ( D ) and paroxetine ( E ) injected animals . ( F ) Quantification of EdU-positive β cells ( arrows in D and E ) plotted as the percentage of all β cells , again , shows a significant increase in β-cell proliferation in paroxetine-injected mice . All p-values were calculated using 2-tailed Student T-test with 95% confidence intervals . *p < 0 . 0001 , n = 5 animals , 150 islets ( DMSO ) ; 7 animals , 210 islets ( paroxetine ) . Scale bar , 25 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 08261 . 017 To verify effects on cell division , we combined SSRI treatments with 5-ethynyl-2′-deoxyuridine ( EdU ) , a thymidine analog that labels proliferating cells ( Salic and Mitchison , 2008 ) . We used another transgenic line labeling β-cell nuclei with GFP , Tg ( ins:hmgb1-EGFP ) jh10 , to facilitate quantification of EdU-labeled cells . Larvae were exposed to compounds and EdU for 2 days ( 3–5 dpf ) , then fixed and sectioned for imaging . As expected , the results show increased numbers of β cells in principal islets of larvae treated with paroxetine ( 37 . 1 + 1 . 2 ) and RO4929097 ( 44 . 1 + 2 . 2 ) , relative to DMSO ( 29 . 7 + 1 . 4; Figure 6B ) . Increased numbers of proliferating ( EdU+ ) β cells were also observed for both paroxetine and RO4929097 ( Figure 6B , EdU+ ) . However , when adjusted for absolute numbers of β cells , only paroxetine-treated larvae showed a significantly higher percentage EdU+ β cells ( 23 . 7 ± 2 . 7% , vs 11 . 0 ± 1 . 5% and 15 . 6 ± 2 . 1% for DMSO and RO4929097 , respectively; Figure 6C–F ) . Combined with our data suggesting paroxetine acts directly on β cells ( Figure 4B–F ) , these results strongly suggest that enhanced serotonergic signaling promotes proliferation of β cells . Serotonin is known to be expressed in human ( Eriksson et al . , 2014 ) and mouse ( Kim et al . , 2010; Ohara-Imaizumi et al . , 2013 ) islets and is implicated in regulating β-cell proliferation during pregnancy ( Kim et al . , 2010 ) and glucose-stimulated insulin secretion ( Ohara-Imaizumi et al . , 2013 ) . Others have shown that serotonin influences insulin secretion ( Isaac et al . , 2013 ) , and consequently glucose levels . In different model systems , elevated glucose levels have been shown to impact β-cell proliferation ( Porat et al . , 2011 ) and differentiation ( Maddison and Chen , 2012 ) . We wanted to know , therefore , whether serotonin signaling directly affects β-cell proliferation in zebrafish larvae or whether its impact is mediated through increased glucose levels . Using a colorimetric assay to quantify larval glucose levels , paroxetine incubation from 3 to 5 dpf demonstrated no effect on larval glycemia—albeit transient increases on days 1 or 2 of treatment cannot be excluded . Indeed of all leads tested , only the glucocorticoid amcinonide significantly elevate glucose levels ( Figure 6—figure supplement 1 ) . Thus , we conclude that serotonin promotes increased β-cell numbers independent from affecting glucose levels . Although serotonin was shown to be present in a subpopulation of cells in the zebrafish intestine , no report has been made regarding its localization in the adult zebrafish pancreas ( Uyttebroek et al . , 2010 , 2013 ) . Using a validated serotonin antibody ( Uyttebroek et al . , 2010 ) , we found this molecule localized to adult pancreatic islets ( Figure 6G , G′ ) . Immunolabeling was evident along a serotonergic nerve that appeared to innervate the pancreas ( indicated by overlap with anti-tubulin staining , that is , ‘yellow’ regions in merged image , Figure 6G ) and was also consistent with possible expression within β cells ( overlap with insulin , compare Figure 6G , G′ ) . The observed expression pattern for serotonin is consistent with immunohistochemistry data from human pancreatic tissue and mammalian model systems . To test if serotonergic signaling also enhances proliferation in the adult zebrafish , we injected mature fish ( >3 month old ) with a 20 µl mixture of paroxetine ( 1 mM ) and EdU ( 25 µM ) every other day for 10 days ( 5 injections ) , followed by an injection of EdU alone on day 12 . Fish were sacrificed on day 14 and pancreata sectioned and immunostained for insulin and EdU . The data show a significant increase in EdU+/insulin+ cells in paroxetine-treated fish ( 3 . 7-fold , p < 0 . 05; Figure 6H–J ) . Finally , to check if the effect of paroxetine is conserved in mammals , we performed intraperitoneal injections of paroxetine ( 15 mg/kg ) in young mice daily from the age of postnatal day 6 ( P6 ) . EdU intraperitoneal injections ( 60 μg per injection ) were carried out every other day from P8 . Pancreata were then collected at P14 and P21 for staining of β cells ( Nxk6 . 1 antibody ) and EdU . The data showed a significant increase in β-cell proliferation in paroxetine-treated animals at P14 ( DMSO control: 11 . 2 + 0 . 4% , n = 4 animals; paroxetine: 13 . 8 + 0 . 4% , n = 5 animals , p < 0 . 0001 ) and P21 ( DMSO: 14 . 8 + 0 . 5% , n = 5 animals; paroxetine: 18 . 06 + 0 . 4% , n = 7 animals , p < 0 . 0001; Figure 6—figure supplement 2 ) . Combined with prior observations in pregnant mice ( Kim et al . , 2010 ) , our data are consistent with serotonergic signaling playing an evolutionarily conserved role in regulating β-cell proliferation in the pancreas . We have established a versatile and sensitive platform for true high-throughput drug discovery in whole-organisms that is applicable to a wide range of in vivo reporter-based assays . We leveraged the high-throughput capacity afforded by ARQiv to reduce false-call rates using qHTS principles—that is , titration-based primary screening ( Inglese et al . , 2006 ) . To our knowledge , this is the first time qHTS has been applied to whole-organism drug discovery utilizing a vertebrate model system . We were motivated to utilize this approach as our chemical library , the JHDL , was assembled to ‘repurpose’ existing drugs for new disease targets ( Shim and Liu , 2014 ) . Thus , a primary goal was to reveal the maximal number of novel therapeutic opportunities afforded by the JHDL , an endpoint which qHTS facilitates by reducing false negatives . Our screen was specifically designed to identify compounds that elevated transgenic insulin reporter activity in larval zebrafish . More specifically , we wanted to find drugs that increased pancreatic β-cell mass , thus , possible new therapeutics for ameliorating β-cell paucity in diabetic patients . To create a platform capable of handling high-throughput volumes , we combined ARQiv with a customized robotic workstation ( Hudson Robotics ) and the COPAS-XL system ( Union Biometrica ) , which we termed ‘ARQiv-HTS’ . Using this system , automated quantification of YFP reporter activity ( i . e . , β-cell numbers ) in more than a half-million transgenic larvae resulted in the identification of 177 hit candidates . Secondary assays on a subset of prioritized hit candidates validated the majority of lead drugs tested as being able to increase β-cell numbers by induction of endocrine differentiation and/or stimulation β-cell proliferation . Compared with our prior manual screen ( Rovira et al . , 2011 ) , ARQiv-HTS significantly increased the number of hits implicated ( 177 vs 62 ) and leads validated ( 24 vs 6 ) . This supports the hypothesis that combining automated large-scale assay platforms , such as ARQiv-HTS , with qHTS can add significant value to whole-organism drug discovery without increasing the time required for the primary screen ( Mathias et al . , 2012; Hasson and Inglese , 2013; Rennekamp and Peterson , 2013 ) . In terms of sensitivity , we estimate ARQiv was able to detect as few as ten additional β cells in the developing pancreas ( e . g . , from 30 to 40 ) . We validated ∼62% of the hit compounds tested as leads ( 24 of 39 , Figure 2D; Tables 1 , 2 ) , a high hit-to-lead validation rate for HTS-based discovery systems ( Hann and Oprea , 2004 ) . This supports the concept that whole-organism screening can overcome inefficiencies in HTS drug discovery , such as high false positive and lead compound attrition rates ( Giacomotto and Segalat , 2010; Mathias et al . , 2012 ) . In support of our findings regarding candidate drugs , a recent manual screen of 883 compounds by the Stainier lab , implicated three pathways in promoting β-cell replication in larval zebrafish: retinoic acid ( RA ) , glucocorticoids , and serotonin ( Tsuji et al . , 2014 ) . We identified several compounds in those categories as well and implicated an additional 11 mechanisms of action in affecting β-cell biology ( Supplementary files 1 , 2 ) . Importantly , their results support our interpretation of the data presented here regarding serotonergic signaling promoting β-cell replication . In our prior manual screen , we found that inhibition of RA signaling can maintain pancreatic progenitor cells in an undifferentiated state ( Rovira et al . , 2011; Huang et al . , 2014 ) . Our results , and those of Tsuji et al . , have now demonstrated another role for RA ( e . g . , tretinoin , compound #31 , Figure 4 ) in stimulating β-cell proliferation . Both studies also found that glucocorticoids induce β-cell proliferation ( e . g . , amcinonide , compound #5 , Figure 4 ) indirectly by elevating glucose levels ( Figure 6—figure supplement 1 ) . Importantly , our screen also revealed a broader range of compound categories that potentially enhance β-cell mass ( Supplementary files 1 , 2 ) . Follow-up of these candidates could suggest multiple new mechanisms for increasing β-cell numbers . Such studies will be facilitated by knowledge of implicated mechanisms of action for most of the compounds in the JHDL . As an example , we explored the role of two mechanisms of action that were potentially shared between two lead drug subsets in regulating endocrine differentiation and β-cell proliferation . A third possibility , compounds stimulating a direct increase in insulin expression without changes in β-cell number remain to be evaluated . The precocious islet assay we developed ( Parsons et al . , 2009; Rovira et al . , 2011; Ninov et al . , 2012; Huang et al . , 2014 ) was used to confirm effects of Hit I compounds on endocrine differentiation ( Figure 3 , Figure 3—figure supplements 1 , 2 ) . Among 20 Hit I compounds tested , 11 were validated as Lead I drugs that promoted endocrine differentiation ( Table 1 ) . It is possible that Lead I compounds stimulated endocrine progenitor proliferation as well , further experimentation will be required to test this . In follow-up studies , we sought to identify common molecular mechanisms of these drugs . Intriguingly , we found that two Lead I compounds , parthenolide and thioctic acid , inhibit the NF-κB pathway ( Ying et al . , 2010; Ghantous et al . , 2013 ) . We subsequently verified that NF-κB signaling was active in pancreatic progenitors and defined a novel role for NF-κB signaling in regulating pancreatic development; inhibition of the pathway enhances endocrine differentiation . In keeping with this finding , NF-κB positively regulates expression of the pancreatic progenitor marker , SOX9 , in human pancreatic cancer stem cells ( Sun et al . , 2013 ) . As we and others have shown , Sox9 is an important transcription factor in the maintenance of pancreatic progenitor cells under regulation of Notch signaling ( Kopp et al . , 2011; Manfroid et al . , 2012; Shih et al . , 2012 ) . Moreover , a recent study shows that proinflammatory cytokines activate the Notch and NF-κB signaling pathways to promote endothelial transdifferentiation to a HSC fate , indicating a requirement for inflammatory regulation of stem cell numbers ( Espin-Palazon et al . , 2014 ) . Taken together , these data suggest a model where NF-κB and Notch signaling maintain transcriptional regulators essential for progenitor maintenance , including Sox9 and Hes/Hey genes , respectively ( Maniati et al . , 2011 ) . We reasoned that compounds which increased β-cell numbers without concomitant effects on endocrine differentiation—that is , no evidence of precocious secondary islet formation—were acting to promote cell division . Accordingly , quantification of β cells within the principal islet was used to confirm 15 of 30 Hit II compounds as Lead II drugs for stimulating proliferation of β cells ( Figure 4A ) . In terms of mechanism of action , two serotonin reuptake inhibitors , paroxetine and amitriptyline ( Sangdee and Franz , 1979; Dechant and Clissold , 1991 ) , were among the 15 leads promoting proliferation ( Table 2 ) . As noted above , serotonin was also implicated in a recent manual screen for factors promoting β-cell proliferation in zebrafish ( Tsuji et al . , 2014 ) . However , unique to this study , our primary screen data suggested that paroxetine acts in a cell-type selective manner; increasing β-cell number without affecting δ cells ( Figure 4B–H ) . Paroxetine is a more SSRI ( Dechant and Clissold , 1991 ) , suggesting cell-specific proliferative effects may be mediated through serotonin . This intriguing possibility was tested further by assessing the effect of serotonin and another SSRI on β-cell numbers ( Figure 6A ) , confirmed by direct assessments of β-cell division using EdU labeling ( Figure 6B–F ) , and supported by expression of serotonin in pancreatic islets ( Figure 6G , G′ ) . These data are in keeping with preferential uptake of serotonin in human β cells in vitro , and in the pancreas of non-human primates and rats ( Eriksson et al . , 2014 ) . We went on to show that paroxetine stimulated β-cell proliferation in both adult fish ( Figure 6H–J ) and neonatal mice ( Figure 6—figure supplement 2 ) . Serotonin signaling also increases β-cell mass and insulin secretion in pregnant mice ( Kim et al . , 2010; Ohara-Imaizumi et al . , 2013 ) . It has been known for over a hundred and 50 years that the pancreas is well innervated . Consistent with a role for neuronal signaling in regulating pancreatic biology , we previously found that disruption of sympathetic innervation in mice leads to abnormal islet structure and loss of functional maturation ( Borden et al . , 2013 ) . Collectively , these findings strongly suggest that neurotransmitters may play significant roles in pancreatic development and β-cell proliferation . Clinical associations between paroxetine and diabetes have been reported but are controversial , with evidence of both beneficial and detrimental effects ( Weber-Hamann et al . , 2006; Paile-Hyvarinen et al . , 2007; Knol et al . , 2008; Derijks et al . , 2009 ) . As roles for serotonin in β-cell function are also inconclusive ( Isaac et al . , 2013; Ohara-Imaizumi et al . , 2013 ) , further study will be required to clarify whether serotonergic signaling is a viable therapeutic target for diabetic patients . In addition , neuromodulator drugs are known to be highly promiscuous ( Bianchi and Botzolakis , 2010 ) , therefore , it will be important to test whether other neurotransmitter pathways also affect islet structure and/or β-cell proliferation . This is also emphasized by the fact that neuromodulators make up the largest subcategory among the 131 ARQiv Call compounds , which remain to be further evaluated ( >20 compounds , first shaded set in Supplementary file 2 ) . In summary , we present the first full-scale implementation of ARQiv-based whole-organism HTS . Like HCS methodologies ( Pardo-Martin et al . , 2010; Rihel et al . , 2010; Sanker et al . , 2013 ) , ARQiv can take advantage of the versatility afforded by a wide array of reporter-based transgenic resources to provide rapid quantitative phenotyping ( Walker et al . , 2012 ) . Thus , both screening strategies can surmount current ‘biological validation’ bottlenecks in drug discovery ( Mathias et al . , 2012 ) . HCS approaches are extremely powerful , providing multi-dimensional data that can be used to speed hit-to-lead transition times , rapidly gain mechanistic insights ( Rennekamp and Peterson , 2013 ) , and identify promising new therapeutics ( Zon and Peterson , 2005 ) . Unlike HCS approaches , ARQiv provides only single-dimension data , quantification of reporter levels—that is , no corresponding images . However , as we have demonstrated here , ARQiv can be coupled to robotics to push the boundaries of throughput for whole-organism drug discovery . Moreover , ARQiv can detect extremely subtle phenotypic changes ( e . g . , as few as ten additional β cells ) using a completely non-subjective methodology ( Walker et al . , 2012 ) . The increase in throughput afforded by ARQiv-HTS has potent practical benefits , supporting HTS ‘best practices’ such as qHTS ( Inglese et al . , 2006 ) . By screening the JHDL at multiple concentrations , we substantially increased the number of hits implicated , thus , increasing our chances of identifying an optimal drug target for increasing β-cell mass . The majority of the validated leads , 18 of 24 ( 75% ) , are already approved for use in humans , thus , facilitating relatively rapid clinical translation of these findings . Mechanism of action investigations revealed two important signaling pathways affecting β-cell biology: NF-κB was implicated in regulating endocrine differentiation , and serotonergic signaling was shown to selectively stimulate β-cell proliferation . Studies in human pancreatic cells could establish if the function of these pathways is conserved , better define the roles of inflammatory and neurotransmitter modulation in pancreatic biology , and help to determine the degree to which these findings have clinical relevance . All studies were carried out in accordance with onsite ACUC protocols . All fish were maintained at 28 . 5°C with a consistent 14:10 hr light: dark cycle . Transgenic lines used were Tg ( ins:PhiYFP-2A-nsfB , sst2:TagRFP ) lmc01 ( ‘β/δ-reporter’; ( Walker et al . , 2012 ) , Tg ( pax6b:GFP ) ulg515 ( Delporte et al . , 2008 ) , Tg ( neurod:EGFP ) nl1 ( Obholzer et al . , 2008 ) , Tg ( 6xNFκB:EGFP ) nc1 ( Kanther et al . , 2011 ) , Tg ( ins:hmgb1-EGFP ) jh10 ( Parsons et al . , 2009 ) , Tg ( tp1:hmgb1-mCherry ) jh11 ( Parsons et al . , 2009 ) . We screened the JHDL , a collection of 3348 compounds ( Chong et al . , 2006b; Rovira et al . , 2011 ) . The majority of the compounds in the JHDL are approved for use in humans: 2290 drugs approved for use by the FDA or international counterparts , another 775 drugs at various stages in clinical trials , and 66 rare drug compounds . In some cases , an active pharmaceutical ingredient ( i . e . , drug ) was included in more than one formulation as a separate compound . However , these were tallied as a single drug , giving a total of 3131 drugs included in the JHDL collection . The salient features of the primary screen performed here are described below . Additional details of the robotics-integrated ARQiv system and the methodologies we apply in pursuing whole-organism HTS in zebrafish larvae can be found here: ( White et al . , 2015 ) . For the primary screen , larvae were derived from in-crosses of homozygous β/δ-reporter fish ( Walker et al . , 2012 ) . The β/δ-reporter transgene labels pancreatic β cells with yellow fluorescent protein ( Phi-YFP , Evrogen ) and neighboring δ cells with red fluorescent protein ( TagRFP , Evrogen ) . At the start of each screening session , customized mass fish breeding chambers were used to collect 3000 to 15 , 000 eggs . At 24 hr post-fertilization ( 24 hpf ) , embryos were transferred into a 0 . 3× Danieu's solution containing 200 nM of 1-Phenyl-2-thiourea ( PTU ) . PTU is a tyrosinase inhibitor , which reduces pigmentation and thereby increases signal-to-noise ratios for ARQiv assays ( Karlsson et al . , 2001 ) . A customized Hudson Robotics system ( Figure 1—figure supplement 1 ) was used to dispense and serially dilute individual JHDL stock solutions 1:2 across a 96-well plate ( Greiner bio-one , #650209 ) such that final concentrations were 4 to 0 . 125 µM in 0 . 1% DMSO ( drug solvent ) . Positive and negative control 96-well plates were prepared such that they bracketed every 10 drug plates , to account for changes in reporter activity over time ( Figure 1—figure supplement 1C ) . Positive control plates consisted of six concentrations of DAPT , a Notch-signaling inhibitor that enhances precocious endocrine differentiation ( Parsons et al . , 2009 ) , serially diluted 1:2 with final concentrations ranging from 200 µM to 6 . 25 µM . Titrating DAPT in every control plate served to account for lot and/or assay variability over the course of the screen . Negative controls consisted of an entire 96-well plate of drug solvent ( 0 . 1% DMSO ) . After drug and control plates had been prepared , a COPAS-XL unit ( Union Biometrica , Holliston , MA ) ( Figure 1—figure supplement 1 ) was used to sort 3 dpf β/δ-reporter larvae for viability and dispense them into individual wells . All plates were then incubated under standard temperature and light cycle conditions for 4 days until reporter levels were quantified at 7 dpf ( Figure 1E ) . 15 min prior to scanning , 10 µl of 0 . 2% Eugenol ( Sigma ) in drug solvent was added to anesthetize larvae . Larvae expressing the β/δ-reporter were analyzed using the ARQiv system to quantify insulin and somatostatin 2 reporter levels ( YFP and RFP , respectively ) . Assay parameters were optimized for HTS as detailed in the text . In addition , plate reader detection parameters ( e . g . , optimal excitation/emission settings , z-dimension ‘focus’ , etc ) were determined empirically using previously described methods ( Walker et al . , 2012 ) . Briefly , all wells were scanned in a 3 × 3 grid , with all grid regions analyzed independently . ‘Signal’ was defined as any region producing a reading greater than or equal to three standard deviations above non-transgenic control fish averages . If more than one region produced ‘signal’ , these values were summed to obtain the total signal for that well . A set ‘gain’ for the plate reader was established for all scans in order to normalize data across numerous days/scans . We developed MATLAB ( Walker et al . , 2012 ) and R-based scripts to analyze and graphically present all primary screen data in real-time ( for an example , see Figure 2 ) . The resultant graphic provided results for each compound in three formats: ( 1 ) box plots showed the range of the data at each concentration; ( 2 ) SSMD values provided a measure of the relative strength of potential hits; and ( 3 ) a heat map of each plate facilitated initial visual follow-ups for detecting 2° islet formation . ARQiv data files from the primary screen were saved in an XML format and processed using MATLAB 2008a and/or R . An extraction algorithm was used to determine the total fluorescent signal of each well and tagged with experimental condition information for future analysis ( Walker et al . , 2012 ) . For wells where the signal could not be detected , the maximum regional value was used . A plate parsing algorithm ( Walker et al . , 2012 ) was used to separate control and drug plates into groups and blocks . Each block consisted of 14 plates including ten drug plates and the flanking sets of positive and negative control plates . The most effective DAPT concentration was parsed out as a subset of 16 per each control plate and used in the drug plate analysis algorithm . Drug plate analysis was performed on each plate in correlation to respective control data . Each drug plate consisted of six concentrations , resulting in eight conditions total for the analysis . Graphical representations of the data were plotted as described above in the text . To ensure accurate SSMD score calculation , outliers ( defined as any well having a signal value greater than three standard deviations from the median of the 16 wells comprising that condition ) were removed from the calculations . Empirically , we found that outlier data correlated with blank wells , multiple fish per well , floating larvae , and dead larvae . All curves in graphed data were drawn using either a best fit ( solid lines ) or single polynomial curve fitting function ( dashed lines ) . The original MATLAB code was updated to an R-based code to facilitate improved graphical outputs . The R-based code for processing a series of drug and control plates configured as described above is provided as Source code 2 . MATLAB ( and/or R ) data plots were used to identify which conditions produced a SSMD ≥1 . 3 . Using fluorescence stereomicroscopy , researchers then evaluated larvae in the corresponding wells ( and in flanking wells ) for evidence of enhanced 2° islet formation . Plates showing evidence of increased 2° islet formation were placed in the Hit 1 subset ( Figure 2D ) . In addition , ARQiv hit calls having a SSMD ≥1 . 75 were placed in the Hit II subset ( Figure 2D ) . It is important to note that this left 131 ARQiv Call compounds , those having an SSMD of 1 . 3–1 . 74 for which no immediate evidence of 2° islets was found; these remain to be further characterized ( Supplementary file 2 ) . For endocrine differentiation validation assays , two transgenic lines facilitating independent tests of Hit I compound effects on endocrine development were used; Tg ( pax6b:GFP ) ulg515 ( Delporte et al . , 2008 ) and Tg ( neurod:EGFP ) nl1 ( Obholzer et al . , 2008 ) . Transgenic embryos were plated in 24-well plates with ≥15 embryos per well at 3 dpf in E3 medium . Newly purchased Hit I compounds were diluted from 25 μM to 0 . 4 μM in a twofold dilution series , added to 24-well plates , and incubated from 3 to 5 dpf . PTU was not required for validation screens and was therefore not used , allowing assessment of lead compounds independent of any potential effects of PTU . As previously described for endocrine differentiation assays ( Huang et al . , 2014 ) , 5 μM of the Notch inhibitor RO4929097 ( Selleck Chemicals ) ( Luistro et al . , 2009 ) was used as a positive control . 0 . 1% DMSO was applied as the negative control . Larvae were fixed at 5 dpf with 4% paraformaldehyde ( PFA ) at 4°C overnight . Larval pancreata were dissected and imaged using a Zeiss Axiovert200M inverted microscope . GFP positive cells in the pancreatic ductal region other than the principal islet were counted as 2° islets ( Rovira et al . , 2011 ) . All assays were evaluated using one-way ANOVA and p-values calculated with a post hoc Dunnett's test . n = 5–10 larvae per condition , and a minimum of three experimental repeats was performed . To validate Hit I and II compound effects in promoting increased β-cell numbers , the ins:hmgb1-EGFP ( Parsons et al . , 2009 ) line was used; this line facilitates β-cell quantification due to nuclear localization of the GFP reporter . The assay was performed similarly to validation I tests except that Hit drugs were tested from 25 μM to 0 . 2 μM using a 1:5 dilution series . Pancreata were dissected and imaged with a Nikon A1-si Laser Scanning Confocal microscopy under a 20× objective . β cells were counted for all Z planes using ImageJ ( NIH ) software . All assays were evaluated using one-way ANOVA ( Analysis of Variance ) and p-values calculated with a post hoc Dunnett's test . n = 5–10 larvae per condition , and a minimum of three experimental repeats was performed . Fish were fixed either in 4% PFA ( 5 dpf larvae ) or 10% formalin ( adults ) at 4°C overnight . Pancreata were dissected and immunohistochemistry performed as described previously ( Huang et al . , 2014 ) . Briefly , pancreata were embedded in paraffin and sectioned at 5 μm . Sections were stained with 4' , 6-diamidino-2-phenylindole ( DAPI ) and processed for immunostaining using the following primary antibodies: serotonin ( 5-HT; 1:100 , Rabbit polyclonal , ImmunoStar ) ; acetylated tubulin ( aTub; 1:400 , Mouse monoclonal , Sigma ) ; green fluorescent protein ( GFP; 1:400 , Rabbit polyclonal , Life Technologies ) , GFP ( 1:400 , Mouse monoclonal , Life Technologies ) , DsRed ( 1:400 , Mouse monoclonal , Clontech ) ; insulin ( 1:400 , Polyclonal Guinea Pig , Dako ) . Fluorescently conjugated secondary antibodies were diluted 1:400 dilution ( Jackson ImmunoResearch Labs ) . Images were collected using a Nikon A1-si Laser Scanning Confocal microscopy . Free glucose level was determined in 5-day-old larvae using a glucose assay kit ( BioVision ) . Briefly , 20 larvae were collected by removing the embryonic media and quickly frozen in liquid nitrogen . Frozen larvae were then thawed on ice and grinded thoroughly in 80 μl glucose assay buffer . Then , glucose concentration were measured and calculated following the manufactory instruction ( BioVision ) . For direct assessment of β-cell proliferation , the Click-iT EdU Alexa Fluor 647 Imaging kit ( Life Technologies ) was used . For larval studies , drug treated 3 dpf larval were treated with EdU ( working concentration 12 . 5 µM ) and fixed at 5 dpf . In adult fish , 25 μM EdU in E3 medium was injected intracoelomically every other day together with paroxetine or 0 . 1% DMSO control for 10 days and alone on the twelfth day . In both cases , pancreata were dissected out , embedded in paraffin blocks , sectioned and stained as per the manufacturer's instructions . Images were collected with a Nikon A1-si Laser Scanning Confocal microscopy . EdU-positive β cells were counted using the ImageJ ( NIH ) software . All p-values were calculated using Student's t-test as comparisons were made only between a single treatment condition and the control . 20 μl of 1 mM paroxetine was injected intracoelomically into adult fish ( >3 month old ) every other day for 10 days . Control fish were injected with same volume of 0 . 1% DMSO . Fish injected with paroxetine exhibited the reported behavioral effects connected to SSRI treatment ( Wong et al . , 2013 ) . Fish were sacrificed 14 days after the first injection and fixed in 10% formalin for further evaluation . Paroxetine was injected intraperitoneally daily at 15 mg/kg to wild-type mice ( 129/C57 mixed background ) at postnatal day 7 ( P7 ) . EdU injections ( 60 μg per injection ) were given intraperitoneally every other day from P8 . Mice were sacrificed via CO2 gas ( followed by cervical dislocation ) and pancreata dissected out . Pancreata were immediately fixed in 4% PFA overnight at 4°C , then transferred to 30% sucrose ( in Phosphate buffered saline , PBS ) and left rocking overnight at 4°C . Before embedding in Optimal Cutting Temperature compound ( OCT ) , pancreata were equilibrated in 1:1 30% sucrose:OCT overnight . Embedded tissues were sectioned and stained as previously described ( Borden et al . , 2013 ) . The primary antibody , anti-mouse Nkx6 . 1 ( Developmental Studies Hybridoma Bank ) , was prepared at a dilution of 1:200 . Images were collected using a Zeiss LSM 700 confocal microscope .	Type 1 diabetes is caused by the body incorrectly destroying the cells in the pancreas—known as β cells—that produce insulin and so control the amount of sugar found in the bloodstream . Drugs that increase the rate at which new β cells form could therefore help to treat this disease . High-throughput screening is a technique that uses automated systems to rapidly test the effects of large numbers of drug-like compounds on living cells . Unfortunately , drugs sometimes produce different effects in animals than those they produce in isolated cells or other more simplified screening systems . Zebrafish are often used in biological studies because the larvae are transparent , making it easier to study what goes on inside them . Wang et al . have now developed a high-throughput screening system that uses genetically engineered zebrafish . The zebrafish contain ‘reporter’ genes that fluoresce when a gene is activated , and the intensity of the fluorescence can be interpreted to work out the effects of an applied drug . To search for compounds that cause β cells to grow , Wang et al . created two reporter genes: one that glows yellow when new β cells form , and one that glows red when other pancreatic cells are stimulated . An initial screen tested the effects of over 3000 drugs , most of which have been approved for use in humans . This screen identified and confirmed 24 drugs that trigger the growth of new β cells or other pancreatic cells in zebrafish larvae . Further investigation uncovered new roles for two signaling pathways that had not previously been linked to pancreatic growth . One pathway—the serotonin pathway , which is better known for transmitting signals in the brain—selectively stimulates the growth of new β cells . The work of Wang et al . therefore presents a number of possible drugs and pathways that could be targeted in the search for a new treatment for type 1 diabetes . Furthermore , this new whole-organism , high-throughput screening system could be used in the future to search for drugs that affect a range of other biological processes .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology", "cell", "biology" ]	2015	First quantitative high-throughput screen in zebrafish identifies novel pathways for increasing pancreatic β-cell mass
Host immune and physical barriers protect against pathogens but also impede the establishment of essential symbiotic partnerships . To reveal mechanisms by which beneficial organisms adapt to circumvent host defenses , we experimentally evolved ecologically distinct bioluminescent Vibrio fischeri by colonization and growth within the light organs of the squid Euprymna scolopes . Serial squid passaging of bacteria produced eight distinct mutations in the binK sensor kinase gene , which conferred an exceptional selective advantage that could be demonstrated through both empirical and theoretical analysis . Squid-adaptive binK alleles promoted colonization and immune evasion that were mediated by cell-associated matrices including symbiotic polysaccharide ( Syp ) and cellulose . binK variation also altered quorum sensing , raising the threshold for luminescence induction . Preexisting coordinated regulation of symbiosis traits by BinK presented an efficient solution where altered BinK function was the key to unlock multiple colonization barriers . These results identify a genetic basis for microbial adaptability and underscore the importance of hosts as selective agents that shape emergent symbiont populations . Identifying traits that are under selection by hosts is crucial to understanding the processes governing nascent symbiotic interactions between animals and microbes . The remarkable efficiency with which some bacteria evolve variation that enhances access to novel host niches indicates that adaptability may be an attribute of some bacterial genomes . Adaptive evolution to a new niche , such as a novel host , may involve reconciliation of constraints imposed by genomic content , conflicting regulation , and pleiotropy ( Morley et al . , 2015; Bedhomme et al . , 2012 ) . Given this context , global regulators could serve as effective targets of selection that drive adaptive leaps made by pathogenic or mutualistic microbes , as long as essential metabolic pathways are both sufficiently insulated from detrimental effects of mutation and available for integration with accessory functions ( Davenport et al . , 2015; Wolfe et al . , 2004; Jansen et al . , 2015 ) . Studies using experimental evolution have often revealed that adaptive evolution initially proceeds through regulatory changes , but few have identified the underlying mechanisms that promote adaptation or linked these processes to natural symbiotic systems ( Morley et al . , 2015; Bedhomme et al . , 2012; Kawecki et al . , 2012; Marchetti et al . , 2010; Guan et al . , 2013 ) . Members of the genus Vibrio , halophilic bacteria with a broad distribution in marine and brackish environments , have repeatedly evolved to colonize varied host niches ( Nishiguchi , 2002; Takemura et al . , 2014; Guerrero-Ferreira and Nishiguchi , 2007 ) , and as such , their study can provide an understanding of adaptability to host association . Bioluminescent Vibrio fischeri can be found among marine plankton ( Lee and Ruby , 1992 ) but the species is best known for its mutualistic light organ symbiosis with squid and fish species . V . fischeri is also well-known for its social quorum-sensing behavior , whereby communities of bacteria use diffusible pheromone signal molecules to synchronize gene expression in response to cell density ( Schuster et al . , 2013; Verma and Miyashiro , 2013; Waters and Bassler , 2005 ) . In squid-symbiotic V . fischeri , quorum sensing occurs through sequential activation by two different pheromone signals: the first signal ( C8-HSL ) 'primes' sensitive perception of the second signal ( 3-oxo-C6-HSL ) through enhanced LitR activity , which increases the levels of the LuxR pheromone sensor , thereby lowering the threshold for signal perception ( Fidopiastis et al . , 2002; Lupp and Ruby , 2004; Miyashiro et al . , 2010 ) . In turn , when LuxR binds to 3-oxo-C6-HSL , LuxR homodimerizes and directly activates the expression of the lux bioluminescence operon to produce light , which squid use for counter-illumination camouflage during their nocturnal foraging behavior ( Lupp et al . , 2003; Jones and Nishiguchi , 2004 ) . The symbiotic association between V . fischeri and the squid Euprymna scolopes has become a powerful system for interrogating mechanisms underlying bacterial colonization of metazoan host mucosal surfaces where colonists must overcome host defenses that limit infection by non-symbiotic bacteria , including pathogens ( Figure 1A ) . Once newly hatched squid entrap bacteria in mucus near the light organ , symbionts aggregate in this mucus and , in response to host attractants , subsequently swim through pores at the entrance of the nascent light organs ( Nyholm et al . , 2000 ) . As V . fischeri bacteria swim down the ducts and into the crypts , they face a 'gauntlet' of defenses that includes host-derived oxidative species ( Davidson et al . , 2004; Weis et al . , 1996; Small and McFall-Ngai , 1999 ) , as well as patrolling macrophage-like hemocytes that attach to other species of marine bacteria with higher affinity , subsequently killing these invading cells ( Nyholm et al . , 2009; Nyholm and McFall-Ngai , 1998; Koropatnick et al . , 2007 ) . These barriers ensure that only the correct symbiotic partner gains access to the crypts where host-provided nutrients support bacterial growth ( Graf and Ruby , 1998; Heath-Heckman and McFall-Ngai , 2011 ) . Striking parallels between beneficial V . fischeri colonization and pathogenic infection suggest that the selective pressures exerted by animal hosts may act on a common repertoire of bacterial traits used to circumvent host defensive obstacles ( Nyholm and McFall-Ngai , 2004 ) . 10 . 7554/eLife . 24414 . 003Figure 1 . Host selection mechanisms that shape adaptive evolution by V . fischeri . ( A ) Dorsal view of juvenile host E . scolopes ( left ) with box indicating the relative position of the ventrally situated symbiotic light organ . On the right , a schematic illustrating the stages at which host-imposed selection occurs during squid–V . fischeri symbiosis: host recruitment ( mucus entrapment , aggregation at light organ pores ) , initiation of symbiosis ( host defenses , including hemocyte engulfment and oxidative stress ) , and colonization and maintenance ( nutrient provisioning , sanctioning of non-luminous cheaters , continued hemocyte patrolling , and daily purging ) . ( B ) Symbiont population growth modeled for a single passage on the basis of growth dynamics of V . fischeri ES114 . Light-organ populations are initiated with as few as ~10 cells ( Wollenberg and Ruby , 2009; Altura et al . , 2013 ) or as much as 1% of the inoculum , but are reduced by 95% following venting of the light organ at dawn ( every 24 hr ) ( Boettcher et al . , 1996 ) . Shaded areas represent night periods whereas light areas represent daylight , which induces the venting behavior . ( C ) Experimental evolution of V . fischeri under host selection as described in Schuster et al . ( 2010 ) . Each ancestral V . fischeri population was prepared by recovering cells from five colonies , growing them to mid-log phase , and sub-culturing them into 100 mL filtered seawater at a concentration sufficient to colonize squid ( ≤20 , 000 CFU/mL ) . On day 1 , ten un-colonized ( non-luminous ) juvenile squid were communally inoculated by overnight incubation , during which bacteria were subjected to the first host-selective bottleneck . Following venting of ~95% of the light organ population , the squid were separated into isolated lineages in individual wells of a 24-well polystyrene plate containing filtered sea water with intervening rows of squid from an un-inoculated control cohort , the aposymbiotc control ( ‘apo control’ ) . Note that only two of the ten passage squid populations are shown . On days 2 , 3 , and 4 , after venting , squid were rinsed and transferred into 2 mL fresh filtered seawater . Luminescence was measured at various intervals for each squid to monitor colonization and the absence of contamination in aposymbiotic control squid . On the fourth day , the squid and half of the ventate were frozen at −80°C to preserve bacteria , and the remaining 1 mL ventate was combined with 1 mL of fresh filtered seawater , and used to inoculate a new uncolonized 24-hr-old juvenile squid . The process continued for 15 squid only for those lineages in which squid were detectably luminous at 48 hr post inoculation . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 003 Not all lineages of V . fischeri excel in symbiosis; this observation is reflective of the varied selective regimes that shape both genetic variation and adaptive potential as symbionts ( Lee and Ruby , 1994a; Nishiguchi et al . , 1998 ) . In habitats where squid hosts are present , they influence local V . fischeri populations by enriching the planktonic community with those strains that are most adept at symbiosis ( Lee and Ruby , 1994b ) . Squid recruit small founder populations ( ~10 bacteria ) and subject these to daily cycles of expulsion ( ‘venting’ ) and regrowth of 95% of light organ populations to >105 bacteria ( Wollenberg and Ruby , 2009 ) ( Figure 1B ) , thereby increasing the relative abundance of their light organ inhabitants in the surrounding seawater ( Lee and Ruby , 1994b ) . The bottlenecks within the venting cycle limit light organ microbial diversity , including variation that impairs symbiosis , such as 'cheaters' that do not contribute to the mutualism but benefit from symbiotic association ( Wollenberg and Ruby , 2009; Ruby and McFall-Ngai , 1999; Visick and McFall-Ngai , 2000 ) . However , host-imposed selection that drives the evolution of some lineages towards efficient colonization could hinder future adaptation and entail fitness trade-offs in other environments ( Soto et al . , 2014; Caley and Munday , 2003 ) . So , by contrast , planktonic V . fischeri strains that reside in habitats without hosts , or that are unable to compete for prime host niches , may maintain greater adaptability while being ineffective as symbionts ( Takemura et al . , 2014 ) . Deficiency in squid colonization correlates with insufficient or excessive luminescence or inadequate production of a symbiotic polysaccharide ( known as Syp ) , which is controlled by a horizontally acquired activator ( RscS ) in squid native strain ES114 ( Nishiguchi et al . , 1998; Yip et al . , 2006; Mandel et al . , 2009 ) . However , the absence of the rscS gene in some symbiotically proficient V . fischeri strains ( and likewise , the presence of rscS in deficient strains ) indicates that this regulator alone does not strictly determine squid colonization capacity ( Figure 1A , Figure 2—figure supplement 1 ) . Genomic similarity among closely related yet ecologically diverse strains has obscured relevant functional differences that are sometimes undetectable except in the symbiotic context ( Yip et al . , 2006; Mandel et al . , 2009; Travisano and Shaw , 2013 ) . For this study , we conducted a series of evolution experiments in which hatchling squid select among V . fischeri populations for mutants that are capable of initiating symbiosis , of persisting in the light organ , and of colonizing new squid when purged from the light organ ( Schuster et al . , 2010 ) . This cycle of host selection was designed to identify traits underlying symbiotic adaptive evolution and to reveal the evolutionary and genomic dynamics of this process . We chose as ancestors of our experimental lineages five V . fischeri strains that had variable aptitudes for squid symbiosis and were isolated from different niches , including the light organs of squid and fish , and various planktonic aquatic environments , including one without known hosts ( Table 1 ) . After we experimentally evolved replicate populations derived from each ancestor in parallel , we evaluated the genetic and phenotypic changes that occurred under host selection to examine how starting fitness and past evolutionary history influenced adaptability to squid symbiosis . To delineate the effects of host selection from neutral mutation accumulation , we also subjected V . fischeri to laboratory evolution in minimal seawater media . Previously , we demonstrated that altered luminescence was associated with several isolates following 15 serial host passages ( Schuster et al . , 2010 ) . Here , we report the genetic basis of this adaptation as well as the population dynamics of the symbionts under host selection . Importantly , we also identify the precise traits under selection that enabled these early-sweeping mutants to bypass key barriers imposed by hosts . 10 . 7554/eLife . 24414 . 004Table 1 . Strains and plasmids used in this study . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 004Strain nameDescriptionReference/sourceVibrio fischeri strains†ES114Isolated from Euprymna scolopes ( Boettcher and Ruby , 1990 ) MJ11Isolated from Monocentris japonica light-organ ( Haygood et al . , 1984 ) EM17Isolated from Euprymna morseii light-organ ( Ruby and Lee , 1998 ) H905Isolated from Hawaiian plankton ( Lee and Ruby , 1992 ) WH1Isolated from Massachusetts plankton ( Lee , 1994 ) RF1A4MJ11 ∆binK::ermB; EmRThis studyRF1A5MJ11 ∆sypK::aphA1; KmRThis studyRF1A6MJ11 ∆binK::ermB ∆sypK::aphA1; EmR KmRThis studyRF1A7MJ11 binK1 ∆sypK::aphA1; KmRThis studyMJ11EP2-3-2MJ11 binK4This studyMJ11EP2-3-3MJ11 binK4This studyMJ11EP2-3-4MJ11 binK4This studyMJ11EP2-3-5MJ11 binK4This studyMJ11EP2-3-6MJ11 binK4This studyMJ11EP2-3-7MJ11 binK4This studyMJ11EP2-3-8MJ11 binK4This studyMJ11EP15-3-1MJ11 binK4This studyMJ11EP15-3-3MJ11 binK4This studyMJ11EP15-3-4MJ11 binK4This studyMJ11EP15-3-7MJ11 binK4This studyMJ11EP15-3-8MJ11 binK4This studyMJ11EP2-4-1MJ11 binK1This studyMJ11EP2-4-3MJ11 binK1This studyMJ11EP2-4-4MJ11 binK1This studyMJ11EP2-4-5MJ11 binK1This studyMJ11EP2-4-6MJ11 binK1This studyMJ11EP15-4-1MJ11 binK1 tadC1G593T ( Schuster et al . , 2010 ) MJ11EP15-4-6MJ11 binK1This studyMJ11EP15-4-7MJ11 binK1This studyMJ11EP15-4-8MJ11 binK1This studyMJ11EP2-5-2MJ11 binK3This studyMJ11EP2-5-3MJ11 binK3This studyMJ11EP2-5-4MJ11 binK3This studyMJ11EP2-5-5MJ11 binK3This studyMJ11EP2-5-6MJ11 binK3This studyMJ11EP15-5-2MJ11 binK4This studyMJ11EP15-5-3MJ11 binK3This studyMJ11EP15-5-4MJ11 binK3This studyMJ11EP15-5-5MJ11 binK3This studyMJ11EP2-6-1MJ11 binK2This studyMJ11EP15-6-1MJ11 binK2 ( Schuster et al . , 2010 ) MJ11EP15-6-2MJ11 binK2This studyMJ11EP15-6-3MJ11 binK2This studyMJ11EP15-6-4MJ11 binK2This studyMJ11EP15-6-5MJ11 binK2This studyMJ11CE4-1MJ11 fliAG80DThis studyMJ11CE5-1MJ11 fliP∆476This studyStrain nameDescriptionReference/sourceEscherichia coli strainsDH5αF− recA1 endA1 hsdR17 supE44 thi-1 gyrA96 relA1Δ ( argF-lacZYA ) U169φ 80lacZΔM15λ −Gibco-BRLDH5αλpirsupE44 ΔlacU169 ( ϕlacZΔM15 ) recA1 endA1 hsdR17 thi-1 gyrA96 relA1; λpir phage lysogen ( Kolter and Helinski , 1978 ) CC118λpirΔ ( arg-leu ) araD ΔlacX74 galE galK phoA20 thi-1 rpsE rpoB argE ( Am ) recA1 , lysogenized with λ pir dam dcm ( Martín-Mora et al . , 2016 ) NEB 10-betaΔ ( ara-leu ) 7697 araD139 fhuA ΔlacX74 galK16 galE15 e14- Φ80dlacZΔM15 recA1 relA1 endA1 nupG rpsL ( SmR ) rph spoT1 Δ ( mrr-hsdRMS-mcrBC ) New England Biolabs , Ipswich , MATOP10F- mcrA Δ ( mrr-hsdRMS-mcrBC ) Φ80lacZΔM15 ΔlacX74 recA1 araD139 Δ ( ara-leu ) 7697 galU galK rpsL ( SmR ) endA1 nupGInvitrogen , Carlsbad , CAPlasmidspCR2 . 1-TOPOCommercial cloning vector; ApR KmRInvitrogen , Carlsbad , CApVSV105Mobilizable vector; ChR ( Dunn et al . , 2006 ) pRAD2E1pVSV105 carrying wild-type binK; ChRThis studypRF2A2pVSV105 carrying binK1; ChRThis studypCLD48pVSV105 carrying ES114 sypE; ChR ( Hussa et al . , 2008 ) pRF2A3pVSV105 carrying MJ11 binA; ChRThis studypVSV104Mobilizable vector; KmR ( Stabb and Ruby , 2002 ) pRF2A1pVSV104 carrying sypE; KmRThis studypRF2A4pVSV104 carrying binA; KmRThis studypKV111Mobilizable vector containing gfp; ChR ( Nyholm et al . , 2000 ) pRF2B7pCR2 . 1-TOPO containing MJ11 ∆sypK::aph1 SOE fragment; KmRThis studypVSV103Mobilizable vector containing lacZ; KmR ( Dunn et al . , 2006 ) pCAW7B1pVSV103 containing lacZ∆147–1080 bp; KmRThis studyApR , ampicillin resistance; ChR , chloramphenicol resistance; EmR , erythromycin resistance; KmR , kanamycin resistance;SmR streptomycin resistance . †Experimentally evolved strains are designated ‘MJ11EP#-#-#’ , where the first and second numbers after the ‘P’ designates the squid passage and population from which the strain was isolated , and the third number designates isolate number; strains derived from evolution in culture are designated ‘MJ11CE’ . To study the dynamic process of adaptation during symbiosis , we capitalized upon the squid’s natural recruitment process to found parallel populations of V . fischeri , and used the daily squid venting behavior to restrict and re-grow bacterial populations , which were passaged through 15 serial squid , encompassing 60 bottlenecking events and an estimated 290–360 generations ( Figure 1C ) ( Schuster et al . , 2010 ) . Multiple populations were derived in parallel from each of five ancestral strains using high-density inocula , up to 10 times the concentration required for native strain colonization , in order to overcome the colonization deficiencies of squid-maladapted strains ( Figure 2A and Materials and methods ) . 10 . 7554/eLife . 24414 . 005Figure 2 . Experimental evolution of Vibrio fischeri produced multiple alleles in the sensor kinase BinK . ( A ) . Phylogenetic relationship , symbiotic capacity , and mutations accrued during squid experimental evolution of ecologically diverse Vibrio fischeri strains . Strain relationships were inferred under maximum likelihood using whole genomes with RealPhy ( Bertels et al . , 2014 ) and with node supports calculated from 1 , 000 bootstraps . Graphic symbols for ecological niches represent the source of isolation . Intrinsic squid symbiotic capacities of the five experimentally evolved strains , as determined by the minimum inoculum concentration required for successful colonization of 90% of squid with a 3 hr ( ES114 , EM17 , and WH1 ) or over-night ( H905 and MJ11 ) inoculum , are represented by color spectrum . Consensus genomes for each of the parallel V . fischeri populations evolved through E . scolopes are shown on the right , with variants indicated by circles . Mutation details are shown in Table 2 . The mutations that were selected in host-passaged populations improved symbiotic capacity rather than general vigor . ( B ) BinK mutations arising in squid-evolved populations of MJ11 occurred in the HAMP and HATPaseC domains . A homo-dimer structural model for BinK using TMPRed and hybrid histidine kinase domain modelling ( Anantharaman and Aravind , 2000; Stewart and Chen , 2010 ) predicts that the accessory sensory Cache1 domain localizes to the periplasm whereas the remaining four functional domains ( accessory HAMP , and conserved HisKA , HATPaseC , and REC phosphorelay domains ) are cytoplasmic ( shown as gray band ) . A position-specific scoring matrix ( PSSM ) analysis for each of the squid-evolved BinK positions indicates whether a given amino acid is more ( positive ) or less ( negative ) likely to be functionally neutral . Scores for the substitutions incurred at these sites are shown in bold . Please refer to Figure 2—figure supplement 1 for a phylogenetic assessment of BinK orthology across Aliivibrio and V . fischeri strains . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 00510 . 7554/eLife . 24414 . 006Figure 2—figure supplement 1 . BinK orthology , conserved domains and squid-adapted binK alleles . ( A ) Unrooted maximum-likelihood ( ML ) phylogeny of all of the hybrid histidine kinases identified in V . fischeri genomes . Gene families were phylogenetically annotated using Escherichia coli references where possible ( not shown ) , otherwise using the ES114 locus tag . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 006 Genome sequencing of evolved isolates revealed that , although few detectable mutations arose during squid passaging , the majority of mutations that arose to a detectable frequency converged in a conserved gene ( locus VF_A0360 in V . fischeri ES114 ) ( Figure 2A–B , Figure 2—figure supplement 1 , Table 2 ) , which was recently identified as a biofilm inhibition kinase ( binK ) in the native symbiotic strain ES114 ( Brooks and Mandel , 2016 ) . Nine independent mutations mapping to the binK locus , most often without other co-occurring mutations , dominated multiple parallel evolved populations of the two strains initially most impaired at squid symbiosis: MJ11 and H905 ( Figure 2A , Table 2 ) . Given that MJ11 is a fish symbiont that lacks rscS , and H905 is a planktonic isolate from the squid habitat that is a poor squid colonizer despite harboring rscS , starting fitness better predicted the path of evolution than rscS content or past evolutionary history as inferred by either lineage or lifestyle ( Figure 2A , Figure 2—figure supplement 1 ) ( Mandel et al . , 2009; Lee and Ruby , 1994a ) . By contrast , very few mutations , all at unique loci , occurred in representative isolates derived from strains EM17 ( an Euprymna morsei squid symbiont ) and WH1 ( a planktonic strain from an environment without known hosts ) ( Figure 2A , Table 2 ) . Both of these strains have relatively greater starting fitness than MJ11 and H905 , further demonstrating that starting symbiont fitness influences its evolutionary path ( Wang et al . , 2016 ) . Finally , mutations were not detected in any of the representative isolates from the native squid symbiont ES114 ( Figure 2A , Table 2 ) , even though several mutations are known to improve its competitive dominance ( Fidopiastis et al . , 2002; Brooks and Mandel , 2016 ) . Laboratory-culture evolution of strain MJ11 that mimicked the population dynamics of squid-induced bottlenecks produced few mutations except for those localizing to flagellar genes fliA and fliP ( Table 2 ) . 10 . 7554/eLife . 24414 . 007Table 2 . Summary of mutations detected following experimental evolution of V . fischeri using Illumina genome resequencing and targeted Sanger sequencing . For culture-evolved populations of V . fischeri MJ11 , five isolates from each evolved population were combined to generate five metagenomes . For squid-evolved populations of MJ11 , EM17 , WH1 and H905 , individual isolates were sequenced from lineages that ultimately survived 15 host passages . Isolates saved from early evolutionary time-points ( host passage 2 ) are shown along with isolate genomes from the endpoint ( host passage 15 ) . Mean read depth and genome coverage for isolates analyzed with WGS are also provided . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 007AncestorEvolved Passage ( EP ) PopulationIsolate†Detected mutations‡Illumina sequencing statisticsbinK ( VFMJ11_A0397 ) tadC1 ( MJ11_0520 ) ; mutation ( reads ) All other mutations detectected by WGS gene ( locus ) ; mutation ( reads ) Reads% Mapped to ancestorCoverageallele/mutationMethod ( reads ) §ChIChIIMJ11211binK3/S311LWGS ( 35 ) ––375335299 . 5135 . 2118MJ11213binK3/S311LWGS ( 32 ) ––371708899 . 6134 . 2113 . 5MJ111514binK3/S311LWGS ( 17 ) ––171614499 . 546 . 842 . 5MJ11233binK4/N292TPCR/SSn . d . n . d . MJ11234binK4/N292TPCR/SSn . d . n . d . MJ11235binK4/N292TPCR/SSn . d . n . d . MJ11236binK4/N292TPCR/SSn . d . n . d . MJ11237binK4/N292TPCR/SSn . d . n . d . MJ11238binK4/N292TPCR/SSn . d . n . d . MJ111531binK4/N292TWGS ( 42 ) ––303114998 . 9104 . 393 . 5MJ111533binK4/N292TWGS ( 63 ) ––377771499 . 4114 . 6105 . 2MJ111534binK4/N292TWGS ( 42 ) ––342021299 . 5106 . 497 . 1MJ111537binK4/N292TWGS ( 41 ) ––330489199 . 590 . 382 . 5MJ111538binK4/N292TWGS ( 63 ) ––294874399 . 685 . 581 . 2MJ11241binK1/R537CWGS ( 62 ) ––2511256998478MJ11243binK1/R537CPCR/SSn . d . n . d . MJ11244binK1/R537CPCR/SSn . d . n . d . MJ11245binK1/R537CPCR/SSn . d . n . d . MJ11246binK1/R537CPCR/SSn . d . n . d . MJ11247binK1/R537CPCRn . d . n . d . MJ11248binK1/R537CPCRn . d . n . d . MJ11249binK1/R537CPCRn . d . n . d . MJ112410binK1/R537CPCRn . d . n . d . MJ112411binK1/R537CPCRn . d . n . d . MJ112412binK1/R537CPCRn . d . n . d . MJ112413binK1/R537CPCRn . d . n . d . MJ112414binK1/R537CPCR/SSn . d . n . d . MJ112415binK1/R537CPCR/SSn . d . n . d . MJ112416binK1/R537CPCR/SSn . d . n . d . MJ111541binK1/R537CWGS ( 131 ) G198V ( 85 ) –412614999 . 4117 . 8106 . 1MJ111546binK1/R537CWGS ( 61 ) G198V ( 55 ) –226682199 . 260 . 852 . 5MJ111547binK1/R537CWGS ( 89 ) G198V ( 93 ) –307443799 . 69283 . 6MJ111548binK1/R537CWGS ( 47 ) G198V ( 96 ) –290297799 . 58477 . 5MJ11252binK3/S311LWGS ( 26 ) ––377104899 . 6132 . 4123 . 7MJ11253binK3/S311LWGS ( 46 ) ––259551899 . 684 . 283 . 7MJ11254binK3/S311LWGS ( 20 ) ––178571399 . 560 . 657 . 2MJ11255binK3/S311LWGS ( 62 ) ––364134699 . 6117 . 4113 . 1MJ11256binK3/S311LWGS ( 81 ) ––412875199 . 6141 . 1134 . 8MJ111552binK4/N292TWGS ( 89 ) ––443082399 . 1152 . 3138 . 4MJ111553binK3/S311LWGS ( 10 ) ––324858099 . 38881 . 1MJ111554binK3/S311LWGS ( 59 ) ––360938299 . 5106 . 897 . 1MJ111555binK3/S311LWGS ( 28 ) ––291557099 . 587 . 482 . 6MJ11261binK2/K482NWGS ( 104 ) ––474856999 . 1164 . 6147MJ11262binK2/K482NPCR/SSn . d . n . d . MJ111561binK2/K482NWGS ( 75 ) ––276491099 . 483 . 275 . 5MJ111562binK2/K482NWGS ( 63 ) ––324096899 . 28872 . 6MJ111563binK2/K482NWGS ( 93 ) ––381436799 . 5108 . 1101 . 7MJ111564binK2/K482NWGS ( 108 ) ––371463899 . 5121 . 485 . 7MJ111565binK2/K482NWGS ( 90 ) ––300636299 . 485 . 572MJ1115Culture1mg–––1031929198272 . 8237 . 8MJ1115Culture3mg–––749684798 . 2196 . 7195MJ1115Culture4mg––fliA ( VF_1834 ) ; G80D ( 63 ) 289416098 . 376 . 667 . 4MJ1115Culture5mg––fliP ( VF_1842 ) ; ∆1 @ 476/870nt ( 110 ) 557143997 . 9148 . 5132 . 1MJ1115Culture2mg–––541103298144 . 2129 . 4WH11541–––727324498 . 6257 . 8251 . 1WH11542–––214438199 . 661 . 465 . 1WH11543–––226023299 . 662 . 166 . 6WH11544–––234142899 . 761 . 665WH11551––NADH oxidase ( VF_A0027 ) ; A402T ( 62 ) 173210699 . 560 . 864 . 7WH11552––NADH oxidase ( VF_A0027 ) ; A402T ( 61 ) 173709599 . 461 . 964 . 9WH11553––NADH oxidase ( VF_A0027 ) ; A402T ( 80 ) 21948479660 . 863 . 4WH11554–––219198699 . 861 . 964 . 9WH11561–––925654799 . 3212 . 6220 . 3WH11562–––213114499 . 66264 . 7WH11563–––190885799 . 562 . 460 . 5EM171562–––261160999 . 693 . 389 . 3EM171571–––669013798 . 6225 . 8227 . 1EM171574–––297742999 . 583 . 482 . 1EM171575––icmF ( VF_0992 ) ;S171N , ( 72 ) 241428899 . 571 . 671 . 5EM171581–––317798199 . 597 . 594 . 6EM171582–––313817599 . 592 . 492 . 3EM171583–––281009999 . 581 . 280EM171585–––523041199 . 6144 . 9143 . 2EM171591–––802293599 . 4184 . 2173 . 5EM171592–––334621699 . 6113 . 7106 . 9EM171593––gdh2 ( VF_1284 ) ; E732D ( 72 ) 348418899 . 595 . 793 . 2EM171595–––244575899 . 572 . 872 . 6H9051511 ( ∆37168 bp/25 genes ) WGS ( 230 ) –IlvY ( VF_2529 ) ; M25I ( 233 ) 764550894 . 2250 . 4222 . 1H9051512 ( ∆37168 bp/25 genes ) WGS ( 167 ) –IlvY ( VF_2529 ) ; M25I ( 112 ) 353111496 . 8117 . 5104 . 4H9051513 ( ∆37168 bp/25 genes ) WGS ( 175 ) –IlvY ( VF_2529 ) ; M25I ( 97 ) 359668997122 . 3109 . 1H9051522∆16 bp@ 498/2595WGS ( 75 ) –purR ( VF_1572 ) ; N71T ( 60 ) 281938797 . 691 . 479 . 6H9051524∆16 bp@ 498/2595WGS ( 94 ) –purR ( VF_1572 ) ; N71T ( 52 ) 299297896 . 9103 . 391 . 4H9051525∆16 bp@ 498/2595WGS ( 90 ) –purR ( VF_1572 ) ; N71T ( 95 ) 384483096 . 3123 . 6109H905231––tadF2 ( VF_A0228 ) ; G21D ( 68 ) 339361190 . 799 . 592 . 2H9051531––tadF2 ( VF_A0228 ) ; G21D ( 140 ) 797477391 . 5147 . 9143 . 9H9051532T195IWGS ( 65 ) –tadF2 ( VF_A0228 ) ; G21D ( 28 ) 198987595 . 565 . 458 . 2H9051533––tadF2 ( VF_A0228 ) ; G21D ( 77 ) 325389996 . 7103 . 894 . 4H9051534––tadF2 ( VF_A0228 ) ; G21D ( 58 ) 324274997 . 1103 . 394 . 7H9051535––tadF2 ( VF_A0228 ) ; G21D ( 25 ) 219077195 . 967 . 559H9051541E43WGS ( 102 ) ––665138592125 . 1130H9051543E43WGS ( 111 ) ––403237396 . 4135 . 9120 . 4H9051544E43WGS ( 187 ) ––612216895 . 8203 . 4179 . 4H9051545E43WGS ( 90 ) ––317781796 . 7100 . 890 . 6H9051551∆1 bp @ 2325/2595ntWGS ( 113 ) ––716687090 . 4134 . 5130 . 9H9051552∆1 bp @ 2325/2595ntWGS ( 94 ) ––370394696 . 7118 . 6108 . 3H9051553∆1 bp @ 2325/2595ntWGS ( 66 ) ––282810297 . 498 . 690 . 4H9051554∆1 bp @ 2325/2595ntWGS ( 109 ) ––472157597158 . 9143 . 8H905261T195IWGS ( 105 ) –tadF2 ( VF_A0228 ) ; G21D ( 28 ) 27436939483 . 373 . 6H9051563T195IWGS ( 142 ) –tadF2 ( VF_A0228 ) ; G21D ( 49 ) 559477197 . 5191 . 7175 . 3H9051564T195IWGS ( 105 ) –tadF2 ( VF_A0228 ) ; G21D ( 37 ) 336120696115 . 9101 . 4†Individual characterized strain collection names assigned to isolates were derived from their ancestral lineage ( e . g . MJ11 ) , evolved passage ( e . g . EP2 ) , the population ( e . g . 1 ) , and isolate number ( e . g . 1 ) , which in the preceding example would give rise to strain collection name of MJ11EP2-1-1 . Isolates in bold served as allelic binK representatives for further assays . mg: metagenome sequencing by pooling five isolates from a population . ‡The presence of mutations was determined from Illumina short read ( 100PE ) whole genome sequencing ( WGS ) , by allele-specific PCR ( PCR ) , and/or by locus PCR-amplification , followed by Sanger sequencing ( SS ) . ‘–' indicates that no mutations were identified at this locus by breseq ( Deatherage and Barrick , 2014 ) in this isolate using WGS . ‘n . d . ’ indicates that the presence of mutations at this locus was not determined . §The number of reads supporting the mutation call from WGS data is provided . Mutations were called for sites with minimum coverage of 20 mappable reads . Mutations identified by Sanger sequencing ( SS ) of PCR-generated amplicons were confirmed from alignments of both forward and reverse reads . Coding genes reference V . fischeri ES114 locus tags . To examine more thoroughly the evolutionary process giving rise to the convergent binK mutations , we focused on lineages derived from the fully sequenced and relatively well-characterized fish symbiont MJ11 . Only five of ten squid exposed to the same inoculum population successfully passaged symbionts to the second recipient squid , and each successful lineage harbored binK variants ( Table 2 ) . Among these were four unique alleles wherein the acquired substitutions mapped to two of the five conserved functional domains of the deduced BinK protein ( Figure 2B , Table 2 ) . Despite standing variation in binK across V . fischeri strains , the four point mutations in experimentally evolved MJ11 lineages occurred at positions that , with the exception of binK3 ( S311L ) , are invariant in natural strains and thus are likely to represent novel allelic variants that are not convergent with the native symbiont ( Figure 2B ) . Further analysis of the acquired mutations using a position-specific scoring matrix ( PSSM ) also provided evidence that the mutations in binK1 ( R537C ) , binK2 ( K482N ) and binK3 ( S311L ) would influence protein function ( Figure 2B ) . In each of the five successful squid-evolved lineages of MJ11 , binK variants dominated the light-organ populations by the third experimental squid ( Table 2 ) . If beneficial variants in this or any other locus were among the remaining five light-organ populations , their failure to colonize the second experimental squid amounted to early extinction of these lineages . The repeated sweeps of novel binK mutations that occurred during squid evolution , but not during laboratory culture evolution , suggested that binK variants were squid-adaptive ( Table 2 ) ( Dillon et al . , 2017 ) . To evaluate the contribution of evolved binK alleles specifically to improved symbiotic colonization , we assessed the colonization efficiency of the squid-evolved isolates and the ancestor using inoculum doses typically used for the native symbiont strain ES114 ( Figure 2A ) . Each squid-evolved binK variant vastly improved colonization efficiency ( Figure 3A ) , but they were not significantly more fit in laboratory culture ( which would be indicative of mutants enhancing general vigor ) when compared to ancestral MJ11 ( Figure 3B ) . Moreover , whereas two of the five culture-evolved populations of MJ11 evolved culture-adaptive flagellar mutations that improved fitness in culture ( Figures 2A and 3B , Table 2 ) , none accrued binK mutations ( Table 2 ) or improved as squid symbionts ( Figure 3A ) . Evolved isolates that have mutations mapping to different binK domains were competitively indistinguishable from each other in symbiotic fitness ( permutation t-test , p=0 . 348 ) ( Figure 3—figure supplement 1 ) , despite evidence that the binK1 allele ( encoding an R537C substitution , Figure 2B , Table 2 ) appeared slightly more efficient at squid colonization when singly inoculated ( Figure 3A ) . 10 . 7554/eLife . 24414 . 008Figure 3 . Evolved binK alleles enhanced host colonization and conferred a fitness tradeoff in non-host environments . ( A ) Symbiotic colonization efficiency of MJ11 and derivatives in squid . Percentage of squid colonized by culture-evolved ( c1–c5 ) and squid-evolved ( binK1- binK4 , bolded isolates in Table 2 ) derivatives of MJ11 . Three hours after a cohort of 10–20 squid were inoculated with 3000 CFU/mL of each MJ11 strain , the squid were separated into individual vials , and colonization percentages determined by detectable luminescence at 24 hr . Bars: 95% CI . ( B ) Growth rates of MJ11 and evolved strains during competition in laboratory culture . Average growth rates ( realized Malthusian parameters ) of ΔbinK , squid-evolved binK and culture-evolved flagellar mutants ( fliA and fliP variants , see Table 2 ) following in vitro culture competition in minimal media with ancestral binK+ MJ11 , estimated using CFU yields of each competitor recovered at regular intervals . Bars: 95% CI . The diagonal line indicates 1:1 growth . Please refer to Figure 3—figure supplement 1 for data on the competitive abilities of binK1 and binK3 during colonization . Please refer to Figure 3—figure supplement 2 for symbiotic yields ( CFU ) of ES114 and MJ11 strains after 24 and 48 hr . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 00810 . 7554/eLife . 24414 . 009Figure 3—figure supplement 1 . Relative competitive ability of binK1 and binK3 variants to colonize squid . In vivo competitions suggest no competitive advantage in squid colonization between evolved V . fischeri MJ11 variants carrying either HAMP or HATPaseC domain mutations . Relative competitive indices for binK1 and binK3 MJ11 variants ( carrying HATPaseC and HAMP domain mutations , respectively ) used to co-inoculate squid across a range of inoculum densities . Points above or below zero represent squid light organs that are dominated by bink3 or bink1 , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 00910 . 7554/eLife . 24414 . 010Figure 3—figure supplement 2 . Growth of strain ES114 and strain MJ11 and its binK variants in squid light organs 24 or 48 hr after inoculation . Yields of symbionts determined by plating serial dilutions of squid homogenate as described previously ( Whistler and Ruby , 2003 ) . Note: the Y-axis is log-scaled . Bars: 95% CI . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 010 To quantify empirically the selective advantage ( selective coefficient: s ) conferred by a representative binK allele that arose to early dominance before co-occurring mutations , we co-inoculated squid with MJ11 and low densities of a binK1 variant ( a fully sequenced second passage squid isolate that we named MJ11EP2-4-1 , see Tables 1 and 2 ) , simulating the conditions under which we predict the variants evolved given the low mutation rate of V . fischeri ( Dillon et al . , 2017 ) ( Figure 4A–B ) . These experiments revealed that even at an extremely low frequency ( e . g . , one binK1 variant per 10 , 000 wild-type MJ11 bacteria , which amounted to only 50 binK1 variant cells in an 104 CFU•ml−1 inoculum for 10 squid ) , the binK1 variant colonized multiple squid ( Figure 4—figure supplement 1 ) . The estimated selective advantage , based on the ratios of the growth rates ( a measure of relative competitiveness ) of wild-type bacteria and the binK1 variant in light-organ populations of co-colonized squid , was independent of initial allele frequencies in the inoculum , consistent with a model of hard selection ( Figure 4B , Figure 4—figure supplement 1 ) ( Saccheri and Hanski , 2006 ) . The estimated selective advantage of the squid-adaptive binK1 allele continued to increase by more than 60% between 24 and 48 hr in squid ( 24 hr: 1 . 1; 48 hr: 1 . 8 ) ( Figure 4B ) . The competitive advantage conferred by binK1 therefore extended beyond the initial colonization events ( the ‘initiation phase’ during the first 24 hr ) to include the period of competitive re-growth following the daily venting of 95% of the bacterial population ( the ‘maintenance phase’ ) , when several different host sanctions are implicated ( Figure 1A–B; Figure 4A–B ) . By contrast , squid-adaptive binK alleles reduced fitness relative to wild-type ( binK+ ) in laboratory planktonic culture ( −0 . 18 > s > −1 ) , demonstrating a modest fitness cost for some alleles in the absence of hosts ( Figure 3B ) . 10 . 7554/eLife . 24414 . 011Figure 4 . Empirical and modeled estimates of selective advantage in evolving V . fischeri symbiont populations . ( A ) Conceptual overview of symbiont population dynamics during growth in inoculum and following host colonization ( black line ) , including daily host-imposed bottlenecks . ( B ) Comparison of the selection coefficients conferred by binK1 in strain MJ11EP2-4-1 ( harboring no other mutations ) relative to binK+ from co-inoculated squid light organs after 24 or 48 hr . The selective advantage ( i . e . , relative competitiveness ) of the evolved allele increased significantly during this period from 1 . 1 to 1 . 8 ( Fisher-Pitman permutation test , p=0 . 0011 ) . Each circle represents the selective advantage of each strain measured from the strain ratios recovered in an individual hatchling . Please refer to Figure 4—figure supplement 1 for the effect of starting binK1 frequencies and inoculum densities on estimates of selective advantage . ( C ) Modeled survival probabilities for new beneficial alleles arising in a growing symbiont population facing host-imposed bottlenecks . The gray shaded curves estimate the survival probability of new mutants following the subsequent population bottleneck , which depends on both the generation of growth in the inoculum or host in which they arise ( x-axis ) and the selective advantage ( s ) conferred by mutation ( gray shading ) . Notably , beneficial variants that arise early in inoculum culture are likely to survive extinction at the subsequent bottleneck , and this probability of survival rapidly decreases even when conferring a large selective coefficient . On the basis of this model , for example , a mutation conferring a large selective advantage ( s ~2 ) would have less than a 10% chance of surviving the subsequent colonization bottleneck if it arose during the tenth generation of inoculum growth ( red line ) . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 01110 . 7554/eLife . 24414 . 012Figure 4—figure supplement 1 . Estimates of the selective advantage of the binK1 allele during squid colonization across a range of starting frequencies and inoculum densities . Comparison of selection coefficients conferred by binK1 in strain MJ11EP2-4-1 ( ‘Evo’ ) ( harboring no other mutations ) relative to binK+ ( ‘Anc’ ) from co-inoculated squid light organs . Each point represents the selective advantage of each strain measured from the strain ratios recovered in an individual hatchling . The estimated selective advantage conferred by the evolved binK1 allele was not influenced by starting frequency ( A ) ( R2 = 0 . 025 , pfrequency = 0 . 62 ) , but it was marginally influenced by density ( B ) ( R2 = 0 . 025 , pdensity = 0 . 03 ) , based on a multiple regression analysis . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 012 Even given the extreme fitness advantage attained by the binK1 variant growing within squid ( Figure 4B ) , the repeated recruitment of binK variants among the few cells that initiated symbiosis is remarkable . Not only must the mutations confer exceptional host-selected advantages , but these rare variants must also survive extinction ( i . e . , loss from the population as the result of genetic drift ) during repeated host-imposed bottlenecks ( Nyholm and McFall-Ngai , 2004; Wollenberg and Ruby , 2009 ) . To examine how mutation timing , strength of selective advantage and population size influenced the ability of rare beneficial variants to attain a high frequency in populations passaged between squid , we modeled the evolutionary dynamics and probability of survival of individual variants within a population experiencing recruitment , growth , and repeated cycles of bottlenecking within a single squid over a theoretical range of selection coefficients , applying generalized population and growth parameters derived from native strain ES114 in the squid–Vibrio symbiosis ( Wollenberg and Ruby , 2009; Altura et al . , 2013; Wahl and Gerrish , 2001 ) ( see Materials and methods ) ( Figure 4C ) . The model predicts that in order for beneficial variation to ensure survival during the extreme bottleneck imposed by the host during initial recruitment , mutants would have to arise early during population expansion and confer s ~6 . Conversely , any beneficial variants arising in light organs during the maintenance of symbiosis , which is characterized by daily venting bottlenecks and re-growth , have increased survival odds even if they confer a lower selective advantage , but the probability of their occurrence is reduced because of the small effective population size ( Materials and methods and Figure 4C ) . Thus , the model suggests that the mutants were most probably present in the starting inoculum and were recruited into symbiosis by members of the first squid cohort . Using a high-resolution measure of the V . fischeri ES114 genomic mutation rate ( Dillon et al . , 2017 ) , we predict that as many as 185 individual mutations could have spontaneously arisen in binK ( see Materials and methods ) during growth of the inoculum ( Figure 4A ) . Despite their low initial frequency , any new alleles that arose by the tenth generation of inoculum growth and ultimately conferred a high selective advantage in squid ( i . e . , s > 1 ) would be expected to survive the first host passage ~10% of the time ( Figure 4C , red line ) . Incidentally , the observed survival of each binK allele amounted to 1 or 2 out of 10 experimental squid . Thus , the empirical estimates of the selective advantage conferred by binK1 in the symbiotic environment are supported by theoretical estimates derived from a model of extraordinarily strong selection during repeated bottlenecks ( Wahl and Gerrish , 2001 ) . The substantial fitness gain conferred by the binK1 allele within the first 24 hr of colonization ( Figure 4B ) suggested that it enhanced the early colonization behaviors of MJ11 ( Figure 1A and B ) ( Nyholm and McFall-Ngai , 2004 ) . Syp mediates the aggregation of native strain ES114 in squid mucus and its overproduction enhances the aggregation ability of this same strain ( Brooks and Mandel , 2016; Nyholm and McFall-Ngai , 2003; Shibata et al . , 2012 ) . Therefore , we evaluated whether aggregation of the squid-evolved binK1 variant was altered . binK1 improved aggregation at the entrance to light organs compared to wild-type MJ11 ( Figure 5A , Figure 5—figure supplement 1 ) . By contrast , it did not cause colony wrinkling ( data not shown ) , a proxy for Syp-mediated biofilm production by strain ES114 ( Brooks and Mandel , 2016; Shibata et al . , 2012 ) . Still , binK1 dramatically increased in vitro biofilm production compared to MJ11 , as determined by surface adherence ( Figure 5B ) , perhaps reflecting the presence of more complex biofilm matrices such as cellulose whose expression was enhanced by the bink1 and ∆binK mutations ( Figure 5—figure supplement 2 , Appendix 1 ) ( Shibata et al . , 2012; Darnell et al . , 2008; Bassis and Visick , 2010 ) . To investigate the basis of increased biofilm formation by the binK1 variant , we overexpressed genes encoding a repressor of Syp , sypE ( Morris and Visick , 2013 ) , and of cellulose , binA ( Figure 5—figure supplement 3 ) ( Bassis and Visick , 2010 ) . Each regulator abolished the enhanced biofilm phenotype of the binK1 variant , indicating that both matrix substrates contributed to this trait ( Figure 5B ) . To test the role of Syp directly , we also introduced a ∆sypK mutation , which functionally eliminates Syp biofilm production by strain ES114 ( Shibata et al . , 2012 ) . The mutation reduced biofilm by the binK1 variant , indicating that the variant's improved biofilm production involved Syp production ( Figure 5B ) . 10 . 7554/eLife . 24414 . 013Figure 5 . Host-adapted binK1 improved initiation phenotypes through enhanced biofilm . ( A ) V . fischeri MJ11 aggregate formation near light-organ ducts . Host tissue stained with CellTracker Orange . Symbionts carry GFP plasmids ( pKV111 ) ( Nyholm et al . , 2000 ) . Micrographs show representative V . fischeri aggregates following the dissection of 30 newly hatched animals incubated with each strain . Aggregates were visualized between 2 and 3hr after of inoculation using a Zeiss LSM 510 Meta laser-scanning confocal microscope . Please refer to Figure 5—figure supplement 1 for additional views of aggregate formation . ( B ) Biofilm production ( crystal violet staining relative to MJ11 ) by wild-type MJ11 ( binK+ ) , squid-adaptive binK1 and ∆binK variants in the presence of either empty vector ( EV , pVSV105 ) ( white fill ) , Syp biofilm repressor sypE ( pCLD48 ) ( hatched fill ) , or cellulose repressor binA ( pRF2A3 ) ( gray fill ) . n = 12–16 biological replicates . See Figure 5—figure supplement 2 for evidence of increased cellulose in binK variants , and Figure 5—figure supplement 3 for biofilm repressor schematic . Followed by influence of a sypK deletion on biofilm production of MJ11 and binK variants . n = 10 biological replicates . ( C ) Binomial mean of survival following exposure to hydrogen peroxide of wild-type MJ11 ( binK+ ) , squid-adaptive binK1 and ∆binK variants in the presence of either empty vector ( EV , pVSV105 ) ( white fill ) , sypE ( pCLD48 ) ( hatched fill ) , or binA ( pRF2A3 ) ( gray fill ) . n = 20–50 biological replicates . Followed by influence of a sypK deletion ( diagonal line overlay ) on population survival of MJ11 and binK variants ( color fill ) . n = 15–106 biological replicates . Error bars 95% CI . Significant p values ( p<0 . 05 ) are indicated above each comparison . p<2 . 2e-16 . Although the effects of overexpression of binA and deletion of sypK on oxidative resistance in the ∆binK variant followed the same trends as these genes in binK1 , the reductions were only marginally significant ( p=0 . 051 and 0 . 15 , respectively ) . Please refer to Figure 5—figure supplement 2 for transcriptomic evidence of reduced expression of two cellulose loci in the ∆binK mutant . A schematic of the impact of the BinA and SypE repressors on biofilm substrates is available as Figure 5—figure supplement 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 01310 . 7554/eLife . 24414 . 014Figure 5—figure supplement 1 . In vivo aggregation behavioral changes conferred by evolved binK1 variant . ( A–B ) Aggregation of ancestral ( A ) and evolved ( B ) MJ11 on host mucosal epithelium prior to colonization . Host tissue stained with CellTracker Orange . Symbionts carry GFP plasmids ( pKV111 ) ( Nyholm et al . , 2000 ) . Micrographs show representative V . fischeri aggregates following the dissection of 30 newly hatched animals incubated with each strain . Aggregates were visualized between 2 and 3 hr after inoculation using a Zeiss LSM 510 Meta laser scanning confocal microscope . Scale bars: 24 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 01410 . 7554/eLife . 24414 . 015Figure 5—figure supplement 2 . Transcriptional shifts associated with binK variants . Transcriptomic differences between wild-type MJ11 ( binK+ ) , squid-adapted MJ11 binK1 , and MJ11 ∆binK for the coding loci in the MJ11 genome as determined by RNA-Seq . Variants were sampled during early log growth ( OD600 ~0 . 25 ) in rich media ( SWTO ) prior to detectable biofilm production from four biological replicates for each strain . Green indicates increased expression; red indicates reduced expression relative to mean expression per locus ( i . e . , read counts z-scaled relative to mean logCPM ) . The heat map only displays loci for which mean expression in a binK variant differed from that in MJ11 at a FDR significance threshold of 0 . 05 ( Table 3 ) . The colored labels refer to compounds whose metabolism , transport , or synthesis are affected by the expression of these genes . Genes involved in cellulose synthesis are indicated with arrows ( VFMJ11_A1000- cellulose synthase operon C protein , and VFMJ11_A1007- cellulose synthase operon protein YhjU ) and log fold change ( logFC ) relative to wild-type is indicated for binK1 and then ∆binK . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 01510 . 7554/eLife . 24414 . 016Figure 5—figure supplement 3 . Schematic of regulation by the biofilm repressors SypE and BinA . SypE represses Syp biofilm production post transcriptionally ( Morris and Visick , 2013 ) . BinA represses cellulose , but not Syp , biofilm formation by increasing phosphodiesterase activity ( Bassis and Visick , 2010 ) . Black-capped lines indicate negative regulation . Gray arrows indicate transcription/translation . Note that because binA is expressed from a syp locus promoter , activation of the syp locus leading to Syp production also leads to repression of cellulose . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 016 Even as the increase in aggregation could confer a fitness gain by binK variants during the initiation phase of symbiosis , aggregation is a trait that is variable enough to call into question whether it could explain the dominance of binK variants . Improved aggregation alone would not cause the 60% increase in fitness observed during maintenance of the symbiosis ( Figure 4B , Figure 3—figure supplement 2 ) . Furthermore , to our knowledge , no study has yet evaluated whether biofilm imparts symbiotic fitness beyond aggregation . Because of the potential that biofilm could confer survival in the face of environmental insults , we evaluated whether binK1 impacted survival upon peroxide exposure , as oxidation is among the host’s defensive arsenal ( Small and McFall-Ngai , 1999; Visick and Ruby , 1998 ) ( Figure 1A ) . The binK1 and ∆binK variants survived oxidation better than MJ11 , and overexpression of the Syp repressor sypE or the cellulose repressor binA decreased survival ( Figure 5C ) . Deletion of sypK in binK variants also reduced survival further , supporting the conclusion that Syp production confers resistance to oxidation ( Figure 5C ) . Enhanced biofilm production and survival following peroxide exposure are correlated , suggesting that Syp and cellulose biofilm contribute to oxidative resistance conferred by binK variants . During migration and upon reaching the squid light organ , potential symbionts must contend with host phagocytic , macrophage-like hemocytes which bind , engulf and destroy bacteria ( Figure 1A ) ( Nyholm and McFall-Ngai , 1998 ) . The ability of squid hemocytes to bind preferentially to non-symbiotic bacterial species is well established , but differential recognition among V . fischeri has only been reported for the native strain ES114 and its genetic variants ( Nyholm et al . , 2009 ) . Therefore , we evaluated whether squid hemocytes preferentially target non-symbiotic MJ11 , and whether the altered biofilm capacity conferred by binK1 promoted evasion of the host’s innate immune system ( Figure 6 , Figure 6—figure supplement 1 ) . Juvenile squid hemocytes bound wild-type MJ11 to a greater extent than they did the native strain ES114 , and this binding was comparable to that observed with other species of bacteria , such as V . harveyi ( Figure 6 ) . In contrast , the binK1 variant resisted host hemocyte binding at a level that was comparable to squid-native strain ES114 ( Figure 6 ) . Overexpression of either sypE or binA reduced immune evasion by ES114 , and sypE also significantly reduced immune evasion by the squid-adaptive binK1 variant , demonstrating that production of Syp and cellulose extracellular matrices mediated this trait . These results provide the first experimental evidence that Syp and cellulose production by native and non-native V . fischeri strains contribute to host hemocyte response . In addition , these findings demonstrate that , by altering biofilm substrate production , binK1 could improve the survival of MJ11 during multiple host-imposed selective checkpoints . 10 . 7554/eLife . 24414 . 017Figure 6 . Biofilm production by squid-adaptive binK1 variants mediates hemocyte evasion . ( A ) Relative efficiency of squid hemocyte binding of GFP-labelled V . fischeri strains including: squid-native symbiont ES114 , binK+ MJ11 , ∆binK MJ11 ( RF1A4 ) , binK1 MJ11 , and shellfish pathogen V . harveyi B392 . ( B ) Relative efficiency of squid hemocyte binding of squid-native symbiont ES114 and squid-adapted bink1 MJ11 carrying the empty vector ( pVSV104 ) , sypE ( pRF2A1 ) or binA ( pRF2A4 ) . N = 30–52 hemocytes quantified per strain . Error bars: 95% CI . Significant p-values ( p<0 . 05 ) are indicated above each comparison . Please refer to Figure 6—figure supplement 1 for micrographs of Vibrio–hemocyte interactions . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 01710 . 7554/eLife . 24414 . 018Figure 6—figure supplement 1 . In vitro response of squid hemocytes to wild , squid-evolved and mutant Vibrio . The micrographs show examples of hemocyte-bound non-symbiotic ( A: Vibrio harveyi ) , squid-symbiotic ( B: V . fischeri ES114 ) , squid-naive ( C: V . fischeri MJ11 binK+ ) and squid-adapted ( D: MJ11 binK1 ) cells . The mean number of GFP-labelled Vibrio cells bound by hemocytes was quantified relative to total bacterial count in a 60 µm radius using confocal microscopy at 63X magnification , following one hour of bacterial exposure . Squid hemocytes in red ( CellTracker Orange ) , Vibrio in green ( GFP ) . Scale bars: 12 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 018 Given the demonstrated importance of Syp to colonization initiation ( Shibata et al . , 2012 ) , we predicted that enhanced Syp production by binK derivatives improved colonization ( Figure 3A ) . Although both Syp and cellulose conferred several phenotypes that are important to the symbiosis ( Figures 5B , C and 6 ) , a role for cellulose during colonization processes has yet to be demonstrated . Here , repression of either Syp ( through expression of sypE ) or cellulose ( through expression of binA ) significantly reduced colonization efficiency by MJ11 and its binK derivatives ( Figure 7A ) . However , sypE impaired colonization by ∆binK to a greater extent than did binA . This suggested to us that Syp may play a greater role than cellulose in colonization , in agreement with the hemocyte evasion results ( Figure 6B ) . Alternatively , sypE could produce other regulatory effects ( Shibata et al . , 2012; Bassis and Visick , 2010; Ray et al . , 2015; Miyashiro et al . , 2014 ) . To address the contribution of Syp to improved colonization more directly , we evaluated the impact of a sypK deletion , which eliminates colonization by the native symbiont ( Shibata et al . , 2012 ) . Loss of sypK had no discernable effect on the colonization of MJ11 , presumably because Syp is already under-produced ( Mandel et al . , 2009 ) , but as expected , it significantly reduced colonization by both binK1 and ∆binK variants ( Figure 7B ) . Notably , deletion of sypK only modestly impaired colonization ( 25% reduction ) by the binK1 variant , suggesting that Syp is not the only contributor to its enhanced colonization . Elimination of sypK had a greater impact on colonization by the ∆binK mutant than by the binK1 variant , reducing its colonization to wild-type levels , which could reflect the greater fitness cost associated with the ∆binK allele ( Figure 3A and B ) or might allude to unique functions associated with the evolved binK1 allele . Together , these results suggest that both Syp and cellulose contribute to enhanced colonization efficiency in the binK1 and ∆binK variants . 10 . 7554/eLife . 24414 . 019Figure 7 . Contribution of Syp and cellulose to improved squid colonization by binK variants . ( A ) Colonization efficiency ( % colonized squid at 24 hr ) by wild-type MJ11 ( binK+ ) , squid-adaptive binK1 and ∆binK variants in the presence of empty vector ( EV , pVSV105 ) ( white fill ) , the Syp repressor sypE ( pCLD48 ) ( hatched fill ) , or the cellulose repressor binA ( pRF2A3 ) ( gray fill ) . n = 15–20 biological replicates . ( B ) Influence of a sypK deletion on colonization efficiency of MJ11 and binK variants . n = 31–52 biological replicates . Error bars: 95% CI . Significant p-values ( p<0 . 05 ) are indicated above each comparison . p<2 . 2e-16 . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 019 Bioluminescence serves as the currency of this symbiosis , and yet the correlation of excessive bioluminescence with poor symbiotic ability suggests that luminescence intensity is a phenotype shaped by host selection ( Lee and Ruby , 1994a; Nishiguchi et al . , 1998; Visick et al . , 2000 ) . Squid-adapted derivatives of MJ11 – where the wild-type ancestor is ≥1 , 000 fold brighter than native symbiont strain ES114 ( Schuster et al . , 2010 ) – evolved a delay in luminescence induction compared to their ancestors . To determine whether quorum-sensing thresholds had been altered by binK mutations , we quantified the production of AinS-synthesized C8-HSL and LuxI-synthesized 3-oxo-C6-HSL signals and the concurrent luminescence production by wild-type MJ11 and by binK1 , and ∆binK variants during the period of induction ( OD600 1 . 1 ) ( Figure 8 ) . For all three strains , luminescence correlated with 3-oxo-C6-HSL concentration ( Figure 8A ) ( r2 = 0 . 857 , p=6 . 4×10−13 ) and not C8-HSL concentration ( r2 = 0 . 105 , p=0 . 1 ) . When compared to the wild-type , both the binK1 and the ΔbinK variant alleles reduced 3-oxo-C6-HSL production and the corresponding luminescence by an order of magnitude ( Figure 8 ) . These significant differences were not caused by MJ11's attaining a higher cell density ( 2 . 0 × 108 CFU•ml−1•OD600−1 ) , as both the binK1 and ΔbinK derivatives produced slightly higher CFU ( Figure 8B ) ( 3 . 2 × 108 CFU•ml−1•OD600−1 and 3 . 7 × 108 CFU•ml−1•OD600−1 , respectively ) ( Figure 8B ) . Although there was a modest ( <2 fold ) increase in the molar concentration of C8-HSL in ΔbinK mutant supernatants , which could inhibit light production through competitive inhibition of LuxR-binding to its cognate 3-oxo-C6-HSL signal ( Kuo et al . , 1996; Schaefer et al . , 1996 ) , there was no discernable difference in C8-HSL production when controlling for the higher cell counts produced by the ΔbinK mutant compared to wild-type MJ11 ( p=0 . 82 ) ( Figure 8B ) . These findings are in agreement with previous biological assays and demonstrate that the binK1 mutation alters quorum sensing and raises the threshold for quorum-sensing activation of luminescence ( Schuster et al . , 2010 ) . 10 . 7554/eLife . 24414 . 020Figure 8 . Host-adapted binK1 attenuates quorum-sensing regulation of luminescence . ( A ) Supernatant concentrations ( nM/OD600 ) of N- ( 3-oxohexanoyl ) homoserine lactone ( C6-HSL ) , as quantified against synthetic standards ( Schaefer et al . , 2000; Pearson et al . , 1994; Duerkop et al . , 2007 ) and corresponding luminescence ( Lum/OD600 ) of 10 independent cultures each for wild-type MJ11 , binK1 and ΔbinK derivatives during quorum-sensing induction of luminescence determined from cultures grown to early log ( Average OD600 1 . 1 , range 0 . 9–1 . 4 , ) . ( B ) Average cell density as measured by absorbance ( OD600 ) , colony-forming units ( CFU ) /mL/OD600 , N- ( 3-oxohexanoyl ) homoserine lactone ( C6 ) nM concentration , N-octanoyl homoserine lactone ( C8 ) nM concentration , and luminescence ( Lum ) /1 mL culture for ten biological replicates of each variant relative to wild-type MJ11 . Error bars: 95% CI . Significant p-values ( p<0 . 05 ) are indicated above each comparison . p<2 . 2e-16 . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 020 Comparisons of the squid-evolved binK1 variant and ΔbinK mutant , especially exemplified by colonization efficiency ( Figure 7B ) , transcriptional profiles and changes in metabolic activity that were convergent with the native symbiont ( Figure 5—figure supplement 2 , Appendix 1 , Appendix 2 ) , suggested that squid selection did not favor outright loss of BinK function in MJ11 . The evolved binK1 and null ∆binK variants did not differ significantly in biofilm production and exhibited similar biofilm-linked traits of oxidation survival and hemocyte evasion ( Figure 5C and 6 ) . Yet , the squid-adapted binK1 variant significantly outperformed the null mutant in culture competition with binK+ ( Figure 3B ) . This enhanced fitness could be due to the maintenance of partial function or to regulatory effects that are unique to the evolved allele . To investigate this further , we assessed the impact of multi-copy expression of wild-type and binK1 alleles . Ancestral binK+ complemented adaptive behaviors conferred by the binK1 and ∆binK mutants , including the abilities to form biofilm and to colonize squid , as would be expected if wild-type BinK function impaired these traits ( Figure 9 ) . Multi-copy expression of binK1 modestly reduced biofilm production by the ∆binK mutant , suggesting that partial function was maintained by this allele , but it also unexpectedly enhanced biofilm production by MJ11 , implying altered function ( Figure 5B and 11 ) . Finally , binK1 significantly enhanced colonization by all variants , even in the presence of a single genomic copy of the wild-type allele , proiding evidence that binK1 is dominant and consistent with its altered function . Even if reduced activity of BinK was sufficient to confer some adaptive traits ( Figures 5–8 ) , these results suggest that improved symbiosis could also arise through phenotypes conferred by alteration of its function ( Figures 9–11 ) . 10 . 7554/eLife . 24414 . 021Figure 9 . Effect of binK on squid colonization and biofilm production . ( A ) Improvement in colonization by multi-copy in trans expression of the evolved binK1 allele and decreased colonization by expression of the ancestral binK+ allele . Colonization assessed by percentage of squid that are luminous after 24 hr . Error bars: 95% CI . N = 15–25 . ( B ) Increased biofilm production resulting from in trans expression of the binK1 allele , and decreased biofilm production resulting from expression of the ancestral binK+ . Comparisons of biofilm production in control-plasmids ( pVSV105= EV ) with that in multi-copy plasmids carrying binK suggest an inhibitory role for BinK in biofilm production , presumably alleviated by the dominance of the binK1 allele . Biofilm production was quantified by absorbance of crystal violet at A550 . Background color depicts strain background in which multicopy plasmid effects were measured , mirroring those used throughout where blue is wild-type MJ11 , green is the evolved binK1 variant and salmon is the ∆binK derivative . Error bars: 95% CI; non-overlap indicates significance . N = 7–8 . Significant p-values ( p<0 . 05 ) are indicated above each comparison . p<0 . 05 , p<0 . 005 , *p<0 . 005 . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 02110 . 7554/eLife . 24414 . 022Figure 10 . Model of BinK regulation of traits adaptive during squid symbiosis . Arrows originating from BinK point to characteristics that are activated or enhanced , and blocked lines point to those that are repressed or blocked by BinK . Hashed lines point to polysaccharides that contribute to biofilm . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 022 In theory , the large population sizes and genetic diversity within bacterial species may enable symbiotic lifestyles with eukaryotic hosts to evolve rapidly ( Fisher , 1930 ) . While the processes leading to pathogen emergence have been intensely studied , much less is known regarding the genetic changes that drive adaptation to novel host niches in nonpathogenic bacteria ( Jansen et al . , 2015; Ochman and Moran , 2001; Kwong and Moran , 2015; Guan et al . , 2013 ) . In pathogens , mobile elements encoded on pathogenicity islands are often cited as the cause of repeated and rapid evolution of host associations , but these elements alone rarely provide bacteria with the ability to colonize hosts ( Reuter et al . , 2014 ) . Further , the selective pressures exerted by new hosts may require synchronized phenotypic changes , limiting the number of adaptive ‘solutions’ available to a microbial genome that is constrained by regulatory structure . Here , rapid adaptation to squid symbiosis occurred in multiple parallel experimental lineages through convergent mutations in a single gene , the binK sensor kinase . These mutations altered multiple functions that are known to contribute to the native symbiosis between strain ES114 and squid ( Figure 10 ) , suggesting that that the regulatory circuits of V . fischeri may have been pre-wired to coordinate diverse symbiotic traits . Many of the BinK-regulated behaviors have established crucial roles in symbiotic association , including quorum-sensing activation of bioluminescence and Syp-mediated aggregation , ( Nishiguchi et al . , 1998; Brooks and Mandel , 2016; Nyholm and McFall-Ngai , 2003; Shibata et al . , 2012; Visick et al . , 2000; Yip et al . , 2005 ) , but we provide the first experimental evidence that two different binK-regulated cell-associated matrix substances , Syp and cellulose , modulate host innate immune interactions that could contribute to strain discrimination during the selection of symbiotic partners . The convergent paths to adaptation taken by independent lineages evolving experimentally through squid reveals that squid hosts exert hard selection on colonizing bacteria , driving the evolution of fitter , symbiotic genotypes . A model of the population-genetic dynamics of bacterial colonization suggests that in order to survive extinction during the host-imposed bottlenecks , binK alleles must confer a massive selective advantage in symbiotic association and must arise early during population growth , most probably— prior to host recruitment—rather than later during symbiotic maintenance ( Figure 4A and C ) . This prediction is consistent with the improved initiation capacity of evolved variants ( Figures 1 , 3A , 7 and 9 ) and explains their detection in the first few squid passages ( Table 2 ) . These mutants would not be expected to rise to detectable frequency considering that alleles that confer enhanced fitness in squid are deleterious in broth culture ( Figure 3B ) . The success of binK mutations , sweeping from undetectable frequency in the ancestral inoculum to fixation in as little as ~50 generations , was only realized when under strong squid host selection . Estimated selective coefficients for the binK1 allele of MJ11 ranged as high as s = 5 . 3 when determined empirically , similar to estimates obtained by population modeling ( s ~6 ) ( see Materials and methods , Figure 4 ) . Selective coefficients above one are rarely reported from nature; however , these are consistent with the stringent selection pressures imposed on pathogens as they colonize new hosts ( Morley et al . , 2015; Bedhomme et al . , 2012; Thurman and Barrett , 2016 ) . This enormous selective advantage is also consistent with the observation that ancestral populations with lower mean fitness ( such as strains MJ11 and H905 ) are more likely than fitter populations ( such as WH1 , EM17 and ES114 ) to make a major adaptive leap ( Lenski and Travisano , 1994 ) . That is , due to their distance from optimal fitness ( e . g . , 100% colonization ) , less fit ancestors are poised to benefit more from mutations of greater selective advantage ( Orr , 2000 , 2003; Wielgoss et al . , 2013 ) . Thus , even though elimination of BinK function also increases competitive fitness by ES114 ( Brooks and Mandel , 2016 ) , it is not surprising that binK mutations did not evolve in populations derived from ancestors with greater starting fitness , such as EM17 , WH1 , and ES114 , as it is unlikely that these mutations could confer a selective advantage sufficient to survive extinction ( Figure 2A and 4C , Table 2 ) . The high predicted selective advantages of binK mutants evolved from squid-maladapted strains MJ11 and H905 support the theory that adaptation from unfit ancestors may initially proceed by large leaps , as opposed to incremental changes of small effect ( Wiser et al . , 2013 ) . Requisite to successful symbiosis with squid is the ability of bacteria to bypass host barriers during initiation: symbionts first aggregate and then migrate though ducts that are policed by hemocytes and eventually reach the oxidative light organ interior ( Figure 1 ) ( Nyholm and McFall-Ngai , 2004 ) . The poor colonization capacity of MJ11 has been attributed to its lack of rscS , a horizontally acquired regulator in the same hybrid histidine kinase family as binK ( Figure 2—figure supplement 1 ) . RscS that activates Syp polysaccharide and allows the native symbiont ES114 to overcome the squid initiation barrier ( Figure 1A ) ( Yip et al . , 2006; Mandel et al . , 2009 ) . Despite its conserved function as a repressor of Syp in ES114 ( Brooks and Mandel , 2016 ) , BinK does not impede symbiosis in that strain , perhaps owing to the activity of RscS ( Yip et al . , 2006 ) . But , notably , strain H905—a close relative to ES114 isolated from the squid habitat and containing rscS—is symbiotically impaired and also evolved convergent mutations in binK during our experiments ( Perry , 2009 ) ( Figure 2—figure supplement 1 , Table 2 ) . This suggests that its colonization deficiency stems from regulatory constraints on Syp production , from ineffective integration of the horizontally acquired RscS regulator with existing regulatory circuitries , or from the evolution of attributes relating to a planktonic lifestyle which impair its ability to access squid light organs ( Lee and Ruby , 1994a ) . Here , MJ11 adapted to experimental squid symbiosis through enhancement not only of Syp ( Figures 5–7 ) , a proposed mechanism for symbiotic evolution in the native symbiont ES114 ( Mandel et al . , 2009 ) , but also by producing additional matrix components such as cellulose ( Figure 5—figure supplement 2 , Appendix 1 ) , both of which improved colonization ( Figure 7 ) . The finding that biofilm phenotypes conferred by evolved binK alleles improved survival of host defenses ( Figure 5 and 6 ) expands our understanding of the nature of host selection , and provides important context for how biofilm can confer enhanced fitness upon individuals . Symbiotic microbes commonly secrete exopolysaccharides or glycosylated compounds to produce biofilm capsules that confer protection against macrophages , antibiotics or toxic substances , and that promote adhesion to epithelial surfaces ( Nizet and Esko , 2009; Sengupta et al . , 2013; Williams et al . , 2013; Hsieh et al . , 2003 ) . Yet beyond its role in aggregate formation , it was not known whether biofilm contributed to squid colonization ( Yip et al . , 2006 ) . The binK1 allele enabled immune evasion by reducing the attachment of host macrophage-like hemocytes to a level comparable with that of squid-native strain ES114 and by enhancing survival when exposed to oxidation ( Figure 5 ) . Both immune evasion and biofilm production were suppressed by overexpression of either the sypE or binA repressors , which indicates that these traits are mediated by Syp and cellulose production ( Figure 5 ) . Squid immune response is mitigated by V . fischeri lipopolysaccharide and other microbe-associated molecular patterns ( MAMPs ) ( Nyholm et al . , 2009; Nyholm and McFall-Ngai , 1998; Koropatkin et al . , 2012; Koropatnick et al . , 2004; Foster et al . , 2000 ) , but this study provides the first evidence that Syp contributes to host immunomodulation by V . fischeri . The genes for Syp share little similarity with those encoding the capsular polysaccharide common to immunomodulating Vibrio species and other pathogens ( Shibata et al . , 2012; Yildiz and Visick , 2009 ) , but the Syp polysaccharide may nonetheless serve a role analogous to that of the polysaccharide ligands of mammalian macrophage receptors produced by gut symbionts , which also exhibit immunosuppressive activity that reduces host inflammatory response ( Mazmanian et al . , 2008; Chu and Mazmanian , 2013; Jones et al . , 2014 ) . Recent evidence in Vibrio parahaemolyticus suggests that the use of Syp is potentially widespread among host-associated Vibrio , mediating virulence and epithelial colonization ( Ye et al . , 2014 ) as well as evasion of host innate immunity ( Hsieh et al . , 2003; Vuong et al . , 2004 ) . The pleiotropic effects of Syp on symbiotic competence suggest why single binK mutations provide such benefit to squid-naïve V . fischeri . Further , they reveal a critical role for cell-associated polysaccharides in the squid–Vibrio interaction , not only mediating group behaviors that improve initiation but also contributing to partner selection on an individual cell basis . Not only do evolved binK alleles increase fitness during the first 24 hr of colonization , they also further enhance fitness between 24 and 48 hr post-colonization during the maintenance phase of symbiosis ( Figure 4B , Figure 3—figure supplement 2 ) when the squid selects on symbiont luminescence intensity and resource utilization ( Graf and Ruby , 1998; Soto et al . , 2014; Schuster et al . , 2010; Visick et al . , 2000; Septer et al . , 2013; Soto and Nishiguchi , 2014 ) ( Figure 1A ) . Although luminescence could be directly under host selection ( Figure 8A ) ( Visick et al . , 2000; Whistler and Ruby , 2003 ) , selection could alternatively favor the altered quorum threshold underlying reduced luminescence in binK variants ( Figure 8B ) . Mutations in luxO and litR—which , like binK variants , attenuate quorum sensing—enhance competitive fitness either in culture or in squid ( Fidopiastis et al . , 2002; Kimbrough and Stabb , 2015 ) . Impaired quorum sensing by other species also enhances competitive growth because of the subsequent de-repression of metabolic functions such as carbohydrate uptake and utilization , and the perturbation of fatty acid and carbohydrate biosynthesis ( Davenport et al . , 2015; An et al . , 2014 ) . Transcriptomics analysis indicated that similar changes occurred in the ΔbinK mutant ( Figure 5—figure supplement 2; Appendix 1 ) . Quorum-regulated metabolic pathways that serve as 'private goods' could be targets of selection if they facilitated the utilization of host-provisioned resources that support symbiont growth in juvenile squid ( Appendices 1 and 2 ) ( Graf and Ruby , 1998; Pan et al . , 2015; Wier et al . , 2010; Miyashiro et al . , 2011; Mandel et al . , 2012; Dandekar et al . , 2012 ) , thereby contributing to a sustained selective advantage following initial colonization ( Figures 1A and 4B ) . The synchronized changes attained through amino acid substitutions in an existing sensor kinase highlight how the conserved but malleable components of signal transduction systems make them key mediators of adaptive evolution ( Figure 2C ) . During bacterial evolution , sensory transduction pathways may serve as pliable targets because of the modularity of their components ( Vogel et al . , 2004; Pasek et al . , 2006 ) . Conserved phosphorelay and accessory domains ( e . g . Figure 2C ) are shared across numerous pathways and facilitate flexible partner interactions , known as 'cross-talk' ( Capra and Laub , 2012 ) . Sensor histidine kinases are effective targets of selective regimes in part because of their dual kinase and phosphatase capabilities , as well as their ability to augment partner interactions through these shared modules which can allow rapid rewiring of networks ( Capra and Laub , 2012; Taylor et al . , 2015; Rowland and Deeds , 2014 ) . The array of phenotypes effected in binK variants ( Figures 5 , 6 and 8 ) implies that BinK sensor kinase may participate in more than one signal transduction pathway ( Nyholm and McFall-Ngai , 2004; Yip et al . , 2006; Miyashiro and Ruby , 2012 ) . Phenotypic changes could be caused by altered interaction with a number of regulators with phosphorelay modules that are already described both for Syp polysaccharide ( Brooks and Mandel , 2016 ) and for the quorum-sensing pathway that controls luminescence ( Miyashiro and Ruby , 2012; Whistler et al . , 2007 ) , although this does not eliminate the possibility that there are unidentified partner ( s ) that mediate these effects . Regardless , evolved BinK enacted global effects by intersecting with pre-existing circuitry , which was presumably shaped by varying interactions with environments including hosts during V . fischeri adaptive evolution ( Gao and Stock , 2013; Mitrophanov and Groisman , 2008 ) . This study demonstrates that some strains of V . fischeri can evolve by leaps in host range that result from single mutations of large effect . That simple point mutations in a regulator can evoke such broad consequences reveals that disparate traits that are important for symbiosis initiation and maintenance are already co-regulated . Such preexisting coordination is almost certainly an evolved ability , perhaps reflective of a history of selection and ‘tinkering’ while fluctuating between the non-host and host-associated environments in which these bacteria naturally reside ( Lee and Gelembiuk , 2008; Jacob , 1977 ) . The immense populations of Vibrio species should , in theory , empower natural selection to refine even subtle traits , promoting the ability to adapt to uncertain conditions through appropriate regulation with remarkable efficacy ( Dillon et al . , 2017 ) . Viewed in this light , this study suggests that the exceptional adaptability of certain bacteria such as Vibrio in forming novel intimate associations with various host organisms may be possible in part due to the structure of existing regulatory pathways formed during thousands of past transient interactions . Such parsimonious reconciliation of genomic constraints with host selection pressures is likely paramount in shaping emerging symbioses . Strains and plasmids are listed in Table 1 . Wild-type Vibrio fischeri including strain MJ11 ( isolated from the fish Monocentris japonica [Haygood et al . , 1984] ) and its derivatives , as well as squid symbiont ES114 , were routinely grown at 28°C in either liquid seawater-tryptone broth ( SWT ) or Luria Bertani broth with added salt ( LBS ) with shaking at 200 rpm , or on LBS medium with 1 . 5% agar ( LBS agar ) ( Graf et al . , 1994 ) . Escherichia coli strains were routinely grown in Luria-Bertani ( LB ) broth ( Sambrook et al . , 1989 ) or in brain heart infusion medium ( BHI ) ( Difco ) at 37°C . When required , media were supplemented with antibiotics at the following concentrations: for V . fischeri , chloramphenicol ( Ch ) at 2 . 5 μg/ml , kanamycin ( Km ) 100 μg/ml and erythromycin ( Em ) at 5 μg/ml; for E . coli , Ch at 25 μg/ml , Km at 50 μg/ml , and Em at 150 μg/ml ( for BHI media ) . For maintaining selection in seawater , these antibiotics were used at half this concentration . When applicable , agar plates were supplemented with 40 mg of 5-bromo-4-chloro-3-indolyl-β-galactopyranosidase ( X-gal ) /ml for visualization of β-galactosidase activity . For biofilm quantification , bacteria were grown in liquid seawater-tryptone broth with added salt ( SWTO ) ( Bose et al . , 2007 ) . To generate transcriptomic libraries , bacteria were grown in 3 mL SWTO supplemented with 0 . 5 mM N-acetyl-D-glucosamine . Bacteria were also grown in variations of HEPES minimal medium ( HMM ) ( Ruby and Nealson , 1977 ) , a seawater-based defined minimal medium with 1x artificial sea water ( ASW: 50 mM MgSO4 , 10 mM CaCl2 , 300 mM NaCl , 10 mM KCl ) , 0 . 333 mM K2HPO4 , 18 . 5 mM NH4Cl , and 0 . 0144% casamino acids , buffered with 10 mM Hepes with a suitable carbon source . Other buffers were substituted and additional nutrients supplemented as follows: for in vitro competition , the medium was supplemented with 0 . 53 mM glucose; for siderophore assessment in reduced iron conditions ( Payne , 1994a ) , the medium was buffered with 100 mM Pipes ( pH 6 . 8 ) , casamino acids were increased to 0 . 3% , and the medium was supplemented with 32 . 6 mM glycerol; and for qualitative detection of siderophores , this medium was additionally supplemented with 1 . 5% Difco bacto-agar and 10% chrome azurol S-hexadecyltrimethylammonium bromide assay solution ( CAS –HDTMA ) ( Lee and Ruby , 1994a; Payne , 1994a; Boettcher and Ruby , 1990; Graf and Ruby , 2000 ) . Plasmids were conjugated between E . coli and V . fischeri as previously described ( Stabb and Ruby , 2002 ) . Integrated DNA Technologies ( Coralville , IA ) synthesized the oligonucleotide primers listed in Table 4 . Routine PCR was performed using AccuStart II PCR Supermix ( Quanta , Houston , TX ) . Phusion High Fidelity DNA polymerase ( New England Biolabs , Ipswich , MA ) was used for cloning and to produce templates for sequencing reactions . PCR cycling was performed according to the manufacturer's protocol in an Eppendorf Mastercycler or Master Cycler Nexus ( Eppendorf , Hamburg , Germany ) . Annealing temperatures used for primers were determined by subtracting 2°C from the melting temperatures ( Tm ) determined by Premiere Biosoft’s Netprimer . The lowest annealing temperature of the primers in the reaction was used during PCR ( Table 4 ) . 10 . 7554/eLife . 24414 . 023Table 3 . Genomes used in phylogenetic analyses . This table lists GenBank accessions for nucleotide genomes used in strain phylogeny and source for gene models used in hybrid histidine kinase phylogeny . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 023StrainNCBI accession/de novoProkka/NCBI gene modelsEscherichia coliNC_000913NCBIAliivibrio wodanisLN554846-51NCBIA . salmonicidaNC_011311–6NCBIA . logeiNZ_AJYJ00000000ProkkaVibrio furnissiiNC_016602 , NC_016628NCBIVibrio parahaemolyticusNC_004603–5NCBIVibrio fischeri SR5NZ_AHIH00000000ProkkaVibrio fischeri ES114NC_006840–2NCBIVibrio fischeri MJ11NC_011184–6NCBIVibrio fischeri EM17De novoProkkaVibrio fischeri WH1De novoProkkaVibrio fischeri ZF211AJYI01ProkkaVibrio fischeri WH4De novoProkkaVibrio fischeri SA1De novoProkkaVibrio fischeri CG101De novoProkkaVibrio fischeri H905De novoProkkaVibrio fischeri PP3De novoProkkaVibrio fischeri VLS2De novoProkka10 . 7554/eLife . 24414 . 024Table 4 . DNA oligonucleotide primers used in this study . DOI: http://dx . doi . org/10 . 7554/eLife . 24414 . 024Primer namePrimer DNA sequence ( 5’−3’ ) Annealing temperatureSource A0397 F5AAGAGTCATGGTATACATCGG51°CThis study A0397 F5TGTAGCTGATGAGACTTTGCG56°CThis study A0397 F8TCATTGAAAGGTTTAATCGGTGT57°CThis study A0397 R11CACTTTATGGATGATCTTCGCT56°CThis study A0397 F3GCTGATGAGACTTTCGCTC52°CThis study A0397 R4GGCTGATTAGATCATCCTGC54°CThis study A0397 F12CAGAAGCACTAAATCATGTGAG52°CThis study A0397 R9TCTGACATGCCAATAATGCCAT59°CThis study MJ11A0397 R KpnIGGTACCCCGAAATTAACGACCAT50°CThis study MJ11A0397 F SalIGTCGACAAATAGAAACACTAACCAC50°CThis study HKSoeA F ( SalI ) GTCGACAATGTAGAAGTGGTAGAACGC50°CThis study HKSoeA2 RGTTTCCGCCATTCTTTGTGGTTAGTGTTTCT350°CThis study HKSoeB2 FAGAAACACTAACCACAAAGAATGGCGGAAAC50°CThis study HKSoeB2 RGCACCGACACTCATCAATTCGATATCAAGCT50°CThis study HKSoeC2 FAGCTTGATATCGAATTGATGAGTGTCGGTGC50°CThis study HKSoeC R ( KpnI ) GGTACCAGCGGCAATAGAATCAGTC50°CThis study TnErm4AATGCCCTTTACCTGTTCC53°CThis study TnErm5CATGCGTCTGACATCTATCTGA55°CThis study A0397 R13GTACACCCGAAATTAACGACCA59°CThis study A0397 F10CAGAGTTATGGGGTTGCTGAGT58°CThis study A0397 WT+ RGTCCCACCAAATTGACG53°CThis study A0397 4+ RGTCCCACCAAATTGACA53°CThis study sypE RF F2GCAGGTTATGTGCGAGG52°CThis study gapA F1GCCGTAGTGTACTTCGAGCG55°C31 gapA R1CCCATTACTCACCCTTGTTTG55°C31 PrRF9AAGCTTATTGGGAATACGGATACCTG53°CThis study PrRF10CATATGCACATCTTCTAACCATTGCTG53°CThis study PrRF19TGTCAGTATCACTCCCCTTCAC55°CThis study PrRF20AGCAGACAGTTTTATTGTTCATTGTTTCACCTCATTTAA50°CThis study PrRF21TTAAATGAGGTGAAACAATGAACAATAAAACTGTCTGCT50°CThis study PrRF22TTTCCTGTTTGTTCTTTTTTAGAAAAACTCATCGAGCA50°CThis study PrRF23TGCTCGATGAGTTTTTCTAAAAAAGAACAAACAGGAAA50°CThis study PrRF24GTTCCTTCTACAAGTCCTATTCC53°CThis study PrRF36ATCCATTGTAATAGTGCTGC53°CThis study PrRF52AATAAGTCCATTTCGTTCTGC54°CThis study PrRF53AAGCGGAAGTAGCGAAAAC54°CThis study VSV105InFGCCTGGGGTGCCTAATG56°CThis study KanINFATACAAGGGGTGTTATGAGCC55°CThis study KanINRCAAGTCAGCGTAATGCTCTGC56°CThis study Standard molecular methods and manufacturer protocols were used for transformations , restriction enzyme digests , ligations , gel electrophoresis , and PCR . Restriction enzymes were purchased from New England Biolabs ( Beverly , MA ) , and T4 DNA Ligase was from Invitrogen ( Waltham , MA ) . Gel isolation and extraction of DNA from restriction digests were done using the Qiagen QIAquick Gel Extraction Kit ( Qiagen , Valencia , CA ) . Plasmids for recombinant work and for sequencing were purified using Zymo Research Zyppy Plasmid Mini Prep ( Irvine , CA ) . Genomic DNA used in PCR reactions was isolated by the phenol/chloroform extraction method ( Wilson , 2001 ) . Routine PCR amplifications were performed with AccuStart II ( Quanta Bio , Beverly , MA ) . Genomic DNA was extracted from mid-log cultures grown in LBS using the Promega Wizard Genomic DNA Purification Kit ( Madison , WI ) . The genomes of V . fischeri strains EM17 , WH1 and H905 were sequenced de novo using single-molecule sequencing ( Pacific Biosciences ) and assembled using HGAP at the Icahn School of Medicine . Gene models for de novo genomes were predicted and annotated using Prokka with strain ES114 serving as the reference ( Seemann , 2014 ) . For all strains derived from experimental evolution ( both squid and culture experiments ) , genomic libraries were prepared on isogenic clones following a modified high-throughput Nextera library construction protocol ( Baym et al . , 2015 ) and were sequenced using the Illumina Hi-Seq 2500 platform at the University of New Hampshire or the New York Genome Center . Nextera PE adapter sequences were removed from raw reads using Trimmomatic ( Bolger et al . , 2014 ) with the clip settings as follows: ILLUMINACLIP = 2:40:15 LEADING:2 TRAILING:2 MINLEN:25 ( Macmanes , 2014 ) . Processed reads were aligned and analyzed against their respective strain reference ( ancestral ) genome to identify mutations , using default settings in breseq ( Deatherage and Barrick , 2014 ) for single isolate genomes and using the ‘—polymorphism’ setting for libraries constructed from pooled isolate gDNA . On average , 99% of the processed reads from each isolate mapped to their reference genome , resulting in an average chromosomal coverage of 95x per isolate ( Table 2 ) for MJ11 . Mutations were called only for regions covered by a minimum of 20 reads . To identify which mutation calls reflected true evolutionary change as opposed to errors in the PacBio or NCBI reference genome , we compared each putative call across all genomes derived from the same ancestor . Potential mutation calls for strain ES114 were cross-referenced with known variants ( Foxall et al . , 2015 ) . Any mutation calls that were shared amongst at least 50% of independently evolved strain genomes were assumed to reflect ancestral genotype and thus discarded . All mutations in the binK locus identified by breseq were subsequently confirmed by targeted PCR amplification and Sanger sequencing by using primers A0397 F3 and A0397 R4 for amplification and to sequence binK1 and binK2 , and primers A0397 F8 and A0397 R9 for amplification and A0397 F3 and A0397 R6 to sequence binK3 and binK4 ( UNH and GeneWiz ) . Nucleotide sequence from published Vibrionaceae genomes ( Vibrio parahaemolyticus , Aliivibrio salmonicida , A . logei , and V . fischeri strains ES114 , MJ11 , SR5 , ZF-211; Table 3 ) and newly generated genomes ( V . fischeri strains H905 , EM17 , SA1 , CG101 , VLS2 , PP3 , WH1 , WH4 ) were analyzed in REALPHY and RAxML to infer whole-genome maximum likelihood phylogeny under the GTRGAMMA model of nucleotide substitution ( Bertels et al . , 2014 ) . Node support was estimated by running 1 , 000 bootstrapped analyses . Squid colonization was conducted as previously described ( Whistler and Ruby , 2003 ) . Squid were bred from adults collected from Maunalua Bay , HI with the original adults collected and bred in December 2006 , and subsequent cohorts collected intermittently from the same location between 2007 and 2016 . Squid were routinely held in 32 ppt Instant Ocean ( IO ) ( Blacksburg , VA ) in diH2O water . For determining colonization efficiency , a cohort of squid was placed in bacterial inoculum derived from mid-log ( OD600 0 . 2 ) SWT broth cultures diluted in filtered IO . The luminescence of squid individually housed in 4 mL IO was monitored daily , and bacterial colonization was determined by plating dilutions of homogenized squid following freezing at −80°C . For starting capacity measurements , squid were exposed to inoculum for 3 hr ( ES114 , EM17 , and WH1 ) or overnight ( H905 and MJ11 ) at increasing concentrations of bacteria ( from 3 , 000 to 20 , 000 CFU/mL ) , until 90% of squid became colonized as determined by luminescence detection at 24 and 48 hr post colonization , and direct plating of light-organ homogenates at 48 hr post colonization . Colonization experiments were completed with at least 10 replicate squid , included aposymbiotic control squid , and were repeated a minimum of three times . Strains MJ11 , EM17 , WH1 , H905 , and ES114 were evolved using squid hosts as previously described ( Schuster et al . , 2010 ) . Briefly , 10 aposymbiotic hatchling squid were inoculated in an ancestral population of each strain ( 20 , 000 CFU/ml in 50 ml filtered IO for H905 and MJ11 , 6 , 000 CFU/ml for WH1 , and 3 , 000 CFU/ml EM17 and ES114 ) . Following overnight incubation , squid were isolated and rinsed in filtered IO . Squid with detectable luminescence after 48 hr served as the founder passage for each parallel replicate population . At 96 hr following initial inoculation , squid hosts were preserved at −80°C while their seawater containing ventate was used to inoculate a new passage of aposymbiotic squid . Half of the ventate was preserved by freezing in 40% glycerol at −80°C . Serial passaging with 1 ml ventate combined with 1 mL fresh IO was initiated with a hatchling squid held overnight to confirm that they were uncolonized on the basis of luminescence measurements . Passaging continued in this manner for a total of 15 host squid per experimental lineage ( see Figure 1C ) . Isolates from various passages of the evolutions were recovered and stored from archived ventate . Ten microliters of the ventate were plated onto SWT agar and incubated at 28°C , and representative colonies that were phenotypically similar to V . fischeri were quadrant streaked for isolation on LBS agar . Isolated colonies were grown in LBS liquid media and preserved by freezing in 40% glycerol at −80°C for subsequent analysis . For isolates whose identity as V . fischeri was suspect due to morphological differences , luminescence was measured from SWT cultures , and the strain diagnostic gapA gene was amplified and sequenced using primers gapA F1 and gapA R1 ( Table 4 ) for confirmation ( Nishiguchi et al . , 1998 ) . To construct a gene tree for hybrid histidine kinase genes across V . fischeri strains and Vibrio relatives , each of the gene models from the complete genomes listed in Table 4 were queried with the PFAM Hidden Markov Models for HATPase C ( PF02518 ) , HisKA ( PF00512 ) , and REC ( PF00072 ) domains using hmmer . Sequences containing all of these conserved domains were then aligned in MAFFT ( Katoh et al . , 2002 ) . A maximum likelihood topology was inferred using RAxML ( Stamatakis , 2006 ) under the PROTGAMMAWAG model of amino acid substitution , following model selection using the Bayesian Information Criterion with IQ-TREE ( Nguyen et al . , 2015 ) . Gene families were annotated based on consensus among strain ES114 , Vibrio parahaemolyticus , and E . coli annotations identified using the BLAST algorithm ( Camacho et al . , 2009 ) . Isolates from the second squid ventate from replicate MJ11 population four were screened for binK and binK1 alleles using forward primer A0397 F5* and allele-specific reverse primers A0397 WT+ R and A0397 4+ R for binK and binK1 , respectively ( Table 4 ) . The presence or absence of amplicons was evaluated against controls including MJ11 ( binK+ ) , binK1 variant MJ11EP2-4-1 and ∆binK variant RF1A4 . PCR amplification was conducted following denaturation at 95°C for 30 s followed by annealing at 53°C for 15 s , and elongation at 72°C for 50 s . To confirm the identity of alleles , the binK region in five isolates was amplified by PCR using A0397 F10 and A0397 R13 , and unconsumed dNTPs and primers were removed using ExoSAP-IT ( Affymetrix Santa Clara , CA ) before Sanger-sequencing at Genewiz ( Cambridge , MA ) using primers A0397 F3 and A0397 R4 ( Table 4 ) . Results were aligned with reference MJ11_A0397 using Lasergene Software programs ( DNASTAR , Inc . Madison , WI ) and the presence of binK1 in the evolved isolates was confirmed . The MJ11 ∆binK::EmR ( RF1A4 ) strain was generated by marker exchange mutagenesis using a construct produced by Splicing and Overlap Extension PCR ( Horton et al . , 1990 ) . Briefly , the primer pairs HKSoeA F ( SalI ) and HKSoeA2 R , HKSoeB2 F and HKSoeB2 R , and HKSoeC2 F and HKSoeC R ( KpnI ) , and the Phusion High Fidelity DNA polymerase were used to amplify the genomic region upstream and downstream of binK from MJ11 genomic DNA , using EmRcolonies and pEVS170 plasmid DNA as the templates ( Tables 1 and 4 ) ( Lyell et al . , 2008 ) . The purified amplicons were then fused using Expand Long Template polymerase ( Roche ) where binK was replaced by an EmR cassette . This purified product was cloned into pCR2 . 1 TOPO and transformed into TOP10 cells ( Invitrogen , Waltham , MA ) , following the manufacturer’s protocol . Putative clones were sequenced by the Sanger method with primers M13 F , M13 R , TnErm4 , and TnErm5 ( Table 4 ) at the Hubbard Center for Genome Studies at the University of New Hampshire before the fragment was sub cloned into the suicide vector pEVS79 , which was used for allelic exchange ( Stabb and Ruby , 2002 ) . Whole genome re-sequencing ( illumina HiSeq ) confirmed that the gene was replaced in MJ11 mutant RF1A4 . The ∆sypK::aphA1 mutant strains RF1A5 , RF1A6 , and RF1A7 were generated by marker exchange mutagenesis using a construct produced by Splicing and Overlap Extension PCR ( Horton et al . , 1990 ) . Briefly , the primer pairs PrRF19 and PrRF20 , PrRF21 and PrRF22 , and PrRF23 and PrRF24 , and the Phusion High Fidelity DNA polymerase were used to amplify the genomic region upstream and downstream of sypK from MJ11 genomic DNA , and using KmR colonies and pVSV103 plasmid DNA as the template ( Tables 1 and 4 ) ( Dunn et al . , 2006 ) . The purified amplicons were then fused using Expand Long Template polymerase ( Roche ) where sypK was replaced by a KmR cassette . This purified product was cloned into pCR2 . 1 TOPO and transformed into TOP10 cells ( Invitrogen , Waltham , MA ) , following the manufacturer’s protocol . Putative clones were sequenced by the Sanger method with primers M13 F , M13 R , KanINF , KanINR ( Table 4 ) at Genewiz in South Plainfield , NJ before the construct , RF2B7 , was used for allelic exchange with a modified chitin competence protocol ( Brooks et al . , 2015 ) . Briefly , V . fischeri cells were grown in minimal media with a chitin derivative ( n-acetyl glucosamine ) until they reached OD600 0 . 2 . Cultures were incubated with 10 µg/mL of pRF2B7 linearized by up to five cycles of freeze-thawing . After incubation with DNA fragments for allelic exchange , cells were recovered , plated onto LBS+Km plates and screened by PCR for incorporation of ∆sypK::aphA1 fragment using primers PrRF36 and KanINR2 ( Table 4 ) . Single colonies of V . fischeri MJ11 and two of its derived strains , squid-evolved binK1 strain ( MJ11EP2-4-1 ) and MJ11 mutant ΔbinK ( RF1A4 ) , were grown in quadruplicate until they had an OD600 of 0 . 25 ( Biophotometer; Eppendorf AG , Hamburg , Germany ) in order to capture populations prior to detectable biofilm activity or flocculation and to minimize effects of spontaneous suppression due to growth defects of binK variants . Cells were pelleted and flash frozen . RNA was extracted following the protocol for the Quick-RNA MiniPrep kit ( Zymo , Irvine , CA ) . Ribosomal RNA was depleted using the RiboZero kit ( Illumina ) . mRNA libraries were constructed using the TruSeq Stranded mRNA library prep kit ( Illumina ) and sequenced using the HiSeq 2500 at New York Genome Center . Quality-trimmed reads were mapped onto the MJ11 reference genome using bowtie2 ( Langmead and Salzberg , 2012 ) and quantified using RSEM ( Li and Dewey , 2011 ) . Differential expression between strains was assessed using edgeR ( Robinson et al . , 2010 ) with a significance threshold of FDR < 0 . 05 . binK and binK1 alleles were cloned into pVSV105 ( Dunn et al . , 2006 ) following amplification of MJ11 and binK1 genomic DNA with forward primer MJ11A0397 F SalI and reverse MJ11A0397 R KpnI ( Table 4 ) . The 2 . 977 Kb product was cloned into pCR2 . 1 TOPO ( Invitrogen ) following the manufacturers’ instructions . The constructs were sequenced using M13F , M13R , A0397 F3 , A0397 F5 , A0397 F8 , A0397 F12 , A0397 R4 , A0397 R9 , and A0397 R11 ( Table 4 ) , and aligned to their respective references to ensure that there were no mutations . The inserts were sub cloned from pCR2 . 1 TOPO into pVSV105 following digestion using the restriction enzymes SalI and KpnI , and ligation using T4 DNA ligase . Ligation reactions were transformed into chemically competent DH5αλpir cells ( Herrero et al . , 1990 ) . Cell lysates of ChR colonies were directly screened for correct insert harboring plasmids by PCR using M13F and A0397 R4 . Positive clones harbored pRAD2E1 ( binK+ ) and pRF2A2 ( binK1 ) . binA was cloned into pVSV105 ( Dunn et al . , 2006 ) following amplification of MJ11 genomic DNA with forward primer PrRF9 and reverse PrRF10 ( Table 4 ) . The 2 . 053 Kb product was cloned into pCR2 . 1 TOPO ( Invitrogen ) following the manufacturers’ instructions . The TOPO constructs were sequenced using M13F , M13R , PrRF9 , PrRF10 , PrRF52 and PrRF53 ( Table 4 ) , and aligned to the genomic sequence in MJ11 using the DNA Star software package ( https://www . dnastar . com/ ) to ensure that no mutations were generated during cloning . The inserts were sub-cloned following digestions with XhoI and NdeI and SalI and NdeI digestions of pVSV105 , and ligation using T4 DNA ligase . Ligation reactions were transformed into chemically competent DH5αλpir cells . Cell lysates of ChR were directly PCR screened for insert-harboring plasmids by PrRF9 and VSV105InF ( Table 4 ) . Positive clones harbored pRF2A3 ( binA+ ) ( Table 1 ) . To make KmR constructs compatible with pKV111 for hemocyte assays , the sypE SphI and SacI fragment was sub-cloned from pCLD48 into SphI and SacI digested pVSV104 ( Stabb and Ruby , 2002 ) . Following transformation into chemically competent DH5αλpir cells , the cell lysates of KmR colonies were directly screened for sypE insert using M13F and sypE RF F2 ( Table 4 ) . Positive clones harbored pRF2A1 ( Table 1 ) . The binA Sph1 and SacI fragment was sub-cloned from TOPO 2 . 1 into pVSV104 digested with SphI and SacI ( Stabb and Ruby , 2002 ) . Cell lysates of KmR colonies were directly screened for binA insert using VSV105InF and PrRF9 ( Table 4 ) . Positive clones harbored pRF2A4 ( Table 1 ) . To mark bacteria for direct competition , the lacZ-expressing plasmid pVSV103 ( Dunn et al . , 2006 ) , which confers a blue colony on media containing X-gal and confers kanamycin resistance , was used along with a derivative of this plasmid ( pCAW7B1 ) in which lacZ was inactivated by removal of an internal 624-bp fragment by digestion with HpaI followed by self-ligation . Estimates of Malthusian growth rates and fitness for MJ11 strains were calculated by measuring relative abundances of marked strains in squid hatchings that were co-inoculated with varying ratios of each strain ( Altered Starting Ratio method sensu [Wiser and Lenski , 2015] ) . Strains were marked with either an intact version of the plasmid pVSV103 ( Dunn et al . , 2006 ) or pCAW7B1 that contains lacZ , which harbors a 200-amino-acid deletion that renders LacZ unable to produce blue pigment in colonies ( Table 1 ) . Squid were inoculated overnight in 50 ml IO containing 25 μg/ml Km and stored at −80°C after 24 or 48 hr ( n = 98 and 59 , respectively ) following initial inoculum exposure if detectably luminous . Inoculations spanned 17 experiments , which contained inoculums with reciprocally marked strains in order to control for potential plasmid effects , ranging both in total cell density ( from 1 , 600 to 26 , 600 CFU/mL ) and in relative strain frequency ( from ~1 binK1 per 10 , 000 binK+ up to approximately equal proportions ) . To estimate CFU abundance for each strain in squid light organs , we counted blue and white colonies after 72 hr of plating squid homogenates onto SWT plates containing 50 μg/ml Km and 1 . 5 mg/ml X-gal . To calculate the selective coefficient ( s ) associated with the evolved variant during competition with the ancestral genotype in squid , we use the derivation in Chevin ( 2011 ) . First , Malthusian growth rates ( M ) ( Fisher , 1930 ) were estimated by taking the natural-log of the ratio of the CFU estimate from each co-colonized light organ to the starting inoculum concentration ( i . e . , starting density ) ( Lenski and Travisano , 1994; Lenski et al . , 1991 ) . The standard plating method to quantify symbionts from squid light organs can detect as few as 15 CFU ( Ruby and Asato , 1993 ) . Then the relative growth rate difference ( sGR ) was used to calculate the selection coefficient: Relative growth rate difference , sGR = ( MEvo– MAnc ) / MAnc Selection coefficient , s = sGR / ln2 Spearman rank correlation tests were then used to test for relationships between Malthusian growth rates and either starting frequency or starting density of inocula . Significant differences in growth rate at either 24 or 48 hr between ancestral and evolved binK1 strains were assessed using exact Fisher-Pitman permutation tests through the ‘oneway_test’ method in the R ‘coin’ package ( Hothorn et al . , 2008 ) . Significant differences in competitive colonization by evolved variants binK1 and binK3 ( mutations in HATPaseC or HAMP domains , respectively ) were assessed with a permutation t-test in the R package ‘DAAG’ using the method ‘onet . permutation’ with 9 , 999 simulations ( Maindonald and Braun , 2015 ) . Malthusian growth rates were estimated similarly to in vivo competitions in which fitness for MJ11 strains was determined following co-inoculation of 150 μl with a single colony from each strain marked with either pVSV103 ( Dunn et al . , 2006 ) or pCAW7B1 . Cultures were grown statically at 28°C and , at 2 hr intervals , a new culture was founded by serial 1/10 dilution into fresh media in a 96-well polystyrene microplate ( Corning ) . At each passage , 20 μl of each competition was diluted , and plated onto SWT plates containing 50 μg/ml Km and 1 . 5 mg/ml X-gal . The total number of blue and white colonies apparent after 72 hr of growth was determined and used for calculations of realized Malthusian parameters . Strain competitions were each conducted with eight replicates and repeated twice . Differences in growth rate ( Malthusian parameter , described above and in Fisher ( 1930 ) were assessed for significance using exact Fisher-Pitman permutation tests through the ‘oneway_test’ method from the R package ‘coin’ ( Hothorn et al . , 2008 ) . To estimate the probability of a neutral mutation occurring within the binK locus during either the inoculum growth phase or during growth cycles in the host , the following parameters were used . References are provided for any parameters based on previously published estimates . ParameterEstimateSourceGenome mutation rate2 . 08 × 10−8 bp-1division−1Dillon et al . ( 2017 ) Genome size of MJ114 , 323 , 877 bpNCBIAvailable non-synonymous binK positions ( approximately 2/3 of codon positions ) 2 , 595 2/3N0 ( Inoculum starting population ) 5 cellsNinoc ( max . population of inoculum prior to dilution ) 2 . 4 × 108 cellsNcol ( V . fischeri founder population size ) 12 ( 2–3 cells per crypt ) Nyholm et al . ( 2000 ) ; Wollenberg and Ruby , ( 2009 ) ; Altura et al . ( 2013 ) Nhost ( Juvenile light organ V . fischeri population capacity ) 5 × 105 cellsKoch et al . ( 2014 ) To place the empirical observations in the context of expectations using the model of Wahl and Gerrish ( 2001 ) , we predict that mutants carrying a selective advantage of s ~ 2 . 8 would have originated within the first 10 generations of inoculum growth , with the probability of any non-synonymous mutation in the locus occurring within the first 10 generations of inoculum growth being 0 . 004 ( under Poisson ) . However , the recovery of four distinct binK alleles suggests that selection could be much greater than this empirical estimation . Although quantification of the selective advantage is central to understanding the dynamics of natural selection during evolution , obtaining accurate estimates is made more difficult as fitness differentials diverge and become extreme ( Wiser and Lenski , 2015 ) . We suspect that empirical estimates of s using competitive co-inoculations may vastly underestimate the strength of selection in this system , not only because of the extreme and diverging fitness differential between ancestor and evolved strains but also because of the difficulty imposed by the recovery and the challenges of accurate enumeration of rare genotypes . Assessment of the capacities of MJ11 and the binK1 variant to form cell aggregates in the squid mucus prior to entry through the ducts was conducted as previously described ( Nyholm and McFall-Ngai , 2003 ) . Briefly , 1 . 5 hr after newly hatched squid were inoculated with ~105 CFU/ml GFP-labeled strains of interest ( harboring pKV111 [Nyholm et al . , 2000] ) , squid were incubated in 1 uM CellTracker Orange ( Invitrogen ) for 30 min , anesthetized in isotonic magnesium chloride and dissected by removing the mantel to expose the intact light organ . Dissected animals were then promptly imaged at 20X and 40X using a Zeiss laser scanning confocal microscope 510 . N = 15–20 squid tested per strain . Biofilm production was quantified using a standard assay with minor modifications ( O'Toole , 2011 ) . Briefly , a colony of bacteria from an agar plate was inoculated into either 150 μl ( in a Costar 96-well plate ) or 2 mL ( in a 15 mm glass tube ) of SWTO and grown shaking at 200 rpm for 17 hr at 28°C . The biofilm that remained after expulsion of liquid , rinsing , and heat fixation at 80°C for 10 min was stained with 0 . 1% crystal violet and then decolorized in a volume of 200 µl for assays in plates or 2 mL for tube assays . Biofilm production was determined by absorbance at 550 nm using a Tecan Infinite M200 plate reader . Experiments were performed in triplicate and contained 3–5 biological replicates per treatment . Differences in means were evaluated for significance using a two-sample Fisher-Pitman permutation test conducted using the exact distribution with the ‘oneway_test’ method from the package coin in R ( Hothorn et al . , 2008 ) . Strains were grown in LBS media at 28°C with shaking at 200 rpm until cultures reach an OD600 between 1 and 1 . 5 , the cultures were normalized to an OD600 of 1 . 0 by dilution and 5 μl was subject , in triplicate , to exposure to hydrogen peroxide at different concentrations ( ranging from 0 . 02% to 0 . 18% ) in 200 μl of LBS media in a 96-well Costar polystyrene plate . The minimum concentrations of hydrogen peroxide that restricted all growth ( MIC ) of wild-type MJ11 and ES114 after over-night incubation was determined for every batch of hydrogen peroxide . Experimental concentrations ranged from 0 . 02% to 0 . 18% . Differences in strain survival ( binomial outcomes ) of at least three combined experiments that contained 106 replicates of strains without plasmids , 15 replicates of ∆sypK variants that were assayed in conjunction with control strains that lacked the mutation ( MJ11 , binK1 , ∆binK ) and 50 replicates of strains with plasmids were evaluated for significance using exact Fisher-Pitman permutation tests with the ‘oneway_test’ method from the R package ‘coin’ ( Hothorn et al . , 2008 ) . The plasmid harboring pRF2A3 ( binA ) was assayed 20 times in the in same experiment as control strains that harbored pVSV105 and pCLD48 ( sypE ) , which was evaluated in the same way . Squid macrophage-like hemocytes were isolated from aposymbiotic hatchling squid using glass adhesion and then stained with Cell Tracker Orange ( Invitrogen ) suspended in Squid-Ringers , prior to exposure to GFP-labeled V . fischeri cells following a previously detailed protocol ( Nyholm et al . , 2009; Collins and Nyholm , 2010 ) , with modifications communicated by Dr Bethany Rader . Hemocytes were exposed for one hour to V . fischeri strains ES114 , MJ11 ( binK+ ) , MJ11EP2-4-1 ( binK1 ) or non-symbiotic Vibrio harveyi B392 , carrying the GFP plasmid pKV111 ( Nyholm et al . , 2000 ) . To test for the effect of Syp biofilm on hemocyte binding , additional assays were conducted using GFP-labeled strains carrying either control plasmid ( pVSV104 ) , sypE expression plasmid ( pRF2A1 ) , or binA expression plasmid ( pRF2A4 ) in addition to GFP plasmid ( pKV111 ) ( Nyholm et al . , 2000 ) ( Table 1 ) . Following exposure , hemocyte response to bacteria was visualized at 63x magnification by confocal microscopy and differential interference contrast using a Zeiss LSM 510 . Hemocyte binding was quantified by enumeration of bound Vibrio relative to total Vibrio within a 60 μm radius surrounding each cell . A minimum of 30 hemocyte interactions were quantified per strain . Significant differences in mean proportional binding across strains were detected using a permutation-based test of independence in the R package ‘coin’ ( ‘independence_test’ method , using the exact distribution ) ( Hothorn et al . , 2008 ) . Siderophore was measured qualitatively as an orange halo appearing around cells cultured on CAS agar ( Graf and Ruby , 2000 ) or from cell free supernatants after 17 hr of growth under iron limited conditions using a chrom-azurol S liquid assay ( Lee and Ruby , 1994a; Payne , 1994b ) . Colorimetric reduction in OD630 was measured in a Tecan Infinite M200 plate reader and % siderohpore units were calculated and normalized by cell density ( Lee and Ruby , 1994a ) . Siderophore units were below the detection limit for MJ11 and its binK1 derivative but not ES114 . Luminescence , cell density and homoserine lactones were quantified from V . fischeri MJ11 and variants grown in a starting volume of 15 mL SWT broth culture in a 125 ml flask , which incrementally decreased in volume with sampling . Luminescence produced by the equivalent of 1 mL of culture was quantified on cells diluted up to 1:1000 , to ensure that measurements were within the range of detection , with a Turner 20/20 luminometer ( Turner Designs , Sunnyvale , CA ) . Concurrently , the optical density ( OD600 ) was determined with a Biophotometer ( Eppendorf AG , Hamburg , Germany ) , with cells diluted into medium . In parallel , colony forming units were determined by standard serial dilution and plating on LBS agar . Published methods were used for the purification and quantification of N- ( 3-oxohexanoyl ) homoserine lactone ( 3-oxo-C6-HSL ) and N-octanoyl homoserine lactone ( C8-HSL ) ( Schaefer et al . , 2000; Duerkop et al . , 2007 ) . Briefly , acyl-HSLs were extracted twice with an equal volume of acidified ethyl acetate from cell-free supernatants of MJ11 and derivatives sampled at a several OD600 levels—representing mid-log ( OD600 ~0 . 7 and 1 . 0 ) , late-log ( ~1 . 7 ) , early stationary ( ~3 . 5 ) , and stationary phase ( ~5 . 3–8 ) —to evaluate the dynamic range of AHL synthesis for each derivative and to determine the optimal OD600 during induction . AHLs were extracted and concentrated from 0 . 5 to 5 mL of MJ11 and variants were detectable and within the assay linear range , identifying that an OD600 of ~1 . 0 was optimal . Replicate experiments were performed in which OD600 was monitored at regular intervals , and AHLs were immediately extracted when cultures reached an OD600 of 0 . 9–1 . 4 . Any binK derivative culture identified as being dominated by suppressor mutants ( i . e . , exhibiting an abnormally fast growth rate accompanied by greater than wild-type luminescence and a high proportion of large colonies when plated ) were discarded . Extracted samples were concentrated by evaporation under anhydrous nitrogen before analysis . 3-oxo-C6-HSL was quantified using the reporter strain E . coli VJS533 harboring plasmid pHV200I− , which responds to 3-oxo-C6-HSL by producing luminescence ( Pearson et al . , 1994 ) . C8-HSL was quantified using the reporter strain E . coli MG4 harboring pQF50 ( bmaI1-lacZ promoter fusion derived from Burkhoderia mallei ) and pJN105 ( an arabinose-inducible R gene ) , which expresses lacZ specifically in response to exogenous C8-HSL with low sensitivity to 3-oxo-C6-HSL ( Duerkop et al . , 2007 ) . LacZ activity was measured by a standard assay ( Miller , 1972 ) and using the Dual-Light Luciferase and β-Galactosidase Reporter Gene Assay System ( Applied Biosystems ) . The amounts of 3-oxo-C6-HSL and C8-HSL were determined by comparing the activity measured from a dilution series of the extracted samples to the linear range ( R2 ≥0 . 98 ) of each standard curve generated from synthetic substrates ( N- ( ß-ketocaproyl ) -L-homoserine lactone and N-octanoyl-L-homoserine lactone ) ( Cayman Chemical ) . A total of 10 cultures for each derivative from five combined experiments were assayed and reported with the exception of CFU , which was from three cultures . Differences in CFU/mL/OD600 , OD600 , nM 3-oxo-C6 , nM C8-HSL , and luminescence ( Lum ) per 1 mL of culture for each variant reported relative to MJ11 were tested for significance using exact Fisher-Pitman permutation tests in the R package ‘coin’ ( ‘oneway_test’ method ) ( Hothorn et al . , 2008 ) . Phenotype MicroArrays ( Biolog , Hayward , CA ) PM1 and PM2A were performed according to manufacturers' protocols ( Bochner et al . , 2001 ) with few modifications for V . fischeri analysis , specifically including supplementation of IF-0 with 1% NaCl . Briefly , for each strain , enough inoculum for two replicate plates was prepared by recovering and mixing bacterial colonies into 16 ml IF-0 to obtain a uniform suspension at OD600 0 . 175 and mixed with dye D mixture ( 1:5 dilutions ) . PM1 and PM2A duplicate ( ES114 , binK1- and ΔbinK-variants ) or triplicate ( MJ11 and blank ) plates were inoculated with 100 μl of suspension per well , and incubated at 28°C for 48 hr . OD490 was recorded by a Tecan Infinite M200 microplate reader every 4 hr to measure kinetic changes in color ( redox state ) of dye D . To determine which substrates elicited different kinetic responses among strains , we performed an ANOVA on OD490 values following normalization against the blank control values for each timed measurement . The significance of strain activity differences for any substrate was determined after correcting for multiple tests using a False Discovery Rate of 0 . 05 . To quantify the overall significance of metabolic responses for MJ11 binK1 and MJ11 ∆binK converging with ES114 while diverging from MJ11 , we used the Exact Binomial Test under the null hypothesis that only 12 . 5% substrates should yield such a pattern across the four strains assayed ( 20 . 54 ) with the R method ‘binom . test’ . Unless otherwise specified , differential responses to colonization and experimental assays for different strains were tested using exact Fisher-Pitman permutation tests with the ‘oneway_test’ in the R package ‘coin’ ( Hothorn et al . , 2008 ) . Results from experiments conducted in triplicate were combined by inclusion of a block variable to account for potential technical artefact .	Most bacteria that associate with animals do not cause harm , and many are essential to health or provide other benefits . An animal’s immune system must permit these beneficial associations and at the same time block harmful microbes . This ultimately means that even beneficial bacteria must adapt to the immune barriers that they encounter . Different species that live in a close relationship with each other are known as symbionts . A species of bacteria called Vibrio fischeri can form a mutually beneficial symbiotic relationship with squid . The squid provide food for the bacteria , but only the bacteria that successfully navigate immune barriers and reach the squid’s “light organ” are fed . In return , the bacteria produce bioluminescence , making the nocturnal squid appear like moonlight in the water . As the bacteria reproduce , some individuals randomly acquire genetic mutations , some of which might improve the bacteria’s chances of survival . Which mutations and associated traits allow bacteria to beat out the competition and evolve to become animal symbionts ? To investigate , Pankey , Foxall et al . grew V . fischeri bacteria from several ancestors that were poor at colonizing squid . Groups of newly hatched squid selected potential symbionts from the resulting mix of bacteria . The selected symbionts were allowed to reproduce within the squid to form a new population of bacteria and were later vented out for a new batch of squid to sort through . This was repeated to ultimately form a final group of bacteria that had passed through 15 squid in turn . Unexpectedly , the bacteria in the final group all found the same solution to help them adapt to symbiotic life with the squid: mutations to the gene that encodes a signaling protein called BinK . Eight distinct mutations arose that dramatically changed how the bacteria interacted with squid . The evolved bacteria created a coating that hid them from squid immune cells and protected them from chemicals that squid use to kill invaders . The mutations also altered how the bacteria communicated with each other . This adjusted the intensity of light that they produced for their host to a more natural level , and improved their ability to grow on squid-provided food . Overall , the results presented by Pankey , Foxall et al . demonstrate that small genetic mutations can transform non-symbionts into symbionts , enabling them to evolve rapidly to form a symbiosis with a new host . This demonstrates that these bacteria already had the ability to coordinate the complex behaviors necessary to overcome the multiple barriers provided to them by the squid immune system . Other beneficial animal–bacteria associations are likely to work on similar principles; the study exemplifies the utility of experimental evolution systems and lays a foundation for further work to investigate these principles in more detail .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "microbiology", "and", "infectious", "disease", "genetics", "and", "genomics" ]	2017	Host-selected mutations converging on a global regulator drive an adaptive leap towards symbiosis in bacteria
To protect against aneuploidy , chromosomes must attach to microtubules from opposite poles ( ‘biorientation’ ) prior to their segregation during mitosis . Biorientation relies on the correction of erroneous attachments by the aurora B kinase , which destabilizes kinetochore-microtubule attachments that lack tension . Incorrect attachments are also avoided because sister kinetochores are intrinsically biased towards capture by microtubules from opposite poles . Here , we show that shugoshin acts as a pericentromeric adaptor that plays dual roles in biorientation in budding yeast . Shugoshin maintains the aurora B kinase at kinetochores that lack tension , thereby engaging the error correction machinery . Shugoshin also recruits the chromosome-organizing complex , condensin , to the pericentromere . Pericentromeric condensin biases sister kinetochores towards capture by microtubules from opposite poles . Our findings uncover the molecular basis of the bias to sister kinetochore capture and expose shugoshin as a pericentromeric hub controlling chromosome biorientation . The accurate segregation of chromosomes during mitosis relies on the capture of newly duplicated sister chromatids by microtubules emanating from opposite poles . This is possible because on each chromosome a multi-subunit kinetochore assembles on a region of DNA , known as a centromere , to mediate attachment to microtubules . The proper attachment of sister kinetochores to opposite poles , called biorientation , generates tension owing to sister chromatid cohesion ( Tanaka , 2010 ) . Sister kinetochores are inherently biased towards capture by microtubules from opposite poles , yet how this is achieved is not known ( Indjeian and Murray , 2007 ) . Kinetochore geometry is thought to position microtubule binding sites in a ‘back-to-back’ orientation during mitosis and this has been hypothesized to contribute to biorientation ( Hauf and Watanabe , 2004 ) . Where erroneous tension-less attachments do occur , they are destabilized by the aurora B kinase , providing a further opportunity for biorientation to be established ( Tanaka , 2010 ) . Shugoshin proteins are localized to the region surrounding the centromere , known as the pericentromere , and have a conserved , yet poorly understood , function in biorientation ( Indjeian et al . , 2005; Huang et al . , 2007; Kiburz et al . , 2008; Gutiérrez-Caballero et al . , 2012 ) . In fission yeast , frogs , and human cells , shugoshins enable biorientation , at least in part , through recruitment of the chromosome passenger complex ( CPC ) containing aurora B to the pericentromere ( Kawashima et al . , 2007; Vanoosthuyse et al . , 2007; Kelly et al . , 2010; Tsukahara et al . , 2010; Wang et al . , 2010; Yamagishi et al . , 2010; Rivera et al . , 2012 ) . Shugoshins also have a more defined role in protecting pericentromeric cohesin from premature loss during meiosis and mammalian mitosis; a function attributed to the recruitment of a specific form of the protein phosphatase 2A ( PP2A ) to the pericentromere ( Katis et al . , 2004; Kitajima et al . , 2004 , 2006; Marston et al . , 2004; Rabitsch et al . , 2004; Riedel et al . , 2006; Tang et al . , 2006; Xu et al . , 2009 ) . Though fundamental for accurate chromosome segregation , the role of shugoshin in biorientation has remained unclear . Budding yeast has a single shugoshin , Sgo1 , which protects pericentromeric cohesin during meiosis but does not regulate cohesion during mitosis ( Katis et al . , 2004; Kitajima et al . , 2004; Marston et al . , 2004; Indjeian et al . , 2005; Kiburz et al . , 2008 ) . We have exploited this system to investigate the sister kinetochore biorientation function of Sgo1 , independently of effects on cohesion . Our analysis leads us to the unanticipated discovery that shugoshin collaborates with the chromosome-organising complex , condensin , in chromosome biorientation . Moreover , we provide the first molecular insight into how sister kinetochores are biased towards capture by microtubules from opposite , rather than the same , pole . To investigate the role of Sgo1 in biorientation we analyzed sgo1 null cells ( sgo1Δ ) together with three missense mutants: sgo1-100 , sgo1-700 and sgo1-3A . The sgo1-3A mutant was engineered to disrupt the binding site for PP2A-Rts1 ( Xu et al . , 2009 ) whereas sgo1-100 and sgo1-700 were isolated in a screen due to their inability to sense a lack of tension ( Indjeian et al . , 2005 ) . All three mutants and sgo1Δ cells have previously been reported to affect biorientation after microtubule perturbation ( Fernius and Hardwick , 2007; Indjeian et al . , 2005; Indjeian and Murray , 2007; Xu et al . , 2009 ) . The Sgo1-3A protein retains its pericentromeric localization ( Xu et al . , 2009; Figure 1A ) . Though the kinetics of cell cycle entry in sgo1-100 and sgo1-700 mutants is similar to that of wild-type cells ( Figure 1—figure supplement 1 ) , Sgo1-100 and Sgo1-700 show only residual initial centromeric recruitment ( Figure 1B ) and are absent from the pericentromeres of cells arrested in mitosis with microtubule-depolymerizing drugs ( Figure 1A ) . We compared the ability of these Sgo1 mutants to establish bipolar attachments at metaphase after entering the cell cycle in the absence of microtubules . We used strains with spindle pole bodies ( SPBs ) labeled with tdTomato ( SPC42-tdTomato ) , the centromere of chromosome IV labeled with GFP ( CEN4-GFP ) and with CDC20 under control of the methionione-repressible promoter ( pMET-CDC20 ) , to enable metaphase arrest by addition of methionine . All strains were released from a G1 arrest into nocodazole- and methionine-containing medium to depolymerize microtubules and induce a metaphase arrest before nocodazole was washed out to allow metaphase spindle formation ( Figure 1C ) . Under these conditions , initial kinetochore-microtubule attachments are frequently erroneous because they occur before spindle pole bodies have migrated apart , leading to a strong reliance on the error correction process driven by Aurora B . We measured the efficiency of biorientation by scoring the splitting of CEN4-GFP signals once metaphase spindles reform after nocodazole washout . The sgo1-100 , sgo1-700 and sgo1-3A mutants showed a similar delay and lower maximum level of biorientation that was not as pronounced as in sgo1Δ cells ( Figure 1D , E ) . 10 . 7554/eLife . 01374 . 003Figure 1 . Sgo1 alleles that affect biorientation . ( A ) Sgo1-3A , but not Sgo1-100 or Sgo1-700 are maintained at the centromere in cells arrested in mitosis by treatment with nocodazole . Cells carrying SGO1-6HA ( AM906 ) , SGO1-100-6HA ( AM6956 ) , SGO1-700-6HA ( AM6957 ) , SGO1-3A-6HA ( AM10011 ) and a no tag control ( AM1176 ) were arrested in mitosis by treating with nocodozole for 3 hr . Cells were harvested for anti-HA ChIP and the levels of each Sgo1 protein at CEN4 were analyzed by qPCR . The mean of three independent experiments is shown with error bars representing standard error . ( B ) Sgo1-100 and Sgo1-700 proteins are initially recruited to centromeres but fail to be maintained there . Strains as in ( A ) were arrested in G1 by treatment with alpha factor . Samples were extracted for analysis by anti-HA ChIP at 15 min intervals after release from G1 . The levels of Sgo1-6HA at CEN4 at the indicated times after release from G1 are shown for a representative experiment . ( C–E ) SGO1 mutants are impaired in biorientation . Wild-type ( AM4643 ) , sgo1-100 ( AM8924 ) , sgo1-700 ( AM8925 ) , sgo1-3A ( AM8923 ) and sgo1Δ ( AM6117 ) cells carrying SPB ( Spc42-tdTomato ) and CEN4 ( CEN4-GFP ) markers were released from a G1 arrest into medium containing nocodazole ( to depolymerize microtubules ) and methionine ( to deplete CDC20 ) . After 3 hr , nocodazole was washed out , and the number of GFP dots was scored in the metaphase-arrested cells as shown in the schematic diagram ( C ) . ( D ) Representative images of cells with one and two GFP dots are shown . ( E ) The percentage of visibly separated centromeres was determined at the indicated intervals after nocodazole washout ( t = 0 ) . Error bars indicate range ( n = 2 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 00310 . 7554/eLife . 01374 . 004Figure 1—figure supplement 1 . The sgo1-100 and sgo1-700 mutations do not affect the timing of cell cycle entry . Strains as in Figure 1A were released from G1 as described in Figure 1B and DNA content was measured at the indicated times by FACS . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 004 The sgo1-3A mutation disrupts the interaction between Sgo1 and PP2A-Rts1 ( Figure 2A ) , which is important for the protection of centromeric cohesion during meiosis ( Xu et al . , 2009 ) . Although the cohesin complex is properly associated with chromosomes in sgo1Δ cells during mitosis and cohesion is not affected ( Indjeian et al . , 2005; Kiburz et al . , 2005; see below ) , PP2A-Rts1 could perform additional functions in biorientation . Rts1 enrichment at the centromere during metaphase is virtually abolished in sgo1Δ , sgo1-3A and sgo1-100 , and modestly reduced in sgo1-700 cells ( Figure 2B ) , even though Sgo1-100 and Sgo1-700 proteins retain the ability to associate with Rts1 ( Figure 2A ) . However , the biorientation defect of the sgo1 mutants cannot be caused by a failure to recruit Rts1 to centromeres because rts1Δ cells achieved biorientation with indistinguishable efficiency to wild-type cells ( Figure 2C ) . Therefore , PP2A-Rts1 is not required for sister kinetochore biorientation and the sgo1-3A mutation must disrupt functions of Sgo1 other than its association with PP2A-Rts1 . 10 . 7554/eLife . 01374 . 005Figure 2 . PP2A-Rts1 recruitment to the centromere by Sgo1 is not required for biorientation . ( A ) Sgo1-100 and Sgo1-700 , but not Sgo1-3A , associate with Rts1 . Cells carrying RTS1-9MYC and SGO1-SZZ ( TAP ) ( AM9144 ) , sgo1-100-SZZ ( TAP ) ( AM9272 ) , sgo1-700-SZZ ( TAP ) ( AM9142 ) , sgo1-3A-SZZ ( TAP ) ( AM9145 ) or no TAP ( AM4721 ) were arrested in nocodazole for 2 hr and treated with the cross-linking reagent dithiobis ( succunimidylpropionate ) ( DSP ) before extract preparation as described in ‘Materials and methods’ . Extracts were incubated with IgG-coupled beads and immunoprecipitates analyzed with the indicated antibodies . ( B ) Sgo1 mutants affect the centromeric localization of Rts1 . Wild-type ( AM8895 ) , sgo1-100 ( AM9439 ) , sgo1-700 ( AM9323 ) , sgo1-3A ( AM9293 ) and sgo1Δ ( AM9624 ) cells carrying RTS1-3PK , as well as a no tag control ( AM1176 ) , were treated with nocodazole for 3 hr before harvesting for anti-PK ChIP . The mean level of Rts1-3PK enrichment at CEN4 from three experimental repeats , determined by qPCR , is shown with bars indicating standard error ( p<0 . 05 , paired t test ) . ( C ) Sister kinetochore biorientation after microtubule depolymerization was measured in wild-type ( AM4643 ) and rts1Δ ( AM5823 ) cells as in Figure 1 ( C ) . The mean of three experimental repeats with error bars representing standard deviation are shown . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 005 In other systems , shugoshins are known to affect the association of the chromosomal passenger complex ( CPC ) containing aurora B kinase with centromeres ( Kawashima et al . , 2007; Vanoosthuyse et al . , 2007; Yu and Koshland , 2007; Kelly et al . , 2010; Tsukahara et al . , 2010; Wang et al . , 2010; Yamagishi et al . , 2010; Rivera et al . , 2012 ) . Budding yeast Sgo1 similarly associates with aurora B ( called Ipl1 ) ( Figure 3A ) . Conditional inactivation of Sgo1 using the auxin-inducible degron ( aid ) system ( Nishimura et al . , 2009; Figure 3B ) revealed that Sgo1 is not required for the initial recruitment of Ipl1 to centromeres but is important for its maintenance ( Figure 3C , D ) . Indeed , in Sgo1-aid cells arrested in metaphase by treatment with nocodazole , Ipl1 was absent from CEN4 ( Figure 3D ) . Early Ipl1 centromere localization also does not require Alk1 and Alk2 ( Figure 3—figure supplement 1 ) , the homologs of Haspin kinase , which is important for centromeric CPC localization in fission yeast and mammals ( Kelly et al . , 2010; Wang et al . , 2010; Yamagishi et al . , 2010 ) . The recruitment of Ipl1 early in the cell cycle may instead be due to association with the Ndc10/Cbf3 kinetochore protein that is known to recruit the CPC to centromeres ( Yoon and Carbon , 1999; Cho and Harrison , 2012 ) . Sgo1-independent Ipl1 localization early in the cell cycle ( Figure 3C ) can explain why Ipl1 , but not Sgo1 , is essential for biorientation in an unperturbed cell cycle , though Ipl1 inhibition and deletion of SGO1 similarly impair biorientation after microtubule depolymerization ( Figure 1E , Figure 3—figure supplement 2; Biggins et al . , 1999; Tanaka et al . , 2002; Indjeian et al . , 2005; Indjeian and Murray , 2007 ) . The Ipl1-Sgo1 interaction ( Figure 3A ) and centromeric localization of Ipl1 ( Figure 3E ) were also similarly decreased in nocodazole-treated sgo1-100 , sgo1-700 and sgo1-3A mutants , which likely contributes to the biorientation defects of these mutants . 10 . 7554/eLife . 01374 . 006Figure 3 . Sgo1 is required for the maintenance of Ipl1 at centromeres , but is dispensable for its initial recruitment . ( A ) Ipl1/aurora B co-immunoprecipitates with Sgo1 . Cells producing SZZ ( TAP ) -tagged Sgo1 ( AM8975 ) , Sgo1-100 ( AM8971 ) , Sgo1-700 ( AM8969 ) , Sgo1-3A ( AM8973 ) or no TAP ( AM3513 ) and carrying IPL1-6HA were treated with nocodazole for 2 hr before cross-linking with DSP . Extracts were prepared as described in ‘Materials and methods’ , incubated with IgG-coupled beads and immunoprecipitates were analyzed by immunoblot using the indicated antibodies . ( B ) Degradation of Sgo1 using the auxin-inducible degron system . Representative anti-Sgo1 immunoblot for the experiments in ( C and D ) showing that NAA treatment leads to Sgo1 degradation . Anti-Pgk1 immunoblot is shown as a loading control . See below for experimental conditions . ( C ) Ipl1 is initially recruited to centromeres in the absence of Sgo1 . Wild-type ( AM3513 ) and SGO1-aid ( AM9619 ) cells carrying IPL1-6HA were released from a G1 block in the presence of auxin ( NAA ) and samples harvested at 15 min intervals for measurement of Ipl1-6HA levels by anti-HA ChIP-qPCR . Also shown is a G1 sample from cells lacking IPL1-6HA ( AM1176; no tag ) . The percentages of metaphase and anaphase spindles after anti-tubulin immunoflurescence were scored as a marker of cell cycle progression and anti-Sgo1 immunoblot confirmed Sgo1-aid degradation ( shown in B ) . A representative experiment is shown from a total of three repeats . ( D ) Wild-type ( AM3513 ) and SGO1-aid ( AM9619 ) cells carrying IPL1-6HA together with a no tag control were arrested in G1 by alpha factor treatment and then released into medium containing NAA and nocodazole for 3 hr before harvesting for ChIP . Levels of Ipl1-6HA were determined at CEN4 and a pericentromeric site ( PERICEN4 ) by qPCR and the mean of three experimental repeats is shown with bars representing standard error ( p<0 . 05 , paired t test ) . ( E ) Ipl1-6HA levels at CEN4 measured by anti-HA ChIP-qPCR in wild type ( AM3513 ) , sgo1-100 ( AM9090 ) , sgo1-700 ( AM9082 ) and sgo1-3A ( AM9076 ) after treating directly with nocodazole for 3 hr are shown , together with a no tag ( AM1176 ) control , treated in the same way . The mean of three independent repeats is shown with bars representing standard error ( p<0 . 05 , paired t test ) . Note that levels of Ipl1-6HA at CEN4 were consistently higher in experiments where cells were directly treated with nocodazole , compared to those treated upon release from G1 ( compare E with D ) . Presumably those cells in the population that are already in mitosis upon nocodazole addition experience an extended arrest during which Ipl1 is continually recruited . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 00610 . 7554/eLife . 01374 . 007Figure 3—figure supplement 1 . The Haspin homologs , Alk1 and Alk2 , are not required for the initial recruitment of Ipl1 to centromeres . Wild-type ( AM3513 ) , alk1Δ alk2Δ ( AM10612 ) and alk1Δ alk2Δ SGO1-aid ( AM10393 ) cells carrying IPl1-6HA as well as a no tag control ( AM1176 ) were arrested in G1 by treatment with alpha factor . Samples were extracted for anti-HA ChIP at the indicated intervals after release from G1 . The levels of Ipl1-6HA at CEN4 were measured by ChIP-qPCR . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 00710 . 7554/eLife . 01374 . 008Figure 3—figure supplement 2 . Defective biorientation in ipl1-as mutants . Biorientation assay showing CEN4-GFP separation in wild-type ( AM4643 ) and ipl1-as ( AM10374 ) cells carrying SPB ( SPC42-tdTomato ) markers . Cells were released from G1 into nocodazole and NAPP1 , before nocodazole was washed out and GFP foci were scored in the metaphase-arrested cells as in Figure 1C . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 008 As an unbiased approach to isolate binding partners that might contribute to biorientation we purified Sgo1 from cycling cells or cells arrested in mitosis by microtubule perturbation ( using a cold-sensitive tubulin mutant; tub2-401 [Huffaker et al . , 1988] ) . To increase the probability of capturing transient interactions , we pre-treated cells with the cross-linking agent dithiobis ( succunimidylpropionate ) ( DSP ) before preparing extracts and immune-precipitating Sgo1-TAP . Associated proteins were identified by mass spectrometry ( Figure 4A , B; Supplementary file 1 ) . Although subunits of PP2A co-purified with Sgo1 , we did not detect peptides of the CPC . Interestingly , we identified four out of five subunits of the condensin complex co-purifying with Sgo1 ( Figure 4A , B; Supplementary file 1 ) . Co-immunoprecipitation of the Ycs4 and Brn1 subunits of condensin ( Figure 4C ) with Sgo1-TAP confirmed the Sgo1-condensin interaction ( Figure 4D , E ) . We confirmed that the Sgo1-Ycs4 interaction is not dependent on either DNA or the pre-treatment of cells with cross-linking agent ( Figure 4—figure supplement 1 ) . This suggests that Sgo1 and condensin form a complex independently of their association with the pericentromeric chromatin . Therefore , Sgo1 associates with three protein complexes during mitosis: PP2A , CPC , and condensin . 10 . 7554/eLife . 01374 . 009Figure 4 . Sgo1 interacts with condensin and recruits it to the pericentromere . ( A and B ) Condensin and PP2A co-purify with Sgo1 . Sgo1 was purified from wild-type or protease-deficient cells that were ( A ) cycling or ( B ) arrested in mitosis using the cold-sensitive tubulin allele tub2-401 as described in ‘Materials and methods’ . Comparable strains lacking TAP were used as a control for non-specific association with the beads . All cells were treated with the cross-linker , DSP , before harvesting and preparing extracts as described in ‘Materials and methods’ . Extracts were incubated with IgG-coupled beads and immunoprecipitates were visualized on silver-stained SDS-PAGE gels . The table shows the number of peptides of subunits of the PP2A and condensin complexes that were identified in the Sgo1-TAP purifications after mass spectrometry . The full list of identified proteins is given in Supplementary file 1 . Strains used in ( A ) were AM7509 ( SGO1-SZZ ( TAP ) , AM1176 ( no tag ) , AM8226 ( protease-deficient , SGO1-SZZ ( TAP ) and AM8184 ( protease-deficient , no tag ) . Strains used in ( B ) were AM8456 ( tub2-401 SGO1-SZZ ( TAP ) , AM2730 ( tub2-401 , no tag ) , AM8455 ( protease-deficient , tub2-401 SGO1-SZZ ( TAP ) and AM8259 ( protease-deficient , tub401 no tag . ( C ) A schematic diagram illustrating the composition of budding yeast condensin is shown . ( D and E ) Cells carrying either YCS4-6HA ( AM9138 ) ( D ) or BRN1-6HA ( AM9266 ) ( E ) and SGO1-SZZ ( TAP ) or no TAP ( AM5705 and AM5708 ) were arrested in nocodazole for 2 hr before treating with DSP . Extracts were incubated with IgG-coupled beads and immunoprecipitates were analyzed with the indicated antibodies by immunoblot . ( F–H ) Sgo1 is required for Brn1 association with the pericentromere . The genome-wide localization of Brn1-6HA was determined in wild-type ( AM5708 ) and sgo1Δ ( AM8834 ) cells by anti-HA ChIP followed by high throughput sequencing ( ChIP-Seq ) after arresting in mitosis by treating with nocodazole for 3 hr . ( F ) Brn1 enrichment along chromosome V along with a magnification of a 50 kb region including the centromere is shown . The number of reads at each position was normalized to the total number of reads for each sample ( RPM: reads per million ) and shown in the Integrated Genome Viewer from the Broad Institute ( Robinson et al . , 2011 ) . ( G ) The number of reads at coordinates corresponding to the rDNA region on chromosome XII is shown for wild-type and sgo1Δ anti-HA ChIP samples normalized to the total number of reads for each sample . Brn1 enrichment at the rDNA is similar in wild-type and sgo1Δ cells . ( H ) Brn1 enrichment in a 50 kb domain surrounding all 16 budding yeast centromeres is shown for wild-type and sgo1Δ cells . For both wild type and sgo1Δ , the ratio of the local maximum in a 100 bp window for ChIP sample/input is calculated at the indicated distance from the centromere for all 16 chromosomes . Box plot of maximum count value for 100 bp windows for 25 kb on both sides of each centromere is shown to give a composite view of all 16 pericentromeres . ( I and J ) Recruitment of Brn1 to centromeres occurs coincident with , and is dependent on , Sgo1 . Wild-type cells carrying SGO1-9MYC and BRN1-6HA ( AM9622 ) as well as sgo1Δ cells ( AM8834 ) carrying BRN1-6HA were arrested in G1 using alpha factor . Samples were extracted at 15 min intervals after release from G1 for anti-HA and anti-Myc ChIP and tubulin immunofluorescence . ( I ) The levels of Brn1-6HA and Sgo1-9Myc at CEN4 were measured at the indicated timepoints by anti-HA and anti-Myc ChIP-qPCR , respectively . Also shown is a G1 sample from cells lacking BRN1-6HA ( no tag; AM1176 ) . ( J ) The percentages of metaphase and anaphase spindles after anti-tubulin immunofluorescence were scored as a marker of cell cycle progression . Shown is a representative experiment from three repeats . ( K ) Schematic diagram illustrating the protein complexes ( PP2A , condensin , CPC ) recruited to the pericentromere by shugoshin . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 00910 . 7554/eLife . 01374 . 010Figure 4—figure supplement 1 . The Sgo1-condensin interaction is not dependent on DNA or treatment with the cross-linking agent , DSP . Cells carrying YCS4-6HA and SGO1-SZZ ( TAP ) ( AM9138 ) or no TAP ( AM5705 ) were arrested in mitosis by treatment with nocodazole for 2 hr . Cultures were either harvested and directly drop-frozen ( −DSP ) or treated with DSP prior to drop freezing ( +DSP ) as described in ‘Materials and methods’ . Extracts were either treated with 25 U of the DNA degrading agent , benzonase ( +benzonase ) and rotated at room temperature or held on ice ( −benzonase ) for 30 min . All samples were incubated with IgG-coupled beads and analyzed by immunoblot with the indicated antibodies . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 01010 . 7554/eLife . 01374 . 011Figure 4—figure supplement 2 . Removal of PCR duplicates does not alter the conclusion that Sgo1 is important for Brn1 enrichment in the pericentromere . This is the same analysis as in Figure 3H , except that only unique reads are included ( therefore eliminating duplicate samples generated during the PCR amplification step ) . Both methods of analysis lead to the conclusion that Brn1 levels in the pericentromere are greatly reduced in sgo1Δ cells . Note that the centromeric peak may be an artifact as centromeric sequences were over-represented in the sgo1Δ sample compared to wild type ( where they were under-represented compared to the rest of the genome ) . Potentially , altered pericentromeric structure in sgo1Δ cells could enable more efficient recovery of these sequences during the purification procedure . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 01110 . 7554/eLife . 01374 . 012Figure 4—figure supplement 3 . Brn1 is reduced around all 16 individual centromeres in sgo1Δ cells . Brn1 enrichment in a 20 kb region surrounding all 16 individual centromeres in wild type and sgo1Δ cells is shown for the experiment described in Figure 4F–H . All reads were included in this analysis . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 012 Condensin complexes structurally organize chromosomes and enable their efficient segregation , though how they do so remains unclear ( Cuylen and Haering , 2011; Hirano , 2012 ) . In budding yeast , condensin is most highly enriched in the rDNA and at each pericentromere ( D’Ambrosio et al . , 2008 ) . Condensin recruitment to the rDNA depends on monopolin ( Csm1/Lrs4 ) ( D’Ambrosio et al . , 2008 ) . Fission yeast monopolin ( Pcs1/Mde4 ) recruits condensin to centromeres where it prevents merotely ( attachment of a single kinetochore to microtubules from both poles ) ( Gregan et al . , 2007; Tada et al . , 2011 ) , unlike budding yeast condensin which is recruited to centromeres independently of monopolin subunit Lrs4 ( Brito et al . , 2010 ) . How condensin is recruited to the pericentromere remains unknown . To test whether the pericentromeric localization of condensin depends on Sgo1 we examined the association of the Brn1 condensin subunit genome wide using chromatin immunoprecipitation followed by high throughput sequencing ( ChIP-seq ) in wild-type and sgo1Δ cells arrested in mitosis by treatment with nocodazole . Although the pattern of reads along chromosome arms and mapping to the rDNA was similar in wild-type and sgo1Δ cells ( Figure 4F , G ) , we observed a clear reduction in pericentromeric levels of Brn1 in sgo1Δ cells , although peaks of variable height remained at some , but not all core centromeres ( Figure 4H , Figure 4—figure supplement 2 , 3 ) . Consideration of all 16 centromeres collectively revealed that in wild-type cells , condensin is enriched on average throughout an approximately 15 kb domain on either side of the centromere and that this enrichment is lost in sgo1Δ cells ( Figure 4H ) . We conclude that Sgo1 is required for condensin association throughout the pericentromere . Sgo1 is absent in G1 and produced only upon cell cycle entry ( Marston et al . , 2004 ) . Although condensin is present in G1 cells and localized to the nucleolus , it begins to co-localize with kinetochores only upon cell cycle entry ( Bachellier-Bassi et al . , 2008 ) . We found that recruitment of condensin to a centromere-proximal site occurs coincidently with , and depends on , Sgo1 ( Figure 4I , J ) . Therefore , in addition to controlling the centromere localization of PP2A-Rts1 and the CPC , Sgo1 recruits condensin to the pericentromere ( Figure 4K ) . Like condensin and shugoshin ( D′Ambrosio et al . , 2008; Kiburz et al . , 2005; Figure 4 ) , cohesin is highly enriched throughout the pericentromere ( Glynn et al . , 2004; Lengronne et al . , 2004; Weber et al . , 2004 ) . What is the relationship between cohesin , condensin , and shugoshin ? Although shugoshins play important roles in regulating the timing of cohesion loss during meiosis and mammalian mitosis ( see Clift and Marston , 2011; Gutiérrez-Caballero et al . , 2012 for reviews ) , this is not the case in budding yeast mitosis . Budding yeast sgo1 mutants are not defective in cohesion ( Indjeian and Murray , 2005; Marston et al . , 2004 ) and cohesin is normally localized to chromosomes ( Kiburz et al . , 2005 ) . We confirmed the proper association of cohesin in sgo1Δ cells arrested in mitosis by ChIP-Seq of its HA-tagged Scc1 subunit ( Figure 5A , B ) . The profile of Scc1 association along chromosome V ( Figure 5A ) and surrounding all 16 budding yeast centromeres in sgo1Δ cells ( Figure 5B , Figure 5—figure supplement 1 ) is indistinguishable from that of wild-type cells . ChIP-qPCR analysis confirmed that the levels of Scc1 cohesin subunit are similar at two tested centromeres in wild-type and sgo1Δ cells ( Figure 5—figure supplement 2 ) . We conclude that Sgo1 is not required for cohesin localization at centromeres , pericentromeres or along chromosomes . 10 . 7554/eLife . 01374 . 013Figure 5 . Sgo1 is not required for cohesin association with chromosomes . Wild-type ( AM1145 ) and sgo1Δ ( AM1474 ) cells carrying SCC1-6HA were arrested in mitosis by treatment with nocodazole for 3 hr . Samples were harvested , anti-HA ChIP was performed and both input and IP samples were sequenced for both strains . ( A ) Scc1-6HA enrichment along chromosome V along with a magnification of a 50 kb region including the centromere is shown . The number of reads at each position were normalized to the total number of reads for each sample and displayed using the Integrated Genome Viewer from the Broad Institute ( Robinson et al . , 2011 ) . ( B ) Box plot of maximum count value for 100 bp windows for 25 kb on both sides of each centromere is shown to give a composite view of all 16 pericentromeres . All reads are included . ( C ) Schematic diagram indicating hierarchy of factors required for condensin association with the pericentromere . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 01310 . 7554/eLife . 01374 . 014Figure 5—figure supplement 1 . Scc1 association with all 16 centromeres is unaffected by SGO1 deletion . Scc1 enrichment in a 20 kb region surrounding all 16 individual centromeres in wild type and sgo1Δ cells is shown from the experiment in Figure 5 . All reads were included in this analysis . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 01410 . 7554/eLife . 01374 . 015Figure 5—figure supplement 2 . ChIP-qPCR analysis showing Scc1-6HA levels at the centromere and pericentromere . Wild type and sgo1Δ cells were treated as described in Figure 5 except that ChIP samples were analyzed by qPCR using primer sets at CEN4 , CEN5 and a site in the pericentromere of chromosome IV . The mean of three independent experiments is shown with bars indicating standard error . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 01510 . 7554/eLife . 01374 . 016Figure 5—figure supplement 3 . Cohesin is required for normal Sgo1 association with the pericentromere . Wild type ( AM906 ) and pMET-SCC1 ( AM6673 ) strains carrying SGO1-6HA together with no tag wild type ( AM1176 ) and pMET-SCC1 controls ( AM1599 ) were arrested in G1 using alpha factor in the presence of methionine ( to deplete Scc1 ) . Strains were released into medium containing nocodazole and methionine for 3 hr and levels of Sgo1-6HA at the indicated sites were measured by ChIP-qPCR . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 01610 . 7554/eLife . 01374 . 017Figure 5—figure supplement 4 . Cohesin loading factors , but not monopolin , are important for proper condensin association with the centromere . Wild type ( AM5708 ) , SCC2-aid ( AM8918 ) , chl4Δ ( AM8885 ) , iml3Δ ( AM5710 ) and lrs4Δ ( AM9766 ) strains carrying BRN1-6HA , as well as a no tag control ( AM1176 ) were treated with nocodazole and NAA ( to degrade Scc2-aid ) for 3 hr before harvesting for ChIP and measuring the levels of Brn1-6HA at CEN4 by ChIP-qPCR . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 017 Conversely , we found that cohesin , and factors required for its loading , are required for the proper association of Sgo1 with the centromere and pericentromere ( Figure 5 , Figure 5—figure supplements 3 and 4 ) . Depletion of the Scc1 subunit of cohesin led to a great reduction in the pericentromeric levels of Sgo1 with only low levels remaining at the centromere itself ( Figure 5—figure supplement 3 ) . These findings suggest that cohesin promotes Sgo1 association with the pericentromere , which , in turn , recruits condensin , implying an indirect role for cohesin in localizing condensin ( through Sgo1 ) ( Figure 5C ) . Consistent with this idea , proper Brn1 association with the centromere in mitosis requires both the Scc2 protein that is required for cohesin loading onto chromosomes and subunits of the kinetochore ( Iml3/Chl4 ) that target Scc2 to centromeres ( Ciosk et al . , 2000; Fernius et al . , 2013; D′Ambrosio et al . , 2008; Ng et al . , 2009; Figure 5—figure supplement 4 ) , However , unlike in fission yeast , the monopolin subunit Lrs4 is not required for Brn1 association with the centromere ( Brito et al . , 2010; Figure 5—figure supplement 4 ) . Overall , we suggest a hierarchy of assembly in which the combined effects of Bub1 kinase and cohesin concentrate shugoshin in the pericentromere , which in turn recruits condensin ( Fernius and Hardwick , 2007; Kawashima et al . , 2010; Yamagishi et al . , 2010; 2008 ) ( Figure 5C ) . Next , we asked whether Sgo1 was sufficient to recruit condensin to chromosomes . Overproduction of Sgo1 , which is known to enable its association with chromosome arms and delay cells in metaphase ( Clift et al . , 2009; Figure 6A ) led to increased levels of condensin at centromere , pericentromere and chromosome arm sites ( Figure 6B ) . SGO1 overexpression also increased Brn1 association with the centromere and pericentromere in cells arrested in mitosis by nocodazole treatment ( Figure 6—figure supplement 1 ) , indicating that increased enrichment of Brn1 in SGO1-overexpressing cells was not purely a consequence of the metaphase arrest . As a more direct test of the ability of Sgo1 to bring condensin to chromosomes , we produced a Sgo1-GFP-TetR fusion protein in cells carrying tetO repeats integrated on a chromosomal arm . In the absence , but not the presence of doxycycline , Sgo1-GFP-TetR is expected to bind to the ectopic site and recruit its binding partners ( Figure 6C ) . Indeed we found that tethered Sgo1-GFP-TetR efficiently recruited the condensin subunit Brn1 , the PP2A subunit , Rts1 , and to a lesser extent the CPC subunit , Ipl1 to a site directly adjacent to the tetOs ( Figure 6D , F; ∼50 bp R ectopic site ) , although centromeric levels were not affected ( Figure 6E , G ) . The recruitment of these proteins to a site ∼800 bp to the left of the tethering site was much less efficient ( Figure 6D , F ) , suggesting that recruitment occurs through direct binding to Sgo1 , rather than an effect of Sgo1 on the surrounding chromatin . Taken together , these results show that Sgo1 is both necessary and sufficient for condensin recruitment . 10 . 7554/eLife . 01374 . 018Figure 6 . Sgo1 is sufficient for condensin recruitment . ( A and B ) Sgo1 overproduction leads to increased levels of Brn1 on chromosomes . Cells carrying BRN1-6HA and that were otherwise wild type ( AM5708 ) or carrying pGAL-SGO1 ( AM10859 ) integrated an ectopic locus , were arrested in G1 in rich medium containing raffinose and adenine using alpha factor ( YEP + R + A ) . After 2 hr 30 min , galactose ( 2% ) was added to induce SGO1 overexpression and 30 min later , cells were released from G1 . Samples were collected at the indicated times after release from G1 for analysis of cell cycle progression by scoring spindle morphology after anti-tubulin immunofluorescence ( A ) or for measurement of Brn1 levels by anti-HA ChIP-qPCR ( B ) . Sites analyzed were at CEN4 , a pericentromeric site or a chromosomal arm site on chromosome IV . A representative experiment from a total of three independent repeats is shown . ( C–G ) Tethered Sgo1 at an ectopic site recruits Brn1 , Rts1 and Ipl1 . ( C ) Schematic diagram showing the expected effects of doxycycline at the ectopic site and at CEN4 , as well as the locations of primer sets used for qPCR ( yellow stars ) . Primer sets used were ∼800 bp left of the tethering site , ∼50 bp right of the tethering site and at CEN4 . ( D–G ) Strains carrying Sgo1-TetR-GFP and tetOs integrated at the HIS3 locus were arrested in nocodazole for 3 hr either in the presence ( +DOX ) or absence ( −DOX ) of doxycycline and harvested for ChIP-qPCR . ( D and E ) Anti-HA ChIP was performed on SGO1-TetR-GFP HIS3::tetOs strains carrying either BRN1-6HA ( AM9847 ) , IPL1-6HA ( AM9940 ) or no tag ( AM9655 ) and levels of Brn1-6HA and Ipl1-6HA were measured by qPCR at the indicated sites adjacent to the ectopic site ( D ) or at CEN4 ( E ) . ( F and G ) Anti-PK ChIP was performed on SGO1-TetR-GFP HIS3::tetOs strains carrying Rts1-3PK ( AM9783 ) or no tag ( AM9655 ) and levels of Rts1-3PK were measured by qPCR at the indicated sites adjacent to the ectopic site ( F ) or at CEN4 ( G ) . In ( D–G ) , the mean of three or four experimental repeats is shown with bars representing standard error ( p<0 . 05 , unpaired t test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 01810 . 7554/eLife . 01374 . 019Figure 6—figure supplement 1 . SGO1 overexpression in metaphase-arrested cells increases Brn1 association with the centromere . Strains AM5708 ( BRN1-6HA ) , AM10859 ( BRN1-6HA pGAL-SGO1 ) were pre-cultured in YEP + R + Ade medium before supplementing with nocodazole and galactose ( 2 hr ) for 3 hr and then harvesting for anti-HA ChIP . The levels of Brn1-6HA were measured at the indicated sites by qPCR . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 019 Biorientation is achieved both because of a bias for sister kinetochores to be captured by microtubules from opposite poles and owing to error correction which destabilizes mono-oriented kinetochores , allowing a further opportunity for biorientation to occur ( Tanaka , 2010 ) . We tested the requirement of condensin for error correction using a conditional degron version of its Ycs5 subunit by monitoring CEN4-GFP separation in metaphase-arrested cells after microtubule depolymerization ( Figure 1C ) . In cells where degradation of condensin’s Ycs5 subunit ( YCS5-aid ) was induced , CEN4-GFP separation was delayed compared to wild-type cells , albeit not to the extent of sgo1Δ cells ( Figure 7A ) , suggesting that condensin facilitates biorientation . To further test whether the error correction process operates normally we developed a live cell microfluidics assay to allow biorientation to be observed directly as microtubules were allowed to reform ( Figure 7B , C; Videos 1 and 2 ) . Overall , as expected , and consistent with a biorientation defect , the number of frames in which cells with split CEN4-GFP foci were observed was reduced in cells lacking SGO1 , where IPL1 was inhibited ( ipl1-as with NAPP1 ) or Ycs5 was degraded ( Ycs5-aid with NAA ) compared to wild-type cells ( Figure 7D ) . However , the distance between separated CEN4-GFP foci was comparable in all strains , suggesting that kinetochore-microtubule attachments , spindle tension , and cohesion are all functional and only the orientation of attachment is defective in sgo1Δ , YCS5-aid and ipl1-as cells ( Figure 7E ) . Furthermore , consistent with a failure to properly biorient chromosomes , unseparated CEN4-GFP tended to be closer to the SPB in sgo1Δ , YCS5-aid and ipl1-as cells than in wild-type cells ( Figure 7—figure supplement 1 ) . We used the switch between one or two CEN4-GFP foci as a measure of kinetochore reorientation during error correction ( Figure 7F ) . The average frequency of switching between 1 and 2 GFP foci was significantly reduced in sgo1Δ and ipl1-as cells , as expected ( Figure 7F ) . We further observed a more modest reduction in switching in YCS5-aid cells , indicating that condensin contributes to proper error correction ( Figure 7F ) . The role of condensin in error correction cannot be to localize Ipl1 , as we found that Ipl1 maintenance at kinetochores requires Sgo1 ( Figure 3D ) , but not Ycs5 ( Figure 7G ) . Rather , we speculate that condensin shapes the pericentromere to place sister kinetochores in a rigid back-to-back orientation that provides the framework for tension-sensing . 10 . 7554/eLife . 01374 . 020Figure 7 . Condensin facilitates effective error correction . ( A ) Sister kinetochore biorientation is defective after nocodazole washout in cells lacking condensin . Strains carrying SPB ( Spc42-tdTomato ) and CEN4 ( CEN4-GFP ) markers were released from a G1 arrest into nocodazole and arrested in metaphase by CDC20 depletion . After 3 hr , nocodazole was washed out ( t = 0 ) and CEN4-GFP separation scored at the indicated intervals as shown in Figure 1C . Error bars represent standard deviation ( wild type and YCS5-aid; n = 3 ) or range ( sgo1Δ; n = 2; reproduced from Figure 1E ) . A representative anti-aid immunoblot is shown to confirm Ycs5 degradation upon NAA addition . Samples were taken in G1 ( −NAA ) and 120 min after release ( +NAA ) . Anti-Kar2 immunoblot is shown as a loading control . ( B–F ) Condensin contributes to efficient error correction . ( B ) Scheme of the live single-cell microfluidics experiment . Wild-type ( AM4643 ) , sgo1Δ ( AM6117 ) , YCS5-aid ( AM9038 ) and ipl1-as ( AM10374 ) cells carrying CEN4 ( CEN4-GFP ) and SPB markers ( SPC42-tdTomato ) were released from a G1 arrest into nocodazole , NAA and NAPP1 and arrested in metaphase by CDC20 depletion . After 30 min , nocodazole was washed out . When the majority of cells had 2 SPBs ( ∼1 hr 30 min later ) , we began imaging and a total of 21 frames were grabbed at approximately 74 s intervals . ( C ) Representative images for wild type and ipl1-as are shown . Numbers indicate time ( s ) each frame was grabbed and asterisks indicate a change in GFP dot number compared to the previous frame . ( D ) The overall percentage of separated CEN4-GFP foci was determined for cells with two visible SPBs from all frames combined . ( E ) The distance between CEN4-GFP foci was measured in cells with separated foci . Box boundaries represent the upper and lower quartiles , respectively . The red cross indicates the mean , the horizontal line indicates the median and error bars show the maximum and minimum values observed . n = 396 ( wild type ) , 108 ( sgo1Δ ) , 267 ( YCS5-aid ) and 154 ( ipl1-as ) . ( F ) The observed frequency of switching between one and two GFP foci was calculated for cells with 2 SPBs . A student t test was used to obtain p values . ( G ) Ycs5 is not required for Ipl1 association with the centromere . The levels of 6HA-tagged Ipl1 in wild-type ( AM3513 ) and YCS5-aid ( AM10334 ) cells , grown in the presence of NAA and nocodazole for 3 hr , were measured at CEN4 by anti-HA ChIP-qPCR and compared to a no tag control ( AM1176 ) . The mean of three independent experiments is shown with bars representing standard error . This is the same experiment as shown in Figure 3D and the wild-type data is reproduced for comparison . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 02010 . 7554/eLife . 01374 . 021Figure 7—figure supplement 1 . Deletion of SGO1 impairs biorientation rather than centromere cohesion . The distance from CEN4-GFP to the nearest SPC42-tdTomato focus was measured for cells with just one visible CEN4-GFP foci from the experiment shown in Figure 7C–F . The fraction of cells with a CEN4-GFP to SPB distance greater or less than the median value for wild type ( 0 . 865 mm ) is plotted for wild type , sgo1Δ , YCS5-aid and ipl1-as cells . p values were obtained using a chi square test . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 02110 . 7554/eLife . 01374 . 022Video 1 . Example video of a wild-type cell in the error correction assay . The video corresponds to the image gallery in Figure 5C ( upper panel ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 02210 . 7554/eLife . 01374 . 023Video 2 . Example video of an ipl1-as cell in the error correction assay . The video corresponds to the image gallery in Figure 5C ( lower panel ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 023 Although it has been recognized that sister kinetochores are intrinsically biased towards capture by microtubules from opposite poles ( Indjeian and Murray , 2007 ) , the factors required were not identified . The molecular basis of the bias towards sister kinetochore biorientation has therefore remained unknown . If our hypothesis that condensin creates a preferred pericentromeric framework upon which the error correction machinery can act is correct , we reasoned that condensin could also impose a bias on kinetochores to biorient . When SPBs are allowed to separate before microtubules attach to sister kinetochores , they tend to biorient normally , even when error correction is impaired ( Indjeian and Murray , 2007 ) . To test the requirement of shugoshin and condensin for sister kinetochore bias , we allowed cells to progress from G1 into the cell cycle for 1 . 5 hr to allow SPB separation , before treating the cells with nocodazole for 30 min . Subsequently , nocodazole was washed out and we simultaneously began filming ( Figure 8A ) . We recorded the percentage of cells with separated SPBs at the start of filming that separated CEN4-GFP foci at least once during the observation period ( approximately 30 min ) ( Figure 8B , C; Videos 3 and 4 ) . Similar numbers of wild-type and ipl1-as cells achieved sister centromere separation , indicating that the error correction process is not required to bias sister kinetochores towards biorientation ( Figure 8B ) . However , remarkably , the frequency of separated CEN4-GFP foci was reduced about two-fold in sgo1Δ and YCS5-aid cells , as compared to wild-type cells ( Figure 8B ) . This indicates that in the absence of shugoshin or pericentromeric condensin , the bias to sister kinetochore biorientation is lost . We conclude that both shugoshin and condensin impose a bias on sister kinetochores to biorient and that this is independent of error correction by Aurora B ( Ipl1 ) . 10 . 7554/eLife . 01374 . 024Figure 8 . Condensin biases chromosomes to biorient . ( A–C ) Condensin and Sgo1 , but not Ipl1 , are required to bias sister kinetochores towards biorientation . ( A ) Scheme of the microfluidics assay to test sister kinetochore bias . Wild-type , sgo1Δ , YCS5-aid and ipl1-as cells as in Figure 5B were released from a G1 arrest into NAA and NAPP1 and arrested in metaphase by CDC20 depletion . SPBs were allowed to separate for 1 hr 30 min before cells were treated with nocodazole for an additional 30 min . After 2 hr total , nocodazole was washed out and frames were grabbed at approximately 94 s intervals for a total of 21 frames . ( B ) The percentage of cells that separated CEN4-GFP foci at least once during the observation period is shown for the indicated strains . p values indicate significance ( chi-square test ) . ( C ) Representative images of wild-type and YCS5-aid cells are shown . Time of image acquisition ( s ) is shown . The asterisk indicates the first time GFP foci are separated . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 02410 . 7554/eLife . 01374 . 025Video 3 . Example video of a wild type cell in the assay to test for a bias towards sister kinetochore biorientation . The video corresponds to the image gallery in Figure 6C ( upper panel ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 02510 . 7554/eLife . 01374 . 026Video 4 . Example video of a YCS5-aid cell in the assay to test for a bias towards sister kinetochore biorientation . The video corresponds to the image gallery in Figure 6C ( lower panel ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 026 Shugoshins are emerging as factors that define a hub at the pericentromere that integrates the functions of multiple protein complexes to ensure the accuracy of chromosome segregation ( Figure 9A ) ( Rattani et al . , 2013 ) . To explore the relationship between shugoshin and its binding partners further , we asked whether the Sgo1-100 , Sgo1-700 and Sgo1-3A proteins retain their interaction with condensin . We found that Sgo1-700 failed to associate with Brn1 , whereas Sgo1-100 and Sgo1-3A retained Brn1 association ( Figure 9B ) . Analysis of Brn1 association with representative centromere-proximal and chromosomal arm sites by ChIP-qPCR revealed that only the sgo1-3A mutant , and not the sgo1-100 or sgo1-700 mutants maintained Brn1 localization at the pericentromere in cells arrested in mitosis by nocodazole treatment ( Figure 9C ) . However , in both sgo1-100 and sgo1-700 mutant cells progressing from G1 into the cell cycle , Brn1-6HA retained a partial ability to associate with the centromere and pericentromere , though only in the case of the sgo1-100 did this occur in a timely manner ( Figure 9—figure supplement 1 ) . As both the Sgo1-100 and Sgo1-700 proteins themselves fail to be maintained at the centromere ( Figure 1B ) , these observations are difficult to interpret . As a more direct test of the ability of the mutant proteins to recruit condensin , we fused Sgo1-100 , Sgo1-700 , and Sgo1-3A to tetR and artificially tethered them to tetO arrays located adjacent to CEN4 in cells that otherwise lacked SGO1 ( Figure 9D–F ) . Tethering of Sgo1-tetR-GFP , Sgo1-100-tetR-GFP , or Sgo1-3A-tetR-GFP , all significantly increased Brn1-6HA levels at CEN4 compared to a no tag control ( Figure 9E ) . In contrast , and consistent with our finding that Brn1 fails to co-immunoprecipitate with Sgo1-700 ( Figure 9B ) , tethering of Sgo1-700-tetR-GFP did not significantly enrich Brn1-6HA at the same site , though it was produced to similar levels as the Sgo1-100-tetR-GFP protein ( Figure 9D , E ) . Despite the ability of Sgo1-100-tetR-GFP and Sgo1-3A-tetR-GFP fusion proteins to recruit Brn1 to the tethering site at CEN4 , only the wild-type Sgo1-tetR-GFP fusion protein was able to partially rescue the segregation of chromosome IV after release from a nocodazole arrest ( Figure 9—figure supplement 2 ) . This is anticipated as none of the mutant proteins are expected to enable proper Ipl1 association with the centromere ( Figure 3E ) . We conclude that Sgo1-100 and Sgo1-3A are able to associate with , and recruit , condensin , whereas Sgo1-700 loses this interaction . 10 . 7554/eLife . 01374 . 027Figure 9 . Shugoshin enables the bias towards sister kinetochore biorientation by condensin recruitment . ( A ) Schematic diagram showing Sgo1-associated complexes and their functions at the pericentromere . ( B ) Sgo1-100 and Sgo1-3A , but not Sgo1-700 , retain association with Brn1 . Cells carrying BRN1-6HA and SGO1-SZZ ( TAP ) ( AM9266 ) , SGO1-100-SZZ ( TAP ) ( AM9149 ) , SGO1-700-SZZ ( TAP ) ( AM9264 ) , SGO1-3A-SZZ ( TAP ) ( AM9262 ) or no TAP ( AM5708 ) were arrested in nocodazole for 2 hr before treating with the cross-linker DSP . Prepared extracts were incubated with IgG-coupled beads and immunoprecipitates analyzed by immunoblotting with the indicated antibodies . ( C ) Brn1 is maintained at the centromere in metaphase-arrested sgo1-3A , but not sgo1-100 or sgo1-700 cells . Wild-type ( AM5708 ) , sgo1Δ ( AM8834 ) , sgo1-100 ( AM9442 ) , sgo1-700 ( AM9291 ) and sgo1-3A ( AM9276 ) cells carrying BRN1-6HA as well as a no tag control ( AM1176 ) were arrested in nocodazole for 2 hr before harvesting for anti-HA ChIP . The levels of Brn1-6HA were measured at the indicated sites by qPCR . ( D–F ) Tethered Sgo1 , Sgo1-100 or Sgo1-3A , but not Sgo1-700 can enrich Brn1-6HA at CEN4 in otherwise sgo1Δ cells . SGO1-tetR-GFP ( AM14012 ) , sgo1-100-tetR-GFP ( AM13902 ) , sgo1-700-tetR-GFP ( AM13907 ) , and sgo1-3A-tetR-GFP ( AM13904 ) were introduced into cells carrying tetOs integrated at CEN4 , producing Brn1-6HA and with SGO1 deleted from its endogenous locus . A strain carrying just tetOs integrated at CEN4 but otherwise wild type was used as a no tag control ( AM11060 ) . All strains were arrested in mitosis by treatment with nocodazole for 3 hr before harvesting for ChIP and immunoblotting . ( D ) Schematic diagram of the tethering locus . ( E ) Levels of Brn1 recruited adjacent to the tethering site ( CEN4 ) when the indicated proteins are fused to TetR-GFP , as measured by ChIP-qPCR . The mean of four independent repeats is shown except for sgo1-3A-tetR-GFP where six repeats are included . Error bars are standard error , significance was calculated using the student t test ( *p<0 . 05 ) . ( F ) Total cellular levels of the Sgo1-tetR-GFP fusion proteins , Brn1-6HA and Pgk1 ( loading control ) were analyzed by immunoblot using the indicated antibodies . ( G ) The bias to sister kinetochore biorientation is absent in sgo1-700 cells . Wild-type ( AM4643 ) , sgo1-100 ( AM8924 ) , sgo1-700 ( AM8925 ) and sgo1-3A ( AM8923 ) cells were released from G1 and treated with nocodazole after SPB separation as in Figure 7A . The percentage of cells that separated CEN4-GFP foci at least once during the observation period is shown for the indicated strains . p values indicate significance ( chi-square test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 02710 . 7554/eLife . 01374 . 028Figure 9—figure supplement 1 . The Sgo1-100 protein can recruit condensin to kinetochores . ( A and B ) Condensin is at least partially recruited to the centromere after release from G1 in sgo1-100 cells . Wild-type ( AM5708 ) , sgo1-100 ( AM9442 ) and sgo1-700 ( AM9291 ) cells carrying BRN1-6HA were arrested in G1 using alpha factor . Samples were extracted for anti-HA ChIP-qPCR at the indicated levels for analysis of Brn1 association with CEN4 ( A ) and the pericentromere of chromosome IV ( B ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 02810 . 7554/eLife . 01374 . 029Figure 9—figure supplement 2 . Sgo1-tetR-GFP , but not Sgo1-100-tetR-GFP , Sgo1-700-tetR-GFP or Sgo1-3A-tetR-GFP tethered to CEN4 can partially rescue the mis-segregation of chromosome IV after nocodazole washout in otherwise sgo1Δ cells . Diploid cells carrying tetR-tdTomato and SGO1-tetR-GFP ( AM14005 ) , sgo1-100-tetR-GFP ( AM14006 ) , sgo1-700-tetR-GFP ( AM14007 ) , sgo1-3A-tetR-GFP ( AM14008 ) or no Sgo1-TetR fusion ( AM14009 ) , with tetOs integrated at CEN4 and with SGO1 deleted from its endogenous locus were treated with nocodazole to depolymerize microtubules and arrest cells in mitosis . Nocodazole was washed out , allowing microtubules to reform and , 60 min later , the position of CEN4-tdTomato foci was scored in the anaphase cells after chromosome segregation . At least 100 cells were scored from each of two experimental repeats with error bars representing the range , except for the sgo1-3A-tetR-GFP strain , where results are shown from a single experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 029 This finding allowed us to test a prediction: if Sgo1-dependent deposition of condensin at the pericentromere is critical for biasing sister kinetochores towards biorientation , then Sgo1-700 , which fails to bind to or recruit condensin to the pericentromere ( Figure 9B–E ) should lack the sister kinetochore bias . In contrast , Sgo1-3A , which recruits condensin to the pericentromere ( Figure 9B , C ) , and Sgo1-100 , which retains at least a partial ability to deposit condensin at the pericentromere ( Figure 9F , Figure 9—figure supplement 1 ) , should enable some degree of sister kinetochore bias . In accordance with these predictions , our live cell assay revealed that only the sgo1-700 mutant was significantly defective in the sister kinetochore bias ( Figure 9G ) . We conclude that condensin recruitment by Sgo1 biases sister kinetochores towards biorientation . Our findings have demonstrated that Sgo1 plays a central role in promoting biorientation , through at least two separate mechanisms . Sgo1 enables error correction by retention of Ipl1 ( aurora B ) at the centromere . Sgo1 separately recruits condensin to the pericentromere , which both imposes a bias on sister kinetochores to biorient and enables effective error correction . Our findings provide the first molecular explanation for the bias to sister kinetochore biorientation . Interestingly , Indjeian and Murray ( 2007 ) demonstrated the existence of a bias to sister kinetochore biorientation in the sgo1-100 mutant , which is defective in the error correction process ( Indjeian et al . , 2005 ) . Consistent with this study , we confirmed that the sgo1-100 mutant retains this bias to sister kinetochore biorientation ( Figure 9G ) . Through analysis of both the sgo1-700 and sgo1Δ mutants we have , nevertheless uncovered a role for Sgo1 in biasing sister kinetochores towards biorientation . Moreover , our data provide a molecular explanation for the loss of sister kinetochore bias in sgo1-700 and sgo1Δ cells: that is the inability to associate with and recruit condensin to centromeres . In contrast , sgo1-100 and sgo1-3A cells , which can enable condensin recruitment to centromeres are proficient in the bias to sister kinetochore biorientation . Condensin is a cohesin-related complex that can form a ring-like structure and has been proposed to organize chromosomes by bringing distant chromosomal sequences together ( Cuylen and Haering , 2011; Cuylen et al . , 2011; Hirano , 2012 ) . The mechanism by which condensin is loaded onto and subsequently maintained on chromosomes is largely unknown . Here , we have identified shugoshin as an important determinant of condensin association with the pericentromere . Indeed , the pericentromeres and rDNA appear to be the predominant , if not the only sites of condensin association with chromosomes in mitosis . Our findings have also suggested a hierarchy of assembly at the pericentromere . In contrast to the idea that the Scc2/4 cohesin-loader complex loads condensin directly ( D’Ambrosio et al . , 2006 ) , we propose that Scc2/4 indirectly affects condensin localization through loading cohesin , which we show is required for the pericentromeric association of Sgo1 . In support of this idea , only cohesin and not condensin subunits were identified in Scc2/4 immunoprecipiates ( Fernius et al . , 2013 ) . Furthermore , since Shugoshin relies on cohesin for its association with the pericentromere ( Kiburz et al . , 2005; Yamagishi et al . , 2008; Figure 5 ) , the importance of cohesin in defining kinetochore geometry ( Sakuno et al . , 2009 ) could be to enable proper condensin association with chromosomes by recruiting shugoshin to the pericentromere . How might condensin shape a favorable pericentromeric geometry ? While the molecular function of condensin is not well understood , condensin is known to alter the structural properties of centromeres that affect their dynamic behavior ( Ribeiro et al . , 2009; Stephens et al . , 2011 ) . We propose that condensin organizes the pericentromeric chromatin to provide a structural integrity to the pericentromere that enables it to adopt a ‘back-to-back’ geometry that orients sister kinetochores in opposite directions , thereby favoring their biorientation ( Figure 10 ) . Although our data indicate that condensin is important to increase the efficiency of biorientation , we do not believe that it is essential for this process in budding yeast , provided that error correction machinery is intact . However , budding yeast centromeres are relatively simple and only a single microtubule contacts each kinetochore ( Winey et al . , 1995 ) increasing the probability that correct attachments can be made by chance . This is not the case for organisms with more complex centromeres where it is likely that kinetochore geometry plays a major role in ensuring sister kinetochore biorientation is achieved . 10 . 7554/eLife . 01374 . 030Figure 10 . Model for dual role of Sgo1 in biorientation is shown . Shugoshin ensures sister kinetochore biorientation through two mechanisms . First , early in the cell cycle , shugoshin mediates the enrichment of condensin within the pericentromere . We propose that condensin enables the pericentromere to adopt a geometry that favors the capture of sister kinetochores by microtubules from opposite poles , thereby biasing them to biorient . Second , shugoshin maintains aurora B at the pericentromere for those kinetochores that fail to biorient and come under tension . Aurora B destabillizes these tension-less attachments , thereby providing a further chance to for sister kinetochores to make the appropriate , tension-generating attachments . We suggest that condensin facilitates this ‘error correction’ process by conferring a rigid structure to the pericentromere upon which aurora B can act . DOI: http://dx . doi . org/10 . 7554/eLife . 01374 . 030 In addition to influencing pericentromeric structure through condensin , shugoshins also confer distinct properties to pericentromeric cohesin through PP2A , both in meiosis and mammalian mitosis , as well as influence kinetochore–microtubule interactions through aurora B . Shugoshins are therefore emerging as functional hubs that define the pericentromere , allowing it to perform specialized functions that are key for the fidelity of chromosome segregation . Strains used in this work are listed in Supplementary file 2A . All yeast strains were derivatives of W303 except the protease-deficient strain , JB811 , used for TAP pulldowns . The sgo1-100 and sgo1-700 alleles were described in Indjeian et al . ( 2005 ) , the sgo1-3A allele was described in Xu et al . ( 2009 ) and sgo1Δ was described in Clift et al . ( 2009 ) . A PCR-based approach was used to tag Sgo1 with SZZ ( TAP ) ; Ipl1 , Brn1 and Ycs4 with 6HA; and Rts1 with 3Pk or 9Myc ( Longtine et al . , 1998; Knop et al . , 1999 ) . Auxin-inducible degron versions of Sgo1 and Ycs5 were constructed as described by Nishimura et al . ( 2009 ) . To generate a strain carrying Sgo1-TetR-GFP , SGO1 was cloned upstream of tetR-GFP in p128 ( TetR-GFP ) ( Michaelis et al . , 1997 ) to generate plasmid AMp769 which was integrated at the LEU2 locus after EcoRV digestion . TetR-GFP fusions to Sgo1-100 , Sgo1-700 or Sgo1-3A were generated by PCR amplication of these alleles from the genomic locus and replacement of SGO1 in AMp769 ( sgo1-700 , sgo1-3A ) or by site-directed mutagenesis using the Quikchange kit ( Agilent Technologies , Santa Clara , CA ) . Ipl1-as5 was described in Pinsky et al . ( 2006 ) . pMET-CDC20 was described in Fernius and Marston ( 2009 ) . The CEN4-GFP label ( CEN4-tetOs::URA3 leu2::TetR-GFP::LEU2 ) was described in He et al . ( 2000 ) ; Tanaka ( 2010 ) and SPC42-tdTomato was described in Fernius and Hardwick ( 2007 ) . Nocodazole was used at 15 μg/ml and re-added to 7 . 5 μg/ml every 1 hr . NAA ( synthetic auxin ) was used at 500 μM and readded to 250 μM every 45 min . Methionine was used at 8 mM and re-added to 4 mM every 45 min . NAPP1 ( to inhibit ipl1-as ) was used at 50 μm and doxycycline was used at 5 μg/ml . ChIP was performed as described using anti-HA 12CA5 , anti-Myc 9E10 or anti-Pk ( V5 ) antibody ( Fernius et al . , 2013 ) . Primers used for qPCR analysis are given in Supplementary file 2B . qPCR was performed in a 20 μl Express SYBR GreenER ( Life Technologies , Carlsbad , CA ) reaction using a Lightcycler machine ( Roche , Switzerland ) . To calculate ChIP enrichment/input , ΔCT was calculated according to: ΔCT = ( CT ( ChIP ) − [CT ( Input ) − logE ( Input dilution factor ) ] ) where E represents the specific primer efficiency value . Enrichment/ input value was obtained from the following formula: E^−ΔCT . qPCR was performed in triplicate , typically for each of three or more independent experimental repeats . Error bars represent standard error . For ChIP-Seq , purified chromatin was recovered using a PCR purification kit ( Qiagen , Netherlands ) followed by drying in a speedvac . Samples were sequenced on a HiSeq2000 instrument ( Illumina , San Diego , CA ) by the EMBL Core Genomics Facility ( Heidelberg , Germany ) . The summary of data obtained is given in Supplementary file 2C . Scripts , data files and workflows used to create the ChIP-Seq data can be found on the github repository: https://github . com/AlastairKerr/Marston2013 . Single reads were mapped using BWA ( Version: 0 . 6 . 1-r104 ) ( Li and Durbin , 2010 ) to the sacCer3 reference genome and were processed with samtools ( Li et al . , 2009 ) to remove duplicated reads for parallel analysis . The region of chrXII containing rDNA ( 451400 bp to 490600 bp ) was removed and studied separately using bedtools ( version v2 . 16 . 2 ) ( Quinlan and Hall , 2010 ) . As this region is highly repetitive and contained a high density of reads , duplicate reads were not removed for this analysis as it would not be possible to differentiate duplicate reads from independent fragments . All data shown are normalized to the number of mapped reads per million total mapped reads [RPM] . Total mapped reads were calculated after any processing was done for rDNA or duplicate read removal . During the amplification step prior to sequencing , multiple identical reads will be generated . Due to the low yield of total DNA precipitating with Brn1-6HA in sgo1Δ cells , we were concerned that the small number of precipitating sequences would be biased for amplification . To avoid this problem , we removed PCR replicates from our analysis . However , since this would also remove bona fide identical sequences from our dataset , we simultaneously analyzed the data with these reads present and compared the two data sets where all 16 centromeres were considered together . To do this , 100 bp windows were examined that extended 25 kb in each direction of every centromere , and a local maximum of the number of mapped reads was taken . The comparison of this data with and without PCR replicates included can be seen in Figure 3H ( unfiltered data ) and Figure 4—figure supplement 2 ( PCR replicates removed ) . Both approaches led to very similar conclusions , therefore for all other data presented we used the unfiltered data . To grow cells for purification of Sgo1 from cycling cells , 4 L of YPDA culture were inoculated to OD600 = 0 . 2 and grown at room temperature for 6 hr . Cells were harvested by centrifugation and the cell pellet was resuspended in 2 μM of dithiobis ( succunimidylpropionate ) ( DSP; Proteochem , Loves Park , IL ) crosslinker for 30 min at room temperature before cell pellets were drop-frozen in liquid nitrogen as described by Fernius et al . ( 2013 ) . To purify Sgo1 from cells arrested in mitosis using the cold-sensitive tub2-401 allele , 4 L of media was inoculated to OD600 = 0 . 02 , grown at 30°C for 8 hr with shaking and then the temperature was reduced to 18°C for 7 hr to induce the arrest before holding the cells at 4°C for up to 5 hr before harvesting , crosslinking , and freezing as described above . Pulldowns and mass spectrometry were performed as described in Fernius et al . ( 2013 ) . For co-immunoprecipitation of TAP-tagged Sgo1 , 200 ml of nocodazole-treated culture was harvested and either firstly cross-linked using DSP as described in Fernius et al . ( 2013 ) , or directly drop-frozen in liquid nitrogen . Frozen pellets were ground in a pestle and mortar for 5 min . Ground lysates , prepared as in Fernius et al . ( 2013 ) , were incubated with 1 mg of IgG-coupled dynabeads for 1 . 5 hr at 4°C . For the DNAase treated samples in Figure 4—figure supplement 1 , benzonase ( 25U ) was added to the extracts before incubating at room temperature for 30 min before proceeding with immunoprecipitation . Protein complexes were eluted from the beads by addition of 30 μl of sample buffer and loaded onto polyacrylamide gels . Western immunoblot was performed as described in Clift et al . ( 2009 ) and visualized by detection of chemiluminesence on autoradiograms except for the data shown in Figure 9E where proteins were visualized using a fluorophore-conjugated antibody and the Odyssey system ( Li-Cor , Lincoln , NE ) . Mouse anti-aid ( CosmoBio , Japan ) , anti-HA11 , and anti-Myc antibodies were all used at a dilution of 1:1000 . Mouse anti-Pgk1 antibodies were used at a dilution of 1:5000 . Rabbit anti-Sgo1 ( a kind gift of Adam Rudner , Ottawa Institute of Systems Biology , Canada ) , anti-GFP ( a kind gift of Eric Schirmer , University of Edinburgh ) and anti-Pgk1 antibodies were used at a dilution of 1:5000 . Rabbit anti-Kar2 antibodies ( laboratory stock ) were used at a dilution of 1:5000 . Indirect immunofluorescence and FACS were performed as described in Clift et al . ( 2009 ) and Fernius and Marston ( 2009 ) , respectively . Cells carrying pMET-CDC20 , CEN4-GFP and SPC42-tdTomato were arrested in G1 using alpha factor ( 4 μg/ml ) in minimal medium lacking methionine ( SD/-met ) at room temperature for 3 hr . Alpha factor was washed out and cells were released from the G1 arrest into rich medium containing methionine and nocodazole ( YPDA + Met + NOC ) plus the appropriate drugs ( NAPP1 , NAA ) . Methionine represses pMET-CDC20 expression , resulting in a metaphase arrest and nocodazole depolymerizes microtubules . After 3 hr , a sample was extracted ( t = 0 ) and nocodazole was washed out by filtering in the presence of methionine and cultures were released into YPDA + Met to allow spindles to reform while maintaining the metaphase arrest . Samples were taken at 20 min intervals . Samples were fixed in 3 . 7% formaldehyde for 10 min , before washing in PBS and resuspending in DAPI for microscopy . Samples with two separated SPC42-tdTomato foci were scored as containing one or two CEN4-GFP foci . Typically 200 , and at least 100 , cells were scored for each timepoint . For live cell imaging , cells were loaded onto the Onix Microfluidic Perfusion system ( CellAsic , Hayward , CA ) and visualized using a Deltavision Elite ( Applied Precision , Issaquah , WA ) coupled to a Cascade 2 EMCCD camera with temperature control to 30°C . Frames were grabbed at the indicated intervals and images were processed in ImagePro software ( Media Cybernetics , Rockville , MD ) . For image analysis , a custom-written macro was developed in ImagePro software ( Media Cybernetics ) , details of which are available upon request . In brief , yeast are selected from a reference DIC image taken at each time point . The yeast are automatically thresholded and the centre pixel from each red and green spot is then calculated . The distances between the spots can then be calculated and the measurements output to a text file for analysis using microsoft excel . Measurements of inter-centromere distance and distance from CEN4-GFP dot to nearest Spc42-tdTomato were obtained using this automated system . To test the efficiency of error correction , cells carrying CEN4 ( CEN4-GFP ) and SPB ( SPC42-tdTomato ) markers together with CDC20 under control of a methionine-repressible promoter ( pMET-CDC20 ) were arrested in G1 using alpha factor and then loaded onto the microfluidic chamber . Cells were released in the chamber into medium containing nocodazole ( to depolymerize microtubules ) and methionine ( to deplete CDC20 and induce a metaphase arrest ) for 30 min . The addition of nocodazole to G1 cells prevents SPB separation because this requires microtubules . Therefore , under these conditions , microtubules do not separate which leads to a high rate of monoorientation once microtubules are allowed to reform . This leads to a strong reliance on the error correction machinery to establish biorientation ( Indjeian and Murray , 2007 ) . After 30 min , nocodazole was washed out , all the time maintaining the CDC20 arrest by inclusion of methionine in the media . After 90% of cells had 2 SPBs ( typically 1 . 5 hr ) , we began imaging at ∼74s intervals for a total of 21 frames . For determination of the overall ability of strains to biorient in Figure 2D all cells in each frame where two SPB foci were detected by the software were scored for the presence of either one or two CEN4-GFP foci . The total percentage of cells with two SPB foci that contained two GFP foci was calculated for all frames for each strain . The number of cell images analyzed was 1116 ( wild type ) , 697 ( sgo1Δ ) , 1251 ( YCS5-aid ) and 968 ( ipl1-as ) . To m As a measure of the efficacy of the error correction machinery , we determined the ability of cells to switch between one and two visible CEN4-GFP foci . Cells in which two SPB foci were detected for at least four consecutive frames were scored for the number of times that the number of CEN4-GFP foci changed from one to two or vice-versa for the frames in which two SPBs were consecutively visible . The ‘switching rate’ was calculated by dividing the number of times a cell alternated between one and two CEN4-GFP foci by the total time in which two SPB foci were consecutively visible . The average switching rate was determined for all cells in which two SPB foci were detected for at least four consecutive frames . In Figure 2F , we analyzed 92 wild-type cells ( 971 frames ) , 55 sgo1Δ cells ( 551 frames ) , 102 YCS5-aid cells ( 1046 frames ) and 86 ipl1-as cells ( 748 frames ) . To test the bias on sister kinetochores to biorient , cells carrying CEN4 ( CEN4-GFP ) and SPB ( SPC42-tdTomato ) markers together with CDC20 under control of a methionine-repressible promoter ( pMET-CDC20 ) were arrested in G1 using alpha factor and then released into medium containing methionine to deplete CDC20 and induce a metaphase arrest . Cells were loaded onto the microfluidics chamber and after approximately 1 . 5 hr , when around 90% of cells had 2 SPBs , cells were treated with YPDA containing nocodazole for 30 min . After 30 min , nocodazole was washed out after which cells were immediately imaged every ∼94 s for a total of 21 frames . We scored the percentage of cells in which separated GFP dots were observed at least once during the observation period .	When a cell divides to create two new daughter cells , it must produce a copy of each of its chromosomes . It is important that each daughter cell gets just one copy of each chromosome . If an error occurs and one cell gets two copies of a single chromosome , it can lead to cancer or birth defects . Fortunately , there are multiple checks to ensure that this does not happen . During cell division the chromosomes line up in a way that increases the likelihood that each daughter cell will have one copy of each chromosome . After this process—which is called biorientation—is completed , microtubules pull the chromosomes to opposite ends of the cell , which then divides . Proteins called shugoshin proteins are known to be involved in biorientation in many organisms . These proteins are found in a region called the pericentromere , which surrounds the area on the chromosomes that the microtubules attach to , but the details of their involvement in biorientation are not fully understood . Now Verzijlbergen et al . have exploited sophisticated genetic techniques in yeast to explore how shugoshin proteins work . These experiments showed that the shugoshin protein helps to recruit condensin—a protein that keeps the DNA organized within the chromosome—to the pericentromere to assist with biorientation . It also keeps aurora B kinase—one of the enzymes that helps to correct errors during cell division—in the pericentromere when a microtubule attaches to the wrong chromosome . These results help us understand how a ‘hub’ in the pericentromere ensures biorientation . The next challenge will be to understand how this hub is disassembled after biorientation to allow error-free cell division to proceed . As shugoshins have been found to be damaged in some cancers , understanding the workings of this hub could also shed new light on how they contribute to disease .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "chromosomes", "and", "gene", "expression", "cell", "biology" ]	2014	Shugoshin biases chromosomes for biorientation through condensin recruitment to the pericentromere
Escherichia coli single-stranded DNA ( ssDNA ) binding protein ( SSB ) is the defining bacterial member of ssDNA binding proteins essential for DNA maintenance . SSB binds ssDNA with a variable footprint of ∼30–70 nucleotides , reflecting partial or full wrapping of ssDNA around a tetramer of SSB . We directly imaged single molecules of SSB-coated ssDNA using total internal reflection fluorescence ( TIRF ) microscopy and observed intramolecular condensation of nucleoprotein complexes exceeding expectations based on simple wrapping transitions . We further examined this unexpected property by single-molecule force spectroscopy using magnetic tweezers . In conditions favoring complete wrapping , SSB engages in long-range reversible intramolecular interactions resulting in condensation of the SSB-ssDNA complex . RecO and RecOR , which interact with SSB , further condensed the complex . Our data support the idea that RecOR--and possibly other SSB-interacting proteins—function ( s ) in part to alter long-range , macroscopic interactions between or throughout nucleoprotein complexes by microscopically altering wrapping and bridging distant sites . Single-stranded DNA ( ssDNA ) binding protein ( SSB ) binds rapidly and avidly to ssDNA generated during the normal processes of DNA replication , recombination , and repair ( Meyer and Laine , 1990 ) . In doing so , SSB protects ssDNA from chemical damage and exonucleolytic degradation , removes secondary structure , and enhances the enzymatic activity of many proteins involved in DNA metabolism ( Shereda et al . , 2008 ) . The extent to which ssDNA is wrapped around a tetramer of SSB is often referred to as a binding mode , defined by the apparent site size or footprint ( i . e . nucleotides bound per tetramer ) . These binding modes are sensitive to salt , temperature , pH , and binding density ( Lohman and Ferrari , 1994 ) . The cooperativity , i . e . nearest neighbor interactions , of SSB is also altered when SSB binds ssDNA in different binding modes ( Lohman et al . , 1986; Bujalowski and Lohman , 1987; Ferrari et al . , 1994 ) . At low salt concentrations , where ssDNA is partially wrapped around SSB , cooperativity is very high or ‘unlimited’ . As such , proteins crowd very close to each other along the ssDNA . At higher more physiological salt concentrations , SSB binds in the fully wrapped binding mode and exhibits ‘limited’ cooperativity , where SSB forms dimers of tetramers ( i . e . octamers ) along the ssDNA ( Bujalowski and Lohman , 1987; Lohman and Ferrari , 1994 ) . Early electron microscopic visualization of SSB-coated ssDNA revealed a beads-on-string structure similar to those observed for nucleosomes bound to dsDNA ( Chrysogelos and Griffith , 1982 ) . These structures are observed at a low binding density of SSB; however , at higher binding densities , the structures form smooth , contoured nucleoprotein complexes that are condensed relative to the contour length of the corresponding dsDNA ( Griffith et al . , 1984; Hamon et al . , 2007 ) . High-resolution atomic force microscopy ( AFM ) imaging of spread SSB-coated ssDNA formed in low and high salt , measured approximately a twofold difference between the contour lengths of the nucleoprotein complexes . This difference in contour length was proposed to reflect the partially wrapped SSB35 and fully wrapped SSB65 binding modes , corresponding to a site size of 35 and 65 nucleotides , respectively ( Hamon et al . , 2007 ) . It is worth noting that an additional , intermediate binding mode , SSB55 , was also observed in direct binding experiments ( Lohman and Overman , 1985; Bujalowski and Lohman , 1986 ) . SSB has been studied extensively using single-molecule FRET on short oligonucleotide substrates ( Roy et al . , 2007 , 2009; Zhou et al . , 2011 ) ; however , relatively little is known about the more complex dynamics of the SSB-coated ssDNA nucleoprotein fiber that forms on the extensive regions of ssDNA during DNA unwinding , resection , and replication . These ssDNA regions can range from a few hundred to tens of thousands of nucleotides in length . More than a dozen proteins interact directly with SSB via its short , unstructured C-terminal tail ( Shereda et al . , 2008; Wessel et al . , 2013; Bhattacharyya et al . , 2014 ) . In the absence of interaction partners or ssDNA , this unstructured peptide tail interacts with the subunits within the SSB tetramer ( Kozlov et al . , 2010a ) . This inter-subunit allostery contributes to the complex , cooperative nature of SSB binding to ssDNA . It has been proposed that the binding modes of SSB might be modulated in vivo for differential roles during ssDNA processing . Direct evidence for such modulation remained elusive for many years ( Shereda et al . , 2008 ) ; however , recent work has shown that PriC remodels the SSB-ssDNA complex to create a DNA structure competent for DnaB loading during replication restart ( Wessel et al . , 2013 ) and that PriA modulates the SSB-ssDNA complex to expose a potential replication initiation site ( Bhattacharyya et al . , 2014 ) . RecO catalyzes the annealing of complementary strands of ssDNA even in the presence of SSB , which otherwise kinetically blocks annealing ( Kantake et al . , 2002 ) ; in this regard , perhaps RecO is mimicking the action of PriA ( Bhattacharyya et al . , 2014 ) . This annealing activity is essential for RecA-independent , homology-directed DNA repair that proceeds through the single-strand annealing ( SSA ) pathway ( Kantake et al . , 2002 ) . RecO also stimulates RecA-dependent homologous recombination by acting with RecR and RecF to promote RecA filament assembly ( Umezu et al . , 1993; Morimatsu and Kowalczykowski , 2003; Handa et al . , 2009 ) . RecR , which does not bind to ssDNA , dsDNA , or SSB , binds to RecO and enhances the affinity of RecO for ssDNA-bound SSB ( Umezu et al . , 1993; Umezu and Kolodner , 1994 ) ; however , neither RecO nor RecOR are capable of physically displacing SSB from ssDNA ( Umezu and Kolodner , 1994; Ryzhikov et al . , 2011 ) . When RecR is bound to RecO , it partially inhibits the annealing activity of RecO but stimulates both the rate of RecA nucleation and filament growth on SSB-coated ssDNA ( Kantake et al . , 2002; Bell et al . , 2012; Morimatsu et al . , 2012 ) . As RecA does not interact with SSB , RecO , or RecR ( Umezu et al . , 1993 ) , this activity must proceed through a RecOR-induced conformational change in the SSB-ssDNA complex ( Ryzhikov et al . , 2011; Zhou et al . , 2011 ) . Using both direct visualization of SSB-coated ssDNA and single-molecule force spectroscopy , we observed the reversible intramolecular condensation of single SSB-coated ssDNA fibers . The extent of this intramolecular condensation increases with salt concentration , but exceeds the expected extent of condensation based on most previous measurements of SSB-ssDNA complexes ( Chrysogelos and Griffith , 1982; Hamon et al . , 2007 ) . We also observe RecO-induced condensation of the SSB-ssDNA complex , as well as long-range intramolecular bridging in the presence of both RecO and RecR . We propose that the nature of this condensation is due to the ability of SSB to interact with distant sites along the ssDNA—either through dimerization of SSB tetramers or through the partial wrapping of distant ssDNA sites on a single SSB protomer—and that one role of RecOR is to enhance these distant interactions , which in turn would facilitate annealing of complementary strands . Our observations raise the possibility that the microscopic changes in ssDNA-binding modes observed for SSB cause macroscopic condensation ( or de-condensation ) of the nucleoprotein fiber that , in turn , might regulate access to ssDNA . We previously described a fluorescent biosensor for ssDNA derived from an engineered mutant , SSBG26C , wherein a fluorophore was conjugated to the protein using Alexa Fluor 488 maleimide to produce SSBAF488 ( Dillingham et al . , 2008 ) . This protein maintains a high , albeit attenuated , affinity for ssDNA ( Bell , 2011; Bell et al . , 2012 ) . SSBAF488-ssDNA nucleoprotein complexes were formed by first denaturing bacteriophage λ genomic dsDNA that had been biotinylated at the 3′-terminated ends using DNA polymerase ( Figure 1A ) . The denatured DNA was mixed with buffer containing SSBAF488 , attached to a glass surface functionalized with streptavidin , extended using flow within a microfluidic chamber , and visualized using total internal reflection fluorescence ( TIRF ) microscopy ( Figure 1B ) . When the concentration of sodium acetate ( NaOAc ) was increased during buffer exchange at a constant flow rate and a constant concentration of fluorescent SSB , the length of single molecules of SSBAF488-coated ssDNA shortened ( Figure 1C , Video 1 , and Figure 1D ) ; however , the fluorescent intensity of individual molecules remained constant ( Figure 1E , Video 1 , and Figure 1—figure supplement 1 ) , indicating that the protein had not dissociated , but rather redistributed , along the ssDNA molecule . In contrast , when the SSBAF488 was exchanged for unmodified wild type SSB , which has a higher affinity for ssDNA , the fluorescence rapidly decreased as SSBAF488 was displaced from the ssDNA ( Figure 1F ) . 10 . 7554/eLife . 08646 . 003Figure 1 . Visualization of salt-induced intramolecular condensation of single molecules of SSBAF488-ssDNA complexes . ( A ) Bacteriophage λ dsDNA ( 48 . 5 kbp ) was biotinylated , denatured , coated with SSBAF488 , and then ( B ) attached to a streptavidin-coated glass coverslip of a microfluidic chamber where it was extended by buffer flow for direct imaging using total internal reflection fluorescence ( TIRF ) microscopy . ( C ) A montage of frames from a video recording the change in length of a single molecule of SSBAF488-coated single-stranded DNA ( ssDNA ) upon increasing [NaOAc] from 0 to 100 mM . The frames were rendered into a topographical intensity map . Time zero corresponds to the time at which the pump was turned on . The dead time of the experiment was ∼25 s due to the liquid volume in the lines between the syringe valve and the microfluidic chamber . ( D ) The length of the molecule in panel C during the change in salt from 0 mM to 100 mM NaOAc was measured for each frame and is plotted as a function of time . The dotted line represents the injection of the buffer into the microfluidic flow chamber . ( E ) The fluorescence intensity of the molecule in panel C was also measured for each frame and is plotted as a function of time . ( F ) The fluorescence intensity of a single molecule of SSBAF488-coated ssDNA is plotted as function of time during a similar experiment where SSBAF488 was exchanged for wild-type , unlabeled SSB . The decrease in fluorescence intensity corresponds to the displacement by wild-type SSB , which has higher affinity for ssDNA than SSBAF488 . The fluorescence intensity ( green circles ) was determined by the mean pixel intensity of region of interest ( ROI ) , and the gray error bars are the standard deviation of the pixels within the ROI . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 00310 . 7554/eLife . 08646 . 004Figure 1—figure supplement 1 . Intensity and length measurements during salt-induced intramolecular condensation . Representative plots showing the change in intensity ( leftmost panels ) or length ( rightmost panels ) of SSBAF488-coated ssDNA molecules for the condition where [NaOAc] was increased from ( A , B ) 0 to 100 mM , ( C , D ) 100 to 200 mM , and ( E , F ) from 200 to 400 mM . Then number of molecules analyzed for each trace is designated in the bottom right corner . Error bars represent standard deviation between molecules . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 00410 . 7554/eLife . 08646 . 005Video 1 . Salt-induced intramolecular condensation of SSBAF488-ssDNA . Video recording of a single molecule of SSBAF488-coated ssDNA , imaged using TIRF microscopy , upon increasing [NaOAc] from 0 to 100 mM . The video frames were rendered into a topological intensity map . Time zero corresponds to the time at which the pump was turned on . The dead time of the experiment was approximately 25 s due to the volume in the lines between the syringe valve and the microfluidic chamber . The molecule in the video corresponds to the molecule presented in Figure 1 , panels C–EDOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 005 High resolution imaging of SSB-coated ssDNA , using electron microscopy ( EM ) and AFM , had previously observed that the length of the nucleoprotein fiber is dependent on the buffer condition in which the complex is formed ( Chrysogelos and Griffith , 1982; Hamon et al . , 2007 ) . However , we were perplexed by the observation that the amount of protein—indicated by the total fluorescent intensity—along the ssDNA remained essentially unchanged during each salt-jump transition , despite the fact that the length had changed substantially . Stopped-flow kinetic studies have previously demonstrated that SSB tetramers can transfer between ssDNA molecules without proceeding through a free protein intermediate ( Kozlov and Lohman , 2002a , b ) and single-molecule experiments have directly demonstrated that SSB tetramers diffuse rapidly on ssDNA and can ‘hop or jump’ across long distances of ssDNA via intersegmental transfer ( Roy et al . , 2009; Zhou et al . , 2011; Lee et al . , 2014 ) . To distinguish between the intramolecular redistribution of SSB along the ssDNA in cis vs dissociation balanced with rebinding during the transition , we asked whether the salt-induced condensation of single molecules might be reversible in the absence of free protein . To address this possibility , SSB-coated ssDNA was tethered in a flow cell and extensively washed with buffer to remove free protein ( ∼100–200 volumes of the flow chamber ) . An injection loop was then used to transiently pulse the tethered molecules with buffer containing either 100 or 400 mM NaOAc , followed by a sufficient volume of buffer to remove the injected salt . When the salt concentration was raised to 100 mM NaOAc , the flow-extended molecules compacted to ∼60% of the length in the absence of salt ( Figure 2A , Video 2 ) , and then returned to the previously extended length when the salt was removed from the flow chamber . Similarly , this condensation was also observed when 400 mM NaOAc was used ( Figure 2B , Video 3 ) ; however , the extent of the condensation was greater , wherein the molecules shortened to ∼12% of the flow-extended length in the absence of salt . In both experiments , the molecules were dimmer at the end of the experiment ( Figure 2—figure supplement 1 ) , where approximately 20% of the SSB dissociated in the 0→100 transition , and ∼60% dissociated in the 0→400 transition . 10 . 7554/eLife . 08646 . 006Figure 2 . The length change upon salt-induced condensation of SSBAF488-coated ssDNA is nearly reversible in the absence of free SSB protein . ( A ) A montage of frames from a video recording of a single molecule of SSBAF488-coated ssDNA contracting in length as the salt concentration is increased from 0 to 100 mM NaOAc , and then subsequently reduced back to zero , conducted in the absence of free SSBAF488 . The flow cell was extensively washed with buffer to remove free SSB protein from the flow cell before beginning the experiment . Video recording began when the pump was turned on , requiring ∼40-50 s for the dead volume to be flushed from the lines to the flow chamber . ( B ) Same as in ( A ) , except the salt concentration was increased from 0 mM NaOAc to 400 mM NaOAc and then back to zero . Each frame of the montages is one micron wide . SSBAF488 was omitted from both of the high salt washes and from the 0 mM wash . Flow is from top to bottom in each image . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 00610 . 7554/eLife . 08646 . 007Figure 2—figure supplement 1 . SSBAF488 partially dissociates from ssDNA during salt transitions in the absence of free protein . ( A ) Schematic showing the injection system used to transiently increase the salt concentration . ( B–E ) Representative plots showing the change in intensity ( leftmost panels ) or length ( rightmost panels ) of SSBAF488-coated ssDNA molecules from the experiments presented in Figure 2 . ( B , C ) for Figure 2A ( top panel , 0 to 100 mM–0 mM NaOAc ) and ( D , E ) for Figure 2B ( bottom panel , 0 mM–400 mM to 0 mM NaOAc ) , where the salt concentration was transiently increased during continuous flow in the absence of free SSBAF488 followed by washing molecules with no salt buffer . Dashed lines indicate the time at which the high salt and low salt buffers reached the flow chamber . SSBAF488 was omitted from both of the high salt washes and from the 0 mM wash . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 00710 . 7554/eLife . 08646 . 008Video 2 . Condensation of SSBAF488 in the absence of free protein during a transient increase from 0 to 100 mM NaOAc . Video recording of a single molecule of SSBAF488-coated ssDNA contracting in length as the salt concentration is increased from 0 to 100 mM NaOAc , and then subsequently reduced back to zero mM , conducted in the absence of free SSBAF488 . The flow cell was extensively washed with buffer to remove free SSB protein before beginning the experiment . Video recording began when the pump was turned on , requiring ∼40-50 s for the dead volume to be flushed from the lines to the flow chamber . SSBAF488 was omitted from both of the high-salt washes and from the 0 mM wash . The video corresponds to the molecule presented in Figure 2A and Figure 2—figure supplement 1 , panels B , C . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 00810 . 7554/eLife . 08646 . 009Video 3 . Condensation of SSBAF488 in the absence of free protein during a transient increase from 0 to 400 mM NaOAc . Video recording of a single molecule of SSBAF488-coated ssDNA contracting in length as the salt concentration is increased from 0 to 400 mM NaOAc , and then subsequently reduced back to zero mM , conducted in the absence of free SSBAF488 . The flow cell was extensively washed with buffer to remove free SSB protein before beginning the experiment . Video recording began when the pump was turned on , requiring ∼40-50 s for the dead volume to be flushed from the lines to the flow chamber . SSBAF488 was omitted from both of the high-salt washes and from the 0 mM wash . The video corresponds to the molecule presented in Figure 2B and Figure 2—figure supplement 1 , panels D , E . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 009 SSBAF488 is particularly suitable for single molecule measurements due to its relative photostability , whereas an alternative biosensor derived from fluorescein-5-maleimide , SSBf , is particularly suitable as an ensemble ssDNA-biosensor due to the large , linear increase in fluorescence upon ssDNA binding ( Dillingham et al . , 2008; Bell , 2011 ) . To determine whether the measured lengths of individual SSBf-coated ssDNA complexes were correlated with the DNA-binding modes of SSB , we measured the stoichiometry of SSBf binding to poly ( dT ) using ensemble fluorescence measurements . Poly ( dT ) was titrated into a fixed concentration of SSBf at various concentrations of NaOAc , and the data were fit to a two-segment line to determine the apparent site size , which reflects the extant binding mode at each salt concentration ( Figure 3A ) . The observed site size increased from ∼43 to ∼70 nucleotides per SSB tetramer over the range of salt concentrations tested as expected; however , we noted that the amplitude of the fluorescence enhancement increased dramatically with salt concentration , indicating the molecular environment of the fluorophore was altered ( Figure 3B ) . In addition to the stoichiometric titrations performed by adding ssDNA to a fixed concentration of SSBf , we performed so-called ‘salt back-titrations’ to determine the concentration at which SSBf dissociates from ssDNA . When pre-formed complexes of SSBf-poly ( dT ) were titrated with an increasing concentration of salt ( Figure 3—figure supplement 1 ) , we observed an initial sharp increase in the fluorescence corresponding to the amplitudes from our direct titrations shown in Figure 3A . The fluorescence peaked between 200 and 400 mM NaOAc , and was followed by a shallow , linear decrease until the concentration reaches approximately 2M NaOAc , where the fluorescence intensity exhibited a sharp decrease due to dissociation ( Figure 3—figure supplement 1 ) . The midpoint of this sharp transition corresponds to the so-called , ‘salt-titration midpoint’ , where ∼50% of the complex is dissociated ( Kowalczykowski et al . , 1981; Newport et al . , 1981 ) . The salt-titration midpoint for this experiment shows that ∼2 M NaOAc in the presence of 5 mM Mg ( OAc ) 2 is required to dissociate half of the protein from the DNA . 10 . 7554/eLife . 08646 . 010Figure 3 . The extent of SSB-ssDNA condensation is greater than anticipated based on known ssDNA-wrapping transitions . ( A ) Poly ( dT ) was titrated into 100 nM SSBf ( tetramer ) and the average fluorescence enhancement of SSBf from three titrations was plotted as a function of ratio of poly ( dT ) to SSB tetramer . The data were fit to a two-segment line , where the breakpoint is the stoichiometric endpoint of the titration corresponding to the site size of SSBf . ( B ) The amplitude of the fluorescence enhancement from the titrations performed in Figure 3A was plotted as a function of [NaOAc] . The error is smaller than the symbols . A larger number of titrations are shown here than in panel A to prevent panel A from being overcrowded; each fold-increase was determined by a full stoichiometric titration where each titration was completely and fully saturated . ( C ) Representative images of single molecules of SSBf-coated ssDNA at increasing [NaOAc] indicated . ( D ) The apparent binding site size ( black circles , ± error of the fits from panel A determined from the titrations performed in panel A were plotted as a function of salt concentration . ( E ) Length of SSBf-coated ssDNA molecules plotted as a function [NaOAc] ( N = 213 ) . ( F ) Length of SSBf-coated ssDNA plotted as a function of [Mg ( OAc ) 2] ( N = 156 ) and ( G ) as a function of [NaGlu] in the absence ( black , closed circles , N = 205 ) and presence ( blue , open circles , N = 214 ) of 1 mM Mg ( OAc ) 2 . Unless otherwise indicated , all error bars represent standard deviation and when not visible were smaller than the symbols . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 01010 . 7554/eLife . 08646 . 011Figure 3—figure supplement 1 . Salt back-titrations to determine the concentration at which SSBf dissociates from ssDNA . Plot of the fluorescence enhancement of pre-formed SSBf-poly ( dT ) complexes as a function of NaOAc concentration , normalized relative to the fluorescence of SSBf in the absence of ssDNA . NaOAc was titrated in the absence ( black circles ) or presence ( blue circles ) of 5 mM Mg ( OAc ) 2 . The concentration of SSBf and poly ( dT ) were constant during the titration . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 01110 . 7554/eLife . 08646 . 012Figure 3—figure supplement 2 . Length distributions of single molecules of SSBf-coated ssDNA as a function of [NaOAc] . Distribution of lengths of SSBf-coated ssDNA at increasing concentrations of NaOAc ( N = 216 ) . The data were fit to a Gaussian distribution , and the mean and standard deviation from the fits were plotted in Figure 3E ( NaOAc ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 01210 . 7554/eLife . 08646 . 013Figure 3—figure supplement 3 . Intensity of SSBf-ssDNA molecules as a function of [NaOAc] . Scatter-plot showing the intensity of individual SSBf-ssDNA complexes imaged at each concentration of NaOAc . The intensity of each molecule was normalized to the average intensity of the molecules at 0 mM NaOAc ( N = 200 , ±s . d . ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 01310 . 7554/eLife . 08646 . 014Figure 3—figure supplement 4 . Length distributions of single molecules of SSBf--coated ssDNA as a function of [Mg ( OAc ) 2] . Distribution of lengths of SSBf-coated ssDNA at increasing concentrations of Mg ( OAc ) 2 ( N = 156 ) . The data were fit to a Gaussian distribution and the mean and standard deviation from the fits were plotted in Figure 3F . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 01410 . 7554/eLife . 08646 . 015Figure 3—figure supplement 5 . Length distributions of single molecules of SSBf-coated ssDNA as a function of [NaGlu] . Distribution of lengths of SSBf-coated ssDNA at increasing concentrations of NaGlu ( N = 205 ) . The data were fit to a Gaussian distribution , and the mean and standard deviation from the fits were plotted in Figure 3G ( black circles ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 01510 . 7554/eLife . 08646 . 016Figure 3—figure supplement 6 . Length distributions of single molecules of SSBf-coated ssDNA as a function of [NaGlu] in the presence of 1 mM Mg ( OAc ) 2 . Distribution of lengths of SSBf-coated ssDNA at increasing concentrations of NaGlu in the presence of 1 mM Mg ( OAc ) 2 ( N = 214 ) . The data were fit to a Gaussian distribution , and the mean and standard deviation from the fits were plotted in Figure 3G ( blue , open circles ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 016 Individual SSBf-ssDNA complexes were also visualized with TIRF microscopy at increasing concentration of NaOAc , and it was evident that the length of the nucleoprotein fibers decreased as the NaOAc concentration increased ( Figure 3C ) . Because we initially considered that the change in length might simply correspond to the change in the salt-dependent binding mode , we plotted the apparent site size determined from the titrations shown in Figure 3A , as a function of increasing [NaAOc] ( Figure 3D ) and compared this to the average length of SSBf-ssDNA complexes ( Figure 3E ) , measured from images such as those in Figure 3C and more thoroughly represented in Figure 3—figure supplement 2 . This comparison shows that the site size changes approximately ∼1 . 7-fold ( 43 nts to 70 nts ) over this range whereas the length of the SSB-ssDNA molecules changes approximately ∼13-fold ( from ∼6 . 5 μm to ∼0 . 5 μm ) . We note that the apparent site size of SSB can vary depending on the ssDNA used owing to exclusion of SSB from regions capable of forming stable secondary structure ( Lohman and Overman , 1985 ) ; however , the reported change in site size for natural M13 ssDNA is only ∼2 . 2-fold ( from 35 to 77 ) over a comparable range ( 1 mM to 300 mM NaCl ) , which is insufficient to account for our observations . If SSBf dissociated from ssDNA during the salt transitions , then formation of secondary structure could explain the additional compaction; however , when we measured the intensity of individual SSBf-ssDNA fibers at each salt concentration ( Figure 3—figure supplement 3 ) , we see an increase in fluorescence intensity similar to––and in good agreement with––the titration performed with poly ( dT ) in Figure 3A , B , and Figure 3—figure supplement 1 . We note that the increase in fluorescence observed in Figure 3—figure supplement 3 is due to the environmental sensitivity of SSBf , and should not be confused with the results from Figure 1 , where we used SSBAF488 . Owing to the complex changes in fluorescence upon ssDNA binding , we cannot completely rule out the possibility that protein partially dissociates from the SSBf-ssDNA; however , we see no evidence of significant net dissociation in our assay in the presence of free protein . In the absence of free protein in solution , dissociation is apparent during the salt transitions ( Figure 2—figure supplement 1 ) , indicating that the net constant intensity we observe in Figure 1 and Figure 1—figure supplement 1 is maintained by mass action and rapid re-binding and redistribution of SSB along the ssDNA polymer . To further assess the condensation state of SSB-coated ssDNA at approximately physiological ionic conditions , we measured the SSBf-ssDNA lengths in the presence of the divalent cation magnesium , which is known to affect SSB binding modes ( Bujalowski et al . , 1988 ) . Mg ( OAc ) 2 induces a large condensation of nucleoprotein fibers that plateaus between 1 and 2 mM , and results in complexes that are as short as those produced with the much higher monovalent salt concentrations ( Figure 3F and Figure 3—figure supplement 4 ) . Escherichia coli maintains its intracellular osmolality by adjusting the intracellular concentration of glutamate , which ranges from ∼30 to 260 mM when cells are grown in media containing from ∼100 to 1100 mM solute ( Richey et al . , 1987 ) . Therefore , we also titrated sodium glutamate ( NaGlu ) in the presence and absence of 1 mM Mg ( OAc ) 2 , which is within the range of the measured intracellular free magnesium ion concentration ( 1–2 mM ) ( Alatossava et al . , 1985 ) . At low concentrations of NaGlu ( i . e . below 100 mM ) , the condensation was dominated by the presence of 1 mM Mg ( OAc ) 2; however , the observed condensation became dominated by NaGlu at higher concentrations ( Figure 3G and Figure 3—figure supplement 5 and 6 ) . In the absence of Mg ( OAc ) 2 , there was a log-linear decrease in length with increasing concentrations of NaGlu , similar to our observation for NaOAc . Extrapolating from this data , the shorter , more condensed molecules that we observe likely represent the physiologically relevant condensation state of the SSB-ssDNA complex as estimated by the in vivo concentration and composition of salts . To further explore the intramolecular condensation of SSB-coated ssDNA , we used a magnetic tweezer instrument to generate force-extension curves ( Gosse and Croquette , 2002; Meglio et al . , 2009 ) . In particular , we reasoned that force spectroscopy would enable us to distinguish between intramolecular collapse owing to secondary structure formation and exclusion of SSB versus intrinsic , protein-mediated folding of the SSB-ssDNA molecule . We further reasoned that because SSBf is a modified variant of SSB ( Dillingham et al . , 2008; Bell , 2011 ) , we could not exclude the possibility that a component of the intramolecular condensation might be due to the fluorescent adduct . This concern prompted us to assess the condensation state of single molecules of ssDNA coated with wild-type , unmodified SSB . Briefly , a ∼13 . 5-kbp DNA substrate with unique flanking restriction sites was PCR amplified from bacteriophage λ DNA . Molecular ‘handles’ were made by PCR amplification of two fragments ( ∼2 kbp each ) using pUC19 as a substrate in the presence of modified nucleotides . One fragment was amplified in the presence of DIG-dUTP , while the other was amplified in the presence of biotin-dGTP . These PCR fragments were ligated to the ends of the 13 . 5 kbp substrate ( Figure 4A , leftmost cartoon ) . The DNA substrate was alkali denatured and then neutralized with buffer ( Figure 4A , center cartoon ) . This resulted in ssDNA with one end that could be attached to the glass surface of a microfluidic cell that had been functionalized with anti-DIG antibodies , and the other end could be attached to a streptavidin-coated paramagnetic bead ( Figure 4A , rightmost cartoon ) . Paramagnetic beads and ssDNA that were not attached to the surface were flushed from the flow cell . Force-extension measurements of individual molecules were performed first in the absence of SSB at increasing salt concentrations . The salt was flushed from the flow cell , and measurements were then repeated in the presence of SSB , again at increasing concentrations of salt . 10 . 7554/eLife . 08646 . 017Figure 4 . The binding of SSB eliminates hysteresis from the force-extension behavior of ssDNA measured by single-molecule magnetic tweezer force spectroscopy ( A ) A DNA substrate was made by ligating biotin- and DIG-containing ‘handles’ ( i . e . ∼2-kb products from PCR containing biotin-dGTP or DIG-dUTP ) to the flanking ends of a 13 . 5-kb DNA substrate . The ligated product was then alkali denatured , attached to magnetic streptavidin-coated beads , and then tethered to a glass surface coated with anti-DIG antibodies within a flow chamber . When present , SSB was added to the flow chamber and bound to the ssDNA in situ . ( B ) A typical time trace of a single molecule of SSB-coated ssDNA during a force-extension experiment . The length was determined at each force applied after the molecule had reached equilibrium ( inset ) . ( C ) The force-extension and relaxation relationship of a single ssDNA molecule is shown in the absence of SSB at increasing concentrations of NaOAc . The plot shows length measurements made while both decreasing ( dashed lines ) and increasing force ( solid ) . ( D ) The force-extension relationship of ssDNA in the presence of 200 nM SSB at increasing concentrations of NaOAc . ( E ) The relative condensation ( L/L0 , where L0 is the length of SSB-coated ssDNA in the absence of salt ) of the molecules measured in panel D were plotted as a function of salt concentration for each applied force and compared to the relative condensation of molecules measured in direct visualization ( TIRF ) experiments from Figure 3C , E . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 01710 . 7554/eLife . 08646 . 018Figure 4—figure supplement 1 . Force extension curve of SSB-coated ssDNA at low force . Data from Figure 4B , C , D re-scaled and plotted from 0 to 1 pN on the y-axis . Black squares are ssDNA alone in 0 mM NaOAc . Circles represented measurements made with SSB-coated ssDNA at the concentration of NaOAc indicated by the color used in the legend . Solid connecting lines represent increasing force; dashed lines represent decreasing force . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 018 The extension of the ssDNA was measured by incrementally increasing and then decreasing the distance between the magnet and the surface , thereby decreasing and increasing the force , respectively . When SSB was bound to the ssDNA , both increasing and decreasing the force kinetically perturbed the length of the SSB-ssDNA complex with apparent exponential kinetics ( Figure 4B , inset ) . Care was taken to insure that the length measurements were made only after the ssDNA or SSB-ssDNA complex was in equilibrium at each force ( Figure 4B ) . Force-extension curves of individual molecules of ssDNA were obtained by first decreasing force ( dashed lines ) and then increasing force ( solid lines ) at increasing concentrations of NaOAc in the absence ( Figure 4C ) and presence of SSB ( Figure 4D and Figure 4—figure supplement 1 ) . The force-extension curves of ssDNA alone show that the force required to stretch ssDNA also increases with respect to increasing concentrations of NaOAc ( Figure 4C ) . The force-extension curves for ssDNA alone at salt concentrations greater than 25 mM NaOAc demonstrate hysteresis ( i . e . the force-extension curves obtained by increasing and decreasing force do not overlap ) due to the formation of secondary structure in the ssDNA ( Bosco et al . , 2014 ) see also , ( Zhang et al . , 2001; Saleh et al . , 2009 ) . Interestingly , this hysteresis was absent in the force-extension curves measured in the presence of SSB ( Figure 4D ) , except at 750 mM NaOAc , the highest salt concentration that we used in our magnetic tweezer experiments . This modest hysteresis at the highest concentration of NaOAc is consistent with our interpretation that the decrease in the amplitude of the fluorescence enhancement measured during equilibrium titrations with SSBf is due to partial dissociation of SSB at concentrations of NaOAc greater than 750 mM ( Figure 3A , B ) . More importantly , the absence of hysteresis in the presence of SSB is strong evidence that the structures are always at equilibrium , and that there are no kinetically trapped intermediates , neither short range nor the more likely long range random interactions ( Schaper et al . , 1991 ) , that would contribute to non-equilibrium behavior . We therefore infer that the intramolecular condensation that we observe is intrinsic to a reversible equilibrium folding of the SSB-ssDNA complex . The length of SSB-coated ssDNA from Figure 4D was normalized to that in the absence of salt in order to determine the relative salt-induced condensation at each force measurement . This relative condensation was calculated by dividing the length , L , by L0 ( i . e . L/L0 ) , where L0 is the length of the SSB-coated ssDNA in the absence of salt ( 0 mM NaOAc ) for each applied force . The relative condensation was then re-plotted as a function of increasing NaOAc concentration for each force measurement ( Figure 4E ) . As the concentration of NaOAc increases , the length of the SSB-coated ssDNA monotonically decreases , or condenses , ( Figure 4E ) ; however , the extent of the condensation is dependent on the force applied . We also compared the salt-dependent length changes measured using magnetic tweezers ( Figure 4D , E ) to the salt-dependent length changes observed in the direct visualization experiments using TIRF microscopy ( Figure 3C , E ) by calculating the relative condensation , L/L0 , to permit a normalized comparison of the different lengths of ssDNA used in each experiment . Since the salt-induced condensation of the molecules measured in the TIRF experiments ( Figure 4E , black squares ) is most similar to the relative condensation observed in the magnetic tweezer experiments that were performed at forces between 0 . 1 and 0 . 5 pN ( Figure 4E , orange and gray circles ) , we estimate that this is approximately the shear force applied by flow during TIRF microscopy within our microfluidic cells . Importantly , because the salt-induced condensation of the SSBf-ssDNA complex is in qualitative agreement with the data obtained with wild-type SSB , we conclude that the salt-induced condensation observed in Figure 3 and Figure 4D , E is dominated by intramolecular bridging in cis , mediated by oligomers of SSB , rather than any property of the modified SSBf ( see Discussion ) . Force spectroscopy directly measures the work required to extend a molecule , and thus can be used determine the energetic consequences of protein binding to DNA ( Liphardt et al . , 2002 ) . The integrated area under a force-extension curve is the work performed on––or absorbed by––the polymer that is being stretched ( Figure 5 ) . We therefore integrated the area under the force-extension curves , relative to the area for ssDNA alone in the absence of salt , to measure the change in energy of the interrogated molecule due to the presence of salt ( Figure 5—figure supplement 1A ) and/or SSB ( Figure 5—figure supplement 1B ) . The measured change in energy ( ΔE ) , in units of pN•nm , was then converted to units of kBT ( i . e . the Boltzmann constant multiplied by the absolute temperature ) using the relationship kBT ∼ 4 . 1 pN•nm at 25°C , which corresponds to the energy contribution from thermal fluctuations ( Nelson , 2004 ) . The energy of salt-induced stabilization of ssDNA secondary structure is apparent in the deviation between the results obtained for pulling ( Figure 5A , increasing force , black filled circles ) and relaxing ( Figure 5A , decreasing force , open circles ) ssDNA in the absence of SSB . In the presence of SSB , the change in energy with respect to salt concentration approximately paralleled the behavior of ssDNA being relaxed from high force to low ( Figure 5A , compare red circles , red line with open black circles , dashed line ) , where long range ssDNA secondary structure does not contribute energetically . Because there was no hysteresis for SSB-ssDNA complexes ( Figure 4D ) , the parallel energetic behavior of SSB-ssDNA and relaxing ssDNA suggests that SSB eliminates the hysteresis seen in ssDNA alone that is introduced by secondary structure formation ( Zhang et al . , 2001 ) and SSB allows the ssDNA within the complex to behave as though long range secondary structure were absent . The addition of SSB resulted in an additional 3100 ( ±550 ) kBT ( represented as ΔΔE ) at salt concentrations from 25 to 250 mM NaOAc relative to relaxing ssDNA ( Figure 5—figure supplement 2 , open red circles , dashed line ) . Only at the highest salt concentration that we measured , 750 mM NaOAc , did we measure a substantial reduction in the ΔΔE , consistent with our interpretation that SSB dissociates at these high salt concentrations , but not measurably below 250 mM NaOAc . While the total energy contribution of SSB binding is nearly constant ( i . e . isoenergetic ) within error , with respect to increasing salt concentrations ( up to ∼250 mM NaOAc ) , the contribution of each individual SSB tetramer should ( and does ) vary due to the transition in the site size across the range of salt conditions . This is––in part––reflected in the complex relationship that increasing salt has on the intrinsic flexibility of the ssDNA ( reflected in the ‘relaxing’ curve , Figure 5A , open black circles ) vs the folding contributions of secondary structure ( reflected in the ‘pulling’ curve , Figure 5A , closed black circles ) . Because SSB eliminates hysteresis , and we interpret this as complete removal of hysteretic secondary structure , we reason that the relevant comparison ( i . e . ΔΔE calculation ) is between the +SSB curve ( Figure 5 , red symbols ) and ‘relaxing’ ssDNA ( Figure 5 , open black symbols ) ( see Discussion ) . Accounting for the change in site size as the concentration of salt increases , the energy of SSB binding to ssDNA in our measurement corresponds to ∼11 ( ±2 ) kBT per SSB tetramer ( assuming ∼200–320 SSB tetramers per ssDNA molecule ) , which is in reasonable agreement with the binding energy previously reported ( Zhou et al . , 2011; Suksombat et al . , 2015 ) . These calculations are limited owing to our inability to actually count the number of SSB tetramers bound to each ssDNA molecule in the magnetic tweezer experiment . 10 . 7554/eLife . 08646 . 019Figure 5 . RecO and RecOR alter SSB-ssDNA wrapping to induce nucleoprotein fiber condensation ( A ) The work ( i . e . ΔE ) stored in the ssDNA or SSB-coated ssDNA molecules was determined from the area under the curves from the data in Figure 4C , D , as shown in Figures 5—figure supplements 1 , 2 , and plotted as a function of the natural logarithm ( bottom x-axis ) of NaOAc concentration ( top x-axis ) for ssDNA ( black-filled circles , increasing force; black open circles , decreasing force ) and for SSB-coated ssDNA ( red filled circles ) . The lines are linear fits where the slope , δkBT/δln[NaOAc] , is 1000 ( ±200 ) for SSB ( red line ) , 1500 ( ±200 ) for ssDNA when decreasing force ( black dashed line ) , and 2200 ( ±150 ) for ssDNA when increasing force ( black solid line ) . ( B ) The force–extension relationship of a single molecule of ssDNA was measured in the absence of salt ( black ) , then again after sequentially adding each of the following: 100 mM NaOAc and 1 mM Mg ( OAc ) 2 ( red ) , 200 nM SSB ( blue ) , 100 nM RecO ( purple ) , and 1 μM RecR ( green ) . The extension in the presence of RecO results in approximately a 10% condensation at each force measured . In the presence of both RecO and RecR , significant hysteresis is observed ( compare solid and dashed green lines ) . ( C ) The change in energy was determined by integrating the area under the curves in panel B relative to the ssDNA alone curve ( black ) , and are plotted for both increasing ( filled bars ) and decreasing force ( open bars ) . ( D ) A cartoon depicting our model for salt-induced intramolecular bridging mediated in cis by oligomers of SSB ( either tetramers or octamers ) . RecOR may mediate bridging either in cis , along the same molecule of SSB-coated ssDNA , or in trans to promote annealing of complementary ssDNA . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 01910 . 7554/eLife . 08646 . 020Figure 5—figure supplement 1 . Diagram of the area under the force-extension curves used to calculate the changes in energy in the absence and presence of SSB . ( A ) The work ( i . e . energy ) stored in the ssDNA was determined from the area under the curves from Figures 4C , D ( 250 mM NaOAc is used here as the example ) and plotted relative to ssDNA alone in the absence of salt ( black lines , solid and dashed , nearly superimposable ) . The light purple area between the curves generated by pulling ( purple solid line ) and relaxing ( purple dashed line ) was interpreted to be the energy from intramolecular secondary structure formation , which is nil in the absence of salt ( black lines ) . The purple and gray striped area between the curve generated by relaxing ssDNA in the presence of salt ( purple dashed line , purple text ) and either of the curves generated by pulling or relaxing ssDNA without salt ( black curves ) was interpreted as resulting from the salt-induced changes in the conformational state of the ssDNA polyelectrolyte . ( B ) The area between the curve obtained in the presence of SSB and 250 mM NaOAc ( red lines and text ) and the curve obtained for ssDNA alone in the absence of salt ( black lines and text ) was used to calculate the change in energy due to SSB-binding to ssDNA plus the salt-induced change to the ssDNA . The curves from panel A ( ssDNA alone at 250 mM NaOAc ) are shown in purple for comparison to the SSB . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 02010 . 7554/eLife . 08646 . 021Figure 5—figure supplement 2 . Difference in the change in energy contributed from SSB binding at increasing salt concentrations . The difference in the work ( i . e . ΔΔE ) stored in the SSB-coated ssDNA molecule , determined by subtracting the ΔE measured for relaxing ssDNA ( open red circles , dashed line ) and pulling ssDNA ( closed red circles , solid line ) from the ΔE measured for SSB-coated ssDNA at increasing concentrations of NaOAc . DOI: http://dx . doi . org/10 . 7554/eLife . 08646 . 021 We next asked whether the intramolecular condensation could also be induced by addition of a protein that interacts with SSB and that is presumed to alter its interactions with ssDNA . Therefore , single-molecule force spectroscopy experiments were also performed in the presence of RecO , with and without RecR ( Figure 5B ) . For naked ssDNA , in the absence of NaOAc and Mg ( OAc ) 2 , the force-extension curves overlap when the applied force is either increased or decreased ( i . e . there is no hysteresis ) ( Figure 5B , black curves ) ; however , when 100 mM NaOAc and 1 mM Mg ( OAc ) 2 were added to mimic physiological conditions , significant hysteresis was observed ( red curve , Figure 5B ) due to the salt-induced stabilization of DNA secondary structure ( Smith et al . , 1996 ) . As documented above , this hysteresis in the presence of 100 mM NaOAc and 1 mM Mg ( OAc ) 2 was also completely eliminated upon the addition of SSB ( blue curve , Figure 5B ) . When RecO was added in a stoichiometric ratio to SSB , the nucleoprotein fiber shortened ∼10% relative to SSB-alone across the entire force spectrum , and hysteresis was not evident ( purple lines , Figure 5B ) . The absence of hysteresis in the presence of RecO is interesting because RecO is known to facilitate annealing of complementary ssDNA in the presence of SSB ( Kantake et al . , 2002 ) , suggesting that RecO functions primarily in trans to promote annealing of complimentary strands , but not in cis to permit formation of non-specific secondary structures . RecR forms oligomers ( dimers/tetramers ) ( Kim et al . , 2012 ) and interacts with RecO ( Umezu and Kolodner , 1994 ) , but does not interact with SSB , ssDNA or dsDNA ( Radzimanowski et al . , 2013 ) . It also negatively regulates RecO-dependent annealing ( Kantake et al . , 2002 ) but promotes SSB-ssDNA remodeling in such a way that enhances both the nucleation and growth of RecA filaments on SSB-ssDNA ( Bell et al . , 2012 ) . When we added RecR in the presence of both SSB and RecO , we observed severe hysteresis in the force-extension curves ( Figure 5B , compare solid and dashed green lines ) . When the force was decreasing ( Figure 5B , green dashed line ) , the curve was essentially indistinguishable from the experiment containing RecO and SSB ( Figure 5B , purple lines ) , but when the force was increasing , significantly more force was required to extend the molecule ( Figure 5B , green solid line ) . This hysteresis suggests that RecR bridges distant contacts , either through protein–protein interactions or by facilitating intramolecular annealing in cis of regions of micro-homology , and that these contacts are disrupted as the fiber is stretched by the magnetic tweezers . Similar to the analysis that we showed in Figure 5—figure supplement 1 and Figure 5A , in Figure 5C we show the integrated area under the force extension curves to determine the change in energy due to the successive and additive inclusion of buffer containing salt ( 100 mM NaOAc and 1 mM Mg ( OAc ) 2 ( red ) ; SSB ( blue ) ; RecO ( purple ) ; and RecOR ( green ) . The bars in the plot represent the work ( expressed as Δ Energy ) done while increasing force ( i . e . pulling; filled bars ) or decreasing force ( i . e . relaxing; open bars ) during the experiment . As expected from our previous experiments , the addition of salt induced hysteresis in the force-extension curve , wherein formation of intramolecular secondary structure accounted for approximately 1400 kBT . The addition of SSB was sufficient to eliminate this hysteresis in the presence or absence of RecO . The energy contribution of RecO to the SSB-ssDNA energy was modest , accounting for ∼250 kBT ( or ∼1 . 2 kBT per SSB tetramer ) . The addition of RecR––so that SSB , RecO , and RecR were all present––again , contributed a modest amount of energy when the complex was relaxing ( an additional ∼220 kBT or ∼1 kBT per SSB tetramer ) ; however , when RecOR was bound to the SSB-coated ssDNA , hysteresis returned to the system , which we interpret as a result of long-range , protein–protein interactions bridged by RecOR bound to SSB-ssDNA . The energy contribution from RecOR-mediated bridging was ∼2000 kBT ( or ∼10 kBT per SSB tetramer ) . Interestingly , this energy contribution is essentially the same as the energetic contribution from SSB binding to ssDNA alone ( ∼9–13 kBT per tetramer , see previous section ) , indicating that RecOR bridges distant SSB tetramers and those tetramers must be disrupted as the molecule is stretched by increasing force . We conclude that when incubated together , RecOR forms a complex that binds to SSB-bound to ssDNA , and in doing so , both modulates the wrapped-state , or binding-mode , of SSB in order to induce macroscopic changes and serves as a scaffold to bridge distant sites along the SSB-ssDNA nucleoprotein fiber . In this work , we comparatively analyzed how the length of SSB-coated ssDNA is modulated by salt and protein binding partners , correlating measurements of ensemble equilibrium binding , direct visualization of single molecules , and single-molecule force spectroscopy . These results provide an extensive physical description of the polymer dynamics of SSB-coated ssDNA and reveal a previously unrealized property of SSB-ssDNA complexes to interact with distant intramolecular sites , which is manifest as condensation of single nucleoprotein fibers . This macroscopic condensation could occur either through the association of stable octamers or through ssDNA by the direct binding of distant sites to a single tetramer . The extent of condensation , however , is greatest under conditions where ssDNA is fully wrapped around the protein in the SSB65 binding mode , making the former possibility more likely . In our force-spectroscopy experiments , the addition of SSB ( or SSB and RecO ) eliminated the salt-induced hysteresis caused by formation of DNA secondary structure through intramolecular annealing in cis . When we compare the energetic contribution of SSB binding to ssDNA , relative to the salt-induced effects on the ssDNA polymer energetics ( excluding the contributions from long range secondary structure by comparing the ‘relaxing’ ssDNA force-extension curves ) , the change in energy due to SSB binding ( when summed over a single molecule of ssDNA ) is nearly isoenergetic across a large salt concentration range ( Figure 5A , compare the slope for SSB , red-filled circles , to relaxing ssDNA alone , open black circles , and Figure 5—figure supplement 2 ) . This is both interesting and unexpected as it implies a homeostasis with regard to the energetics of SSB , relative to ssDNA , at physiological salt concentrations , making the absolute intracellular salt concentration nearly unimportant relative to other cellular processes working on or with the SSB-ssDNA complex . We propose that this relatively constant energy contribution across the ssDNA is made possible by a net change in the microscopic binding modes of SSB in such a way that all ( or most ) ssDNA is effectively coated by SSB , albeit with each SSB tetramer engaged in a different number of nucleotides and a different number of SSB tetramers engaged at each salt concentration . In this way , the sum of the energy changes ( see Suksombat et al . , 2015 ) for each tetramer is balanced across a wide range of physiological conditions . Importantly , this ‘isoenergetic landscape’ is independent of salt only when we compare the SSB-ssDNA curves with the relaxing conditions for ssDNA alone ( Figure 5A and Figure 5—figure supplement 2 ) , where secondary structure considerations are experimentally removed . From the perspective of displacing SSB from ssDNA by another protein , this is the biologically relevant comparison because the absence of hysteresis in the SSB-ssDNA curves shows that there is no long range secondary structure in the SSB-ssDNA complexes . When we calculate the ΔΔE relative to the ‘pulling’ condition with ssDNA alone where the contributions of secondary structure are clearly evident ( Figure 5A , solid black circles and Figure 5—figure supplement 2 , solid red circles , solid red line ) , the ΔΔE decreases until it becomes negative ( and therefore unfavorable ) at approximately 400 mM NaOAc , corresponding to a concentration of salt where secondary structure may become more stable than the SSB–ssDNA interaction ( consistent with our observations in Figure 1—figure supplement 1 ) . Therefore , this net energetic accounting reflects what would be apparent in a traditional ensemble measurement , where SSB-binding and DNA secondary structure formation are in competitive equilibrium , and reconciles our observations with previous work that has interrogated the salt-dependence of SSB-ssDNA interactions where the standard state for free energy calculations is the free ssDNA that will form secondary structure . Previous AFM imaging of SSB bound to M13mp7 ssDNA ( 7249 nts ) measured contour lengths of SSB-ssDNA of ∼920 nm in the low salt , 35-nucleotide binding mode , and 560 nm in the high salt , 65-nucleotide binding mode; this change represents a 1 . 6-fold increase upon going from high to low salt , or a decrease of 60% upon going from low to high salt ( Hamon et al . , 2007 ) . Since denatured λ-phage DNA is 6 . 6-fold longer than M13mp7 ssDNA , we expected our SSB-ssDNA would be approximately 6 . 1 μm long in low salt and compacted by 1 . 6-fold to approximately 3 . 7 μm long in high salt ( up to ∼300 mM Na+ ) ; however , we observed an approximately fourfold compaction over this range . A substantially greater degree of compaction of the complexes was measured upon further increase in salt concentration – from 6 . 5 μm in the absence of salt to 0 . 5 μm at 750 mM NaOAc – which is a 13-fold compaction . If the site size was the dominant factor determining the condensation state of SSB-coated ssDNA , then the maximum change in length should be approximately twofold , yet we measure a ∼13-fold decrease in length . We propose that an explanation derives from the fact that SSB does not simply bind to ssDNA as an array of globular units , but instead exhibits limited cooperativity between tetramers , at higher salt concentrations , where it also forms octamers ( Lohman and Bujalowski , 1988; Bujalowski and Lohman , 1989a , b ) which might serve as an intramolecular bridges between distant sites along the ssDNA resulting in the condensation that we observe ( Chrysogelos and Griffith , 1982; Griffith et al . , 1984; Hamon et al . , 2007 ) . Although the molecular nature of the condensing species is unknown , we also note that the population of the various SSB-binding modes depends not only on intracellular solution conditions , but also on the SSB concentration itself due to the established effects of ‘competition’ between DNA-binding modes with different site sizes , affinities , and cooperativities ( Schwarz and Stankowski , 1979; Bujalowski et al . , 1988 ) ; therefore , with SSB in excess over ssDNA – which is the cellular situation – multiple modes coexist and may serve to keep the amounts of bound SSB nearly constant . Finally , although the high degree of salt-induced compaction of SSB-ssDNA that we observed was surprising based on previous AFM and EM studies , our results are in good qualitative agreement with early , largely unexplained , ultracentrifugation experiments performed with SSB-saturated M13-phage ssDNA ( Schaper et al . , 1991 ) : our changes in length with salt concentration map exactly on the reported changes in sedimentation coefficients for the SSB-M13 ssDNA complexes . Importantly , the force-extension measurements of unlabeled , wild-type SSB recapitulates the relative condensation of SSBf-coated ssDNA observed in the direct visualization experiments , indicating that the observed condensation of the fluorescently modified nucleoprotein complex in Figure 1C and Figure 3C–G is not a consequence of using modified SSB , SSBAF488 , or SSBf , but rather is due to an intrinsic , salt-induced conformational change resulting in intramolecular re-organization along the nucleoprotein fiber . The absence of hysteresis in our force-extension curves in the presence of SSB is strong evidence that the intramolecular condensation that we observe is not due to the formation of distant ssDNA–ssDNA contacts , but rather is driven by the microscopic reorganization of SSB along the ssDNA , which in turn contributes to a macroscopic folding of the molecule . Several structural polymer models might explain the condensed molecules that we observe , including the formation of solenoid or fractal structures; however , several interesting properties of SSB support the idea that intramolecular folding or condensation is protein-mediated . First , dimerization of SSB tetramers ( i . e . octamer formation ) is a well-established phenomenon at increasing but still physiologically relevant intracellular salt concentrations ( Chrysogelos and Griffith , 1982; Bujalowski and Lohman , 1987 ) , which can linearly vary from 0 . 23 to 0 . 93 molal K+ ion and from 0 . 03 to 0 . 26 molal glutamate ion in response to changes in the osmolality of the growth medium ( Richey et al . , 1987 ) see also , ( Epstein and Schultz , 1965 ) ( Epstein and Schultz , 1966 ) . Second , SSB exhibits long-range , intersegmental transfer ( Lee et al . , 2014 ) , the latter of which likely proceeds through direct , tetramer transfer within a ternary intermediate comprising SSB and two ssDNA molecules without proceeding through a free-protein intermediate ( Kozlov and Lohman , 2002a , b ) . This property of SSB is possible owing to multiple , high affinity binding sites distributed around the tetramer , or as the case may be in the high-salt mode , octamers . This phenomenon has primarily been described as a transient intermediate during rapid , stopped flow kinetics; however , we believe that the intramolecular condensation that we observe here provides evidence that the sum of these transient interactions across the SSB-ssDNA fiber might explain how many SSB tetramers can simultaneously engage in distant ssDNA sites to contribute to an intramolecular , folded polymer that is highly dynamic and ‘fluid’ , undergoing constant , steady–steady state protein turnover and diffusion at equilibrium . This is corroborated by the rapid exchange of SSB protein ( Figure 1F ) and the absence of net protein loss under steady-state conditions ( Figure 1D–E and Figure 1—figure supplement 1 ) , but the rapid dissociation in the absence of free protein during salt-jump experiments ( Figure 2—figure supplement 1 ) , as well as the ability of SSB labeled with different fluorophores to exchange and form mixed complexes ( JCB , unpublished observations ) , a phenomenon also demonstrated for eukaryotic RPA ( Gibb et al . , 2014 ) . Similar to nucleosomes , SSB binds and wraps a DNA polymer around itself ( albeit ssDNA instead of dsDNA ) , interacts with dozens of proteins via a short acidic peptide tail , and exhibits complex cooperative and anti-cooperative behavior that is modulated by salt concentration . Our observation that SSB-ssDNA is macroscopically organized and regulated through microscopic interactions is surprisingly similar to the most basic organization of eukaryotic chromatin and highlights the important role of SSB , not simply as a kinetic trap for ssDNA , but as an organizational and regulatory scaffold during DNA metabolism ( Shereda et al . , 2008; Sun et al . , 2015 ) . This organizational and regulatory role is likely controlled by the acidic , intrinsically disordered C-terminal tail of SSB , which is required for cooperative binding of SSB to ssDNA ( Kozlov et al . , 2015 ) . In the absence of interaction partners or ssDNA , this unstructured peptide tail interacts with the subunits within the SSB tetramer ( Kozlov et al . , 2010a ) . Recent studies have identified a cadre of proteins , including the χ subunit of DNA polymerase III , PriA , PriB , RecG , RNaseHI , Exonuclease I , and RecO , that bind to the C-terminal tail of SSB and either remodel the SSB-ssDNA complex or regulate enzymatic ssDNA metabolism ( Cadman and McGlynn , 2004; Shereda et al . , 2007 , 2008; Lu and Keck , 2008; Kozlov et al . , 2010a , 2010b , 2015; Wessel et al . , 2013; Bhattacharyya et al . , 2014; Petzold et al . , 2015; Sun et al . , 2015 ) . Extrapolating from our observations in this work , these many proteins may regulate access to ssDNA by binding to SSB and altering either compaction/de-compaction most likely by perturbing the microscopic binding state of SSB . Indeed , we demonstrate that this condensation occurs not only with increasing osmolality , but also by the addition of RecO , which binds directly to SSB , in the absence and presence of RecR ( Ryzhikov et al . , 2011 ) . As the C-terminal tail of SSB interacts with more than a dozen proteins involved in DNA replication , recombination , and repair ( Shereda et al . , 2008 ) , our observation supports and expands upon the idea that these proteins might modulate the macroscopic condensation state of the SSB-ssDNA fiber by microscopically altering the binding mode , and therefore either grant or restrict access to the ssDNA . In the case of RecO and RecOR , altering this macromolecular state by bridging distant sites could reduce the three-dimensional space required to facilitate homology-dependent annealing of ssDNA ( Berg et al . , 1981; Forget and Kowalczykowski , 2012 ) , which occurs by a second order kinetic process ( Berg et al . , 1981; Kantake et al . , 2002; Wu et al . , 2006; Bell et al . , 2012 ) . Similarly , RecO has been shown to slow the rate of one-dimensional diffusion , or sliding , of SSB on ssDNA ( Zhou et al . , 2011 ) , which is consistent with the small energy contribution of RecO and RecOR binding we observe here , where RecO contributes an additional ∼1–2 kBT per SSB tetramer . By slowing the rate of diffusion of SSB on ssDNA , RecO might facilitate both annealing and RecA nucleation by increasing the lifetime of transiently exposed ssDNA . The long range interactions induced by RecR could also be due to phasing of SSB via its interaction with RecO , creating microscopic gaps on the ssDNA that allow distant sites with micro-homology to anneal , forming intramolecular secondary structure ( Figure 5D ) . In its biological context , these microscopic gaps could also facilitate the nucleation of RecA filaments during homologous recombination by exposing short segments of ssDNA between SSB tetramers , either by inducing a conformational change that physically disrupts the SSB-ssDNA complex to create gaps or by increasing the lifetime of transiently exposed ssDNA created during SSB sliding along the ssDNA ( Bell et al . , 2012 ) . Fluorescent SSB was generated by conjugating either Alexa Fluor 488 maleimide or fluorescein-5-maleimide ( Life Technologies ) to SSBG26C as previously described ( Dillingham et al . , 2008 ) . Bacteriophage λ DNA ( 1 . 5 nM molecules ) was biotinylated by incorporating biotin-dGTP ( 50 μM ) at the 3′-ends of DNA using T7 Polymerase ( 10 Units ) in NEB Buffer 2 ( 10 mM TrisHCl ( pH 7 . 9 ) , 50 mM NaCl , 10 mM MgCl2 , and 1 mM dithiothreitol ( DTT ) ) in the presence of 50 μM dATP , dCTP , and dTTP for 15 min at 12°C . The reaction was terminated by the addition of 20 mM EDTA and incubated at 75°C for 10 min . The biotinylated dsDNA was purified from unincorporated biotin-dGTP using an S-400 spin column equilibrated with 20 mM TrisHCl ( pH 7 . 5 ) and 0 . 1 mM EDTA . This biotinylated dsDNA was then diluted to 250 pM ( molecules ) in 10 μl of 0 . 5 M NaOH for 10 min at 37°C and subsequently diluted into 400 μl of buffer containing 20 mM TrisOAc ( pH 8 . 0 ) , 20% sucrose , 50 mM DTT , and 200 nM of the indicated SSB-derived biosensor . The final concentration of ssDNA was 12 . 5 pM molecules or 600 nM nucleotides . The ssDNA-nucleoprotein complexes were then injected into a flow cell and tethered to the surface of a coverslip . Flow cells ( 4 mm × 0 . 4 mm × 0 . 07 mm ) were assembled using a glass slide , a coverslip , and double-sided tape ( 3M Adhesive Transfer Tape 9437 ) . Ports were drilled into the glass microscope slide , and flow was controlled using a motor-driven syringe pump ( Amitani et al . , 2010; Forget and Kowalczykowski , 2010 , 2012 ) . The surface of the coverslip was cleaned by the subsequent injection of 1 M NaOH for 10 min , rinsed with water and equilibrated in buffer containing 20 mM TrisOAc ( pH 8 . 0 ) , 20% sucrose and 50 mM DTT . The surface was then functionalized by injecting the above buffer containing 2 mg/ml biotin-BSA ( Pierce ) and incubated for 10 min , rinsed with buffer , equilibrated with 0 . 2 mg/ml streptavidin ( Promega ) for 10 min and then blocked with 1 . 5 mg/ml Roche Blocking Reagent ( Roche ) for 10 min . For imaging , the nucleoprotein complexes were allowed to incubate in the flow cell in the absence of flow for approximately 5–15 min until a sufficiently desired density of molecules were tethered to the surface , then visualized using TIRF microscopy while extended by flow at volumetric flow rate of 4000 μl/hr . Unless otherwise noted , imaging was performed in 20 mM TrisOAc ( pH 8 . 0 ) , 50 mM DTT , 20% sucrose , and the indicated concentration of NaOAc or Mg ( OAc ) 2 . Unless otherwise indicated ( as in Figure 2 ) , the concentration of SSB was 200 nM ( monomers ) . Titrations to monitor the binding of SSBf to ssDNA were performed by monitoring the fluorescence enhancement at 25°C , using a Shimadzu fluorescence spectrophotometer set at an excitation wavelength of 495 nm and an emission wavelength of 520 nm . Excitation and emission slits were set to a bandwidth of 3 and 10 nm , respectively . The concentration of SSBf was 100 nM ( tetramer ) . Titrations were performed in 20 mM TrisOAc ( pH 8 . 0 ) , 1 mM DTT , and the indicated concentration of salt . The fluorescence values were corrected for dilution and normalized to the fold increase in fluorescence ( fluorescence intensity of SSBf plus ssDNA divided by the SSBf fluorescence in the absence of ssDNA ) . The site size of SSB was determined by fitting the data to a two-segment line , where the x- and y-intercepts of the first segment and the slope of the second segment were constrained to zero . The x-intercept between the segments was taken to be the stoichiometric breakpoint of the titration . Data fitting was performed using GraphPad Prism version 5 . 0d . All equilibrium titrations were performed in triplicate and report the mean and standard deviation from each experiment . The multiple-DIG and multiple-biotin labeled 2-kb DNA handles were prepared using pUC19 , which was linearized by HindIII , as the template . The primer sequences for multi-DIG labeled 2-kb DNA handle were primer-1 ( 5′-GTT GTG GGC CCG GCG TAA TCA TGG TCA TAG CTG-3′ ) and primer-2 ( 5′-CAA CAT TTC CGT GTC GCC CTT ATT CCC-3′ ) . Primer-1 creates a restriction site for ApaI ( underlined ) ; dsDNA length is 2036 bp after PCR and 2026 bp after Apa1 digestion . PCR was performed in the presence of 0 . 2 mM dATP , dGTP , dCTP , 0 . 18 mM dTTP , and 0 . 02 mM DIG-11-dUTP ( Roche ) using Taq DNA polymerase ( NEB ) . For the DNA handle containing biotin , primer-3 ( 5′-GTT GTG CTA GCG GCG TAA TCA TGG TCA TAG CTG-3′ ) was used instead of primer-1 . Primer 3 creates a restriction site for NheI ( underlined ) ; dsDNA length is 2036 bp after PCR and 2030 bp after NheI digestion . The PCR was performed in the presence of 0 . 2 mM dATP , dCTP , dTTP , 0 . 18 mM dGTP , and 0 . 02 mM biotin-11-dGTP ( Perkin–Elmer ) . The ∼13 . 5-kb DNA was prepared using lambda DNA as the template using primer 4 ( 5′-GTT GTG GGC CCA CCA CCT CAA AGG GTG ACA G-3′ ) and primer 5 ( 5′-GTT GTG CTA GCA CGG TGG AAA CGA TAC TTG C-3′ ) to produce dsDNA 13 , 572 bp in length . These primers create restriction sites for ApaI and NheI ( underlined ) , respectively . PCR was performed using Expand 20kbPLUS PCR system ( Roche ) . The PCR products were digested with appropriate restriction enzymes to yield dsDNA 13 , 552 bp in length and purified using a Qiagen PCR purification kit ( Qiagen ) . All three pieces of DNA were ligated in a single step . Flow cells were assembled by sandwiching double-sided tape ( 3M Adhesive Transfer Tape 9437 ) from which a rectangular channel had been cut with a precision controlled razor blade printer ( Craft Robo CC200-20 , Graphtec ) between a Mylar sheet ( 0 . 002′′ , McMaster ) and a coverslip ( No . 1;113 Corning ) . Flow cells were washed with water , phosphate buffered saline ( PBS; Gibco #10010; 1 mM KH2PO4 , 3 mM Na2HPO4 , and 155 mM NaCl , pH 7 . 4 ) and then coated with 0 . 2 mg/ml anti-digoxigenin ( Roche ) in PBS by incubating at 37°C overnight . Unbound anti-digoxigenin was rinsed with PBS . The surface was blocked for at least 2 hr at 37°C with a solution containing 10 mg/ml BSA ( Sigma ) , 3 . 3 mg/ml poly-L-glutamic acid ( Sigma ) in 45 mM NaHCO3 ( pH 8 . 1 ) and 50 mM DTT . The blocking agent was rinsed from the flow cell using single molecule buffer ( SMB ) containing 20 mM TrisOAc ( pH 8 . 0 ) and 50 mM DTT , and then blocked again using 1 . 5 mg/ml Roche blocking reagent ( RBR ) dissolved in SMB plus 1 M NaOAc for 30 min , followed by a successive incubation of 1 . 5 mg/ml RBR in SMB ( no salt ) for an additional 30 min . The flow cell was mounted onto a PicoTwist microscope ( PicoTwist , Paris , France ) so that the Mylar is under tension . The position of the magnets was carefully adjusted so that the distance between the magnets and the flow cell surface was accurate and calibrated according to the manufacturer's specifications . The DNA substrate was incubated in 0 . 5 M NaOH to denature the dsDNA to ssDNA , which was then attached to magnetic beads ( 1 μm MyOne C1 Dynal ) by mixing biotinylated ssDNA and the beads in ∼5:1 molar ratio in SMB and incubating on a slow rotator for 15 min at room temperature . The ssDNA-bead mixture was added to the flow cell . After 10-min incubation at 25°C , untethered beads were eliminated by extensive washing with SMB . Changing the position of the magnets controlled the force . Flow was driven by gravity . The flow cell was sealed by switching off the inlet and outlet valves after buffer exchange , the magnets were moved to their destination position , and data collection began . After each force-extension curve was obtained , the buffer in the flow cell was exchanged to increase the concentration of salt , and the experiment was repeated . Experiments containing SSB were similarly obtained , where the force-extension curve of ssDNA alone was obtained in the absence of salt , then plus 200 nM SSB , and finally incrementally increasing the salt concentration but maintaining a constant concentration of SSB . All experiments were performed in SMB plus the indicated concentration of salt at 25°C . RecO and RecR were purified as previously described ( Kantake et al . , 2002 ) . Experiments were performed by sequentially adding each of the following components to SMB and injecting the solution into the flow cell after ssDNA-bead complexes were tethered to the surface: 100 mM NaOAc and 1 mM Mg ( OAc ) 2 , 200 nM SSB , 100 nM RecO , and 1 μM RecR . Each additional component was added to the previous buffer , and the molecules were allowed to equilibrate for 5–10 min before the experiment . The change in energy was measured using the ‘Area Under Curve’ ( AUC ) function in GraphPad Prism ( v5 . 0d ) for each molecule at either increasing or decreasing force and subtracting the AUC for ssDNA alone in the absence of salt . At the highest salt concentration measured ( 750 mM NaOAc ) , 17 . 5 pN was sufficient to completely dissociate SSB from ssDNA , as ascertained by the convergence of the force-extension curve with ssDNA alone . At all other salt concentrations , the maximum force applied was 10 . 5 pN , and the curve was completed by extrapolating between the 10 . 5 pN and 17 . 5 pN in order to complete the curves to calculate the integrated area . By comparing the measurements in the 750 mM NaOAc data set with a linear extrapolation from 10 . 5 pN to 17 . 5 pN , we calculate that this analysis contributes no more than 370 pN•nm to the error in our measurement , which is reflected in the error bars in Figures 6B and 6D . The AUC was converted from energy units of pN•nm to kBT using the conversion , 1 kBT ∼ 4 . 1 pN•nm ( Nelson , 2004 ) .	Chromosomes consist of two strands of DNA that are intertwined as a helix . These strands can peal apart to form single-stranded DNA before the DNA is copied and for other processes in cells . Single-stranded DNA can also form if double-stranded DNA is damaged by harmful radiation or chemicals so that only one strand can be copied or when the damaged strand is selectively degraded by enzymes during the course of repair . Proteins called single-stranded binding proteins ( or SSBs for short ) bind to single-stranded DNA molecules to protect them . A molecule of single-stranded DNA wraps around a group of four SSB proteins ( known as a tetramer ) . The degree to which DNA is wrapped around the SSB tetramer depends on the environmental conditions . For example , in the presence of high levels of salt—which is typical inside cells – single-stranded DNA wraps around all four subunits of the SSB . However , at lower salt levels , the DNA only wraps around some of the units in the SSB tetramer . A process called recombination can repair breaks in DNA . During this process , a broken DNA molecule that contains single-stranded DNA can pair with a matching ( or complementary ) strand from an intact double-stranded DNA molecule that carries an identical genetic sequence . A protein called RecO helps to anneal two complementary DNA strands together with the help of the RecR protein . However , for RecR and RecO to achieve this task , they need to work together with the resident SSB proteins that occupy single-stranded DNA . How they find matching sequences when SSB proteins are in the way is not clear . Bell et al . used techniques called TIRF microscopy and single-molecule force spectroscopy to directly observe how SSB from the bacterium E . coli binds to and coats individual molecules of single-stranded DNA . The experiments show that when the levels of salt increase , single-stranded DNA that is coated with SSB proteins becomes compacted and the length of the DNA molecules decreases , a process referred to as ‘intramolecular condensation’ . Bell et al . found that condensation occurred because two SSB tetramers that are associated with different regions of the single-stranded DNA interact to form stable ‘octamers’ . In the presence of RecO and RecR , the single-stranded DNA compacted even further . Bell et al . propose that these recombination proteins act as a scaffold to bring together distant partner sites of single-stranded DNA . This condensation allows two DNA sequences that can be far apart in the cell to find one another more quickly . The next challenge is to understand how the matching regions of single-stranded DNA are identified , and what causes the SSBs to move to allow other repair proteins to gain access to the DNA .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "biochemistry", "and", "chemical", "biology" ]	2015	Imaging and energetics of single SSB-ssDNA molecules reveal intramolecular condensation and insight into RecOR function
SWELL1 ( LRRC8A ) is the only essential subunit of the Volume Regulated Anion Channel ( VRAC ) , which regulates cellular volume homeostasis and is activated by hypotonic solutions . SWELL1 , together with four other LRRC8 family members , potentially forms a vastly heterogeneous cohort of VRAC channels with different properties; however , SWELL1 alone is also functional . Here , we report a high-resolution cryo-electron microscopy structure of full-length human homo-hexameric SWELL1 . The structure reveals a trimer of dimers assembly with symmetry mismatch between the pore-forming domain and the cytosolic leucine-rich repeat ( LRR ) domains . Importantly , mutational analysis demonstrates that a charged residue at the narrowest constriction of the homomeric channel is an important pore determinant of heteromeric VRAC . Additionally , a mutation in the flexible N-terminal portion of SWELL1 affects pore properties , suggesting a putative link between intracellular structures and channel regulation . This structure provides a scaffold for further dissecting the heterogeneity and mechanism of activation of VRAC . VRAC is a ubiquitously expressed mammalian anion channel implicated in diverse physiological processes including volume regulation , cell proliferation , release of excitatory amino acids , and apoptosis ( Hyzinski-García et al . , 2014; Nilius et al . , 1997; Pedersen et al . , 2016 ) . It is suggested to play a role in a variety of human diseases including stroke , diabetes , and cancer ( Hyzinski-García et al . , 2014; Planells-Cases et al . , 2015; Zhang et al . , 2017 ) . A causative link has been established between a chromosomal translocation in the SWELL1 ( LRRC8A ) gene and a human B cell deficiency disease , agammaglobulinemia ( Sawada et al . , 2003 ) . Previous studies have shown that SWELL1 is required for VRAC activity , and that the presence of other LRRC8 subunits dictates functional characteristics of VRAC , including pore properties ( Qiu et al . , 2014; Syeda et al . , 2016; Voss et al . , 2014 ) . While SWELL1 and at least one other LRRC8 subunit are required for canonical whole-cell VRAC currents , purified homomers of SWELL1 reconstituted in lipid bilayers are activated by osmotic stimuli and blocked by VRAC antagonist , DCPIB ( Syeda et al . , 2016 ) . Interestingly , CRISPR-engineered HeLa cells lacking all LRRC8 subunits ( LRRC8-/- HeLa cells ) exhibited very small but significant DCPIB-sensitive hypotonicity-induced currents after SWELL1 overexpression ( Figure 1—figure supplement 1 ) , supporting previous bilayer results . Since the number and composition of functional native oligomeric assemblies remains unknown , we decided to first elucidate the structure of SWELL1 homomers . To produce homomeric SWELL1 , human SWELL1-FLAG was recombinantly expressed in LRRC8 ( B , C , D , E ) -/- HEK293-F suspension cells , then solubilized in 1% decyl maltose neopentyl glycol ( DMNG ) detergent , followed by purification and exchange into 0 . 05% digitonin for structure determination by cryo-EM ( Figure 1—figure supplement 2 ) . Image analysis and reconstruction yielded a ~4 Å resolution map that was used to build a molecular model of SWELL1 ( Figure 1—figure supplements 3–4 , Supplementary file 1 ) . SWELL1 is organized as a hexameric trimer of dimers with a four-layer domain architecture and an overall jellyfish-like shape ( Figure 1A ) . The transmembrane ( TM ) and extracellular domains ( ECDs ) surround the central pore axis , and share a previously unappreciated structural homology with the connexin ( Maeda et al . , 2009 ) and innexin ( Oshima et al . , 2016 ) gap junction channels ( Figure 1—figure supplement 5A–D ) . The ECD is composed of two extracellular loops ( ECL1 and ECL2 ) that are stabilized by three disulfide bonds ( Figure 1B–C and Figure 1—figure supplement 5E–F ) . ECL1 contains one strand of a small beta-sheet and a helix ( ECH ) that faces the center of the ECD while ECL2 contains two additional antiparallel beta strands of the beta-sheet that faces the outside of the ECD . Each subunit contains four TM helices ( TM1-4 ) . TM1 lies closest to the central pore axis and is tethered to a short N-terminal coil ( NTC ) that is parallel to the inner leaflet of the membrane . In the cytosol , the intracellular linker domains ( ILD ) create a tightly packed network of helices connecting the channel pore to the LRR domains . Each ILD is composed of two-four helices from the TM2-TM3 cytoplasmic loop ( LH1-4 ) , and five helices from the TM4-LRR linker ( LH5-9 ) ( Figure 1C ) . Each protomer terminates in 15–16 LRRs which form a prototypical solenoid LRR fold ( Figure 1B–C ) . LRRs from the six protomers dimerize into three pairs , which interact to form a Celtic knot-like assembly ( Figure 1A ) . Perhaps the most striking architectural feature of VRAC is the symmetry mismatch between the cytosolic LRR domains and the pore-forming domains of the channel , despite its homo-hexameric assembly ( Figure 2 ) . The ECDs , TMs , and ILDs all share the same 6-fold symmetric arrangement ( Figure 2B ) ; however , in the cytosol , LRR domains dimerize in a parallel fashion with each LRR at either a 10 or −20° offset relative to the rest of its protomer , producing a 3-fold symmetric trimer of dimers ( Figure 2C ) . The nonequivalence between identical subunits arises from a hinge around the conserved residue L402 in a helix of the TM4-LRR linker ( Figure 2D and Figure 1—Figure supplements 6 and 7 ) . This hinge allows the LRR domains to shift as rigid bodies , producing sufficient flexibility for them to interface at their edges via several charged residues ( Figures 2D and 3A ) . As a result , the helical C-termini of the two subunits in a dimer pair make two different sets of interactions with the neighboring LRR ( Figure 3B ) . Focused 3D classification of the LRR domains revealed several arrangements of LRRs suggesting that flexibility of the LRR domains may play a functional role in channel gating ( Figure 2—figure supplement 1 ) , similar to the intracellular domains of the CorA magnesium channel ( Matthies et al . , 2016 ) . Interestingly , the outer LRR subunit in the dimer exhibits helical density in the C-terminal half of the TM2-TM3 linker that rests on top of the outer protomer’s LRR domain , adding an additional layer of intricacy to the network of cytosolic interactions ( Figure 1—figure supplement 5A–B ) . Symmetry mismatch is also observed in the homotetrameric AMPA receptor GluA2 , which similarly forms local dimers in different domain layers ( Sobolevsky et al . , 2009 ) . Furthermore , the dimer-of-dimers topology of homotetrameric AMPA-subtype ionotropic glutamate receptors ( iGluRs ) defines the subunit organization of di- and tri-heteromeric NMDA-subtype iGluR structures ( Karakas and Furukawa , 2014; Lee et al . , 2014; Lü et al . , 2017 ) . By analogy , we speculate that the trimer-of-dimers assembly of SWELL1 is recapitulated in , and influences the composition of , heteromeric VRACs . Unlike other ion channels , there is little domain swapping between the subunits of the pore-forming domains of the SWELL1 channel . The individual helical bundles are loosely packed with one another and lined with hydrophobic residues . The inter-protomer space may be occupied by hydrophobic membrane components like lipid or cholesterol that might be important for channel assembly or lipid signaling . Such densities are observed in the inter-subunit space in innexin-6 and have been proposed to have a stabilizing role in the conformation of the helix bundles ( Oshima et al . , 2016 ) . At the upper faces of the extracellular domains , on mostly flexible loops , resides a three residue KYD motif previously shown to be involved in voltage-dependent inactivation and selectivity ( Ullrich et al . , 2016 ) ; interestingly , KYD extends laterally towards the neighboring subunit ( Figure 4—figure supplement 1 ) , suggesting that subunit interactions in this region contribute to these channel properties . The ECDs , TMs , and ILDs of all six subunits contribute to the ion-conducting pore ( Figure 4A–B ) . Below that , windows of 35 by 40 Å between LRR dimer pairs are sufficiently large to allow ions and osmolytes to freely pass . In the extracellular domain , 25 Å above the membrane , a ring of arginines ( R103 ) at the N-terminal tip of the extracellular helix forms the narrowest constriction in the channel structure ( Figure 4A–C ) . We hypothesized that these arginines , only conserved between SWELL1 and the LRRC8B subunit ( R99 ) ( Figure 1—figure supplement 6 ) , might directly interact with permeant anions . To test this hypothesis , we mutated positively-charged R103 to phenylalanine , and determined whether ion selectivity was altered in SWELL1-R103F + LRRC8C heteromeric channels heterologously expressed in HeLa LRRC8 ( A , B , C , D , E ) -/- cells . We determined the reversal potential ( Vrev ) for hypotonicity-induced Cl- currents mediated by SWELL1-R103F + LRRC8C channels . The Vrev of currents mediated by SWELL1-R103F + LRRC8C was significantly reduced compared to wildtype channels , indicating that the channels are less selective for Cl- ( Figure 4D ) ( Ackerman et al . , 1994; Jackson and Strange , 1995; Tsumura et al . , 1996 ) . Furthermore , extracellular ATP at concentrations that block ~75% of wildtype VRAC currents was ineffective on channels containing R103F ( Figure 4E and Figure 4—figure supplement 1 ) . Therefore , R103 is a critical residue within SWELL1 that impacts ion selectivity as well as pore block of heteromeric VRAC channels . Within the pore , constrictions are observed at pore-facing residues T44 and T48 ( Figure 4A–B ) . Interestingly , we had previously identified residue T44 via the substituted cysteine accessibility method ( SCAM ) on heteromeric channels as likely to be at or near the pore ( Qiu et al . , 2014 ) . Near the bottom of the pore cavity , a constriction at the intracellular face of the membrane corresponds to a short N-terminal coil ( NTC ) sitting parallel to the inner leaflet of the membrane . The first 14 residues of the N-terminus of the channel are not resolved in the cryo-EM density , presumably due to flexibility . The absence of these residues is conspicuous; in the Cx26 and innexin-6 structures , an N-terminal helix forms a pore funnel structure that is the narrowest constriction in the structures of these channels and is thought to contribute to trafficking , selectivity , and gating ( Kyle et al . , 2008; Maeda et al . , 2009; Oshima , 2014; Oshima et al . , 2016 ) . In our reconstruction , the short portion of the NTC that is resolved is highly coordinated by cytosolic domains and positioned to respond to conformational changes in the cytosolic domains of one protomer , as well as movements of the neighboring protomer ( Figure 4F ) . Due to the similarities in pore structure between VRAC and connexin/innexin ( Figure 1—figure supplement 5 ) , we conducted functional assays to interrogate the role of the NTC in VRAC . We focused on residue T5 because the homologous residue is involved in stabilizing the pore funnel through a hydrogen bonding network in the Cx26 structure ( Maeda et al . , 2009 ) . We made the mutation T5C to test whether extracellular addition of the negatively-charged , membrane-impermeable thiol-reactive reagent , 2-sulfonatoethyl methanethiosulfonate ( MTSES ) , could alter VRAC activity in heteromeric channels composed of SWELL1-T5C + LRRC8C in HeLa LRRC8 ( A , B , C , D , E ) -/- cells via cysteine modification . While MTSES has no effect on wildtype heteromeric channels ( Qiu et al . , 2014 ) or channels containing SWELL1-T5R ( Figure 4G ) , whole-cell currents mediated by SWELL1-T5C + LRRC8C are strongly suppressed upon the addition of MTSES , suggesting that T5C is part of a constriction narrow enough to block the pore upon covalent modification by MTSES ( Figure 4G and Figure 4—figure supplement 2 ) . We next determined the role of T5 in anion selectivity . Although SWELL1-T5C-containing channels have similar relative permeability to wildtype , SWELL1-T5R-containing channels are significantly more selective to iodide compared to chloride , confirming that this residue is close to or part of the channel pore ( Figure 4H and Figure 4—figure supplement 2 ) . Thus , the unresolved portion of the N-terminus plays a role in pore constriction in native channels composed of SWELL1 and LRRC8C . Its absence in our structure is likely due to either the high flexibility of the region or a peculiarity of the homomeric assembly of the channel . Here we report the architecture and homo-hexameric assembly of SWELL1 channels . Electrophysiological analyses presented here demonstrate that the homomeric SWELL1 structure retains properties of more complex heteromers , as mutations based on the structure proved to be relevant for VRAC currents in a cellular context . The structure of SWELL1 also provides hints as to how VRAC gating is regulated . Since decreases in intracellular ionic strength cause activation ( Syeda et al . , 2016 ) , gating would likely be initiated by movement of intracellular domains in response to changes in salt concentration . We speculate that the multitude of charge-mediated interactions in the LRRs endows the SWELL1 structure with ionic-strength sensitivity , and via interactions with the N-terminus , the ILDs couple LRR movement to the transmembrane channel . Knock-out of LRRC8 genes in HeLa and suspension Freestyle HEK293-F cell line was completed using CRISPR/Cas9-mediated gene disruption ( Ran et al . , 2013 ) . SWELL1 ( LRRC8A ) , LRRC8B , LRRC8D , and LRRC8E genes were targeted using guideRNA ( gRNA ) sequences reported by Voss et al . ( 2014 ) ; the LRRC8C gene was targeted with a gRNA sequence reported by Syeda et al . ( 2016 ) . Cloning of the gRNAs into PX458-mCherry plasmid was completed as reported in Syeda et al . ( 2016 ) . Multiple plasmids were transfected simultaneously using either Lipofectamine 2000 or PEI max . After 48–72 hr , fluorescent mCherry positive cells were single-cell sorted into 96-well plates . Successful knock-out was determined by genotyping targeted regions for frameshift mutations and verified by mass spectrometry analysis . For HeLa cells ( LRRC8-/- HeLa cells ) , complete knock-out was verified for all five LRRC8 genes . For HEK293-F suspension cells , complete knock-out was verified for LRRC8B-E ( LRRC8 ( B , C , D , E ) -/- HEK293-F cells ) . One SWELL1 allele remained intact in all surviving suspension culture lines . All cell lines tested negative for mycoplasma contamination . Human SWELL1 ( LRRC8A ) ( Origene #RC208632 ) was cloned with a C-terminal FLAG-tag ( DYKDDDDK ) separated by a triple glycine linker ( SWELL1-GGG-FLAG ) into a pcDNA3 . 1/Zeo ( - ) vector using Gibson cloning . HEK293-F LRRC8 ( B , C , D , E ) -/- cells were transfected at a cell density of 1 . 8*10^6 cells/mL with 1 mg/L cells of SWELL1-GGG-FLAG plasmid DNA combined with 3 mg/L cells of PEI max . After 48 hr , cells were pelleted and solubilized in solubilization buffer ( 20 mM Tris pH 8 , 150 mM NaCl , 1% DMNG , 2 mg/mL iodoacetamide , and EDTA-free protease inhibitor cocktail ( PIC ) ) at 4°C with vigorous shaking . The cell lysate was ultracentrifuged at 90 , 000 x g for 30 min at 4°C and the supernatant was collected and combined with 1 mL/L cells of FLAG M2 affinity resin for 1 hr batch incubation at 4°C with gentle shaking . Resin was washed in a gravity column with 5 mL per mL of resin ( column volumes; CV ) of solubilization buffer ( 20 mM Tris pH 8 , 150 mM NaCl , 1% DMNG , 2 mg/mL iodoacetamide , and EDTA-free PIC ) , 5CV of high salt wash buffer ( 20 mM Tris pH 8 , 150 mM NaCl , 0 . 05% digitonin , and EDTA-free PIC ) , and 10CV of wash buffer ( 20 mM Tris pH 8 , 150 mM NaCl , 0 . 05% digitonin , and EDTA-free PIC ) . Protein was eluted using elution buffer ( 20 mM Tris pH 8 , 150 mM NaCl , 0 . 05% digitonin , EDTA-free PIC and 3x FLAG peptide ( Sigma or in-house peptide production ) ) . Sample was concentrated and injected onto Shimadzu HPLC and separated using a Superose 6 Increase column equilibrated with running buffer ( 20 mM Tris pH 8 , 150 mM NaCl , 0 . 05% digitonin , and EDTA-free PIC ) . The peak corresponding to SWELL1 homomeric oligomers ( ~800 kDa ) was collected and used for cryo-EM grid preparation . The sample was concentrated to ~8 mg/mL using 100 kDa MWCO concentrators . Protein ( 3 μl ) was applied to plasma cleaned UltrAuFoil 1 . 2/1 . 3 300 mesh grids , blotted for 6 s with 0 blot force , and plunge frozen into nitrogen cooled liquid ethane using a Vitrobot Mark IV ( ThermoFisher ) . Images were collected at 200 kV on a Talos Arctica electron microscope ( ThermoFisher ) with a K2 direct electron detector ( Gatan ) at a nominal pixel size of 1 . 15 Å . Leginon software was used to automatically collect micrographs ( Suloway et al . , 2005 ) . The total accumulated dose was ~55 e-/Å2 and the defocus range was 0 . 8–1 . 5 µm . Movies were aligned and dose-weighted using MotionCor2 ( Zheng et al . , 2017 ) . Images were assessed for quality and edges of gold holes were masked using EMHP ( Berndsen et al . , 2017 ) . CTF values were estimated using Gctf ( Zhang , 2016 ) . Template-based particle picking was completed using FindEM template correlator ( Roseman , 2004 ) . Particles were extracted using Relion 2 . 1 ( Scheres , 2012 ) then subjected to 2D classification using cryoSPARC ( Punjani et al . , 2017 ) . 130 , 054 particles corresponding to good 2D class averages were selected for further data processing . An ab initio initial model was created in cryoSPARC followed by iterative angular reconstitution and reconstruction . The resulting density map was used as a seed for refinement of the data set in Relion 2 . 1 . Resolution of the resulting map was 4 . 6 Å . The map showed significant disorder in the LRR regions; however the map reveals that LRR regions arrange pairwise around a three-fold symmetry axis . As the transmembrane and extracellular domains were well-resolved , refinement was pursued imposing C3 symmetry and introducing a mask that excluded density outside of the well-defined , three-fold symmetric transmembrane/extracellular domains . Resolution of the resulting map was 4 . 0 Å; transmembrane/extracellular domains were well-resolved whereas LRR regions were largely disordered . This map was then used to create suitable projections that were subtracted from particles , thereby creating a particle data set corresponding mostly to LRR densities . This new data set was then subjected to 3D classification in Relion 2 . 1 ( K-means split of 12 ) . One of the resulting classes showed order in the pairwise LRR arrangement around the three-fold symmetry axis . Particles corresponding to this class ( 25 , 719 ) were then refined locally around the previously obtained coordinate assignment imposing three-fold symmetry resulting in an LRR density map at 5 . 0 Å resolution . Additionally – due to the overall higher degree of order – original particles corresponding to the 25 , 719 density-subtracted particles were refined under three-fold symmetry constraints . Resolution of the resulting map was 4 . 4 Å . An initial model of an N-terminal portion of SWELL1 was generated with RobettaCM using innexin-6 ( 5H1Q ) as a template structure ( Oshima et al . , 2016; Song et al . , 2013 ) . The SWELL1 topology was predicted using OCTOPUS ( Viklund and Elofsson , 2008 ) . Predicted transmembrane regions were manually aligned to the transmembrane helices of the template structure 5H1Q ( Oshima et al . , 2016 ) . Intervening regions of SWELL1 were aligned to 5H1Q using BLASTp . 10 , 000 independent homology models were generated with RosettaCM and clustered using Calibur ( Li and Ng , 2010 ) . The resulting model with the lowest Rosetta energy from the largest cluster was used as a guide for ab initio building of the transmembrane helices , extracellular domains , and intracellular linker domain . Sequence register was aided by bulky side chains and disulfide bonds in the extracellular domain . A Robetta-generated model of the SWELL1 LRR domain was docked into the EM density corresponding to the LRR of the outer subunit , which was better resolved than the inner subunit ( Kim et al . , 2004 ) . This LRR model was adjusted manually to fit the density , then copied and docked into the LRR density of the inner subunit , followed by further adjustments . During the building process , manual building in COOT ( Emsley and Cowtan , 2004 ) was iterated with real space refinement using Phenix ( Adams et al . , 2010 ) or RosettaRelax ( DiMaio et al . , 2009 ) . Structures were evaluated using EMRinger ( Barad et al . , 2015 ) and MolProbity ( Chen et al . , 2010 ) . The final model contains residues 15–68 , 94–174 , 232–802 in the inner subunit and 15–68 , 94–175 , 214–802 in the outer subunit . Side chains of residues 15–21 , 359–364 , 787–802 of both subunits and 214–233 of the outer subunit were trimmed to Cβ because of limited resolution and lack of well-defined secondary structures in these regions . Structure figures were made in Pymol ( Schrodinger , , 2017 ) and UCSF Chimera ( Pettersen et al . , 2004 ) . Pore radii were calculated using HOLE ( Smart et al . , 1996 ) . The APBS plugin in pymol was used to calculate surface representations of electrostatic potentials . Electrophysiology experiments were completed with HeLa LRRC8-/- cells . HeLa LRRC8-/- cells were transfected 1–3 days earlier with SWELL1 constructs together with LRRC8C-ires-GFP in a 2:1 ratio ( 0 . 8 and 0 . 4 γ/ml for each coverslip ) . VRAC currents using a 2:1 ratio of SWELL1:LRRC8C were at least twice as large as those using a 1:1 ratio ( data not shown ) . Only one cell per coverslip was tested for its response to hypotonic solution . In experiments aimed at determining whether HeLa LRRC8-/- cells transfected with SWELL1 only could express VRAC currents , the extracellular solution contained ( in mM ) 90 NaCl , 2 KCl , 1 MgCl2 , 1 CaCl2 , 10 HEPES , 110 mannitol ( isotonic , 300 mOsm/kg ) or 30 mannitol ( hypotonic , 230mOsm/kg ) , pH 7 . 4 with NaOH; recording pipettes were filled with intracellular solution containing ( in mM ) : 133 CsCl , 5 EGTA , 2 CaCl2 , 1 MgCl2 , 10 HEPES , 4 Mg-ATP , 0 . 5 Na-GTP ( pH 7 . 3 with CsOH; 106 nM free Ca2+ ) and had resistances of 2–3 MΩ . Experiments testing R103F and T5 mutants used extracellular solutions described in Qiu et al . ( 2014 ) ( ‘bianionic’ ) and intracellular solution used in Syeda et al . ( 2016 ) ( 130 mM CsCl , 10 HEPES , 4 Mg-ATP , pH 7 . 3 ) . These were used to determine relative permeability PI/PCl . An agar bridge was used between the ground electrode and the bath in all experiments .	Every cell needs to regulate its internal volume or it will burst . Most of a cell’s volume is a watery mixture of salts , proteins and other molecules . A cell can take in more water from its surroundings , diluting this mixture and causing the cell to expand . If a cell starts to take up too much water , it will open channel proteins in its outer membrane called volume regulated anion channels ( or VRACs for short ) . An open VRAC allows negatively charged ions to leave the cell , and in the process causes water to leave the cell too . This relieves the pressure inside the cell , and the cell starts to shrink . The structure of a VRAC is thought to contain six subunits , and most include at least two different kinds of subunit . Some of the subunits must be a protein called SWELL1 ( which is also known as LRRC8A ) . The other subunits can be any of four similar proteins from the same protein family . Since a VRAC can contain additional subunits drawing from this pool of five proteins , many structures are possible . But it remains unclear exactly how the structure of a VRAC allows it to sense and regulate the volume of a cell . This is partly because scientists do not have enough information about the architecture of this protein to understand how it might work . Using electron microscopes , Kefauver et al . have now captured detailed images of a VRAC composed entirely of human SWELL1 proteins . The overall structure of VRAC resembles a six-legged jellyfish , with a pore on the cell’s exterior passing through a constricted dome followed by three pairs of arms that extend into the cell’s interior . Given the observed structure , Kefauver et al . speculate that the arms of the SWELL1 proteins sense salt concentrations within the cell ( to tell if its become diluted by an influx of water ) and then interact with the rest of the channel . In response to these interactions , the domed part of the VRAC constricts or dilates to help regulate the cell’s volume . Molecular biologists can now use these structural details to further study the fundamentals behind how cells regulate their volume . This model will also improve scientific understanding of how diverse VRAC structures differ in their responses to changes in pressure within cells .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "structural", "biology", "and", "molecular", "biophysics" ]	2018	Structure of the human volume regulated anion channel
Imprinted gene expression occurs during seed development in plants and is associated with differential DNA methylation of parental alleles , particularly at proximal transposable elements ( TEs ) . Imprinting variability could contribute to observed parent-of-origin effects on seed development . We investigated intraspecific variation in imprinting , coupled with analysis of DNA methylation and small RNAs , among three Arabidopsis strains with diverse seed phenotypes . The majority of imprinted genes were parentally biased in the same manner among all strains . However , we identified several examples of allele-specific imprinting correlated with intraspecific epigenetic variation at a TE . We successfully predicted imprinting in additional strains based on methylation variability . We conclude that there is standing variation in imprinting even in recently diverged genotypes due to intraspecific epiallelic variation . Our data demonstrate that epiallelic variation and genomic imprinting intersect to produce novel gene expression patterns in seeds . Diploid sexually reproducing organisms inherit an allele of each gene from both parents , which masks deleterious effects of recessive mutations . However , a subset of genes in flowering plants and mammals are subject to imprinting , whereby genes are expressed predominantly from one allele in a parent-of-origin dependent manner , such that traits controlled by these genes reflect the genotype of only one parent . Imprinted gene expression affects fetal growth regulation and postnatal behavior in mammals and the formation of viable seeds and the inhibition of interspecies hybridization in plants ( Tycko and Morison , 2002; Jiang and Köhler , 2012; Kradolfer et al . , 2013 ) . Imprinting is primarily restricted to the endosperm in plants , the triploid tissue that develops alongside the embryo and is necessary for normal embryo patterning and growth . Genome-wide surveys for imprinted expression have identified several dozen to hundreds of imprinted genes in rice , maize , Arabidopsis , mice , mules , and hinnies ( Gehring et al . , 2011; Hsieh et al . , 2011; Luo et al . , 2011; Wolff et al . , 2011; Zhang et al . , 2011; DeVeale et al . , 2012; Waters et al . , 2012; Xin et al . , 2013; Wang et al . , 2013b ) . Differential DNA methylation and histone modifications between maternally-inherited and paternally-inherited alleles are important regulators of imprinted gene expression ( Köhler et al . , 2012 ) . Endosperm DNA is maternally hypomethylated at thousands of discrete loci ( Gehring et al . , 2009; Hsieh et al . , 2009; Ibarra et al . , 2012 ) in a process dependent on the 5-methylcytosine DNA glycosylase DME ( Gehring et al . , 2006 , 2009; Hsieh et al . , 2009; Ibarra et al . , 2012 ) . Maintenance and de novo methylation pathways also appear to be compromised in the central cell and during early endosperm development ( Jullien et al . , 2012; Belmonte et al . , 2013; Vu et al . , 2013 ) , which might further contribute to loss of methylation from the maternally inherited genome . Endosperm DMRs ( differentially methylated regions ) are enriched for TE sequences , although not all imprinted gene are associated with a neighboring TE ( Gehring et al . , 2009 ) . TE methylation dynamics during reproduction appear to be an important driver of imprinted gene expression , yet the epigenetic modification of TEs and their presence or absence in genomes can be variable on short evolutionary timescales . Although very few TEs are presently active in Arabidopsis thaliana , most differences between genomic sequences of Arabidopsis strains are due to variation in TEs ( Cao et al . , 2011 ) . Many euchromatic TEs and related sequences are targeted for RNA-directed DNA methylation ( RdDM ) , an active process through which 24-nt small RNAs derived from longer non-coding RNA transcripts direct DNA methyltransferases to cognate sequences ( Law and Jacobsen , 2010 ) . RdDM is important for maintaining transcriptional silencing of TEs . Gene expression is negatively correlated with the proximity of TEs targeted by small RNAs and methylated TEs are under stronger purifying selection when they are near genes ( Hollister and Gaut , 2009; Wang et al . , 2013a ) . TE methylation is quite stable , although loss of methylation at TEs can occur spontaneously at very low frequency ( Becker et al . , 2011; Schmitz et al . , 2011 ) . However , a quarter of TEs in Arabidopsis are not methylated ( Ahmed et al . , 2011 ) and only 68% are associated with small RNAs ( Hollister et al . , 2011 ) . Thus the epigenetic modification status of TEs can be variable within the species , between different classes of elements , or even among elements of the same family . The potential role of TEs in establishing or maintaining imprinted expression coupled with the evolutionary forces that select for parent-of-origin specific expression suggest that substantial intraspecific variation in imprinting could exist . Indeed , the first imprinted gene described , the maize R gene ( Kermicle , 1970 ) , is an example of allele-specific imprinting; only alleles that have a Doppia TE inserted in the promoter are imprinted ( Kermicle , 1978; Walker , 1998; Alleman and Doctor , 2000 ) . Once imprinted gene expression arises , the kinship or parental conflict theory of imprinting ( Haig , 2013 ) posits that it could be evolutionarily selected because asymmetrically related kin ( e . g . , half-siblings that have the same mother but different fathers ) compete for maternal resources . Thus , maternally and paternally inherited alleles of genes that influence maternal resource transfer to offspring have different optima for total gene expression levels . Plants adopt a range of different strategies with regards to maternal resource transfer to offspring—producing a few large seeds , or many small seeds . Intraspecific variation in this trait could potentially be linked to differences in the set of genes subject to imprinting in each strain . To systematically evaluate whether gene imprinting varies on short evolutionary time scales and to further understand the role of genetic and epigenetic polymorphisms in this process , we have investigated the conservation and variability of imprinting , DNA methylation , and small RNA production in reciprocal crosses among three strains of Arabidopsis . Arabidopsis is an ideal system in which to ask these questions because of the availability of genotyped and epigenotyped strains that have diverged for only a few thousand years . Here we discovered 12 examples of allele-specific imprinting , about half of which were associated with endosperm demethylation of a TE that was variably methylated within the strains we examined . We further evaluated intraspecific methylation variability at regions targeted for CG DNA demethylation during female reproductive development for 140 strains where vegetative methylation patterns are known ( Schmitz et al . , 2013 ) . Approximately 11% of imprinted genes are associated with an endosperm DMR that is variably methylated among strains . From this analysis we predicted and experimentally validated allele-specific imprinting in additional strains for two genes . The ability to predict imprinting status based on strain-to-strain variation in vegetative methylation patterns suggests that these genes are strong candidates for allelic variation in imprinting due to epigenetic differences at TEs , and thus act as epialleles . Our data demonstrate that epiallelic variation and genomic imprinting intersect to produce novel gene expression patterns in seeds . Thus , naturally occurring epialleles could have the strongest phenotypic effect during the reproductive phase of plant development , when patterns of methylation are altered . We first identified genes with consistent parentally biased expression in embryo and endosperm using mRNA-seq data from six different crosses representing three sets of reciprocals: Col-Ler , Col-Cvi , and Ler-Cvi ( Figure 1 , Figure 1—source data 1 ) . Imprinting could be evaluated for 16 , 646 loci in at least one set of reciprocal crosses and 8088 loci in all three sets of reciprocal crosses based on SNPs and sequencing depth . As previously , we implemented a series of filters to define imprinted genes ( Gehring et al . , 2011 ) ( 'Materials and methods' ) except that we added an additional filtering step to require endosperm maternally biased genes to have at least 85% maternal reads in each direction of the cross and paternally biased genes to have at least 50% paternal reads in each direction of the cross ( the maternal and paternal cutoffs in the embryo were 70% ) ( Figure 1 ) . In the endosperm between 122 and 145 maternally expressed imprinted genes ( MEGs ) were identified for each pair of reciprocal crosses ( Col-Ler , Col-Cvi , or Ler-Cvi ) along with between 43 and 52 paternally expressed imprinted genes ( PEGs ) for a total of 285 possible MEGs ( including 5 TEs ) and 103 PEGs in the union of all crosses ( Figure 1B , Figure 1—source data 2 ) . Many of these genes have previously been identified as imprinted genes ( Figure 1—source data 4 ) . Consistent with previous results ( Gehring et al . , 2011; Hsieh et al . , 2011 ) , very few potential imprinted genes were detected in the embryo ( Figure 1C , Figure 1—source data 3 ) . Imprinting calls based on whole-genome mRNA-seq were validated by sequencing or performing CAPs digestion on RT-PCR amplicons of 29 genes from independently isolated embryo and endosperm RNA samples ( Figure 1—figure supplement 2 , Figure 1—source data 5 ) ; results were mostly consistent with the mRNA-seq data . 10 . 7554/eLife . 03198 . 003Figure 1 . mRNA-seq identifies genes with biased expression . ( A ) Proportion of maternal ( m ) and paternal ( p ) reads for all three sets of reciprocal crosses in the endosperm . One replicate of each reciprocal cross is shown . Biases represented by each quadrant are depicted for Col-Ler endosperm crosses but apply to all graphs . Orange and pink dots represent MEGs ( pink dots are MEGs in all three sets of reciprocal crosses ) , blue and green dots represent PEGs ( blue dots are PEGs in all three sets of reciprocal crosses ) . Crosshairs indicate the expected log ratio for genes that lack biased expression . ( B ) Overlap of MEGs and PEGs in the endosperm among three sets of reciprocal crosses . Pink and blue circles: Col-Ler; brown and purple circles: Col-Cvi; yellow and gray circles: Ler-Cvi . ( C ) Proportion of maternal ( m ) and paternal ( p ) reads for Col-Cvi and Cvi-Ler reciprocal crosses in the embryo . Colored dots as in part A . Figure 1—figure supplement 1 shows seeds used in the experiment . Figure 1—figure supplement 2 shows validation of an imprinted gene . Figure 1—figure supplement 3 examines maternal:paternal ratios of imprinted genes identified in one set of crosses in the other two sets of reciprocal crosses . Figure 1—figure supplement 4 examines overall expression levels of imprinted genes at other stages of development . Information on mRNA-seq library metrics is in Figure 1—source data 1 and allele-specific expression information for all genes in endosperm and embryo is in Figure 1—source data 2 and Figure 1—source data 3 , respectively . Figure 1—source data 4 shows the overlap among imprinted genes identified in this study and those identified in previous efforts and Figure 1—source data 5 includes independent validation of imprinted genes . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 00310 . 7554/eLife . 03198 . 004Figure 1—source data 1 . mRNA-seq libraries generated in this study . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 00410 . 7554/eLife . 03198 . 005Figure 1—source data 2 . Endosperm imprinting data for all genes . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 00510 . 7554/eLife . 03198 . 006Figure 1—source data 3 . Embryo imprinting data for all genes . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 00610 . 7554/eLife . 03198 . 007Figure 1—source data 4 . Overlap among published imprinted gene lists . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 00710 . 7554/eLife . 03198 . 008Figure 1—source data 5 . Validation of imprinted genes . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 00810 . 7554/eLife . 03198 . 009Figure 1—figure supplement 1 . Seed development in the crosses used in this study . ( A ) Seeds cleared with chloral hydrate and imaged 6 days after pollination . Scale bar = 100 microns for all panels . ( B ) Mature seeds . Scale bar = 500 microns . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 00910 . 7554/eLife . 03198 . 010Figure 1—figure supplement 2 . Validation of AT4G00750 allele-specific imprinting by RT-PCR and CAPs digestion . Endosperm cDNA from the indicated crosses ( female in cross listed first ) was amplified using intron-spanning primers that flank a C>G polymorphism between Col and Ler or Cvi and then restriction digested with Hpy188I . The PCR amplifies a 324 bp product . After digestion with Hpy188I , Col remains uncut but Ler or Cvi alleles are digested to 209 and 115 bp . Consistent with the RNA-seq data , AT4G00750 expression is primarily from the maternally inherited allele except when Ler is the male parent . AT4G00750 is a MEG in both directions of the cross for Col-Cvi . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 01010 . 7554/eLife . 03198 . 011Figure 1—figure supplement 3 . Consistency of imprinting among different sets of reciprocal crosses . Allele-specific expression ratios of imprinted genes identified in one set of reciprocal crosses in the other two sets of reciprocal crosses . Pink dots , MEGs in both sets of crosses being compared; orange dots , MEGs not shared with the dataset being plotted; blue dots , PEGs in both sets of crosses being compared; green dots , PEGs not shared with the dataset being plotted . Most pink and orange dots are in the upper right quardrant and most blue and green in the lower left , indicating consistent parental bias . m , maternal; p , paternal . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 01110 . 7554/eLife . 03198 . 012Figure 1—figure supplement 4 . Imprinted genes are expressed at multiple stages of development . ( A ) Expression of 199 MEGs and 82 PEGs in leaves , shoot apex ( Sh ) , flowers , roots ( R ) , pollen ( P ) , and seeds at various stages of development . Tissue series data was downloaded using the e-Northern expression tool from the Bio-Analytic Resource ( Toufighi et al . , 2005 ) . ( B ) Expression of MEGs and PEGs in whole seeds ( WS ) , embryo proper ( EP ) , suspensor ( S ) , micropylar endosperm ( MCE ) , peripheral endosperm ( PEN ) , chalazal endosperm ( CZE ) , general seed coat ( SC ) and chalazal seed coat ( CSC ) . Data is from Belmonte et al . , 2013 . Each tissue is organized by increasing developmental age from pre-globular to mature green . Data was clustered and visualized using GENE-E . Gene order is the same between A and B . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 012 We concluded that most genes that show strong evidence for imprinting in one cross have evidence for the same parental bias in other crosses ( Figure 1—figure supplement 3 ) . The intersection of the endosperm datasets revealed 28 MEGs and 6 PEGs in common among all three pairs of reciprocal crosses ( Figure 1B ) . An additional 53 MEGs and 23 PEGs were identified in two of three sets of reciprocal crosses . Most MEGs and PEGs that were identified in only one set of reciprocal crosses lacked sufficient data to assess imprinting in the other crosses , due to an absence of SNPs or because of low read counts , rather than because they were clearly not parentally biased . For example , of the 73 Col-Cvi MEGs that were not among the Col-Ler MEGs , 62 lacked sufficient allele-specific data to be assessed for imprinting in Col-Ler . Of the remaining 11 genes , 8 showed strong evidence for maternal bias but did not meet all criteria for imprinting ( usually failing to meet the requirement for 85% maternal reads in both directions of the cross ) . We also examined the expression of imprinted genes at other stages of the plant life cycle ( Figure 1—figure supplement 4 ) . Analysis of published microarray expression data ( Toufighi et al . , 2005 ) showed that in Arabidopsis most imprinted genes are expressed at other stages of plant development ( Figure 1—figure supplement 4 ) . Within seeds , imprinted genes are most commonly expressed in chalazal endosperm ( Figure 1—figure supplement 4 ) , consistent with previous findings from individual loci ( Ingouff et al . , 2005 ) . Several PEGs and MEGs are most highly expressed in mature pollen ( Figure 1—figure supplement 4 ) , probably reflecting expression in the pollen vegetative nucleus , which also undergoes active DNA demethylation ( Schoft et al . , 2011; Calarco et al . , 2012; Ibarra et al . , 2012 ) . Consistent with our previous study ( Gehring et al . , 2011 ) , compared to all genes that could be assessed for imprinting , the PEGs identified in any of the three sets of reciprocal crosses ( n = 103 ) were enriched for genes encoding proteins with a SRA-YDG domain ( 65 . 6-fold; p value=3 . 3E−6 ) , genes involved in the biological processes regulation of RNA metabolic processes ( 4 . 1-fold; p value=4 . 7E−4 ) , DNA-dependent regulation of transcription ( 4 . 1-fold , p value=8 . 5E−4 ) , and DNA binding proteins ( 2 . 6-fold; p=2 . 5E−4 ) . Overall , PEGs consisted of many genes known or predicted to be involved in transcription and epigenome regulation . Maize PEGs are also enriched for chromatin modifiers ( Waters et al . , 2013 ) . The Arabidopsis MEGs were not enriched for any particular class of genes except for a slight enrichment for transcription factor activity ( 2 . 1-fold; p=0 . 035 ) , particularly of the MYB and homeodomain types . We identified 9 PEGs and 3 MEGs that exhibited allele-specific imprinting ( Figure 2—source data 1 ) . Our method to identify imprinted genes explicitly relies on agreement between reciprocal crosses ( ‘Materials and methods’ ) . However , genes that exhibit allele-specific imprinting will only be parentally biased when a particular strain is the male or female parent; for example a gene could be a PEG in all crosses except when Cvi is the male parent ( Figure 2 ) . From the ratio of maternal/paternal mRNA-seq reads in each cross we identified those loci that were potentially biased in one direction of the cross but not the other ( ‘Materials and methods’ ) . Because each allele was included twice as the maternal or paternal parent in our experimental design , we were able to identify loci that consistently showed maternal or paternal bias when a particular strain was the male or female parent ( Figure 2 ) . These lists were then compared to the union of all MEGs and PEGs ( Figure 1 ) to identify genes that are imprinted in one set of reciprocal crosses , but only in one direction in the other two sets of reciprocal crosses ( Figure 2 ) . We performed CAPs analysis or sequenced RT-PCR amplicons for 7 of the 12 allele-specific imprinted genes and further confirmed that they exhibited allele-specific imprinting with few exceptions ( Figure 1—figure supplement 2 , Figure 1—source data 5 , Figure 2—source data 1 ) . 10 . 7554/eLife . 03198 . 013Figure 2 . A subset of genes is only imprinted when a certain strain is the male or female parent . Process for identifying allele-specific imprinted genes that are PEGs except when Cvi is the male parent . Genes that are paternally biased in Cvi x Col but not Col x Cvi ( blue dots ) were identified . These genes were overlapped with the Ler-Cvi maternal/paternal log ratios for the same genes ( green dots ) to generate a list of candidate loci that are not PEGs when Cvi is the male parent . Intersection with Col-Ler PEGs ( pink dots ) identifies strain-specific imprinted genes that are PEGs except when Cvi is the male parent , including AT2G32370 and AT3G14205 . All candidate allele-specific imprinted genes are in Figure 2—source data 1 . m , maternal; p , paternal . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 01310 . 7554/eLife . 03198 . 014Figure 2—source data 1 . Candidate allele-specific imprinted genes . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 014 To explore the potential relationship between DNA methylation and conserved and allele-specific imprinting , libraries for single base resolution DNA methylation profiling by bisulfite sequencing ( BS-seq ) were prepared from embryo and endosperm DNA ( Figure 3—source data 1 ) . All BS-seq libraries had a high cytosine to thymine conversion rate ( at least 99 . 70% ) , indicating efficient bisulfite treatment ( Figure 3—source data 1 ) . Methylation profiles were generated from non-redundant uniquely mapping reads ( ‘Material and methods’ ) . Endosperm DNA was consistently less methylated compared to embryo DNA in all sequence contexts ( Figure 3—source data 1 ) , as shown previously ( Gehring et al . , 2009; Hsieh et al . , 2009; Ibarra et al . , 2012 ) . Embryo tissue from crosses between strains primarily displayed additive total DNA methylation ( Figure 3—source data 1 ) . In the endosperm , total methylation was more closely aligned with the methylation level of the female parent , consistent with the 2:1 ratio of maternal to paternal genomic DNA in the endosperm ( Figure 3—source data 1 ) . Interestingly , CHH methylation was not substantially reduced on a global scale in Cvi x Col endosperm ( 3 . 0% ) compared to the embryo ( 3 . 1% ) ( Figure 3—source data 1 ) . Our analysis revealed an unusual methylation profile in Cvi . CG methylation in Cvi embryos ( 16 . 3% ) was lower than in Col and Ler embryos ( 28 . 1% and 22 . 4% , respectively ) ( Figure 3—source data 1 ) . We further investigated the nature of CG hypomethylation in Cvi to determine whether it was specific to certain classes of sequences or whether CG methylation was uniformly reduced across the genome . Of the three strains , Cvi had the lowest median CG methylation levels in both genes and TEs in embryos ( Figure 3 ) , but was largely unaffected in those sequences in either the CHG or CHH contexts ( Figure 3C , Figure 3—figure supplement 1 ) . Analysis of Cvi CG methylation data from leaves ( Schmitz et al . , 2013 ) confirmed that hypomethylation was not specific to embryo and endosperm but is a general property of this strain ( Figure 3—figure supplement 1 ) . Loss of CG methylation was most pronounced in gene bodies ( Figure 3B , C , Figure 3—figure supplement 1 ) , where it was 50% lower in Cvi compared to either Ler or Col ( Figure 3—source data 2 ) . CG methylation in TEs was 14% lower in Cvi than in Col , but at the same level as in Ler ( Figure 3—source data 2 ) . Reduced gene body methylation in Cvi is unlikely to be a technical artifact of mapping biases: gene bodies have fewer SNPs and indels than TEs and thus mapping efficiency is better to genes than to TEs . All of our locus-specific bisulfite sequencing confirmed the whole-genome BS data . In Arabidopsis , gene body methylation is primarily in the CG context and is maintained after DNA replication by the maintenance methyltransferase MET1 ( Lister et al . , 2008 ) . Therefore , non-CG methylation pathways seem to operate normally in Cvi , but maintenance methylation appears to be compromised . 10 . 7554/eLife . 03198 . 015Figure 3 . Cvi is hypomethylated in CG contexts . ( A ) Box and whiskers plots of CG DNA methylation levels of genes and TEs in Col , Ler , and Cvi embryos . Line: median; gray dots: outliers . ( B ) Average CG DNA methylation profiles of genes ( blue colors ) and TEs ( orange colors ) in Col , Ler , and Cvi embryos . Relative to Col , mean Cvi methylation level was decreased by 56% in genes ( p=0 . 00 , Tukey's HSD test ) and by 14% in TEs ( p=0 . 00 , Tukey's HSD test ) . ( C ) DNA methylation in Col , Ler and Cvi embryos at a representative genomic region that includes genes and TEs . CG ( red ) , CHG ( blue ) and CHH ( green ) methylation . Tick marks below the line indicate cytosines for which data was present but no methylation was detected . Figure 3—figure supplement 1 contains additonal analyses , Figure 3—source data 1 has statistics on BS-libraries and Figure 3—source data 2 shows the complete statistical analysis of methylation in Cvi compared to other strains . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 01510 . 7554/eLife . 03198 . 016Figure 3—source data 1 . BS-Seq libraries generated in this study . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 01610 . 7554/eLife . 03198 . 017Figure 3—source data 2 . Statistical analysis of strain differential methylation in genes and TEs . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 01710 . 7554/eLife . 03198 . 018Figure 3—figure supplement 1 . Cvi is hypomethylated in CG contexts regardless of tissue type but is not as hypomethylated in CHG and CHH contexts . Box and whisker plots of DNA methylation levels of genes ( left ) and TEs ( right ) . ( A ) CHG methylation in Col , Ler , and Cvi embryos . Relative to Col , mean Cvi methylation level was decreased by 22% in genes ( p<0 . 05 , Tukey's HSD test ) and by 2% in TEs ( p>0 . 05 , Tukey's HSD test ) . ( B ) CHH methylation in Col , Ler , and Cvi embryos . Relative to Col , mean Cvi methylation level was decreased by 26% in genes ( p<0 . 05 , Tukey's HSD test ) and by 9% in TEs ( p<0 . 05 , Tukey's HSD test ) . ( C ) Box and whisker plots of % CG DNA methylation of genes ( left ) and TEs ( right ) in Col and Cvi embryos in comparison to Col and Cvi leaves , using methylation data from Schmitz et al . ( 2013 ) . Relative to Col , mean Cvi methylation level was decreased by 56% ( embryo ) and 54% ( leaf ) in genes and by 14% in TEs in both tissues ( p<0 . 05 , Tukey's HSD test ) . Line: median; gray dots: outliers . Statistics are in Figure 3—source data 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 018 Genomic regions that are subject to active DNA demethylation during endosperm development in at least one strain but are variably methylated among other strains could be important contributors to an epigenetic mechanism for allele-specific imprinting . To identify embryo-endosperm DMRs and strain DMRs , we calculated weighted methylation levels ( Schultz et al . , 2012 ) in overlapping 300 nucleotide windows across the entire genome , requiring each cytosine to have at least five reads coverage to be included in the analysis ( ‘Materials and methods’ ) . We then ran pairwise comparisons between all windows with sufficient coverage for embryo libraries and their matching endosperm libraries ( embryo-endosperm DMRs ) and for Col-0 , Ler and Cvi embryo libraries against one another ( strain DMRs ) . The distribution of differences highlighted global differences in methylation ( Figure 4—figure supplement 1 ) . To identify DMRs the analysis was restricted to differences in weighted methylation fraction of at least 35% for CG or CHG methylation , with a minimum overlap of three informative Cs between windows ( i . e . , at least 3 Cs at the exact same positions had sufficient coverage in both embryo and endosperm ) , and 10% for CHH methylation , with a minimum overlap of 10 informative Cs . We retained DMRs that had a FDR corrected p-value<0 . 01 , reflecting whether the fraction of methylated/unmethylated counts was the same for both samples . From 365 , 000 to 500 , 000 informative 300 bp windows , 12 , 000–14 , 000 Col-Cvi and Col-Ler positive CG strain DMRs were identified , corresponding to approximately 8000 features . Most strain DMRs were in genes ( Figure 4A ) . In Col-Ler comparisons we also identified 7453 features where Ler was more methylated than Col , but only 1749 features where Cvi was more methylated than Col because of the overall reduction of CG gene body methylation in Cvi ( Figure 3 , Figure 4 , Figure 4—figure supplement 1 ) . In contrast to CG DMRs , strain CHH DMRs mostly mapped to TEs and intergenic regions ( Figure 4B ) , which are the sequences most likely to contain non-CG methylation . Similar results were obtained for Ler-Cvi comparisons ( Figure 4—figure supplement 2 ) . Independent validation by methylation-sensitive PCR or locus-specific BS-PCR of six loci confirmed our genome-wide analyses ( Figure 4—figure supplement 3 ) . 10 . 7554/eLife . 03198 . 023Figure 4 . Strain DMRs and embryo-endosperm DMRs are in distinct genomic regions . ( A ) Number of features overlapping strain DMRs between Col and Ler or Col and Cvi embryos . ( B ) Number of features overlapping embryo-endosperm DMRs in Col x Cvi and Cvi x Col crosses . ( C and D ) 24 nt small RNA quantities ( reads per million ) corresponding to Col-Cvi strain ( C ) and Col x Cvi or Cvi x Col embryo-endosperm DMRs ( D ) . ( E ) Overlap between Col-Cvi strain positive CG DMRs ( more methylated in Col than Cvi ) and the union of Col x Cvi and Cvi x Col embryo-endosperm CG DMRs ( embryo more methylated than endosperm ) corresponding to genes , TEs , and intergenic regions . Figure 4—figure supplement 1 show the distribution of all methylation differences; Figure 4—figure supplement 2 shows DMR analysis in Ler-Cvi crosses and other datasets; Figure 4—figure supplement 3 validates DMRs identified in this analysis; Figure 4—figure supplement 4 examines small RNAs at TEs and Figure 4—figure supplement 5 shows the overlap of embryo-endosperm CpG DMRs with previous studies . sRNA-seq library metrics are in Figure 4—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 02310 . 7554/eLife . 03198 . 024Figure 4—source data 1 . Whole seed sRNA-seq libraries generated in this study . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 02410 . 7554/eLife . 03198 . 025Figure 4—figure supplement 1 . Distribution of endosperm-embryo and strain CG DNA methylation differences . Histograms showing the distribution of all the 300 nt comparisons irrespective of the associated p-value . x axis: difference in weighted methylation; y axis: number of windows . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 02510 . 7554/eLife . 03198 . 026Figure 4—figure supplement 2 . Ler and Cvi strain DMRs and embryo-endosperm DMRs in additional datasets . ( A ) Number of features overlapping strain DMRs between Ler and Cvi embryos . ( B ) Number of features overlapping embryo-endosperm DMRs in Cvi x Ler and Ler x Cvi crosses . ( C ) Number of features overlapping embryo-endosperm DMRs in Ler x Col and Col x Ler crosses ( analysis of dataset from Ibarra et al . , 2012 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 02610 . 7554/eLife . 03198 . 027Figure 4—figure supplement 3 . Validation of BS-seq results with locus-specific BS-PCR or McrBC-PCR . ( A ) AT4G21430 . ( B ) AT5G17320 . ( C ) AT1G65330 . Top: CG , CHG and CHH methylation profiles of Col , Ler , and Cvi embryos and strain DMRs selected for validation . Bottom: methylation of individual sequenced clones from locus-specific BS-PCR . Filled circles indicate methylation , whereas unmethylated positions remain unfilled . ( D ) McrBC digestion of leaf genomic DNA followed by PCR of AT2G34880 and AT1G48910 . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 02710 . 7554/eLife . 03198 . 028Figure 4—figure supplement 4 . Distribution of TE superfamilies and small RNAs within embryo-endosperm DMRs . ( A ) TE superfamilies overlapped by CG or CHH DMRs ( embryo > endosperm methylation in Cvi x Cvi ) in comparison to the whole genome TE representation . TE superfamilies are as defined by TAIR10 . ( B ) Box plots depicting the number of 21–24 nt small RNAs ( reads per million ) overlapping: all TEs of the designated class ( black ) , CG DMRs ( red ) or CHH DMRs ( green ) . p-values were calculated using the Wilcoxon-Mann-Whitney test , followed by a Bonferroni correction . p<0 . 05; p<0 . 01; p<0 . 001; *p<0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 02810 . 7554/eLife . 03198 . 029Figure 4—figure supplement 5 . Overlap of embryo-endosperm CG DMRs with previous studies . This study: DMRs identified from all matched comparisons , Gehring et al . , 2009: Col-glxCol-gl and LerxLer DMRs , Ibarra et al . , 2012: ColxLer and LerxCol DMRs combined . To identify the DMRs , this study and Ibarra et al . ( 2012 ) datasets were analyzed with the same analysis pipeline described in ‘Materials and methods’ . Minimum overlap required between DMRs = 100 nt . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 029 We identified fewer embryo-endosperm DMRs than strain DMRs , although lower BS-seq coverage in the endosperm was likely a major contributing factor ( Figure 4B , Figure 3—source data 1 ) . From 23 , 000 informative windows in the Col x Cvi embryo-endosperm comparison we identified 2305 positive CG DMRs , corresponding to 1100 features where the embryo was more methylated than the endosperm ( Figure 4B ) . In contrast to strain DMRs and consistent with previous findings ( Gehring et al . , 2009; Ibarra et al . , 2012 ) , most regions hypomethylated in the endosperm in both the CG and CHH contexts correspond to intergenic regions and annotated transposable element fragments . Helitron and Mu elements were most commonly represented among demethylated TEs overlapped by a DMR , reflecting their abundance in the genome ( Figure 4—figure supplement 4; Gehring et al . , 2009 ) . It is important to note that although Cvi has a global methylation profile distinct from Col and Ler ( Figure 3 , Figure 3—source data 1 ) , endosperm demethylation dynamics appear to be the same . This reflects the fact that regions targeted for active DNA demethylation are depleted of genes , which is where most strain-specific methylation differences in Cvi reside . Using our analysis pipeline we also identified embryo-endosperm DMRs from published BS-seq data from Col x Ler and Ler x Col embryo and endosperm isolated at a slightly later developmental stage ( Ibarra et al . , 2012 ) . These datasets have higher endosperm coverage and slightly lower embryo coverage than our datasets . 40% of DMRs were in common when our union set of DMRs ( identified in any matching embryo-endosperm comparison among our datasets; n = 21 , 973 ) was compared to the Ibarra embryo-endosperm DMRs ( Figure 4—figure supplement 5 ) . 61% of the embryo-endosperm DMRs we previously identified in Col-gl and Ler seeds by meDIP-seq ( Gehring et al . , 2009 ) were identified in our current study ( Figure 4—figure supplement 5 ) , indicating a high degree of overlap between this study and others . Because methylation of TEs and other repetitive sequences is often associated with small RNAs and because small RNAs are abundant in seeds ( Mosher et al . , 2009 ) , we sequenced small RNAs from whole seeds at 6 DAP , obtaining 20–30 million high-quality reads for biological replicates of each sample ( Figure 4—source data 1 ) . Data from whole seeds cannot distinguish among small RNAs from the seed coat , embryo , or endosperm , but at 6 DAP RdDM pathway genes are expressed in both embryo and endosperm ( Jullien et al . , 2012; Belmonte et al . , 2013 ) , suggesting that both tissues have the ability to produce small RNAs . Strain specific CG DMRs have very low levels of small RNAs , consistent with these DMRs residing mainly in genes , which are not targeted by the RdDM pathway ( Figure 4C ) . In contrast , CG DMRs where the embryo is more methylated than the endosperm are enriched for small RNAs compared to strain DMRs ( Wilcoxon-Mann-Whitney test , p<0 . 0001 ) or compared to a set of random genomic loci ( Figure 4D ) . TEs overlapping embryo-endosperm CG DMRs have on average higher levels of small RNAs in whole seeds than do all TEs on average from that family ( Figure 4—figure supplement 4 ) . Both strain and embryo-endosperm CHH DMRs are associated with small RNAs ( Figure 4C , D ) . We examined the overlap of the union set of imprinted genes ( including within the gene and 2 kb from the 5′ and 3′ ends ) and the CG and/or CHH embryo-endosperm DMRs . Over 40% of the MEGs ( 121/285 ) overlapped a CG DMR within these regions , but this was not a significant enrichment compared to a random set of the same number of genes ( Fisher's exact test p-value=0 . 3477 ) , nor was the overlap ( 176/285 ) when Ibarra et al . CG DMRs were also included in the analysis . In contrast , PEGs were significantly enriched for CG DMRs ( 64/103; p=0 . 0174 ) ( Figure 5 ) . Furthermore , of the 29 PEGs that are in common among at least two of three sets of reciprocal crosses ( Figure 1B ) , 22 are associated with a proximal TE , primarily within 1 kb 5′ of the transcription start site ( Figure 5 ) . Fewer MEGs exhibit a correlation with presence of a TE ( 40/85 ) , consistent with the lower correlation between DNA demethylation and imprinted expression for MEGs ( Figure 5 ) . 10 . 7554/eLife . 03198 . 030Figure 5 . Correspondence between DNA methylation , TEs , and sRNAs for imprinted genes . ( A ) Average CG methylation in embryo and endosperm for the union set of PEGs , MEGs and all genes . ( B ) Percentage of genes with TE at indicated position . ( C ) Distribution of TEs and 24 nt small RNAs around endosperm imprinted MEGs ( n = 85 ) and PEGs ( n = 29 ) identified in at least two of three sets of reciprocal crosses . TE heatmap indicates the presence or absence of TEs according to TAIR10 annotation . 24 nt small RNA data is from ColxCvi whole seeds . Other libraries showed the same overall small RNA profile . Values were calculated in 200 nt windows extending 2 kb upstream and downstream from the 5′ and 3′ ends of the gene and 1 kb into the gene body . White indicates the absence of data . Figure 5—figure supplement 1 shows H3K27me3 profiles around imprinted genes in vegetative tissues . Figure 5—figure supplement 2 and Figure 5—figure supplement 3 further explore the distribution and allelic contribution of small RNAs associated with imprinted genes . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 03010 . 7554/eLife . 03198 . 031Figure 5—figure supplement 1 . Histone H3 lysine 27 trimethylation ( H3K27me3 ) profiles of PEGs and MEGs in vegetative tissues . H3K27me3 leaf data from Lafos et al . ( 2011 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 03110 . 7554/eLife . 03198 . 032Figure 5—figure supplement 2 . Small RNA levels around imprinted genes . Box plots depicting 24 nt sRNAs in reads per million reads ( RPM ) within 1 kb windows associated with MEGs , PEGs , or all genes that could be evaluated for imprinted expression within that cross . Asterisks indicate significance when compared to all genes analyzed . p-values were calculated using the Wilcoxon-Mann-Whitney test , followed by a Bonferroni correction . p<0 . 05; p<0 . 01; p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 03210 . 7554/eLife . 03198 . 033Figure 5—figure supplement 3 . Fraction of maternal small RNAs near the 5’ end of imprinted genes . Boxplots illustrating the fraction of classified 24 nt sRNA reads identified as derived from the maternally inherited genome for the set of all genes that were analyzed for imprinting , the union of all identified MEGs , and the union of all identified PEGs ( Figure 1B ) . Windows had to exceed a threshold of five classified reads ( i . e . , reads that could be assigned to the maternal or paternal genome based on SNPs ) to be included in the analysis . p-values were calculated using the Wilcoxon-Mann-Whitney test . p<0 . 05; p<0 . 01; *p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 033 We also examined the distribution of total and allele-specific small RNAs around MEGs and PEGs . The highest average levels of small RNAs are found at the 5′ end of the gene ( Figure 5—figure supplement 2 ) . Despite the lack of DMR enrichment , MEGs are enriched for small RNAs within the first kilobase of the gene in all crosses examined and in the first kb 5′ of the gene in a subset of the samples ( Figure 5—figure supplement 2 ) . Small RNAs associated with PEGs are enriched in the first kb 5′ of the gene—where most PEG-associated TEs are located ( Figure 5 ) —and in the gene body , although levels 5′ of the gene are much higher ( Figure 5—figure supplement 2 ) . We examined whether MEGs or PEGs differed in the maternal/paternal fraction of associated small RNAs . Previous data suggested that small RNAs corresponding to TEs associated with MEGs accumulate in sperm cells ( Calarco et al . , 2012 ) ; in these instances the silent paternal allele would already be targeted for RdDM in sperm . Paternal small RNAs , which in whole seeds must be derived from the endosperm or embryo genomes , constituted 6–24% of RNAs that could be assigned to a specific allele , depending on the cross ( Figure 4—source data 1 ) . We calculated the fraction of maternal small RNA reads for the region 1 kb 5′ and 3′ of the transcription start site of imprinted genes , retaining only those regions with at least five allele-specific reads in our analysis . MEGs were enriched for small RNAs derived from the paternally-inherited genome compared to all genes that could be evaluated for imprinting ( Figure 5—figure supplement 3 ) . This suggests that silencing of the paternal allele of MEGs is associated with cis acting small RNAs produced from those alleles in the endosperm . Small RNA data from specific compartments of the seed will be necessary to conclusively address this question . We identified regions of the genome that are subject to DNA demethylation in endosperm in at least one background but that are variably methylated among strains . Overlap between the strain DMRs and embryo-endosperm DMRs ranged from 12% for TEs to 31% for genes ( Figure 4E ) . This suggests that there are sufficient epigenetic polymorphisms at embryo-endosperm DMRs to facilitate the formation of allele-specific imprinting . Differences in imprinting among alleles tied to differences in DNA methylation could be due to genetic or epigenetic differences ( Figure 6—figure supplements 1 , 2 , and 3 ) . Of the 12 allele-specific imprinted genes we identified ( Figure 2 , Figure 2—source data 1 ) , 10 were associated with coincident CG or CHH embryo-endosperm DMRs and strain DMRs , 6 of which occurred at TEs ( Figure 2—source data 1 , Figure 6 , Figure 6—figure supplement 1 , Figure 6—figure supplement 2 ) . For all 6 genes we confirmed by sequencing that the TE annotated in Col was present in the same genomic location in Ler and Cvi with no major sequences changes except for a few SNPs . These 6 genes were also included among imprinting validation assays ( Figure 1—source data 5 , Figure 2—source data 1 ) . We more closely investigated three allele-specific imprinted genes with strong differences in the ratio of maternal to paternal transcripts in imprinted and non-imprinted crosses: AT2G32370 , AT2G34890 , and AT3G14205 ( Figure 2 , Figure 6 , Figure 6—figure supplement 2 ) . AT2G32370 , HDG3 , was originally identified as a PEG because of its association with a Col and Ler embryo-endosperm DMR and because it was expressed specifically in the endosperm ( Gehring et al . , 2009 ) . Our new data showed that HDG3 is not a PEG when Cvi is the male parent ( Figure 2 , Figure 6 ) . The embryo-endosperm DMR associated with HDG3 is located in a Helitron fragment ( ATREP10D ) 5′ of the gene , and the methylated paternal allele is predominantly expressed . This region is not methylated in Cvi ( Figure 6B , Figure 6—figure supplement 1 ) . In crosses between Cvi females and Col or Ler males , maternal and paternal alleles are differentially methylated and the gene is imprinted ( the naturally hypomethylated Cvi maternal allele has the same methylation profile as an actively demethylated maternal allele ) . But in reciprocal crosses between Col or Ler females and Cvi males , both maternal and paternal alleles are hypomethylated and the gene is biallelically expressed in the expected 2:1 maternal:paternal ratio ( Figure 2 , Figure 6 ) . This suggests that differential methylation of maternal and paternal alleles , rather than simply demethylation of the maternal allele , is required for imprinted expression . A similar logic applies to AT3G14205; the Cvi allele is hypomethylated at a 5′ RC/Helitron fragment ( ATREP1 ) compared to Col and Ler and the gene is not a PEG when it is transmitted through the Cvi male ( Figure 2 , Figure 2—source data 1 , Figure 6—figure supplement 2 ) . AT2G34890 was a MEG except when Ler is the male parent . The Ler AT2G34890 allele is hypomethylated at a 5′ MuDR element in comparison to Cvi and Col , suggesting that loss of methylation of the paternal allele could lead to its transcription ( Figure 6—figure supplement 2 ) . However , consistent with a more ambiguous relationship between DNA demethylation and MEGs ( Figure 5 ) , closer inspection of AT2G34890 shows that at a region 5′ of the TE both the Ler and Cvi alleles are hypomethylated compared to Col , suggesting that a clear methylation distinction between imprinted ( Col and Cvi ) and non-imprinted ( Ler ) paternal alleles does not exist ( Figure 6—figure supplement 2 ) . 10 . 7554/eLife . 03198 . 019Figure 6 . Expression and methylation analysis of HDG3 , an allele-specific imprinted gene . ( A ) HDG3 is a PEG except when Cvi is the paternal parent . Blue bars , % paternal allele expression; red bars , % maternal allele expression from combined mRNA-seq data; vertical line , expected percent paternal allele expression for a non-imprinted gene . ( B ) Methylation of HDG3 5′ flanking region in Col embryo , Ler embryo and endosperm , and Cvi embryo ( additional analysis in Figure 6—figure supplement 1 ) . Red track , CG; green track , CHH . ( C ) Methylation profile of maternal and paternal HDG3 alleles in Col x Cvi and Cvi x Col endosperm as determined by locus-specific bisulfite PCR . Red circles , CG; blue circles , CHG; green circle , CHH . Filled circles indicate methylation , whereas unmethylated positions are unfilled . ( D ) Methylation profile of HDG3 in Col , Ler , Cvi , Kz_9 and An_1 in leaves ( http://neomorph . salk . edu/1001_epigenomes . html ) . ( E ) HDG3 is not imprinted in 6 DAP endosperm when another hypomethylated strain ( Kz_9 ) is the pollen parent , but is a PEG in a cross with another methylated strain ( An_1 ) , as determined by sequencing RT-PCR products that span informative SNPs . Blue bars , % paternal allele expression; red bars , % maternal allele expression; vertical line , expected paternal allele expression for a non-imprinted gene . The number of RT-PCR clones sequenced is indicated . p value represents a binomial test of whether the observed maternal:total ratio is less than the expected 2:3 ratio . ( F ) Cartoon representation of results . Expression and methylation results for AT3G14205 and AT2G34890 are in Figure 6—figure supplement 2 . Examples of genetic differences causing methylation differences are in Figure 6—figure supplement 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 01910 . 7554/eLife . 03198 . 020Figure 6—figure supplement 1 . Methylation analysis of HDG3 . ( A ) Allele-specific CG methylation of maternal and paternal HDG3 alleles in Cvi x Col and Col x Cvi embryos from whole genome BS-seq data . ( B ) Methylation profile of the HDG3 DMR in different strains and tissues and of maternal and paternal HDG3 alleles in Col x Cvi and Cvi x Col embryos as determined by locus-specific bisulfite PCR . Filled circles indicate methylation , whereas unmethylated positions remain unfilled . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 02010 . 7554/eLife . 03198 . 021Figure 6—figure supplement 2 . Expression and methylation analysis of other variably imprinted genes . ( A ) AT3G14205 is a PEG except when Cvi is the paternal parent ( mRNA-seq data ) . Blue bars , % paternal allele expression; red bars , % maternal allele expression from combined mRNA-seq data; vertical line , expected % paternal expression for a non-imprinted gene . ( B ) Methylation of AT3G14205 5′ flanking region in Col embryo , Ler embryo and endosperm , and Cvi embryo . Red track , CG; green track , CHH . ( C ) Leaf methylation profile of AT3G14205 in Col , Ler , Kz_9 , Cvi and Seattle_0 ( http://neomorph . salk . edu/1001_epigenomes . html ) . ( D ) AT3G14205 is not imprinted when another hypomethylated strain ( Seattle_0 ) is the pollen parent , but is a PEG in a cross with another methylated strain ( Kz_9 ) , as determined by sequencing RT-PCR products that span informative SNPs . The number of RT-PCR clones sequenced is indicated . p value , binomial test of whether the observed maternal:total ratio is less than the expected 2:3 ratio . ( E ) AT2G34890 is a MEG except when Ler is the paternal parent ( mRNA-seq data ) . Vertical line , expected maternal allele expression for a non-imprinted gene . ( F ) Methylation of AT2G34890 5′ flanking region in Col and Ler embryo and Cvi embryo and endosperm . ( G ) Leaf methylation profile of AT2G34890 in Col , Cvi , Ler and Es_0 ( http://neomorph . salk . edu/1001_epigenomes . html ) . ( H ) AT2G34890 is still imprinted when another hypomethylated strain ( Es_0 ) is the pollen parent . p value: binomial test of whether the observed maternal:total ratio is greater than the expected 2:3 ratio . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 02110 . 7554/eLife . 03198 . 022Figure 6—figure supplement 3 . Genetic difference between strains can underlie differential methylation . Differences in methylation between strains can be due to genetic differences ( e . g . , absence of a sequence in one strain ) . To uncover possible genetic differences between Col and Cvi strains , we compared the set of Col-Cvi methylation difference positive windows to regions of the Cvi genome not covered by any reads in the 1001 Genomes resequencing project ( http://signal . salk . edu/atg1001/index . php ) . We validated DMRs at one MEG ( AT5G17165 ) and one PEG ( AT1G57820 ) that were polymorphic between the two strains using PCR and sequencing . ( A ) In Cvi , the TE at the 3′ end of AT5G17165 lacks 600 nt , corresponding to a methylated region in Col and Ler . Red track , CG; blue track , CHG; green track , CHH . ( B ) In Cvi , the 86 nt long TE at the 5′ end of AT1G57820 has 9 SNPs , and the upstream and downstream intergenic DNA sequences have insertions and deletions compared to Col . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 022 Allele-specific imprinted genes could represent epialleles whose expression phenotypes are observed in the endosperm . To assess how widespread allele-specific imprinting might be within the species , we determined the strain variability in methylation at regions we identified as being targeted for endosperm demethylation , using the methylation profiling data of leaves or floral buds from 140 strains ( Schmitz et al . , 2013 ) . This allowed us to more broadly estimate the potential for allele-specific imprinting outside of Col-Ler-Cvi strains . We divided regions corresponding to embryo-endosperm DMRs into five classes based on the range of methylation variability across all 140 strains: those with very low variability in methylation ( less than 0 . 2 mean methylation difference across all strains ) , low variability ( between 0 . 2 and 0 . 4 mean methylation difference across all strains ) , a strongly bimodal class ( DMRs with a range greater than 0 . 4 but where most of the density of the distribution in the outer 50% of the range ) and two intermediate classes , weakly bimodal and not bimodal ( Figure 7A , B ) . DMRs with a high or intermediate methylation range and less strong clustering in the outer 50% of the distribution were considered weakly bimodal . All remaining DMRs were classified as ‘not bimodal’—these regions tend to have a more uniform or unimodal distribution across a large range of scores ( Figure 7A , B ) . The majority of DMRs ( 69% ) were consistently methylated across all strains examined , falling into the very low range , low range , or not bimodal categories ( Figure 7A , B ) . However , about 16% of DMRs were in the strongly bimodal category , where methylation is consistently high or low across strains except for one or more strong outliers ( Figure 7A , B ) . 10 . 7554/eLife . 03198 . 034Figure 7 . Natural epigenetic variability across strains at embryo-endosperm CG DMRs . ( A ) Methylation variability across strains for regions targeted for endosperm demethylation . Classification of methylation range in the Schmitz et al . , 2013 dataset ( total of 140 strains ) for all all embryo-endosperm CG DMRs ( n = 10 , 370 ) identified in this study . Only DMRs with at least 5 CG sites and a minimum of five reads coverage at each site in the Schmitz et al . dataset were classified . Additionally , only DMRs with at least 70 strains with sufficient data were included . ( B ) Examples of low range ( gray ) , not bimodal ( blue ) , weak bimodal ( orange ) and strongly bimodal ( red ) DMRs . ( C ) Association of the classified CG endosperm-embryo DMRs with PEGs . This study: DMRs identified from all pairwise matched endosperm-embryo comparisons from bisulfite datasets in Figure 3—source data 1; Ibarra et al . , 2012: ColxLer and LerxCol DMRs combined . Allele-specific PEGs are listed around the pie chart . n/a = not classifiable because gene was associated with DMR of more than one type . DOI: http://dx . doi . org/10 . 7554/eLife . 03198 . 034 We overlapped our union set of imprinted genes with the classified DMRs . 21 MEGs and 9 PEGs were associated with a very low or low variability DMR within the gene or two kb 5′ or 3′ . These include the MEG FWA , a locus with high levels of promoter methylation in all strains . We thus expect that in all strains in the endosperm FWA will have a demethylated maternal allele and a highly methylated paternal allele , and be consistently imprinted within the species . In contrast , 27 MEGs and 17 PEGs ( 11% of all imprinted genes ) were associated with strongly bimodal DMRs , including four PEGs we identified from our Col , Ler , and Cvi mRNA-seq and BS-seq data as exhibiting allele-specific imprinting and being associated with a shared embryo-endosperm and strain DMR ( Figure 7C , Figure 2—source data 1 ) . This includes the DMRs associated with the allele-specific imprinted genes HDG3 and AT3G14205 ( Figure 7C ) . We tested whether the methylation status of the embryo-endosperm DMRs associated with allele-specific imprinted genes was predictive for imprinting . If the difference in imprinting among HDG3 alleles was due to the cis epigenetic difference at the 5′ TE , we predicted that crosses with other male parents carrying naturally hypomethylated alleles would exhibit lack of imprinting in F1 endosperm . The strain Kz_9 has reduced methylation at the 5′ TE , although it is more methylated than in Cvi ( Figure 6D ) . We performed reciprocal crosses between Col and Kz_9 , extracted the RNA from endosperm 6 DAP and performed RT-PCR around a Col/Kz_9 SNP . Cloning and sequencing of the PCR products confirmed that HDG3 was a PEG when Col was the male parent . In the reciprocal cross , when the hypomethylated HDG3 allele was inherited from the Kz_9 male , HDG3 was not imprinted ( Figure 6E , F ) . HDG3 remains imprinted in crosses between Col and another strain with methylation , An_1 ( Figure 6D–F ) . This suggests that the epigenetic state of the HDG3 allele is sufficient to predict imprinting in other naturally occurring strains and is thus likely causal for lack of imprinting in Cvi . Similarly , the DMR variability analysis ( Figure 7 ) identified another strain , Seattle_0 , where the TE 5′ of AT3G14205 was hypomethylated . Similar to crosses with Cvi males , in crosses where Seattle_0 was the male parent , AT3G14205 was no longer a PEG ( Figure 6—figure supplement 2 ) , but was still a PEG when Seattle_0 was the female and Col the male . As expected for AT2G34890 , where the methylation state of the 5′ region was variable but did not seem to correlate with whether the allele was imprinted or not among Col , Ler , and Cvi , we found that AT2G34890 is a MEG in crosses with Col regardless of whether Es_0 , a strain with a hypomethylated allele like Ler , served as the female or the male parent ( Figure 6—figure supplement 2 ) . As we have shown for HDG3 and AT3G14205 , crosses involving a methylated allele and a naturally hypomethylated allele can result in allele-specific imprinting in one direction of the cross . Thus , in addition to the allele-specific imprinted genes we identified based on our mRNA-seq data , the additional MEGs and PEGs associated with embryo-endosperm DMRs that are strongly bimodal for methylation within the population might not be imprinted in strains where the allele is naturally hypomethylated . This suggests that 11% of imprinted genes have the potential to be variably imprinted when a particular strain is the male or female parent . By examining crosses among three of the thousands of Arabidopsis strains , we were able to identify genes imprinted among all crosses and a small number of strong candidates for allele-specific imprinting caused by a cis methylation difference at a proximal transposable element . We found that most imprinted genes identified in reciprocal crosses between two strains are imprinted in crosses between other strains , or have some evidence for parental bias even if all imprinting criteria were not met . Arabidopsis allele-specific imprinted genes represent a small fraction of all possible imprinted genes , approximately 6% in our mRNA-seq datasets . These results are consistent with the extent of allele-specific imprinting in maize , where around 12% of imprinted genes fall into this category ( Waters et al . , 2013 ) . Allele-specific imprinting could represent genes that have recently come under the control of a TE and may or may not have an endosperm function , or genes that are imprinted specifically in one strain and not the other due to differences in control over endosperm growth and development . Under the conflict theory ( Haig , 2013 ) , differences in seed size could reflect strains that have reached different optima for imprinting—strains producing small seeds could be considered more maternalized and those producing larger seeds more paternalized . Arabidopsis allele-specific imprinted genes encode multiple types of proteins , including a putative transcription factor ( HDG3 ) , a heat shock protein , a phosphoinositide phosphatase ( AT3G14205 ) , three chromatin proteins , and a gene required for normal levels of phytic acid accumulation in seeds ( Kim and Tai , 2010 ) . Further experimentation exploring the function of these genes during seed development in each genetic background will be required to determine if differences in imprinting among strains contribute to seed phenotypes . By considering population methylation variation at regions demethylated during reproductive development , we were able to estimate the possible extent of allele-specific imprinting within the species . Of the set of 388 imprinted genes we identified , 11% were associated with embryo-endosperm DMRs that are variably methylated in vegetative tissues in at least one of 140 strains ( the strongly bimodal DMRs ) . Based on their methylation patterns , these genes have the potential to act as epialleles and exhibit allele-specific imprinting , as we demonstrated for HDG3 and AT3G14205 . However , we caution that imprinted genes associated with a strongly bimodal DMR do not always exhibit allele-specific imprinting when alleles with different methylation patterns are combined in F1 endosperm . For example , we originally identified AT4G23110 as a candidate imprinted gene because it was associated with an embryo-endosperm DMR and only expressed in seeds ( Gehring et al . , 2009 ) , which was later confirmed by mRNA-seq ( Gehring et al . , 2011 ) . In this study we find that the embryo-endosperm DMR in the 5′ region of the gene is strongly bimodal within the population , with 118 strains exhibiting between 60–100% CG methylation in a 500 bp 5′ region , and 10 strains with no methylation . However , when we performed reciprocal crosses between one of the hypomethylated strains and Col , a methylated strain , imprinting was maintained in both directions of the cross . Thus , we expect that 11% is the maximal fraction of genes that would exhibit allele-specific imprinting due to strain-to-strain differences in methylation , with the actual number being less than this . The predictive power for whether a gene will exhibit allele-specific imprinting is greater for PEGs than for MEGs . PEGs were more often associated with embryo-endosperm DMRs than MEGs , and showed stronger correlation with the presence of a TE , and the presence of H3K27me3 in vegetative tissues ( Figure 5—figure supplement 1; Lafos et al . , 2011 ) . Our data and that of many others ( Köhler et al . , 2012; Gehring , 2013; Zhang et al . , 2014 ) suggest that the mechanism of imprinting for PEGs in Arabidopsis and maize is that the methylated paternal allele is expressed while the hypomethylated maternal allele is silenced by the PRC2 complex . Thus , the imprinting state of PEGs is much easier to predict based on known genetic and epigenetic characteristics . MEGs represent a more diverse class of genes , and their imprinting status is more difficult to predict based on DNA methylation or TE presence , although we do find that MEGs are enriched for small RNAs in seeds . The RC/Helitron class of TEs , the most abundant type in euchromatin , is prevalent among DMRs . RC/Helitrons are also one of the most variably methylated TEs , with more than half being poorly or unmethylated in Col ( Ahmed et al . , 2011 ) . The susceptibility of Helitrons to loss of methylation coupled with their overabundance among embryo-endosperm DMRs further suggests that imprinting could be highly dynamic if considered on a population scale . McClintock described the ability of TEs to cycle in nature and between active and inactive epigenetic states ( McClintock , 1965; Slotkin and Martienssen , 2007 ) . A conceptually similar phenomenon could cause imprinting to vary between closely related genotypes . Our study also yielded several unanticipated findings . We discovered that the Cvi strain is globally hypomethylated , and primarily loses CG methylation in gene bodies . Despite its ubiquity , the function of CG gene body methylation in plants and animals is still unclear , although it may be important for regulating alternative splicing or preventing spurious transcription ( Zilberman et al . , 2007; Shukla et al . , 2011 ) . Mutations that reduce CG gene body methylation ( e . g . , mutations in the maintenance DNA methyltransferase ) affect CG methylation in all genomic contexts . Because Cvi has little gene body methylation but nearly normal levels of CG methylation in other genomic features , Cvi could serve as a model genotype to further explore the function of gene body methylation in relation to gene expression or chromatin structure . Cytologically , Cvi has reduced heterochromatin and dispersed 45S rDNA repeats and DNA methylation compared to Col and Ler ( Tessadori et al . , 2007 , 2009 ) . Decondensed chromatin might facilitate increased production of small RNAs ( Schoft et al . , 2009 ) , and Cvi x Col endosperm showed little reduction in CHH methylation compared to the embryo . Consistent with this , small RNAs produced from the maternal allele represented a greater fraction of small RNAs when Cvi was the female parent in a cross ( Figure 4—source data 1 ) . We also observed , in contrast to a previous study ( Mosher et al . , 2009 ) , that small RNAs derived from the paternally inherited genome were readily detected in seed tissue from all crosses . As expected , maternal small RNAs were much more abundant than paternal small RNAs , but because we could not distinguish the three components of the seed ( embryo , endosperm , and seed coat ) , it is unknown whether this was due to predominantly maternal expression in the endosperm , or reflective of the overall maternal bias expected in whole seeds . However , the fraction of paternal small RNAs ( 6–24% ) was comparable to the fraction of paternal reads when mRNAs were sequenced from whole seeds ( 15–20% ) ( Figure 1—source data 1 , Figure 4—source data 1 ) , perhaps suggesting that small RNAs in seeds reflect the ratio of maternal:paternal genomes in each tissue . Our analysis of the previously published data from Col x Ler and Ler x Col siliques ( Mosher et al . , 2009 ) also revealed the presence of paternally derived small RNAs , although at lower levels than in our whole seed datasets ( Figure 4—source data 1 ) . Recent analysis of small RNAs in rice inter-strain crosses detected maternally and paternally derived small RNAs in the endosperm and suggested rice might be different from A . thaliana in this regard ( Rodrigues et al . , 2013 ) . Our data instead suggest that rice and Arabidopsis are likely similar in terms of parental small RNA composition in the endosperm , although endosperm-specific Arabidopsis profiles will be necessary to conclusively evaluate this . Interestingly , at 6 DAP the largest subunit of Pol IV , NRPD1a , was paternally biased in all crosses . NRPD1a has a 5′ embryo-endosperm DMR associated with small RNAs , further suggesting that there may be a complex interplay and crosstalk between active DNA demethylation and the small RNA production pathway . Other genes that promote DNA methylation were also primarily paternally expressed in all crosses ( e . g . , the VIM family genes ) . A major unsolved question is how the maternal alleles of genes that are demethylated before fertilization remain hypomethylated several days after fertilization , in the spite of the presence of small RNAs that normally target DNA methylation to these sequences . It is possible that changes to maternal endosperm chromatin structure could alter the efficacy of RNA-directed DNA methylation . In conclusion , our study demonstrates that epiallelic variation and genomic imprinting mechanisms intersect to produce novel gene expression patterns in seeds . We propose that the phenotypic impact of epialleles is likely to be most pronounced in the endosperm because changes in DNA methylation are a programmed part of endosperm development . Epialleles naturally circulating in plant populations might significantly impact seed development and lead to the production of novel phenotypes in hybrids . Plants were grown in a greenhouse with 16-hr days at ∼21 C . Flowers were emasculated 2 days before pollination . Seeds were dissected at 6 DAP , which corresponds to the torpedo stage of embryogenesis under our growth conditions ( Figure 1—figure supplement 1 ) . RNA was isolated from endosperm , embryo , and whole seeds 6 days after pollination as described ( Gehring et al . , 2011 ) using either the RNAqueous kit with Plant RNA Isolation Aid or the RNAqueous Micro Kit ( Ambion , Life Technologies Corporation , Carlsbad , CA ) . At least 600 ng of DNAse I-treated RNA ( Invitrogen , Life Technologies Corporation , Carlsbad , CA ) was used to prepare mRNA-Seq libraries as described ( Gehring et al . , 2011 ) , except that Illumina TruSeq primers were used in the final amplification step . Amplification was for 12 cycles . Strand-specific RNA-seq libraries were generated from at least 100 ng of total RNA using the Integenex PolyA prep protocol ( Wafergen Biosystems , Fremont , CA ) with 15 cycles of amplification . See Figure 1—source data 1 for details of library prep for specific samples . Endosperm mRNA-seq was performed in triplicate ( 18 samples ) . Because we previously found little evidence for imprinting in embryos at this stage of development ( Gehring et al . , 2011 ) , embryo mRNA-seq was performed on single samples ( Figure 1—source data 1 ) . Single-end sequencing of mRNA-Seq libraries was performed on an Illumina HiSeq machine . Read length was 40 bp or 80 bp ( Figure 1—source data 1 ) . Sequencing quality was assessed using fastqc , and raw reads were filtered for overrepresented adapter sequences using fastx_clipper . Low quality reads were removed with fastq_quality_filter using the options –q 20 and –p 80 ( http://hannonlab . cshl . edu/fastx_toolkit/ ) . Filtered reads were then aligned to the TAIR10 version of the Arabidopsis genome using Tophat v2 . 0 . 8 ( Trapnell et al . , 2009 ) . For 40 bp libraries the options -solexa1 . 3-quals --segment-length 18 --segment-mismatches 1 --max-segment-intron 11 , 000 were used and for 80 bp libraries the options were --solexa1 . 3-quals --segment-length 30 --max-segment-intron 11 , 000 . Reads counts for each gene and TE annotated in TAIR10 were quantified using htseq-count ( http://www-huber . embl . de/users/anders/HTSeq/doc/index . html ) with options –m intersection-strict –-stranded = no ( for non-strand-specific libraries ) or –stranded = yes ( for strand-specific libraries ) . After sorting reads by genome position , single nucleotide polymorphisms ( SNPs ) ( Supplementary file 2 ) were used to classify reads by strain using a custom script ( Supplementary file 3 ) . Reads were discarded if classification at two SNP positions within the same read conflicted . Htseq-count with the same options was run on each allele-specific set of mapped reads to generate allele counts for each gene and TE . Libraries ranged in depth from 50 to 160 million high-quality reads per sample , of which approximately 4–10 million reads could be assigned to a specific parental allele ( informative reads ) . The highest proportion of informative reads was in crosses between Ler and Cvi , which have the most SNPs ( Figure 1—source data 1 ) . We slightly modified our previously published analysis method ( Gehring et al . , 2011 ) to identify imprinted genes . We used Fisher's exact test on each set of reciprocal crosses to test the null hypothesis that p1 = 2p2 = 0 . 67 ( p1 = portion of strain A reads in A female × B male and p2 = portion of strain A reads in B female × A male ) for endosperm or p1 = 2p2 = 0 . 5 for embryo . We considered genes with a Benjamini corrected p value less than 0 . 01 . We further filtered the list by removing genes with an imprinting factor ( Gehring et al . , 2011 ) less than 2 and by removing genes that were more than twofold higher expressed in the seed coat than embryo or endosperm at the linear cotyledon stage , using data from Belmonte et al . ( 2013 ) . To obtain values from the Belmonte et al . data , for endosperm we averaged the RMA values from the MCE , PEN , and CZE samples and for seed coat we averaged values from the CZSC and SC , all from the linear cotyledon stage . Averaged values were log transformed and genes with a seed coat-endosperm or seed coat-embryo differences <1 retained . Finally , for maternally biased genes we required that at least 85% of informative reads were maternal in both directions of the reciprocal cross and for paternally biased genes that at least 50% of informative reads in both directions of the reciprocal cross were paternal . For embryo libraries the final filtering step required at least 70% of reads to be maternal in both directions of the cross for maternally biased genes and less than 30% maternal for paternally biased genes . For each set of endosperm reciprocal crosses we sequenced mRNA from three biological replicates . For a gene to be called imprinted in a particular set of endosperm crosses ( Col-Ler , Ler-Cvi , or Col-Cvi ) , it had to be called imprinted in 2 of 3 reciprocal cross pairs of biological replicates . We chose two of three instead of three of three because of variation in sequencing depth among libraries . We calculated the ratio of ( maternal reads ( strain A × strain B ) + 1 ) / ( paternal reads ( strain A × strain B ) + 1 ) and plotted it against the ratio of ( maternal reads ( strain B × strain A ) + 1 ) / ( paternal reads ( strain B × strain A ) + 1 ) for all loci with >0 informative reads and seed coat-endosperm expression <1 ( Belmonte et al . , 2013 ) . The Euclidean distance of every point to the lines x = 2 and y = 2 ( no imprinting ) was calculated using MATLAB . Loci within the distance of 1 to each line were retained for further analysis . For each locus we also calculated a parental bias factor , b ( g ) ( b ( g ) = log2 ( maternal reads + 1 ) − log2 ( 2 × paternal reads + 1 ) ) and a normalized parental bias factor bnorm ( g ) = b ( g ) – mean ( b ( g ) ) /stdev ( b ( g ) ) . For non-imprinted genes the value of b ( g ) = 0 and bnorm ( g ) = 0 . Loci within 1 of x = 2 or y = 2 that also fell above the 95th ( maternal bias ) or below the 5th ( paternal bias ) percentile distribution of bnorm ( g ) values exhibited parental bias in only one set of reciprocal crosses . Loci that showed maternal or paternal preference when a particular strain was the parent were determined by intersecting lists from each cross ( e . g . , intersect Col-Ler maternal biases with Col-Cvi maternal biases to generate a list of loci that are maternally biased when Col is the female parent ) . To generate the final list of genes that exhibit potential allele-specific imprinting , the loci described above were intersected with the list of imprinted loci from the reciprocal crosses not involving the strain exhibiting the bias ( e . g . , intersection of loci maternally biased when Col is the male parent but not when it is the female parent with MEGs in Ler-Cvi reciprocal crosses ) . Validation of the global RNA-seq analysis was by amplicon sequencing of RT-PCR products using miSeq or Sanger sequencing , or by CAPs digestion . RNA was collected from dissected embryo and endosperm , reverse transcribed , treated with DnaseI , and amplified with ExTaq for 30 cycles ( PCR primers in Supplementary file 1 ) . For miSeq analysis , amplicons from the same cross and tissue type were pooled and libraries were constructed using the Illumina NexteraXT kit ( Illumina , Inc . , San Diego , CA ) . Paired-end sequencing of RT-PCR amplicons was performed on an Illumina MiSeq machine , generating 150 bp reads . Raw reads were aligned to a pseudometagenome ( see small RNA methods ) using Tophat v2 . 0 . 8 alignment options –read-mismatches 10 –edit-distance 12 . After sorting reads by genome position , SNPs ( Supplementary file 2 ) were used to classify reads by strain just like for mRNA-seq . A goodness-of-fit binomial exact test was used to test the null hypothesis that the observed fraction of reads derived from the maternal allele for each assayed locus was described well by the binomial distribution parameterized by p = the observed fraction maternal reads from the combined counts of the RNA-seq libraries analyzed in this study . GO analysis was performed using the DAVID bioinformatics resource version 6 . 7 ( Huang et al . , 2009a , 2009b ) . Reported p-values are corrected using the Benjamini method . Whole genome bisulfite sequencing library construction and data analysis are described in detail at Bio-protocol ( Pignatta et al . , 2015 ) . Seeds from 30 to 40 siliques per sample were dissected and DNA was extracted as described ( Gehring et al . , 2009 ) . At least 1 μg of RNAse-treated DNA was used for library preparation ( Figure 2—source data 1 ) . Libraries were made using an Illumina TruSeq kit ( Illumina , Inc . ) , with the following modifications . DNA was sheared using a Covaris instrument ( settings: peak power 175 W , duty factor 10 , cycles/burst 200 , time 6 min , 6 C ) , purified using Agencourt AMPure beads ( 1 . 4× DNA:beads ) ( Beckman Coulter , Inc . , Brea , CA ) , and resuspended in 50 μl of water followed by end repair and 3′ end adenylation . Illumina TruSeq DNA adapters , which contain 5-methylcytosines instead of cytosines , were ligated in a 50-μl overnight reaction at 16°C with 2 . 5 μl adapters , 5000 units T4 DNA Ligase ( New England BioLabs , Ipswich , MA ) , and 1 × T4 Ligase buffer with ATP . DNA was cleaned twice using Agencourt AMPure beads before bisulfite treatment with the MethylCode Bisulfite Conversion Kit as per manual instructions ( Invitrogen , Life Technologies Corporation ) . Bisulfite-treated DNA was eluted in 10 μl . Three μl were used as a template in each of two PCR reactions with 0 . 5 units Pfu Turbo , Cx Hotstart DNA Polymerase ( New England BioLabs ) , 1 μl 10 mM dNTPs , and 1 × Turbo Cx buffer . PCR conditions were: 95°C for 2 min , 12–15 cycles ( 95°C for 20 s , 60°C for 30 s , 72°C for 1 min ) , and 72°C for 7 min . Libraries were subjected to QC on a bioanalyzer before sequencing on a Illumina HiSeq2000 using a single read 80 base pair protocol except for one library which was sequenced using 2 × 100 paired end reads . Adapters and low quality reads ( less than 75% quality scores above 25 ) were discarded after running quality control of sequencing reads with fastqc ( http://hannonlab . cshl . edu/fastx_toolkit/ ) . Libraries prepared from Col and Cvi were aligned to TAIR10 genome using Bismark ( Krueger and Andrews , 2011 ) with the following parameters: -n 1 -l 50 , where n is the maximum number of mismatches and -l the length of seed ( first number of nt that are mapped with less than n mismatches ) . For Cvi , reads that failed to map to TAIR10 were then mapped against the Cvi pseudogenome ( TAIR10 genome with Cvi SNP substitutions ) . All mapped reads were combined . Libraries prepared from Ler were aligned to the Ler-0 genome ( Gan et al . , 2011 ) . After mapping , a 2-strain alignment was used to convert Ler-0 genome coordinates to TAIR10 coordinates , allowing subsequent pairwise comparisons between libraries . Redundant mapped reads were eliminated from each library starting from a sorted SAM file , keeping only one sequence per strand that mapped to the same position . To do this , reads were sorted by decreasing prevalence followed by increasing number of mismatches ( not counting bisulfite conversions ) to the genome . The most prevalent read with the total highest quality string was kept . In the case of a tie , the read with the fewest number of mismatches was retained . Bismark's methylation extractor script was used to calculate a methylation value for each cytosine . For F1 hybrid libraries , we first mapped the reads to one of the parental genomes ( Col-Cvi reads to TAIR10 and Ler-Cvi reads to Ler ) . Reads that failed to map to either the TAIR10 or Ler genomes were mapped against the Cvi pseudogenome using the same alignment parameters ( -n 1 -l 50 ) . We discarded redundant reads and combined the remaining Cvi-mapped reads to the ones already mapped against Col-0 ( TAIR10 ) or Ler . To assign reads to a particular strain and to retain as many unique reads as possible , we separated the reads by strand and ignored C>T SNPs for forward reads and G>A SNPs for reverse reads . Reads were classified as maternal , paternal , no evidence for either genome ( for reads not overlapping any SNP ) , or both ( conflicting data ) based on their sequence at known SNP positions . After classification , redundant reads from each class were eliminated as described above , and methylation extractor was run for each class as well as for all reads combined . The mean bisulfite conversion rate for each library was calculated based on the methylation status of each cytosine from reads mapping to the chloroplast genome , which are expected to be unmethylated . Bismark's methylation extractor output was summarized by chromosome position by converting the methylation string into ummethylated counts , methylated counts , and percent methylation . The genome was divided into 300 nt windows that overlapped by 100 nt . Using Bismark's methylation extractor output files as input , weighted methylation levels for each window were calculated as described ( Schultz et al . , 2012 ) , with the requirement of at least 5-read coverage at each site . Differential methylation was assayed by calculating the difference ( sample 1 − sample 2 of weighted methylation fractions ) and confidence ( p-value from Fisher's exact test ) for each window in all sequence contexts . p values were corrected with the Benjamini and Hochberg False discovery rate ( FDR ) . We defined CG and CHG DMRs as windows with a weighted methylation difference of at least 35 , with a minimum overlap of three informative Cs between windows and a corrected p value<0 . 01 . CHH DMRs had a weighted methylation difference of at least 10 , with a minimum 10 overlapping informative cytosines and a p value<0 . 01 . To compare methylation levels between strains , methylation fractions of sites in genes and TEs were scaled relative to the mean level of the Col strain in that context . Following analysis by ANOVA , the magnitudes of differences in the normalized methylation levels between strains were calculated using Tukey's HSD test , with alpha set at 0 . 05 ( Figure 3—source data 2 ) . To visualize methylation profiles around imprinted genes , the methylation level of each assayed position ( with at least five informative reads ) was summarized into a set of bed files and used as input to calculate the average methylation in 200 nt windows spanning extended gene bodies ( from 2 kb upstream of the transcription start to 2 kb downstream of the transcription end ) of the conserved imprinted genes . We also compared Col , Ler , and Cvi embryo CG average methylation using 50 nt windows spanning extended bodies of genes and TEs . Using a set of genome features of a specific type ( e . g . , list of genes , TEs ) , we summarized the methylation level across that set of features . We calculated the weighted mean methylation ( wmean ) for a particular region ( R ) containing n sites ( i ) with methylation data ( me_i ) with the equation: wmean ( R ) = Σ[i = 1 to n] ( me_i/tot_i ) where tot_i is the total number of reads . The results of these analyses were displayed using R ( Figure 5 ) . Plots for Figure 3 and Figure 3—figure supplement 1 were also generated using R . DNA from embryo , endosperm and leaves was extracted using a CTAB protocol . Bisulfite treatment was performed using the MethylCode Bisulfite Conversion Kit ( Invitrogen , Life Technologies Corporation ) following the manufacturer's protocols . PCRs were performed using the primer pairs listed in Supplementary file 1 . PCR products were gel purified , cloned and sequenced . Sequences were aligned using SeqMan and methylation was measured using Kismeth ( Gruntman et al . , 2008 ) . For validation using McrBC , approximately 800 ng of genomic DNA were digested overnight at 37°C with 50 U of McrBC ( New England Biolabs ) in a 30 μl reaction . 5 μl were used as template in PCR along with an untreated DNA sample . Primer pairs are listed in Supplementary file 1 . To determine how variably the identified embryo-endosperm DMRs were methylated across various wild-type strains , we downloaded BS-seq data from NCBI GEO accession GSE43857 ( Schmitz et al . , 2013 ) . For strains with both leaf and bud data , only the leaf data was used , resulting in data for 140 different Arabidopsis strains . Methylation data were extracted from the Schmitz et al . data for all of our embryo-endosperm DMRs; contiguous or overlapping DMRs were merged prior to the analysis . For each DMR we assigned a methylation score equal to the weighted mean methylation at each CpG site in the DMR ( Schultz et al . , 2012 ) for each of the 140 strains . Only CpG sites with at least five reads of support and only strains with five or more CpG sites for a DMR were included in the analysis . DMRs were censored from the analysis if more than 70 of the 140 strains had missing scores . The remaining DMRs ( n = 10 , 370 ) were classified into five categories that describe the distribution of methylation across the Schmitz et al . strains . DMRs with a low range of methylation scores , defined as a difference of less than 0 . 4 between the scores of the most and least methylated strains , were considered to have roughly consistent methylation patterns across all strains . This was further divided into a ‘very low range’ subcategory consisting of all DMRs with a range of methylation scores less than 0 . 2 across all strains . These DMRs have very consistent methylation scores across all strains , and tend to correspond to highly methylated transposable elements or regions near centromeres . DMRs with a score range greater than 0 . 4 were further subdivided according to whether the strain scores tended to be unimodal or uniformly distributed across the range of the data , or whether they tended to be bimodally distributed . This was determined by counting the number of strains whose methylation scores fell in the middle 50% of the data range , and comparing this to the number of strains whose methylation scores fell in the top or bottom 25% of the range . DMRs with a high range but with most of the density of the distribution in outer 50% of the range of the data were considered ‘strongly bimodal’ ( range >0 . 7 and fraction strains in upper or lower 25% of the range ≥0 . 8 ) . These DMRs include cases where methylation is consistently high or low across strains except for one or two strong outliers . DMRs with high or intermediate range and less strong clustering in the outer 50% of the distribution were considered ‘weakly bimodal’ ( range >0 . 7 and fraction in outer 50% ≥0 . 5 or 0 . 7 ≥range ≥0 . 4 and fraction in outer 50% ≥0 . 8 ) . All remaining DMRs were classified as ‘not bimodal’—these tend to have a more uniform or unimodal distribution across a large range of scores . RNAs less than 200 bp were isolated from whole seeds 6 DAP using the miRVana RNA isolation kit ( Ambion , Life Technologies Corporation ) . Libraries for Illumina sequencing were constructed following the method of Grimson et al . ( 2008 ) , with only minor modifications . Instead of 32P-labelled oligos , 18 nt and 30 nt unlabeled marker RNAs were used in conjunction with SYBR-Gold for size selection and monitoring of ligation reactions . Marker RNAs were kept in separate lanes on the polyacrylamide-urea gels instead of being directly mixed with the RNA samples , but were processed in an identical manner . We enabled multiplexing of libraries by using four different 3′ PCR primers ( Supplementary file 1 ) during library amplification , each of which was 94 bases in length as opposed to the 44 nt 3′ PCR primer from the referenced protocol . As a result , the sequences obtained in the final gel purification step ranged in size from 135 to 155 nt in length . Single-end sequencing of sRNA libraries was performed on an Illumina HiSeq machine ( four libraries of 40 bases per lane ) . We trimmed low-quality read ends ( with fastq_quality_trimmer –t 20 and –l 25 ) and removed adapters ( fastx_clipper tool –a TCGTATGCCGTCTTCTGCTTG –i 18; http://hannonlab . cshl . edu/fastx_toolkit/ ) . Reads were aligned using Bowtie 1 . 0 . 0 ( Langmead et al . , 2009 ) using the parameters –v 2 and --best , such that up to two mismatches were allowed and any read mapping to multiple locations was randomly assigned to one of the locations that had the best match to the read . We used the resequenced Ler genome ( Gan et al . , 2011 ) , and TAIR10 for the Col genome . We constructed a Cvi pseudogenome in which Cvi SNPs and 1 bp indels ( obtained from http://signal . salk . edu/atg1001/download . php ) were used to modify the TAIR10 genome at the appropriate positions . To facilitate unbiased mapping , sRNA reads from hybrids were aligned to a metagenome composed of the two parental genomes . The reads were then converted to TAIR10 coordinates , regardless of the parent strain of origin , and were classified using the same SNP classification approach described for the mRNA-Seq analysis . All reads that overlapped annotated tRNAs , snRNAs , rRNAs , or snoRNAs were removed . We normalized libraries by converting read values within windows into RPM ( reads per million ) values . The conversion from reads to RPM used the total number of reads aligning to the genome for each library following the subtraction of structural RNAs . To generate a Col/Ler SNP list , we used the previously described Col/Ler SNP list ( Gehring et al . , 2011 ) supplemented with novel SNPs having an unambiguous consensus base ( A , C , T , G ) , PHRED ≥ 25 , detection score = 1 from the resquenced Ler genome ( Gan et al . , 2011 ) ( http://mus . well . ox . ac . uk/19genomes/variants . SDI/ ) . In total , this yielded a list of 384 , 612 SNPs . To generate an initial Col/Cvi SNP list , we downloaded the SALK Cvi_0 ( accession CS28198 ) data from http://signal . salk . edu/atg1001/download . php . From the quality_variant_filtered_Cvi_0 . txt file , we removed SNPs with <0 . 95 or less concordance ( n = 66 , 272 ) as well as 1 bp deletions ( n = 11 , 444 ) . This yielded 579 , 310 remaining SNPs . We derived an initial Ler/Cvi SNP list using the following logic: ( 1 ) if the SNP was present in Col/Cvi list but not Col/Ler list , the Col/Cvi SNP was added to the Ler/Cvi list; ( 2 ) if the opposite scenario was true ( present in Col/Ler but absent from Col/Cvi ) , the Col/Ler SNP was inverted ( e . g . , C>T becomes T>C ) and added to the Ler/Cvi SNP list; ( 3 ) if the same SNP was found in both Col/Ler and Col/Cvi SNP lists , it was not added to the Ler/Cvi list since these SNPs arise from a difference between Col and both Ler and Cvi genomes . This yielded a list of 645 , 212 SNPs . In order to assess the rates of erroneous read classification using these SNP lists , we used them to classify reads of known origin from mRNA-seq and small RNA-seq libraries made from Col x Col , Ler x Ler , and Cvi x Cvi embryo and endosperm or whole seeds . SNPs that systematically misclassified reads were filtered out by using a one-tailed binomial hypothesis test with the null hypothesis that ‘good SNPs’ have an underlying acceptable error rate of ≤5% ( H_0: Percent_misclassified ≤ 5% ) . SNPs with p<0 . 05 were removed . This filtration method removed 5869 and 24 , 137 SNPs from the Col/Ler and Col/Cvi SNP lists respectively , after which we regenerated the Ler/Cvi SNP list to create the final lists of 378 , 743 Col/Ler SNPs , 555 , 801 Col/Cvi SNPs , and 619 , 477 Ler/Cvi SNPs ( Supplementary file 2 ) . Data is deposited under GEO accession number GSE52814 and is also available from the Dryad Digital Repository: http://dx . doi . org/10 . 5061/dryad . gv536 .	When animals or plants reproduce sexually , the DNA in a sperm or pollen is combined with that in an egg cell to generate an offspring that inherits two copies of each gene , one from each parent . For a very small number of genes , the copy from one of the parents is consistently turned off . This process—called imprinting—means that the same gene can have different effects depending on if it is inherited from the mother or the father . In plants , imprinting is vital for the production of seeds and typically occurs in the endosperm: the tissue within a seed that provides nourishment to the plant embryo . One way genes can be imprinted is by adding small chemical marks—called methyl groups—on to the DNA that makes up the gene or nearby sequences . These marks can either switch on , or switch off , the expression of the gene . DNA methylation also immobilises stretches of DNA called transposable elements , stopping them from moving from one location to another in the genome . These stretches of DNA are identified and targeted for methylation by small molecules of RNA that match their DNA sequences . Genes that are imprinted in the endosperm of the model plant Arabidopsis are often associated with transposable elements , which can be methylated differently in the naturally occurring varieties , or strains , of Arabidopsis . However it is unclear how many genes are differently imprinted between these different strains . Pignatta et al . looked for differences in gene imprinting , DNA methylation and small RNA production in the seeds , embryos and endosperm tissue from three strains of Arabidopsis . They also examined seeds from crosses between these three strains . While most genes had the same imprinting pattern in all strains and crosses examined , 12 genes were imprinted differently depending on whether they were inherited from the male or female of a given strain . For example , for some genes the copy inherited from the male parent is always turned off , unless it is inherited via the pollen of one specific Arabidopsis strain . Half of this variation could be explained by a transposable element near to each gene that was methylated differently among the strains . By comparing the differentially methylated regions in the genomes of 140 Arabidopsis strains , Pignatta et al . found that differences in methylation may affect 11% of imprinted genes—and went on to confirm variable imprinting in some Arabidopsis strains based on the presence or absence of DNA methylation . Future work is needed to understand how variation in gene imprinting might affect the traits of hybrid seeds , and how it might affect the evolution of new traits in hybrid plants .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "plant", "biology", "genetics", "and", "genomics" ]	2014	Natural epigenetic polymorphisms lead to intraspecific variation in Arabidopsis gene imprinting
Adjunctive dexamethasone reduces mortality from tuberculous meningitis ( TBM ) but not disability , which is associated with brain infarction . We hypothesised that aspirin prevents TBM-related brain infarction through its anti-thrombotic , anti-inflammatory , and pro-resolution properties . We conducted a randomised controlled trial in HIV-uninfected adults with TBM of daily aspirin 81 mg or 1000 mg , or placebo , added to the first 60 days of anti-tuberculosis drugs and dexamethasone ( NCT02237365 ) . The primary safety endpoint was gastro-intestinal or cerebral bleeding by 60 days; the primary efficacy endpoint was new brain infarction confirmed by magnetic resonance imaging or death by 60 days . Secondary endpoints included 8-month survival and neuro-disability; the number of grade 3 and 4 and serious adverse events; and cerebrospinal fluid ( CSF ) inflammatory lipid mediator profiles . 41 participants were randomised to placebo , 39 to aspirin 81 mg/day , and 40 to aspirin 1000 mg/day between October 2014 and May 2016 . TBM was proven microbiologically in 92/120 ( 76 . 7% ) and baseline brain imaging revealed ≥1 infarct in 40/114 ( 35 . 1% ) participants . The primary safety outcome occurred in 5/36 ( 13 . 9% ) given placebo , and in 8/35 ( 22 . 9% ) and 8/40 ( 20 . 0% ) given 81 mg and 1000 mg aspirin , respectively ( p=0 . 59 ) . The primary efficacy outcome occurred in 11/38 ( 28 . 9% ) given placebo , 8/36 ( 22 . 2% ) given aspirin 81 mg , and 6/38 ( 15 . 8% ) given 1000 mg aspirin ( p=0 . 40 ) . Planned subgroup analysis showed a significant interaction between aspirin treatment effect and diagnostic category ( Pheterogeneity = 0 . 01 ) and suggested a potential reduction in new infarcts and deaths by day 60 in the aspirin treated participants with microbiologically confirmed TBM ( 11/32 ( 34 . 4% ) events in placebo vs . 4/27 ( 14 . 8% ) in aspirin 81 mg vs . 3/28 ( 10 . 7% ) in aspirin 1000 mg; p=0 . 06 ) . CSF analysis demonstrated aspirin dose-dependent inhibition of thromboxane A2 and upregulation of pro-resolving CSF protectins . The addition of aspirin to dexamethasone may improve outcomes from TBM and warrants investigation in a large phase 3 trial . New host-directed therapies are urgently required for all forms of tuberculosis , but especially for tuberculous meningitis ( TBM ) , the most lethal form of the disease , which kills or disables around half of sufferers ( Thwaites , 2013 ) . Therapeutic strategies to improve outcomes can be broadly divided into those directed against the bacteria and their enhanced killing , and those directed at the host and the control of the inflammatory response . To date , attempts to optimise bacteria-directed anti-tuberculosis regimens have not been shown to clearly benefit patients with TBM ( Ruslami et al . , 2013; Heemskerk et al . , 2016 ) . In contrast , host-directed therapy with adjunctive anti-inflammatory corticosteroids has been shown to reduce mortality from TBM , although without reduced disability amongst survivors ( Prasad et al . , 2016 ) . There is , therefore , an urgent need to explore alternative therapeutic strategies that may prevent the long-term neurological sequelae of TBM and complement the short-term survival benefits of dexamethasone . Cerebral infarction is the commonest cause of irreversible neurological injury from TBM ( Lammie et al . , 2009 ) . TBM-related infarcts are typically located in the territories of the proximal middle cerebral artery and the medial lenticulostriate and thalamoperforating vessels , where the basal meningeal inflammatory exudate is most intense ( Lammie et al . , 2009; Hektoen , 1896; Misra et al . , 2011 ) . Their pathogenesis remains controversial , in particular the role of vessel thrombosis . Some autopsy studies have either failed to find arterial thrombosis associated with infarcts , or found it to be uncommon ( Doniach , 1949 ) ; whereas others have reported that thrombosis is common , especially when associated with tuberculous vasculitis ( Poltera , 1977 ) . The limited available evidence suggests that TBM-related infarcts are caused by a combination of vasospasm , intimal proliferation , and thrombosis ( Lammie et al . , 2009 ) . Aspirin acts by irreversibly inhibiting the cyclooxygenase pathway of arachidonic acid metabolism and the production of prostanoids ( Vane , 1971 ) . Low dose aspirin ( 75–150 mg ) prevents ischaemic cerebrovascular disease ( Richman and Owens , 2017 ) and higher dose aspirin ( up to four grams daily ) is used for the treatment of some inflammatory conditions ( e . g . rheumatic fever ) ( Cilliers et al . , 2015 ) . Its anti-inflammatory effects are thought to occur at doses >600 mg daily , through the inhibition of pro-inflammatory prostaglandins ( e . g . PGE2 , PGF2α and PGD2 ) and the unstable prostanoid , thromboxane A2 ( TXA2 ) ( Botting , 2010 ) . Low dose aspirin causes less inhibition of pro-inflammatory prostaglandins , but causes clinically significant inhibition of TXA2 and platelet aggregation . Until recently , the inhibitory effect on platelets and thrombus formation was thought to explain aspirin’s well-documented reduction in the risk of death from stroke and myocardial infarction ( Raju et al . , 2011 ) . However , these effects may be augmented by aspirin’s ability to trigger the production of 15-epi-lipoxins , 17R-resolvins and protectins , molecules that alongside the recently discovered maresins actively promote the resolution of inflammation ( Spite and Serhan , 2010 ) . The ‘pro-resolution’ properties of aspirin are not shared with any other non-steroidal anti-inflammatory drugs ( NSAID ) , or corticosteroids . It represents a potentially unique mode of action by which aspirin , alongside the prevention of thrombosis , might prevent infarctions and speed resolution of intra-cerebral inflammation and improve outcomes from TBM . Furthermore , there are intriguing data from murine models of tuberculosis which suggest aspirin and other non-steroidal anti-inflammatory drugs may enhance Mycobacterium tuberculosis killing ( Byrne et al . , 2007; Vilaplana et al . , 2013 ) . Two previous trials of adjunctive aspirin for TBM have been reported . The first randomised 118 Indian adults with TBM to standard anti-tuberculosis chemotherapy , with or without aspirin ( 150 mg daily ) ( Misra et al . , 2010 ) . By 3 months , brain magnetic resonance imaging ( MRI ) proven infarction occurred in 13 ( 43% ) in the placebo arm and 8 ( 24% ) in the aspirin group ( p=0 . 18 ) . Aspirin was associated with a reduction in mortality ( 43% versus 22% , p=0 . 02 ) without a significant increase in adverse events . The results are hard to interpret , however , because of the variable use of prednisolone between the treatment arms . Participants who received prednisolone and aspirin appeared to benefit the most . The second trial randomised 146 South African children with TBM to standard anti-tuberculosis chemotherapy plus placebo ( n = 50 ) , low-dose aspirin ( 75 mg/day ) ( n = 47 ) , or high-dose aspirin ( 100 mg/kg/day ) ( n = 49 ) ( Schoeman et al . , 2011 ) . Aspirin had no significant impact on survival , motor or cognitive outcomes . We hypothesised that aspirin prevents TBM-related brain infarction by its anti-thrombotic , anti-inflammatory , and pro-resolution effects . We chose to investigate two aspirin doses: a low dose ( 81 mg/day ) with anti-thrombotic but minimal anti-inflammatory activity; and a higher dose ( 1000 mg/day ) with both anti-thrombotic and anti-inflammatory activity . Our primary objective was to demonstrate the safety , tolerability , and potential efficacy of 81 mg and 1000 mg aspirin when added to dexamethasone for the first 60 days of TBM treatment . Our secondary objective was to investigate the potential mechanisms of actions of the two aspirin doses by examining the profiles of lipid mediators , including the pro-inflammatory eicosanoids and aspirin triggered pro-resolving mediators , in the cerebrospinal fluid ( CSF ) of participants . Baseline characteristics were balanced between the three treatment groups ( Table 1 ) . The predominant gender was male ( 65 . 8% ) , the median age was 41 years and duration of illness was 10 days . Most participants had mild to moderate illness severity , with only 15 ( 12 . 5% ) MRC grade III at enrollment . Baseline MRI revealed ≥1 infarct in 40/114 ( 35 . 1% ) participants; the placebo and aspirin 1000 mg groups had a higher proportion with infarcts ( 42 . 5% and 38 . 5% respectively ) than the aspirin 81 mg group ( 22 . 9% ) . According to the published diagnostic criteria 92/120 ( 76 . 7% ) had definite TBM , 17/120 ( 14 . 2% ) probable , and 9/120 ( 7 . 5% ) possible TBM ( Marais et al . , 2010 ) . Of the 92 participants with definite TBM , 42 ( 45 . 7% ) had M . tuberculosis cultured from the CSF and acid-fast bacilli were seen in the CSF in 50 others ( 54 . 3% ) . Amongst patients with culture-confirmed disease , 10/42 ( 23 . 8% ) had isoniazid resistant infection and one ( 2 . 4% ) had MDR TBM . The primary safety outcome of gastro-intestinal or cerebral bleeding occurred in 21/111 ( 18 . 9% ) : 5/36 ( 13 . 9% ) given placebo , and in 8/35 ( 22 . 9% ) and 8/40 ( 20 . 0% ) given 81 mg and 1000 mg aspirin respectively ( p=0 . 59 ) ( Table 2 ) . Only one new cerebral bleed occurred ( in the aspirin 81 mg group ) : an asymptomatic micro-haemorrhagic transformation of a lacunar infarct . Four gastro-intestinal bleeding events were defined as either serious , or grade 3 or 4: two serious haematemesis events in the placebo group and one in the aspirin 81 mg group , and one grade 3 episode of melena in the aspirin 1000 mg . The majority of bleeding events ( 16/20; 80% ) were defined as either grade 1 or two events , 15/16 described as >5 mls of changed or fresh blood aspirated from a nasogastric tube , and 1 episode of melena ( supplementary file 3 ) . None of these events required active management , but on each occasion the study drug was stopped immediately . The primary efficacy outcome of new MRI-proven brain infarction or death occurred in 25/112 ( 22 . 3% ) : 11/38 ( 28 . 9% ) given placebo , 8/36 ( 22 . 2% ) given aspirin 81 mg , and 6/38 ( 15 . 8% ) given 1000 mg aspirin . The observed absolute risk reductions in the aspirin 81 mg and aspirin 1000 mg groups versus placebo were −6 . 7% ( 95% confidence interval ( CI ) −25 . 7% to +13 . 1% ) and −13 . 2% ( 95% CI −31 . 0% to 5 . 7% ) , respectively , although the differences were not statistically significant ( p=0 . 40 ) ( Table 2 ) . The observed risk of a new MRI-proven brain infarction was lower in the aspirin treated patients compared to placebo , although not statistically significant ( p=0 . 18 ) ( Table 2 ) . In addition , 9/15 ( 60 . 0% ) of brain infarcts seen at baseline in the aspirin 1000 mg group resolved by day 60 , whereas resolution only occurred in 1/7 ( 14 . 2% ) in the aspirin 81 mg group and 6/14 ( 42 . 9% ) in the placebo group ( p=0 . 14 ) . There was only one death in the aspirin 1000 mg treated participants by day 60 . In the per-protocol population new infarction or death occurred by day 60 in 19/95 ( 20 . 0% ) : 10/34 ( 29 . 4% ) given placebo , 6/31 ( 19 . 3% ) given aspirin 81 mg , and 3/30 ( 10 . 0% ) given 1000 mg aspirin ( p=0 . 16 ) ( Table 3 ) . The observed absolute risk reductions in the aspirin 81 mg and aspirin 1000 mg groups versus placebo were −10 . 1% ( 95% CI −29 . 7% to +11 . 0% ) and −19 . 4% ( 95% CI −37 . 4% to +0 . 6% ) , respectively ( p=0 . 16 ) . No deaths occurred in the aspirin 1000 mg group , compared to 13% and 11% in the aspirin 81 mg and placebo groups respectively ( p=0 . 11 ) . Planned sub-group analyses for the primary efficacy outcome are reported in Table 4 . No clear subgroup signal was seen for any subgrouping variable except for the diagnostic category which showed a significant interaction with the aspirin treatment effect ( Pheterogeneity = 0 . 01 ) and suggested a potential reduction in new infarcts and deaths by day 60 in the aspirin-treated participants with microbiologically confirmed TBM ( 11/32 ( 34 . 4% ) events in placebo vs . 4/27 ( 14 . 8% ) in aspirin 81 mg vs . 3/28 1000 ( 10 . 7% ) in aspirin 1000 mg; p=0 . 06 ) . These beneficial effects were most marked in the aspirin 1000 mg group ( aspirin 81 mg vs placebo: odds ratio ( OR ) 0 . 33 , 95% CI 0 . 09–1 . 20 , p=0 . 093; aspirin 1000 mg vs . placebo: OR 0 . 23 , 95% CI 0 . 06–0 . 93 , p=0 . 039 ) ( Figure 2 ) . These effects equate to the number-needed-to-treat ( NNT ) to prevent an infarct or death by day 60 of 5 for aspirin 81 mg and 4 for aspirin 1000 mg . In the ITT population there was no significant difference in death or disability by day 60 or month eight across the treatment groups ( Table 5 ) . The 8-month mortality was 14/118 ( 11 . 9% ) : 5/39 ( 12 . 8% ) in the placebo group versus 6/39 ( 15 . 4% ) and 3/40 ( 7 . 5% ) in the 81 mg and 1000 mg aspirin groups respectively ( p=0 . 50; Figure 2 panels ) . Although the observed mortality was lowest in the aspirin 1000 mg group at 60 days and 8 months , the proportion of participants in this group with moderate ( 6/40 ( 15 . 0% ) ) or severe disability ( 2/40 ( 5 . 0% ) ) by 8 months was not significantly different from the aspirin 81 mg ( 4/39 ( 10 . 3% ) and 2/39 ( 5 . 1% ) ) and placebo treated participants ( 7/38 ( 18 . 4% ) and 0/38 ( 0 . 0% ) ) ( supplementary file 4 ) . In the per-protocol population , however , there was a trend to better 8 month outcomes in the aspirin 1000 mg group ( p=0 . 13 ) ( Table 5 ) . Aspirin at either dose was not associated with a significant reduction in hospital stay ( median 32 days for each group; p=0 . 84 ) . MRI brain imaging abnormalities ( other than infarcts ) were similar between the groups by day 60 and 8 months ( supplementary file 5 ) . Hydrocephalus , however , was less common by day 60 in the aspirin 1000 mg group ( 2/38 ( 5 . 3% ) ) than the aspirin 81 mg ( 5/32 ( 15 . 6% ) ) and placebo groups ( 8/35 ( 22 . 9% ) ( p=0 . 09 ) . None of the participants in the trial with hydrocephalus underwent ventriculoperitoneal shunting . The proportion of participants in each group with infarcts by month eight did not differ significantly . Overall , aspirin was not associated with a significant increase in grade 3 or four or serious adverse events , with the possible exception of more cardiac events in the aspirin groups ( p=0 . 08 ) ( Table 6 ) . The numbers of participants with ≥1 serious adverse event were 12 ( 29 . 3% ) in the placebo arm , 15 ( 38 . 5% ) in the aspirin 81 mg arm and 9 ( 22 . 5% ) in the 1000 mg arm ( p=0 . 31 ) . Adverse events resulting in study drug stop or interruption occurred in 7 ( 17 . 1% ) given placebo , 10 ( 25 . 6% ) 81 mg aspirin , and 10 ( 25 . 0% ) 1000 mg aspirin ( p=0 . 56 ) . The commonest reason was mild gastro-intestinal bleeding ( 20/27; supplementary file 3 ) . Hyponatraemia ( plasma sodium <125 mmol/L ) was more common in those treated with placebo ( 33 ( 80 . 5% ) ) than aspirin 81 mg ( 24 ( 61 . 5% ) ) or 1000 mg ( 25 ( 62 . 5% ) ) ( p=0 . 11 ) . We investigated the impact of aspirin on the concentrations of lipid mediators of inflammation , extracted , identified and quantified from CSF using lipid mediator profiling and LC-MS/MS ( Figure 3 - child ) . Partial least squared discriminant analysis 2-dimensional score plot of CSF taken from all surviving participants 30 days from randomization showed clustering of lipid mediators according to treatment group suggesting dose-dependent effects ( Figure 3A ) . Furthermore , in those participants who received >30 days of study drug we compared baseline with day 30 CSF and found dose-dependent inhibition of TXB2 ( the stable metabolite of TXA2 ) and up-regulation of pro-resolving protectins , with significant differences observed in the aspirin 1000 mg group compared to placebo ( Figure 3B; supplementary file 6 ) . There is much current interest in novel host-directed therapies against tuberculosis ( Wallis et al . , 2016 ) . We conducted a phase two randomised controlled trial with the aim of showing the safety and potential efficacy of either low ( 81 mg/day ) or higher ( 1000 mg/day ) dose aspirin when added to anti-tuberculosis drugs and dexamethasone for the treatment of HIV-uninfected adults with TBM . We found that aspirin was not associated with a significant increase in grade 3 or four adverse events . In both the ITT and the per-protocol population , the observed risk of death or new brain infarction by day 60 was lower in the aspirin arms compared to placebo , although this was not statistically significant . Planned sub-group analyses , however , suggested that aspirin 1000 mg may benefit those with microbiologically confirmed TBM . This finding was supported by an analysis of CSF lipid mediators of inflammation , which demonstrated aspirin 1000 mg was associated with significant inhibition of pro-thrombotic TXA2 and upregulation of pro-resolution protectins . The important characteristics of the trial participants , which influences the generalisability of the findings , were that they were HIV-uninfected , had relatively mild disease ( 87 . 5% MRC grade I or II ) , a high proportion ( 35 . 1% ) had brain infarcts evident at baseline , and most ( 76 . 7% ) had a microbiologically confirmed diagnosis of TBM . The high proportion of microbiologically confirmed disease is especially relevant , as many centres report much lower proportions ( typically 20–50% ) . The characteristics of populations of suspected but unconfirmed case of TBM may vary substantially between centres and influence treatment effects . In addition , all participants were treated with adjunctive dexamethasone , which is known to reduce deaths from TBM in this population ( Thwaites et al . , 2004 ) . Combining dexamethasone with aspirin did not significantly increase gastro-intestinal bleeding of any severity , or any other category of grade 3 or four adverse event . There was a non-significant increase in non-severe ( grade 1 or 2 ) gastro-intestinal bleeding events ( mostly small volumes of digested blood aspirated from nasogastric tubes ) in the aspirin-treated participants and in all these cases the study drug was stopped immediately . A larger trial is needed to determine whether aspirin truly increases these events and to assess their clinical significance . However , as the risk of severe gastric bleeding appears to be very low , the future management of minor bleeding events in aspirin-treated patients could be less conservative , especially as the per-protocol analysis suggested participants who received >30 days of aspirin may benefit more than those who stopped aspirin earlier . Our findings are similar to the previous trial of aspirin ( 150 mg/day ) for adults with TBM conducted in India ( Misra et al . , 2010 ) , which reported aspirin in combination with prednisolone was associated with a reduction in brain infarcts ( 22% versus 55% in controls; p=0 . 08 ) and death ( 13% verus 14% in controls; p=0 . 05 ) . Treatment with aspirin without prednisolone was not associated with improved outcomes . In both the Indian trial and the trial conducted in South African children ( Schoeman et al . , 2011 ) , which included a high dose ( 100 mg/kg/day ) arm , the use of aspirin was not associated with increased adverse events . In particular , aspirin did not appear to increase the risk of gastro-intestinal bleeding in either study . If these data are taken together with the results of the current study , they strongly suggest aspirin at doses ranging from 81 mg to 1000 mg per day can be safely added to anti-tuberculosis and corticosteroid therapy . Determining which dose is likely to be most effective is difficult , but our findings suggest that higher doses ( 1000 mg/day or equivalent in children ) are likely to more effective . The strength of our trial is that it addressed both the potential clinical role of aspirin and its mechanism of action by serial brain imaging and analysis of a panel of 71 lipid mediators , their precursors , pathway markers and further metabolites in the CSF . There are , however , some important limitations . First , assessment of the primary efficacy endpoint required participants to be well enough to have an MRI at baseline and day 60 . Three screened patients were judged too unwell to have baseline imaging and enter the trial , and five participants were too unwell for imaging at day 60 . Therefore , our findings may not be generalisable to those with very severe disease at baseline . The trial was not powered to show an impact on longer term survival or neurodisability and therefore does not provide definitive , practice-changing evidence that adjunctive aspirin improves outcomes in all adults with TBM . However , the findings support the hypothesis that aspirin has effects on tuberculosis-associated neuro-inflammation that are independent of dexamethasone and may lead to additional improvements in clinical outcomes . The clinical findings need to be interpreted cautiously , but the planned sub-group analysis suggested a clinical benefit of aspirin in those with microbiologically confirmed disease , especially at 1000 mg . This potential clinical effect is supported by the CSF analysis , which showed dose-dependent inhibition of TXA2 by aspirin , modest inhibition of prostaglandins , and the upregulation of potentially protective protectins . The serial brain images also support the assertion that aspirin’s benefit may be driven by the upregulation of these pro-resolving molecules: 60% of infarcts seen at baseline had resolved by day 60 in the aspirin 1000 mg group compared with 14 . 2% in the aspirin 81 mg group and 42 . 9% in the placebo group . In summary , this phase two randomised placebo-controlled trial suggests that daily aspirin 81 mg or 1000 mg can be given safely with dexamethasone and anti-tuberculosis drugs for the treatment of HIV-uninfected adults with TBM . The trial also provides new data that indicate aspirin induces dose-dependent inhibition of TXA2 and upregulation of protectins within the central nervous system that may reduce the incidence and promote the resolution of TBM-associated brain infarcts and inflammation and thereby improve outcome . These findings provide strong support for the conduct of a large phase 3 trial of adjunctive aspirin for TBM , but may also have relevance for the treatment of other forms of tuberculosis , adding to the growing evidence that aspirin and other non-steroidal anti-inflammatory drugs may be useful novel adjunctive agents in tuberculosis treatment ( Kroesen et al . , 2017 ) . We conducted a parallel group , double blind , randomised , placebo controlled trial in HIV-uninfected adults with TBM to assess the safety and efficacy of either 81 mg or 1000 mg aspirin daily for the first 60 days of treatment with standard anti-tuberculosis drugs and dexamethasone ( full study protocol provided in supplementary file 7 ) . The trial enrolled in-patients at the Hospital for Tropical Diseases , a 550-bed tertiary referral hospital in Ho Chi Minh City , Vietnam . The trial was approved by the Oxford Tropical Research Ethics Committee and the Institutional Review Board of the Hospital for Tropical Diseases and the Ethical Committee of the Ministry of Health , Vietnam . Adults ( ≥18 years old ) with suspected TBM ( at least 5 days of meningitis symptoms , nuchal rigidity , and CSF abnormalities ) and a negative HIV test were eligible to enter the trial . Written informed consent to participate in the study was obtained from all participants or from their relatives if the participant could not provide consent due to incapacity . Published diagnostic criteria were used to categorise participants retrospectively into definite , probable , or possible TBM once the results of all investigations returned ( supplementary file 1 ) ( Marais et al . , 2010 ) . Patients were excluded if they or their family did not give written informed consent to participate; they had received >2 days anti-tuberculosis chemotherapy for the current infection; they were unlikely , for any reason , to be able to have MRI brain imaging within 5 days of randomisation; they had known or suspected infection with multi-drug resistant ( MDR ) tuberculosis ( resistant to at least isoniazid and rifampicin ) ; they were unable to take isoniazid , rifampicin , or pyrazinamide at recommended doses for any reason; they had a history of peptic ulceration or gastro-intestinal bleeding , or active gastro-intestinal bleeding was suspected; they had taken >1 dose of aspirin ( at any dose ) or any other NSAID for any reason within 2 weeks of screening; aspirin was considered mandatory for any reason; dexamethasone was contraindicated for any reason; or the patient was pregnant or breast feeding . Randomisation was 1:1:1 to placebo , 81 mg aspirin , or 1000 mg aspirin according to a computer-generated randomization list using block randomization with variable blocks of length 3 ( with 25% probability ) and 6 ( with 75% probability ) and with stratification by MRC disease severity grade ( defined in Table 1 ) . Participant numbers were stratified and assigned sequentially at randomisation , with each participant receiving a pre-prepared numbered identical package of blinded study drugs . Treatment allocation was concealed by each treatment pack containing two bottles of study treatment: one containing 81 mg tablets of identical aspirin or placebo , the second containing identical 500 mg tablets of aspirin or placebo . Trial participants and the entire clinical and study team were blind to the treatment allocation for the duration of the trial . For 60 days , participants took one 81 mg tablet/placebo and two 500 mg tablets/placebo ( taken every 12 hr ) , according to their randomised allocated treatment . Participants unable to swallow were given crushed tablets ( which did not reveal the allocation ) via a nasogastric tube at the same doses . Anti-tuberculosis treatment followed Vietnam’s tuberculosis treatment guidelines , consisting of oral isoniazid ( 5 mg/kg/day; maximum 300 mg/day ) , rifampicin ( 10 mg/kg/day ) , pyrazinamide ( 25 mg/kg/day; maximum 2 g/day ) and ethambutol ( 20 mg/kg/day; maximum 1 . 2 g/day ) and intramuscular streptomycin ( 20 mg/kg/day; maximum 1 g/day ) for 3 months , followed by rifampicin and isoniazid at the same doses for a further 6 months . All patients received adjunctive dexamethasone for the first 6 to 8 weeks of treatment as previously described ( Thwaites et al . , 2004 ) and oral ranitidine ( 300 mg at night ) . For patients infected with M . tuberculosis resistant to isoniazid , the treatment was adjusted according to local guidelines and the susceptibility of the organism . Lumbar puncture was performed before the start of treatment and on days 30 and 60 as per normal clinical care . All CSF specimens were stained and cultured by standard methods for pyogenic bacteria , fungi , and mycobacteria and tested by GeneXpert MTB/RIF assay ( Cepheid , USA ) . Isolates of Mycobacterium tuberculosis were tested for susceptibility to isoniazid , rifampicin , ethambutol and streptomycin by mycobacterial growth indicator tube method ( Ardito et al . , 2001 ) . Brain MRI ( 1 . 5 tesla; 64 slices ) with T1 volume pre and post contrast , T2 , FLAIR , gradient echo and DWI sequences was acquired ±5 days from randomisation and then at day 60 ( ±10 days ) and day 240 ( ±30 days ) . All images were separately interpreted by two independent neuroradiologists ( one consultant and one fellow ) blind to the treatment allocation , who then determined a consensus opinion . Clinical progress and neurological and drug-related adverse events were assessed daily until discharge from hospital . After discharge , monthly visits were scheduled for clinical evaluation and laboratory monitoring until 8 months , when a final clinical assessment was made . All participants were genotyped for leukotriene A4 hydrolase ( LTA4H ) , which has been shown to influence TBM pathophysiology and outcome , using previously described methods to investigate its influence on CSF inflammation and aspirin effect ( Thuong et al . , 2017 ) . The primary safety endpoint was the occurrence of clinically significant upper gastro-intestinal bleeding and any cerebral bleeding confirmed by brain imaging by 60 days from randomisation . Clinically significant upper gastro-intestinal bleeding was defined as vomiting fresh or changed blood of any volume; passing melena; an unexplained drop in haemoglobin concentration of >2 g/L; or >5 mls of fresh or changed blood aspirated from nasogastric tube . The primary efficacy endpoint was any new MRI-proven brain infarction or death by 60 days . The secondary endpoints were the number of grade 3 and four and serious adverse events by day 60 from randomisation; mortality over the first 240 days from randomisation; duration of hospital stay; neurological disability ( as assessed by the modified Rankin score ) by days 60 and 240; the proportion of patients with MRI-proven infarction by day 240; and the resolution of CSF inflammation by day 30 through measurement of lipid mediators . Lipid mediators were measured by liquid Chromatography-tandem mass spectrometry ( LC-MS/MS ) on baseline and day 30 CSF ( archived at −80°C ) . Methods have been previously described , ( Walker et al . , 2017 ) and are briefly summarised here . Baseline and day 30 CSF ( archived at −80°C ) was placed in two volumes ice-cold methanol containing deuterium labelled PGE2 ( d4-PGE2 ) ; d4-LTB4 , d5-LXA4 d5-RvD2 , d5-LTC4 , d5-LTD4 , and d5-LTE4 ( 500 pg each; Cayman Chemicals ) . These were kept at −20°C for 45 min to allow for protein precipitation and lipid mediators were extracted using C-18 based Solid Phase Extraction as previously described ( Walker et al . , 2017 ) . Methyl formate fractions were brought to dryness using a TurboVap LP ( Biotage ) and products suspended in water-methanol ( 80:20 vol:vol ) for Liquid Chromatography-tandem mass spectrometry ( LC-MS/MS ) based profiling . A Shimadzu LC-20AD HPLC and a Shimadzu SIL- 20AC autoinjector ( Shimadzu , Kyoto , Japan ) , paired with a QTrap 5500 ( ABSciex , Warrington , UK ) were utilised and operated as previously described ( Colas et al . , 2014 ) . To monitor each lipid mediator and deuterium labelled internal standard , a Multiple Reaction Monitoring method was developed using parent ions and characteristic diagnostic ion fragments ( Walker et al . , 2017 ) . This was coupled to an Information Dependent Acquisition and an Enhanced Product Ion scan . Identification criteria included matching retention time to synthetic standards and at least six diagnostic ions in the MS-MS spectrum for each molecule . Calibration curves were obtained for each molecule using authentic compound mixtures and deuterium labelled lipid mediator at 0 . 78 , 1 . 56 , 3 . 12 , 6 . 25 , 12 . 5 , 25 , 50 , 100 , and 200 pg . Linear calibration curves were obtained for each lipid mediator , which gave r ( Ruslami et al . , 2013 ) values of 0 . 98–0 . 99 . The sample size of 40 per arm was chosen based on clinical and feasibility considerations . Our objective was to provide estimates of safety and efficacy , alongside potential mechanisms of action , which would inform the design and execution of a larger phase III trial; we did not expect to show clinically definitive efficacy in this phase two trial . We assumed a risk of new MRI-proven brain infarction or death within 60 days of approximately 40% in the control arm . Based on the results of the trial performed by Misra et al , ( Misra et al . , 2010 ) we assumed that 81 mg aspirin daily may reduce this risk to 20% and the risk in the 1000 mg aspirin daily arm to between 20–40% . Given these estimates our three arm trial would have approximately 75% power to detect such an effect at the one-sided 10% significance level ( i . e . to generate ‘mild evidence’ ) and approximately 50% power to detect it at the conventional one-sided 2 . 5% significance level . Cerebral bleeding associated with TBM is extremely rare ( estimated at <0 . 01% of all patients ) , but it is possible it may be more common in those treated with aspirin . It was therefore included in the primary assessment of safety . The proportion of HIV-uninfected patients with clinically significant gastro-intestinal bleeding from our most recent trial of hyper-intensive anti-tuberculosis chemotherapy in Vietnam was about 1% ( 3/278 ) ( Heemskerk et al . , 2016 ) . Assuming this same risk of bleeding in the aspirin arms , the probability that the upper limit of the 95% confidence interval remains below 12 . 9% is more than 92% . If the risk of bleeding is 10% in one of the aspirin arms , the probability that the lower limit of the 95% confidence interval remains above 2 . 5% is 78% . Statistical analysis followed the protocol and a predefined statistical analysis plan . The main population for all analyses was the intention-to treat population ( ITT ) , which included all randomized participants , analysed according to the randomized treatment arm . A per-protocol population was defined by excluding participants with a confirmed alternative diagnosis to TBM; those with confirmed MDR-TBM; and those who received <30 days of study drug for reasons other than death . The risk of clinically significant upper-gastro-intestinal bleeding and image-proven cerebral bleeding by 60 days were summarized as numbers and proportion in each group together with two-sided 95% confidence intervals for risk differences between groups calculated by the Wilson score method . Comparison between the three arms was based on the chi-square test of independence . The primary efficacy endpoint of new MRI-proven brain infarction or death by 60 days was analysed in the same way . For the secondary endpoint , a linear-by–linear trend test ( also called Cochran–Armitage test for trend ) was performed to assess the association between disability score and the three arms ( Agresti , 2002 ) . Subgroup analyses for the primary efficacy endpoint were conducted using logistic regression in pre-defined subgroups according to TBM grade ( I , II , or III ) ; previous tuberculosis treatment; TBM diagnostic category ( definite versus probable/possible ) ; drug resistance ( MDR-TB , rifampicin mono-resistance , isoniazid resistance ( with or without streptomycin resistance ) , no or other resistance ) ; and leukotriene A4 hydrolase ( LTA4H ) genotype ( CC , CT , TT ) . We fitted a logistic regression model using Firth’s correction to the likelihood because there were subgroups without events ( Firth , 1993 ) . Heterogeneity of the treatment effect across sub-groups was tested via a likelihood ratio test for the interaction term between treatment and the grouping variable in a logistic regression model . To assess differences of lipid mediators between the different treatment groups partial least squares discriminant analysis was conducted using SIMCA 13 . 0 . 3 software ( Umetrics , San Jose , CA ) , ( Colas et al . , 2014 ) where mediators displaying a Variable Importance in Projection scores greater than one were taken as displaying significant correlation with the treatment group . This parameter estimates the importance of a variable in the Partial Least Squares projections with scores greater than one indicating that a specific variable is important in a given model . No imputation of missing data was performed and the threshold for assuming statistical significance was p<0 . 05 for all analyses . For the analysis of gastro-intestinal/cerebral bleeding by day 60 , patients lost to follow-up or dead before day 60 ( without prior bleeding ) were excluded from the analysis to avoid under estimating the true proportion of bleeding in the aspirin groups . The analysis of the primary efficacy endpoint excluded patients with missing baseline or follow-up MRI scans ( except for prior death ) . All statistical analyses were performed with the statistical software R v3 . 1 . 2 ( R Core Team , 2017 ) . An independent data and safety monitoring board reviewed the unblinded safety data after 39 and 94 participants were enrolled . The trial was not stopped early . The trial was registered on clinicaltrials . gov , NCT02237365 . The funders played no part in the design , implementation , or analysis of the study or in the decision to publish the results . The corresponding author has full access to all the data in the study and had final responsibility for the decision to submit for publication . The Oxford University Clinical Research Unit ( OUCRU ) operates managed open access to the research data it generates , which complies with the policies of its major funder , the Wellcome Trust , UK . The objective is not to restrict access to data , but to monitor who uses the data and for what purpose , and to ensure those responsible for collecting and curating the data are appropriately acknowledged by those using it . Therefore , those wishing to acquire the anonymized dataset from which the results presented in this manuscript were produced should email the trial Chief Investigator and corresponding author , Professor Guy Thwaites ( gthwaites@oucru . org ) .	The deadliest form of tuberculosis is tuberculosis meningitis ( TBM ) , which causes inflammation in the brain . Even with the best treatment available , about half of patients with TBM become disabled or die , often because they have a stroke . Strokes are caused by blood clots or other blockages in blood vessels in the brain . Aspirin is known to prevent blood clots and helps reduce inflammation . Some scientists wonder if it might help patients with TBM by preventing blockages in blood vessels . Now , Nguyen et al . show that adding aspirin to existing TBM treatments may reduce strokes in some patients . In the experiments , 120 patients with TBM were randomly assigned to receive a low dose of aspirin ( 81 mg/day ) , a high dose of aspirin ( 1000mg/day ) , or an identical tablet that contained no medication . All the patients also took the anti-tuberculosis drugs and steroids usually used to treat the condition . Both doses of aspirin appeared to be safe . Patients who received aspirin were less likely to have a stroke or die in the first two months of treatment than patients who received the fake pill . But the difference was so small it could have been caused by chance . In the 92 patients with clear evidence of tuberculosis bacteria in their brains , the benefit of aspirin was larger and unlikely to be due to chance . The benefit was greatest for those who received the higher dose of aspirin , only 10 . 7% of these patients died or had a stroke , compared with 14 . 8% of those who received a low dose of aspirin , or 34% of those who received the fake pill . Next , Nguyen et al . looked at brain fluid taken from the TBM patients before and after they received the aspirin or fake medication . The experiments showed that patients treated with high dose aspirin had much lower levels of a clot-promoting substance called thromboxane A2 and more anti-inflammatory molecules . Larger studies are needed in children and adults to confirm that aspirin helps prevent strokes or death in patients with TBM . Studies are also needed on patients who have both TBM and HIV infections . But if more studies show aspirin is safe and effective , adding this medication to TBM treatment may be an inexpensive way to prevent death or disability .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "medicine", "microbiology", "and", "infectious", "disease" ]	2018	A randomised double blind placebo controlled phase 2 trial of adjunctive aspirin for tuberculous meningitis in HIV-uninfected adults
Monocytes are circulating short-lived macrophage precursors that are recruited on demand from the blood to sites of inflammation and challenge . In steady state , classical monocytes give rise to vasculature-resident cells that patrol the luminal side of the endothelium . In addition , classical monocytes feed macrophage compartments of selected organs , including barrier tissues , such as the skin and intestine , as well as the heart . Monocyte differentiation under conditions of inflammation has been studied in considerable detail . In contrast , monocyte differentiation under non-inflammatory conditions remains less well understood . Here we took advantage of a combination of cell ablation and precursor engraftment to investigate the generation of gut macrophages from monocytes . Collectively , we identify factors associated with the gradual adaptation of monocytes to tissue residency . Moreover , comparison of monocyte differentiation into the colon and ileum-resident macrophages revealed the graduated acquisition of gut segment-specific gene expression signatures . The recent past has seen major advance in our understanding of the diverse origins of tissue macrophages , as well as their discrete maintenance strategies . Macrophages were shown to arise from three distinct developmental pathways that differentially contribute to the tissue compartments in the embryo and adult ( Ginhoux and Guilliams , 2016 ) . In the mouse , embryonic tissue macrophages first develop from primitive macrophage progenitors that originate in the yolk sac ( YS ) . In the brain , YS-macrophage-derived macrophages persist throughout adulthood , while in most other tissues these cells are replaced by fetal monocytes that derive from multi-potent erythro-myeloid progenitors ( EMP ) . Definitive hematopoiesis commences from E10 . 5 with the generation of hematopoietic stem cells ( HSC ) that first also locate to the fetal liver , but eventually seed the bone marrow ( BM ) to maintain adult hematopoiesis . Most EMP-derived tissue macrophage compartments persevere throughout adulthood without major input from HSC-derived cells; however , in certain barrier tissues , as well as selected other organs , like the heart , embryonic macrophages are progressively replaced by HSC-derived cells involving a blood monocyte intermediate ( Varol et al . , 2015 ) . Monocytes are continuously generated in the BM involving a sequence of developmental intermediates , before extravasation into the circulation ( Ginhoux and Jung , 2014; Mildner et al . , 2016 ) . Once in the blood , murine classical Ly6C+ monocytes have a limited half-life ( Yona et al . , 2013 ) . On demand , Ly6C+ monocytes can be rapidly recruited to sites of injury and challenge , where they complement tissue-resident macrophages and dendritic cells . In absence of challenge , some Ly6C+ monocytes give rise to vasculature-resident Ly6C- cells , which patrol the vessel walls ( Auffray et al . , 2007 ) . These cells display macrophage characteristics including extended life spans ( Yona et al . , 2013; Mildner et al . , 2017 ) . In addition , Ly6C+ monocytes replenish the above-mentioned selected steady state tissue macrophage compartments , including the gut and skin ( Ginhoux and Jung , 2014 ) . Given their mobility , plasticity and key role in pathologies , the manipulation of monocytes and their differentiation could bear considerable therapeutic potential . However , monocyte differentiation into tissue resident cells remains incompletely understood . Gut macrophages , which reside in the connective tissue underlying the gut epithelium , the lamina propria , are considered key players for the maintenance of intestinal homeostasis . As such , they constantly sense their environment and respond to the unique microbiota and food challenge ( Bain and Mowat , 2011; Zigmond and Jung , 2013 ) . Recent studies revealed that monocyte-derived lamina propria macrophages comprise in mice two populations , that is short-lived cells and long-lived cells with self-renewing capacity ( Shaw et al . , 2018 ) ; the latter population might also include remnants of embryonic populations , as could additional intestinal long-lived macrophage populations that reside near blood vessels , nerves and in the Peyer's Patches ( De Schepper et al . , 2018 ) . Evidence for macrophages with different half-lives is also emerging for the human small intestine ( Bujko et al . , 2018 ) . Collectively , these findings highlight the existence of considerable macrophage heterogeneity , not only between different organs , but also within given tissues . Monocyte differentiation into intestinal macrophages involves phenotypic changes with respect to Ly6C , CD64 and MHCII expression , a sequence described as ‘monocyte waterfall’ ( Tamoutounour et al . , 2012 ) . Mature steady-state gut macrophages tolerate the commensal microbiota and food antigens ( Bain and Mowat , 2011; Zigmond and Jung , 2013 ) . Their relative unresponsiveness is thought to rely on regulatory circuits that balance the expression of pro- and anti-inflammatory gene products , such as cytokines and molecules participating in pattern recognition receptor signaling cascades . Macrophages located in different tissues display characteristic enhancer landscapes and gene expression profiles , which have been attributed to the exposure of instructing factors of the microenvironment they reside in Amit et al . ( 2016 ) . Gut segments display distinct anatomy , function and microbiota load ( Mowat and Agace , 2014 ) and macrophages of small and large intestine hence also likely differ . Despite the known distinct susceptibility of the colon and ileum to pathology , such as to the IL10R deficiency ( Glocker et al . , 2009; Zigmond et al . , 2014 ) and in ulcerative colitis , so far no comparative analysis of their macrophages has been reported . Likewise , our general understanding of monocyte-derived tissue resident macrophages remains scarce and is largely restricted to settings of inflammation . Here we investigated monocyte differentiation into intestinal macrophages in the small and large intestine . Using adoptive monocyte transfers into macrophage-depleted recipients ( Varol et al . , 2007; Varol et al . , 2009 ) , we synchronized the macrophages in terms of development , isolated colonic and ileal macrophages and subjected them to transcriptome profiling . Our data establish the distinct identities of gut segment resident macrophages and shed light on the kinetics and gradual gene expression of specific factors for their establishment of their identities . Tissue macrophages display distinct gene expression profiles and enhancer landscapes ( Amit et al . , 2016 ) . This holds also for intestinal macrophages residing in small and large intestine . Transcriptomes of Ly6C+ BM monocytes , that is the macrophage precursors , and transcriptomes of their progeny in colon and ileum displayed 6200 genes differentially expressed at least 2-fold across all analyzed data sets out of a total of 12345 detected genes ( Figure 1—figure supplement 1A–C ) . 2255 genes were expressed in monocytes and down-regulated in macrophages ( cluster I ) . Conversely , cluster II comprised genes whose expression was absent from monocytes , but shared by both small and large intestinal macrophages . Finally , 1087 and 987 genes were either preferentially or exclusively expressed in ileal or colonic macrophages , respectively . To further characterize adult monocyte-derived gut macrophages , we took advantage of an experimental system involving monocyte engraftment of macrophage-depleted animals ( Varol et al . , 2007; Varol et al . , 2009 ) . Analysis of transferred cells at different intervals from engraftment allows the study of intestinal macrophage development over time , since monocyte differentiation is synchronized . For the cell ablation we used [CD11c-DTR > WT] BM chimeras , in which diphtheria toxin ( DTx ) receptor ( DTR ) transgenic intestinal macrophages can be conditionally ablated by DTx injection ( Varol et al . , 2007; Varol et al . , 2009 ) ( Figure 1A ) . Two days prior to monocyte transfer , DTx was applied to the recipients to clear their intestinal macrophage niche and mice were subsequently treated with DTx every second day . The monocyte graft was isolated from BM of Cx3cr1GFP/+ mice ( Jung et al . , 2000 ) and defined as CD117- CD11b+ CD115+ Ly6C+ GFPint cells; donor animals also harbored an allotypic marker ( CD45 . 1 ) ( Figure 1A , Figure 1—figure supplement 1A ) . Grafted cells could be visualized in recipient gut tissue and underwent expansion , as reported earlier ( Varol et al . , 2009 ) ( Figure 1—figure supplement 2 ) . Intestinal tissues of recipient mice were harvested on day 4 , 8 and 12 post engraftment ( Figure 1A ) to isolate graft-derived macrophages according to CD45 . 1 and CX3CR1/GFP expression; these cells were CD11b+ CD64+ Ly6C- and mainly CD11chi ( Figure 1B ) . Of note , specifics of the system preclude harvest of graft-derived cells at later time points ( Varol et al . , 2009 ) . The monocyte graft and colonic and ileal macrophage populations were subjected to bulk RNA-seq using MARS-Seq technology ( Jaitin et al . , 2014 ) . Gene expression profiling revealed robust changes already at day four post engraftment; changes appeared to be tissue- rather than time-specific , with the exception of two day 4 samples of ileal macrophages , which clustered with the colonic samples ( Figure 1C , Figure 1—figure supplement 3A ) . Transcriptomes of the retrieved engrafted macrophages lacked expression of pro-inflammatory genes , such as Saa3 , Lcn2 , Il1b , Il6 and Tnf , as opposed to cells retrieved from colitic animals treated with Dextran Sulfate Sodium ( DSS ) ( Okayasu et al . , 1990 ) ( Figure 1—figure supplement 4 ) . This supports the earlier notion that the cell ablation results in a transient tissue response , but the monocyte transfer system mimics cell differentiation close to steady-state conditions ( Varol et al . , 2009 ) . Comparative transcriptome analysis of grafted monocytes , their progeny retrieved from the recipient intestines , and resident ileal and colonic macrophages that were independently retrieved from Cx3cr1GFP/+ mice revealed 4213 differentially expressed genes ( DEG ) ( >4 fold differences in any pairwise comparison among a total of 12878 genes ) ( Figure 1D ) . 748 genes were exclusively expressed in the monocyte graft , including Ly6c2 , Ccl6 and Cebpe ( Cluster I ) ( Figure 1D , E ) . 793 genes shared expression in graft-derived cells and gut macrophages in colon and ileum ( Cluster II ) . These included Irf6 , Gata6 , Ly6A and Smad7 . Cluster III comprised 634 genes that were either exclusively or preferentially expressed in colonic macrophages , such as Pparg , Foxa1 and Tlr1 ( Figure 1E ) . Cluster IV comprised 539 genes exclusively or preferentially expressed in ileal macrophages , such as Nos2 , Sox4 and Mmp9 ( Figure 1E ) . Metascape analysis ( Zhou et al . , 2019 ) highlighted that genes associated with cluster II , II and IV were associated with distinct pathways , particularly with respect to epithelial cell communication and cell metabolism ( Figure 1—figure supplement 3B ) . We also identified genes that differed in expression between engrafted and resident macrophages . Specifically , 796 and 467 genes displayed high or low expression levels in monocytes , respectively , were highly expressed in resident colonic macrophages , but either low or absent in the graft-derived cells ( Cluster V and VI ) . Finally , cluster VII comprised 236 genes that were expressed in monocytes , and down-regulated in resident gut macrophages , but not in the grafted cells . Volcano plot analysis revealed that early monocyte differentiation ( graft vs day 4 ) was characterized by abundant changes in gene expression in both colon and ileum . In the colon 3407 genes were up- and 3608 genes were down-regulated; in the ileum 2975 genes were up- and 3206 genes were down-regulated , with up to 10 fold change ( Figure 1F ) . Later time points ( day 4 to day 8 and day 8 to day 12 ) were characterized by less pronounced alterations , both with respect to the number of DEG and their fold change ( Figure 1G , H ) . In the colon 289 genes ( 66% of all significantly-changed genes ) were induced between day 4 and 8 and only 111 genes ( 19% ) were up-regulated between day 8 and 12 . In the ileum this trend was reversed , with 95 genes ( 21% ) up-regulated between day 4 and 8 , but 321 genes ( 85% ) induced between day 8 and 12 . Collectively , this establishes that monocytes that enter distinct gut segments rapidly acquire characteristic transcriptomic signatures . Our experimental set up allows us to focus on cells that entered the gut in a defined time window . Interestingly , even by day 12 , monocyte graft-derived cells differed in expression profiles when compared to resident ileal and colonic macrophages ( Figure 2A ) . Differences included genes absent from , and exclusively expressed by engrafted cells ( Figure 1D clusters V - VII , Figure 2B–D ) . These differences might be attributable to incomplete macrophage maturation or to the recently reported heterogeneity of the intestinal macrophage compartment ( Shaw et al . , 2018; De Schepper et al . , 2018 ) . Notably , the majority of the cells we retrieve likely comprise lamina propria/mucosa resident macrophages , rather than the less abundant macrophages of the submucosa or muscularis layer ( Shaw et al . , 2018; De Schepper et al . , 2018 ) . Generation of a CD4+ Timd4+ subpopulation of lamina propria macrophages was reported to require prolonged residence in the tissue ( Shaw et al . , 2018 ) . Although grafted cells in both colon and ileum acquired with time some expression of a hallmark of the long-lived cells , the phosphatidyl serine receptor Timd4 ( Tim4 ) ( Figure 2B , Figure 1D cluster VI ) , other signature genes , such as Cd209f , C2 and Rusc2 ( Shaw et al . , 2018 ) , were not expressed in the time frame analyzed here ( Figure 2B ) . The hypothesis that 12 days were insufficient for engrafted macrophages to acquire the full gene expression profile of resident cells was furthermore corroborated by the delayed onset of MHCII ( encoded by H2-ab1 ) expression , one of the markers for intestinal macrophage maturation ( Tamoutounour et al . , 2012; Schridde et al . , 2017 ) ( Figure 2C ) . Engrafted macrophages were also characterized by lack of expression of Irf1 and the member of the TAM receptor kinase family Axl ( Figure 2C ) . Genes , whose expression was low to absent in resident macrophages from both colon and ileum , but prominent in the engrafted cells ( Figure 1D cluster VII ) , included the ones encoding ribosome-associated proteins and histones ( Figure 2D ) . As previously shown ( Varol et al . , 2009 ) , the reconstitution of emptied intestinal tissue with monocyte-derived cells involves clonal expansion , that is less likely to occur in physiological setting . In line with this notion , out of the 6383 genes significantly differing between engrafted and resident macrophages from either colon or ileum , 897 were annotated with a Gene Ontology ( GO ) term associated with ‘proliferation’ and ‘cell cycle’; 99 of these DEG were low to absent in resident macrophages from both tissues retrieved from non-engrafted Cx3cr1GFP/+ mice , such as Hmga1 ( Figure 1D ) . We recently reported the requirement of IL10Ra on colonic macrophages for gut homeostasis . Mice lacking this cytokine receptor on intestinal macrophages develop severe gut inflammation in the colon , but not the ileum ( Zigmond et al . , 2014; Bernshtein et al . , 2019 ) , as do children that harbor an IL10RA deficiency ( Glocker et al . , 2009 ) . Interestingly , Il10ra was not induced in engrafted colonic macrophages , even 12 days after tissue entry , while engrafted ileal macrophages displayed Il10ra transcripts as early as day 4 ( Figure 2E ) . Expression of the cytokine IL10 itself was almost absent from monocytes and engrafted macrophages in both tissues , but significantly present in resident macrophages retrieved from non-engrafted Cx3cr1GFP/+ mice , to a larger extent in the colon than the ileum ( Figure 2E ) . Of note , Il1b expression displayed a similar pattern in line with an earlier suggestion that IL1 might induce macrophage IL-10 expression ( Foey et al . , 1998 ) . Likewise , genes induced following macrophage exposure to Il10 , such as Ccr5 ( Houle et al . , 1999 ) and Socs3 ( Cassatella et al . , 1999 ) displayed similar expression patterns . This suggests that in our model the IL10/IL10R axis is inactive in newly differentiated macrophages and established only after further maturation in the tissue . Flow cytometric analysis of colon tissue of WT C57BL/6 mice identifies a small population of CD11b+ Ly6C+ MHCII- cells that likely represent recent monocyte immigrants into the tissue ( Figure 2—figure supplement 1A ) . These rare cells probably entered the lamina propria to maintain the steady state macrophage pool of the intestine , before differentiating and have been referred to as P1 population of a ‘monocyte waterfall’ ( Tamoutounour et al . , 2012 ) . Global RNAseq analysis of this population , alongside the Ly6C+ MHCII- macrophages revealed that , like the day four graft , these endogenous gut immigrants down-regulated monocyte markers and gained a gut macrophage signature , including expression of the Forkhead transcription factor ( TF ) FoxD2 and the nuclear receptor Nr3c2 ( Figure 2—figure supplement 1B , C ) . Endogenous gut immigrants did not display a signature indicating proliferation , such as Rpl10 or Hmga1 . However , like the grafted cells , these early colonic immigrants lacked signature genes associated with long-lived gut macrophages , including Timd4 and CD209f , as well as expression of Il10ra and Il10 . To further corroborate our data , we used a distinct cell ablation model and performed adoptive monocyte transfers into DTx-treated [CX3CR1-DTR > WT] BM chimeras ( Diehl et al . , 2013; Aychek et al . , 2015 ) . The gene lists of upregulated and down regulated genes in macrophages retrieved from engrafted [CD11c-DTR > WT] and [CX3CR1-DTR > WT] chimeras showed with 71% and 59% , respectively , considerable overlap ( Figure 2—figure supplement 2 ) . While our adoptive transfer inherently aims at the reconstitution of ablated cells which differ in the two models , the observed coherence suggests robustness of the approach . Collectively , these data show that despite some differences , monocyte graft-derived cells recapitulate the ‘monocyte waterfall’ ( Tamoutounour et al . , 2012 ) . We next focused on factors that might be involved in the generation of segment-specific macrophages , that is genes whose expression differed between colonic and ileal macrophages . 458 genes were up-regulated during monocyte differentiation in a segment-specific manner – 351 in colonic macrophages and 107 in ileal macrophages ( Figure 3A ) . Monocyte differentiation into ileal macrophages was accompanied by induction of Gata and Hbox TF family members , such as Gata5 and Hbox3 ( Figure 3A ) , as well as genes encoding the chemokine Ccl5 and the chemokine receptor CCR9 . Monocytes that entered colon tissue preferentially up-regulated Foxd2 , the nuclear receptor Nr3c2 , and the dominant negative helix-loop-helix protein Id2 ( Figure 3A ) . Of the genes , which were specifically down-regulated in only one intestinal segment , 78 genes followed this trend in colonic and 99 in ileal macrophages . The latter down-regulated genes included 7 TFs , such as Foxp1 and Trim16 , while Arid5a and Elk3 were specifically down-regulated in the colon ( Figure 3B ) . Many genes related to immune reaction and response to challenge displayed higher expression in ileal macrophages than their colonic counterparts . Examples are: Arid5a , whose gene product regulates IL6 ( Masuda et al . , 2016 ) ; Elk3 , which encodes a member of the ETS TF family and was reported to modulate the phagocytosis of bacteria by macrophages ( Tsoyi et al . , 2015 ) and Ano6 , that is down-regulated in colonic macrophages ( Figure 3B ) , and reportedly supports microbiocidal activity of macrophages involving P2X7 receptor signaling ( Ousingsawat et al . , 2015 ) . In contrast , the enzyme Sod1 , which was reported to impair macrophage-related parasite killing in cutaneous Leishmaniasis ( Khouri et al . , 2009 ) , showed lower expression in ileal macrophages ( Figure 3B ) . Another interesting group of genes are those , which are expressed in monocytes , but further up-regulated in one tissue upon differentiation and down-regulated in the other . These genes might encode factors whose expression is incompatible with segment-specific macrophage fates . 54 such genes were expressed in colonic engrafted macrophages and silenced in their ileal counterparts; 15 genes followed an opposite trend ( Figure 3C ) . Only one TF was found in both groups , KLF4 ( Figure 3C ) . Aldh2 encoded by Aldh2 , mostly known for its role in alcohol detoxification , was recently reported to play a role in repression of ATP6V0E2 , which is critical for proper lysosomal function , autophagy , and degradation of oxidized LDL ( Zhong et al . , 2019 ) ( Figure 3C ) . Hdac7 encoded by Hdac7 and up-regulated in ileal macrophages and down-regulated in colonic macrophages , was reported to interfere with the myeloid gene expression pattern and to inhibited macrophage-specific functions ( Barneda-Zahonero et al . , 2013 ) ( Figure 3C ) . Finally , Fmnl3 participates in filopodia generation ( Harris et al . , 2010 ) ( Figure 3C ) . Collectively , these data establish that monocytes establish gut segment specific gene expression patterns , likely under the influence of local cues . Transcriptomes of colonic and ileal macrophages 4 days after monocyte tissue entry were alike , with many genes sharing expression in both tissues when compared to their monocyte progenitors . Overall , by day 4 , expression of 2007 genes was down-regulated more than 2-fold in the monocyte graft following differentiation into macrophages in both tissues ( out of 12485 genes expressed ) . 2404 genes were induced in both the colon and ileum , arguably as part of a generic transcriptome signature of intestinal macrophages ( Figure 4A ) . Notably , 919 of the genes induced during monocyte differentiation into generic intestinal macrophages displayed very low prior expression in monocytes – below 50 reads ( Figure 4B ) . In contrast , expression of fewer transcripts ( 183 ) seemed to be actively silenced during the differentiation process , as seen in the violin blot in Figure 4B . This implies that monocytes actively acquire macrophage identities by de novo mRNA synthesis , while much of the monocytic gene expression is compatible with the differentiation process . The top 5 GO-terms associated with genes up-regulated in intestinal macrophages related to cell adhesion and migration processes ( Figure 4C ) , including the chemokine Cxcl1 , Ptprk which regulates cell contact and adhesion , and the integrin Itga6 ( Figure 4D ) . The top 5 GO-terms associated with down-regulated genes in macrophages included cell cycle and division , as well as mRNA processing and DNA repair ( Figure 4C , D ) , for example DNA polymerase beta ( Polb ) , a cell cycle checkpoint regulator ( Rad17 ) and an RNA helicase ( Setx ) . Collectively , these data are in line with the transformation of circulating monocytes into non-migratory tissue resident cells and suggest a role for DNA damage-associated molecules during the differentiation process . Gene expression changes are driven by TFs . In the case of intestinal macrophages , three major TF families seem to participate in monocyte-macrophage differentiation: CCAAT-enhancer-binding proteins ( C/EBPs ) , E2 transcription factors ( E2F ) and early growth response TFs ( Egr ) . Four of the Cebp family members ( Cebpa , b , d , g ) and 5 E2F family members ( E2f2 , 4 , 6 , 7 , 8 ) were significantly down-regulated upon monocyte differentiation into macrophages ( Figure 5A ) . In contrast , Egr1 , 2 and 3 were up-regulated . Other down-regulated TFs included the regulators of immune response Bach1 , Bcl6 , Irf9 , Nfatc3 , Nfkb1 and Rela , as well as the master macrophage TF Spi1 ( PU . 1 ) ( Figure 5B ) . The list of induced genes was enriched with homeobox TFs such as Sox13 , Pbx1 , Foxa1 and others ( Figure 5C ) . In addition , this group comprised TFs that had previously been reported to be critical for the development of other tissue macrophages , such as the nuclear receptor LXRα encoded by Nr1h3 for splenic macrophages and Gata6 for peritoneal macrophages ( Varol et al . , 2015; Rosas et al . , 2014 ) . Monocytes are generated in the BM to be subsequently disseminated throughout the body via blood vessels . Under inflammation , the cells are rapidly recruited to the site of injury . In absence of challenges , Ly6C+ monocytes can have distinct fates . A fraction of them gives rise to vasculature-resident Ly6C- ‘patrolling’ cells ( Auffray et al . , 2007 ) . Other cells contribute to the homeostatic replenishment of selective tissue macrophage compartments ( Figure 6A ) . To gauge the impact of the blood environment , as compared to a solid tissue such as the intestine , on the differentiation process , we next compared transcriptomes of Ly6C- blood cells and gut macrophages to their Ly6C+ monocyte precursors ( Figure 6—figure supplement 1 ) . A heat map of all 1303 genes , whose expression significantly differed between Ly6C+ monocytes compared to blood- and gut tissue-resident cells ( day 4 ) , revealed five clusters ( Figure 6B ) . Monocyte progeny , whether in vasculature or tissue shared signatures , showed considerable similarities as reflected in the expression pattern of two thirds of the genes ( 66 . 7% ) . Specifically , clusters I and II comprised 780 genes that were down-regulated upon Ly6C+ monocyte differentiation , including hallmark monocyte markers , such as Ly6c , Ccr2 and Mmp8 and Myd88 ( Figure 6B , C ) . 160 genes were induced in both blood- and tissue resident monocyte-derived cells , albeit to different extend , including as Pparg , Ets2 and Tgfbr2 ( Cluster III ) ( Figure 6B , D ) . Cluster IV and V spanned one third of the genes differentially expressed by Ly6C+ monocytes and their progenies , but distinct in tissue and vascular resident cells . Specifically , cluster IV comprised 253 genes up-regulated in blood-resident cells and down-regulated in gut macrophages . This included Csf2ra , Nfkb1 , Il10ra and Spi1 ( PU . 1 ) . Cluster V comprised 110 genes induced in gut macrophages but not vasculature-resident cells , such as the mitochondrial master regulator Ppargc1b and the metalloprotease Adam19 ( Figure 6B ) . Concerning TFs , Cebpb was induced in Ly6C- blood monocytes , as was previously reported ( Mildner et al . , 2017 ) , while Cebpa and Cebpd were down-regulated in both blood- and gut-resident cells ( Figure 6E ) . Irf family members 7 , 8 and 9 were down-regulated during Ly6C+ monocyte differentiation in blood and gut . Collectively , these data establish that vasculature-resident Ly6C- monocytes and gut macrophages that derive both from Ly6C+ monocytes display considerable overlap in transcriptomic signatures , but also display gene expression patterns that are likely associated with their specific environments . Adult tissue macrophages can derive from distinct origins ( Ginhoux and Guilliams , 2016; Varol et al . , 2015 ) . Most tissue macrophages are currently believed to be generated in the embryo from EMP via a fetal liver monocyte intermediate and subsequently maintain themselves through self-renewal . Selected macrophages residing in barrier tissues , such as gut and skin , however rely on constant replenishment from blood monocytes . Here we report the study of this macrophage generation from monocyte precursors . Following tissue damage and infection , classical monocytes , defined as CD14+ cells in humans , and Ly6C+ cells in mice critically contribute to inflammatory reactions by promoting and resolving acute challenges ( Ginhoux and Jung , 2014; Mildner et al . , 2016 ) . At the sites of injury , monocytes can give rise to cells with both macrophage and DC features . Monocyte differentiation during inflammation has been studied in various pathophysiological settings , including experimental auto-immune encephalitis ( Yamasaki et al . , 2014; Masuda et al . , 2016 ) , colitis ( Rivollier et al . , 2012; Zigmond et al . , 2012 ) and others ( Avraham-Davidi et al . , 2013 ) . To study less well understood physiological monocyte differentiation in absence of overt inflammation , we took advantage of an experimental system that allows synchronized reconstitution of macrophage compartments by monocyte engraftment ( Varol et al . , 2007; Varol et al . , 2009 ) . The majority of changes in gene expression , both in fold change and numbers , occurred in the transition from monocytes to either colonic or ileal macrophages at day four post transfer , that is immediately after extravasation . Engrafted colonic and ileal macrophages segregated in gene expression according to tissue . Tissue-specific macrophage imprinting occurs hence early during development , shortly after tissue infiltration . Interestingly , two samples of engrafted ileal macrophages clustered with the colonic samples rather than the ileal ones , suggesting that the colonic signature is default . Colonic macrophages at day 12 post engraftment were also more distinct from endogenous colonic macrophages , compared to their ileal counterparts , which implies that the mature colonic gene signature might take longer time to develop or could be more heterogeneous . Recent studies have noted heterogeneity within murine blood monocytes , in particular with respect to an intermediate between the Ly6C+ and Ly6- populations ( Mildner et al . , 2017 ) . While we currently cannot formally rule out that , colonic and ileal macrophages could hence derive from distinct precursors , we consider this however unlikely . Mowat and colleagues have reported a transcriptome analysis of monocytes and colonic macrophages , including intermediates of the ‘waterfall’ ( Schridde et al . , 2017 ) . The authors highlighted the critical role of TGFb in the differentiation process . While a comparison of these data to ours confirmed the late onset of genes that characterize long-lived gut macrophages , the use of the distinct platforms and distinct experimental set up precluded further direct alignment . Of note , monocyte-derived cells are in our system synchronized with respect to development and therefore allow additional temporal resolution , especially with respect to final population of the scheme ( P4 ) , which comprises in the cited study ( Schridde et al . , 2017 ) a heterogeneous conglomerate . With their extended half-life , Ly6C- monocytes that patrol the endothelium , have been proposed to represent vasculature-resident macrophages ( Ginhoux and Jung , 2014 ) . Indeed , these cells shared gene signatures with the gut resident macrophages , such as the reduction in IRF TFs following monocyte differentiation and induction of characteristic macrophage genes , such as PPARg and TGFbR . However , the comparison of these blood-resident cells to gut tissue-resident macrophages revealed also considerable differences likely associated with the residence in vasculature and the solid tissue , respectively . Though anatomically close , the small and large intestine represent very distinct tissues , including structural dissimilarities , such as the extended ileal villi and Peyer’s Patches , characteristic distinct abundance of immune cells , as well as different luminal microbiome content ( Mowat and Agace , 2014 ) . Highlighting these differences , ileum and colon display also unique susceptibility to perturbations , as for instance to a IL10R deficiency ( Zigmond et al . , 2014; Bernshtein et al . , 2019 ) . Our comparative analysis of colonic and ileal macrophages , including their generation from monocytes , might provide critical insights into the mechanism underlying segment-specific pathology resistance or - susceptibility in the gut . Together with earlier reports ( Mildner et al . , 2017; Schridde et al . , 2017 ) , our data sets can provide a starting point for hypothesis-driven experiments . To conclude , we characterized here monocyte-derived intestinal macrophages generated under conditions avoiding overt inflammation . We highlight specific genes and TFs which are regulated following monocyte differentiation to generic or segment-specific intestinal macrophages . By comparing transcriptomes of early intestinal macrophages and blood-resident Ly6C- cells , we show that the populations which share a common ancestor – the Ly6C+ blood monocytes – show considerable overlap in gene expression , while they also display adaptation to their specific environments . Our data provide a gateway and reference point to further studies on monocyte differentiation to macrophages . Mice were kept in a specific-pathogen-free ( SPF ) , temperature-controlled ( 22 ± 1°C ) facility on a reverse 12 hr light/dark cycle at the Weizmann Institute of Science . Food and water were given ad libitum . Mice were fed regular chow diet ( Harlan Biotech Israel Ltd , Rehovot , Israel ) . The following mice strains all on C57BL6 background were used: Cx3cr1gfp/+ mice ( Jung et al . , 2000 ) , CD11c-DTR transgenic mice ( B6 . FVB-Tg [Itgax-DTR/GFP] 57Lan/J ) ( Jung et al . , 2002 ) and CX3CR1-DTR transgenic mice ( Diehl et al . , 2013 ) . BM chimeras were generated by engraftment of 7–10 weeks old recipient mice that were irradiated the day before with a single dose of 950 cGy using a XRAD 320 machine ( Precision X-Ray ( PXI ) . Femurs and tibiae of donor mice were removed and BM was flushed with cold PBS . BM was washed with cold PBS twice and filtered by 100 μm filter . BM cells were suspended in PBS and 5 × 106 cells were injected IV into irradiated recipient . Mice were handled and experiments were performed under protocols approved by the Weizmann Institute Animal Care Committee ( IACUC ) in accordance with international guidelines . Femurs and tibias of donor mice were removed and BM was flushed with cold PBS . BM was washed with cold PBS twice and filtered by 100 μm filter . Cells were suspended with PBS and loaded on equal amount of Ficoll ( GE healthcare ) . Tubes were centrifuged 920 g in room temperature for 20 min without breaks and Buffy coats were collected and washed with cold PBS . CD11c-DTR > wt] . Cells were stained and sorted according to the following markers: CD117- CD11b+ CD115+ Ly6C+ GFPint . BM chimeras were treated with 18 ng / gram bodyweight Diphtheria toxin ( DTx ) ( Sigma-Aldrich , Cat # D0564 ) for two consecutive days before transfer . At the day of transfer mice were injected with 106 BM monocytes IV . At days 1 , 3 , 5 , 7 and 9 after transfer mice were injected with 9 ng / gr bodyweight DTx . Intestines were removed and fecal content flushed out with PBS; tubes were opened longitudinally and cut into 0 . 5 cm sections . Pieces were placed in 5ml/sample ( up to 300gr of tissue ) of Hanks' Balanced Salt Solution ( HBSS ) with 10% heat-inactivated FCS/FBS , 2 . 5 mM EDTA and 1 mM DL-Dithiothreitol ( ( DTT ) , Sigma-Aldrich Cat# D9779 ) and incubated on a 37°C shaker at 300 rpm for 30 min to remove mucus and epithelial cells . Following incubation , samples were vortexed for 10 s and filtered through a crude cell strainer . Pieces that did not pass the strainer were collected and transferred to 5 ml/sample of PBS +/+ with 5% heat-inactivated FCS/FBS , 1 mg/ml Collagenase VIII ( Sigma-Aldrich Cat# C2139 ) and 0 . 1 mg/ml DNase I ( Roche Cat# 10104159001 ) . Tissue was incubated in a 37°C shaker at 300 rpm for 40 min ( colon ) or 20 min ( ileum ) in the solution . After incubation , samples were vortexed for 30 s until tissue was dissolved , then filtered through a crude cell strainer . The strainer was washed with PBS - /- and centrifuged in 4ºC , 375G for 6 min . Cells were stained and subjected to FACS analysis or sorting . Blood was retrieved from the vena cava , immediately placed in 150 U/ml Heparin and loaded on Ficoll ( GE healthcare ) . Tubes were centrifuged 920 g in room temperature for 20 min without breaks and Buffy coats were collected and washed with cold PBS . Cells were sorted according to the following parameters: CD45+ CD11b+ CD115+ Ly6C+/- . Samples were suspended and incubated in staining medium ( PBS without calcium and magnesium with 2% heat-inactivated Fetal Calf/Bovine Serum ( FCS/FBS ) and 1 mM EDTA ) containing fluorescent antibodies . Following incubation , cells were washed with staining buffer only or staining buffer with DAPI , centrifuged , filtered through 80 μm filter and read . For FACS analysis , LSRFortessa ( BD Biosciences ) was used . For cell sorting , FACSAria III or FACSAria Fusion ( BD Biosciences ) were used . Results were analyzed in FlowJo software ( Tree Star ) . Staining antibodies ( clones indicated within brackets ) : anti-CD45 ( 30-F11 ) , CD11b ( M1/70 ) , CD115/CSF-1R ( AF598 ) , Ly-6C ( HK1 . 4 ) , CD64/FcγRI ( X54-5/7 . 1 ) , CD11c ( N418 ) , anti-I-Ab ( MHCII ) ( AF6-120 . 1 ) , DAPI . RNA-seq of populations was performed as described previously ( Diehl et al . , 2013; Jaitin et al . , 2014 ) . Cells were sorted into 100 µl of lysis/binding buffer ( Life Technologies ) and stored at 80°C . mRNA was captured using Dynabeads oligo ( dT ) ( Life Technologies ) according to manufacturer’s guidelines . A derivation of MARS-seq ( Jaitin et al . , 2014 ) was used to prepare libraries for RNA-seq , as detailed in Shemer et al . ( 2018 ) . RNA-seq libraries were sequenced using the Illumina NextSeq 500 . Raw reads were mapped to the genome ( NCBI37/mm9 ) using hisat ( version 0 . 1 . 6 ) . Only reads with unique mapping were considered for further analysis . Gene expression levels were calculated and normalized using the HOMER software package ( analyzeRepeats . pl rna mm9 -d < tagDir > count exons -condenseGenes -strand + -raw ) . Gene expression matrix was clustered using k-means algorithm ( MATLAB function kmeans ) with correlation as the distance metric . PCA was performed by MATLAB function pca . Gene ontology was performed by DAVID ( https://david . ncifcrf . gov ) . Data on molecules and pathways was partly obtained by Ingenuity Pathway Analysis ( IPA ) , Ingenuity Target Explorer , Qiagen and Metascape Pathway analysis ( Zhou et al . , 2019 ) . Results are presented as mean ± SEM . Statistical analysis was performed using Student’s t test . * p-value<0 . 05 p-value<0 . 01 * p-value<0 . 001 .	Macrophage cells play a crucial role in keeping the body free of disease-causing microbes and debris . They surveille the tissues , detect and clear infections , and tidy up dead cells . Most internal organs contain a population of macrophages that move into the organ during development and then persist throughout an organism’s life . However , tissues in contact with the outside world , such as the gut , need a constant supply of fresh macrophages . This supply depends on immune cells called monocytes moving into these tissues from the blood and maturing into macrophages when they arrive . The macrophages in the gut have a challenging job to do . They need to be able to detect infections amongst healthy gut bacteria and foreign food particles . Macrophages from other tissues would overreact if they encountered this complicated environment , but gut macrophages learn to tolerate their surroundings by switching genes on and off as they mature . The exact combination of genes macrophages in the gut use depends on whether they are in the small or large intestine , which have different anatomies and resident microbes . To understand how monocytes mature into macrophages in the gut , previous studies have focused on what happens during an infection . However , it remains unclear how monocytes develop into mature gut macrophages in the healthy gut . To address this question , Gross-Vered et al . have looked at mice in which gut macrophages can be killed when a drug is applied . This made it possible to replace the mice’s own macrophages with fluorescently labelled cells derived from monocytes . Fluorescent monocytes were introduced into the bloodstream and arrived in the small and large intestine after the drug had been administered . Gross-Vered et al . then collected cells derived from these labelled monocytes and examined the genes that they were using . This revealed that once the monocytes entered the gut they began sensing their new environment and switching thousands of genes on and off . These changes happened rapidly at first and continued more gradually as the macrophages matured . Comparing the fluorescent macrophages from the small and large intestines revealed many similarities , but there were also hundreds of genes that differed . In the small intestine , macrophages switched on genes involved in catching and consuming bacteria , whereas macrophages in the large intestine , which has more resident healthy bacteria , turned on fewer of these bacteria-eating genes . Inflammatory bowel disorders like ulcerative colitis and Crohn's disease both involve gut macrophages . Comparing the genes that macrophages use in the healthy and diseased gut may reveal information about these disorders . For example , ulcerative colitis only affects the large intestine , so understanding how and why the monocytes mature differently there could shed light on new ways to treat the disease .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "immunology", "and", "inflammation" ]	2020	Defining murine monocyte differentiation into colonic and ileal macrophages
The bivalent hypothesis posits that genes encoding developmental regulators required for early lineage decisions are poised in stem/progenitor cells by the balance between a repressor histone modification ( H3K27me3 ) , mediated by the Polycomb Repressor Complex 2 ( PRC2 ) , and an activator modification ( H3K4me3 ) . In this study , we test whether this mechanism applies equally to genes that are not required until terminal differentiation . We focus on the RE1 Silencing Transcription Factor ( REST ) because it is expressed highly in stem cells and is an established global repressor of terminal neuronal genes . Elucidation of the REST complex , and comparison of chromatin marks and gene expression levels in control and REST-deficient stem cells , shows that REST target genes are poised by a mechanism independent of Polycomb , even at promoters which bear the H3K27me3 mark . Specifically , genes under REST control are actively repressed in stem cells by a balance of the H3K4me3 mark and a repressor complex that relies on histone deacetylase activity . Thus , chromatin distinctions between pro-neural and terminal neuronal genes are established at the embryonic stem cell stage by two parallel , but distinct , repressor pathways . Undifferentiated pluripotent cells present a unique dilemma with regard to gene regulation; genes that promote differentiation must be repressed to maintain pluripotency , yet this repression must be reversible to allow for rapid response to developmental cues . The repressed status , often referred to as poised , is conferred by epigenetic modifications established at loci encoding developmental regulators . Specifically , global histone modification patterns in embryonic stem cells ( ESCs ) have revealed the coexistence of trimethylation of histone H3 at lysines 4 and 27 ( H3K4me3 and H3K27me3 ) at promoters of genes encoding key lineage-determining factors ( Bernstein et al . , 2006 ) . This dual chromatin status has been termed bivalence to reflect the juxtaposition of modifications typically associated with functionally active and transcriptionally repressed promoters , respectively . The H3K27me3 mark is established by the methyltransferase EZH2 within the Polycomb Repressor Complex 2 ( PRC2 ) ( Pengelly et al . , 2013 ) , which effectively provides a counterbalance to factors that promote H3K4me3 and active expression ( Bernstein et al . , 2006 ) . This PRC2-dependent state has been proposed as a universal mechanism to confer pluripotency by controlling all developmental lineages , but its application to the neuronal lineage has not been tested rigorously . This is of interest because for most lineages the key developmental regulators are activators , while in the neuronal lineage a key regulator is the transcriptional repressor REST ( NRSF ) . Specifically , REST is a master developmental regulator that controls a large suite of genes that encode proteins critical for neuronal maturation , such as cellular migration , axonal pathfinding , and synaptic transmission ( Johnson et al . , 2007 , 2008; Otto et al . , 2007 ) . Further , REST is expressed at very high levels in embryonic stem cells , contrary to other developmental regulators . A global REST knockout results in embryonic lethality , pointing to an essential function for REST in general embryonic development following the ESC stage ( Chong et al . , 1995; Schoenherr and Anderson , 1995 ) . In neural progenitors , REST levels decrease until it completely leaves the chromatin at terminal differentiation of most neurons . Preventing its dismissal from chromatin delays greatly neuronal maturation in vivo ( Mandel et al . , 2011 ) and alters neural progenitor pool identities in vitro ( Covey et al . , 2012 ) . In stem/progenitor cells , developmental genes required for neuronal lineage decisions are repressed , including pro-neural and REST-regulated genes ( Buckley et al . , 2009 , Ballas et al . , 2005 ) , but whether the mechanisms that regulate these classes of genes are the same or different remains an open question . Prior studies have shown an important role for PRC2 repression on poised genes of multiple lineages . On the one hand , although the terminal neuronal genes regulated by REST are poised in stem cells , REST is itself a repressor and may not require the additional repression mechanism of Polycomb . On the other hand , the non-coding RNA ( ncRNA ) HOTAIR has been shown to act as an adapter between the core PRC2 component EZH2 and the REST co-factor Kdm1a ( Tsai et al . , 2010 ) , suggesting a connection between PRC2 and REST in ESCs . In addition , other groups have observed biochemical interaction between REST and PRC2 members ( Dietrich et al . , 2012; Mozzetta et al . , 2014 ) and recruitment of H2K27me3 to RE1 sites ( Arnold et al . , 2013 ) . Therefore , we performed three studies to test directly for the existence of a functional relationship between PRC2 and REST in ESCs . First , we performed a mass spectrometric analysis of REST complexes to identify ESC-specific co-factors in an unbiased manner . Second , we asked whether REST-occupied neuronal genes were marked by H3K27me3 , and furthermore , whether PRC2 activity was compromised in Rest−/− ESCs . Finally , exploiting a Rest−/− ESC line , we examined the consequences of the loss of REST on chromatin marks and gene expression . Previous studies of REST-interacting proteins in ESCs used a candidate approach and focused on co-factors characterized in differentiated cells ( Ballas et al . , 2005; Yu et al . , 2011 ) . In the current study , we considered the possibility that ESC-specific co-factors might be involved in regulatory mechanisms of REST that were unique to pluripotent cells . To test this idea we performed a mass spectrometric analysis of REST complexes using a mouse ESC line that stably expressed both the biotin conjugating enzyme , BirA ( Kim et al . , 2009 ) , and REST tagged with a biotin acceptor sequence . The stable line expressed approximately five-fold higher levels of REST than normal ESCs with no differences in pluripotency markers compared to WT cells ( not shown ) . Multidimensional Protein Identification Technology ( MudPIT ) analysis was performed on three independent streptavidin purifications . Proteins that were co-purified with REST in at least two of three pull-downs and were weakly represented , if at all , in the BirA control pull-downs are shown in Table 1 . None of the known epigenetic regulators identified as co-factors by mass spectrometry were specific to ESCs . However , we did identify almost all known REST co-factors including CoREST1 and Sin3a as well as the chromatin modifying enzymes , HDAC1 and 2 , Kdm1a and G9a/Glp , and the G9a-associated adaptors CDYL and WIZ1 , all of which have been shown biochemically to be present within REST complexes in terminally differentiated cell types ( Andres et al . , 1999; Grimes et al . , 2000; Hakimi et al . , 2002; Roopra et al . , 2004; Mulligan et al . , 2008 ) , thus validating our approach . We noted that an additional CoREST family member , CoREST2 , was also present in the pull-downs . We confirmed the presence of CoREST2 , as well as a subset of other co-factors , at RE1 sites in ESCs by chromatin immunoprecipitation ( ChIP , Figure 1—figure supplement 1 ) . We also identified several new factors , some with known functions ( Smarca5 , Mdc1 ) and some with no known function ( D1Pas1 , Table 1 ) . In contrast to these factors , components of the Polycomb repressor complexes were not identified according to our criteria . It was possible that the specific conditions used to generate the whole-cell extracts used in the MudPIT analysis precluded identification of Polycomb proteins . Therefore , we repeated mass spectrometry analysis on streptavidin pull-downs from nuclear extracts ( Abmayr et al . , 2006 ) . Under these conditions , we did identify the PRC2 complex members Suz12 ( 3 and 4 peptides in BioT REST pull-down replicates , 0 and 0 peptides in Control ) and Ezh2 ( 3 and 5 peptides in BioT REST , 0 and 0 peptides in Control ) . Co-immunoprecipitation analysis using nuclear extract confirmed only the Suz12 interaction , as well as the interactions with known REST co-repressors ( Figure 1—figure supplement 2A ) . Importantly , however , the members of the PRC2 complex required for the methyltransferase activity , Ezh2 , and for complex formation , Eed ( Montgomery et al . , 2005 ) , were both absent from the co-immunoprecipitation ( Figure 1—figure supplement 2A ) . These results indicate that REST protein does not interact with an enzymatically active PRC2 complex in ESCs . To supplement this proteomic approach , and as an independent test for the role of PRC2 members in REST regulation , we used a genome-wide ChIP-seq approach . 10 . 7554/eLife . 04235 . 003Table 1 . Co-factors identified within REST complexes were purified from ESCsDOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 00310 . 7554/eLife . 04235 . 004Table 1—Source data 1 . REST-bound genomic regions with repeated consensus RE1 motifs . Columns list the chromosome and base pair coordinates ( Region Start & Region End ) of the REST-binding domain identified by PeakRanger analysis of ChIP-Seq read distribution . RE1 Start and RE1 End columns give the coordinates corresponding to the positions of individual RE1 motifs found by FIMO within the corresponding region . Orientation column lists whether the RE1 motif is on the forward ( + ) or reverse ( − ) DNA strand , and the p-value column gives the calculated log-odds score from the comparison of a discovered motif to a position weighted matrix corresponding to the full consensus RE1 motif . DOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 004FunctionalExperiment 1Experiment 2Experiment 3categoryGeneBioT RESTControlBioT RESTControlBioT RESTControlBaitREST413727CorepressorRcori484Rcor2821938Sin3a8116Histone tailHDAC1111273modifyingHDAC27924enzymeLSD11826213Prmt5222Wdr532Ehmt2/G9a5188Ehmti46Wiz46AdaptorCdyl553Cdyl233ChromatinSmarca53452remodelerSupt16h3446Ssrpi32OtherGata2b32repressorMBD324F-boxFbxwi 1294proteinBtrc26TransposaseLin28A42Trim7122DNAMdd22bindingBclafi22Utf123UnclearBxdc2223D1Pas18411Gcdh362Pdcd11242Pop1222Wwox24Pura32Dimti22Proteins are listed were identified in all streptavidin purifications of biotin-tagged REST ( 3 out of 3 ) but not represented in more than one of the negative control samples . Columns list the functional category , protein symbol , and the number of unique peptides detected in REST and negative control purifications . A Polycomb complex was not represented in our analysis of REST complexes , but it was possible that the streptavidin pull-downs might not co-purify ncRNA-mediated associations . Therefore , we compared the genomic distributions of REST and H3K27me3 enrichment to determine whether PRC2 is recruited to REST-bound sites in ESCs . 2136 genomic regions targeted by REST were identified by our ChIP-seq in mouse ESCs , which is comparable to prior REST ChIP-seq studies in human T cells ( Johnson et al . , 2007 ) and a different mouse ESC line ( Johnson et al . , 2008 ) . The DNA sequence of REST-bound regions was analyzed and 96 . 6% ( 2064 REST-bound sites ) contained either the complete or partial consensus RE1 sequence motif ( Otto et al . , 2007 ) ( Figure 1—figure supplement 3B ) . This sequence analysis also showed that the RE1 sequence is the central determinant for REST recruitment because regions with the highest enrichment contained multiple repeats of complete RE1 sites aligned with the same strand orientation ( Zhang et al . , 2006; Jothi et al . , 2008 ) ( Figure 1—figure supplement 3A ) . Conversely , regions that contained a single right half of the RE1 motif were associated with low levels of enrichment ( Otto et al . , 2007 ) ( Figure 1—figure supplement 3A ) . REST-binding and relative enrichment indicated by ChIP-seq were confirmed by ChIP-quantitative PCR analysis for a subset of loci ( Figure 1—figure supplement 3C ) . Although ncRNA-mediated interactions linking REST to PRC2-bound chromatin have been proposed ( Tsai et al . , 2010 ) , the strong correlation between REST-binding and RE1 DNA sequences suggests that any alternative mechanisms of stable recruitment to chromatin were not prevalent in ESCs . This did not preclude , however , the inverse possibility that REST could recruit PRC2 to chromatin adjacent to RE1 sites . However , assessment of H3K27me3 domains showed that only a small minority ( ∼3% ) of REST-bound sites were associated with significant enrichment of H3K27me3 relative to input , and only 0 . 5% of H3K27me3-enriched domains were associated with REST binding ( Figure 1A ) . Even if the effective footprint of REST sites was extended 1 kb in both directions , the proportion overlapping with H3K27me3 peaks was only 12 . 6% of REST peaks and 2 . 3% of H3K27me3 peaks . Furthermore , our own analysis of ChIP-seq results published previously show that the PRC2 factors Suz12 and Ezh2 bind at extremely low levels , if at all , at REST sites relative to sites of H3K27me3 enrichment ( Figure 1—figure supplement 2B ) . None of these data sets supports a strong functional connection between these distinct complexes , or between RE1 sites and PRC2 , in ESCs . 10 . 7554/eLife . 04235 . 005Figure 1 . PRC2 establishes H3K27me3 in ESCs independent of REST repression . ( A ) A limited number of REST-occupied sites are associated with domains of H3K27me3 enrichment in ESCs , even if defined more broadly ( +/− 1 kb ) . ( B ) H3K27me3 levels are stable in Rest−/− ESCs in the majority of regions targeted by PRC2 . The scatter-plot shows the relative enrichment of H3K27me3 ChIP-Seq signal in wild type ( WT , x-axis ) and Rest−/− ESCs ( y-axis ) at regions targeted by PRC2 in WT ESCs . ( C ) As in ( B ) , but at identified REST-binding sites . ( D ) Chromatin immunoprecipitation analysis showing H3K27me3-enrichment changes at RE1 sites near PRC2-targeted regions in WT and Rest−/− ESCs ( * indicates p < 0 . 05 ) , normalized for H3 density . DOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 00510 . 7554/eLife . 04235 . 006Figure 1—figure Supplement 1 . REST is required for recruitment of co-factors to RE1 sites in ESCs . ChIP assays were performed in WT and Rest−/− ESCs using antibody against Sin3a , Cdyl , CoREST1 , CoREST2 , Hdac2 , G9a , and Kdm1a to compare recruitment of endogenous co-factors at the RE1 sites near Igsf21 , Kcnk9 , Erich1 , Npas4 , and Rad51 . ChIP assays done in parallel with normal rabbit IgG are included as a negative control . DOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 00610 . 7554/eLife . 04235 . 007Figure 1—figure Supplement 2 . Detection of REST binding to PRC2 members is biochemically possible , but a true interaction is unlikely . ( A ) Immunoprecipitation was performed with streptavidin beads from biotin-tagged REST ESCs or BirA control ESCs and the demarcated proteins were assayed by Western blot . ( B ) Previously published ChIP-seq data sets were interrogated over H3K27me3 peaks and REST peaks normalized to the signal found at the H3K27me3 peaks . DOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 00710 . 7554/eLife . 04235 . 008Figure 1—figure Supplement 3 . Characteristics of REST-bound loci . ( A ) Regions with multiple RE1 motifs show a strong association with REST . REST ChIP-Seq data displayed as sequence tags aligned to the mouse genome assembly ( mm9 ) viewed in the UCSC genome browser . Green and red hash marks represent sequences matching the forward and reverse strands , respectively . Consensus RE1 sequence motifs are indicated by black hash marks . All views are shown at an equal size relative to the scale bar . ( B ) FIMO definition of the RE1 motif was derived from ChIP-seq peaks . ( C ) Relative enrichment of REST measured by ChIP-Seq was confirmed by ChIP-qPCR . ChIP assays were performed using antibody raised against a peptide corresponding to a fragment of mouse REST in WT ( red bars ) and Rest−/− ( blue bars ) in ESCs at regions associated with RE1 motifs and regions lacking an RE1 site ( Oct4 and MageA8 ) . ( D ) REST targets associated with trimethylation of histone H3K4 or H3K27 enrichment are preferentially localized near gene promoters . Graph shows the percentage of REST-bound regions that overlap with domains of the bivalent histone modification pattern consisting of H3K4me3 and H3K27me3 enrichment ( red bars ) , H3K4me3 enrichment alone ( green bars ) , or without overlap to either modification ( tan bars ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 00810 . 7554/eLife . 04235 . 009Figure 1—figure Supplement 4 . H3K27me3 levels from WT and Rest−/− ESCs are as similar as H3K27me3 levels from different published reports . ( A ) Normalized enrichment values for H3K27me3 at defined H3K27me3 peaks were derived from several previously published data sets and plotted against one another . Numbers indicate Pearson coefficient . DOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 00910 . 7554/eLife . 04235 . 010Figure 1—figure Supplement 5 . Ezh2-enrichment at REST-bound loci . ( A ) Ezh2 occupancy was increased at RE1 sites within H3K27me3 domains that gained H3K27me3 in the absence of REST . ChIP assays were performed with anti-Ezh2 to compare enrichment at RE1 sites between WT ( light orange ) and Rest−/− ( dark orange ) ESCs . DOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 010 Despite the limited association between H3K27me3 enrichment and REST binding , we asked whether promoters targeted by both repressive mechanisms ( REST and PRC2 ) represented specific gene classes , because H3K27me3 marks several key developmental factors in ESCs . The dual PRC2/REST-occupied genes were primarily canonical REST-regulated mature neuronal genes rather than pro-neural or developmental genes per se , and therefore showed no ontological category enrichment ( data not shown ) . Developmental regulators of multiple lineages , such as Cdx4 and Runx1 , which are associated with the bivalent marks H3K27me3 and H3K4me3 ( Mikkelsen et al . , 2007 ) , similarly showed no gene ontology differences between those occupied by REST and those that were not . These results suggest that there exists no specific functional class of genes that is regulated by REST and Polycomb in tandem . To determine whether PRC2 activity at REST-occupied sites , when it did occur , was dependent on REST binding , we asked whether H3K27me3 was lost from these regions in Rest−/− ESCs . We integrated the ChIP-seq signal across both narrow REST-binding domains and across a continuous broad domain to avoid nucleosome occupancy fluctuations due to loss of REST binding . By this analysis , we found that levels of H3K27me3 at defined H3K27me3 sites were largely maintained ( Figure 1B , Pearson's coefficient = 0 . 53 ) . The changes observed between WT and Rest−/− ESCs were very similar to those observed between WT data sets published previously ( Figure 1—figure supplement 4 ) . Specifically , we found that >95% of H3K27me3-enriched regions associated with REST-bound sites in WT cells were also enriched for H3K27me3 in Rest−/− ESCs ( Figure 1A and C , Pearson's coefficient = 0 . 85 ) . The number of H3K27me3-overlapping domains was also essentially the same between wild-type and REST knockout ESCs ( Figure 1A ) . Similar results were observed when only REST sites within 5 kb of gene promoters were analyzed ( data not shown ) . In the small number of instances where H3K27me3 levels did change , some genes lost H3K27me3 in Rest−/− ESCs , including Scn8a , Galnt9 , and Vgf ( Figure 1D and Table 2 ) , while other genes , including Dner , Otop3 , and Cacng2 , gained H3K27me3 ( Figure 1D and Table 2 ) . The losses and gains were validated by quantitative ChIP-PCR ( Figure 1D ) . The HoxA11 and Oct4 genes , which are not bound by REST in ESCs , represent positive and negative controls for the H3K27me3 mark , respectively . The occupancy of EZH2 at these same regions was altered similar to H3K27me3 ( Figure 1—figure supplement 5 ) . These results taken together indicated that REST was not required for establishment or maintenance of H3K27me3 , throughout the genome generally or at loci targeted by REST specifically . However , in a very limited number of cases , H3K27me3 was lost in Rest−/− ESCs , reflecting either direct or indirect influence of REST on PRC2-binding . The increase in H3K27me3 at select sites may reflect block of PRC2 binding by REST due to close proximity of their binding sites . Why certain dually occupied genes lost or gained H3K27me3 in response to loss of REST was not obvious based on the function of the encoded proteins , but could be related to the timing of expression in vivo . 10 . 7554/eLife . 04235 . 011Table 2 . REST-associated genes with significant changes in H3K27me3 levels were measured in Rest−/− ESCsDOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 011Gene SymbolGene NameChange in Rest−/− ESCPrune2Prune homolog 22 . 1FosbFBJ osteosarcoma oncogene B2 . 0Mast1Microtubule associated serine/threonine kinase 11 . 8Celf4Bruno-like 4 , RNA binding protein1 . 7Kiaa1152Uncharacterized protein C14orf118 homolog1 . 7DnerDelta/notch-like EGF-related receptor1 . 6Cacng2Stargazin1 . 6BdnfBrain derived neurotrophic factor1 . 6Hes3Hairy and enhancer of split 31 . 6Otop3Otopetrin 31 . 5A330050F15RikUncharacterized protein LOC3207221 . 5Skor2SKI family transcriptional corepressor 21 . 4Nmnat2Nicotinamide nucleotide adenylyltransferase 2−1 . 3Cnnm1Cyclin M1−1 . 3Cabp1Calcium binding protein 1−1 . 5Kcnb1K+ voltage gated channel , Shab-related subfamily−1 . 6Celsr3Flamingo homolog 1−1 . 7MaptMicrotubule-associated protein tau isoform a−1 . 7BsnBassoon−2 . 2VgfVGF nerve growth factor inducible−4 . 4Sarm1Sterile alpha and TIR motif containing 1−12 . 1Galnt9Polypeptide Gal NAc transferase 9LossSmpd3Sphingomyelin phosphodiesterase 3LossScn8aNa2+ voltage-gated channel , type VIM , alphaLossList of genes located near REST bound regions that were associated with PRC2 in WT ESCs and showed a significant difference in H3K27me3 enrichment relative to Rest−/− ESCs . Although the above experiments ruled out a major role for PRC2 in REST-regulated repression , our mass spectrometry results using whole-cell extracts revealed three histone modifying enzymes in the purified REST complex that are often associated with repression: the histone H3K9 methyltransferase , G9a , histone deacetylases ( HDACs ) 1 and 2 , and the histone H3K4me1/2 demethylase , Kdm1a . Chromatin immunoprecipitation analysis showed that G9a recruitment was lost at RE1 sites in Rest−/− ESCs ( Figure 1—figure supplement 1 ) , and we observed a significant reduction in the levels of H3K9me2 in the region of the RE1 sites in 12/16 genes ( Figure 2A ) . Two control genes expressed in ESCs but lacking RE1s did not show any change ( Figure 2A ) . Consistent with the above findings , there was no correlation between changes in H3K9me2 in WT and Rest−/− ESCs and changes in H3K27me3 ( R2 = 0 . 002 ) , again underscoring the independence of REST and PRC2 in chromatin remodeling . 10 . 7554/eLife . 04235 . 012Figure 2 . Chromatin modification changes due to loss of REST . ( A ) REST-dependent establishment of 2Me-H3K9 , measured by ChIP , is impaired at RE1 sites in Rest−/− ESCs irrespective of changes in H3K27me3 levels . Oct4 and MageA8 are genes expressed in ESCs that lack RE1 sites . ( B ) Increased histone acetylation is detected at most RE1-associated promoters in the absence of REST , irrespective of changes in H3K27me3 levels . Oct4 and Gapdh promoter regions are expressed in ESCs and lack RE1 sites . ( C ) H3K4me3 enrichment is increased at most RE1-associated promoters in Rest−/− ESCs , independent of H3K27me3 levels ( * indicates p < 0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 012 The presence of HDACs in the REST complex predicted increased H3K9ac enrichment at RE1 sites in Rest−/− ESCs , which we observed in 11/16 analyzed genes , with no change in the controls that lacked RE1 sites ( Figure 2B ) . Importantly , there was no correlation between enrichment of H3K9ac and gain or loss of H3K27me3 ( R2 value = 0 . 056 ) due to the loss of REST . Although MLL proteins were not present in the REST immuno-complex , we tested for the presence of the H3K4me3 mark because it is associated with active or ‘poised’ promoters in ESCs in opposition to H3K27me3 ( Bernstein et al . , 2006 ) or REST ( Ballas et al . , 2005 ) . Of 16 genes containing RE1 sites , H3K4me3 was increased significantly in eight of them in Rest−/− ESCs , independent of the presence of the H3K27me3 mark and whether it was altered by the loss of REST ( Figure 2C; R2 = 0 . 029 ) . Thus , in the context of the bivalent hypothesis , although Polycomb is not an active component for REST-regulated genes , the presence or absence of the H3K4me3 mark may be an important aspect of the chromatin signature orchestrated by REST . As further evidence for this , we found that 37% ( 441/1202 ) of REST sites within 20 kb of target genes overlapped with H3K4me3 peaks , a number that increased to 62% for those REST sites within 5 kb of the TSS ( 417/617 ) . To determine the functional consequences of chromatin modification changes due to the loss of REST repression , we performed RNA-seq on transcripts from WT and Rest−/− ESCs . As expected , numerous REST target genes ( binding site identified within 20 kb of the transcription start site ) show an expected increase in expression levels in Rest−/− ESCs . However , the expression data show no correlation with changes in H3K27me3 levels , either at the REST-binding site ( Figure 3A ) or at the TSS ( Figure 3B ) . From this analysis we conclude that even the small H3K27me3 changes observed due to the loss of REST have little effect on the expression of REST target genes , further supporting the functional independence of REST from Polycomb . When REST target genes are further categorized according to their promoter status regarding H3K27me3 and H3K4me3 ( Young et al . , 2011 ) in WT ESCs , it is evident that all classes of REST target genes are de-repressed , irrespective of other marks ( Figure 3C ) . In comparison , REST target genes are not de-repressed in Eed−/− ESCs ( Ferrari et al . , 2014 ) ( Figure 3D ) , which show drastic decreases in the H3K27me3 mark . Combined , these results support the conclusion that REST is the primary repressor of its target genes and the roles of Polycomb and the H3K27me3 mark are functionally dispensable for its activity . 10 . 7554/eLife . 04235 . 013Figure 3 . REST-dependent changes in expression of REST targets are correlated significantly with REST-dependent changes in H3K4me3 , not H3K27me3 . ( A ) RNA-seq log2 ( Fold Change ) results for Rest−/− ESCs are not correlated with changes in H3K27me3 levels at REST sites or ( B ) REST target transcriptional start sites ( TSS ) . ( C ) All REST target genes are de-repressed in Rest−/− ESCs regardless of H3K27me3 or H3K4me3 status . ( D ) In contrast , REST targets show no transcriptional changes in Eed−/− ESCs , which have highly reduced levels of H3K27me3 , and genes with this mark show significant increases in expression ( p < 0 . 005 ) . ( E ) Changes in H3K4me3 enrichment in Rest−/− ESCs strongly correlate with REST target gene expression changes ( p < 0 . 01 ) . ( F ) Expression levels of H3K4me3-marked REST target genes are significantly reduced relative to H3K4me3-marked genes and de-repressed in Rest−/− ESCs ( p < 0 . 05 , *p < 0 . 001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 013 Bivalent developmental genes that become activated during differentiation are proposed to lose the repressive H3K27me3 mark but maintain the active H3K4me3 mark . Having shown that the REST and Polycomb pathways were largely independent , we asked which REST-dependent chromatin marks at the ESC stage might influence transcript levels of these targets . To this end , we performed a multiple regression analysis to determine which chromatin changes due to the loss of REST at the ESC stage were most likely associated with the observed expression changes . From this analysis , only the chromatin mark H3K4me3 correlated significantly with the expression changes observed in Rest−/− ESCs ( p <0 0 . 02 , Figure 3E ) . Further support for the importance of the H3K4me3 mark at REST targets is provided by the absolute levels of expression of REST target genes when categorized as above . Specifically , genes occupied by REST , marked either by just H3K4me3 or by H3K4me3 together with H3K27me3 , show significantly lower expression levels than non-REST target genes ( Figure 3F , p < 0 . 001 and p < 0 . 05 , respectively ) . When REST is deleted from ESCs , the expression levels of these H3K4me3-marked REST target genes are increased and approaches that of non-target H3K4me3-marked genes . This REST-dependent repression of H3K4me3-enriched promoters suggests that one of the primary functions of REST in ESCs is to counter RNA Pol II recruitment and maintenance of this activating mark . Despite functioning independently , Polycomb and REST repressor complexes in ESCs can generate similar downstream molecular effects by blunting H3K4me3 signaling at transcriptionally poised genes required for differentiation to proceed . To identify the mechanism for the increases in H3K4me3 after the loss of the REST complex , we monitored H3K4me3 levels at REST sites in ESCs that were mutant for the co-repressors G9a , Kdm1a , or HDACs ( Figure 4A ) . We used the histone deacetylase inhibitor trichostatin-A ( TSA ) as a proxy for HDAC loss , due to the redundancy of HDAC family members ( Montgomery et al . , 2007 ) . Only TSA treatment correlated significantly with increased H3K4me3 ( p < 0 . 01 , Figure 4B ) , consistent with previous studies that have indicated a negative interaction between de-acetylation at lysine 9 by HDAC activity and trimethylation at lysine 4 ( Lee at al . , 2006b ) . As expected , we also observed elevated acetylated H3K9 levels at RE1 sites after TSA treatment ( Figure 4—figure supplement 1 ) . Additionally , we utilized gene expression data sets published previously to analyze the transcriptional consequences of co-repressor removal . This analysis revealed that REST targets are de-repressed only in the absence of HDAC activity , but not when G9a and Kdm1a are mutated ( Figure 4C ) . By focusing on those genes for which we observed H3K4me3 effects , we also found a significant correlation between the magnitude of the change in RNA levels when REST is deleted and that when histone deacetylase activity is strongly inhibited by TSA ( p < 0 . 005 , Figure 4D ) . These results suggest that REST repression in ESCs is mediated primarily by recruited HDACs that serve as a counterbalance to H3K4me3 levels and basal RNA polymerase II activity , although the nature of the cross-talk between HDACs and H3K4 trimethylation in this context awaits future investigation . 10 . 7554/eLife . 04235 . 014Figure 4 . REST antagonizes H3K4me3 in ESCs through histone deacetylase activity . ( A ) Only treatment with the histone deacetylase inhibitor trichostatin-A ( TSA ) results in the increased H3K4me3 enrichment seen in Rest−/− ESCs . The active Oct4 and GAPDH promoter regions that lack RE1 sites were included as control regions enriched for H3K4me3 . ( B ) Changes in H3K4me3 enrichment at RE1 sites due to the loss of REST are significantly correlated with those due to TSA treatment ( p < 0 . 01 ) . ( C ) Microarray analysis reveals that HDAC inhibition by trichostatin-A ( TSA ) preferentially de-represses REST targets , unlike the loss of G9a or Kdm1a . ( D ) Changes in expression of select REST target genes due to REST loss significantly correlate with changes in expression due to HDAC inhibition with TSA ( p < 0 . 001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 01410 . 7554/eLife . 04235 . 015Figure 4—figure Supplement 1 . H3K9ac levels increase after TSA treatment . ( A ) H3K9ac enrichment is increased after treatment with histone deacetylase inhibitor trichostatin-A ( TSA ) but not when other co-factors are eliminated from the REST complex . DOI: http://dx . doi . org/10 . 7554/eLife . 04235 . 015 The Polycomb-mediated bivalent pattern of histone modifications , consisting of H3K4me3 and H3K27me3 , has been proposed as a central mechanism for maintenance of a poised transcriptional status in undifferentiated stem cells . However , a study on early zebra fish embryos showed that only 36% of inactive gene promoters were associated with a bivalent histone modification pattern , while 28% exhibited enrichment of H3K4me3 alone , yet remained inactive ( Vastenhouw et al . , 2010 ) . This suggests that in addition to PRC2 , alternative repressor mechanisms exist for recruiting chromatin modifiers to poised genes , although such proteins have not been identified . We propose that the REST/HDAC repressor mechanism represents one such alternative mechanism for genes in the neuronal lineage . By extension , our results indicate that both neuronal cell fate determining genes and neuronal genes expressed later in the differentiation program are poised in ESCs , albeit by different mechanisms . We identified three classes of REST-bound sites: 1 ) sites that lacked trimethylation at either H3K4 or H3K27 , 2 ) sites that exhibited the bivalent modification pattern H3K4me3 and H3K27me3 , and 3 ) sites marked by H3K4me3 only ( Figure 1—figure supplement 3D ) . The first class , lacking trimethylation , was preferentially located distal to promoter regions . Preliminary studies on these distal sites show that some overlap with the enhancer mark H3K4me1 and/or H3K27ac ( data not shown ) , potentially indicating a role for REST-directed repression at specified distal enhancer regions , an intriguing hypothesis that awaits future analysis . However , because it is currently unclear which promoters/genes these distal sites regulate , we focused on the other two subclasses , which were located within 20 kb of annotated TSSs . The majority of these REST-associated regions were enriched for H3K4me3 , consistent with our idea that the balance of REST-recruited HDACs and H3K4me3 was sufficient to poise neuronal genes independent of Polycomb . The REST-bound promoters with H3K27me3 raised the possibility of a functional link between REST and PRC2 in ESCs at these sites . Additionally , a previous report that the ncRNA HOTAIR can link REST and PRC2 suggested that ncRNA-mediated interactions in ESCs could result in PRC2 recruitment to REST-bound RE1 sites , and conversely , that REST complexes could be recruited to PRC2 bound regions independent from recognition of the RE1 motif ( Tsai et al . , 2010 ) . Our results are not consistent with either of these scenarios . First , REST-binding sites within the ESC genome appeared to be dependent exclusively on the underlying DNA sequence , because REST binding was correlated strictly with the presence of RE1 motifs . Second , the Polycomb complex member Eed , which is required for H3K27me3 deposition ( Montgomery et al . , 2005 ) , was absent from the REST complexes characterized by mass spectrometry and co-immunoprecipitation , undermining the likelihood of either repressive complex directly targeting the other . Third , only a minority of REST-bound sites was associated with H3K27me3 enrichment and PRC2 localization ( ∼3% ) . Moreover , H3K27me3 was not preferentially enriched at regions with multiple RE1 sites and thus did not show strong association with REST . Finally , more than 97% of the RE1 sites associated with PRC2 in wild-type cells also showed H3K27me3 enrichment in Rest−/− ESCs , including at gene promoters , indicating that REST was not required for PRC2 recruitment at these regions . Taken together , we conclude that REST and PRC2 act largely independently , even at shared target genes , in ESCs . The term ‘developmental regulators’ has been used to describe Polycomb targets in ESCs ( Boyer et al . , 2006; Lee et al . , 2006a ) . Therefore , we considered the possibility that the small subclass of REST/PRC2 targets might represent a specialized set of genes for promoting neural development . Gene ontology analysis , however , revealed no apparent distinction between biological functions in this subclass and the biological functions associated with the REST pattern alone . Both subclasses contained genes known to influence neurodevelopment , many of which persist in the adult nervous system , as well as other categories considered to be late neuronal genes involved in mature neuronal function , such as synaptic components and voltage-gated channels . Thus , PRC2 does not specifically target regulators of neurodevelopment within the REST-regulated network of genes . Different criteria used to define and quantify H3K27me3 domains may explain the discrepancies between our conclusions and those of others suggesting that REST mediates PRC2 recruitment in ESCs ( Dietrich et al . , 2012 ) . A critical distinction is that our analysis defined H3K27me3-enriched regions before comparing the computed H3K27me3 signals between WT and Rest−/− ESCs . By applying this initial binary condition , our analysis avoids the contribution of fluctuations in background signal . We argue that comparing the computed H3K27me3 ChIP-seq signals at all REST sites without considering initial H3K27me3 background signal would always find some level of relationship between REST and PRC2 and therefore eliminate the null hypothesis a priori . Therefore , we limited our comparisons of H3K27me3 domains to regions that also showed the clear presence of PRC2 activity in WT ESCs . Additionally , the loss of REST may generate small but reproducible effects in measured histone modifications due to local changes in nucleosome density , rather than actual changes in a specific modification ( Zheng et al . , 2009 ) . Similar to a recent study that showed the presence of REST evicts nucleosomes at RE1 DNA sequence motifs ( Valouev et al . , 2011 ) , we evaluated in vivo nucleosome positioning in ESCs and found that phasing of nucleosomes centered at RE1 motifs was displaced in Rest−/− cells ( data not shown ) . Therefore , although it can appear that PRC2 activity is increased specifically at RE1 sites by the loss of REST ( Dietrich et al . , 2012 ) ; this likely reflects a secondary consequence of the gain of a nucleosome at the RE1 site , due to the loss of the REST protein and subsequent fill-in of its footprint by a single histone octamer . How REST-associated nucleosome positioning generally affects gene expression is not yet known , but there are well-documented examples in lower eukaryotes of dynamic nucleosome positioning as a mechanism of gene regulation ( Bai and Morozov , 2010 ) . In addition to maintaining nucleosome-depleted regions , our results indicate that REST likely counterbalances RNA Pol II activity primarily through recruitment of histone deacetylase activity in undifferentiated cells , echoing results observed in human T cells ( Zheng et al . , 2009 ) . Acetylated histone tails have been shown to interact with bromodomains of transcription factors , such as Brd4 , which promotes recruitment of Mediator complexes or positive transcription elongation factor b ( P-TEFb ) and release of paused RNA Pol II ( Jang et al . , 2005; Yang et al . , 2005; Wu and Chiang , 2007 ) . These interactions may explain the observed dependence of H3K4me3 on TSA and H3K9 acetylation . The net transcriptional effect on genes in Rest−/− ESCs was variable and depended on the locus ( Johnson et al . , 2008; Jorgensen et al . , 2009 ) , which is likely due to specific activators being present or absent in ESCs , as well as additional repressive mechanisms that may be acting at the same target . However , changes in the H3K4me3 mark ( and histone acetylation ) due to the loss of REST were significantly correlated with changes in gene expression , while the other histone modifications we analyzed were not . This suggests that REST-directed repression of H3K4 methyltransferases or activation of H3K4me3 demethylases is important to restrict the amount of expression from these genes . A potential candidate demethylase is SMCX ( Jarid1C ) , which binds REST in HeLa cells and can regulate promoter H3K4me3 levels ( Tahiliani et al . , 2007 ) , although we found no evidence of SMCX binding in our mass spectrometry results . In addition , there is evidence that H3K4me3 ‘primes’ non-expressed genes for acetylation and increased gene expression after histone deacetylase loss ( Wang et al . , 2009; Lopez-Atalaya et al . , 2013 ) . Thus , as neuronal differentiation proceeds and REST/HDAC levels on target chromatin decrease dramatically , those genes previously marked with H3K4me3 increase this mark simultaneously with H3K9 acetylation in a rapid feed-forward mechanism . Based on our results , we propose that the loss of the REST or Polycomb repressor complexes from different sets of genes , in conjunction with the recruitment of transcriptional activators , allows for finely tuned , graded expression changes over the course of differentiation . In stem/progenitor cells , REST is a key repressor of genes crucial to the terminally differentiated neuron , while PRC2 is a repressor of a REST independent pathway regulating pro-neural genes that are required at earlier differentiation stages ( Mohn et al . , 2008 ) . Finally , ‘terminal selector’ genes , which are transcriptional activators in mature neurons , also drive their own expression to maintain the terminally differentiated phenotype ( Hobert , 2011 ) . In a similar but reversed case , the REST gene , which itself contains a REST-binding site , may function to reduce its own expression so that differentiation can proceed unidirectionally . It will be interesting in the future to see whether repressors in other cell lineages play similar roles in poising terminal genes in stem/progenitor cells . A recent study has suggested that structural genes encoding mature cardiac cell functions are regulated primarily by transcriptional activators rather than by H3K27me3 ( Paige et al . , 2012 ) . An alternative possibility , based on our study , is that these temporally delayed cardiac genes are repressed by factors , still to be identified , which recruit chromatin modifiers other than Ezh2 in order to balance the activation mark in stem cells . In neurons , direct reprogramming can occur by introducing pro-neural ( Ascl1 ) along with terminal genes ( e . g . Myt1l ) into somatic cells ( Vierbuchen et al . , 2010 ) perhaps because they represent distinct regulatory pathways . Better knowledge of the factors regulating terminally differentiated gene chromatin could provide insight into the mechanisms underlying direct reprogramming of fibroblasts into different types of cells ( Nam et al . , 2013 ) . Mouse REST ( mREST ) CDS , lacking the start and stop codons and flanked by BamH1 sequences , was amplified from pcDNA3 . 1A ( − ) -mREST-Myc-His ( Mandel , unpublished ) using the following PCR primers: 5′- GCG CGG ATC CCC ACC CAG GTG ATG GGG CA -3′ ( JL70112a ) and 5′- GCG CGG ATC CCT ACT CCT GCT CCT CCC GC -3′ ( JL70705a ) ( underlined are BamHI sites ) . The fragment was cloned into a TOPO-TA vector , released by BamH1 , and then cloned in frame into the BamH I site in pEFrFLAG-BIOpGKpuropAv1 ( pFL-Big ) ( Wang et al . , 2006 ) , kindly provided by Jianlong Wang and Stuart Orkin ( Harvard Medical School ) . The pFL-Big plasmid was kindly provided by Jianlong Wang and Stuart Orkin ( Harvard Medical School ) , the N6 and N8 ESC lines were provided by Zhou-Feng Chen , ( Washington University in St . Louis ) . Plasmid pFBmR was linearized with Sca I and transfected with Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) into BirA-J1 ES cells that stably express the Escherichia coli Bir A ligase ( Wang et al . , 2006 ) ( kindly provided by Jianlong Wang and Stuart Orkin ) and maintained in 15% FBS in DMEM ( #11965; Gibco ) supplemented with penicillin/streptomycin , 2 mM L-glutamine , non-essential amino acids ( #M7145; Sigma ) , 0 . 1 mM 2-mercaptoethanol , 8 mg/l adenosine , 8 . 5 mg/l guanosine , 7 . 3 mg/l uridine , 7 . 3 mg/l cytidine , 2 . 4 mg/l thymidine , and 10 U/ml LIF ( # ESG1107; Chemicon/Millipore ) on tissue culture plates coated with 0 . 1% gelatin ( Sigma ) . Stable BioT-REST expressing cells were selected in 2 µg/ml puromycin and individual clones were hand picked under a microscope . The clones were then screened by Western blot analysis for REST protein level , using an antibody raised against the C-terminus of hREST ( Ballas et al . , 2005 ) . One clone expressing REST at levels ∼5 fold that of endogenous REST ( clone #60 ) was used in the streptavidin pull-down experiment for mass spectrometric analysis . ES cells expressing BioT-REST ( clone #60 ) from 10 , 15 cm dishes were used for each pull down . The cells were harvested and pelleted , then lysed in 2 . 5 ml cold lysis/binding buffer ( 0 . 5 mM EDTA , 150 mM NaCl , 0 . 5% Triton X-100 , 10% glycerol , 1 mM NaF , 1 mM Na3VO4 , 0 . 5 mM DTT in pH 7 . 5 , 50 mM Tris–Cl with 1× Roche complete protease inhibitors cocktail ) with the help of sonication on ice ( 4 rounds of 20 strokes , output 4 , 40% duty cycle , Sonifier ) . The cell lysate was cleared by centrifuge at 4°C and incubated with buffer-exchanged 200 µl streptavidin M-280 magnetic beads ( Dynal beads/Invitrogen ) at 4°C for 3 hr in lysis/binding buffer . After the incubation , the beads were pelleted and washed three times with 1 ml cold lysis/binding buffer and three times with 1 ml cold PBS . The beads were then eluted twice with 200 µl and 100 µl elution buffer ( 1:1 ( vol/vol ) acetonitrile/H2O in 0 . 1% trifluoroacetic acid ) at 65°C for 10 min . The two eluates were combined and SpeedVac dried under no heat and subjected to MudPIT analysis as powder . The parental BirA-J1 ES cells , which express E . coli BirA ligase but no BioT-tagged REST , were processed and analyzed in parallel as the negative control pull down . The eluted REST complex was solubilized in 8 M urea containing 10 mM dithiothreitol and incubated at 60°C for 30 min . The solution was cooled to room temperature and iodoacetamide was added to a final concentration of 15 mM and incubated at room temperature for 20 min in dark . The solution was then diluted to a final urea concentration of 2 M with 100 mM Tris–HCl . The proteins were digested with 1 μg of trypsin at 37°C overnight . The digestion was terminated by adding formic acid to 5% , and centrifuged . Half of the peptides containing supernatant were used for liquid chromatography coupled with mass spectrometry analysis to identify proteins . Peptides from each pull-down sample were pressure-loaded onto a 250 µm i . d . fused silica capillary column packed with a 3 cm , 5 µm Partisphere strong cation exchanger ( SCX , Whatman , Clifton , NJ ) and a 3 cm , 10 µm Aqua reversed-phase C18 material ( Phenomenex , Ventura , CA ) , with the SCX end fritted with immobilized Kasil 1624 ( PQ Corperation , Valley forge , PA ) . After desalting , a 100 µm i . d . capillary with a 5 µm pulled tip packed with a 10 cm , 54 µm Aqua C18 material was attached to a ZDV union , and the entire split-column was placed inline with an Agilent 1100 quaternary HPLC ( Agilent , Palo Alto , CA ) and analyzed using a modified , six-step multi-dimensional protein identification technology ( MudPIT ) described previously ( Washburn et al . , 2001 ) . As the peptides were eluted from the microcapillary column , they were electrosprayed directly into an LTQ linear ion trap mass spectrometer ( ThermoFinnigan , San Jose , CA ) with the application of a distal 2 . 5 kV spray voltage . A cycle of one full-scan mass spectrum ( 400–1400 m/z ) followed by 5 data dependent MS/MS scan at a 35% normalized collision energy was repeated continuously throughout each step of the multidimensional separation . The resulting MS/MS spectra were searched with the SEQUEST algorithm ( Griffin et al . , 1995 ) against a mouse IPI database ( version 3 . 30 , released at 28 June 2007 ) that was concatenated to a decoy database in which the sequence for each entry in the original database was reversed . The search parameters include a static cysteine modification of 57 amu and no trypsin specificity . The database search results were assembled and filtered using the DTASelect program ( Tabb et al . , 2002 ) requiring a protein level false discovery rate less than 1% , all peptides identified are required to be tryptic , and at least two peptides are required for a protein to be identified . Under such filtering conditions , no peptide hit from the reverse database was found . Mouse ESCs cultures , N6 ( WT ) and N8 ( Rest−/− ) ( Jorgensen et al . , 2009 ) , were cultured in DMEM medium described above . ESCs were cultured on feeder layers of irradiated mouse embryonic fibroblasts and passaged three times on plates coated with 0 . 1% gelatin to eliminate MEFs before harvesting cells for RNA or chromatin purification . Total RNA was extracted using TRIzol ( Invitrogen ) followed by on-column DNAse treatment with RNase-free DNase and RNesay mini kit ( Qiagen ) . 2 μg total RNA was used to make one sequencing library . Two biological replicates were made for each condition: WT ESC and REST−/− ESC . Indexed libraries were prepared using the Illumina TruSeq RNA Sample Preparation Kit v2 ( San Diego , CA ) . Four libraries were mixed at equal concentration and sequenced by an Illumina HiSeq 2000 sequencer at the OHSU Massively Parallel Sequencing Shared Resource ( MPSSR ) . Reads were mapped using Subread ( Liao et al . , 2013 ) and gene counts assigned using FeatureCounts ( Liao et al . , 2014 ) . Differential expression analysis was performed using edgeR ( Robinson et al . , 2010 ) with p-values assessed by both tag-wise and common dispersion analysis . Primary reads and mapped gene counts can be found at GSE59442 . Total RNA was isolated by disrupting cultured cells with Trizol reagent ( Invitrogen ) followed by chloroform extraction and ethanol precipitation/wash according to the manufacturer's instructions . For each sample , 1 μg of purified RNA was used as template for first strand cDNA synthesis with random hexamer primers and SuperScript III reverse transcriptase ( Invitrogen ) following the standard manufacturer’s protocol . cDNA quantities were evaluated by quantitative real-time PCR measuring SYBR Green fluorescence on an ABI 7900HT . Following activation of the hot start polymerase at 95°C for 10 min , reactions were cycled 40 times at 95°C for 15 s and 60°C for 1 min . Experimental cDNA samples were run in triplicate . Primer sequences used for amplification are listed in Table 1—Source data 1 . Relative gene expression for genes of interest ( GOI ) was calculated using the ΔΔCt method and normalized to Gapdh levels to control for variation in reaction inputs . Standard deviation of the normalized expression was calculated as; SD = ( normalized value ) × ln ( 2 ) × √ ( ( SDGapdh ) 2 + ( SDGOI ) 2 ) . ChIP analyses were performed as described previously ( Ballas et al . , 2005 ) . Briefly , cells were treated with 1% formaldehyde for 10 min at RT to form protein–DNA crosslinks . Crosslinking reaction was quenched by addition of glycine to a final concentration of 0 . 125 M and incubating for 5 min at RT , followed by two washes with PBS . Harvested cells were resuspended in nuclei isolation buffer ( 5 mM HEPES , pH 8 . 0 , 85 mM KCl , and 0 . 5% Triton X-100 ) and incubated for 10 min on ice . Pelleted nuclei were resuspended in nuclei lysis buffer ( 50 mM Tris–HCl , pH 8 . 0 , 10 mM EDTA , and 1% SDS ) at an approximate concentration of 107 cells per ml prior to shearing chromatin by sonication to a final size range of ∼100–750 bp . Chromatin lysate was diluted 1:10 with ChIP dilution buffer and specific antibodies were added for overnight incubation at 4°C . The following antibodies were used for immunoprecipitations: anti-H3K4me3 ( 07-473; Millipore ) , anti-Ac-H3K9 ( H9286; Sigma ) , anti-2Me-H3K9 ( ab1220; Abcam ) , anti-H3K27me3 ( 9733; Cell Signaling ) , anti-H3 ( 2650; Cell Signaling ) , anti-REST-C ( Ballas et al . , 2005; Otto et al . , 2007 ) , anti-Ezh2 ( 5246; Cell Signaling ) , LSD1 ( Kdm1a ) antibody from Yang Shi ( Harvard Medical School ) . Protein A conjugated magnetic beads that had been blocked with BSA were used to purify immunocomplexed chromatin fragments by incubating with sample lysates for 3 hr at 4°C . Beads were sequentially resuspended in low salt , high salt , and LiCl wash buffers followed by two final washes in TE buffer . Immunoprecipitated chromatin was eluted from the beads resuspended in elution buffer ( 50 mM Tris HCl , pH 8 . 0 , 100 mM NaHCO3 , 1% SDS , and 200 mM NaCl ) during reversal of formaldehyde crosslinks by overnight incubation at 65°C . Elutions were treated with RNase A ( 1 hr at 37°C ) and proteinase K ( 2 hr at 55°C ) prior to a final purification of DNA by column chromatography ( Qiagen PCR Purification ) . Quantities of immunoprecipitated DNA were measured relative to signal from input samples by real-time PCR and analyzed using the ΔΔCt method . Primer sequences used for ChIP analysis are listed in Table 1—source data 1 . Data were analyzed using linear regression analysis ( Figures 3E , Figure 4B , D ) , or Student's t-test ( all other figures ) . A threshold of p < 0 . 05 was interpreted as significant . ChIP-isolated DNA was pooled ( three technical replicates done in parallel from each of the two independent biological replicates ) and fragments were processed to blunt ends followed by A-tailing to facilitate ligation of Illumina oligo adapters . PCR amplification was run for 12–14 cycles with primers complementary to adapter sequence to amplify the pool of ChIP DNA with addition of the adapter sequence . PCR products in the range of 200–300 bp were isolated by agarose gel electrophoresis followed by gel extraction . 5 ng of sheared DNA purified from chromatin samples without immunoprecipitation was also processed in this manner as an input control . DNA fragments were sequenced using the Illumina Genome Analyzer II platform . The number of unique reads aligned to the mm9 assembly for each ChIP-Seq was: REST ( WT ESC ) 11 , 954 , 736 , H3K27me3 ( WT ESC ) 17 , 893 , 323 , H3K27me3 ( Rest−/− ESC ) 14 , 102 , 052 , and Input ( WT ESC ) 12 , 092 , 824 . Raw reads were aligned with Bowtie and only uniquely mapped reads were kept . After alignment , PeakRanger ( Feng et al . , 2011 ) and MACS ( Zhang et al . , 2008 ) were used to call peaks and the overlap peak set was retained . Overlapped regions may have different boundaries . To identify H3K27me3 peaks conserved between cell lines and called by independent means , we used only those MACS-called peaks that overlapped between our H3K27me3 peak set and those identified by the Encode project ( Grant et al . , 2011 ) . To find the genomic regions with increased levels of H3K27me3 after REST knock out , the H3K27me3 ChIP in REST knock-out is used as the ‘treatment’ and H3K27me3 ChIP in wild-type as the ‘control’ for both of the two software programs and the same significance threshold was set for both . The data sets were then swapped and analyzed for regions with decreased levels of H3K27me3 . To get the reads for histograms shown in Figure 1—figure supplement 3A , the ‘wig’ module of PeakRanger parsed all aligned reads and counted reads within the specified regions . To find the REST-binding motif ( RE1 ) , REST peak coordinates were used as input for FIMO ( Grant et al . , 2011 ) . Previously published ChIP-seq data sets used in the analysis of H3K27me3 were GSE51006 ( Ferrari et al . , 2014 ) , GSE48172 ( Hu et al . , 2013 ) , GSE27341 ( Arnold et al . , 2013 ) , and GSE49431 ( Kaneko et al . , 2013 ) . Previously published ChIP-seq data set used in the analysis of PRC2 components was GSE49431 ( Kaneko et al . , 2013 ) . Previously published ChIP-seq data sets used in the analysis of REST complex components were GSE27841 ( Whyte et al . , 2012 ) and GSE24841 ( Williams et al . , 2011 ) . Previously published ChIP-seq data set for H3K4me3 peaks was GSM1003756 ( Stamatoyannopoulos et al . , 2012 ) . Data were collected as or converted to bigwig format using a combination of BEDtools ( Quinlan and Hall , 2010 ) , SAMtools ( Li et al . , 2009 ) , and analyzed using the bigWigAverageOverBed module from the Kent source tools available from the UCSC genome browser ( Kent et al . , 2010 ) . Gene lists derived from methods above and previous publications ( Young et al . , 2011 ) were formatted and uploaded to the AMIGO GO Enrichment tool and analyzed for enrichment in biological processes ( Carbon et al . , 2009 ) . Funding was provided by the Howard Hughes Medical Institute and the National Institutes of Health .	When an embryo is developing , genes are switched on or off at different times , for many different reasons . Many of these genes are switched off , or repressed , by making the DNA inaccessible to the various proteins and molecules that control gene activity . This is achieved by altering the way that the DNA is packaged into a compacted structure called chromatin . A host of proteins modify the structure of chromatin: it can be made more tightly packaged , which keeps genes switched off; or it can be made more loosely packaged , which allows the genes within to be accessed and switched on . The stem cells in an embryo are able to give rise to many different types of specialized cell . Genes that determine which cell type a stem cell will eventually become are often kept in a so-called ‘poised’ state , and have chromatin modifications that encourage genes to be switch on , as well as modifications that switch genes off . Current thinking is that this poised state allows these genes to be switched on or off rapidly in response to the signals that the cell receives during development . The only known protein complex that causes the chromatin to become more compacted in this poised state is the Polycomb complex . This complex binds to specific regions of DNA and is thought to allow stem cells to remain able to become different cell types by repressing the genes required for adopting a specialized cell fate . However , it is unclear if this poised state also regulates those genes that control the final stages of a cell becoming a specific cell type . McGann et al . investigated genes that are involved in the final stages of a nerve cell's development . These genes are regulated by another protein called REST , which acts to repress the genes in non-neuronal cells . McGann et al . found that the genes that are regulated by REST in embryonic stem cells from mice also have their chromatin modified in two contrasting ways . Some of the modifications are linked to switching genes on , while others are linked to keeping genes switched off . Thus these genes are also in a poised state . However , for these genes , this state is acquired without the activity of the Polycomb complex . The results of McGann et al . show that two similar , but distinct , mechanisms keep the genes required for the early and the late stages of nerve cell development in a poised state . If this poised state aids the development of other cell types ( for example muscle or fat cells ) , uncovering how it is achieved could improve our ability to direct stem cells to develop into specific cell types and tissues .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "chromosomes", "and", "gene", "expression", "developmental", "biology" ]	2014	Polycomb- and REST-associated histone deacetylases are independent pathways toward a mature neuronal phenotype
Working memory and conscious perception are thought to share similar brain mechanisms , yet recent reports of non-conscious working memory challenge this view . Combining visual masking with magnetoencephalography , we investigate the reality of non-conscious working memory and dissect its neural mechanisms . In a spatial delayed-response task , participants reported the location of a subjectively unseen target above chance-level after several seconds . Conscious perception and conscious working memory were characterized by similar signatures: a sustained desynchronization in the alpha/beta band over frontal cortex , and a decodable representation of target location in posterior sensors . During non-conscious working memory , such activity vanished . Our findings contradict models that identify working memory with sustained neural firing , but are compatible with recent proposals of ‘activity-silent’ working memory . We present a theoretical framework and simulations showing how slowly decaying synaptic changes allow cell assemblies to go dormant during the delay , yet be retrieved above chance-level after several seconds . Prominent theories of working memory require information to be consciously maintained ( Baars and Franklin , 2003; Baddeley , 2003; Oberauer , 2002 ) . Conversely , influential models of visual awareness hold information maintenance as a key property of conscious perception , highlighting synchronous thalamocortical activity ( Tononi and Koch , 2008 ) , cortical recurrence ( Lamme and Roelfsema , 2000 ) , or the sustained recruitment of parietal and dorsolateral prefrontal regions ( i . e . , the same areas as in working memory; Naghavi and Nyberg , 2005 ) in a global neuronal workspace ( Dehaene and Changeux , 2011 , 2001 ) . Experimentally , non-conscious priming only lasts a few hundred milliseconds ( Dupoux et al . , 2008; Greenwald et al . , 1996 ) and unseen stimuli typically fail to induce late and sustained cerebral responses ( Dehaene et al . , 2014 ) . Conscious perception , in contrast , exerts a durable influence on behavior , accompanied by sustained neural activity ( King et al . , 2014; Salti et al . , 2015; Schurger et al . , 2015 ) . The hypothesis of an intimate coupling between conscious perception and working memory is thus grounded in theory and supported by numerous empirical findings . Recent behavioral and neuroimaging evidence , however , has questioned this prevailing view by suggesting that working memory may also operate non-consciously . Unseen stimuli may influence behavior for several seconds ( Bergström and Eriksson , 2015; Soto and Silvanto , 2014 ) . Soto et al . ( 2011 ) , for instance , showed that participants recalled the orientation of a subjectively unseen Gabor cue above chance-level after a 5s-delay . Functional magnetic resonance imaging suggests that prefrontal activity may underlie such non-conscious working memory ( Bergström and Eriksson , 2014; Dutta et al . , 2014 ) . The verdict for non-conscious working memory is far from definitive , however . Delayed performance with subjectively unseen stimuli was barely above chance ( Soto et al . , 2011 ) and could have arisen from a small percentage of errors in visibility reports , with subjects miscategorizing a seen target as unseen ( miscategorization hypothesis ) . If this were the case , then the blindsight trials , on which subjects correctly identified the target while denying any subjective awareness of the stimulus , should display similar , if not identical , neural signatures and contents as the seen trials . Alternatively , participants could also have ventured a guess about the target as soon as it appeared and consciously maintained this early guess ( conscious maintenance hypothesis ) . Many priming studies have shown that fast guessing results in above-chance objective performance with subjectively unseen stimuli ( Merikle et al . , 2001 ) . The observed blindsight effect would then reflect a normal form of conscious working memory ( Stein et al . , 2016 ) . This alternative hypothesis is hard to eliminate on purely behavioral grounds; it can only be rejected by tracking the dynamics of working memory activity , for instance using brain-imaging , and determining whether this activity occurs immediately after the target even on unseen trials . Here , we set out to address these issues , focusing on four main objectives: First , we probed the replicability of the long-lasting blindsight effect reported by Soto et al . ( 2011 ) as well as its robustness with respect to interference from distraction and a conscious working memory load in order to delineate it from other forms of prolonged iconic or sensory memory . Second , we interrogated the link between conscious perception and conscious working memory , examining whether the maintenance period in working memory could be likened to a prolongation of a conscious episode . Third , we tested the reality of non-conscious working memory by systematically examining the neural correlates of the blindsight effect and using them to assess the above two alternative hypotheses ( the miscategorization and conscious maintenance hypothesis ) . Lastly , we propose a neuronal theory to offer a mechanistic account of conscious and non-conscious working memory . We first examined objective performance in the working memory task as a function of target visibility . Overall , subjects reported the exact target location with high accuracy on seen trials ( collapsed across visibility ratings > 1: Mcorrect = 69 . 1% , SDcorrect = 17 . 4%; chance = 5%; t ( 16 ) = 15 . 2 , p<0 . 001 , 95% CI = [55 . 2% , 73 . 1%]; Cohen’s d = 3 . 7 ) . As subjective visibility of the target increased from glimpsed ( visibility = 2 ) to clearly seen ( visibility = 4 ) , there was a corresponding monotonic increase in accuracy ( Figure 1B; ps<0 . 05 for all pair-wise comparisons ) . Crucially , performance remained above chance even on unseen trials ( rating = 1: Mcorrect = 22 . 4% , SDcorrect = 13 . 8%; t ( 16 ) = 5 . 2 , p<0 . 001 , 95% CI = [10 . 3% , 24 . 4%]; Cohen’s d = 1 . 3 ) . This blindsight remained substantial after a 4s-delay ( Mcorrect = 21 . 1% , SDcorrect = 14 . 7%; t ( 16 ) = 4 . 5 , p<0 . 001 , 95% CI = [8 . 5% , 23 . 7%]; Cohen’s d = 1 . 0 ) . Spatial distributions of participants’ responses were concentrated around the target ( Figure 2A ) . To correct for small errors in localization , we computed the rate of correct responding with a tolerance of two positions ( ±36° ) surrounding the target location . In subjects displaying above-chance blindsight ( chance = 25%; p<0 . 05 in a χ2-test; n = 13 ) , we estimated the precision of working memory as the standard deviation of the distribution within this tolerance interval ( Materials and methods ) . Performance was better on seen than on unseen trials , both in terms of rate of correct responding ( F ( 1 , 16 ) = 198 . 5 , p<0 . 001; partial η2 = 0 . 925 ) and precision ( F ( 1 , 12 ) = 36 . 7 , p<0 . 001; partial η2 = 0 . 754 ) . There was neither an effect of the distractor on these measures ( all ps>0 . 079 ) , nor any significant interactions between distractor and visibility ( all ps>0 . 251 ) , indicating that distractor presence did not affect retention for seen or unseen targets . Restricting the analyses to trials within one position of the actual target location ( ±18° ) or to the subgroup of 13 subjects included in the MEG analyses did not change these findings qualitatively . 10 . 7554/eLife . 23871 . 004Figure 2 . Behavioral evidence for non-conscious working memory . Spatial distributions of responses ( 0 = correct target location; positive = clockwise offset ) as a function of visibility and distractor presence ( A ) , conscious working memory load ( B ) and delay duration ( C ) . Insets show rate of correct responding ( within ±2 positions of actual location ) and precision of working memory representation separately for seen and unseen trials . Error bars represent standard error of the mean ( SEM ) across subjects and horizontal , dotted line indicates chance-level ( 5% ) . p<0 . 05 , p<0 . 01 , and *p<0 . 001 in a paired sample t-test . Del = delay , Dis = distractor , L = load . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 00410 . 7554/eLife . 23871 . 005Figure 2—figure supplement 1 . Perceptual sensitivity does not correlate with working memory performance on unseen trials . ( A ) Scatter plots depicting the relationship between detection d’ and accuracy ( left ) , the rate of correct responding ( middle ) , and precision ( right ) in the working memory task of experiment 1 as a function of visibility . ( B ) Same as in ( A ) , but for experiment 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 005 While target detection d’ exceeded chance-level ( M = 1 . 5 , SD = 0 . 7; t ( 16 ) = 8 . 9 , p<0 . 001 , 95% CI = [1 . 2 , 1 . 9]; Cohen’s d = 2 . 1 ) and correlated with accuracy and the rate of correct responding on seen trials ( both Pearson rs > 0 . 762 , both ps<0 . 001 ) , there was no relationship between our participants’ sensitivity to the target and any of our performance measures on the unseen trials ( all Pearson rs < 0 . 342 , all ps>0 . 179; Figure 2—figure supplement 1A ) . Thus , target visibility predicted performance in the objective working memory task only on seen trials , but not on unseen trials . Overall , these results confirm , with much higher non-conscious performance , the observations of previous studies ( Soto et al . , 2011 ) : Non-conscious information may be maintained for up to 4 s and successfully shielded against distraction from a salient visual stimulus , independently of overall subjective visibility . To probe the similarity between conscious working memory and the observed long-lasting blindsight effect , in a second behavioral experiment with 21 subjects , we examined whether imposing a load on conscious working memory ( remembering digits ) affected non-conscious performance . On each trial , 1 ( low load ) or 5 ( high load ) digits were simultaneously shown for 1 . 5 s , followed by a 1s-fixation period and the same sequence of events ( target and mask ) as in experiment 1 . After a variable delay ( 0 or 4 s ) , participants had to ( 1 ) localize the target , ( 2 ) recall the digits in the correct order , and ( 3 ) rate target visibility . Subjects again chose the exact target position with high accuracy on seen trials ( Mcorrect = 77 . 8% , SDcorrect = 13 . 9% ) and remained above chance on unseen trials ( Mcorrect = 25 . 6% , SDcorrect = 11 . 8%; chance = 5%; t ( 18 ) = 7 . 6 , p<0 . 001 , 95% CI = [14 . 9% , 26 . 3%]; Cohen’s d = 1 . 7 ) . While , as in experiment 1 , cue detection d’ was greater than chance ( M = 1 . 7 , SD = 0 . 8; t ( 20 ) = 10 . 2 , p<0 . 001 , 95% CI = [1 . 4 , 2 . 1]; Cohen’s d = 2 . 2 ) , no correlations were observed with objective task performance on the unseen trials ( all Pearson rs < 0 . 366 , all ps>0 . 115; seen trials: all Pearson rs > 0 . 443 , all ps<0 . 051; Figure 2—figure supplement 1B ) . As expected , participants were better at recalling 1 rather than 5 digits in the correct order ( M = 93 . 3% vs . 89 . 5% , F ( 1 , 17 ) = 4 . 7 , p=0 . 045 ) , irrespective of target visibility or delay duration ( all ps>0 . 135 ) . Analyzing only the trials with correctly recalled digits , we observed an impact of load on the precision with which target location was retained ( F ( 1 , 13 ) = 7 . 3 , p=0 . 018; partial η2 = . 360 ) . Crucially , load modulated the relationship between precision and visibility ( interaction F ( 1 , 13 ) = 8 . 7 , p=0 . 011; partial η2 = . 400 ) , with no effect on seen ( t ( 13 ) = 0 . 6 , p=0 . 561 ) and a strong reduction of precision on unseen trials ( t ( 13 ) = −3 . 6 , p=0 . 004 ) . There was no effect of working memory load on the rate of correct responding ( all ps>0 . 229; Figure 2B ) . Delay duration ( 0 or 4 s ) also did not influence the rate of correct responding ( all ps>0 . 082; Figure 2C ) . It did , however , affect overall precision ( F ( 1 , 15 ) = 9 . 3 , p=0 . 008; partial η2 = . 383 ) and the relationship between precision and visibility ( interaction F ( 1 , 15 ) = 5 . 2 , p=0 . 037; partial η2 = . 259 ) . This interaction was driven by higher precision on no-delay than on 4s-delay trials , exclusively when subjects had seen the target ( t ( 15 ) = −5 . 7 , p<0 . 001; unseen trials: t ( 15 ) = −0 . 6 , p=0 . 559 ) . Overall , these results highlight the replicability and robustness of the long-lasting blindsight effect and suggest that it does not just constitute a prolonged version of iconic memory: Even in the presence of a concurrent conscious working memory load , unseen stimuli could be maintained , with no detectable decay as a function of delay . However , the systems involved in the short-term maintenance of conscious and non-conscious stimuli interacted , because a conscious verbal working memory load diminished the precision with which non-conscious spatial information was maintained . To tackle our second objective – a detailed examination of the link between conscious perception and conscious working memory – , we turned to our MEG data and first ensured that the mechanisms underlying conscious perception were stable across experimental conditions . The subtraction of the event-related fields ( ERFs ) evoked by unseen trials from those evoked by seen trials revealed similar topographies for the perception and working memory task ( Figure 3A ) : Starting at ~300 ms and extending until ~500 ms after target onset , a response emerged over right parieto-temporal magnetometers . This divergence resulted primarily from a sudden increase in activity on seen trials ( ‘ignition’ ) in the perception ( pFDR<0 . 05 from 384 to 416 ms and from 504 to 516 ms ) and working memory task ( pFDR<0 . 05 from 328 to 364 ms and from 396 to 404 ms; Figure 3B ) . The observed topographies and time courses fall within the time window of typical neural markers of conscious perception , including the P3b ( e . g . , Del Cul et al . , 2007; Salti et al . , 2015; Sergent et al . , 2005 ) . Consciously perceiving the target stimulus therefore involved comparable neural mechanisms , irrespective of task . 10 . 7554/eLife . 23871 . 006Figure 3 . Neural signatures for conscious perception and maintenance in working memory . ( A ) Sequence of brain activations ( −200–800 ms ) evoked by consciously perceiving the target in the perception ( top ) and working memory ( bottom ) task . Each topography depicts the difference in amplitude between seen and unseen trials over a 100 ms time window centered on the time points shown ( magnetometers only ) . ( B ) Average time courses of seen and unseen trials ( −200–800 ms ) after subtraction of target-absent trials in a group of parietal magnetometers in the perception ( left ) and working memory ( right ) task . Shaded area illustrates standard error of the mean ( SEM ) across subjects . Significant differences between conditions are depicted with a horizontal , black line ( Wilcoxon signed-rank test across subjects , uncorrected ) . For display purposes , data were lowpass-filtered at 8 Hz . T = target onset . ( C ) Temporal generalization matrices for decoding of visibility category as a function of training and testing task . In each panel , a classifier was trained at every time sample ( y-axis ) and tested on all other time points ( x-axis ) . The diagonal gray line demarks classifiers trained and tested on the same time sample . Please note the event markers in any panel involving the perception task: Mean reaction time ( target-present trials ) for the visibility response is indicated as vertical and/or horizontal , dotted lines . Any classifier beyond this point only reflects post-visibility processes . Time courses of diagonal decoding and of classifiers averaged over the P3b time window ( 300–600 ms ) and over the working memory maintenance period ( 0 . 8–2 . 5 s ) are shown as black , red , and blue insets . Thick lines indicate significant , above-chance decoding of visibility ( Wilcoxon signed-rank test across subjects , uncorrected , two-tailed except for diagonal ) . For display purposes , data were smoothed using a moving average with a window of eight samples . AUC = area under the curve . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 006 We next directly probed the relationship between conscious perception and information maintenance in conscious working memory . Does the latter reflect a prolonged conscious episode , or does it involve a distinct set of processes recruited only during the retention phase ? If conscious working memory can indeed be likened to conscious perception , one might expect the same patterns that index such perception to be sustained throughout the working memory maintenance period . Linear multivariate pattern classifiers were trained to predict visibility ( seen or unseen ) from MEG signals separately for each task . Classification performance was assessed during an early time period ( 100–300 ms ) , the critical P3b time window ( 300–600 ms ) , and the first ( 0 . 6–1 . 55 s ) and second part ( 1 . 55–2 . 5 s ) of the delay period . Decoding of the visibility effect was comparable in the two tasks ( Figure 3C and Supplementary file 1 ) : Classification performance rose sharply between 100 and 300 ms and peaked during the P3b time window ( all ps<0 . 007 , except 100–300 ms in the working memory task , where p=0 . 066 ) . It then decayed slowly from ~1 s onwards in both tasks , yet remained above chance during the 0 . 6–1 . 55 s interval ( all ps<0 . 001 ) . Similar time courses were also observed when training in one task and testing for generalization to the other . Though rapidly dropping to chance-level after ~1 s , classifiers trained in the perception task performed above chance during the first three time windows on working memory trials ( and vice versa; all ps<0 . 014 ) , indicating that , early on , both tasks recruited similar brain mechanisms . Temporal generalization analyses ( King and Dehaene , 2014 ) were used to evaluate the onset and duration of patterns of brain activity . If working memory were just a prolonged conscious episode , classifiers trained at time points relevant to conscious perception ( e . g . , the P3b window ) should generalize extensively , potentially spanning the entire delay . Our findings supported this hypothesis only in part . The temporal generalization matrix for the working memory task presented as a thick diagonal , suggesting that brain activity was mainly characterized by changing , but long-lasting patterns . Though failing to achieve statistical significance over the entire 0 . 6–1 . 55 s interval ( all ps>0 . 101 ) , at a more lenient , uncorrected threshold , classifiers trained during the P3b time window ( 300–600 ms ) in the working memory task remained weakly efficient until ~692 ms ( AUC = 0 . 54 ±- 0 . 02 , puncorrected=0 . 023 ) . Similarly , classifiers trained during the same time period in the perception task and tested in the working memory task persisted up to ~860 ms ( AUC = 0 . 53 ± 0 . 01 , puncorrected=0 . 028 ) . Brain processes deployed for the conscious representation of the target were thus partially sustained during the working memory delay . The reverse analysis , in which we trained classifiers during the retention period in the working memory task ( 0 . 8–2 . 5 s ) , did not reveal any generalization to the P3b time window in the perception task ( p= 0 . 101 ) . These results confirm that seeing the target entailed a similar unfolding of neural events in two task contexts: Conscious perception primarily consisted in a dynamic series of partially overlapping information-processing stages , each characterized by temporary , metastable patterns of neural activity . The same neural codes appeared to be recruited at the beginning of the maintenance period ( up to ~1 s ) . As such , these findings corroborate previous accounts linking conscious perception to an ‘ignition’ of brain activity ( Del Cul et al . , 2007; Gaillard et al . , 2009; Salti et al . , 2015; Sergent et al . , 2005 ) and suggest that , in part , working memory implies the prolongation of a conscious episode , and , in part , a succession of additional processing steps . Our focus so far has been on evoked brain activity . However , other reliable neural signatures of conscious perception have been identified in the frequency domain ( Gaillard et al . , 2009; Gross et al . , 2007; King et al . , 2016; Wyart and Tallon-Baudry , 2009 ) . We thus turned to time-frequency analyses and first contrasted seen trials with both our target-absent control condition as well as unseen trials in both tasks ( Figure 4A and Figure 4—figure supplement 1A ) . In order to qualify as a signature of conscious perception , any candidate characteristic should exist in the perception-only control condition ( without any working memory requirement ) and be specific to seen trials . Cluster-based permutation analyses singled out a desynchronization in the alpha band ( 8–12 Hz ) as the principal correlate of conscious perception in the perception task ( seen – target-absent: pclust=0 . 004; seen – unseen: pclust=0 . 009 ) , with seen trials displaying a strong decrease in power ( relative to baseline ) compared to either the target-absent or the unseen trials . Initially left-lateralized in centro-temporal sensors , this effect moved to fronto-central channels and extended between ~300 and 1700 ms . A similar , albeit later ( 500–1700 ms ) and more bilateral fronto-central , desynchronization was also observed in the beta band ( 13–30 Hz; seen – target-absent: pclust<0 . 001; seen – unseen: pclust=0 . 01 ) . No differences between the unseen and target-absent trials were found in the alpha ( pclust>0 . 676 ) or beta band ( pclust>0 . 226 , apart from a short-lived , weak difference between ~0 . 9 and 1 . 3 s , where pclust=0 . 020 ) , suggesting that unseen trials strongly resembled trials without a target . 10 . 7554/eLife . 23871 . 007Figure 4 . A sustained decrease in alpha/beta power as a marker of conscious working memory . ( A ) Average time-frequency power relative to baseline ( dB ) as a function of task and visibility category in a group of occipital ( left ) and fronto-central ( right ) magnetometers . Mean reaction time ( target-present trials ) for the visibility response in the perception task is indicated as a vertical , dotted line . ( B ) Beta band activity ( 13–30 Hz; 0–2 . 1 s ) related to conscious working memory ( seen – unseen trials ) as shown in magnetometers ( top ) and source space ( bottom; in dB relative to baseline ) . Black asterisks indicate sensors showing a significant difference as assessed by a Monte-Carlo permutation test . ( C ) Same as in ( A ) and ( B ) but for unseen correct and unseen incorrect trials in the alpha band ( 8–12 Hz ) . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 00710 . 7554/eLife . 23871 . 008Figure 4—figure supplement 1 . Alpha- and beta-band desynchronizations serve as a general signature of conscious processing and conscious working memory . ( A ) Perception task: Topographies represent the power difference ( magnetometers ) for seen vs target-absent trials ( top ) , seen vs unseen trials ( middle ) , and unseen vs target-absent trials ( bottom ) in the alpha ( 8–12 Hz ) and beta ( 13–30 Hz ) frequency bands as a function of time ( 0–2 . 1 s ) . Black asterisks indicate sensors showing a significant difference as assessed by a cluster-based permutation test . ( B ) Working memory task: Topographies and panels are as in ( A ) . ( C ) Working memory task: Topographies represent the power difference ( magnetometers ) for unseen correct vs target-absent trials ( top ) , unseen incorrect vs target-absent trials ( middle ) , and unseen correct vs unseen incorrect trials ( bottom ) in the alpha ( 8–12 Hz ) and beta ( 13–30 Hz ) frequency bands as a function of time ( 0–2 . 1 s ) . Black asterisks indicate sensors showing a significant difference as assessed by a cluster-based permutation test . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 00810 . 7554/eLife . 23871 . 009Figure 4—figure supplement 2 . Seen and unseen correct trials do not share the same discriminative decoding axis . ( A ) Temporal generalization matrices for a decoder trained on ERFs to distinguish seen from unseen trials in the perception task and tested in the working memory task , either with the same labels ( visibility decoder; left ) or the unseen correct and incorrect trials ( accuracy decoder; right ) . In each panel , a classifier was trained at every time sample ( y-axis ) and tested on all other time points ( x-axis ) . The diagonal gray line demarks classifiers trained and tested on the same time sample . Please note the additional event marker: Mean reaction time ( target-present trials ) for the visibility response is indicated as a horizontal , dotted line . Any classifier beyond this point only reflects post-visibility processes . Time courses of diagonal decoding are shown as black insets . Thick lines indicate significant , above-chance decoding ( Wilcoxon signed-rank test across subjects , uncorrected , one-tailed ) . For display purposes , data were smoothed using a moving average with a window of eight samples . ( B ) Same as in ( A ) , except that the decoder was trained and tested on average power ( relative to baseline ) in the alpha band ( 8–12 Hz ) . For display purposes , data were smoothed using a moving average with a window of one sample . ( C ) Same as in ( B ) , except that the decoder was trained and tested on average power ( relative to baseline ) in the beta band ( 13–30 Hz ) . AUC = area under the curve . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 00910 . 7554/eLife . 23871 . 010Figure 4—figure supplement 3 . Bayesian statistics for the time-frequency analyses . ( A ) Time courses of average alpha band activity ( 8–12 Hz; −0 . 2–2 . 1 s ) in a group of frontal sensors as a function of visibility ( left ) and accuracy on the unseen trials ( right; correct = within ±2 positions of the actual target location ) . Shaded area demarks standard error of the mean ( SEM ) across subjects . Thick line represents significant difference in power relative to baseline ( Wilcoxon signed-rank test across subjects ) . Insets show Bayes Factors ( as assessed in a two-tailed t-test ) in four time windows: 100–300 ms ( early ) , 300–600 ms ( P3b ) , 0 . 6–1 . 55 s ( Del1 ) , and 1 . 55–2 . 1 s ( Del2 ) . Del1 = first part of the delay , Del2 = second part of the delay , T = target onset . ( B ) Same as in ( A ) , but for average beta band ( 13–30 Hz ) activity . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 010 Most importantly , when comparing seen and target-absent/unseen trials in the working memory task , we again observed a similar , but now temporally sustained , pattern of alpha/beta band desynchronization ( Figure 4B and Figure 4—figure supplement 1B ) . Starting at ~300 to 500 ms , seen targets evoked a power decrease in central , temporal/parietal , and frontal regions in the alpha ( seen – target-absent: pclust=0 . 003; seen – unseen: pclust=0 . 003 ) and beta band ( seen – target-absent: pclust=0 . 009; seen – unseen: pclust<0 . 001 ) . Crucially , this desynchronization spanned the entire delay period and was specific to seen trials ( Figure 4A ) , with no differences in power between the unseen and target-absent trials in either band ( alpha: pclust>0 . 729; beta: pclust>0 . 657 ) and only a couple of interspersed periods of residual desynchronization persisting in the target-absent control trials . No task- or visibility-related modulations in power spectra were found in occipital areas , and the desynchronization originated primarily from a parietal network of brain sources ( Figure 4A and B ) . In conjunction with the afore-mentioned results , these findings imply that alpha/beta desynchronization is a correlate of conscious perception ( Gaillard et al . , 2009 ) and a neural state common to conscious perception and conscious working memory . Having identified markers of conscious perception and working memory in both multivariate and time-frequency analyses , we can now test the reality of non-conscious working memory by confronting it with several alternative hypotheses . The miscategorization hypothesis suggests that the long-lasting blindsight resulted from a small set of seen trials erroneously labeled as unseen . Unseen correct trials should thus display similar neural signatures as seen trials , including a shared discriminative decoding axis and a desynchronization in the alpha/beta band . An analogous reasoning holds for the conscious maintenance hypothesis , according to which the observed blindsight effect arises from the conscious maintenance of an early guess: Conscious processing would occur on unseen trials and we should thus find a sustained decrease in alpha/beta power similar to the one on seen trials . Conversely , a clear distinction between brain responses on seen trials and on unseen ( correct ) trials would suggest that blindsight resulted from a distinct non-conscious mechanism of information maintenance . We first probed the alternative hypotheses with the ERF data . Training a decoder to distinguish seen from unseen trials in the perception task and applying it to the unseen correct and incorrect trials in the working memory task , we directly assessed the classifier’s ability to generalize from seen to unseen correct trials ( accuracy decoder ) . If , indeed , the latter had actually been seen , such a decoder should look similar to the above-described generalization analysis , in which a classifier had been trained on seen/unseen trials in the perception task and tested on the same labels in the working memory task ( visibility decoder ) . As shown in Figure 4—figure supplement 2A , this was not the case . Whereas the temporal generalization matrix for the visibility decoder presented as a thick diagonal , no discernable pattern emerged for the accuracy decoder . The time courses of diagonal decoding were also quite dissimilar . For the visibility decoder ( see also above ) , classification performance first rose above chance at ~148 ms ( AUC = 0 . 54 ± 0 . 01 , pFDR=0 . 023 ) , peaked at ~640 ms ( AUC = 0 . 58 ± 0 . 02 , pFDR=0 . 001 ) , and then decayed rapidly by ~1 s ( first three time windows , all ps<0 . 001 ) . In contrast , classification for the accuracy decoder was erratic and transient: It first sharply peaked at ~180 ms ( AUC = 0 . 55 ± 0 . 01 , puncorrected=0 . 037 ) , dropped to chance-level , and then exceeded chance between ~372 and 724 ms with a peak at 444 ms ( AUC = 0 . 57 ± 0 . 02 , puncorrected=0 . 007 ) . Much unlike any of the previous decoders involving the perception task , long after the visibility response , it rose a third time between ~1 . 44 and 1 . 74 s , peaking with similar magnitude as before at ~1 . 58 s ( AUC = 0 . 57 ± 0 . 02 , puncorrected=0 . 010; P3b and last time window: all ps<0 . 023 ) . Although the level of noise evident in the accuracy decoder thus precludes any definitive conclusion , the visibility and accuracy decoders had little in common , rendering it unlikely for the unseen correct trials to have simply been mislabeled . We next returned to our time-frequency analysis . When averaging over all unseen trials in the working memory task , there was no indication of a desynchronization remotely comparable to the one on seen trials ( Figure 4A and Figure 4—figure supplement 1C ) . Indeed , Bayesian statistics indicated that , on the unseen trials , evidence for the null hypothesis ( i . e . , no relative change in alpha/beta power ) was at least similar ( at the very end of the epoch ) or stronger than evidence for the alternative hypothesis . By contrast , on seen trials , evidence for the alternative hypothesis was always strongly favored ( Figure 4—figure supplement 3 ) . Even when analyzing the unseen correct trials separately , there was no appreciable trace of any alpha/beta desynchronization ( Figure 4C and Figure 4—figure supplement 3 ) . Only one short-lived effect , reversed relative to conscious trials , was observed in the alpha band ( pclust=0 . 040 ) in a set of posterior central sensors , corresponding to primarily occipital sources: Starting at ~1 . 5 s and extending until ~1 . 9 s , unseen correct trials exhibited a stronger increase in alpha power than their incorrect counterparts . Given the difference in performance on these two types of unseen trials , such small variations are not surprising and could , perhaps , reflect a stronger suppression of interference from the distractor on the unseen correct trials . Unseen correct trials thus appeared to be nearly indistinguishable from the unseen incorrect and target-absent trials . As multivariate analyses might be more sensitive than univariate ones in detecting similarities between conditions , we also performed the above decoding analysis separately for average alpha ( 8–12 Hz ) and beta ( 13–30 Hz ) power . Overall , these analyses confirmed our previous findings , albeit more clearly so in the alpha than in the beta band . A visibility decoder trained on alpha power to distinguish seen from unseen trials in the perception task and tested in the working memory task again exhibited a thick diagonal , with above-chance decoding between ~180 ms and 1 . 18 s ( first three time windows: all ps<0 . 016 ) . There was no evidence for any generalization to the unseen correct trials ( Figure 4—figure supplement 2B; all time windows: ps>0 . 211 ) . Similarly , a visibility decoder trained on average beta power entirely failed to generalize to the unseen correct trials ( Figure 4—figure supplement 2C; all time windows: ps>0 . 191 ) . Considering the weak , although statistically significant ( all four time windows , ps<=0 . 05 ) , initial generalization from the perception to the working memory task , probably due to the slightly later onset of the beta desychronization in the former , this failure is less informative than the one observed in the alpha band and should be replicated in future investigations . Taken together , we found a clear distinction in the brain responses of seen and unseen ( correct ) trials . Converging evidence from our decoding analyses in the ERFs and alpha/beta band suggests that there was no apparent discriminative axis shared between the seen and the unseen correct trials . Similarly , the desynchronization in alpha/beta power characterizing the seen targets did not emerge on the unseen ( correct ) trials . These findings therefore argue against the miscategorization and conscious maintenance hypotheses and instead suggest that non-conscious working memory is a genuine phenomenon , distinct from conscious working memory . We next set out to identify the neural mechanisms supporting both conscious and non-conscious working memory and first determined where and how the specific contents of working memory were stored . Circular-linear correlations between the amplitude of the ERFs and target location ( across all working memory trials ) revealed a strong and focal association ( relative to a permuted null distribution ) over posterior channels , starting at ~120 ms and lasting until 904 ms ( early and P3b time windows: all ps<0 . 001; all BFs>109 . 60; Figure 5A and Supplementary files 2 and 3 ) . Similarly , distractor position could be tracked between ~194 and 570 ms after its presentation ( early and P3b time windows: all ps<0 . 009; all BFs>14 . 47 ) . The position of our stimuli could thus be faithfully retrieved in visual areas . In a subsequent step , we investigated how target location would be maintained in the context of conscious and non-conscious working memory ( Figure 5B ) . Target position was transiently encoded via slowly decaying activity in occipital as well as bilateral temporo-occipital cortex from ~120 to 800 ms on seen trials ( early and P3b time windows: all ps<0 . 001 and all BFs>24 . 07 , with the exception of the 100–300 ms period in right temporo-occipital channels , where p=0 . 064 and BF=2 . 31 ) and in occipital and left temporo-occipital brain areas from ~180 to 504 ms on unseen trials ( early time window: all ps<0 . 047; all BFs>2 . 58 ) . A clear correlation with target location was therefore found for both seen and unseen trials . In fact , although it was more short-lived on the latter , it was of comparable magnitude as the one observed on the seen trials during the early time window ( occipital/left temporo-occipital channels: all ps>0 . 110 when directly comparing the correlation scores of seen and unseen trials in a Wilcoxon signed-rank test ) . In the case of seen trials , both occipital and left temporo-occipital cortex also maintained the target representation at least throughout the first part of the delay period ( all ps<0 . 024; all BFs>3 . 77 ) , though , intriguingly , this was not accompanied by continuously sustained activity . Target ‘decodability’ instead waxed and waned , appearing and disappearing periodically . No such activity was observed for the maintenance of unseen targets ( first and second part of the delay: all ps>0 . 446; all BFs<0 . 047 ) . This absence of ‘decodability’ during the maintenance period persisted , even when considering unseen correct and unseen incorrect trials separately ( Figure 5C ) . There was only a trace of residual decoding of target location on unseen correct trials in left temporo-occipital areas during the delay period , but this did not reach significance , potentially due to the low number of trials in this condition . Note that in the perception task , seen targets could be retrieved similarly to their counterparts in the working memory task between ~232 and 1184 ms in occipital and bilateral temporo-occipital regions ( all ps>0 . 068 , except for the 100–300 ms time window in occipital channels where p=0 . 008 , when directly comparing the correlation scores of seen targets in both tasks in a Wilcoxon signed-rank test; Figure 5—figure supplement 1 ) . 10 . 7554/eLife . 23871 . 011Figure 5 . Tracking the contents of conscious and non-conscious working memory . ( A ) Topographies ( top ) and time courses ( bottom; −0 . 2–2 . 5 s ) of average circular-linear correlations between the amplitude of the MEG signal ( gradiometers ) and target/distractor location . Shaded area demarks standard error of the mean ( SEM ) across subjects . Thick line represents significant increase in correlation coefficient as compared to an empirical baseline ( one-tailed Wilcoxon signed-rank test across subjects , uncorrected ) . ( B ) Average time courses ( −0 . 2–2 . 5 s ) of circular-linear correlation coefficients between amplitude of the ERFs and target location as a function of visibility in the working memory task in a group of left temporo-occipital ( left ) , occipital ( middle ) , and right temporo-occipital ( right ) gradiometers . Shaded area demarks standard error of the mean ( SEM ) across subjects . Thick line represents significant increase in correlation coefficient as compared to an empirical baseline ( one-tailed Wilcoxon signed-rank test across subjects , uncorrected ) . Insets show average correlation coefficients ( relative to an empirical baseline ) in four time windows: 100–300 ms ( early ) , 300–600 ms ( P3b ) , 0 . 6–1 . 55 s ( Del1 ) , and 1 . 55–2 . 5 s ( Del2 ) . White asterisks denote significant differences to baseline ( one-tailed Wilcoxon signed-rank test across subjects ) , black asterisks significant differences between conditions ( two-tailed Wilcoxon signed-rank test across subjects ) . For display purposes , data were lowpass-filtered at 8 Hz . p<0 . 05 , p<0 . 01 , and p<0 . 001 . Del1= first part of delay , Del2 = second part of delay , T = target onset . ( C ) Same as in ( B ) , but as a function of accuracy on the unseen trials ( correct = within ±2 positions of the target ) . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 01110 . 7554/eLife . 23871 . 012Figure 5—figure supplement 1 . Representation of seen target locations during conscious perception and working memory . Average time courses of circular-linear correlation coefficients between amplitude of the ERFs and target location on seen trials as a function of task ( perception and working memory ) in a group of left temporo-occipital ( left ) , occipital ( middle ) , and right temporo-occipital ( right ) gradiometers . Shaded area demarks standard error of the mean ( SEM ) across subjects . Mean reaction time ( target-present trials ) for the visibility response in the perception task is indicated as a vertical , dotted line . Thick line represents significant increase in correlation coefficient as compared to an empirical baseline ( one-tailed Wilcoxon signed-rank test across subjects , uncorrected ) . Insets show average correlation coefficients ( relative to baseline ) in four time windows: 100–300 ms ( early ) , 300–600 ms ( P3b ) , 0 . 6–1 . 55 s ( Del1 ) , and 1 . 55–2 . 5 s ( Del2 ) . White asterisks denote significant differences to baseline ( one-tailed Wilcoxon signed-rank test across subjects ) , black asterisks significant differences between conditions ( two-tailed Wilcoxon signed-rank test across subjects ) . For display purposes , data were lowpass-filtered at 8 Hz . p<0 . 05 , p<0 . 01 , and p<0 . 001 . Del1 = first part of the delay period , Del2 = second part of the delay period , T = target onset . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 01210 . 7554/eLife . 23871 . 013Figure 5—figure supplement 2 . Circular-linear correlations and multivariate decoding reveal similar time courses for target location . ( A ) Average time courses of circular-linear correlation coefficients between amplitude of the ERFs and target location as a function of task ( perception and working memory ) and visibility ( seen and unseen ) in a group of left temporo-occipital gradiometers . Shaded area demarks standard error of the mean ( SEM ) across subjects . Thick line represents significant increase in correlation coefficient as compared to an empirical baseline ( one-tailed Wilcoxon signed-rank test across subjects , uncorrected ) . Insets show average correlation coefficients ( relative to baseline ) in four time windows: 100–300 ms ( early ) , 300–600 ms ( P3b ) , 0 . 6–1 . 55 s ( Del1 ) , and 1 . 55–2 . 5 s ( Del2 ) . White asterisks denote significant differences to baseline ( one-tailed Wilcoxon signed-rank test across subjects ) . For display purposes , data were lowpass-filtered at 8 Hz . p<0 . 05 , p<0 . 01 , and p<0 . 001 . Del1 = first part of the delay period , Del2 = second part of the delay period , T = target onset . ( B ) Average time courses of a linear support vector regression trained to predict target angle as a function of task ( perception and working memory ) and visibility ( seen and unseen ) . Thick line represents significant increase in decoding accuracy ( in radians ) as compared to a baseline ( one-tailed Wilcoxon signed-rank test across subjects , uncorrected ) . Insets show average correlation coefficients ( relative to baseline ) in four time windows: 100–300 ms ( early ) , 300–600 ms ( P3b ) , 0 . 6–1 . 55 s ( Del1 ) , and 1 . 55–2 . 5 s ( Del2 ) . White asterisks denote significant differences to baseline ( one-tailed Wilcoxon signed-rank test across subjects ) . For display purposes , data were lowpass-filtered at 8 Hz . p<0 . 05 , p<0 . 01 , and p<0 . 001 . Del1 = first part of the delay period , Del2 = second part of the delay period , T = target onset . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 01310 . 7554/eLife . 23871 . 014Figure 5—figure supplement 3 . Tracking target/response location on unseen correct and incorrect trials with multivariate decoding . ( A ) Average time courses of a linear support vector regression trained on seen correct trials to predict target angle on the unseen correct ( top ) and unseen incorrect ( bottom ) trials . Thick line represents significant increase in decoding accuracy ( in radians ) as compared to a baseline ( one-tailed Wilcoxon signed-rank test across subjects , uncorrected ) . Insets show average correlation coefficients ( relative to baseline ) in four time windows: 100–300 ms ( early ) , 300–600 ms ( P3b ) , 0 . 6–1 . 55 s ( Del1 ) , and 1 . 55–2 . 5 s ( Del2 ) . White asterisks denote significant differences to baseline ( one-tailed Wilcoxon signed-rank test across subjects ) . For display purposes , data were lowpass-filtered at 8 Hz . p<0 . 05 , p<0 . 01 , and p<0 . 001 . Del1 = first part of the delay period , Del2 = second part of the delay period , T = target onset . ( B ) Same as in ( A ) , but for response location . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 014 Given the univariate nature of the circular-linear correlations , one might again wonder whether a multivariate strategy would be more sensitive in detecting subtle associations between the MEG data and target location . We therefore used linear support vector regressions ( SVR ) to predict target angle from the MEG signal as a function of visibility ( Materials and methods ) . As can be seen in Figure 5—figure supplement 2 , this method resulted in similar , albeit more noisy , time courses as the ones obtained with the circular-linear correlations: Seen targets were again encoded and maintained intermittently between ~268 ms and 1 . 4 s ( P3b time window and first part of the delay: ps<0 . 05 ) . No statistically significant decoding emerged for unseen target locations . Due to the fact that subjects responded correctly on approximately half of all unseen trials ( see Supplementary file 4 for average trial counts ) , we attempted to evaluate the dynamics of the encoding and maintenance of unseen correct and incorrect target locations by training the regression model on the strongest case , the seen correct trials , and applying it separately to the unseen correct and incorrect trials . We again observed no evidence for any generalization at all ( Figure 5—figure supplement 3A ) , though this likely reflects the sensitivity of the analysis more so than any meaningful effect . Taken together , in line with previous research ( Harrison and Tong , 2009; King et al . , 2016 ) , these results suggest that posterior sensory regions may initially encode seen and unseen memoranda via slowly decaying neural activity . In the case of conscious working memory , these then seem to be maintained by those same areas through an intermittently reactivated , neural code ( Fuentemilla et al . , 2010 ) . In contrast , no such periodically resurfacing activity appears to accompany non-conscious working memory . The correlation between target location and brain activity affords an additional way to interrogate the conscious maintenance hypothesis . If subjects quickly guessed the location of an unseen target and then held it in conscious working memory , in addition to observing a signature of conscious processing on the unseen trials , we should observe a correlation with the location of their response long before it occurs . Potentially , remembering the response might recruit brain systems completely different from the ones representing the target . Circular-linear correlations rendered this prediction unlikely . Associations between response location and the MEG signal were again primarily confined to posterior channels , with more frontal areas being recruited preferentially at the time of the response ( Figure 6A ) . As such , the topographical patterns were highly similar to the ones observed for the correlation with target location . Importantly , no additional regions were identified on the unseen trials and none of these areas showed any appreciable correlation before the presentation of the response screen ( Figure 6—figure supplement 1 ) . This suggests that , irrespective of stimulus visibility , common brain networks supported memories for the target stimulus and the ensuing decision and that , in the case of non-conscious working memory , these did not come online until the response . 10 . 7554/eLife . 23871 . 015Figure 6 . Tracking response location in conscious and non-conscious working memory . ( A ) Topographies of average circular-linear correlations between the amplitude of the MEG signal ( gradiometers ) and response location . R = onset of the response screen . ( B ) Average time courses ( left: stimulus-locked , −0 . 2–2 . 5 s; right: response-locked , −0 . 5–0 . 8 s ) of circular-linear correlation coefficients between the amplitude of the ERFs and response location as a function of visibility in the working memory task in a group of occipital ( top , left ) , frontal ( top , right ) left temporo-occipital ( bottom , left ) and right temporo-occipital ( bottom , right ) gradiometers . Shaded area demarks standard error of the mean ( SEM ) across subjects . Thick line represents significant increase in correlation coefficient as compared to an empirical baseline ( one-tailed Wilcoxon signed-rank test across subjects , uncorrected ) . Insets show average correlation coefficients ( relative to an empirical baseline ) in four stimulus-locked time windows , 100–300 ms ( early ) , 300–600 ms ( P3b ) , 0 . 6–1 . 55 s ( Del1 ) , and 1 . 55–2 . 5 s ( Del2 ) , and two response-locked time windows , −0 . 5–0 . 0 s ( Del3 ) and 0 . 0–0 . 8 s ( Resp ) . White asterisks denote significant differences to baseline ( one-tailed Wilcoxon signed-rank test across subjects ) , black asterisks significant differences between conditions ( two-tailed Wilcoxon signed-rank test across subjects ) . For display purposes , data were lowpass-filtered at 8 Hz . p<0 . 05 , p<0 . 01 , and p<0 . 001 . Del1= first part of delay , Del2 = second part of delay , Del3 = last 500 ms before response screen , R = response screen onset , T = target onset . ( C ) Same as in ( B ) , but as a function of accuracy on the unseen trials ( correct = within ±2 positions of the target ) . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 01510 . 7554/eLife . 23871 . 016Figure 6—figure supplement 1 . Topographies for circular-linear correlations with response location as a function of visibility . Topographies of circular-linear correlations with response location as a function of time ( in s ) for seen ( left ) and unseen ( right ) trials . The first three time bins are relative to target , the last two relative to response screen onset . R = response screen onset . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 01610 . 7554/eLife . 23871 . 017Figure 6—figure supplement 2 . Circular-linear correlations and multivariate decoding reveal similar time courses for response location . ( A ) Average time courses of circular-linear correlation coefficients between amplitude of the ERFs and response location as a function of task ( perception and working memory ) and visibility ( seen and unseen ) in a group of left temporo-occipital gradiometers . Shaded area demarks standard error of the mean ( SEM ) across subjects . Thick line represents significant increase in correlation coefficient as compared to an empirical baseline ( one-tailed Wilcoxon signed-rank test across subjects , uncorrected ) . Insets show average correlation coefficients ( relative to baseline ) in four time windows: 100–300 ms ( early ) , 300–600 ms ( P3b ) , 0 . 6–1 . 55 s ( Del1 ) , and 1 . 55–2 . 5 s ( Del2 ) . White asterisks denote significant differences to baseline ( one-tailed Wilcoxon signed-rank test across subjects ) . For display purposes , data were lowpass-filtered at 8 Hz . p<0 . 05 , p<0 . 01 , and p<0 . 001 . Del1 = first part of the delay period , Del2 = second part of the delay period , T = target onset . ( B ) Average time courses of a linear support vector regression trained to predict response angle as a function of task ( perception and working memory ) and visibility ( seen and unseen ) . Thick line represents significant increase in decoding accuracy ( in radians ) as compared to a baseline ( one-tailed Wilcoxon signed-rank test across subjects , uncorrected ) . Insets show average correlation coefficients ( relative to baseline ) in four time windows: 100–300 ms ( early ) , 300–600 ms ( P3b ) , 0 . 6–1 . 55 s ( Del1 ) , and 1 . 55–2 . 5 s ( Del2 ) . White asterisks denote significant differences to baseline ( one-tailed Wilcoxon signed-rank test across subjects ) . For display purposes , data were lowpass-filtered at 8 Hz . p<0 . 05 , p<0 . 01 , and p<0 . 001 . Del1 = first part of the delay period , Del2 = second part of the delay period , T = target onset . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 017 The time courses of the circular-linear correlations further solidified this interpretation ( Figure 6B ) . On seen trials , response position was maintained throughout the majority of the epoch in occipital and left temporo-occipital brain areas ( first three time windows: all ps<0 . 020; all BFs>4 . 16 ) . This was not the case on the unseen trials: No correlation patterns appeared in any of the posterior channels during the course of the epoch ( all time windows: all ps>0 . 064; all BFs<1 . 32 ) . In contrast , a strong correlation emerged for both seen and unseen trials during the response period ( 0–800 ms with respect to the onset of the letter cue ) . Response location could be tracked with similar time courses and magnitude on seen and unseen trials in occipital , bilateral temporo-occipital , and frontal channels ( all ps<0 . 024; all BFs>13 . 73; when directly comparing the correlation scores of seen and unseen targets in a Wilcoxon signed-rank test: all ps>0 . 216 , except for left temporo-occipital channels , where p=0 . 040 ) . When we further distinguished unseen correct from unseen incorrect trials , the results remained similar , though much noisier ( Figure 6C ) : There was no clear correlation pattern before the onset of the response screen on either the unseen correct or the unseen incorrect trials ( all ps>0 . 096; all BFs<1 . 47 ) . Only after the appearance of the letter cues did we observe a correlation with response location . Multivariate decoding analyses confirmed this picture: Whereas response location for seen targets could be tracked similarly to actual target location at least throughout the first part of the delay period ( P3b time window and first part of the delay: ps<0 . 05; Figure 6—figure supplement 2 ) , no such pattern was observed on the unseen trials ( all ps>0 . 153 ) . This absence of decodability persisted on the unseen correct and incorrect trials , even when training the regression model on the seen correct trials ( Figure 5—figure supplement 3B ) . Overall , these results are incompatible with the hypothesis that the long-lasting blindsight is only due to the conscious maintenance of an early guess , as , in this case , brain responses linked to the subjects’ responses should have been observed shortly after the presentation of the target stimulus . What mechanism might permit above-chance recall without any continuously sustained brain activity ? Recent modelling suggests that sustained neural firing may not be required to maintain a representation in conscious working memory . Mongillo et al . ( 2008 ) proposed a theoretical framework for working memory , in which information is stored in calcium-mediated short-term changes in synaptic weights , thus linking the active cells coding for the memorized item . Once these changes have occurred , the cell assembly may go dormant during the delay , while the synaptic weights are slowly decaying . At the end of the delay period , a non-specific read-out signal may then suffice to reactivate the assembly . Furthermore , reactivation of the assembly may also occur spontaneously during the retention phase , similar to the rehearsal process postulated by Baddeley ( 2003 ) , thus refreshing the weights and permitting the bridging of longer delays . Could this ‘activity-silent’ mechanism also constitute a plausible neural mechanism for non-conscious working memory ? To test this hypothesis , we simulated our experiments using a one-dimensional recurrent continuous attractor neural network ( CANN ) based on Mongillo et al . ( 2008 ) . The CANN encoded the angular position of the target and was composed of neurons aligned according to their preferred stimulus value ( Figure 7A ) . Transient short-term plasticity between the recurrent connections , with a 4s-decay constant , was implemented as described by Mongillo et al . ( 2008; Figure 7B ) . Timing of the simulated events was comparable to the experimental paradigm: A target signal was briefly presented at a random location , followed by a mask signal to all neurons and a non-specific recall signal after a 3s-delay . 10 . 7554/eLife . 23871 . 018Figure 7 . Activity-silent neural mechanisms underlying conscious and non-conscious working memory . ( A ) Structure of a one-dimensional continuous attractor neural network ( CANN ) . Neuronal connections J ( θ , θ’ ) are translation-invariant in the space of the neurons’ preferred stimulus values ( -π , π ) , allowing the network to hold a continuous family of stationary states ( bumps ) . An external input Ie ( θ , t ) containing the stimulus information triggers a bump state ( red curve ) at the corresponding location in the network . ( B ) Model of a synaptic connection with short-term potentiation . In response to a presynaptic spike train ( bottom ) , the neurotransmitter release probability u increases and the fraction of available neurotransmitter x decreases ( middle ) , representing synaptic facilitation and depression . Effective synaptic efficacy is proportional to ux ( top ) . ( C ) Firing rate of neurons ( top ) and sequence of events ( bottom; target and mask signal ) when simulating conscious working memory with Amask = 50 Hz < Acritical . ( D ) Same as in ( C ) for non-conscious working memory when Amask = 65 Hz > Acritical . ( E , F ) Performance of the network ( distribution of responses ) when mask amplitude was near the critical level , Amask = 62 Hz ~ Acritical , and noise had been added to the system . Out of 4000 trials , 2035 resulted in the conscious ( E ) and the remainder in the non-conscious regime ( F ) . In both cases , performance remained above chance with the responses concentrated around the initial target location . DOI: http://dx . doi . org/10 . 7554/eLife . 23871 . 018 If the activity-silent mechanism constituted a plausible neurophysiological correlate of conscious and non-conscious working memory , these simulations should capture our principal findings . A stimulus presented at threshold should entail one of two different maintenance regimes: a first distinguished by near-perfect recall with spontaneous reactivations of the memorized representation throughout the retention period ( thus resembling the prolonged , yet fluctuating , ‘decodability’ of seen target locations ) , and a second characterized by above-chance objective performance in the almost complete absence of delay activity ( thereby portraying the time course of the circular-linear correlations for the unseen stimuli ) . In a noiseless model , there indeed existed a critical value of mask amplitude , Acritical , which separated two distinct regimes: Just as was the case for our seen trials , when Amask < Acritical , the neural assembly coding for the target spontaneously reactivated during the delay ( Figure 7C ) . However , when Amask > Acritical , the system evolved into a state without spontaneous activation of target-specific neurons , yet with a reactivation in response to a non-specific recall signal , mimicking our unseen trials ( Figure 7D ) . When fixing mask amplitude near Acritical and adding noise continuously or just to the inputs , the network exhibited both types of regimes in nearly equal proportions: 50 . 8% of trials were characterized by an activity-silent delay interspersed with spontaneous reactivations and 49 . 2% by an entirely activity-silent delay period . Reminiscent of our behavioral results , sorting the trials according to the existence or absence of these reactivations and computing the histograms of recalled target position relative to true location produced two distributions of objective working memory performance: one , in which target position was nearly accurately stored ( Figure 7E ) , and one , in which performance remained above chance despite a higher base rate of errors ( Figure 7F ) . These simulations replicate our experimental findings ( in particular Figures 2 and 5 ) and suggest the activity-silent framework as a likely candidate mechanism for both conscious and non-conscious working memory . Consistent with introspective reports and research on visual awareness and working memory ( Baddeley , 2003; Dehaene et al . , 2014 ) , we observed a close relationship between conscious perception and maintenance in conscious working memory . In both tasks , classifiers trained to separate seen and unseen trials resulted in thick diagonals up to ~1 s after target onset , even when generalizing from one task to the other . Such long diagonals have repeatedly been observed in recent studies and are thought to reflect sequential processing ( King and Dehaene , 2014; Marti et al . , 2015; Salti et al . , 2015; Stokes et al . , 2015; Wolff et al . , 2015 ) . Irrespective of context , conscious perception and early parts of conscious maintenance thus involve a similar series of partially overlapping processing stages . Time-frequency decompositions reinforced and extended this conclusion . Seen trials in the perception task were distinguished from both a target-absent control condition and unseen trials by a prominent decrease in alpha/beta power over fronto-central sensors , corresponding to a distributed network centered on parietal cortex . A similar desynchronization , sustained throughout the retention period , was also observed for conscious working memory . Alpha/beta band desynchronizations such as these have previously been linked with conscious perception ( Gaillard et al . , 2009; Wyart and Tallon-Baudry , 2009 ) and working memory ( Lundqvist et al . , 2016 ) . Modelling suggests that the memorized item is encoded by intermittent gamma bursts , which interrupt an ongoing desynchronized beta default state ( Lundqvist et al . , 2011 ) . Such a decreased rate of beta bursts , once averaged over many trials , would have resulted in the apparently sustained power decrease we observed . Increases in gamma power have also been shown in some studies on conscious perception ( e . g . , Gaillard et al . , 2009 ) , but we failed to detect it here , perhaps because our targets were brief , peripheral , and low in intensity . Circular-linear correlations further highlighted the similarity between conscious perception and working memory . Location information could be tracked for ~1 s on perception-only trials and for at least 1 . 5 s of the working memory retention period . The mental representation formed during conscious perception was therefore either maintained or repeatedly replayed during conscious working memory . Even when subjects indicated not having seen the target , they still identified its position much better than chance up to 4 s after its presentation . This long-lasting blindsight effect was replicated in two independent experiments and exhibited typical properties of working memory , withstanding salient visible distractors and a concurrent demand on conscious working memory . Those results corroborate previous research showing that information can be maintained non-consciously ( e . g . , Bergström and Eriksson , 2014 , 2015; Dutta et al . , 2014; Soto et al . , 2011 ) . However , these prior findings could have arisen due to errors in visibility reports . If , for example , a participant had been left with a weak impression of the target ( and , consequently , its location ) , he or she might not have had adequate internal evidence to refer to this perceptual state as seen , thus incorrectly applying the label unseen . A small number of such errors would have produced above-chance responding . Another explanation could have been the conscious maintenance of an early guess , whereby subjects would have ventured a prediction as to the correct target position immediately after its presentation and then consciously maintained this hunch . The MEG results provide evidence against these possibilities . First , whereas seen trials were characterized by a sustained desynchronization in the alpha/beta band in parietal brain areas , no comparable desynchronization was observed on unseen trials , even when subjects correctly identified the target location . On the contrary , the only , short-lived , difference between unseen correct and unseen incorrect trials emerged around the time of the distractor and was reversed in direction: Unseen correct trials were accompanied by an increase in power in the alpha band with respect to their incorrect counterpart , an effect that might relate to a successful attempt to reduce interference from the distractor ( Cooper et al . , 2003; Jensen and Mazaheri , 2010 ) . Otherwise , unseen correct and incorrect trials were indistinguishable in their power spectra and similar to the target-absent control condition . Second , there was no clear evidence for a shared discriminative decoding axis between the seen and the unseen correct trials: Generalization was entirely unsuccessful when the classifier was trained on the time-frequency data , and highly dissimilar from the original visibility decoder when trained on the ERFs . While it is impossible to draw definitive conclusions just from the current dataset and future research should replicate these results , the majority of our evidence thus points against an interpretation , in which the unseen correct trials constituted either just a subset of seen trials , or arose from the conscious maintenance of an early guess . Instead , inasmuch as the observed desynchronization serves as a faithful indicator of conscious processing , it argues in favor of a differential state of non-conscious working memory with a distinct neural signature . Circular-linear correlations as well as multivariate regression models between the amplitude of the MEG signal and response location support this interpretation . On seen trials , response position was coded akin to target location: Initially maintained via slowly decaying neural activity in posterior brain areas , the response code subsequently resurfaced intermittently in the same as well as more frontal regions . There was no detectable evidence for such a code on the unseen trials . Only during the very last part of the delay , right before the response , did response-related neural activity emerge and ramp up to the same level as on seen trials during the response period . As such , the absence of any prior delay-period activity does not appear to be an artifact attributable to low statistical power or an increase in noise on the unseen trials . Instead , in conjunction with the absence of any signature of conscious processing on these trials , these findings imply that subjects did not consciously maintain an early guess and rather relied on genuine non-conscious working memory to perform the task . In this context , an interesting avenue for future investigations might be to delineate the boundary conditions of such non-conscious working memory . Although the short-term maintenance of information certainly lies at the heart of most theories of working memory ( Eriksson et al . , 2015 ) , there exist additional criteria for working memory that were not investigated in the present study . It is thus an interesting empirical question whether these other working memory processes may also occur without subjective awareness . Is it , for example , possible to manipulate information non-consciously ? Though speculative , in light of the proposed activity-silent code for non-conscious maintenance ( without any spontaneous reactivations; see below ) , it seems unlikely . Being an entirely passive process , it is not clear how stored representations could be transformed without being persistently activated and thus becoming conscious . Future research is , however , needed to provide a definitive answer . Target-related activity was not continuously sustained throughout the delay period , even when the target square had been consciously perceived . It instead fluctuated , disappearing and reappearing intermittently . This feature was even more pronounced on the unseen trials , with no evidence for any such retention-related activity beyond ~1 s . We presented a theoretical framework , based on Mongillo et al . ( 2008 ) and the concept of ‘activity-silent’ working memory ( Stokes , 2015 ) , that may provide a plausible explanation for maintenance without sustained neural activity . According to this model , short-term memories are retained by slowly decaying patterns of synaptic weights . A retrieval cue presented at the end of the delay may then serve as a non-specific read-out signal capable of reactivating these dormant representations above chance-level . Support for this model comes from experiments in which non-specific , task-irrelevant stimuli ( Wolff et al . , 2017 , 2015 ) , neutral post-cues ( Sprague et al . , 2016 ) , or transcranial magnetic stimulation ( TMS ) pulses ( Rose et al . , 2016 ) presented during a delay restore the decodability of representations . Direct physiological evidence for the postulated short-term changes in synaptic efficacies also exists ( Fujisawa et al . , 2008 ) . The present non-conscious condition provides further support for such an activity-silent mechanism . In this framework , a stimulus that fails to cross the threshold for sustained activity and subjective visibility may still induce enough activity in high-level cortical circuits to trigger short-term synaptic changes . Such transient non-conscious propagation of activity has been simulated in neural networks ( Dehaene and Naccache , 2001 ) and measured experimentally in temporo-occipital , parietal , and even prefrontal cortices ( Salti et al . , 2015; van Gaal and Lamme , 2012 ) . In the present work , we indeed observed some residual , transiently decodable activity over left temporo-occipital sensors on unseen correct trials . The memory of target location could therefore have arisen from posterior visual maps ( Roelfsema , 2015 ) , although future research should test this prediction further . Note that activity-silent mechanisms need not apply solely to prefrontal cortex as originally proposed by Mongillo et al . ( 2008 ) , but constitute a generic mechanism that may be replicated in different areas , possibly with increasingly longer time constants across the cortical hierarchy ( Chaudhuri et al . , 2014 ) . Only some of these areas/spatial maps may be storing the information on unseen trials . A key feature of the model by Mongillo et al . ( 2008 ) and the present simulations is that , even for above-threshold ( ‘seen’ ) stimuli , delay activity is not continuously sustained . Occasional bouts of spontaneous reactivation instead refresh the synaptic weights and maintain the memory for an indefinite time . The time courses of the circular-linear correlations and of the multivariate decoding we observed on seen trials match this description: While target location was encoded and maintained in temporo-occipital areas , target ‘decodability’ was not constantly sustained , but waxed and waned throughout the delay . Fuentemilla et al . ( 2010 ) also observed that , during a delay period , decodable representations of memorized images recurred at a theta rhythm . More recently , single-trial analyses of monkey electrophysiological recordings in a working memory task have confirmed the absence of any continuous activity and instead identified the presence of discrete gamma bursts , paired with a decrease in beta-burst probability ( Lundqvist et al . , 2016 ) . Such periodic refreshing of otherwise activity-silent representations could potentially serve as the neural correlate of conscious rehearsal , a central feature of working memory according to Baddeley ( 2003 ) . It also suggests , however , that even consciously perceived items may not always be ‘in mind . ’ Future research might attempt to more directly simulate activity-silent mechanisms in the context of conscious and non-conscious perception by , for example , relying on more elaborate models capturing decreases in alpha/beta power ( Lundqvist et al . , 2011 ) . In conjunction with prior evidence ( King et al . , 2016; Salti et al . , 2015 ) , our findings therefore indicate that there may be two successive mechanisms for the short-term maintenance of conscious and non-conscious stimuli: an initial , transient period of ~1 s , during which the representation is encoded by active firing with a slowly decaying amplitude , and an ensuing activity-silent maintenance via short-term changes in synaptic weights , during which activity either intermittently resurfaces ( conscious case ) or vanishes ( non-conscious case ) . Such activity-silent retention need not necessarily be specific to working memory . Recent investigations have , for instance , demonstrated the existence of recognition memory for invisible cues ( Chong et al . , 2014; Rosenthal et al . , 2016 ) . As delay periods ranged in the order of minutes rather than seconds , persistent neural activity seems to be an unlikely candidate mechanism of maintenance . Activity-silent codes might have been at play , though they probably depended on mechanisms with longer time constants than the relatively rapidly decaying patterns of synaptic weights discussed in the context of the present experiments . Nevertheless , activity-silent representations may constitute a general mechanism for maintenance across the whole spectrum of temporal delays ( from seconds over minutes/hours to days/weeks/decades ) , thus forming a generic property of memory . Our study presents limitations that should be addressed by future research . Due to the nature of the current investigation ( a working memory task with long trials and subjectively determined variables ) , a relatively small number of unseen trials was acquired , thus making it difficult to detect subtle effects . While our conclusions are supported by Bayes Factor analyses , converging evidence from univariate and multivariate techniques , and similar results obtained with larger samples in the domain of activity-silent conscious working memory ( e . g . , Rose et al . , 2016; Wolff et al . , 2017 ) , a number of our observations are based on null effects , and it remains a possibility that we missed some target- and/or response-related activity on the unseen trials . Future research should thus aim at replicating the present findings with larger datasets or with more sensitive techniques , such as intra-cranial recordings . In particular , it might be interesting to further probe the relationship between seen , unseen correct , and unseen incorrect targets: A specific prediction of the proposed model is that unseen correct trials should possess enough activity to modify synaptic weights in high-level cortical circuits , yet without crossing the threshold for sustained activity and consciousness ( ‘failed ignition’ ) . Unseen correct trials should thus share some of the processes that are found on seen trials and future research is necessary to directly test this hypothesis . In contrast to a widely-held belief , our findings support the existence of genuine working memory in the absence of either conscious perception or sustained activity . Our proposal is that , following a transient encoding phase via active firing , non-conscious stimuli may be maintained by ‘activity-silent’ short-term changes in synaptic weights without any detectable neural activity , allowing above-chance retrieval for several seconds . Similar activity-silent codes also subserve conscious maintenance , though in this case periodic refreshing appears to stabilize the stored representations throughout the delay . Our findings thus highlight the need to refine our understanding of working memory , and to continuously challenge the limits of non-conscious processing . 38 healthy volunteers participated in the present study ( experiment 1: N = 17 , Mage = 23 . 3 years , SDage = 2 . 8 years , 10 men; experiment 2: N = 21 , Mage = 24 . 3 years , SDage = 3 . 8 years , 9 men ) . They gave written informed consent and received 80 or 15€ as compensation for the imaging and behavioral paradigms . Due to noisy recordings , only 13 of the 17 subjects in experiment 1 were retained for the MEG analyses . Although sample size had not specifically been estimated for our study , it thus was reasonable given typical experiments in the field . Participants performed variations of a spatial delayed-response task , designed to assess the retention of a target location under varying levels of subjective visibility ( Figure 1A ) . Each trial began with the presentation of a central fixation cross ( 500 ms ) , displayed in white ink on an otherwise black screen . In experiment 1 , a faint gray target square ( RGB: 89 . 25 89 . 25 89 . 25 ) was flashed for 17 ms in 1 out of 20 equally spaced , invisible positions along a circle centered on fixation ( radius = 200 pixels; eight repetitions/location ) . Another fixation cross ( 17 ms ) preceded the display of the mask ( 233 ms ) . Mask elements were composed of four individual squares ( two right above and below , and two to the left and right of the target stimulus ) , arranged to tightly surround the target square without overlapping it . They appeared simultaneously at all possible target locations . Mask contrast was adjusted on an individual basis in a separate calibration procedure ( see below ) . A variable delay period with constant fixation followed the mask ( 2 . 5 , 3 . 0 , 3 . 5 , or 4 . 0 s ) . On 50% of the trials in experiment 1 , an unmasked distractor square , randomly placed and with the same duration as the target , was presented 1 . 5 s into the delay period . After the delay , 20 letters – drawn from a subset of lower-case letters of the alphabet ( excluded: e , j , n , p , t , v ) – were randomly presented in the 20 positions ( 2 . 5 s ) . Participants were asked to identify the target location by speaking the name of the letter presented at the location . They were instructed to always provide a response , guessing if necessary . A trial ended with the presentation of the word Vu ? ( French for seen ) in the center of the screen ( 2 . 5 s ) , cueing participants to rate the visibility of the target on the 4-point Perceptual Awareness Scale ( PAS; 1: no experience of the target , 2: brief glimpse , 3: almost clear experience , 4: clear experience; Ramsøy and Overgaard , 2004 ) using the index , middle , ring , or little finger of their right hand ( five-button non-magnetic response box , Cambridge Research Systems Ltd . , Fiber Optic Response Pad ) . We instructed subjects to reserve a visibility rating of 1 for those trials , for which they had absolutely no perception of the target . The target square was also replaced by a blank screen on 20% of the trials , in order to obtain an objective measure of participants’ sensitivity to the presence of the target . The inter-trial interval ( ITI ) lasted 1 s . Subjects completed a total of 200 trials of this working memory task , divided into four separate experimental blocks . They also undertook two blocks of 100 trials each of a perception-only control paradigm , identical to the working memory task in all respects except that the delay period and target localization screen were omitted , such that the presentation of the mask immediately preceded subjects’ visibility ratings . Task order ( perception vs . working memory ) was counterbalanced across participants . Experiment 2 was designed to investigate the impact of a conscious working memory load on non-conscious working memory . Apart from the following exceptions , it was identical to experiment 1: A screen with either 1 ( low load ) or 5 ( high load ) centrally presented digits ( 1 . 5 s ) – randomly drawn ( without replacement ) from the numbers 1 through 9 – as well as a 1s-fixation period were shown prior to the presentation of the target square . Following either a 0s- or a 4s-delay period , subjects first identified the target location by typing their responses on a standard AZERTY keyboard ( 4 s ) . The French word for numbers ( Numéros ? ) then probed participants to recall the sequence of digits in the correct order . Responses were again logged on the keyboard during a period of 4 . 5 s . Subjects last rated target visibility as in experiment 1 ( 3 s ) . The ITI varied between 1 and 2 s . Participants completed two experimental blocks of 100 trials each . Prior to the experimental tasks , each participant’s perceptual threshold was estimated in order to ensure roughly equal proportions of seen and unseen trials . Subjects completed 150 ( experiment 1: three blocks ) or 125 ( experiment 2: five blocks ) trials of a modified version of the working memory task ( no distractor , delay duration: 2 s in experiment 1 and 0 s in experiment 2 ) , during which mask contrast was either increased ( following a visibility rating of 2 , 3 , or 4 ) or decreased ( following a visibility rating of 1 ) on each target-present trial according to a double-staircase procedure . Individual perceptual thresholds to be used in the main tasks were derived by averaging the mask contrasts from the last four switches from seen to unseen ( or vice versa ) of each staircase . We analyzed our behavioral data in Matlab R2014a ( MathWorks Inc . , Natick , MA; code available upon request ) and SPSS Statistics Version 20 . 0 ( IBM , Armonk , NY ) , using repeated-measures analyses of variance ( ANOVAs ) . Only meaningful trials without missing responses were included in any analysis . Distributions of localization responses were computed for visibility categories with at least five trials per subject . Objective working memory performance was quantified via two complementary measures . The rate of correct responding was defined as the proportion of trials within two positions ( i . e . , ±36° ) of the actual target location and served as an index of the amount of information that could be retained . Because 5 out of 20 locations were counted as correct , chance on this measure was 25% . The precision of working memory was estimated as the dispersion ( standard deviation ) of spatial responses . In particular , we modeled the observed distribution of responses D ( n ) as a mixture of a uniform distribution ( random guessing ) and an unknown probability distribution d ( ‘true working memory’ ) : ( 1 ) D ( n ) =pN+ ( 1−p ) d ( n ) where p refers to the probability that a given trial is responded to using random guessing; N to the number of target locations ( N = 20 ) ; and n is the deviation from the true target location . We assumed that d ( n ) = 0 for deviations beyond a fixed limit a ( with a = 2 ) . This hypothesis allowed us to estimate p from the mean of that part of the distribution D for which one may safely assume no contribution of working memory: ( 2 ) p^=∑D ( n ) \|noutside[−a , a] ( N−2a−1 ) ∗N where the model is designed in such a way as to ensure that p^=1 if D is a uniform distribution ( i . e . , 100% of random guessing ) and p^=0 if D vanishes outside the region of correct responding ( i . e . , 0% of random guessing ) . There needs to be at least chance performance inside the region of correct responding , so ( 3 ) ∑D ( n ) \|nϵ[−a , a]≥[2a−1]N which ensures 0≤p^≤1 . This is the reason why , when computing precision , we included only subjects whose rate of correct responding for unseen trials , collapsed across all experimental conditions , significantly exceeded chance performance ( i . e . , 25% ) in a χ2-test ( p<0 . 05 ) . An estimate of d , d^ , can then be derived in two steps from Equation 1 as ( 4 ) δ ( n ) =D ( n ) −p^N1−p^ ( 5 ) d^ ( n ) =δ ( n ) \|nϵ[−a , a]∑δ ( n ) \|nϵ[−a , a] . We note that the distribution δ has residual , yet negligible , positive and negative mass ( due to noise ) outside the region of correct responding . In order to obtain d^ , we therefore restricted the distribution δto[−a , a] , set all negative values to 0 , and renormalized its mass to 1 . The precision of the representation of the target location in working memory was then defined as the standard deviation of that distribution . In experiment 1 , we recorded MEG with a 306-channel ( 102 sensor triplets: 1 magnetometer and 2 orthogonal planar gradiometers ) , whole-head setup by ElektaNeuromag ( Helsinki , Finland ) at 1000 Hz with a hardware bandpass filter between 0 . 1 and 330 Hz . Eye movements as well as heart rate were monitored with vertical and horizontal EOG and ECG channels . Prior to installation of the subject in the MEG chamber , we digitized three head landmarks ( nasion and pre-auricular points ) , four head position indicator ( HPI ) coils placed over frontal and mastoïdian skull areas , and 60 additional locations outlining the participant’s head with a 3-dimensional Fastrak system ( Polhemus , USA ) . Head position was measured at the beginning of each run . Our preprocessing pipeline followed Marti et al . ( 2015 ) . Using MaxFilter Software ( ElektaNeuromag , Helsinki , Finland ) , raw MEG signals were first cleaned of head movements , bad channels , and magnetic interference originating from outside the MEG helmet ( Taulu et al . , 2004 ) , and then downsampled to 250 Hz . We conducted all further preprocessing steps with the Fieldtrip toolbox ( http://www . fieldtriptoolbox . org/; Oostenveld et al . , 2011 ) run in a Matlab R2014a environment . Initially , MEG data were epoched between −0 . 5 and +2 . 5 s with respect to target onset for all stimulus-locked , and between −0 . 5 and +0 . 8 s with respect to the onset of the response screen for all response-locked analyses . Trials contaminated by muscle or other movement artifacts were then identified and rejected in a semi-automated procedure , for which the variance of the MEG signals across sensors served as an index of contamination . To remove any residual eye-movement and cardiac artifacts , we performed independent component analysis separately for each channel type , visually inspected the topographies and time courses of the first 30 components , and subtracted any contaminated component from the MEG data . Except for analyses requiring higher spatial precision ( i . e . , circular-linear correlations and decoding ) , results are presented for magnetometers only . Further preprocessing steps depended on the nature of the subsequent analysis: Epochs retained for investigations based on evoked responses ( i . e . , ERFs , decoding , circular-linear correlations ) were low-pass filtered at 30 Hz , while time-frequency decompositions relied on entirely unfiltered data . In the latter case , a sliding , frequency-independent Hann taper ( window size: 500 ms , step size: 20 ms ) was convolved with the unfiltered epochs in order to extract an estimate of power between 1 and 99 Hz ( in 2 Hz steps ) to identify the neural correlates of conscious and non-conscious perception and working memory in the frequency domain . Prior to univariate or multivariate statistical analysis , data ( ERFs , time-frequency power estimates ) were baseline corrected using a period between −200 and −50 ms . To localize and track the neural representations of target , response , and distractor location , filtered epochs were transformed into circular-linear correlation coefficients . Following King et al . ( 2016 ) , we combined the two linear correlation coefficients between the MEG signal and the sine and cosine of the angle defining the location in question ( i . e . , target , distractor , or response ) . An empirical null distribution was generated for each condition separately by shuffling the labels ( i . e . , target , distractor , or response location ) at the corresponding time points and averaging the resulting distribution from 1000 such permutations . Due to the spatial nature of our task , there is a possibility that subjects could have systematically moved their eyes after the presentation of the target , thus contaminating the correlation analyses . However , several lines of evidence suggest that this was not the case: First , participants were carefully instructed not to move their eyes . A close inspection of the EOG traces confirmed that subjects successfully implemented this request and did not display any strategic eye movements . Second , we carefully removed any trials contaminated by such movements as part of our preprocessing procedure . Third , the topographical patterns of the correlations show that the signal primarily originated in occipital and parietal channels . Eye movements therefore unlikely have driven the circular-linear correlations . Individual anatomical magnetic resonance images ( MRI ) , obtained with a 3D T1-weighted spoiled gradient recalled pulse sequence ( voxel size: 1 1 * 1 . 1 mm; repetition time [TR]: 2300 ms; echo time [TE]: 2 . 98 ms; field of view [FOV]: 256 * 240 * 176 mm; 160 slices ) in a 3T Tim Trio Siemens scanner , were first segmented into gray/white matter as well as subcortical structures with FreeSurfer ( https://surfer . nmr . mgh . harvard . edu/ ) . We then reconstructed the cortical , scalp , and head surfaces in Brainstorm ( http://neuroimage . usc . edu/brainstorm; Tadel et al . , 2011 ) and co-registered these anatomical images with the MEG signals , using the HPI coils and the digitized head shape as a reference . Current density distributions on the cortical surface were subsequently estimated separately for each condition and subject . Specifically , we employed an analytical model with overlapping spheres to compute the leadfield matrix and modeled neuronal current sources with an unconstrained ( dipole orientation loosening factor: 0 . 2 ) weighted minimum-norm current estimate ( wMNE; depth-weighting factor: 0 . 5 ) and a noise covariance obtained from the baseline period of all trials . Average time-frequency power in the alpha ( 8–12 Hz ) and beta ( 13–30 Hz ) bands was then estimated with complex Morlet wavelets using the Brainstorm default parameters , the resulting transformations projected onto the ICBM 152 anatomical template ( Fonov et al . , 2011 , 2009 ) , and the contrasts between the conditions of interest computed . Group averages for spatial clusters of at least 150 vertices are shown in dB relative to baseline and were thresholded at 60% of the maximum amplitude ( cortex smoothed at 60% ) . We employed the Scikit-Learn package ( Pedregosa et al . , 2011 ) as implemented in MNE 0 . 13 ( Gramfort et al . , 2013 , 2014 ) in order to conduct our multivariate pattern analyses ( MVPA ) . Following Marti et al . ( 2015 ) and King et al . ( 2016 ) , we fit linear estimators at each time sample within each participant to isolate the topographical patterns best differentiating our experimental conditions . Support vector machines ( Chang and Lin , 2011 ) were trained in the case of categorical data ( i . e . , visibility/accuracy ) and a combination of two linear support vector regressions was used for circular data ( i . e . , target/response location ) to estimate an angle from the arctangent of the separately predicted sine and cosine of the labels of interest . A 5- ( for categorical variables ) or , due to the much larger number of labels , 2-fold ( for circular variables ) , stratified cross-validation procedure was used in order to avoid overfitting: MEG data were first split into five ( two ) sets of trials with the same proportion of samples for each class . Within each fold , four ( one ) of these sets served as the training data and the remainder as the testing data . Model fitting , including all preprocessing steps , was exclusively performed on the training set . 50% of the most informative features ( i . e . , channels ) were selected by means of a simple , univariate analysis of variance to reduce the dimensionality of the data ( Charles et al . , 2014; Haynes and Rees , 2006 ) , the remaining channel-time features z-score normalized , and a weighting procedure applied in order to counteract the effects of any class imbalances . The classifier was then trained on the resulting data and applied to the left-out trials in order to identify the hyperplane ( i . e . , topography ) best suited to separate the classes . This sequence of events ( univariate feature selection , normalization , training and testing ) was repeated five ( two ) times , ensuring that each trial would be included in the test set once . Within the same cross-validation loop , we also evaluated the ability of each classifier to discriminate the experimental conditions of interest at all other time samples ( i . e . , generalization across time ) . This kind of MVPA results in a temporal generalization matrix , in which each entry represents the decoding performance of each classifier trained at time point t and tested at time point t’ , and in which the diagonal corresponds to classifiers trained and tested on the same time points ( King and Dehaene , 2014 ) . Importantly , when interrogating the capacity of our classifiers to generalize across tasks or labels ( e . g . , from the perception to the working memory task , or from seen to unseen correct target locations ) , we modified the aforementioned cross-validation procedure to capitalize on the independence of our training and testing data ( see http://martinos . org/mne/dev/auto_examples/decoding/plot_decoding_time_generalization_conditions . html#example-decoding-plot-decoding-time-generalization-conditions-py ) . As such , classifiers from each training set were directly applied to the entire testing set and the respective predictions averaged . Classifiers for categorical data generated a continuous output in the form of the distance between the respective sample and the separating hyperplane for each test trial . In order to be able to compare classification performance across subjects , we then applied a receiver operating characteristic analysis across trials within each participant and summarized overall effect sizes with the area under the curve ( AUC ) . Unlike average decoding accuracy , the AUC serves as an unbiased measure of decoding performance as it represents the true-positive rate ( e . g . , a trial was correctly categorized as seen ) as a function of the false-positive rate ( e . g . , a trial was incorrectly categorized as seen ) . Chance performance , corresponding to equal proportions of true and false positives , therefore leads to an AUC of 0 . 5 . Any value greater than this critical level implies better-than-chance performance , with an AUC of 1 indicating a perfect prediction for any given class . In contrast , classifiers for circular data were first summarized by computing the mean absolute difference between the predicted and the actual angle ( range: 0 to π; chance: π/2 ) and then transformed into an ‘accuracy’ score ( range: -π/2 to π/2; chance: 0 ) . To facilitate comparability between different conditions , an additional baseline-correction was then performed . We performed statistical analyses across subjects . For the ERF and time-frequency data , cluster-based , non-parametric t-tests with Monte Carlo permutations were used to identify significant differences between experimental conditions ( Maris and Oostenveld , 2007 ) . Further planned comparisons of ERF time courses ( seen vs . unseen ) in a-priori defined spatio-temporal regions of interest ( i . e . , P3b time window: 300–600 ms ) were conducted with non-parametric signed-rank tests ( puncorrected<0 . 05 ) . A correction for multiple comparisons was then applied with a false discovery rate ( pFDR<0 . 05 ) . Non-parametric signed-rank tests ( puncorrected<0 . 05 ) were also employed to evaluate decoding performance and the strength of circular-linear correlations . Specifically , we assessed whether classifiers could predict the trials’ classes better than chance ( categorical data: AUC > 0 . 5; circular data: rad > 0 ) and whether circular-linear correlation coefficients deviated from an empirical baseline ( Δrho > 0 ) . We report temporal averages over four a-priori time bins , corresponding to an early perceptual period ( 100–300 ms ) , the P3b time window ( 300–600 ms ) , and the first ( 0 . 6–1 . 55 s ) and second ( 1 . 55–2 . 53 s ) part of the delay period . To capitalize on the increased spatial selectivity of gradiometers , averaged time courses of these two channels are shown for circular-linear correlations . Bayesian statistics , based on either two- ( time-frequency analyses ) or one-sided ( circular-linear correlations ) t-tests , were also computed when appropriate with a scale factor of r = 0 . 707 ( Rouder et al . , 2009 ) . A one-dimensional , recurrent continuous attractor neural network ( CANN ) model ( Mongillo et al . , 2008 ) was adapted in order to simulate the experimental findings ( Figure 7A ) . Individual neurons were aligned according to their preferred stimulus value , enabling the network to encode angular position of a target stimulus ( range: -π to π; periodic boundary condition ) . JIE and JEI represent the connection strength between excitatory and inhibitory neurons . All excitatory neurons received a constant background input , Ie , reflecting the arousal signal when the neural system was engaged in a working memory task . δ1ξ1 is background noise; Ie , any external stimulus ( e . g . , target , mask , and recall signal ) ; and δ1ξ1 ( t ) the noise related to those external stimuli . u ( θ , t ) and x ( θ , t ) denote the short-term synaptic facilitation ( STF ) and depression ( STD ) effects at time t of neurons with preference θ , respectively . The short-term plasticity dynamics are characterized by the following parameters: J1 ( absolute efficacy ) , U ( increment of the release probability when a spike arrives ) , τf and τd ( facilitation and depression time constants ) . The STF value u ( θ , t ) is facilitated whenever a spike arrives , and decays to the baseline U within the time τf . The neurotransmitter value x ( θ , t ) is utilized by each spike in proportion to u ( θ , t ) and then recovers to its baseline , 1 , within the time τd . J ( θ , θ′ ) is the interaction strength from neurons at θ to neurons at θ′ and is chosen to be ( 10 ) J ( θ , θ′ ) ={J1cos[B∗ ( θ−θ′ ) ]−J0ifB∗ ( θ−θ′ ) ϵ[−arcos ( −J0/J1 ) , arcos ( −J0/J1 ) ] , −J0 , else where J0 , J1 , and B are constants which determine the connection strength between the neurons . Note that J ( θ , θ′ ) is a function of θ−θ′ , i . e . , the neuronal interactions are translation-invariant in the space of neural preferred stimuli . The other parameters of the system were as follows: τ = 0 . 008 s , τf = 4 s , τd = 0 . 3 s , J1 = 12 , J0 = 1 , JEI = 1 . 9 , JIE = 1 . 8 , Ib = -0 . 1 Hz , δ1 =0 . 3 , δ2 = 9 , N = 100 , α = 1 . 5 , B = 2 . 2 . During our simulations , we first presented a target signal with an amplitude of Atarget = 390 Hz at a random location ( 50 ms ) , waited for 17 ms , and then applied a mask signal to all the neurons in the system ( 200 ms ) . The amplitude of the mask signal was initially varied in order to determine a critical value which would produce two distinct maintenance patterns , but was then fixed at a threshold of Amask = 62 Hz . At the end of a 3s-delay period , a non-specific recall signal was given for 50 ms with Arecall = 10 Hz . Remembered target position was calculated as the population vector angle during this time period .	Many everyday activities require you to store information in your brain for immediate use . For example , imagine that you are cooking a meal: You have to remember the ingredients , add them in the correct order , and operate the stove . This ability is called working memory . Researchers have long believed that , whenever we store information in our working memory , we are conscious of that information . That is , if someone asks you , you can report the information . Scientists usually also think that working memory comes with constant brain activity . This means that for as long as you have to remember something , the cells in your brain that code for that information will be active . Trübutschek et al . now show that we can sometimes store information in working memory without being conscious of it and without the need for constant brain activity . As part of the experiment , a barely visible square-shaped target was briefly flashed in 1 of 20 different locations on a computer screen . Human volunteers had to locate the square and indicate whether they had seen it or not . Importantly , they had to guess the location of the target whenever they had not seen it . While the volunteers performed this task , their brain activity was monitored using magnetoencephalography , a noninvasive technique that captures the magnetic fields created by electrical signals in the brain . Even when the volunteers had not seen the target , they could often correctly guess where it had been up to four seconds later , more often than would be predicted by chance alone . The experiment ruled out the possibility that this so-called “blindsight” was simply due to the volunteers accidentally reporting not having seen a target , when they had actually seen it . It also excluded the possibility that the volunteers guessed the location long before they had to report it and simply consciously stored that guess . Instead , without the participant knowing , the brain appears to have stored the target location in working memory using parts of the brain near the back of the head that process visual information . Importantly , this non-conscious storage did not come with constant brain activity , but seemed to rely on other , “activity-silent” mechanisms that are hidden to standard recording techniques . Although Trübutschek et al . show that the brain can unknowingly store information , they did not test other aspects of working memory . Future studies are needed to examine whether the brain can also non-consciously manipulate or use information in its working memory . In addition , future research also needs to investigate the exact mechanism that stores information without constant brain activity .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2017	A theory of working memory without consciousness or sustained activity
The Golgi complex is the central sorting compartment of eukaryotic cells . Arf guanine nucleotide exchange factors ( Arf-GEFs ) regulate virtually all traffic through the Golgi by activating Arf GTPase trafficking pathways . The Golgi Arf-GEFs contain multiple autoregulatory domains , but the precise mechanisms underlying their function remain largely undefined . We report a crystal structure revealing that the N-terminal DCB and HUS regulatory domains of the Arf-GEF Sec7 form a single structural unit . We demonstrate that the established role of the N-terminal region in dimerization is not conserved; instead , a C-terminal autoinhibitory domain is responsible for dimerization of Sec7 . We find that the DCB/HUS domain amplifies the ability of Sec7 to activate Arf1 on the membrane surface by facilitating membrane insertion of the Arf1 amphipathic helix . This enhancing function of the Sec7 N-terminal domains is consistent with the high rate of Arf1-dependent trafficking to the plasma membrane necessary for maximal cell growth . In eukaryotes , trafficking of membranes and membrane proteins from their site of synthesis at the endoplasmic reticulum through the secretory pathway proceeds via the Golgi apparatus . This trafficking , mediated by membrane vesicles and tubules , must achieve the specificity to ensure that proteins are targeted to the correct destinations and with the correct post-translational modifications while retaining the throughput required to provide the bulk of the lipid content of the plasma membrane’s growth over the course of cell division . Much of this process is controlled at the trans-Golgi network ( TGN ) , where the final processed forms of proteins are recognized by cargo adaptors and packaged into vesicles for transport to endosomes , lysosomes , and the plasma membrane ( Guo et al . , 2014 ) . In both humans and model organisms , Arf GTPases act as the central coordination point for vesicle formation at the TGN , directly regulating a cascade of cargo adaptors ( Donaldson and Jackson , 2011 ) . Arf activation is regulated in turn by guanine nucleotide exchange factors ( GEFs ) that exchange bound GDP for GTP ( Casanova , 2007 ) . The accompanying conformational change in the Arf GTPase rearranges ‘switch’ regions for recognition by effectors and exposes a myristoylated amphipathic N-terminal helix that inserts into the membrane ( Antonny et al . , 1997; Goldberg , 1998 ) . Arf-GEFs thereby stand upstream of Arf GTPases as the initiator of the cascade of events leading to vesiculation of TGN cisternae . At the TGN , the Arf1-5 GTPases are activated by the BIG1/2 proteins in humans ( Mansour et al . , 1999; Yamaji et al . , 2000 ) and the Arf1/2 GTPases are activated by Sec7 in the model organism Saccharomyces cerevisiae ( Achstetter et al . , 1988; Peyroche et al . , 1996 ) . These TGN-localized Arf-GEFs are universally conserved in eukaryotes , and their importance is underscored by neurological disorders associated with mutations in the BIG2/ARFGEF2 gene ( Banne et al . , 2013; Sheen et al . , 2004 ) . The TGN-localized Arf-GEFs comprise roughly 1800 functional residues including a central catalytic domain of ~200 residues mediating nucleotide exchange ( Chardin et al . , 1996; Morinaga et al . , 1996; Peyroche et al . , 1996 ) . This catalytic domain , commonly called the Sec7 domain , will be referred to here as the GEF domain for clarity . Conserved domains C-terminal to the GEF domain , termed HDS1-4 ( homology downstream of Sec7 ) ( Mouratou et al . , 2005 ) , act to integrate the signals from several small GTPases , including Arf1 itself , to switch Sec7 from a strongly autoinhibited to a strongly autoactivated form ( McDonold and Fromme , 2014; Richardson et al . , 2012; Richardson and Fromme , 2012 ) . The N-terminal DCB ( dimerization and cyclophilin binding ) and HUS ( homology upstream of Sec7 ) domains , though conserved in all Golgi Arf-GEFs and essential for Golgi function ( Ramaen et al . , 2007 ) , remain more of an enigma . Numerous functions have been proposed , including cyclophilin binding , regulated dimerization , Arl1 and Arf1 GTPase binding , and enhancement of GEF activity , but studies are frequently contradictory and fail to assemble into a unified understanding of function ( Christis and Munro , 2012; Grebe et al . , 2000; Lowery et al . , 2013; Mouratou et al . , 2005; Ramaen et al . , 2007; Richardson et al . , 2012 ) . Here , we present the crystal structure of the regulatory DCB and HUS domains from Thielavia terrestris Sec7 and demonstrate that they form a single continuous structural domain . We find that dimerization of this N-terminal domain is not conserved and that Sec7 dimerization is primarily mediated by the C-terminal HDS4 domain . We describe a new function of the DCB/HUS regulatory domain: this domain enhances the activation of Arf1 in a manner dependent on both lipids and the N-terminal membrane insertion element of Arf1 , implying a role in chaperoning the insertion of Arf1 into the Golgi membrane . To gain structural insights into the function of the DCB and HUS domains , we purified and attempted to crystallize constructs from ten species , including S . cerevisiae and the thermophiles Chaetomium thermophilum , Myceliophthora thermophila , and T . terrestris ( Amlacher et al . , 2011; Berka et al . , 2011 ) . Following correction of the annotated intron ( Supplementary file 1 ) , a construct comprising residues 1–458 of T . terrestris produced crystals diffracting to 2 . 6 Å . Using experimental phase determination by single-wavelength anomalous diffraction of selenomethionine-substituted protein , the structure of the DCB and HUS domains was determined with four copies in the asymmetric unit . The best resolved chains in the model lack only unresolved loops and the final ~35 conserved residues of the HUS domain absent from the crystallized construct ( Figure 1A and Figure 1—figure supplement 1 , Table 1 ) . Extensive crystal contacts between chains A and B stabilize them sufficiently to produce excellent electron density; the N-terminal portions of chains C and D , with fewer crystal contacts , were less well resolved and had only enough visible density to be modeled as copies of chains A and B ( Figure 1—figure supplements 1 and 2 ) . 10 . 7554/eLife . 12411 . 003Figure 1 . Crystal structure of T . terrestris Sec7 DCB/HUS domain ( residues 1–458 ) ( A ) Chains A ( green ) and D ( white ) are shown; chain A is colored light to dark N to C , and helices are numbered N to C . The entire asymmetric unit is shown in supplement 1 . Electron density is shown in supplement 2 . The DCB/HUS interface is shown in supplement 3 . ( B ) The charge potential surface of chain A alone , as calculated by APBS ( Baker et al . , 2001; Dolinsky et al . , 2007 ) , is colored on a red-white-blue gradient from -10 kT/e to +10 kT/e . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 00310 . 7554/eLife . 12411 . 004Figure 1—figure supplement 1 . Asymmetric unit of crystal structure . The entire asymmetric unit is shown on the left , with each chain colored light to dark N to C . A=green , B=red , C=blue , D=gray . The asymmetric unit is shown in B-factor putty form on the right ( thicker chain indicates larger relative atomic B-factors ) . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 00410 . 7554/eLife . 12411 . 005Figure 1—figure supplement 2 . Electron density of crystal structure . Weighted composite omit maps of helix 4 including residues D297 , K301 , and F305 are shown . Chain A , above , was used for structural analysis; chain D , below , was modeled on the basis of chain A . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 00510 . 7554/eLife . 12411 . 006Figure 1—figure supplement 3 . Magnified view of the DCB/HUS interface . Packing of helices 7 and 8 ( DCB ) and 10 and 11 ( HUS ) is shown , including participating side chains . Conserved residues are colored in green . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 006 The DCB and HUS domains together form a single continuous armadillo repeat joined by a conserved ~1400 Å2 hydrophobic interface , comprising helices 7 and 8 from the DCB domain and helices 10 and 11 from the HUS domain . ( Figure 1A and Figure 1—figure supplement 3 ) . We therefore regard the N-terminal regulatory region of Sec7 as a single DCB/HUS structural domain . In most species , a poorly conserved ~100 amino acid linker connects the conserved DCB and HUS domains . In the T . terrestris DCB/HUS domain structure , residues 245–271 of this interdomain region ( between helices 8 and 10 , including helix 9 ) are resolved in their packing against the armadillo repeat , and the remaining 46 and 21 residue stretches are unresolved in the structure . Interestingly , the mutation E209K in human BIG2 associated with neuronal disease ( Sheen et al . , 2004 ) maps to this unresolved region . A striking conserved positively charged patch is seen at the interface of helices 7 and 10 ( Figure 1B ) . 10 . 7554/eLife . 12411 . 007Table 1 . Data collection and refinement statisticsDOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 007T . terrestris Sec7 DCB/HUS domain ( residues 1-458 ) Wavelength ( Å ) 0 . 987Resolution range ( Å ) 50 - 2 . 6 ( 2 . 64–2 . 6 ) Space groupP 21 21 21Unit cella=62 . 472Å b=132 . 024Å c=247 . 664Å α=β=γ=90°Total reflections569136Unique reflections59606Multiplicity9 . 5 ( 4 . 7 ) Completeness ( % ) 98 . 77 ( 88 . 46 ) Mean I/sigma ( I ) 8 . 46 ( 1 . 48 ) Wilson B-factor65 . 26R-work0 . 2119 ( 0 . 3113 ) R-free0 . 2568 ( 0 . 3665 ) Number of atoms10944Macromolecules10887Water57Protein residues1383RMS ( bonds ) 0 . 009RMS ( angles ) 1 . 23Ramachandran favored ( % ) 98Ramachandran outliers ( % ) 0 . 075Clashscore7 . 63Average B-factor89 . 7Macromolecules89 . 8Solvent60 . 3 The crystallized construct forms a dimer in the crystal via its C-terminal helices 14 and 15 , with helix 15 of each copy aligned antiparallel to each other . Dimerization is known to occur in N-terminal constructs of Sec7 and its homologs ( Grebe et al . , 2000; Ramaen et al . , 2007; Richardson et al . , 2012 ) , and small-angle X-ray scattering ( SAXS ) analysis confirmed that the observed dimerization mode occurred in solution as well as in the crystal ( Figure 2A ) . However , the fact that the construct dimerizes via the end of a DCB/HUS domain construct that was truncated raised concern , as truncation of helical domains has been shown in the past to result in artificial dimer interfaces ( Richardson et al . , 2009 ) . Therefore , we purified a longer T . terrestris Sec7 construct comprising the entire DCB/HUS domain ( residues 1–492 ) , for analysis by SAXS . While slight divergence is seen , the intact T . terrestris DCB/HUS domain SAXS data closely fits that predicted from a monomer ( Figure 2B ) . This strongly suggests that the observed dimerization interface is an artifact resulting from truncation of the C-terminal helix of the HUS domain , although it is possible that this dimerization mode represents a regulated conformation of the N-terminus . 10 . 7554/eLife . 12411 . 008Figure 2 . Sec7 dimerizes primarily via the HDS 4 domain . ( A ) CORAL ( Petoukhov et al . , 2012 ) was used to fit the T . terrestris Sec7 DCB/HUS domain structure ( residues 1–458 ) to SAXS data collected on the same construct , accounting for the presence of unresolved loops modelled as dotted lines . For comparison , a similar calculation using only a single chain is shown with a significantly worse fit . ( B ) A single monomer from ( A ) , fixing the previously modeled loops in place and adding additional residues at the C-terminus , was fit to SAXS data collected on T . terrestris Sec7 ( 1–492 ) ; as the added region is expected to comprise an alpha helix in addition to a connecting loop , BUNCH was used in place of CORAL for modeling . A similar fit of the dimeric form is shown for comparison . ( C ) The solution molecular weights of the indicated S . cerevisiae and T . terrestris constructs were determined by MALS . Comparison to the predicted monomeric mass based on primary sequence yields the stoichiometry . SAXS results from S . cerevisiae Sec7ΔC are shown in supplement 1 . Results from corresponding S . cerevisiae Gea2 constructs are shown in supplement 2 . Original MALS traces of all S . cerevisiae and T . terrestris constructs are shown in supplement 3 . ( D ) Schematic model of S . cerevisiae cis and trans interactions of truncated constructs . Red represents a hypothesized interface between HDS1 and DCB/HUS , the latter half of which can self-stabilize by dimerization in the absence of HDS1 . Orange represents the HDS4 dimerization interface . T . terrestris is hypothesized to possess a divergent DCB/HUS interaction interface with less need of stabilization by dimerization . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 00810 . 7554/eLife . 12411 . 009Figure 2—figure supplement 1 . SAXS analysis of S . cerevisiae Sec7ΔC . BUNCH ( Petoukhov and Svergun , 2005 ) and CRYSOL ( Svergun et al . , 1995 ) were used to model T . terrestris Sec7 DCB/HUS domain ( residues 1–458 ) , S . cerevisiae Gea2 GEF domain ( residues 570–714 ) , and appropriate linker residues to SAXS data collected on S . cerevisiae Sec7∆C ( residues 203–1017 ) with P2 symmetry . While too many degrees of freedom exist to interpret the resulting model as an accurate structural assembly , the placement of the GEF domain at a distance from the DCB/HUS domain in order to fit the data suggests a flexible linkage . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 00910 . 7554/eLife . 12411 . 010Figure 2—figure supplement 2 . MALS analysis of S . cerevisiae Gea2 . Solution molecular weights of the indicated S . cerevisiae Gea2 constructs were determined by MALS . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 01010 . 7554/eLife . 12411 . 011Figure 2—figure supplement 3 . MALS traces of S . cerevisiae and T . terrestris Sec7 constructs . Normalized gel filtration peak profiles of all Sec7 constructs are shown with MALS molecular weight measurements superimposed . The right-hand molecular weight axis for each panel is scaled to each construct’s calculated mass , with monomeric , dimeric , and trimeric masses indicated . Due to the wide variation in construct size , a single gel filtration column was not used for every construct , leading to the variation in elution profiles seen . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 011 These observations , together with a report that a portion of the HDS4 domain mediates dimerization ( Mariño-Ramírez and Hu , 2002 ) , prompted us to perform an in-depth characterization of the multimerization behavior of different Sec7 constructs from both T . terrestris and S . cerevisiae . Many constructs failed to yield reliable SAXS data , so we turned to multi-angle light-scattering ( MALS ) to identify the oligomeric state of a series of truncation constructs . We found that full-length T . terrestris Sec7 and the corresponding S . cerevisiae Sec7f ( residues 203–2009 ) construct were both dimeric ( Figure 2C ) . Strikingly , removal of the C-terminal HDS4 domain resulted in a monomer for constructs from both species , whereas a construct corresponding to a predicted coiled-coil from the HDS4 domain ( Mariño-Ramírez and Hu , 2002 ) was itself dimeric . This indicates that the HDS4 domain mediates dimerization of the intact Sec7 proteins from both T . terrestris and S . cerevisiae . To examine the role of the HDS4 domain in cells , we attempted to introduce a GFP-Sec7ΔHDS4 construct into cells lacking the essential SEC7 gene . Virtually all such cells were unable to grow , similar to the previously described lethality arising from removal of the HDS2-4 domains ( Figure 3A ) ( Richardson et al . , 2012 ) . To examine whether this effect was due to mis-localization of the construct , we imaged an otherwise wild-type strain harboring the GFP-Sec7ΔHDS4 construct . Although we detected a low level of cytoplasmic mislocalization , GFP-Sec7ΔHDS4 was largely localized correctly to the TGN , as observed by its colocalization with endogenous Sec7-RFP ( Figure 3B ) . In contrast , localization of the shorter GFP-Sec7ΔC+HDS1 construct was more severely compromised , as seen previously ( Richardson et al . , 2012 ) . Therefore , the HDS4 domain is critically important for Sec7 function in cells but does not appear to play a major role in its localization . 10 . 7554/eLife . 12411 . 012Figure 3 . The HDS4 domain of Sec7 is important for function but dispensable for TGN localization . ( A ) Centromeric plasmids encoding GFP-Sec7 constructs expressed from the SEC7 promoter were introduced into a SEC7 plasmid shuffling strain ( CFY409 ) . Growth on 5-FOA measures the ability of the construct to complement the sec7∆ mutation . ( B ) The same plasmids were imaged in an otherwise wild-type strain expressing endogenously tagged Sec7-RFPMars ( ‘Sec7-RFP’ ) ( CFY578 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 012 Further C-terminal truncation of S . cerevisiae Sec7 resulted in a surprising series of observations . Whereas a construct lacking the HDS4 domain is monomeric , a construct lacking the HDS2-4 domains was an equilibrium mixture of monomeric and dimeric species ( Figure 2C ) . Removal of additional domains , generating constructs lacking the HDS1-4 domains with or without the GEF domain , resulted in stable dimers . However , the corresponding constructs of T . terrestris Sec7 were monomeric ( Figure 2C ) . The simplest model accounting for dimerization of the isolated S . cerevisiae N-terminus , dimerization via the HDS4 domain , and loss of dimerization when the HDS1 domain is present , invokes an interaction between the HDS1 and DCB/HUS domains ( Figure 2D ) . When present , the HDS1 domain likely masks a site by which the DCB/HUS domain dimerizes in its absence , but Sec7 retains its dimerization via the HDS4 domain . Whether this masked dimerization is an artifact or serves a regulatory purpose remains unknown , but the presence of an exposed interaction domain in truncated constructs provides an attractive explanation for the complicated oligomerization results seen in prior work ( Grebe et al . , 2000; Ramaen et al . , 2007 ) , as well as for the seemingly absolute requirement of the DCB/HUS domain for soluble expression of most constructs containing HDS domains . Unfortunately , this latter phenomenon also precludes more thorough biochemical testing of this hypothesis , leaving this model partially speculative . It is also evident from the behavior of the T . terrestris constructs that dimerization of the DCB/HUS domain is not a conserved behavior . We note that this analysis does not exclude the possibility of membrane- or temperature-dependent effects on dimerization or possible influences on Sec7 dimerization by other proteins . While reliable SAXS data could not be obtained from T . terrestris constructs including the GEF domain , modeling our N-terminal structure and a previously solved GEF domain structure ( Renault et al . , 2002 ) to SAXS data collected on S . cerevisiae Sec7 ( 203–1017 ) ( Sec7ΔC ) suggests that the GEF domain is flexibly connected to the DCB/HUS domain rather than continuing the armadillo repeat ( Figure 2—figure supplement 1 ) . As such , it appears unlikely that the intact DCB/HUS construct ( 1–492 ) is prone to the same artifactual dimerization issues at its C-terminus as is the truncated Sec7 ( 1–458 ) construct . Given our finding that the HDS4 domain appears to be the primary mediator of Sec7 dimerization , we decided to investigate the multimerization state of the distantly related early-Golgi Arf-GEF Gea2 from S . cerevisiae , which has evolved without an HDS4 domain . MALS analysis revealed that Gea2 full-length and C-terminal truncation constructs are dimeric ( Figure 2—figure supplement 2 ) . This suggests that dimerization in some form is critical to the regulation of all the Golgi Arf-GEFs , and that in the absence of an HDS4 domain , N-terminal dimerization may have evolved to acquire additional importance . We previously observed that the DCB/HUS domain confers a significant enhancement of activity on the GEF domain , in the physiologically relevant context of a myristoylated Arf1 substrate in the presence of liposome membranes ( Richardson et al . , 2012 ) . We sought to investigate the mechanism for this enhancement on the basis of the DCB/HUS crystal structure . We used an established in vitro GEF activity assay taking advantage of the innate change in tryptophan fluorescence of Arf1 as it transitions from binding GDP to binding GTP ( Bigay and Antonny , 2005; Kahn and Gilman , 1986 ) to track reaction kinetics without the need for artificial substrates ( Figure 4A ) . Extending our previous results , we were surprised to observe that when we removed membranes from the reaction and truncated the N-terminal membrane insertion helix of Arf1 to preserve its ability to exchange nucleotide ( ΔN17Arf1 ) , the DCB/HUS domain of S . cerevisiae Sec7 no longer stimulated GEF activity . Indeed , the Sec7ΔC construct was slightly less active than the GEF domain alone , possibly due to a reduced rate of diffusion ( Figure 4A , B ) . Therefore , the DCB/HUS domain stimulates GEF domain activation of Arf1 on membranes . 10 . 7554/eLife . 12411 . 013Figure 4 . Stimulation of GEF activity by the DCB/HUS domain depends on the presence of lipids . ( A ) Triplicate nucleotide exchange curves of S . cerevisiae Sec7ΔC ( green traces ) or isolated GEF domain constructs ( yellow traces ) acting on myristoylated Arf1 substrate in the presence of liposomes are shown against a mock exchange reaction ( black trace ) ( left ) . The curves were fit to a single exponential and normalized to Sec7 concentration to extract the exchange reaction rates ( right ) . Error bars denote 95% confidence intervals , n=3 . Purity of all constructs is demonstrated in supplement 1 . These measurements of exchange by tryptophan fluorescence are compared to complementary measurements of Arf1 membrane binding in supplement 2 . ( B ) Exchange reaction rates of S . cerevisiae Sec7 constructs acting on the ΔN17Arf1 substrate in the absence of lipids . GEF activity of T . terrestris constructs is shown in supplement 3 . GEF activity following Arl1 preincubation is shown in supplement 6 , with corresponding physical interaction analysis in supplements 4 and 5 . ( C ) Parallel reactions of S . cerevisiae Sec7 constructs acting on myristoylated Arf1 and ΔN17Arf1 in the presence of liposomes and micelles . ( D ) Nucleotide exchange by S . cerevisiae Sec7 constructs on non-myristoylated L8K-Arf1 in the presence and absence of liposomes . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 01310 . 7554/eLife . 12411 . 014Figure 4—figure supplement 1 . Measurement of Sec7 GEF kinetics by liposome flotation . 2 μM myristoylated Arf1-GDP was incubated with liposomes , excess GMPPNP , and . 67 μM Sec7ΔC or GEF domain for the indicated amount of time and put on ice to stop the reaction . Lipid-bound protein ( i . e . activated Arf1 ) was assayed by liposome flotation as described previously ( Richardson et al . , 2012 ) . ( A ) Input fractions and float/lipid-bound fractions were analyzed by SDS-PAGE and colloidal Coomassie; float fraction load amounts were normalized to the recovered lipid fraction on the basis of fluorescence of DiR dye included in the liposomes . Note that band staining intensity correlates with molecular weight , and the GEF domain construct is ~¼ the mass of the Sec7ΔC construct . ( B ) Quantified band densities ( circles and dashed lines ) are overlaid on the reaction kinetics measured by fluorescence in Figure 4A ( solid lines ) . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 01410 . 7554/eLife . 12411 . 015Figure 4—figure supplement 2 . GEF activity of T . terrestris Sec7 . T . terrestris Sec7ΔC and isolated GEF constructs were assayed for rate of nucleotide exchange of myristoylated Arf1 in the presence of liposomes ( left ) and ΔN17Arf1 in the absence of liposomes ( right ) . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 01510 . 7554/eLife . 12411 . 016Figure 4—figure supplement 3 . GST pulldown analysis of Arf1 and Arl1 interaction with Sec7ΔC . Stable interaction of Arf1 and Arl1 with S . cerevisiae Sec7ΔC was assayed by pulldown of Sec7 with a great excess of GST-GTPase bound to GDP ( denoted D ) or GMPPNP ( denoted T* ) . Following SDS-PAGE , protein was visualized by Coomassie staining . There was no apparent nucleotide-dependent interaction observed between Sec7ΔC and Arf1-GTP or Arl1-GTP in this experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 01610 . 7554/eLife . 12411 . 017Figure 4—figure supplement 4 . Liposome pelleting analysis of Arf1 and Arl1 interaction with Sec7ΔC . Stable interaction of membrane-bound Arf1 and Arl1 with S . cerevisiae Sec7ΔC ( as a potential effector ) was assayed in the context of membranes by liposome pelleting as described previously ( Paczkowski et al . , 2012 ) following EDTA-mediated loading with the indicated nucleotide . The fraction recovered is calculated relative to a baseline of liposome-free pelleting . There was no apparent interaction observed between Sec7ΔC and membrane-bound Arf1-GTP or Arl1-GTP in this experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 01710 . 7554/eLife . 12411 . 018Figure 4—figure supplement 5 . Effect of Arl1 preincubation on Sec7 GEF activity . S . cerevisiae Sec7ΔC was assayed for rate of nucleotide exchange of Arf1 in the presence of liposomes and Arl1-GMPPNP as described previously ( McDonold and Fromme , 2014 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 01810 . 7554/eLife . 12411 . 019Figure 4—figure supplement 6 . Purity of constructs used for kinetic assays . 2 . 5 μg of each construct used for biochemical assays were separated by SDS-PAGE and stained by Coomassie to assess purity . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 019 To determine whether the membrane-localized activation of Arf1 monitored by tryptophan fluorescence corresponded to the concomitant membrane association of activated Arf1 with membranes , we performed liposome flotation following a GEF-mediated exchange timecourse to measure the stable membrane association of activated Arf1 ( Figure 4—figure supplement 1 ) . The kinetics of Arf1 membrane insertion as measured in this assay directly correlated with the kinetics of Arf1 activation as measured by tryptophan fluorescence and further confirmed the role of the DCB/HUS domain in stimulating Arf1 activation at the membrane surface . Similar results were also observed using equivalent T . terrestris constructs on the basis of tryptophan fluorescence ( Figure 4—figure supplement 2 ) . As the stimulatory effect is observed in both dimeric ( S . cerevisiae ) and monomeric ( T . terrestris ) constructs , we infer that DCB/HUS domain stimulation of GEF activity occurs independently of dimerization . Two potential mechanisms of action thereby present themselves: either the DCB/HUS domain binds to the membrane surface to recruit the GEF domain to its site of action , or the DCB/HUS domain assists in membrane-insertion of the N-terminal amphipathic helix of Arf1 . To distinguish between these two possibilities , we changed the lipids in the reaction mixture from physiologically relevant TGN-like liposome membranes to non-physiological DMPC/cholate micelles known to support nucleotide exchange of myristoylated Arf1 ( Kahn and Gilman , 1986 ) . In the presence of these micelles , the activity of both constructs on myristoylated Arf1 was higher , indicating an increase in the intrinsic rate of nucleotide exchange , yet the Sec7ΔC construct was again more active than the isolated GEF domain ( Figure 4C ) . As this effect is not seen on ΔN17Arf1 in the presence of micelles , which displays a uniformly reduced rate of exchange , it cannot be attributed merely to the lipids themselves . In a separate experiment we introduced the L8K mutation into the membrane insertion helix of Arf1 , which reduces its hydrophobicity and enables Arf1 activation in the absence of membranes ( Luo et al . , 2007; Yoon et al . , 2004 ) . We observed that the DCB/HUS domain did not stimulate activity of the GEF domain towards L8K-Arf1 in either the absence or presence of membranes ( Figure 4D ) . Taken together , these results suggest that GEF stimulation by the DCB/HUS domain involves insertion of the Arf1 amphipathic helix into a hydrophobic and presumably lipidic environment , yet this effect is indifferent to the precise nature of the lipids . Our interpretation of these observations is that the DCB/HUS domain assists in overcoming a kinetic activation barrier associated with insertion of the myristoylated Arf1 N-terminal helix into membranes . An alternative possibility , that the DCB/HUS domain has indiscriminate affinity for lipid surfaces and therefore increases the rate of membrane-localized encounters between the GEF domain and its substrate , is addressed further below . To pair these observations to the structure of the DCB/HUS domain , we performed a targeted functional screen by generating an extensive panel of mutants in S . cerevisiae , selecting conserved residues found at the surface of the T . terrestris DCB/HUS domain structure ( Figure 5A ) . We investigated whether alanine or aspartate substitutions in these conserved surface residues resulted in cellular viability phenotypes at elevated temperature in a sensitized strain in which the levels of Arf1/2 have been knocked down by 90% through disruption of the ARF1 gene ( Stearns et al . , 1990 ) ( Figure 5B ) . The established sec7-1 temperature sensitive mutation ( S402L in S . cerevisiae ) ( McDonold and Fromme , 2014; Novick et al . , 1980 ) maps to a conserved serine at residue 156 in the α7–8 loop ( Figure 6—figure supplement 1 ) . The leucine substitution arising from the sec7-1 mutation likely perturbs the local structure of this region critical to forming the DCB/HUS interface . Several other mutants displayed temperature-sensitive growth phenotypes; the most significant growth defects were caused by mutations in residues located on a conserved surface of helices 4 , 6 , and 8 , on the opposite side of the protein from the conserved positively charged patch ( Figure 5A , B ) . 10 . 7554/eLife . 12411 . 020Figure 5 . Conserved DCB/HUS surface regions mediate Sec7 function . ( A ) Locations of all S . cerevisiae mutants tested are shown as space-filling spheres mapped on the T . terrestris structure , with backbone colored by conservation . Positions of mutations resulting in stronger temperature sensitive growth phenotypes are colored red ( e . g . , S . cerevisiae Q365/R368 ) , positions with weaker phenotypes are colored orange ( e . g . , S . cerevisiae D297/K301/F305 ) , and positions with no growth phenotype are colored blue; the position corresponding to the sec7-1 mutation ( S402L ) is colored magenta . ( B ) Using a plasmid-shuffling assay , CEN plasmids bearing GFP-tagged Sec7 or Sec7f with the indicated mutations expressed via their endogenous promoter were tested for their ability to rescue a genomic sec7 deletion in a sensitized arf1Δ/ARF2 strain ( CFY863 ) . Growth of serial 10-fold dilutions after 3 days at 37°C is shown , comparing the shuffled strains on 5-FOA to their parents growing in parallel on synthetic complete media ( SC ) . Changes in the number or size of colonies indicates a growth defect . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 020 To explore whether any of these mutations affected localization of Sec7 to the TGN , we imaged mutant GFP-Sec7 plasmids in the sensitized arf1Δ strain . All of the new mutants that we examined expressed well and exhibited largely punctate localization ( Figure 6A ) . Although some of the mutants were partially mislocalized to the cytoplasm , none were as severe as either the sec7-1 mutant or previously identified mutants in the HDS1 domain ( Richardson et al . , 2012 ) . For example , although the D297A/K301D/F305D mutant exhibited significant cytoplasmic mislocalization , it was also strongly localized to punctate structures ( Figure 6A ) . In contrast , the sec7-1 mutant only occasionally exhibits robust punctate localization . Therefore , the growth defects displayed by the new DCB/HUS domain mutants do not appear to be attributable to loss of proper localization . 10 . 7554/eLife . 12411 . 021Figure 6 . Helices 4 and 6 of the DCB/HUS domain mediate GEF stimulation . ( A ) sec7Δ/arf1Δ strains bearing the indicated GFP-Sec7 alleles on a centromeric plasmid expressed from the SEC7 promoter were imaged at permissive and restrictive temperatures . ( B ) Surface residue conservation is shown on the basis of a 361-sequence MUSCLE alignment comprising all BLAST hits of the T . terrestris Sec7 N-terminus following removal of incomplete sequences and sequences with more than 95% pairwise identity . Green represents conservation . Residues mutated for biochemical assays are shown in colors matching the resultant bars . A closer view of the residue mutated in sec7-1 is shown in supplement 1 . ( C ) Mutants purifiable as S . cerevisiae Sec7ΔC constructs were assayed for rate of nucleotide exchange of myristoylated Arf1 in the presence of liposomes . Activity of the same mutants toward ΔN17Arf1 in the absence of liposomes is shown in supplement 2 . ( D ) The two HUS-box mutants purifiable as S . cerevisiae Sec7ΔC constructs were assayed for rate of nucleotide exchange of myristoylated Arf1 in the presence of liposomes . Viability and in vivo stability are assessed in supplements 3 and 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 02110 . 7554/eLife . 12411 . 022Figure 6—figure supplement 1 . Atomic basis of the sec7-1 phenotype . The temperature sensitive allele sec7-1 represents an S402L mutation , corresponding to T . terrestris Sec7 residue S156 . This serine stabilizes a loop near the interface between the DCB and HUS regions . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 02210 . 7554/eLife . 12411 . 023Figure 6—figure supplement 2 . Sec7ΔC mutant activity in the absence of membranes . Purifiable mutants in the S . cerevisiae Sec7ΔC construct were assayed for rate of nucleotide exchange of ΔN17Arf1 in the absence of membranes . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 02310 . 7554/eLife . 12411 . 024Figure 6—figure supplement 3 . Viability of HUS box mutants . Missense mutations in the HUS box were tested for viability at room temperature by plasmid shuffle , spotted in half-log dilutions left to right . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 02410 . 7554/eLife . 12411 . 025Figure 6—figure supplement 4 . Expression of HUS box mutants . Expression of the indicated GFP-Sec7f alleles was assayed by anti-GFP immunoblot . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 025 To test whether any of the mutations affecting growth also affected GEF activity , we prepared mutant Sec7ΔC proteins for in vitro analysis . The use of the Sec7ΔC truncation enabled us to directly monitor the role of the DCB/HUS domain without confounding affects from the C-terminal HDS1-4 domains . While several mutants could not be purified , including sec7-1 , a sufficient number representing both conserved regions were purified to allow us to test the involvement of each region in the DCB/HUS stimulatory activity ( Figure 6B , C ) . We found that mutations on the surfaces of helices 4 and 6 ( S . cerevisiae residues 297–305 and 364–368 ) reduced both cell viability and membrane-dependent GEF activity of Sec7ΔC , whereas mutations in the positively charged patch ( i . e . , S . cerevisiae residues Arg505 and Lys513 ) had little effect in vitro despite modestly affecting growth . None of the tested mutations reduced GEF activity in the absence of membranes ( Figure 6—figure supplement 2 ) , indicating the mutations that reduce membrane-proximal GEF activity do not simply cause misfolding of these contructs . Therefore , the surfaces of helices 4 and 6 are important for both GEF activity and cell viability . A notable absence from both the structure presented here and the available structures of the GEF domain is the highly conserved ‘HUS-box’ located between helix 15 and the helix 16 truncated from our crystallized construct ( Mouratou et al . , 2005 ) . A previous study reported that the HUS-box was required for a yeast 2-hybrid interaction between the DCB and HUS domains ( Ramaen et al . , 2007 ) , but our structural data do not support such a role . To assess whether the HUS-box plays a role in GEF stimulation by the DCB/HUS domain , we generated single alanine mutants in the HUS-box ( Figure 6D—figure supplements 3 and 4 ) . One mutation , N653A , was both inviable in the sensitized strain and compatible with purification . The N653A Sec7ΔC construct showed no difference in GEF activity compared to the wild-type allele in the presence of membranes , indicating that the HUS-box plays no role in the observed exchange rate enhancement by the DCB/HUS domain . Another potential role of the DCB/HUS domain which would not have been uncovered in our in vitro assay is its interaction with Arl1 ( Christis and Munro , 2012 ) . Despite extensive effort , we could not detect any stable interaction between S . cerevisiae Sec7ΔC and Arl1 in the presence or absence of membranes , nor did preincubation of Sec7ΔC with membrane-bound Arl1 show any effect on catalytic rate ( Figure 4—figure supplements 3–5 ) . As we previously reported an interaction between Arl1 and longer Sec7 constructs ( McDonold and Fromme , 2014 ) , this interaction appears to depend on one or more of the HDS domains in addition to the DCB/HUS domain in S . cerevisiae Sec7 . Finally , to distinguish between different models for lipid-dependent DCB/HUS function , we assayed the ability of the isolated DCB/HUS domain to compete with the Sec7ΔC construct in the GEF activity assay . Addition of a 16-fold excess of DCB/HUS to an exchange reaction inhibited activity to near the levels of the isolated GEF domain , indicative of a function other than direct stimulation of the GEF domain ( Figure 7A ) . 10 . 7554/eLife . 12411 . 026Figure 7 . The DCB/HUS domain can inhibit GEF activity in trans . ( A ) S . cerevisiae Sec7ΔC and isolated GEF constructs were assayed for rate of nucleotide exchange of myristoylated Arf1 in the presence of liposomes , with 16-fold excess DCB/HUS construct or sixfold additional liposomes added as indicated . ( B ) Wild-type S . cerevisiae Sec7ΔC was assayed for nucleotide exchange of myristoylated Arf1 in the presence of liposomes and a 12-fold excess of DCB/HUS constructs bearing the indicated mutations . The range of activity of interest is bounded by the WT and mock rates and is left unshaded . ( C ) A speculative model of DCB/HUS domain and GEF domain cooperation in Arf1 activation . DOI: http://dx . doi . org/10 . 7554/eLife . 12411 . 026 Introduction of mutations into the competing DCB/HUS construct partially alleviated competition correspondent to their inhibitory effects when assayed in the Sec7ΔC construct , confirming the identification of the site responsible for activity ( Figure 7B ) . We considered the possibility that the excess competing DCB/HUS domain was simply saturating the membrane surface and therefore interfering with membrane-proximal Arf activation . Therefore , we tested whether addition of excess liposome membranes could relieve the competitive inhibition . Addition of a sixfold excess of membranes failed to relieve even partially the inhibitory effect of excess competing DCB/HUS domain ( Figure 7A ) . This indicates that inhibition of Sec7ΔC GEF activity by excess DCB/HUS domain does not occur through competition for binding to the membrane surface . Therefore , excess DCB/HUS domain likely competes with Sec7ΔC for binding to the substrate Arf1 . Taken together , our results are consistent with a direct , and likely transient , interaction of the DCB/HUS domain with the Arf1 substrate and imply that the GEF domain and DCB/HUS domain each make distinct contacts with Arf1 during the exchange reaction . Analysis of SAXS data collected on S . cerevisiae Sec7ΔC suggests flexible linkage between the DCB/HUS and GEF domains ( Figure 2—figure supplement 1 ) , supporting the possibility of simultaneous DCB/HUS and GEF domain binding to Arf1 . In this study , we determined that the regulatory DCB and HUS domains of Sec7 form an unexpected single structural unit that plays a direct role in Arf1 activation at the membrane surface . Previous studies described roles of the DCB/HUS domain in regulation via oligomerization and Arl1 binding ( Christis and Munro , 2012; Ramaen et al . , 2007 ) . The strength of the DCB/HUS Arl1 interaction appears to differ among Sec7 orthologs from different species; although Arl1 is clearly important for BIG1/2 localization in fly and human cells ( Christis and Munro , 2012 ) , it is dispensable for Sec7 localization in S . cerevisiae ( McDonold and Fromme , 2014; Setty et al . , 2004 ) and is not required for Sec7 function in all fly tissues ( Torres et al . , 2014 ) . This suggests eukaryotic cells have evolved multiple ways to regulate Arf activation at the TGN using the same Arf-GEF . We have now described an additional and likely conserved function of the DCB/HUS domain: residues of helices 4 and 6 stimulate Arf1 nucleotide exchange activity dependent on the presence of lipids and the GEF domain . Based on the data we present here , a revised model for DCB/HUS function must account for the requirement of an intact Arf1 membrane insertion helix , for the requirement of lipids , and for a presumably transient interaction with Arf1 . One speculative model consistent with all observations is presented in Figure 7C . A consequence of Arf1 acting to protect its own hydrophobic membrane insertion moiety tightly coupled with nucleotide state is that the nucleotide exchange transition state may briefly expose the Arf1 N-terminal myristoylated amphipathic helix to aqueous solution before its insertion into the membrane . Exchange of nucleotide by the Sec7 GEF domain may position the flexibly linked DCB/HUS domain appropriately for stabilization of this transition state , either via direct transient binding to the amphipathic helix or via positioning Arf1 optimally relative to the adjacent membrane surface . The Golgi Arf-GEFs regulate the central membrane trafficking pathway involved in growth of eukaryotic cells by delivering the bulk of lipids to the plasma membrane ( Drubin and Nelson , 1996 ) . We have previously observed a maximum in vitro Sec7-mediated Arf1 exchange rate of 2 × 106 M-1s-1 , requiring intact Sec7f and activating small GTPases ( McDonold and Fromme , 2014 ) . Using existing estimates of membrane flux and cellular and vesicular protein concentrations ( Chong et al . , 2015; Dodonova et al . , 2015; Layton et al . , 2011; Presley et al . , 2002 ) , we estimate the rate of Arf1 nucleotide exchange required to support maximal cell growth to be approximately 6 × 104 M-1s-1 ( Supplementary file 3 ) . Sec7 must additionally process enough Arf1 to counterbalance endolysosomal trafficking , as well as ‘overhead’ for non-productive exchanges not leading to vesiculation . While these latter rates are much more difficult to estimate , it is entirely plausible that the four- to eightfold increase in activity provided by the DCB/HUS domain would prove essential to achieving the Arf1 activation throughput required to support maximal cell growth . While multimerization via the DCB and HUS domains has been observed in several species and has been proposed to regulate Sec7 activity ( Grebe et al . , 2000; Ramaen et al . , 2007 ) , the ability of the DCB/HUS domain to function as a monomer in the T . terrestris Sec7ΔC construct suggests that dimerization is not essential for DCB/HUS domain function . Sec7 is autoinhibited by both its HDS1 and HDS4 domains , and appears to adopt open and closed conformations ( McDonold and Fromme , 2014; Richardson et al . , 2012 ) . Interactions between the DCB/HUS and HDS domains may provide a mechanism for autoinhibition by occlusion of the GEF domain in a closed conformation . The absence of the HDS domains in a truncated construct would expose an unstable interaction region in the DCB/HUS domain that becomes stabilized by dimerization . Dimerization of the intact DCB/HUS domain may be an artifact of truncation , but perhaps also corresponds to a bona fide regulatory conformation of the full-length protein induced through interactions with known regulators ( McDonold and Fromme , 2014 ) . Our observations that the HDS4 domain mediates both dimerization and autoinhibition imply that Sec7 dimerization is indeed a means of regulation . In the absence of separation of function mutants , it is currently not possible to determine whether dimerization or autoinhibition is the more important function of the HDS4 domain , or whether these two functions are intimately coupled and therefore inseparable . Interestingly , a recent study reported that dimerization of human GBF1 , which lacks an HDS4 domain , is not required for its function in cultured cells ( Bhatt et al . , 2015 ) . We have demonstrated that the DCB/HUS domain of Sec7 stimulates GEF activity by facilitating Arf1 amphipathic helix insertion into a lipid environment during nucleotide exchange . Further studies are now needed to elucidate the role of the HUS-box and how the DCB/HUS domain functions in concert with the HDS domains , as well as the basis for the apparent differences in regulation between the early-Golgi and late-Golgi Arf-GEFs . Sec7 and Arf1 constructs were purified as previously described ( Richardson et al . , 2012; Richardson and Fromme , 2015 ) , with no adjustments to the S . cerevisiae construct protocols required for T . terrestris . Briefly , all S . cerevisiae and T . terrestris Sec7 constructs were expressed with an N-terminal His6 tag in 2–12 L cultures of E . coli in Terrific Broth , grown overnight at 18°C . Following lysis , proteins were purified via Ni . NTA resin ( Qiagen ) in batch , followed by MonoQ ion exchange and Superdex 200 gel filtration ( GE Healthcare ) , with a final buffer composition of 20 mM HEPES pH 7 . 5 , 150 mM NaCl , and 2 mM DTT . ΔN17Arf1 constructs were purified as per Sec7 , with additional 2 mM MgCl2 added to all buffers . Full-length Arf1 was coexpressed with NMT1 to myristoylate , then following lysis was purified via batch incubation with DEAE-sephacel , batch incubation with ToyoPearl phenyl resin , and Superdex 200 gel filtration . Arf1 L8K purification followed an almost identical protocol , but without NMT1 coexpression . Myristoylated Arf1 was assumed to be fully GDP-bound; Arf1 L8K and ΔN17Arf1 were treated with EDTA in the presence of excess GDP to convert them to their GDP-bound form prior to activity assays . Selenomethionine substituted T . terrestris His6-Sec7 ( 1–458 ) at 16 mg/ml was crystallized via the hanging drop method , mixing 1:1 with 6% Jeffamine ED-2001 , 150 mM MES pH 6 , 3% DMSO , and 10 mM DTT at 4°C . Crystals were cryoprotected in a three-stage shift to synthetic well solution plus 30% DMSO . The native construct was crystallized and cryoprotected similarly , using a well solution of 5% Jeffamine ED-2001 , 150 mM MES pH 6 , and 6% DMSO . Diffraction data were collected locally at CHESS beamline A1 on an ADSC Quantum-210 CCD detector and processed using HKL-2000 ( Otwinowski and Minor , 1997 ) ; to improve completeness of collected reflections , datasets from two crystals from the same drop were merged to obtain the final native dataset . Experimental phases were obtained via single-wavelength anomalous diffraction using PHENIX ( Adams et al . , 2010 ) , permitting the auto-building of the alpha-helical structure by PHENIX , followed by serial manual building in Coot ( Emsley et al . , 2010 ) and re-refinement including TLS modeling ( Painter and Merritt , 2006 ) . The anomalous signal permitted assignment of residues 245–271 of the interdomain loop , containing two asymmetrically positioned methionines . All protein models were visualized using PyMol ( Schrödinger ) . Small-angle X-ray scattering data were collected at CHESS beamline F2 at room temperature on samples purified to homogeneity as described . Multiple serial exposures of each construct at 4 mg/ml and 3–4 successive twofold dilutions were collected against exactly matched blanks and processed using BioXTAS RAW ( Nielsen et al . , 2012 ) to confirm absence of radiation- or concentration-dependent aggregation . Theoretical models based on crystal structures were calculated and fit to the experimental data using BUNCH and CORAL ( Petoukhov et al . , 2012; Petoukhov and Svergun , 2005 ) Proteins purified to homogeneity were exchanged into fresh buffer by serial concentration and redilution to a final concentration of 5 mg/ml , and run through a Wyatt WTC-050S5 gel filtration column coupled to DAWN HELEOS-II light scattering and Optilab T-rEX refractive index detectors ( Wyatt Technology ) at room temperature . Data were analyzed via ASTRA 6 software to obtain the molecular weight of the sample , and compared to that predicted from the sequence to determine oligomeric state . Arf1 nucleotide bound state was measured in real-time as described previously ( Richardson et al . , 2012 ) , with the exception that [Sec7] was increased to equimolar with [Arf1] to permit accurate measurement of the slow GEF construct reaction rate and avoid potentially confounding results from unstable mutant constructs . To a final reaction volume of 150 μl , HKM buffer ( 20 mM HEPES pH 7 . 5 , 150 mM KOAc , 2 mM MgCl2 ) , 200 μM lipids ( lipsomes or micelles ) , 670 nM Sec7 construct , 670 nM Arf1 construct , and 200 μM GMPPNP were sequentially added while monitoring fluorescence , waiting 1–3 min between steps for the fluorescence to stabilize . After addition of GMPPNP , the fluorescence was monitored for an additional 40 min and fit to a single exponential curve to determine the rate of exchange . In the absence of Sec7 , fluorescence traces were observed to remain stable , indicating that the observed changes in fluorescence were solely due to the activity of Sec7 , and were not a lipid-mediated effect . The calculated rate of exchange was then normalized to the native fluorescence of the input Sec7 construct to obtain kreact . Note that the per-molecule exchange rates calculated from these reactions are lower than reported previously , in part due to the higher concentrations of GEF constructs used here and variations in behavior between liposome batches . Triplicates of each condition were collected for statistics; error bars represent 95% confidence as determined by t test or ANOVA with Tukey’s or Dunnett’s test in postprocessing , as appropriate . Sec7 alleles to be assayed were cloned with an N-terminal GFP tag into pRS415 ( LEU2 marked ) , and transformed into yeast containing genomic sec7 and arf1 disruptions and a URA3 plasmid harboring wild-type SEC7 ( CFY863 ) . Following overnight growth in –Leu synthetic media , serial half-log dilutions were spotted on 5-FOA and synthetic complete or dropout media and grown for three days at 37°C . Cells were grown in synthetic dropout media and imaged in log phase ( OD600 ~ 0 . 5 ) . For the temperature shift experiment , cells were grown shaking in a 37°C water bath for 30 min before imaging . Live cells were imaged on a DeltaVision RT wide-field deconvolution microscope ( Applied Precision ) . Images were deconvolved using SoftWoRx 3 . 5 . 0 software ( Applied Precision ) . Images were further processed in ImageJ , adjusting only min/max light levels for clarity , and using equivalent processing for all images within an experiment . Membrane binding assayed by liposome pelleting and flotation experiments was performed as described ( Richardson et al . , 2012; Paczkowski et al . , 2012; Richardson and Fromme , 2015 ) . Additional details are provided in the corresponding figure legends . The PDB accession number for the T . terrestris Sec7 DCB-HUS domain is 5HAS .	The cells of plants , animals and other eukaryotes are subdivided into different membrane-bound compartments . One of these compartments – called the Golgi complex – has been likened to the 'Grand Central Station' of the eukaryotic cell , because it serves as the cell’s centralized sorting compartment . Small , spherical structures called vesicles arrive at the Golgi complex from other cellular compartments , and the material within these vesicles is then sorted , packaged into new vesicles , and sent out to different destinations . Regulatory proteins are responsible for making decisions about when to turn on different incoming and outgoing pathways to or from the Golgi complex . In particular , one regulatory protein named Sec7 controls many of the outgoing vesicles that leave the Golgi complex . Sec7 is a fairly large protein and has different parts , or domains , that regulate how the protein works . It was known that two of these regulatory domains ( named 'DCB' and 'HUS' ) were required for Sec7 to work , but it was not known what these domains actually did . Richardson et al . have now used a technique called X-ray crystallography to reveal the three-dimensional structure of the regulatory DCB and HUS domains of Sec7 from a species of yeast . The Sec7 protein has been conserved throughout evolution with few changes , and so the structure of this yeast protein is expected to be the same as that of the corresponding protein in humans . Unexpectedly , Richardson et al . discovered that DCB and HUS are not structurally separate domains and actually form a single 'DCB/HUS' domain . Biochemical experiments then showed that the DCB/HUS domain helps Sec7 work on the surface of membranes . One of the jobs of Sec7 is to insert another regulatory protein ( called Arf1 ) into the membranes of the Golgi complex , and these new findings reveal that the DCB/HUS domain helps Sec7 overcome the challenges associated with this task . Researchers have now uncovered structural information for approximately half of the Sec7 protein , and so an important future challenge will be to reveal the structure of the remaining regulatory domains of Sec7 . This achievement will help researchers to figure out how all of the parts of Sec7 work together to control how this protein makes decisions .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology", "structural", "biology", "and", "molecular", "biophysics" ]	2016	The Sec7 N-terminal regulatory domains facilitate membrane-proximal activation of the Arf1 GTPase
ADP-ribosylation ( ADPr ) is a posttranslational modification ( PTM ) of proteins that controls many cellular processes , including DNA repair , transcription , chromatin regulation and mitosis . A number of proteins catalyse the transfer and hydrolysis of ADPr , and also specify how and when the modification is conjugated to the targets . We recently discovered a new form of ADPr that is attached to serine residues in target proteins ( Ser-ADPr ) and showed that this PTM is specifically made by PARP1/HPF1 and PARP2/HPF1 complexes . In this work , we found by quantitative proteomics that histone Ser-ADPr is reversible in cells during response to DNA damage . By screening for the hydrolase that is responsible for the reversal of Ser-ADPr , we identified ARH3/ADPRHL2 as capable of efficiently and specifically removing Ser-ADPr of histones and other proteins . We further showed that Ser-ADPr is a major PTM in cells after DNA damage and that this signalling is dependent on ARH3 . ADP-ribosylation ( ADPr ) is a chemical modification of macromolecules that regulates a wide variety of cellular processes , such as DNA damage repair , transcription , translation , aging , stress responses , microbial pathogenicity and many others ( Gupte et al . , 2017; Hottiger , 2015; Jankevicius et al . , 2016; Palazzo et al . , 2017 ) . The modification reaction involves the transfer of the ADP-ribose moiety from NAD+ onto the target molecule and the release of nicotinamide ( Gupte et al . , 2017; Langelier et al . , 2012; Schreiber et al . , 2006 ) . There are several ADP-ribosyl transferase ( ART ) enzyme families that can specifically modify proteins ( Bütepage et al . , 2015; Hassa et al . , 2006; Rack et al . , 2015 ) . The most thoroughly characterised class of ARTs is the poly ( ADP-ribose ) polymerase ( PARP ) family . PARPs are widespread throughout Eukarya and are also found sporadically in bacteria ( Perina et al . , 2014 ) . The human genome encodes 17 members of the PARP family , which are grouped into five distinct classes according to protein domain architecture and involvement in different cellular processes ( Perina et al . , 2014 ) . The majority of these PARPs can catalyse the addition of only one unit of ADP-ribose ( mono-ADPr; MAR; MARylation ) onto target proteins ( Bütepage et al . , 2015; Kleine et al . , 2008; Vyas et al . , 2014 ) . However , several PARP family members such as PARP1 , PARP2 , and Tankyrases can generate long chains of the repeating ADP-ribose units on their protein targets ( poly-ADPr; PAR; PARylation ) ( D'Amours et al . , 1999; Gibson and Kraus , 2012 ) . Furthermore , PARP family members have displayed differential activities with respect to the preferred amino acid for ADP-ribose attachment . Several amino acids have been reported as acceptors of PARP-mediated modification; most commonly glutamate , but also aspartate , lysine , arginine and cysteine ( Daniels et al . , 2015; Gagné et al . , 2015; Martello et al . , 2016; Vyas et al . , 2014 ) . Most recently , we identified serine as an acceptor of ADPr modification by PARP1 and PARP2 ( Bonfiglio et al . , 2017a; Leidecker et al . , 2016 ) . We also discovered that the PARP1/2 interacting protein HPF1 ( Histone PARylation Factor 1 ) is responsible for introducing this shift in amino acid specificity ( Bonfiglio et al . , 2017a; Gibbs-Seymour et al . , 2016 ) . Further analyses , by utilising mass spectrometry of human cells treated with DNA damaging agents , revealed large numbers of serine ADP-ribosylation ( Ser-ADPr ) modifications across a variety of human proteins involved in regulation of genome stability , including histones and PARP1 itself ( Bonfiglio et al . , 2017a; Bilan et al . , 2017 ) . Protein ADP-ribosylation can affect structure , function and stability of target proteins , and hence , requires strict control . One mode of regulation is the degradation and/or removal of the ADPr signal by specific enzymes ( Feijs et al . , 2013; Palazzo et al . , 2016 , 2015; Rack et al . , 2016 ) . PAR glycohydrolase ( PARG ) is the most well characterized enzyme in humans for PAR hydrolysis , which utilises a macrodomain fold to bind ADPr and specifically cleaves the ribose–ribose bonds between the subunits of the PAR chains ( Lin et al . , 1997; Slade et al . , 2011 ) . ADP-ribosylhydrolase 3 ( ARH3 , also called ADPRHL2 ) is also able to degrade PAR chains on proteins , but features a different structural composition to PARG , and hydrolyses PAR less efficiently ( Hatakeyama et al . , 1986; Mueller-Dieckmann et al . , 2006; Oka et al . , 2006 ) . However , neither PARG nor ARH3 were previously shown to be able to process a single unit of ADPr ( mono-ADPr ) attached to a protein ( Oka et al . , 2006; Slade et al . , 2011 ) . This MARylation is instead regulated by a different suite of proteins that do not degrade PAR chains , but rather specifically cleave amino acid-linked ADPr moieties; terminal ADPr protein glycohydrolase ( TARG1 ) , MACROD1 , MACROD2 and ARH1 ( Barkauskaite et al . , 2015; Feijs et al . , 2013; Jankevicius et al . , 2013; Mashimo et al . , 2014; Rosenthal et al . , 2013; Sharifi et al . , 2013 ) . TARG1 , MACROD1 and MACROD2 have been shown to hydrolyse acidic residue ( Asp/Glu ) linked mono-ADPr , whilst ARH1 is capable of removing ADPr from arginine ( Glowacki et al . , 2002; Jankevicius et al . , 2013; Rosenthal et al . , 2013; Sharifi et al . , 2013 ) . Our recent discoveries that HPF1 enables PARP1-mediated Ser-ADPr suggest a new mechanism of ADPr signalling that would potentially require precise regulation . Additionally , understanding the regulation of Ser-ADPr of histones could provide significant insights to the already complex tapestry of histone modification . Here we endeavoured to determine if Ser-ADPr is a reversible modification and if so , which hydrolase is responsible for the release of this form of ADPr . Our investigations show that DNA damage induced Ser-ADPr modification of histones is reversible in human cells , and that ARH3 is a hydrolase capable of efficiently removing Ser-ADPr from proteins . Here we also establish ARH3 as a much-needed experimental tool to expand the investigations of Ser-ADPr dynamics as our understanding of this recently unveiled form of ADPr ( Leidecker et al . , 2016 ) is still in its infancy . PARP-dependent ADP-ribosylation signalling is a rapid and dynamic process in human cells . Within minutes of incurring DNA damage , the peak PARylation signal is observed , before it quickly diminishes ( Figure 1A ) ( Hakmé et al . , 2008; Mortusewicz et al . , 2007; Tallis et al . , 2014 ) . Our recent studies have shown that Ser-ADPr is catalysed by PARP1 and PARP2 in a HPF1-dependent manner ( Bonfiglio et al . , 2017a; Gibbs-Seymour et al . , 2016 ) , and we identified a large number of Ser-ADPr target proteins involved in response to DNA damage . This raised the question of whether Ser-ADPr was a reversible modification as seen for other forms of ADPr and for virtually all the PTMs playing a role in cell signalling . To investigate this , we carried out quantitative proteomics experiments using stable isotope labeling by amino acids in cell culture ( SILAC ) ( Ong et al . , 2002 ) in combination with our recently developed mass spectrometric approach ( Bonfiglio et al . , 2017a; Leidecker et al . , 2016 ) . U2OS heavy labeled ( Lys8 ) cells were treated with H2O2 at different time points ( 0 min , 10 min and 120 min ) and then combined with light labelled H2O2-tretead cells ( Lys0 ) that were used as a spike-in SILAC standard ( Figure 1B ) ( Geiger et al . , 2011 ) . As expected , all the detected Ser-ADPr marks increased in cells exposed to oxidative DNA damage for 10 min ( Figure 1C–D and Figure 1—figure supplement 1 , Figure 1—source data 1 ) ( Leidecker et al . , 2016 ) . However , after 120 min of H2O2 treatment , Ser-ADPr returned to levels similar to the untreated cells ( Figure 1C–D and Figure 1—figure supplement 1 , Figure 1—source data 1 ) , demonstrating that Ser-ADPr is also a reversible modification . 10 . 7554/eLife . 28533 . 003Figure 1 . Histone serine ADP-ribose modification is reversible . U2OS cells were treated with H2O2 and analysed at indicated time-points . ( A ) The samples were lysed and the proteins were separated by SDS-PAGE , analysed by western blot and probed for PAR ( left ) or pan-ADPr ( middle ) . Ponceau-S staining was used as loading control ( right ) . ( B ) Schematic representation of the SILAC-based strategy to quantify core histone Ser-ADPr marks after different time points of 2 mM H2O2–induced DNA damage . Light labeled ( Lys0 ) cells treated for 10 min with 2 mM H2O2 were used as SILAC Standard . ( C ) The relative abundance of Ser-ADPr modification on histone proteins H2B ( Ser6 ) and H3 ( Ser10 , Ser28 ) was calculated and plotted as a function against time of H2O2 treatment . ( D ) MS1s of a Ser-ADPr H2B peptide ( H2B Ser6-ADPr ) at different time points of 2 mM H2O2 treatment . The heavy peptide was derived from cells treated with H2O2 for the indicated time point , and the light peptide was derived from the SILAC Standard ( 10 min H2O2-treated cells ) . Each inset ( right ) shows a ∼1:1 ratio ( heavy/light ) of a non-ADP-ribosylated peptide from the same experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 28533 . 00310 . 7554/eLife . 28533 . 004Figure 1—source data 1 . MaxQuant evidence table related to Figure 1—figure supplement 1 . ADPr identified peptide features from SILAC experiments performed to quantify ADPr after different time points of 2 mM H2O2–induced DNA damage ( Figure 1—figure supplement 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 28533 . 00410 . 7554/eLife . 28533 . 005Figure 1—figure supplement 1 . Histone serine ADP-ribose modification is reversible . ( A ) Schematic representation of the SILAC-based strategy to quantify core histone Ser-ADPr marks after different time points of 2 mM H2O2–induced DNA damage . Light labeled ( Lys0 ) cells treated for 10 min with H2O2 were used as SILAC Standard ( top panels ) . MS1s of a Ser-ADPr H3 peptide ( H3-Ser10-ADPr ) at different time points of 2 mM H2O2 treatment ( bottom panels ) . The heavy peptide was derived from cells treated with H2O2 for the indicated time point , and the light peptide was derived from the SILAC Standard ( 10 min H2O2-treated cells ) . Each inset ( right ) shows a ∼1:1 ratio ( heavy/light ) of a non-ADP-ribosylated peptide from the same experiment . ( B ) Log2 of summed ADPr peptide intensities were plotted against log2 of ADPr Heavy/Light SILAC ratios for each time point . ADPr peptides detected after 0 min of 2 mM H2O2 are colored in black . ADPr peptides detected after 10 min 2 mM H2O2 are colored in red . ADPr peptides detected after 120 min 2 mM H2O2 are colored in blue . DOI: http://dx . doi . org/10 . 7554/eLife . 28533 . 005 As shown above , histone Ser-ADPr marks are reversed at a late time point after DNA damage , roughly mirroring the global PARylation and total ADPr profiles ( Figure 1 ) . This raised the question of how ADPr attached to serine residues is removed from its protein targets . We reasoned that a known hydrolase or its homologue could be responsible for the removal of Ser-ADPr . Previous research has found protein ADP-ribosyl hydrolase activity in proteins bearing one of two evolutionarily unrelated domains: a ADP-ribosylglycohydrolase ( DraG-like ) fold ( pfam PF03747 ) and a macrodomain ( pfam PF01661 ) ( Glowacki et al . , 2002; Oka et al . , 2006; Rack et al . , 2016 ) . Therefore , we screened all known human ADP-ribosylhydrolases , belonging to either the macrodomain family ( PARG , TARG1 , MACROD1 , and MACROD2 ) or to DraG family ( ARH1 , ARH2 and ARH3 ) for their ability to cleave Ser-ADPr . We also tested human macrodomains ( such as ALC1 and MACROH2A ) that are thought to be devoid of catalytic activity ( Ahel et al . , 2009; Timinszky et al . , 2009 ) . We used a well-defined histone H3 peptide fragment ( aa 1-21 ) as a known substrate for serine mono-ADP-ribosylation ( Bonfiglio et al . , 2017a ) which we then incubated with each of the previously mentioned proteins ( Figure 2A ) . Strikingly , only ARH3 was able to catalyse removal of ADPr from the histone peptide , implicating a serine ADP-ribosylhydrolase activity . Preceding studies with ARH3 found poly ( ADP-ribosyl ) hydrolase activity , but inability to remove terminal arginine , or other amino acid , ADP-ribose linkages that were known prior to discovery of Ser-ADPr ( Oka et al . , 2006 ) . As expected , we also observed that both PARG and ARH3 were able to degrade the PAR chains on automodified PARP1 ( Figure 2A , lanes 2 and 8 ) , albeit ARH3 was less efficient in removing the chains ( Figure 2B , Figure 2—figure supplement 1A ) ( Mueller-Dieckmann et al . , 2006 ) . We then performed a similar experiment using full length recombinant histone H1 , which can be ADP-ribosylated by PARP1 on serine residues in the presence of HPF1 ( Bonfiglio et al . , 2017a ) . Whilst ARH3 was able to remove both the PARylation and mono ( ADP-ribosyl ) ation of H1 , PARG was only able to degrade the PAR chains on histone H1 and PARP1 , leaving a mono ( ADP-ribosyl ) ation signal that was visible as two distinct bands at the sizes of H1 and PARP1 ( Figure 2B ) . This may be expected given the inability of PARG to process mono ( ADP-ribosyl ) ation ( Slade et al . , 2011 ) . The other glycohydrolases had no discernible effect on the modification of either PARP1 or histone H1 . A double digestion with PARG/ARH3 confirmed that most of the ADP-ribosylated chains had been attached to serine residues ( Figure 2—figure supplement 1 ) . 10 . 7554/eLife . 28533 . 006Figure 2 . ARH3 hydrolyses Ser-ADPr . ( A ) Glycohydrolysis of mono-ADP-ribosylated H3 histone peptide ( aa 1-21 ) . ADP-ribosylated H3 peptide was obtained by incubation with PARP1 and HPF1 using radioactively labelled NAD+ as an ADPr donor . After stopping the modification reaction with PARP inhibitor , the indicated glycohydrolases and related proteins were added to the substrate as shown on the figure . Lower panel – Coomassie stained gel of the proteins used in the reaction that also serves as a loading control . ( B ) Removal of poly-ADP-ribosylation from the recombinant histone H1 substrate by various glycohydrolases . Reaction performed as described in A . ( C ) Comparison of the hydrolase reactions on the poly- and mono-ADP-ribosylated histone H1 substrates . Lower panel – Coomassie stained gel of the proteins used in the reaction that also serves as a loading control . ( D ) Schematic representation of the SILAC-based strategy to quantify ADPr removal upon in vitro incubation of purified Ser-ADPr histones with or without recombinant ARH3 ( top panels ) . Log2 of summed peptide intensities were plotted against log2 Heavy/Light SILAC ratios for each condition ( bottom panels ) . ADPr peptides are colored in red . ( E ) Autoradiogram analysis of Ser mono-ADPr hydrolysis with a non-histone protein substrate . The ADP-ribosylated automodification domain of PARP1 ( aa 374-525 ) was used in this assay and the reactions were supplemented with the indicated hydrolases . In panel panels A , B , C and E , the reaction products were separated by SDS-PAGE and analysed by autoradiography . The signals relating to the specific ADP-ribosylated protein are indicated in each panel . DOI: http://dx . doi . org/10 . 7554/eLife . 28533 . 00610 . 7554/eLife . 28533 . 007Figure 2—source data 1 . MaxQuant evidence table related to Figure 2D . ADPr identified peptide features from SILAC experiments performed to quantify ADPr removal upon in vitro incubation of purified Ser-ADPr histones with or without recombinant ARH3 ( Figure 2D ) . DOI: http://dx . doi . org/10 . 7554/eLife . 28533 . 00710 . 7554/eLife . 28533 . 008Figure 2—figure supplement 1 . PARP1 E988Q generates mono Ser-ADPr on itself and on substrates . ( A ) Removal of poly-ADP-ribosylation from the recombinant histone H1 substrate by the glycohydrolases PARG , ARH3 , PARG and ARH3 together , and TARG1 as indicated . ADP-ribosylated histone H1 was obtained by incubation with PARP1 and HPF1 using radioactively labelled NAD+ as an ADPr donor . After stopping the modification reaction with PARP inhibitor , the indicated glycohydrolases were added to the substrate as shown on the figure . ( B ) High-resolution ETD fragmentation spectrum of a PARP1 E988Q peptide modified by ADP-ribose on Ser499 . ( C ) High-resolution ETD fragmentation spectrum of a H1 . 0 peptide modified by ADP-ribose on Ser103 . DOI: http://dx . doi . org/10 . 7554/eLife . 28533 . 008 Next , we contrasted the poly- and mono-ADP-ribosylation of PARP1 and histone H1 . We prepared these substrates by incubating H1 and HPF1 together with either wild type , or E988Q mutant PARP1 for a modification reaction . The E988Q mutation alters the activity of PARP1 to that of mono-ADP-ribosylation , so in this way we were able to generate Ser mono-ADPr modifications on a protein target that is otherwise PARylated under wild type conditions ( Figure 2—figure supplement 1B–C ) . The activities of the hydrolases tested against PARylation products yielded the same result as before in Figure 2A – both PARG and ARH3 degraded the PAR ( Figure 2C , lanes 2 and 4 - upper panel ) ; and collapsed the protein signal for PARP1 and H1 proteins as seen by Coomassie stain ( Figure 2C , compare lane 1 with lanes 2 and 4; - lower panel ) . However , the serine mono-ADP-ribosylated histone H1 samples were only hydrolysed by ARH3 ( Figure 2C , lane 9 ) , with no notable reduction in mono-ADP-ribose band intensity by the other hydrolases , including PARG . Although it cannot be resolved well in these gels , we consistently observed a small but notable shift in the stained protein signal after the reaction with ARH3 , suggesting the reversal of serine mono ( ADP-ribosyl ) ated H1 into its unmodified form ( Figure 2C compare lane 9 to lanes 6 , 7 , 8 , and 10 ) . To investigate whether ARH3 is also capable of removing ADPr from histones purified from cells that had been modified on serine upon DNA damage ( Bonfiglio et al . , 2017a; Leidecker et al . , 2016 ) , we carried out quantitative proteomics experiments using a SILAC approach . Core histones purified from heavy labeled H2O2-treated cells ( Lys8 ) were incubated in the presence or absence of ARH3 for 30 min . To quantitatively evaluate the removal of Ser-ADPr by ARH3 , core histones purified from light labelled H2O2-treated cells ( Lys0 ) were added as a spike-in SILAC standard . All the identified histone ADPr peptides were significantly decreased in samples treated with recombinant ARH3 ( Figure 2D , Figure 2—source data 1 ) , demonstrating that this hydrolase is also able to remove Ser-ADPr from endogenous histones . We have recently characterised the Ser-ADPr sites on the isolated automodifcation domain of PARP1 ( aa 374-525 ) when incubated in the presence of full length PARP1 and HPF1 ( Bonfiglio et al . , 2017a ) . During this reaction , the isolated domain is modified on 3 serine residues and thus we were able to use it as a well-defined model to analyse removal of the Ser-ADPr from an ADP-ribosylated non-histone protein . We used purified PARP1 automodification domain with HPF1 and either wild type PARP1 or PARP1 E988Q mutant , to produce PARylated and MARylated substrates ( respectively ) for the hydrolase reactions and then tested them with ARH3 , MACROD2 , TARG1 or ARH1 ( Figure 2E ) . ARH3 was efficient at removing Ser-ADPr modifications from both the mono- and poly-ADP-ribosylated automodification domain , confirming the capability to hydrolyse serine linked ADP-ribose from protein targets besides histones . To precisely characterise the activity of ARH3 on Ser-ADPr and to exclude the effect of PARP1 and HPF1 that were present in the previous hydrolase reactions , we modified the H3 histone peptide ( aa 1-21 ) with PARP1 and HPF1 as described above , and then purified the peptide fragment away from proteins for incubation with the same suite of hydrolases as before ( Figure 3A ) . The autoradiogram demonstrated that ARH3 alone was sufficient for the complete removal of Ser-ADPr from H3 histone peptide in the absence of PARP1 and HPF1 . 10 . 7554/eLife . 28533 . 009Figure 3 . Characterisation of the serine ADP-ribosylhydrolase activities of ARH3 . Glycohydrolase assay using the purified serine ADP-ribosylated histone peptides as substrates . Reactions were performed and analysed as described in Figure 2A . ( A ) Activity of different hydrolases against ADP-ribosylated H3 peptide ( aa 1-21 ) . ( B ) The same as in ( A ) except purified histone H2B peptide was used . ( C ) ARH3 structure ( PDB ID: 2FOZ ) showing the amino acid residues in the catalytic site coordinating Mg ions ( green ) . The putative ADPr binding site is highlighted in red . ( D ) Mutating ARH3 catalytic residues or addition of EDTA to the reaction abolishes ARH3 activity . ( E ) Reaction performed as ( D ) except histone H2B peptide was used . ( F ) ARH3 activity is enzyme concentration dependent . ARH3 enzyme concentrations were in the range from 3 μM to 5 nM . ( G ) ARH3 activity is time dependent . 0 . 2 µM ARH3 was used in this assay . DOI: http://dx . doi . org/10 . 7554/eLife . 28533 . 009 Our previous study established that Ser6 of H2B is ADP-ribosylated by PARP1 in the presence of HPF1 ( Bonfiglio et al . , 2017a ) , and similarly as for H3 , we could show that the purified ADP-ribosylated H2B peptide is efficiently and specifically de-ADP-ribosylated by ARH3 ( Figure 3B ) . Earlier research on ARH3 identified the critical acidic amino acids Asp77 and Asp78 that are required for coordination of a magnesium ion and poly ( ADP-ribose ) hydrolysis ( Oka et al . , 2006 ) ( Figure 3C ) , so we generated single site ( D77N and D78N ) as well as double site ( D77N D78N ) ARH3 mutants to confirm whether the same residues were necessary for Ser-ADPr hydrolysis ( Figure 3D and E ) . Only wild type ARH3 was able to remove the Ser-ADPr modification from the histone H3 and H2B peptides . These results show that Asp77 and Asp78 are necessary for both PAR and serine-ADPr hydrolysis . Further , in the presence of the metal chelating agent EDTA , ARH3 was unable to hydrolyse the substrate , confirming that the activity is Mg2+ dependent ( Figure 3D and E ) . Furthermore , we showed that ARH3 enzymatic activity is concentration and time dependent ( Figure 3F and G ) . Notably , ARH3 was highly efficient against Ser-ADPr and quantitatively hydrolysed the substrate at nanomolar enzyme concentrations . To assess the amino acid specificity of ARH3 , we asked whether ARH3 presents activity on ADP-ribosylations linked to other amino acids in addition to O-linked Ser-ADPr . To explore this hypothesis we performed a PARP1 automodifcation assay with PARP1 E988Q in the absence of HPF1 , a reaction that we have previously shown to produce glutamate linked mono-ADPr ( Sharifi et al . , 2013 ) . The mono-ADP-ribosylated PARP1 E988Q protein was then used as a substrate and incubated with ARH3 , along with two characterised glutamate mono-ADP-ribose hydrolases; MACROD2 and TARG1 , and ARH1 which has been shown to possess arginine mono-ADP-ribose hydrolysis activity ( Jankevicius et al . , 2013; Oka et al . , 2006; Sharifi et al . , 2013 ) . The autoradiogram shows that both MACROD2 and TARG1 , but not ARH1 , nor ARH3 , were able to catalyse the efficient removal of glutamate mono-ADP-ribose from the PARP1 protein ( Figure 4A ) . We also tested ARH3 against arginine-linked ADPr . We prepared the substrates for this reaction by treating K562 human myelogenous leukemia cell extract with recombinant ARTC2 . 2 protein , an arginine ADP-ribosyltransferase ( Adriouch et al . , 2008 ) in the presence of [32P]NAD . Figure 4B shows that only ARH1 ( that was used as a positive control ) , but not ARH2 , ARH3 or TARG1 , efficiently cleaved the arginine ADP-ribosylated proteins in the cell extracts . 10 . 7554/eLife . 28533 . 010Figure 4 . ARH3 specifically cleaves Ser mono-ADPr , but not Glu , Arg or Lys mono-ADPr . Reactions were analysed as in Figure 2A . ( A ) Analysis of ARH3 activity on a glutamate-ADP-ribosylated protein . The mono-ADP-ribosylated recombinant PARP1 E988Q protein was treated with the indicated glycohydrolases . ( B ) Analysis of ARH3 activity on arginine-ADP-ribosylated proteins . Cellular extracts from K562 cells were ADP-ribosylated by recombinant ARTC2 . 2 protein . The reactions were then supplemented with the indicated glycohydrolases . ( C ) Analysis of ARH3 activity on a lysine-ADP-ribosylated peptide . A chemically modified histone H3 peptide with mono-ADPr on lysine residues was incubated with the indicated glycohydrolases or 0 . 5 M KOH for 30 min . DOI: http://dx . doi . org/10 . 7554/eLife . 28533 . 010 We also tested the ARH3 and TARG1 against H3 peptide with chemically generated lysine-linked ADPr ( Jankevicius et al . , 2013 ) ( Figure 4C ) . We observed no activity of ARH3 , nor TARG1 against this substrate . All together , these findings confirm that ARH3 is unable to catalyse the efficient removal of glutamate , arginine or lysine linked ADP-ribose moieties , and is specific for Ser-ADPr hydrolysis . To confirm the relevance and the enzymatic activity of ARH3 in cells , we generated ARH3 knock-out ( KO ) U2OS cells lines using CRISPR-Cas9 genome editing technology . Control and ARH3 KO cells were treated with 2 mM H2O2 and harvested at 0 , 10 min and 120 min post DNA damage . In order to detect the effects of ARH3 KO on ADPr levels we used two different antibodies; the widely used anti-PAR antibody which recognises only long PAR chains , and a macrodomain recombinant fusion protein that binds all forms of ADPr including mono and short oligo-ADPr ( described here as a pan-ADPr antibody ) which has been recently developed ( Gibson et al . , 2016 ) ( Figure 5A ) . When compared to control cells , ARH3 KO cells showed higher levels of ADP-ribosylated proteins under unstimulated conditions . As shown in Figure 5A , ADP-ribosylation is dramatically induced in both wt and ARH3 KO cells following DNA damage ( see 10 min samples ) , particularly on PARP1 and Histone proteins . However , the difference between wt and ARH3 KO cells became dramatic at 2 hr post DNA damage , in the ARH3 KO cells the ADPr signal persisted whilst in the wild type cells the ADPr signal was greatly reduced . This observation was most clear with the short oligo- and mono-ADPr as detected by pan-ADPr antibody , while turnover of long chains ( see the signal at PARP1 detected by PAR antibody ) was much less affected ( Figure 5A ) . This may suggest that PARG is required in an early phase of DNA-damage response , shortening long chains of Ser-ADPr substrates , producing mono ( and likely very short chains ) which subsequently are substrates of ARH3 enzymatic activity . 10 . 7554/eLife . 28533 . 011Figure 5 . ARH3 is necessary for removal of Ser-ADPr in cells . ( A ) Control and ARH3 KO U2OS ( ARH3-/- ) cells were treated with 2 mM H2O2 for the indicated time points . After treatment , cells were lysed and proteins were separated by SDS-PAGE , analysed by western blot and probed for PAR , pan-ADPr , ARH3 , H3 , and GAPDH antibodies . Additionally , Ponceau-S staining was used as loading control ( B ) Cell extracts obtained from ARH3 KO cells treated with 2 mM H2O2 for 10 and 120 min were incubated with buffer or ADP-ribosylhydrolases ARH3 , MACROD2 and PARG . Samples were separated by SDS-PAGE , analysed by Western blot and probed for pan-ADPr , H3 , GAPDH , and Ponceau-S staining were used as loading control . ( C ) Schematic representation of the SILAC-based strategy to quantify core histone Ser-ADPr marks from Control U2OS cells ( top panel ) or ARH3 KO U2OS cells ( bottom panel ) after H2O2 treatment for 120 min . Heavy labeled ( Lys8 ) Control U2OS cells treated for 10 min with H2O2 were used as SILAC Standard . ( D ) MS1s of a Ser-ADPr H2B peptide ( H2B Ser6-ADPr ) from Control U2OS cells ( top panel ) or ARH3 KO U2OS cells ( bottom panel ) after H2O2-treatment for 120 min . The light peptide was derived from Control ( top panel ) or ARH3 KO ( bottom panel ) cells treated with 2 mM H2O2 for 120 min , and the heavy peptide was derived from the SILAC Standard ( 10 min H2O2 treated cells ) . Each inset shows a ∼1:1 ratio ( heavy/light ) of a non-ADP-ribosylated peptide from the same experiment . In the ARH3 KO U2OS cells , the chosen H2B mark ( H2B S6-ADPr ) resulted ~40 times more abundant than in the Control U2OS cells . DOI: http://dx . doi . org/10 . 7554/eLife . 28533 . 01110 . 7554/eLife . 28533 . 012Figure 5—source data 1 . MaxQuant ADPr sites table related to Figure 5C–D . List of identified Ser-ADPr sites from SILAC experiments performed to quantify core histone Ser-ADPr marks from Control and ARH3 KO U2OS cells ( Figure 5C–D ) . DOI: http://dx . doi . org/10 . 7554/eLife . 28533 . 012 To further confirm that deficiency of ARH3 is directly responsible for the persistence of the ADPr signal , we supplemented the ARH3 KO extracts ( collected post DNA damage ) with addition of purified recombinant ARH3 . As seen in Figure 5B , ARH3 erases the persisting ADPr signal ( at 120 min post DNA damage ) , confirming that ARH3 is both necessary and sufficient for removal of these modifications . MACROD2 and PARG were not capable of hydrolysing the same substrates , when added to the extracts and probed with pan-ADPr , suggesting that accumulation of monoADPr was on serine residues ( Figure 5B ) . To directly confirm that Ser-ADPr was indeed accumulated in these conditions , we analysed by mass spectrometry the isolated histones from these cell extracts . Histones purified from U2OS heavy labelled H2O2-treated cells ( Lys8 ) were used as a spike-in SILAC standard ( Figure 5C ) . As expected , after 120 min of DNA damage , the levels of histone Ser-ADPr were dramatically increased in ARH3 KO cells compared the wild type cells . ( Figure 5D , Figure 5—source data 1 ) . Mass spectrometry is currently the only available technique for the detection of Ser-ADPr . Given that we have determined that ARH3 is specific for Ser-ADPr and that this hydrolase does not remove ADPr from several other known target amino acids , we set to employ ARH3 for the establishment of a simple alternative approach for monitoring the modification of known Ser-ADPr target proteins . We reasoned that the new pan-ADPr reagent ( Gibson et al . , 2016 ) , which by itself does not distinguish between different amino acid specificities , in combination with ARH3 could discern the presence of histone Ser-ADPr by immunoblotting . Identically to what we observed for in vitro Ser-ADPr on peptides and proteins , ARH3 completely removes ADPr from core histones derived from DNA damage treated cells ( Figure 6A ) . This confirms our previous mass spectrometric analyses that conclusively identified histone ADPr on serine , but not other residues ( Leidecker et al . , 2016 ) , thus providing further indication of serines as the exclusive modification sites on core histones under these experimental conditions . Next , we implemented this newly established approach to confirm the reversibility of histone Ser-ADPr in DNA repair indicated by our SILAC spike-in experiment ( Figure 1C , D and Figure 1—figure supplement 1 ) . As expected , following a strong induction of histone Ser-ADPr after 10 min of treatment with peroxide , the modification is completely reversed after 120 min ( Figure 6B ) . This strategy based on ARH3 is set to enhance and significantly facilitate any further investigation of Ser-ADPr on established substrates . 10 . 7554/eLife . 28533 . 013Figure 6 . ARH3 as a tool for recognizing histone Ser-ADPr . ( A ) Core histones were purified from U2OS cells treated with H2O2 for the indicated time points . Recombinant ARH3 or MACROD1 or reaction buffer was added to histones purified from 10 min H2O2-treated cells . After treatment , proteins were separated by SDS-PAGE , analysed by western blot and probed for pan-ADPr . Ponceau-S staining was used as loading control . ( B ) Purified core histones from U2OS cells treated with H2O2 for the indicated time points were separated by SDS-PAGE , analysed by Western blot and probed for pan-ADPr . Ponceau-S staining was used as loading control . ( C and D ) A schematic representation of the specificity of the ADP-ribosylhydrolases PARG , ARH3 , TARG1 , MACROD1 , MACROD2 and ARH1 for PARylated ( C ) and MARylated ( D ) proteins . DOI: http://dx . doi . org/10 . 7554/eLife . 28533 . 013 PTMs modulate virtually all cellular processes by acting as molecular switches that modify properties of target proteins including function , interactions and stability . As with all homeostatic and stress control systems , this level of regulation relies on reversibility to maintain balance , in this case the fine-tuning of the attachment and removal of PTMs . In fact , disruption of the enzymes responsible for deconjugational ( and conjugational ) dynamics of PTMs often leads to disease states . This also applies to ADP-ribosylation , as disregulation of mono- or poly-ADPr processes can result in neurodegenerative disorders and cancer ( Bütepage et al . , 2015; Hanai et al . , 2004; Rouleau et al . , 2010; Sharifi et al . , 2013 ) . We recently discovered serine ADP-ribosylation ( Ser-ADPr ) as a novel modification of proteins involved in genome stability ( Bonfiglio et al . , 2017a; Leidecker et al . , 2016 ) . Our data identified nearly 300 hundred Ser-ADPr modification sites in proteins extracted from living cells , suggesting that this PTM is widespread across a breadth of pathways including; DNA repair , chromatin organisation , mitotic nuclear division , DNA recombination , transcriptional regulation and mRNA splicing amongst others ( Bonfiglio et al . , 2017a ) . We further showed that at least a fraction of these Ser-ADPr targets are modified by the HPF1/PARP1/2 complex , including histones , PARP1 itself and High Mobility Group ( HMG ) proteins , as we were able to reconstitute the sites in vitro using recombinant proteins ( Bonfiglio et al . , 2017a; Gibbs-Seymour et al . , 2016 ) . These findings raised an obvious question of whether Ser-ADPr modification is reversible and which enzyme is responsible for said activity . ARH3 has previously been demonstrated to cleave PAR chains , although less rapidly than PARG , but also to hydrolyse O-acetyl-ADP-ribose by-products generated by Sir2 deacetylases ( Mashimo et al . , 2013; Oka et al . , 2006; Ono et al . , 2006 ) . Here , we established that ARH3 is also capable of efficiently removing Ser-ADPr in a magnesium-dependent manner , and that mutation of key aspartate residues ( which are also critical for PAR cleavage ) ablates this activity . Given our previous discoveries that Ser-ADPr is a widespread modification in the human proteome following DNA damage , the identification of ARH3 as the first enzyme that is able to hydrolyse Ser-ADPr may prove a critical advance in understanding control of NAD signalling and ADP-ribosylation in regulation of genome stability ( Bonfiglio et al . , 2017a; Leidecker et al . , 2016 ) ( Figure 6C and D ) . Prior observations suggest a wide distribution of ARH3 throughout the cell; cytosol , nucleus and mitochondria—an interesting array of localisations given that the ARH3 protein possesses a mitochondrial targeting peptide at the N-terminus ( both in Human and Murine proteins ) ( Mashimo et al . , 2013; Niere et al . , 2012 ) . Despite the wide cellular distribution , all described ARH3 functions so far seem to converge on safeguarding genome stability . Previous studies suggested that ARH3 functioned in the degradation of PAR chains that had been released by both nuclear and cytosolic PARG following the oxidative stress exposure . As a result of this cooperation , ARH3 buffers the Parthanatos response ( a Caspase-independent cell death induced by the transport and binding of PAR to mitochondrial protein AIF ) ( Mashimo et al . , 2013 ) . Furthermore , ARH3 KO MEFs were found to be sensitive to hydrogen peroxide compared to wild type cells , and in the absence of ARH3 protein PAR was observed to accumulate in the nucleus ( Mashimo et al . , 2013 ) . Additionally , the sensitivity of the ARH3 KO MEFs to hydrogen peroxide was rescued by treatment with a PARP1 inhibitor , linking the function of ARH3 to the PARP1 controlled DNA damage response ( Mashimo et al . , 2013 ) . Here , we have described ARH3’s Ser-ADP-ribosylhydrolase activity which offers a more parsimonious explanation of the susceptibility of ARH3 KO cells to DNA damage . We have previously observed DNA damage induced Ser-ADP-ribosylation of histones and other proteins ( Bonfiglio et al . , 2017a ) , and now we report that ARH3 is necessary for the reversal of these modifications . We also detected persistence of ADPr on numerous proteins in human ARH3 KO cells and found that ARH3 is a major hydrolyse that demodifies these proteins post DNA damage . These data suggest a conserved role for ARH3 in the response to DNA damage and the regulation of ADPr signalling . The ADP-ribose linked to serine via an O-glycosidic bond is chemically similar to the ribose-ribose bond within the ADP-ribose polymer ( Bonfiglio et al . , 2017b; Leidecker et al . , 2016 ) . Consequently , it may be unsurprising that ARH3 can both degrade PAR chains and remove MARylation from serine residues . This observation raises a discrepancy between PARG and ARH3; both are able to hydrolyse PAR , neither are able to cleave ester bonds between the proximal ADPr unit and protein ( Barkauskaite et al . , 2013; Oka et al . , 2006 ) , yet ARH3 can cleave Ser-ADPr glycosidic bonds and completely remove the ADPr chains attached to serine residues in proteins . Architecturally , both enzymes are clearly distinct from each other , with PARG adopting a macrodomain fold with a unique catalytic loop allowing for hydrolysis of ADPr , whilst ARH3 has an eponymous ARH fold featuring a binucleate Mg2+ catalytic site ( Barkauskaite et al . , 2013; Mueller-Dieckmann et al . , 2006; Slade et al . , 2011 ) . Both enzymes perform acid-base catalysis in PAR hydrolysis , but PARG has a preference for binding to the long PAR chains and is inefficient in hydrolysing short oligo-ADPr fragments and incapable or removing mono-ADPr ( Barkauskaite et al . , 2013; Hatakeyama et al . , 1986; Mueller-Dieckmann et al . , 2006 ) . Altogether , these observations suggest functional differences between PARG and ARH3 , but also potential redundancies . More elaborate future structural and functional studies will be needed to resolve these questions as well as the Ser-ADPr specificity of ARH3 . Advanced mass spectrometry has played an indispensable role in the discovery of Ser-ADPr as a widespread signal in DNA repair and in the identification of the enzymes and cofactors responsible for its attachment ( Bonfiglio et al . , 2017a; Leidecker et al . , 2016 ) . However , these findings have opened up many questions that currently cannot be addressed efficiently as only a limited number of researchers have both expertise and access to advanced instrumentation needed for unbiased and unambiguous mass spectrometric analysis of ADPr ( Bonfiglio et al . , 2017b ) . Thus to broaden the study of the amino acid specificities of ADPr we employed the strikingly specific activity of ARH3 as a practical analytical tool to discern the presence of Ser-ADPr without the need for mass spectrometry ( Figure 6C and D ) . In combination with available biological techniques and tools , such as the anti-pan ADPr binding reagent ( Gibson et al . , 2016 ) , we showed that ARH3 allows a simple exploration of histone Ser-ADPr dynamics during DNA damage response . In summary , here we present ARH3 as a serine ADP-ribosylhydrolase that functions to reverse the PARP1/HPF1 mediated modification of histones and other proteins modified on serine residues . This thread of investigation has delineated a novel aspect of cellular ADP-ribosylation , an intricate level of control of ADPr signal transduction that requires specific protein tools . Understanding the activities , tendencies and regulation of these proteins may provide key insights into diseases and disorders that either rely on common ADP-ribosylation pathways , or suffer from the lack of a key protein that breaks the carefully balanced ADPr homeostasis . Anti-PAR Polyclonal Antibody ( rabbit ) was purchased from Trevigen ( Gaithersburg , MD , US ) . Pan-ADPr antibody ( a macrodomain recombinant fusion protein that binds all forms of ADPr ) was purchased from Millipore ( Billerica , MA , US ) . Rabbit anti-ARH3/ADPRHL2 ( HPA027104; RRID:AB_10601330 ) was purchased from Atlas Antibodies . Mouse anti-GAPDH ( RRID:AB_2107445 ) and rabbit anti-H3 ( RRID:AB_417398 ) were purchased from Millipore . The relevant genes were cloned from their corresponding cDNAs and cloned into pET28 vectors unless stated otherwise . The expression construct for ARH3 was a gift from Prof Paul Hergenrother ( University of Illinois ) . ARH1 and ARH2 were cloned into pDEST17 vector . pASK60‐OmpA‐mARTC2 . 2 6xHis‐Flag tag was a gift from Prof Friedrich Koch‐Nolte ( Universitätsklinikum Hamburg‐Eppendorf ) . ARH3 catalytic mutants D77N , D78N , D77N D78N were made using the QuikChange Lightning Site-Directed Mutagenesis Kit purchased fromAgilent Technologies ( Santa Clara , Ca , US ) as per the manufacturer’s recommendations . Most of the proteins were expressed and purified essentially as described ( Dunstan et al . , 2012 ) , but with addition of a size-exclusion chromatography purification using HiLoad 16/60 Superdex 75 column . In the case of ALC1 and histone MACROH2A proteins only the macrodomains were purified rather than full length proteins . Mouse ARTC2 . 2 protein was purified as previously described ( Mueller-Dieckmann et al . , 2002 ) . PARP1 wild type and the E988Q mutant were expressed and purified as previously reported ( Langelier et al . , 2011 ) . PARG was purified as described ( Lambrecht et al . , 2015 ) . HPF1 was expressed and purified as reported ( Gibbs-Seymour et al . , 2016 ) . ARH1 , ARH2 , ARH3 proteins were all expressed and purified using the method described ( Kernstock et al . , 2006 ) . Histone H1 ( full length ) was purchased from NEB ( Ipswich , MA , US ) , Histone H3 peptide ( aa 1-21 ) from Sigma ( Saint Louis , MO , US ) and Histone H2B peptide ( aa 1-21 ) from Millipore . Recombinant proteins or peptides were ADP-ribosylated by PARP1 in the presence or absence of HPF1 to produce substrates for the hydrolase reaction . The PARP reaction buffer contained 50 mM Tris-HCl pH 8 . 0 , 100 mM NaCl , 2 mM MgCl2 , activated DNA and 50 µM NAD+ spiked with 32P-NAD+ . The modification reaction proceeded at room temperature for 20 min before addition of the PARP inhibitor Olaparib at 1 µM . This modification reaction was used as a substrate and incubated with various glycohydrolases for 30 min at room temperature . Reactions were then analysed by SDS-PAGE and autoradiography . PARP1 concentration in the assays was 1 µM unless stated otherwise , HPF1 was always equimolar to PARP1 , histone H1 was used at the final concentration of 2 µM , histone peptides were used at 0 . 5 µg per reaction , ARH3 and other glycohydrolase and macrodomain containing proteins were used at 3 µM unless stated otherwise . H3 and H2B Histone peptides were modified by PARP1 and HPF1 as described above . Subsequently , PARP1 and HPF1 were removed from the samples by filtering the reaction with a 10 kDa cut-off concentration column ( Millipore ) . Excess of NAD+ was removed using a G25 spin column ( GE HealthCare , UK ) . In vitro modification of proteins from K562 cell extracts by ARTC2 . 2 recombinant protein was performed as described ( Palazzo et al . , 2016 ) . Briefly , 8 × 106 cells were washed twice in PBS and lysed in 50 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 0 . 5% Triton X‐100 , 0 . 2 mM DTT , 1 μM Olaparib , 4 mM Pefabloc SC PLUS ( Sigma‐Aldrich ) at 4°C . The cell extract was clarified by centrifugation and diluted five times in no-Triton X‐100 buffer and supplemented with 15 mM MgCl2 and 1 μCi ( 37 kBq ) of 32P-NAD+ . 67 μL extract aliquots were then incubated with or without 1 μM recombinant mARTC2 . 2 protein for 15 min at 30°C . After 15 min incubation , lysates were further incubated in presence of hydrolases for 45 min at 30°C . Samples were then analysed by SDS-PAGE and autoradiography . Lysine ADP-ribosylation of Histone H3 peptide was performed as described ( Jankevicius et al . , 2013 ) . Briefly , 2 µg of H3 peptide was incubated with ADP-ribose in 25 mM Tris-HCl pH 8 . 3 at 37°C for 3 days . Excess nucleotides were removed by purification of the peptide using ZipTip Pipette Tips ( Millipore ) . Filter tips were pre-washed with 100% acetonitrile ( ACN ) , and subsequently equilibrated 4 times with 0 . 1% trifluoroacetic acid ( TFA ) . TFA was then added to Lys-ADP-ribosylated histone H3 to a final concentration of 0 . 1% and applied to the filter . The filter was then washed twice with 0 . 1% TFA before the histone H3 peptide was eluted with 50% ACN and 0 . 1% TFA . The eluate was desiccated and then resuspended in 25 mM Tris-HCl pH 8 . 0 , 150 mM NaCl , 1 mM DTT and 2 mM MgCl2 . The ADP-ribosylated peptide was then analysed by the glyocohydrolase assay described above . Incubation with 0 . 5 M KOH at 37°C for 15 min was used as a positive control for the reaction . Ser-ADP-ribosylated histone H3 peptide was incubated with 200 nM ARH3 . At the indicated time points , samples were taken , containing 0 . 5 µg H3 Histone peptide , and the reactions were stopped with 1 mM ADPr and LDS sample buffer ( Life Technologies , Carlsbad , CA , US ) and incubated for 2 min at 90°C . The samples were analysed by SDS-PAGE and subsequent autoradiography using phosphor screen and read out with Bio-Rad Molecular Imager PharosFX Systems ( Bio-Rad , Hercules , CA , US ) . Band intensities were quantified with the Image Lab software ( Bio-Rad , version 5 . 0 ) . Human U2OS osteosarcoma cells were acquired from ATCC ( ATCC HTB-96; RRID:CVCL_0042 ) , identity was confirmed by STR profiling , and absence of mycoplasma contamination confirmed by MycoAlert Mycoplasma Detection Kit . Cells were cultured in DMEM supplemented with 10% FBS and penicillin–streptomycin ( 100 U/ml ) at 37°C , 5% CO2 . U2OS were regularly tested for mycoplasma by PCR-based detection analysis and discarded if positive . For SILAC labeling ( Ong et al . , 2002 ) , U2OS cells were grown in medium containing unlabeled L-lysine ( L8662 , Sigma-Aldrich ) for the light condition or isotopically labeled L-lysine ( 13C6 , 15N2 , 608041 , Sigma-Aldrich ) for the heavy condition . Both light and heavy DMEM were supplemented with 10% dialyzed FBS ( Thermo Scientific ) . Cells were cultured for more than seven generations to achieve complete labeling . Incorporation efficiency ( >99% ) was determined by MS . For the generation of ARH3 KO cell lines , a pair of sgRNA were designed to generate deletion into the ARH3 gene . More specifically , sgRNA 210 ( GCGCTGCTCGGGGACTGCGT ) and sgRNA 212 ( GGGCGAGACGTCTATAAGGC ) , were used to remove part of ARH3 exon 1 and part of the downstream intron , including the splice donor site . sgRNAs were cloned into epX459 ( 1 . 1 ) , which Dr Joey Riepsaame , ( GEO facility , Sir William Dunn of Pathology , Oxford ) generated by subcloning enhanced Cas9 ( eSpCas9 ) v1 . 1 into plasmid pX459 . U2OS cells were transfected with control sgRNA or co-transfected with sgRNAs 210 and 212 ( 1:1 ratio ) using TransIT-LT1 Transfection Reagent ( Mirus ) , following the manufacturer’s instructions . After 24 hr , transfected cells were treated with 1 μg/mL Puromycin ( InvivoGen ) for 36 hr . After selection , control and ARH3 KO cells were diluted to 0 . 4 cells/100 μl and seeded on 96-well plates . Single colonies were amplified and screened by western blot using anti-ARH3 antibody ( HPA027104 Atlas Antibodies ) . Colonies containing Flag-tagged Cas9 were discarded . U2OS cells were stimulated with 2 mM H2O2 for the indicated time points and lysed in the following buffer: 50 mM Tris-HCl pH 8 . 0 , 100 mM NaCl , 1% Triton X-100 . Immediately before lysing the cells , the lysis buffer was supplemented with 5 mM MgCl2 , 1 mM DTT , proteases and phosphatases inhibitors ( Roche ) , 1 μM ADP-HPD , and 1 μM Olaparib . After the cell pellet was re-suspended in the supplemented lysis buffer , Benzonase ( Sigma ) was added . ARH3 KO cells from 10 and 120 min post H2O2 time points were lysed as previously described and diluted 1:5 in PARP reaction buffer supplemented with 1 μM Olaparib . Aliquots of diluted extract were incubated for 1 hr at room temperature with buffer or 0 . 5 μM ARH3 , MACROD2 and PARG . Reactions were stopped with loading buffer and subjected to a standard SDS-PAGE method . SILAC-labeled U2OS cells were stimulated with 2 mM H2O2 for the indicated time points and core histones were purified as previously described ( Leidecker et al . , 2016 ) . Briefly , cells were washed twice with ice-cold PBS and lysed by rotation in 0 . 1 M H2SO4 at 4°C for 2 hr . The lysate was centrifuged at 2200 g at 4°C for 20 min . The pellet with non-soluble proteins and cell debris was discarded . Sulfuric acid-soluble proteins were neutralized with 1 M Tris-HCl pH 8 . 0 . NaCl , EDTA and DTT were added to a final concentration of 0 . 5 M , 2 mM , 1 mM , respectively . For ion exchange chromatography , sulfopropyl ( SP ) -Sepharose resin was packed into a column and pre-equilibrated with 10 volumes of binding buffer ( 50 mM Tris-HCl pH 8 . 0 , 0 . 5 M NaCl , 2 mM EDTA , 1 mM DTT ) . The neutralized supernatant containing H2SO4-soluble proteins was passed through the column . The resin was washed with 10 volumes of binding buffer and 30 volumes of Washing Buffer ( 50 mM Tris-HCl pH 8 . 0 , 0 . 6 M NaCl , 2 mM EDTA , 1 mM DTT ) . Proteins were eluted with elution buffer ( 50 mM Tris-HCl pH 8 . 0 , 2 M NaCl , 2 mM EDTA , 1 mM DTT ) in 10 fractions . Eluted proteins ( mainly core histones ) were precipitated overnight in 4% ( v/v ) PCA at 4°C . The fractions were then centrifuged at 21 , 000 g at 4°C for 45 min and the resulting pellets were washed with 4% PCA ( 2 × 1 ml ) , 0 . 2% HCl in acetone ( 2 × 1 ml ) , acetone ( 2 × 1 ml ) . For Western blot analysis , samples were subjected to a standard SDS-PAGE method . Proteins were transferred to PVDF membranes ( Merck Millipore ) . Membranes were then blocked with TBS-T buffer ( 25 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 0 . 05% Tween 20% and 5% non-fat dried milk ) and incubated overnight with primary antibodies at 4°C , followed by a one hour incubation with peroxidase-conjugated secondary antibodies at room temperature . Blots were developed using ECL Select and signals were captured using a ChemiDoc MP System ( Bio-Rad ) . Dilutions used for the primary antibodies were: Anti-poly-ADP-ribose: diluted at 1:2000 , Anti-pan-ADP-ribose binding reagent: diluted 1:1000 . Proteins were digested using partial FASP as previously described ( Leidecker et al . , 2016 ) . After digestion , peptides were desalted on either C18 cartridges ( 3M Empore ) or using in-house manufactured StageTips ( Rappsilber et al . , 2003 ) , depending on the peptide amounts . Eluted peptides were dried down in a Speedvac concentrator and resuspended in 0 . 1% FA prior to LC-MS/MS analysis . Liquid chromatography and MS for all LC-MS/MS runs were performed as previously described ( Bonfiglio et al . , 2017a ) . For unambiguous identification of ADPr sites on PARP1 E988Q and H1 from in vitro reactions ( Figure 2—figure supplement 1 ) , ETD fragmentation was used , with the same parameters as previously described ( Bonfiglio et al . , 2017a ) . For histone Ser-ADPr quantification ( Figure 1D and E , and Figure 1—figure supplement 1 ) HCD fragmentation was used , using the same conditions as previously described ( Bonfiglio et al . , 2017a ) . Raw files were analyzed with MaxQuant proteomics suite of algorithms ( version 1 . 5 . 3 . 17 ) ( Cox and Mann , 2008 ) , using the same parameters as previously described ( Bonfiglio et al . , 2017a ) . For Figure 1C , normalized SILAC ratios from ADPr ( S ) sites table were extracted from MaxQuant , log2 converted and relativized to the non-treated condition to create the scatterplots comparing the relative abundance versus the time of 2 mM H2O2 . For Figures 1D and 5D and Figure 1—figure supplement 1A representative MS spectra were manually selected and annotated . For Figure 2D and Figure 1—figure supplement 1B evidence tables from MaxQuant were analyzed using Perseus software ( http://www . perseus-framework . org ) together with an in-house script ( Source code 1 . ) to create the scatterplots comparing SILAC ratios versus peptide intensities ( Bonfiglio et al . , 2017a ) .	Inside cells , genetic information is stored within molecules of DNA . If any of the DNA becomes damaged , the cell has a suite of proteins that can help to repair the DNA . Many of these proteins act as signals that alert the cell to the presence of damaged DNA . One such signal involves adding a molecule called ADPr onto specific proteins that are near the damaged section of DNA . There are several enzymes that can attach ADPr molecules to proteins and other enzymes known as ADP-ribosylhydrolases can halt the signal by removing the ADPr molecules . Together , these two groups of enzymes control how strong the ADPr signal is , how long it lasts , and therefore control the DNA repair process . Proteins are made up of building blocks called amino acids . Previous studies have shown that ADPr molecules can be attached to several different amino acids including glutamate , aspartate and cysteine . Specific ADP-ribosylhydrolase enzymes are known to be responsible for removing ADPr molecules from these amino acids . In 2016 , a group of researchers found that ADPr can also be added to an amino acid called serine . However , it is not known if cells are able to remove ADPr molecules from serine , or which ADP-ribosylhydrolases might be involved . Fontana , Bonfiglio , Palazzo et al . – including some of the researchers involved in the earlier work – used biochemical techniques to investigate if any human enzymes are able to remove ADPr molecules that have been attached to serines on proteins . The experiments reveal that the serine ADPr signal increases after DNA damage , before reducing over time . However , in human cancer cells that lack an ADP-ribosylhydrolase known as ARH3 , the serine ADPr signal persists after DNA damage . This suggests that adding ADPr molecules to the amino acid serine is a key signal that controls DNA repair and that ARH3 is the main enzyme responsible for erasing this signal . Drugs that inhibit some of the enzymes that attach ADPr molecules to proteins are used to treat some breast , ovarian and prostate cancers . Therefore , understanding how cells remove these signals from proteins may aid the development of new therapies for these conditions . The next steps following on from this work are to find out more about the structure of ARH3 and to understand how cells that lack this enzyme behave .	[ "Abstract", "Introduction", "Results", "Discussion", "Material", "and", "methods" ]	[ "biochemistry", "and", "chemical", "biology" ]	2017	Serine ADP-ribosylation reversal by the hydrolase ARH3
Neural connectomics has begun producing massive amounts of data , necessitating new analysis methods to discover the biological and computational structure . It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function . Here we developed a non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data . It combines the information traditionally used by biologists in a principled and probabilistically coherent manner , including connectivity , cell body location , and the spatial distribution of synapses . We show that the approach recovers known neuron types in the retina and enables predictions of connectivity , better than simpler algorithms . It also can reveal interesting structure in the nervous system of Caenorhabditis elegans and an old man-made microprocessor . Our approach extracts structural meaning from connectomics , enabling new approaches of automatically deriving anatomical insights from these emerging datasets . Emerging connectomics techniques ( Zador et al . , 2012; Morgan and Lichtman , 2013 ) promise to quantify the location and connectivity of each neuron within a tissue volume . These massive datasets will far exceed the capacity of neuroanatomists to manually trace small circuits , thus necessitating computational , quantitative , and automatic methods for understanding neural circuit structure . The impact of this kind of high-throughput transition has been seen before—the rise of sequencing techniques necessitated the development of novel computational methods to understand genomic structure , ushering in bioinformatics as an independent discipline ( Koboldt et al . , 2013 ) . The brain consists of multiple kinds of neurons , each of which is hypothesized to have a specific role in overall computation . Neuron types differ in many ways , for example , chemical or morphological , but they also differ in the way they connect to one another ( Seung and Sümbül , 2014 ) . In fact , the idea of well defined , type-dependent local connectivity patterns ( microcircuits ) has a long history ( Passingham , 2002 ) , and is prominent in many areas , from sensory ( e . g . , retina; Masland , 2001 ) to processing ( e . g . , neocortex; Mountcastle , 1997 ) to movement ( e . g . , spinal cord; Grillner et al . , 2005 ) . These types of repeated computing patterns are a common feature of computing systems , even occurring in man-made computing circuits . It remains an important challenge to develop algorithms to use connectivity-based anatomical data ( connectomics ) to automatically back out underlying microcircuitry . The discovery of structure is a crucial aspect of network science . Early approaches focused on global graph properties , such as the types of scaling present in the network ( Watts and Strogatz , 1998 ) . While this approach leads to an understanding of the global network , more recent work aims at identifying very small-scale repeat patterns , or motifs , in networks ( Milo et al . , 2002 ) . These motifs are defined not between different node types , but rather represent repeated patterns of topology . The discovery of structure in probabilistic graphs is a well-known problem in machine learning . Commonly used algorithms include community-based detection methods ( Girvan and Newman , 2002 ) and stochastic block models ( Nowicki and Snijders , 2001 ) . While these approaches can incorporate the probabilistic nature of neural connections ( Hill et al . , 2012 ) , they do not incorporate the additional richer structure present in connectomics data—the location of cell bodies , the spatial distribution of synapses , and the distances between neurons . It is of particular importance that the probability of connections has a strong spatial component , a factor that is hard to reconcile with many other methods . A model attempting to fully capture the variation in the nervous system should take into account the broad set of available features . When it comes to neuroscience and other computing systems , we expect patterns of connectivity much more complex than traditional motifs , exhibiting a strong spatial dependence arising from the complex genetic , chemical , and activity-based neural development processes . To address these challenges , here we describe a Bayesian non-parametric model that can discover circuit structure automatically from connectomics data: the cell types , their spatial patterns of interconnection , and the locations of somata and synapses . We show that by incorporating this additional information , our model both accurately predicts the connection as well as agrees with human neuroanatomists as to the identification of cell types . We take as inspiration previous work on identifying cell types automatically from morphology ( Guerra et al . , 2011 ) and electrophysiology ( Druckmann et al . , 2013 ) . We primarily focus on the recently released mouse retina connectome ( Helmstaedter et al . , 2013 ) , but additionally examine the Caenorhabditis elegans connectome ( White et al . , 1986 ) . Comparing the cell types discovered by the algorithms with those obtained manually by human anatomists reveals a high degree of agreement . We thus present a scalable probabilistic approach to infer microcircuitry from connectomics data available today and in the future . To evaluate the quality of the model fit , we need to use information that quantifies aspects of the data for which we have ground truth information . We focus on two aspects of performance . First , if the model works well , then the probability that a pair of neurons is of the same type should be high if the neurons actually are of the same type . Second , the model should assign a high probability of connection between two cells if they have a connection in the underlying data . We term these two factors clustering accuracy and link-prediction accuracy . To assess the accuracy of a clustering compared to that determined by neuroanatomists , we employ three metrics—clustering homogeneity , clustering completeness , and the ARI . All metrics equal 1 . 0 when two clusterings completely agree . Homogeneity reflects the degree to which a found cluster or type contains only a single true type . Completeness measures how much of a true type is contained within a single identified type—a completeness of 1 . 0 means no true type is split into multiple subtypes . ARI is a metric that reflects both measures ( see the supplemental material for more information ) . To assess the accuracy of the model for connections , we use link prediction accuracy . If our model accurately captures the true structure of the data , it should be good at predicting if a link exists . We thus train the model on the data with a subset of the links marked as unobserved and thus compute our predictive accuracy . We perform 10-way cross-validation on a given dataset ( Guerra et al . , 2011 ) , learn the resulting model , and use that model to predict the missing synapses . Each potential link between cells is assigned a probability , and we compute the AUC for the resulting ROC curve . An AUC of 1 . 0 means that we perfectly predict the presence and absence of the missing synapses . We use link prediction accuracy to quantify how good the model is at discovering the underlying connectivity . To validate our model , we performed a series of simulations to test if the model can accurately recover the true underlying network structure and cell type identity . We thus simulate data for which we know the correct structure and compare the estimated structure based on the algorithm ( see ‘Materials and methods’ ) with the one we used for simulation . We find that the model does a good job of recovering the correct number of cell types ( Figure 2A ) , the cell identities ( Figure 2B ) , and the spatial extent of each type ( Figure 2C ) . For comparison , we show the results using the infinite stochastic block model ( iSBM ) instead ( Figure 2A–C , black line ) which assumes that only cell type matters , and thus finds small neighborhoods of connected nodes ( instead of global connectivity patterns ) . This contrast shows that while the regular block model can not correctly deal with distance-dependent connectivity , our model can . Our model converges relatively quickly ( see 'Mixing of Markov chains' ) to an estimate of the most probable values for the cell types , which is enabled by using a combination of simulated annealing and parallelized Markov-chain Monte Carlo ( MCMC ) ( see ‘Materials and methods’ for details ) . Thus our model at least is promising for application to biological datasets . 10 . 7554/eLife . 04250 . 003Figure 2 . Correct recovery of true numbers of hidden types in synthetic data when incorporating spatial information . ( A ) The infinite stochastic block model ( which only uses connectivity information ) over-estimates the number of classes as it fails to take distance into account , whereas our modeling of the combination of distance and connectivity finds close to the true number of classes . Conn: connectivity; dist: distance . ( B ) As we increase the true number of types , our method continues to find the correct clustering ( as measured by the adjusted Rand index , ARI ) whereas the infinite stochastic block model ( iSBM ) overclusters and thus poorly matches ground truth . ( C ) We examine the spatial extent ( size ) of the discovered types ( clusters ) by measuring the two-dimensional standard deviation of the cell locations . The y-axis indicates what fraction of the discovered types had a given spatial extent . Without incorporating distance , we identify a large number of small , spatially-localized types . With distance , we see a correct recovery of the spatial extent of each type . DOI: http://dx . doi . org/10 . 7554/eLife . 04250 . 003 We next analyze how our model performs in cases where the data are generated with assumptions different from ours . To understand the properties of our model , we attempt connectivity inference on four sets of synthetic data . This helps us understand what our model would do if the data do not obey our assumptions . We thus generate 10 sets of synthetic data from each of four existing models . The distance-dependent stochastic block model assumes type depends on distance , the traditional stochastic block model has no notion of distance , the mixed membership block model assumes type is combinatorial , and the latent position cluster model assumes that type is clustered-but-continuous . If the data are sampled from our model , inference according to our model , unsurprisingly , is good by all measures . It correctly estimates the number of cell types , it is good at predicting connectivity ( high area under the curve , AUC ) , it agrees with human classification ( Rand index ) , it discovers all types , and leads to homogeneous estimates ( Figure 3 , first row ) . If the data come from a block model without distance dependence , we see that it still does well on all meaningful measures ( Figure 3 , second row ) . This is unsurprising , as our model learns the distance dependence , even its absence . For the mixed membership model ( Figure 3 , third row ) , the model grossly overestimates the number of types , by basically allocating a type for each combination of memberships . Otherwise , it still performs relatively well . Lastly , for the latent position clustering model ( Figure 3 , fourth row ) , the model does poorly . If type is continuous instead of discrete , then our model is basically trying to cover a continuous set with a discrete scenario leading to rather poor performance . However , as we do expect cell types to have a discrete biological basis , we might expect our model to do well with real data . 10 . 7554/eLife . 04250 . 004Figure 3 . Model inferences when the true generating model differs from our distance-block-model prior . Horizontal columns show results with synthetic data generated according to the distance-dependent stochastic block model , the non-distance-dependent stochastic block model , the mixed membership block model , and the latent position cluster model . In all cases histograms represent posterior distribution over the indicated metric . ( A ) The number of types found by the model; the vertical dashed line indicates the ‘true’ type number ( not applicable to the mixed membership model ) . ( B ) The area under the receiver operating characteristic ( ROC ) curve , indicating link prediction accuracy . ( C , D , E ) Clustering metrics quantifying degree of type agreement with known ground truth . DOI: http://dx . doi . org/10 . 7554/eLife . 04250 . 004 Connectomic efforts so far have reconstructed only small sections of neural tissue . Consequently , many connections to cells outside that tissue volume will be lost . We are concerned that this selective elimination of connectivity along the boundary might give the appearance of distance-dependent connectivity when there is none . We thus performed simulations to check if edge effects could destroy spatial structure and if edge effects could introduce artificial , spurious spatial structure . We measure the degree to which distance-dependent effects can arise from selecting regions that are smaller than the ‘scale’ of connectivity ( Figure 4 ) . We do this by generating two collections of synthetic datasets—one with distance-dependent connectivity and one without . We then in each dataset randomly examine contiguous circular regions with area varying from zero to the entire volume , and empirically calculate the spatial variance in type-dependent connectivity . We find that , if there is no distance dependence , edge effects do not artificially introduce distance dependence . However , if the section we are examining is too small , our model can miss the distance dependence . Thus with respect to distance-dependent connectivity inference , our model errs on the side of caution . But we also find that for spatial extent that is similar to the currently available datasets , the effects of this are quite limited . 10 . 7554/eLife . 04250 . 005Figure 4 . Two sets of generated synthetic data , one with spatially dependent connectivity and one without . We measure the variance in the connectivity-distance plot for randomly selected regions of each dataset , ranging from single cells to the entire volume . We see that while selecting too small a region can destroy the appearance of distance-dependent connectivity , it does not create it in non-spatial data . DOI: http://dx . doi . org/10 . 7554/eLife . 04250 . 005 The mouse retina ( Masland , 2001 ) is a neural circuit which we expect to have connectivity patterns that are well approximated by our generative model . It is known that there are multiple classes of cells that can be broadly grouped into: ganglion cells that transmit information to the rest of the brain; bipolar cells that connect between different cells; and amacrine cells that feed into the ganglion cells . Recent research ( Helmstaedter et al . , 2013 ) has produced a large dataset containing both the types of cells from orthogonal approaches , and also the connectivity matrix between all reconstructed cells ( Figure 5A ) . 10 . 7554/eLife . 04250 . 006Figure 5 . Discovering cell classes in the mouse retina connectome . Here we show the maximum a posteriori ( MAP ) estimate for the types in the mouse retina data . ( A ) Input connectivity data for 950 cells for which soma positions were known . ( B ) Clustered connectivity matrix; each arbitrary color corresponds to a single type and will be used to identify that type in the remainder of the plot . ( C ) The spatial distribution of our cell types—each cell type tessellates space . Colors correspond to those in ( B ) . ( D ) Connectivity between our clusters as a function of distance—the cluster consisting primarily of retinal ganglion cells ( brown nodes on the graph ) exhibits the expected near and far connectivity . Conn prob: probability of connection . DOI: http://dx . doi . org/10 . 7554/eLife . 04250 . 006 The algorithm took 8 hr to perform inference , dividing neurons into a set of cell types which reflect known neuroanatomical distinctions ( Figure 5 shows the MAP result ) . For each pair of neurons there is a specific distance-dependent connection probability ( Figure 5D ) , which is well approximated by the model fit . Moreover , each type of cell is rather isotropically distributed across space ( Figure 5C ) as should be expected for true cell types . Comparing the results of the algorithm to other information sources allows evaluation of the quality of the type determination . Our types closely reflect the ( anatomist-determined ) segmentation of cells into retinal ganglion , narrow amacrine , medium/wide amacrine , and bipolar cells ( Figure 6B ) . We find that the types we find tend to reflect the known laminar distribution in the retina ( Figure 6C ) as well as the known synaptic density profiles . 10 . 7554/eLife . 04250 . 007Figure 6 . Visualizing type inference uncertainty . Our fully Bayesian model gives a confidence estimate ( posterior probability ) that any two given cells are of the same type . In ( A ) we visualize that cell–cell coassignment matrix , showing the probability that cell i is of the same type as cell j on a range from 0 . 0 to 1 . 0 . The block structure shows subsets of cells which are believed to all belong to the same type . For comparison , ( B ) shows the anatomist-defined type for each cell , grouped broadly into the coarse types identified in the previous panel . ( C ) Link versus cluster accuracy . ( D ) The posterior distribution of receiver operating characteristic ( ROC ) curves from 10-fold cross-validation when predicting connectivity , as well as ( E ) the area under the curve ( AUC ) and ( F ) the type agreements with known neuroanatomist types . ARI: adjusted Rand index . Model comparison , showing using human-discovered types with and without distance information , as well as our model incorporating just connectivity , connectivity and distance , or connectivity , distance , and synaptic depth ( as well as the alternative latent position cluster model , see text ) . ( G ) A comparison of the predictive accuracy ( AUC ) for hand-labeled anatomical data , versus inclusion of additional sources of information , as well as the clustering accuracy . Note that our model sacrifices very little predictive accuracy for additional clustering accuracy . By comparison , conventional methods fail at one or both . ARI: adjusted Rand index . ( H ) The spatial extent ( in depth and area ) of the types identified by humans and our various algorithmic approaches . DOI: http://dx . doi . org/10 . 7554/eLife . 04250 . 007 The algorithm yields a separation of neurons into a smaller number of types than the fully granular listing of 71 types found by the original authors of the paper ( Helmstaedter et al . , 2013 ) , although it is still highly correlated with those finer type distinctions ( see section ‘Mouse retina’ ) . It is our expectation that , with larger datasets , even closer agreement would be found . Our fully Bayesian model produces a distribution over probable clusterings . Figure 6 shows this posterior distribution as a cell–cell coassignment matrix , sorted to find maximum block structure . Each large , dark block represents a collection of cells believed with strong probability to be of the same type . When we plot ( Figure 6B ) the anatomist-derived cell types along the left , we can see that each block consists of a roughly homogeneous collection of types . We evaluate our model along three sets of parameters ( Figure 6 ) : how closely does our clustering agree with neuroanatomists' knowledge ? Given two cells , how accurately can our model predict the link between them ? And how closely does the spatial extent ( within a layer ) of our identified types agree with the spatial extent of types identified by neuroanatomists ? For our model we show the receiver operating characteristic ( ROC ) curve ( Figure 6D ) which shows how the true and false positive rates trade off . We plot the posterior distribution of the area under this curve in Figure 6E . We then plot the posterior distribution for cluster agreement metrics—completeness , homogeneity , and adjusted Rand index ( ARI ) ( Figure 6F ) . We see that our model tends to over-cluster—cells which are of distinct type ( at the finest granularity of neuroanatomist-identified type ) are grouped as a single type by our model . We compare link-prediction accuracy across the methods , including our own ( Figure 6G , AUC , red ) . We find that given the dataset , many techniques allow for good link-predictive accuracy . All the methods allow decent link prediction with an AUC in the 0 . 9 range . However , our algorithm clearly outperforms the simple statistical models that only use connectivity . As a second measure we compare link-prediction accuracy across the methods ( Figure 6G , ARI , blue ) . We find that our algorithm far outperforms the controls . We also find that when it is based on more of the same information used by anatomists , then it gets better at agreeing with these anatomists . In particular , using connectivity , distance , synapse distribution , and soma depth leads to the highest ARI . When using the available information , the algorithm produces a good fit to human anatomist judgments . Finally we look at the spatial extent of the discovered types both within a layer and between layers ( Figure 6H ) . We see that , in the absence of distance information , mere connectivity information results in types which only span a small region of space—essentially local cliques . Incorporation of distance information results in types which span the entire extent of the layer . The depth variance of all models continues to be substantially larger than that predicted by human anatomists—future directions of work include attempting to more strongly encode this prior belief of laminarity . Having shown our model to work on the repeating tessellated , laminar structure of the mammalian retina , we then apply our model to a structurally very different connectome—the whole body of a small roundworm: C . elegans is a model system in developmental neuroscience ( White et al . , 1986 ) , with the location and connectivity of each of 302 neurons developmentally determined , leading to early measurement of the connectome . Unlike the retina , only the motor neurons in C . elegans exhibit regular distribution in space—along the body axis . Most interneurons are concentrated in various ganglia that project throughout the entire animal , and the sensory neurons are primarily located in a small number of anterior ganglia . C . elegans also differs from the retina in that the measured connectome is actually two separate graphs—one of directed chemical synapses and another of undirected electrical synapses . As this is a very different connectome , it allows an interesting generalization test: how well will our model work on such a distinct dataset ? Using both the chemical and electrical connectivity ( see ‘Materials and methods’ ) , we determined the underlying cell types explained by connectivity and distance ( Figure 7A ) . A superficial inspection of the results shows clustering into groups consisting roughly homogeneously of motor neurons , sensory neurons , and interneurons . Closer examination reveals agreement with the classifications originally outlined by White in 1986 ( White et al . , 1986 ) . 10 . 7554/eLife . 04250 . 008Figure 7 . Discovering connectivity and type in C . elegans . ( A ) Posterior distribution on cell connectivity as a function of discovered type , similar to Figure 6 . In ( B ) we plot neuroanatomist-derived types along with their labels . Our model shows a high probability of motor neurons , sensory neurons , and various interneuron classes being of the same type . Soma positions along the body axis are plotted in ( C ) where we see that we cluster spatially distributed motor neurons together , whereas head sensory neurons are more likely to be grouped together as well . ( D ) The receiver operating characteristic ( ROC ) curves for held-out link probability for both the electrical synapses ( gap junctions ) and chemical synapses in C . elegans . ( E ) The posterior distribution of the area under the ROC curve ( AUC ) for the curves in ( D ) . ( F ) Measurements of the agreement of our identified cell types compared to neuroanatomists . The high completeness but low homogeneity ( and corresponding low adjusted Rand index , ARI ) reflects our model's tendency to group multiple types into a single type . DOI: http://dx . doi . org/10 . 7554/eLife . 04250 . 008 Note our clustering does not perfectly reflect known divisions—several combinations of head and sensory neurons are combined , and a difficult-to-explain group of mostly VB and DB motor neuron types , with VC split between various groups . Our identified cell types thus reflect a ‘coarsening’ of known types , based entirely on connectivity and distance information , even when the organism exhibits substantially less spatial regularity than the retina . To show the applicability of our method to other connectome-style datasets , we obtained the spatial location and interconnectivity of the transistors in a classic microprocessor , the MOS Technology 6502 ( used in the Apple II ) ( James et al . , 2010 ) . Computer architects use common patterns of transistors when designing circuits , with each transistor having a ‘type’ in the circuit . We identified a region of the processor with complex but known structure containing the primary 8-bit registers X , Y , and S ( Figure 8 ) . 10 . 7554/eLife . 04250 . 009Figure 8 . Discovering connectivity and type in the MOS 6502 microprocessor . ( A ) The micrograph of the original microprocessor , with the region containing the registers under study highlighted . ( B ) Our graph consists of the interconnections of MOS field-effect transistors with three terminals , Gate , C1 , and C2 . The reconstruction technique did not permit resolution of C1 and C2 into source and drain . ( C ) The spatial distribution of the transistors in each cluster show a clear pattern . ( D ) The clusters and connectivity versus distance for connections between Gate and C1 , Gate and C2 , and C1 and C2 terminals on a transistor . Purple and yellow types have a terminal pulled down to ground and mostly function as inverters . The blue types are clocked , stateful transistors , green control the ALU and orange control the special data bus ( SDB ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04250 . 009 Our algorithm identifies areas of spatial homogeneity that mirror the known structure in the underlying architecture of the circuit , segmenting transistor types recognizable to computer architects . Using the original schematics , we see that one identified type contains the ‘clocked’ transistors , which retain digital state . Two other types contain transistors with pins C1 or C2 connected to ground , mostly serving as inverters . An additional identified type controls the behavior of the three registers of interest ( X , Y , and S ) with respect to the SB data bus , either allowing them to latch or drive data from the bus . The repeat patterns of spatial connectivity are visible in Figure 8C , showing the man-made horizontal and vertical layout of the same types of transistors . We have presented a machine learning technique that allows cell types and microcircuitry to be discovered from connectomics data . We have shown its applicability to regularly structured laminar neural circuits like the retina , as well as a less structured whole neuronal organism ( C . elegans ) and a classic processor . When compared to existing methods , we show how the incorporation of all of this data yields results that combine both high link-prediction accuracy and high agreement with human anatomists . We have found that combining the available data types allows us to discover cell types and microcircuitry that were known to exist in the systems based on decades of previous research and allows good prediction of connectivity . For our probabilistic models , no known solution exists to exactly find the most probable parsing of the neurons into cell types and connectivity patterns . We employ a collection of MCMC techniques ( see ‘Materials and methods’ ) , but while different initializations converge to similar ultimate values , we can never realistically obtain the global optimum . There are a broad range of techniques that may offer good approximations to the global optimum and future work could adapt them to find more precise solutions to our problem . For our probabilistic model , inference becomes slower as the amount of data increases . Our algorithm required several hours for 1000 neurons . Scaling this class of probabilistic model is an active area of research , and recent results in both variational methods ( Hoffman et al . , 2013 ) and spectral learning ( Anandkumar et al . , 2012 ) and future work could adapt them to find faster approximate solutions to our problem . Larger datasets will allow algorithms to distinguish more distinct types and we expect closer agreement with existing anatomical knowledge as more data become available . Moreover , in general , for such problems precision increases with the size of the dataset and the cells that we have are not sufficient to statistically distinguish all the cell types known in anatomy ( such as the ∼70 in the retina ) . Still , using only connectivity and distance , it is possible to meaningfully divide neurons into types . Our small collection of hand-selected distance-dependent likelihood functions is clearly non-exhaustive , and assumes monotonicity of connectivity probability—for a given class , closer cells are never less likely to connect . This is known to be insufficient for various neural systems . Future models could incorporate a wider variety of likelihood functions , or even learn the global functional form from the data . There are a range of previous approaches to the discovery of neural microcircuitry ( Mountcastle , 1957; Douglas and Martin , 1991; Freund and Buzsáki , 1998; Barthó et al . , 2004 ) . These generally involve a great deal of manual labor and ad hoc determination of what constitutes a type of cell—to this day there are disagreements in the literature as to the true types in the mammalian retina . Much as phylogenomics has changed our understanding of animal ontologies , modern large scale data will allow the efficient unbiased discovery of cell types and circuits . The sheer amount of available data demands the introduction of algorithmic approaches . The development of automatic identification and quantification of cell type may also provide a new computational phenotype for quantifying the effect of disease , genetic interventions , and developmentally experienced neural activity . Our method can in principle identify neuron types across non-connected graphs , for example , across animals . For example , the types of neurons in one animal can be associated with the types of neurons in another animal , in the same way as this is already possible through molecular markers ( Brown and Hestrin , 2009 ) . This could be particularly important if cell types appear that are due to properties of the stimuli and experience as opposed to just the molecular properties of cells , such as color and orientation selective types in primary visual cortex ( Lennie and Movshon , 2005; Sincich and Horton , 2005 ) . This would allow comparative quantitative anatomy across animals , and aid the search for the ultimate causes of connectivity . Our model combines connectivity , cellular and synaptic properties , and suggests the way towards combining even richer data . Distinct cell types differ in morphology , connectivity , transcriptomics , relation to behavior or stimuli , and many other ways . Algorithms combining these data and type information may allow us to synthesize all the available information from one experiment or even across experiments into a joint model of brain structure and function . Our work shows how rich probabilistic models can contribute to computational neuroanatomy . Eventually , algorithms will have to become a central tool for anatomists , as it will progressively become impossible for humans to parse the huge datasets . This transition may follow a similar transition to that of molecular biology ( with gene-finding algorithms ) and evolutionary biology ( with computational phylogenetics ) . Ultimately , computational approaches may help resolve the significant disagreements across human anatomists . Our model is a extension of the iSBM ( Kemp et al . , 2006; Xu et al . , 2006 ) to incorporate spatial relations between entities , inspired by attempts to extend these models with arbitrary discriminative functions ( Murphy , 2012 ) . We take as input a connectivity matrix R defining the connections between cell ei and ej , as well as a distance function d ( ei , ej ) representing a ( physical ) distance between adjacent cells . See the supplemental material for extension to multiple connectivity matrices . We assume there exist an unknown number K of latent ( unobserved ) cell types , k∈{1 , 2 , 3 , … , K} , and that each cell ei belongs to a single cell type . We indicate a cell ei is of type k using the assignment vector ( c ) , so ci = k . The observed connectivity between two cells R ( ei , ej ) then depends only on their latent type and their distance through a link function f ( ⋅ , d ( ei , ej ) ) . We assume f is parameterized based on the latent type , ci = m and cj = n , via a parameter ηmn , as well as a set of global hyperparameters θ , such that the link function is f ( d ( ei , ej ) \|ηmn , θ ) . We then jointly infer the MAP estimate of the class assignment vector ( c ) = {ci} , the parameter matrix ηmn , and the global model hyperparameters θ: ( 2 ) p ( c , η , θ\|R ) ∝∏i , jp ( R ( ei , ej ) \|f ( d ( ei , ej ) \|ηcicj ) , θ ) ∏m , np ( ηmn\|θ ) p ( θ ) p ( c\|α ) p ( α ) p ( θ ) . We describe the spatial ‘logistic-distance Bernoulli’ function here , and others in the supplemental material . The ‘logistic-distance Bernoulli’ spatial model assumes that , if cell ei is of type m and cell ej is of type n , then ηmn = ( μmn , λmn ) , and the probability that two cells ei and ej are connected is given by ( 3 ) p=1 . 01+expd ( ei , ej ) −μmnλmn , ( 4 ) p=p⋅ ( pmax−pmin ) +pmin , where pmax and pmin are global per-graph parameters . We place exponential priors on the latent parameters: ( 5 ) μmn∼exp ( μ\|μhp ) , ( 6 ) λmn∼exp ( λ\|λhp ) , using λhp and μhp as global per-graph hyperparameters . We use a Dirichlet-process prior on class assignments , which allows the number of classes to be determined automatically . In brief , for N total cells , the probability of a cell belonging to a class is proportional to the number of data points already in that class , Nk , such that p ( ci=k ) ∝mkN+α and the probability of the cell belonging to a new class k′ is p ( ci=k′ ) ∝αN+α . α is the global concentration parameter—larger values of α make the model more likely to propose new classes . We grid the parameter α and allow the best value to be learned from the data . Where we model cell depth , we assume that each cell type has a typical depth , and thus a Gaussian distribution of si . We assume si∼N ( μk ( s ) , σk2 ( s ) ) , where the ( s ) superscript indicates these model parameters are associated with the soma-depth portion of our model . We use a conjugate prior for ( μk ( s ) , σk2 ( s ) ) with μk ( s ) ∼N ( μhp ( s ) , σk2 ( s ) /κhp ( s ) ) and σk2 ( s ) ∼χ−1 ( σhp2 ( s ) , νhp ( s ) . The use of conjugacy simplifies inference while allowing for each cell type to have its own depth mean and distribution . We model synapse depth profile that each cell type has a characteristic depth distribution of synaptic contact points , and mixture of Gaussian distributions over cell is Ni contact points , gi . We do this by assuming the gji are drawn from an M = 3-component mixture of Gaussians . Thus associated with each cell type k is a vector of M Gaussian means ( μk , 1g , ⋯ , μk , Mg ) , and a mixture vector πk . This representation can thus model depth distributions of contact points that have up to three modes , an assumption that is well matched in the bulk of anatomical studies of cell-type-dependent connectivity . We perform posterior inference via MCMC , annealing on the global likelihood during the traditional burn-in phase . MCMC transition kernels for different parts of the state space can be chained together to construct a kernel whose ergodic distribution is the target ergodic distribution over the entire state space . Our first transition kernel ( ‘structural’ ) performs Gibbs sampling of the assignment vector p ( c\|η , θ , α ) . The lack of conjugacy in our likelihood model makes an explicit evaluation of the conditional assignment probabilities impossible , motivating us to use an auxiliary variable method ( Neal , 2000 ) in which a collection of ephemeral classes is explicitly represented for the duration of the Gibbs scan . We then employ a transition kernel to update the per-component parameter values ηmn . Conditioned on the assignment vector c and the model hyperparameters θ , α the individual ηmn are independent . We slice sample ( Neal , 2003 ) each component's parameters , choosing the slice width as a function of the global hyperparameter range . The global hyperparameters , both α and θ , are allowed to take on a discrete set of possible values . As θ is often a tuple of possible values , we explore the Cartesian product of all possible values . We then Gibbs sample ( our final transition kernel ) , which is always possible in a small , finite , discrete state space . We chain these three kernels together , and then globally anneal on the likelihood from a temperature of T = 64 down to T = 1 over 900 iterations unless otherwise indicated , and then run the chain for another 100 iterations . We then generate at least 20 samples , each taken from the end of a single Markov chain initialized from different random initial points in the state space . For visualization we pick the chain with the highest log likelihood , but for all numerical comparisons ( including link probability and cluster accuracy ) we use this full collection of samples from the posterior distribution to estimate the resulting statistics . To compute link-prediction accuracy , we compute the probability of a link between two cells using each model , trained via 10-fold cross-validation . We use a full collection of posterior samples when computing the link probability , and then compute the area under the ROC curve for each . We compare our model with a standard network clustering model , the latent-position clustering model . This model assumes each cell belongs to one of K clusters , and each cluster is associated with a d-dimensional Gaussian distribution . The probability of a link is then a function of the distance between the data points in this continuous space . We use a variational implementation provided in R ( Salter-Townshend and Murphy , 2013 ) , parametrically varying the number of latent dimensions and the number of requested groups . While this model provides reasonable link-predictive accuracy , the clusterings dramatically disagree with those from human anatomists . Hierarchical generative models can be sensitive to hyperparameter settings , thus for most hyperparameters we perform inference . In cases where we cannot , we run separate collections of Markov chains at separate settings and show the results across all pooled parameters . For the case of the mouse retina data , we consider maximum link probability pmax∈{0 . 95 , 0 . 9 , 0 . 7} , variance scales for the synapse density profile of σ2∈{0 . 01 , 0 . 1 , 1 . 0} ( of normalized depth ) , and K∈{2 , 3} possible synapse density profile mixture components . For the connectivity-distance-only model we actually perform inference over both pmax and pmin . Evaluating whether or not approximate inference methods , such as MCMC , produce samples which are valid approximations of the posterior distribution is an ongoing area of research in the computational statistics community . We use a rough proxy here—synthetic likelihood evaluation . For synthetic datasets of sizes comparable to our real data size , do we recover known ground truth information after running our Markov chains for the appropriate amount of time ? Figures 9 and 10 show the cluster accuracy ( ARI ) to ground truth and the total log score as a function of runtime . We see dramatic changes in log score initially as we vary the temperature , stabilizing as runtime progresses , for each chain . Then we see the characteristic jumps between nearby modes towards the end of the run , in both log score and ARI . Importantly , regardless of whether our model over- or under-estimates the exact posterior variance about the network , we find points in the latent variable space that are both predictive and parsimonious , largely agreeing with the human anatomists and predicting existing connections . 10 . 7554/eLife . 04250 . 011Figure 9 . Adjusted Rand index ( ARI ) for synthetic data as a function of run iteration . DOI: http://dx . doi . org/10 . 7554/eLife . 04250 . 01110 . 7554/eLife . 04250 . 012Figure 10 . Total model score ( log score ) versus wall clock time . DOI: http://dx . doi . org/10 . 7554/eLife . 04250 . 012 We reparameterized the logistic-distance Bernoulli likelihood to better capture the microprocessor data structure . We are explicitly setting the maximum probability p of the logistic function on a per-component basis , drawing from a global p∼Beta ( αhp , βhp ) . Then λ is set for each component as a global hyperparameter , λ . The ‘logistic-distance Poisson’ spatial model is used to explicitly model the count of synapses , c , between two neurons . The probability of c synapses between two neurons is distributed c∼Poisson ( c\|r ) , where r ( the ‘rate’ ) is generated by a scaled logistic function ( the logistic function has range [0 , 1] ) . For each component ηmn we learn both the threshold μmn and the rate scaling factor rmn . Thus if cells m and n are likely to have on average 20 synapses if they are closer than 5 μm , then μmn = 5 and rmn = 20 . Thus the probability of R ( ei , ej ) = c synapses between two cells ei and ej is given by: ( 7 ) r=1 . 01+expd ( ei , ej ) −μmnλ , ( 8 ) r=r⋅ ( rmn−rmin ) +rmin , ( 9 ) R ( ei , ej ) ∼Poisson ( c\|r ) , where λ and rmin are per-graph parameters and we have per-component parameters μmn∼Exp ( μ\|μhp ) and rmn∼Exp ( rmn\|rscalehp ) . All source code and materials for running experiments can be obtained from the project website , at http://ericmjonas . github . io/connectodiscovery/ . All preprocessed data has been made publically available as well . The model can handle multiple graphs Rq simultaneously with a shared clustering by extending the likelihood to include the product of the likelihoods of the individual graphs . ( 10 ) p ( c , ηq , θq\|Rq ) ∝∏q ( ∏i , jp ( Rq ( ei , ej ) \|f ( d ( ei , ej ) \|ηcicjq , θq ) ∏m , np ( ηmnq\|θq ) p ( θq ) ) p ( c\|α ) p ( α ) . For the mouse retina logistic-distance Bernoulli model , we gridded μhp and λhp into 40 log10-spaced points 1 . 0 and 80 . For the C . elegans data with the logistic-distance Poisson model , we gridded μhp and λ into 20 log10-spaced points between 0 . 2 and 2 . 0 , and the ratescalehp parameter into 20 log10-spaced points between 2 . 0 and 20 . 0 . We globally set ratemin = 0 . 01 . For the microprocessor with the logistic-distance with fixed lambda parameter and Bernoulli likelihood , we gridded muhp into 50 log10-spaced points between 10 and 500 and set λ = μhp/10 . pmin∈{0 . 001 , 0 . 01 , 0 . 02} and both pα and pβ∈{0 . 1 , 1 . 0 , 2 . 0} . We compare discovered types to known types via cluster comparison metrics: cluster homogeneity , cluster completeness , and the ARI . Homogeneity measures how many true types are in a given found type . If every cell type is split into two types , each subtype is still completely homogeneous . Completeness measures how many members of a given true type are split across found types . ARI takes into account both effects ( Hubert and Arabie , 1985 ) —two identical clusterings have an ARI of 1 . 0 , while progressively more dissimilar clusters have lower ARIs , becoming slightly negative as the clustering gets anti-correlated . Figure 11 shows the result of taking 20 different clusters and moving data points between them according to the following operations . •distribute: take a class and distribute its elements uniformly among the remaining types . •merge: take a type and merge it into another existing type . •split: take a type and split it into two distinct types . 10 . 7554/eLife . 04250 . 013Figure 11 . Type agreement evaluation metrics as a function of splitting types , merging types , and randomly distributing cells between types . DOI: http://dx . doi . org/10 . 7554/eLife . 04250 . 013 We can see the impact on ARI , completeness , and homogeneity as we perform these operations on more of the original 20 types . In all cases , ‘distribution’ of one type among the others is detrimental to the metric . Splitting impacts completeness but not homogeneity , and merging impacts homogeneity but not completeness .	The human brain is made up of billions of neurons , which are organised into networks via trillions of connections . The study of the nature of these connections will be central to understanding how the brain works . In recent years , a number of new methods for imaging the brain have made it possible to visualise and map these connections , generating striking images and creating an additional field of neuroscience known as ‘connectomics’ . However , the sheer volume of data generated by connectomics is now beginning to exceed the capacity of researchers to analyse it . Just as the advent of genome sequencing required the development of statistical techniques to analyse the resulting data , so the emergence of connectomics has created a need for similarly powerful mathematical models in neuroscience . Jonas and Kording have developed one such algorithm that can classify the component units of circuits , both biological and man-made , and identify the connections between them . When applied to connectomics data for 950 neurons in the mouse retina , the algorithm generated predictions regarding cell types and patterns of connectivity . The predicted cell types agreed closely with those identified by human neuroanatomists . Results were similarly convincing when the algorithm was applied to the nervous system of the nematode worm and genetic model organism , Caenorhabditis elegans , and even when it was asked to classify electronic components and connectivity patterns in a man-made microprocessor . Algorithms such as that developed by Jonas and Kording will soon be essential for making sense of the vast quantities of data generated by connectomic studies of the human brain . At present , an analysis of 950 neurons requires several hours , thus refinements that make the process faster will likely be required prior to the analysis of larger human datasets . Such algorithms will open up a range of possibilities for examining the structure of the healthy brain , as well as the changes triggered by developmental abnormalities and disease .	[ "Abstract", "Introduction", "Model", "Results", "Discussion", "Methods" ]	[ "tools", "and", "resources", "neuroscience" ]	2015	Automatic discovery of cell types and microcircuitry from neural connectomics
Reward perception guides all aspects of animal behavior . However , the relationship between the perceived value of a reward , the latent value of a reward , and the behavioral response remains unclear . Here we report that , given a choice between two sweet and chemically similar sugars—L- and D-arabinose—Drosophila melanogaster prefers D- over L- arabinose , but forms long-term memories of L-arabinose more reliably . Behavioral assays indicate that L-arabinose-generated memories require sugar receptor Gr43a , and calcium imaging and electrophysiological recordings indicate that L- and D-arabinose differentially activate Gr43a-expressing neurons . We posit that the immediate valence of a reward is not always predictive of the long-term reinforcement value of that reward , and that a subset of sugar-sensing neurons may generate distinct representations of similar sugars , allowing for rapid assessment of the salient features of various sugar rewards and generation of reward-specific behaviors . However , how sensory neurons communicate information about L-arabinose quality and concentration—features relevant for long-term memory—remains unknown . In an environment filled with various stimuli , the positive experiences an animal remembers are widely assumed to be rewarding and salient . Long-term associative memories in particular are supposed to reflect the intensity of past responses to rewards . The experiences we remember , however , are not always those we expect to remember . How immediate reward perceptions influence future actions is therefore of wide interest . Among various positive rewards , food , and in particular sweet food , has been most revealing since it is a source of both pleasure ( immediate value ) and nutrition ( long-term value ) . Food is also a complex reward . Having evolved in distinct ecological niches , different species of Drosophila display distinct food preferences and discriminate between potential sources of nutrition ( Dethier , 1976 ) . For example , while some species of Drosophila prefer rotting fruits , others prefer mushrooms , cacti , or hibiscus flowers ( Markow and O'Grady , 2005 ) . Identifying and remembering relevant food , therefore , is essential for survival . Moreover , food is often not a single substance but a mixture of various compounds , and not all are equally rewarding: rotting fruits contain various sugars , alcohols , and acids that produce varying responses ( Yarmolinsky et al . , 2009; Charlu et al . , 2013 ) . Food in natural contexts is also always part of an environment filled with other features , including predators , and therefore quick evaluation of potential food sources requires simultaneous processing of multiple stimuli . Finally , the attraction to food , and memories of it , are influenced by the internal state of the organism , such as whether the animal is hungry or satiated ( Colomb et al . , 2009; Krashes et al . , 2009; Toshima and Tanimura , 2012; Dethier , 1976 ) . It is therefore likely that contingent on their internal state , animals use certain components of food sources to quickly recognize those that are appropriate for feeding and , if worthwhile , to form memories of these sources for future visits . How these different aspects of food very quickly generate appropriate memories that guide future food-seeking behavior , however , remains unclear . One possibility is that whatever components of food are most salient for long-term behavior are the same features that animals find immediately rewarding . This would predict that the more appealing ( or palatable ) a sugar is , the better it will be remembered . Another possibility is that certain components of food can reinforce memory relatively independent of the food’s immediate appeal , because they indicate specific attributes of the food ( e . g . nutritional content ) that are of long-term relevance . In a complex environment , where an animal needs to process multiple stimuli simultaneously , such processing may ensure that regardless of the immediate response , stimuli of long-term relevance will be remembered . In the course of exploring both the immediate appeal of various natural sugars and their ability to generate long-term associative memories , we serendipitously discovered that these two processes are separable . A specific illustration of this phenomenon is seen with the two chemically similar sugars , D- and L-arabinose: flies greatly prefer D-arabinose to L-arabinose , but better remember an odor paired with L-arabinose than with D-arabinose . We have also begun to explore how an animal assesses whether an experience that is rewarding in the moment is also of long-term relevance . Many studies have characterized higher order systems , particularly the neuromodulatory systems such as dopaminergic ( Schwaerzel et al . , 2003; Huetteroth et al . , 2015; Berry et al . , 2012; Liu et al . , 2012; Yamagata et al . , 2015; Musso et al . , 2015 ) , octopaminergic ( Burke et al . , 2012; Schwaerzel et al . , 2003 ) , neuropeptide F ( Krashes et al . , 2009 ) and mushroom body neurons ( Aso et al . , 2014; Kirkhart and Scott , 2015; Vogt et al . , 2014 ) underlying long-term sugar reward memory in Drosophila . How various sugars differentially engage the higher order reward system , however , remains unclear . We find that D- and L-arabinose differentially activate the same peripheral Gr43a-expressing neurons , and that activating Gr43a in some but not all manners can substitute for the sugar reward , indicating that sensory neurons can at least partially mediate this discrimination process . However , the exact mechanism by which these sensory neurons communicate the relevant features of L-arabinose to higher order systems remains unclear at this stage . To explore how animals evaluate salient features of food , we used an associative-appetitive memory paradigm ( henceforth referred to as the ‘memory paradigm’ ) with Drosophila melanogaster that approximates food-seeking behavior ( Colomb et al . , 2009; Krashes and Waddell , 2011; Tempel et al . , 1983 ) . In this paradigm , hungry flies are trained for 2 min to associate an odor with a rewarding sweet sugar; trained flies subsequently seek out the sugar-associated odor for several days afterwards , indicating that they have formed an associative memory ( Figure 1A ) . We have used this paradigm for three reasons: one , it is an ethologically relevant behavior; two , both the internal state ( hunger ) of the fly and characteristics of the sugar dictate the duration of memory ( Burke and Waddell , 2011; Fujita and Tanimura , 2011; Colomb et al . , 2009 ) ; and three , salient features of the sugar are evaluated rapidly within the 2-min training as reported by others ( Burke and Waddell , 2011 ) and similarly confirmed by us ( Figure 1—figure supplement 1A and B ) . 10 . 7554/eLife . 22283 . 003Figure 1 . Flies’ immediate preference for a sugar is not predictive of their long-term memory: various sugars from fruits . ( A ) Schematic of behavioral assays . In the appetitive associative memory paradigm , hungry flies are trained for 2 min with the sugar-odor pair and memory is assayed by subsequently giving a choice between the two odors . In the preference assay , hungry flies are given a choice between two sugars mixed with different colors; after 5 min color of the abdomen is used to assess consumption . ( B ) Schematic of selected pectic polysaccharides present in fruits’ cell walls , adapted from Harholt et al . ( Harholt et al . , 2010 ) . ( C ) Survival percentages for flies given solely 1 M sugar solutions . n = 10 ( 50 flies per n ) for each time point . ( D ) Two-choice tests comparing flies’ preference for each of four sugars when both sugars are presented side-by-side for 5 min ( 50 flies per n ) . ( E ) Short-term ( 5 min ) associative memory scores for the sugars . ( n = 7–11 ) ( F ) Long-term ( 24 hr ) associative memory scores for the sugars . L-fucose is a component of pectin as well , although the amount is low compared to L-arabinose . ( n = 20–24 ) Memory scores labeled a are significantly different ( <0 . 05 ) from bars labeled b , analyzed by one way ANOVA with Tukey’s multiple comparisons test . Detailed explanations of what constitutes a single n is found in Materials and methods . Results with error bars are means ± s . e . m . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 00310 . 7554/eLife . 22283 . 004Figure 1—figure supplement 1 . The CS-US association occurs during the two-minute training . ( A ) Flies trained with sucrose , which produces robust 24 hr memory , were immediately provided either rich nutritious food or water . If sucrose’s nutritional value is assessed beyond the 2-min training , nutritious food immediately after training may interfere with the fly’s ability to attribute its nutritional status to the 2-min training , and thus interfere with memory formation . However , flies trained with 1 M sucrose and given food or no food for 3 hr post-training showed similar memory , unpaired t-test , p=0 . 480 . ( B ) Flies trained with L-sorbose—a sweet but non-nutritious sugar that produces short- but not long-term memory—were immediately fed sucrose . If the nutritional evaluation occurred after the 2-min training , immediate feeding on sucrose may substitute as a nutritional cue , resulting in enhanced long-term memory . However , flies trained with 1 M L-sorbose and given sucrose immediately after training showed similar memory to flies not given sucrose , suggesting that the critical association period was confined to the 2-min training , unpaired t-test , p=0 . 207 . ( C ) Matrix of flies’ preference for one sugar ( top ) when paired side-by-side with another sugar ( side ) for 5 min . Each comparison was tested with four independent trials , 50 flies per trial . Numbers are the proportion eating the sugar listed at top . ( Proportions may not sum to 1 . 0; often several flies would not eat either sugar . ) Short-term memory and long-term memory averages shown below with ± s . e . m . Memory scores labeled a are significantly different ( p<0 . 05 ) from bars labeled b , analyzed by one-way ANOVA with Tukey’s multiple comparisons test . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 004 In the course of training flies ( Figure 1A ) with various sugars , including those that are present in Drosophila melanogaster’s natural diet of ripening fruits ( Figure 1B ) some of which are non nutritious ( Figure 1C ) , we observed that the relative appeal of a sugar ( preference , Figure 1D ) or short-term memory ( minutes after training , Figure 1E ) does not always predict its ability to act as a rewarding stimulus for long-term ( 24 hr after training ) associative memory ( Figure 1F and Figure 1—figure supplement 1C ) . This was apparent for multiple sugars , but nowhere so striking as the difference between two structural isomers , D- and L-arabinose ( Figure 2A ) . D- and L-arabinose both taste sweet ( Figure 2—figure supplement 1 ) and are both non-nutritious ( Figure 2B ) . Flies overwhelmingly preferred D-arabinose to L- ( Figure 1D and Figure 1—figure supplement 1C ) , and form similar short-term memories of both sugars ( Figure 1E and Figure 2C ) . However , it is L-arabinose , not D- , that is more effective in producing long-term memory ( Figure 1F and Figure 2C ) . The relative ineffectiveness of D-arabinose in producing long-term memory is consistent with other studies ( Burke and Waddell , 2011; Fujita and Tanimura , 2011; Cervantes-Sandoval and Davis , 2012 ) . 10 . 7554/eLife . 22283 . 005Figure 2 . Flies’ immediate preference for a sugar is not predictive of their long-term memory: L- vs D-arabinose . ( A ) Structures of D- and L-arabinose . ( B ) Survival percentages for flies given solely 1 M sugar solutions . n = 10 ( 50 flies per n ) for each time point . ( C ) Short- and long-term memory of sucrose and D- and L-arabinose . ( D ) Two-choice tests comparing flies’ preference for D- and L-arabinose when both sugars are presented side-by-side for 5 mins . n = 4 ( 50 flies per n ) . ( E ) Long-term ( 24 hr ) memory scores for increasing concentrations of L-arabinose . Results with error bars are means ± s . e . m . ns , not significant . ≤0 . 01 , ≤0 . 001 and *≤0 . 0001 . The significant differences ( p<0 . 05 ) between conditions in Figure 2C and E were analyzed by one-way ANOVA with Tukey’s multiple comparisons test and differences are denoted by different letters . Detailed explanations of what constitutes a single n is found in Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 00510 . 7554/eLife . 22283 . 006Figure 2—figure supplement 1 . Palatability of D- and L-arabinose over a concentration range . Fifty male flies per trial were given water alone for 24–36 hr , then put on a microtiter plate checkered with food-dye-labeled water and either D-arabinose or L-arabinose at various concentrations . After 5 min , flies were removed and the color visible in the abdomen was scored . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 00610 . 7554/eLife . 22283 . 007Figure 2—figure supplement 2 . Specificity of L-arabinose memory . ( A ) L-arabinose from different sources ( USB and Sigma ) generated equivalent long-term memory , controlling for other contaminating sugars . ( B ) Canton-S flies obtained from two different labs show similar long-term memory when trained with L-arabinose . ( C ) Since flies’ feeding behavior can be influenced by the time of day , flies were tested at different times on consecutive days by different experimenters . Under all testing conditions , flies formed long-term memories of L-arabinose . ( D ) While L-arabinose forms memory , an L-arabinose-galactose polymer does not; neither does another natural L sugar , L-rhamnose , demonstrating the selectivity of L-arabinose memory . ( E ) Flies fed a cocktail of three broad-spectrum antibiotics ( kanamycin , ampicillin , and tetracycline ) for the 48 hr before training show memory indistinguishable from untreated control flies . For two samples unpaired two tailed t-tests and for multiple samples one-way ANOVA with Tukey’s multiple comparisons test were performed , and significant differences ( p<0 . 05 ) are denoted by different letters . Results with error bars are means ± s . e . m . ns , not significant . ≤0 . 01 , ≤0 . 001 and ≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 00710 . 7554/eLife . 22283 . 008Figure 2—figure supplement 3 . Although both are sweet , D-arabinose is preferred over L-arabinose . ( A ) At equal concentrations , flies overwhelmingly prefer D-arabinose to L-arabinose; the preference begins to shift when L-arabinose is ≥3 times more concentrated than D-arabinose . ( B ) CAFÉ assay quantifying flies’ intake of D- and L-arabinose across a range of concetrations . ( C ) In 5 min , flies consume more radioactive [32]P-mixed D-arabinose than radioactive [32]P-mixed L-arabinose during two-choice tests when either sugar is tested separately against water . ( D ) Single flies monitored by video spend more time on 1 M D-arabinose than 1 M L-arabinose when the two are presented side-by-side . ( E ) The greater the concentration of D-arabinose , the better the memory score . This suggests that the flies are not eating so much D-arabinose that they become sick , and would otherwise remember if not for eating large amounts of sugar . Memory does not improve at lower concentrations . For multiple samples , one way ANOVA with Tukey’s multiple comparisons test was performed , and significant differences ( p<0 . 05 ) are denoted by different letters . Results with error bars are means ± s . e . m . ns , not significant . ≤0 . 01 , ≤0 . 001 and ≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 00810 . 7554/eLife . 22283 . 009Figure 2—figure supplement 4 . Detection and memory . ( A ) Proboscis extension reflex response to D- and L-arabinose . Flies were given a drop of water , then 10 mM D-ara , then 100 mM D-ara , then 1 M D-ara , water , followed by 10 mM L-ara , 100 mM L-ara , and 1 M L-ara . Alternate flies were given D-ara or L-ara first . ( n = 26 for each concentration ) . ( B ) Flies only begin to have trouble detecting sucrose in the two-choice test ( paired with water ) when sucrose is ≤1 mM . ( C ) Flies form better memories with increasing concentrations of sucrose . 10 mM sucrose ( the maximum possible contamination given L-arabinose purity of ≥99% ) does not produce robust long-term memory . For multiple samples , one-way ANOVA with Tukey’s multiple comparisons test was performed , and significant differences ( p<0 . 05 ) are denoted by different letters . Results with error bars are means ± s . e . m . ns , not significant . ≤0 . 01 , ≤0 . 001 and ≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 009 A trivial explanation for the observed memory with L-arabinose would be contamination with nutritious sugars . But L-arabinose bought from different sources generated similar survival curves and memory scores ( Figure 2—figure supplement 2A ) . The L-arabinose memory is also not due to particular wild-type flies used in the experiment ( Figure 2—figure supplement 2B ) , the training conditions or the particular experimenter ( Figure 2—figure supplement 2C ) . Neither arabinogalactan ( a polymer of L-arabinose and galactose ) nor the natural L-sugar rhamnose , produce significant memory , indicating that not all L-arabinose-containing components of fruits’ cell wall or natural L-sugars are conducive to memory formation ( Figure 2—figure supplement 2D ) . Bacteria are known to utilize L-arabinose ( Watanabe et al . , 2006 ) , but the flies’ resident bacteria had no evident contribution to L-arabinose memory , since giving the flies a cocktail of antibiotics for the 2 days prior to behavioral training had no effect on L-arabinose memory ( Figure 2—figure supplement 2E ) . Taken together , these results suggest that L-arabinose can act as a rewarding stimulus for long-term associative memory . We wondered whether the behavioral differences between D- and L- were due to the high concentration ( 1 M ) of sugars , although 1 M to 3M sugar is standard in memory assays ( Yamagata et al . , 2015; Cervantes-Sandoval and Davis , 2012; Burke and Waddell , 2011 ) . However , the preference for D-arabinose ( Figure 2D ) persisted when the sugars’ concentrations were both reduced 100-fold ( 10 mM ) , and began to shift only when the concentration of D-arabinose was reduced to less than a third of L-arabinose ( Figure 2—figure supplement 3A ) . A similar difference between D- and L-arabinose has also been reported in the blowfly Phormia regina , where the taste threshold for D-arabinose is reported to be five times lower than that of L-arabinose ( Hassett et al . , 1950 ) . When we measured consumption by Capillary Feeder ( CAFÉ ) assays over a concentration range ( Figure 2—figure supplement 3B ) or by mixing radioactive [32]P in the food in fixed concentration ( Figure 2—figure supplement 3C ) , the flies consumed more D- than L-arabinose . Over time , however , flies consumed less D-and L-arabinose than nutritious sugars ( data not shown ) , consistent with other studies ( Dus et al . , 2011; Stafford et al . , 2012 ) , and consumption reached a plateau in ~14 min for D- and ~30 min for L . We also monitored by video the behavior of single flies as they fed on colorless D- and L-arabinose solutions ( Figure 2—figure supplement 3D ) and observed that they spend much more time on D-arabinose than L-arabinose , consistent with higher overall consumption . These differences are not due to differences in mere detection of D- and L- arabinose: detection rates were very similar at high concentrations and began to differ only when concentrations were dropped to ≤50 mM ( Figure 2—figure supplement 1 ) . Likewise , the ability of L-arabinose to generate long-lasting memory persisted even at a 10-fold lower concentration , albeit with much weaker efficacy ( Figure 2E ) . Moreover , lowering D-arabinose concentration , where the flies still detect D-arabinose but consume less of it , there was no increase in memory ( Figure 2—figure supplement 3E ) ruling out the possibility that consuming too much non-nutritious sugar , such as D-arabinose , is somehow a negative reinforcement . In addition to consumption , we also measured the proboscis extension response ( PER ) , which reports immediate acceptance of a taste stimuli . Curiously , PER response was similar between D- and L-arabinose over a concentration range ( Figure 2—figure supplement 4A ) , consistent with other reports that PER depends more on the intensity than chemical nature of the sugar ( Masek and Scott , 2010; Stafford et al . , 2012 ) . However , mere detection and acceptance of the sugar is not sufficient for long-term memory: A choice test between water and various concentrations of sucrose ( a potent inducer of long-term memory ) showed that there was no difference in the likelihood of consumption between 1 M and 10 mM sucrose; only when sucrose concentration is reduced to 1 mM did detection begin to fall ( Figure 2—figure supplement 4B ) . However , only sucrose concentrations ≥ 100 mM reliably produced robust long-term memory ( Figure 2—figure supplement 4C ) . Therefore , various sugar-associated behavioral responses , such as detection , acceptance , and assessment of immediate and long-term relevance are not a single process and are likely dictated by various attributes of the sugar . Taken together , these results suggest that even two chemically similar sugars can elicit quite distinct short- and long-term behaviors , and that immediate behavioral responses are not always predictive of long-term behavioral consequences: while flies find D-arabinose more immediately appealing , L-arabinose is more salient for long-term memory . What is the neural basis for the difference in behavioral responses to D- and L-arabinose ? There are two possibilities , not mutually exclusive: the two sugars engage distinct neural pathways , or they activate the same neural pathways in a distinct manner . The intial step in sugar detection and consumption are the gustatory-receptor-expressing ( Gr ) neurons that respond to sweet substances . To date , Gr5a , Gr43a , Gr61a , and Gr64a , b , c , d , e , and f have been implicated in sweet sugar detection ( Dahanukar et al . , 2007; Jiao et al . , 2008; Yavuz et al . , 2014; Freeman et al . , 2014; Miyamoto et al . , 2013 ) . We therefore used Gr-GAL4 drivers to express the inward rectifying potassium channel Kir2 . 1 ( Gr-GAL4/+; UAS-Kir2 . 1/+ ) , silencing these sets of Gr-expressing neurons ( Baines et al . , 2001 ) in order to determine the neurons involved in D vs L preference and L-arabinose memory . Preference and memory for a sugar starts with detecting the sugar; silencing neurons required for detection could cause a general decline in consumption of a particular sugar or all sugars . We therefore first measured the flies’ ability to detect and consume L- or D-arabinose following silencing of specific Gr-expressing neurons ( Figure 3—figure supplement 1A and B ) . Silencing Gr5a-expressing neurons reduced L-arabinose detection by about 80% , while silencing Gr61a-expressing neurons reduced both L-and D-arabinose detection by ~50% . Silencing Gr64e and Gr64f neurons almost completely abolished detection , discrimination and memory , consistent with previous reports that these receptors are likely expressed in all neurons responsible for sugar detection ( Jiao et al . , 2008; Wisotsky et al . , 2011 ) . Silencing neurons required for discrimination would result in equivalent consumption of D- and L-arabinose . Upon silencing of Gr5a- , Gr43a- , Gr64a- , or Gr64d-expressing neurons , flies still overwhelmingly preferred D-arabinose ( Figure 3A ) . Only silencing Gr61a neurons reduced D-arabinose consumption while increasing L-arabinose consumption ( and ~30% flies did not eat any sugar ) , indicating that without Gr61a-expressing neurons flies were beginning to have trouble discriminating between the two sugars ( Figure 3A ) . 10 . 7554/eLife . 22283 . 010Figure 3 . Gr43a- and Gr61a-expressing neurons are involved in D vs L preference and in L-arabinose memory . ( A ) Silencing of Gr61a-expressing neurons with Kir2 . 1 impaired D > L discrimination and preference; silencing Gr64e- and Gr64f-expressing neurons nearly eliminated detection of both sugars . ( B ) Silencing Gr43a- and Gr61a-expressing neurons impaired L-arabinose memory . Gr64aGAL4 and Gr64dGAL4 , whose expression is restricted to LSO and VCSO neurons did not impair L-arabinose memory . Silencing Gr64f- and Gr5a-neurons reduce L-arabinose memory , but they also impair L-arabinose detection . ( see Figure 3—figure supplement 1 ) . ( C ) Gr43a and Gr61a receptors are important for L-arabinose memory . ( D ) No single receptor mutant impaired D > L preference . For multiple samples , one-way ANOVA with Tukey’s multiple comparisons test was performed , and significant differences ( p<0 . 05 ) are denoted by different letters . Results with error bars are means ± s . e . m . ns , not significant . ≤0 . 01 , ≤0 . 001 and ≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 01010 . 7554/eLife . 22283 . 011Figure 3—figure supplement 1 . Gr expressing neurons involved in D- and L-arabinose detection . ( A ) Fifty male flies per trial were given water alone for 24–36 hr , then put on a microtiter plate checkered with food-dye-labeled water and 1 M L-arabinose . After 5 min , flies were removed and the color visible in the abdomen was scored . n = 4 for all silencing and mutant two-choice experiments , 50 flies per n . Silencing Gr5a- , Gr64e- , and Gr64f-expressing neurons nearly eliminated L-arabinose detection; silencing Gr43aGAL4 neurons had no detectable effect on L-arabinose detection . ( B ) Silencing Gr64f-expressing neurons virtually eliminates D-arabinose detection; silencing of Gr61a- and Gr43a-expressing neurons reduces but does not eliminate D-arabinose detection compared to the corresponding GAL4/+ control . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 01110 . 7554/eLife . 22283 . 012Figure 3—figure supplement 2 . L- and D-arabinose detection do not rely on any single receptor . ( A ) Gr43a mutants have a small L-arabinose detection deficit while Gr61a mutants have a moderate detection deficit . ( B ) No single receptor removal impairs D-arabinose detection . Results with error bars are means ± s . e . m . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 012 In contrast to D-arabinose preference , silencing of Gr5a- , Gr43a- , Gr61a- , and Gr64f- but not Gr64a- or Gr64d-expressing neurons , significantly impaired L-arabinose memory ( Figure 3B ) . Since silencing Gr5a- and Gr64f-expressing neurons also impairs L-arabinose consumption , the memory impairments may very well be due to an inability to detect L-arabinose ( Figure 3—figure supplement 1A ) . Since silencing Gr61a neurons reduces detection , discrimination and memory , they may play a more general role in L- and D-arabinose detection and subsequent processing . Interestingly , silencing of Gr43a neurons had no effect on L-arabinose detection ( Figure 3—figure supplement 1A ) or D-arabinose preference ( Figure 3A ) , but resulted in loss of L-arabinose memory ( Figure 3B ) , suggesting Gr43a-expressing neurons play an important role in L-arabinose memory . Single gustatory neurons express multiple gustatory receptors . To determine which receptor within Gr43a neurons—Gr43a or some other receptor expressed by these neurons—is important for L-arabinose memory we trained receptor mutants with L-arabinose . ∆gr43a , ∆gr61a , and ∆gr43a-61a flies all showed a significant reduction ( p<0 . 01 ) in long-term memory at 24 hr ( Figure 3C ) . However , D-arabinose preference is maintained in the absence of any single known sugar receptor ( Figure 3D ) . To determine whether L-arabinose memory phenotypes were simply due to detection deficits , we tested the mutants’ ability to detect L-arabinose . Deletion of Gr43a had a small effect on L-arabinose detection , and deletion of Gr61a resulted in ~40% reduction ( Figure 3—figure supplement 2A ) . D-arabinose detection was not altered by any single receptor deletion ( Figure 3—figure supplement 2B ) . Taken together , these results suggest that Gr43a and Gr43a-expressing neurons are important to form long-term memory of L-arabinose , while Gr61a and Gr61a-expressing neurons are important for L- and D-arabinose detection and discrimination . These results , however , do not rule out the possibility that there may be an unidentified receptor that exclusively mediates D-arabinose preference or that L-arabinose memory uses other receptors in addition to Gr43a and Gr61a . In Drosophila , gustatory receptors are present on the antennae , legs , wings , and labellae , and in the pharynx , gut , and central brain ( Joseph and Carlson , 2015 ) . The wide expression pattern , presence of multiple receptors in the same neurons , and different combinations of receptors in different neurons indicate that gustatory-receptor-expressing neurons in various locations may respond quite differently to different sugars ( Thoma et al . , 2016; Miyamoto and Amrein , 2014 ) . We focused particularly on Gr43a-expressing neurons for their specific involvement in L-arabinose memory , and previous studies suggested they act as nutrient sensors ( Miyamoto et al . , 2012 ) . We therefore sought to determine whether all Gr43a-expressing neurons or only a subset of Gr43a neurons are important for L-arabinose memory . As reported by others ( Miyamoto et al . , 2012; Park and Kwon , 2011 ) , Gr43aGAL4 expression is consistently detected in four dorsolateral protocerebrum ( DLP ) neurons in the central brain , the LSO and VCSO neurons in the proboscis , two f5 neurons in the distal tarsi , and in the proventricular ganglion of the gut ( Figure 4A ) . We selectively silenced the central brain DLP neurons using a Gr43aGAL4:ChaGAL80 ( Miyamoto et al . , 2012 ) combination ( Figure 4B ) or the LSO and VCSO neurons using Gr64aGAL4 and Gr64dGAL4 ( Figure 3B ) . While silencing all Gr43a neurons impaired L-arabinose memory , silencing of just the DLP ( Figure 4B ) , or just the LSO and VCSO neurons ( Figure 3B ) had no significant effect , suggesting that some combination of Gr43a-expressing neurons that includes the tarsal and/or gut neurons are the necessary Gr43a-expressing neurons for L-arabinose memory . Because silencing of Gr61a- and Gr5a-expressing neurons each blocked L-arabinose memory ( Figure 3B ) and neither Gr5a nor Gr61a expression can be detected in the gut , it seems that the tarsal Gr43a-expressing neurons are the important ones for L-arabinose memory . Previous studies suggested that in the f5 neurons in the distal tarsi , Gr43a is coexpressed with Gr61a ( Figure 4—figure supplement 1A ) ( Freeman and Dahanukar , 2015 ) . There were uncertainities about the coexpression of Gr43a and Gr5a in distal tarsi . However , split-GAL4 reconstitution assay suggests that Gr5a and Gr43a are likely to be coexpressed in one f5 neuron ( likely f5V ) in the distal tarsi ( Figure 4—figure supplement 1B ) , in agreement with previous work ( Miyamoto et al . , 2012 ) . Taken together , these results suggest that f5 neurons in the distal tarsi coexpressing Gr43a and some combination of Gr5a and Gr61a are involved in L-arabinose memory . However , these results do not rule out the possibility that other Gr43a neurons or other Gr-expressing neurons are involved in L-arabinose memory . 10 . 7554/eLife . 22283 . 013Figure 4 . Tarsal Gr43a neurons are critical for L-arabinose memory . ( A ) Gr43aGAL4 neurons are observed in the dorsolateral protocerebrum , central brain , proboscis , leg , and gut ( not shown ) . ( B ) Silencing only the dorsal protocerebral ( DLP ) and VCSO neurons does not impair L-arabinose memory . Left panel: memory score in various genetic backgrounds . Right panel , top: in Gr43aGAL4/+; Kir2 . 1/+ flies , all indicated neurons are silenced ( including proventricular neurons , not pictured ) . Bottom: in Gr43aGAL4:ChaGal80/+; UAS-Kir2 . 1/+ flies , only the neurons indicated in red type are silenced . For multiple samples , one-way ANOVA with Tukey’s multiple comparisons test was performed , and significant differences ( p<0 . 05 ) are denoted by different letters . Results with error bars are means ± s . e . m . ns , not significant . ≤0 . 01 , ≤0 . 001 and ≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 01310 . 7554/eLife . 22283 . 014Figure 4—figure supplement 1 . Expression patterns of Gr-GAL4s . ( A ) Distal tarsi expression patterns of Gr5aGAL4 ( 3 neurons ) , Gr43aGAL4 ( 2 neurons ) , and Gr61aGAL4 ( 6 neurons ) . Scale bar 50 μm . ( B ) Gr5a-Gr43a splitGal4 labeling ( Pfeiffer et al . , 2010 ) . GAL4 DNA-binding domain ( DBD ) and VP16 transcription activation domain ( AD ) fused to leucine zipper dimerization domains ( Leu-Zip ) were inserted into the Gr5a ( DBD ) and Gr43a ( AD ) genomic locus using CRISPR-Cas9 . The neurons coexpressing both receptors drive mCD8eGFP from the UAS promoter . f5 neurons in the distal tarsi are marked by this technique . In some animals , an f5 neuron is the only neuron marked ( top panel ) . However , in other animals , additional neurons were also marked in the leg ( bottom panel ) and proboscis . Scale bar 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 014 To understand how D- and L-arabinose generate different behavioral responses , we analyzed electrophysiological responses of f5V sensilla in the distal tarsi , which host neurons expressing Gr43a . D-arabinose consistently generated significantly more spikes over a range of concentrations ( Figure 5A and B ) . Differences in electrophysiological response also manifested in calcium levels measured by GCaMP6 , a genetically encoded calcium indicator ( Chen et al . , 2013 ) . In Gr43a-expressing f5 neurons in distal tarsi , the peak calcium level was higher and reached more rapidly for D-arabinose than L-arabinose ( Figure 5C ) . Removal of the Gr43a receptor from these neurons significantly reduced response to L-arabinose but not D-arabinose ( Figure 5D ) , consistent with the idea that D-arabinose activates multiple receptors . In the proboscis LSO neurons , there was a quicker rise and fall in response to L-arabinose with a slower but more sustained activation in response to D-arabinose ( Figure 5—figure supplement 1A ) . However , such differences between D- and L-arabinose-provoked responses are not universal: the average D- and L- arabinose responses of the central brain DLP neurons were similar in both magnitude and shape ( Figure 5—figure supplement 1B ) . These results indicate that D- and L-arabinose can activate the same gustatory neurons to different extents and that differential activation depends on properties specific to each neuron . 10 . 7554/eLife . 22283 . 015Figure 5 . Tarsal Gr43a neurons respond differentially to D- and L-arabinose . ( A ) Spikes per second of recorded f5V tarsal neuron in response to D- or L-arabinose at various concentrations . ( B ) Spikes per second binned by 100 ms over the first two seconds of response . ( C ) The evoked-calcium activity of Gr43aGAL4 neurons in the distal tarsi . ( D ) Removal of Gr43a receptor impairs fructose and L-arabinose activation of Gr43aGAL4 neurons . Results with error bars are means ± s . e . m . ns , not significant . ≤0 . 01 , ≤0 . 001 and ≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 01510 . 7554/eLife . 22283 . 016Figure 5—figure supplement 1 . Evoked-calcium activity of Gr43a neurons in response to D- and L-arabinose . ( A ) Imaging of calcium responses of LSO neurons in proboscis . The left panel shows curve-aligned fluorescence to compare the shape of the responses , and the right panel plots peak magnitudes as ∆F/Fo . ( B ) Imaging of calcium responses of dorsal protocerebral neurons . The left panel shows curve-aligned fluorescence to compare the shape of the responses , and the right panel plots peak magnitudes as ∆F/Fo . Results with error bars are means ± s . e . m . ns , not significant . ≤0 . 01 , ≤0 . 001 and ≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 016 Because Gr43aGAL4 offered the most restricted set of neurons that was critical for L-arabinose memory , we sought to determine whether they also represented the minimum set of gustatory neurons sufficient for appetitive long-term memory formation . To this end , we asked whether activating Gr43aGAL4 neurons in the memory paradigm—in the absence of sugar—could generate an associative-appetitive memory ( Figure 6A ) . dTrpA1 , a temperature-sensitive cation channel , causes continuous activation of neurons at temperatures above 26°C ( Hamada et al . , 2008 ) . Activation of Gr43aGAL4 neurons by dTrpA1 , however , failed to substitute for the sugar reward ( Figure 6B ) , although similar activation of a subset of dopaminergic neurons ( R58E02-GAL4/+; dTrpA1/+ ) produced long-term appetitive memory as reported by others ( Liu et al . , 2012 ) ( Figure 6—figure supplement 1A ) . These results suggested either that activation of Gr43aGAL4 neurons is necessary but not sufficient for L-arabinose memory , or that dTrpA1 does not approximate the activation required to produce long-term memory . Consistent with the latter possibility , activation of Gr43a neurons with the red-shifted channelrhodopsin variant ReaChR , a light-gated cation channel that depolarizes neurons in response to red light ( Lin et al . , 2013 ) produced associative memory: when flies expressing ReaChR in Gr43aGAL4 neurons were exposed to one odor without the light , and a second odor in the presence of red light , the flies subsequently preferred the light-associated odor ( Figure 6C ) . Intriguingly , activation by the same amount of light evenly distributed was not effective in producing long-term memory , suggesting that these patterns evoked different levels or patterns of activity in Gr43a neurons; the nature of this activation is unknown at this time ( Figure 6C ) . Finally , starvation is an important regulator of memory strength in the associative-appetitive paradigm—the hungrier the flies are , the better memories they form ( Krashes et al . , 2009; Colomb et al . , 2009 ) . Starvation also influenced the memory strength following Gr43a-neuron activation: the same pulsated light activation produced memory in starved but not fed flies ( Figure 6D ) . 10 . 7554/eLife . 22283 . 017Figure 6 . Activation of Gr43a neurons is sufficient to form rewarding associative memory . ( A ) Schematic of heat and light-activated associative olfactory training . ( B ) Activation of Gr43aGAL4 neurons by dTrpA1 ( at 31 °C ) does not induce long-term memory . ( C ) A 20 Hz , 15 ms pulse-width activation for 2 . 5 s , repeated every 20 s , induces long-term memory in flies expressing ReaChR in Gr43aGAL4 neurons; genetic controls do not show significant memory , and the same amount of light using the same pulse-width but distributed uniformly over the 2 min generates no memory ( red ) . Schematics of light patterns are not to scale . ( D ) Optogenetic activation of Gr43aGAL4 neurons induces memory only in hungry flies , not in flies fed ad libitum . ( E ) Optogenetic activation of Gr43a- and Gr5a-expressing neurons leads to substantial 24 hr memory; activation of Gr61a- or Gr64a-expressing neurons does not . For multiple samples , one-way ANOVA with Tukey’s multiple comparisons test was performed , and significant differences ( p<0 . 05 ) are denoted by different letters . Results with error bars are means ± s . e . m . ns , not significant . ≤0 . 01 , ≤0 . 001 and ≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 01710 . 7554/eLife . 22283 . 018Figure 6—figure supplement 1 . dTrpA1 activation of R58E02 neurons does not produce long-term memory . ( A ) Activation of R58E02GAL4 dopaminergic neurons , either with light ( ReaChR; 1 Hz , 500 ms pulse-width for 20 s , repeated three times over 2 min , or 10 Hz , 10 ms pulse-width , repeated continuously for 2 min; data combined ) or with heat ( dTrpA1; 31 °C for two minutes ) is sufficient to generate robust 24 hr memory . ( B ) Silencing of R58E02GAL4 neurons eliminates 24 hr L-arabinose memory . ( C ) Supplementing L-arabinose with 1 M sorbitol does not increase L-arabinose memory . For multiple samples , one-way ANOVA with Tukey’s multiple comparisons test was performed , and significant differences ( p<0 . 05 ) are denoted by different letters . Results with error bars are means ± s . e . m . ns , not significant . ≤0 . 01 , ≤0 . 001 and *≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 22283 . 018 Since the f5 neurons in the distal tarsi express Gr5a in addition to Gr43a , we also activated Gr5a-expressing neurons . Similar to Gr43a neurons , activation of Gr5a-expressing neurons resulted in robust long-term memory ( Figure 6E ) . Activation of Gr64a-expressing neurons , which labels the LSO and VCSO neurons , did not produce significant long-term memory ( Figure 6E ) . Interestingly , Gr61a-expressing neurons are necessary but not sufficient to generate associative memory ( Figure 6E ) , suggesting that activation of some Gr43a and Gr5a expressing neurons could be critical for memory processes , or that activation of the additional Gr61a-expressing neurons somehow weakens the co-expressing neurons’ likelihood of generating memory . Taken together , these results suggest that activation of a subset of Gr43a-expressing neurons is sufficient to generate long-lasting associative memory . These observations further suggest that activation of the same neurons by different methods , perhaps leading to different activity levels/patterns , give rise to substantially different behavioral outcomes , consistent with other reports ( Clark et al . , 2013; Seeger-Armbruster et al . , 2015 ) . However , further work is necessary to determine exactly which subsets of neurons contribute to L-arabinose memory , and whether these neurons needs to be activated in a specific pattern to elicit long-term memory . The caloric value of a sugar has been found to be an important determinant of long-term appetitive memory ( Burke and Waddell , 2011; Fujita and Tanimura , 2011; Musso et al . , 2015 ) , implying that flies quickly metabolize the sugar and that caloric evaluation somehow provides cues necessary to elicit long-term memory . We find that sugar with no caloric value can also produce long-term appetitive memories . One obvious possibility is that memories of sweet nutritious sugars are distinct from memories of sweet non-nutritious sugars . However , this seems so far not to be the case: a subset of higher order dopaminergic neurons ( R58E02GAL4 ) necessary for long-term memory of nutritious sucrose ( Liu et al . , 2012 ) is also required for non-nutritious L-arabinose ( Figure 6—figure supplement 1B ) . Similarly , addition of sorbitol , a tasteless but nutritious sugar , enhances the memory of non-nutritious sugars like xylose and D-arabinose , but does not enhance the memory of nutritious sugars ( Burke and Waddell , 2011 ) . Adding sorbitol to L-arabinose had no additive effect on long-term memory ( Figure 6—figure supplement 1C ) . It therefore appears that L-arabinose memory uses at least some of the same downstream neural circuitry as memory of nutritious sugars . Whether memory of L-arabinose , a non-nutritious sugar , is an exception or represents a more general phenomenon is unclear since we have tested only a limited number of sugars in a particular behavioral paradigm . However , in addition to L-arabinose , L-fucose can also produce memory ( Figure 1E ) ; both are components of the pectin in many fruits’ cell walls ( Dick and Labavitch , 1989; Ahmed and Labavitch , 1980 ) . It is therefore possible that these sugars may signal some specific attributes of ripening fruit—ripening is accompanied by breakdown of the fruit’s cell walls—although neither of these sugars are present in fruits near the concentrations ( 1 M ) used in memory assays . Nonetheless , these observations suggest that flies can quickly assess salient features of sugars—a sort of leading indicator of nutritional value—without the sugar’s metabolic breakdown . This approach to memory formation may allow flies to quickly recognize and remember potential foods using specific cues , a time advantage that could be vital in natural contexts . Do insects distinguish structurally similar sugars ? The taste modality of insects , particularly Drosophila , is reported to have limited discriminatory power and be primarily based on the intensity of the stimuli as opposed to the chemical nature of the sugar ( Masek and Scott , 2010 ) . Indeed we find that , apart from flies’ differential preference for various sugars at equal concentrations , for immediate and short-term behavior this is largely true . However , we did not observe any obvious correlation between immediate behavior and long-term memories: flies immediate preference is L-fucose > D-arabinose > L-arabinose > L-sorbose ( Figure 1D ) ; for short-term memory , L-sorbose = D-arabinose ≥ L-arabinose = L-fucose ( Figure 1E ) ; but in order of long-term memory score , L-arabinose ≥ L-fucose ≥ D-arabinose = L-sorbose ( Figure 1F ) . These results indicate that while short-term responses are guided by palatability , long-term behavioral reponses are guided by additional attributes of the sugars . It is not yet clear why D-arabinose is a less effective stimulus . Since D- and L-arabinose are both sweet , they may generate positive sensations in a different manner , or perhaps D-arabinose carries a negative value that over time reduces the positive association formed initially ( or dampens the behavioral output ) . The gustatory receptors Gr5a , Gr43a , Gr61a , and Gr64a-f have been implicated in sugar detection ( Fujii et al . , 2015; Freeman and Dahanukar , 2015; Dahanukar et al . , 2007; Scott et al . , 2001; Dunipace et al . , 2001; Montell , 2009; Jiao et al . , 2007 , 2008; Joseph and Carlson , 2015 ) . Although exactly which Gr receptors are responsible for detecting which sugar remains somewhat controversial , two features of sweet-sensing gustatory receptors are generally agreed upon: first , different gustatory neurons express a number of Gr receptors in unique combinations; second , more than one receptor is typically involved in detecting a sugar ( Fujii et al . , 2015 ) . However , the physiological consequences of this combinatorial expression of semi-redundant gustatory receptors remain uncertain . This study raises the possibility that gustatory neurons in different locations , expressing unique combinations of receptors , are responsible for discriminating chemically similar sugars and eliciting different behavioral responses . Consistent with this idea , previous studies suggested that Gr43a neurons in the central brain monitor hemolymph fructose levels and modulate feeding behavior ( Miyamoto et al . , 2012 ) , while we find that these neurons are dispensable for L-arabinose memory , and that peripheral Gr43a-neurons are likely sufficient to signal the presence of a rewarding sugar and generate associative memories . These differences likely arise from the locations of these neurons , differentially expressed receptors , the presence or absence of various co-receptors , and the second-order neurons to which these neurons project . Exactly which or how many Gr43a- , Gr61a- , and Gr5a-expressing neurons in the periphery are sufficient for L-arabinose memory is currently unclear . We also find that activation of Gr43a-expressing neurons by ReaChR but not dTrpA1 is able to generate appetitive memory , while artificially activating a subset of dopaminergic neurons ( R58E02GAL4 ) by heat ( dTrpA1 ) or light ( ReaChR ) both led to long-term memory ( Figure 6—figure supplement 1A ) . How a difference in activity at the sensory level is conveyed to higher-order neurons , and how that difference is interpreted by the higher-order neurons , remains unclear . More concretely , why is dTrpA1 activation of a subset of dopamine neurons sufficient to generate memory , but dTrpA1 activation of Gr43a-expressing neurons is not ? One possibility is that the activity requirements of neuromodulatory systems are less stringent than those for sensory coding , and that temporal selectivity occurs before the signal reaches these dopamine neurons . Alternatively , recent studies have indicated that dopaminergic neurons are functionally diverse , and that distinct population of dopaminergic neurons are involved in appetitive associative memory ( Cohn et al . , 2015; Huetteroth et al . , 2015; Krashes et al . , 2009; Berry et al . , 2012; Aso et al . , 2014; Yamagata et al . , 2015; Berry et al . , 2015; Musso et al . , 2015; Schwaerzel et al . , 2003 ) . These reports raise the possibility that differing sensory inputs could activate different subsets of dopaminergic neurons . How can structurally similar sugars generate differential activation ? It is likely that although these sugars bind to some of the same receptors , the relative affinity of the receptors vary . In this regard , the fly sweet taste system may be similar to that of the mammalian system , where a single heteromeric receptor ( T1R2 and T1R3 ) is responsible for detecting a large number of sweet substances , with multiple discrete ligand-binding sites in each receptor responsible for generating diverse responses ( Yarmolinsky et al . , 2009 ) . We suspect that the differential engagement of multiple gustatory receptors leads similar chemicals to generate differential activation of the same neurons , and that differential activation and different ensembles of activated neurons allows higher-order neurons to decode the relevant features of sugars . We speculate that , at least in Drosophila , evaluation of a sugar’s long-term salience may be encoded in the activation pattern of subsets of gustatory neurons , which allows rapid evaluation and remembering of nutritious food in complex environments . Flies were generously shared by Dr . John Carlson ( Gr43aGAL4-9/CyO; Gr61aGAL4-9/CyO; Gr5aGAL4 , Gr64GAL4 ) , Dr . Hubert Amrein ( UAS-Gr43a; Gr43aGAL4 , with first coding exon replaced with GAL4 , serving as ΔGr43a and used in crosses for behavioral training ) , Dr . Anupama Dahanukar ( Gr61a-null mutant , Gr64a-null mutants and Gr5a-null mutants ) , Dr . Toshihiro Kitamoto ( UAS-Shibirets ) , and Dr . Paul Garrity ( UAS-dTrpA1 ) . The wild-type Canton-S flies were generously provided by Dr . Scott Waddell and Dr . Troy Zars . Other fly stocks were obtained from Bloomington Fly Stock Center ( UAS-Kir2 . 1 RRID:BDSC_6595; UAS-GCaMP3 RRID:BDSC_32235; UAS-GCaMP6m RRID:BDSC_42748; UAS-ReaChR RRID:BDSC_53749; Gr64eGAL4 RRID:BDSC_57667; Gr64fGAL4 RRID:BDSC_57669; Gr64dGAL4 RRID:BDSC_57665; ΔGr64d/e RRID:BDSC_23628; ΔGr64f RRID:BDSC_27883 ) . Sugars were obtained from the following sources: D-arabinose , Sigma , cat#A3131-25G , lot# SLBB3223V , 100M1365V and Fisher Bioreagents , cat# BP250425 , lot# 114986; L-arabinose , Sigma , cat# A3256-100G , lot# BCBB3602V , 098K0164 and USB Corporation , cat# 11406 , lot# 4131874; L-sorbose , Sigma , cat# 85541 , lot# BCBD8834V; L-fucose , Sigma , cat# F2252 , lot# SLBB1522V; L-rhamnose monohydrate , Sigma , cat# R3875 , lot# BCBD8824V; D-sorbitol , Sigma , cat# S1876 , lot# 017 K0092; sucralose , Sigma , cat# 69293 , lot# BCBF8524V; and saccharin sodium salt hydrate , Sigma , cat# S1002 , lot# BCBF4560V; arabinogalactan , Food Science of Vermont , item# 026664342010 . The two-choice tests were performed essentially as previously described ( Weiss et al . , 2011 ) : 1- to 2 -day-old male flies were collected in groups of 50 , allowed to recover for 3 days , and food-deprived for approximately 22 hr in plastic tubes ( VWR ) containing kimwipes wetted with 3 ml of water . 1% agarose ( Sigma ) was mixed into 1 M sugar solution along with red or green food dye ( 1% , McCormick ) , and 15 µl drops were pipetted into 60-well minitrays ( Thermo Scientific ) . A hole large enough to fit a funnel was melted into the lid , and the 50 flies were allowed to feed for 5 min in complete darkness , with tape covering the lid hole . At the end of 5 min , the color in their abdomens was assessed under a dissecting microscope , and flies were counted as eating a sugar if any dye was visible in their abdomens or thorax . Flies eating a mix of the two were scored half for L-arabinose , half for D-arabinose . Preference and detection indices were calculated as ( number of flies eating sugar ) / ( total number of flies ) . To rule out the color bias in the cases of choice between two sugars , half the experiments had the colors reversed . The feeding assay was carried out for 5 min , instead of a period of hours , because in the context of our particular behavioral paradigm the choices made by flies over a longer time period are not relevant . Two-choice radioactivity experiments were performed as described above , with the addition of 1 µL of 1:5 diluted cytidine 5’-triphosphate [α−32P] ( 3000 Ci/mmol 10mCi/ml , 1MCi; PerkinElmer ) into 1 . 5 ml 1 M sugar solutions without dye; again sugars were pipetted onto the 60-well microtiter plate . After the 5-min feeding , flies were immediately placed on dry ice blocks , and five flies chosen at random were placed in each scintillation vial ( Denville Scientific ) , homogenized , covered with 5 ml LSC-cocktail ( ScintiSafe , Fisher Scientific ) , and counted by scintillation counter ( LS6500; Beckman Coulter ) . Video monitoring of feeding flies was performed using webcams ( C160; Logitech ) . Four colorless drops of 1% agarose and 1 M sugar solution were placed on an empty 35 mM Petri dish ( Falcon ) , one in each quadrant; two were L-arabinose and two were D-arabinose . Video was recorded for 30 min; trials in which the flies never found the sugars were discarded from analysis . Once the fly encountered a sugar solution , the behavior for next 5 min were quantified . We also examined the preference for other sugars , including sweet versus non-sweet sugars , to ensure that the experimental conditions did not influence the flies’ choices . Antibiotic experiments were carried out by placing approximately fifty 1- to 3-day-old flies into plastic tubes with kimwipes and 3 ml of either 1 M sucrose or 1 M sucrose with 100 µg/ml kanamycin , 500 µg/ml ampicillin , and 50 µg/ml tetracycline , for 24 hr . The antibiotic concentrations were chosen based on previously published work ( Ridley et al . , 2013; Brummel et al . , 2004; Sultan and Baker , 2001 ) . Flies were subsequently transferred to tubes with either 3 ml water or 3 ml water with 100 µg/ml kanamycin , 500 µg/ml ampicillin , and 50 µg/ml tetracycline , for another 22 hr . They were then trained with 1M L-arabinose as described below . Survival curves were generated by placing fifty 3–5 day old flies in plastic tubes with kimwipes soaked in 2 . 5 ml of 1 M sugar solution . For each sugar solution tested , ten individual tubes were tracked , thus n = 10 for each solution . The number of dead flies was counted at 12 , 24 , 36 , 60 , and 72 hr . Olfactory training was carried out largely as previously described ( Krashes and Waddell , 2011 ) : 1- to 3-day-old flies were made hungry by placing groups of 50–70 flies in plastic tubes with kimwipes and tap water ( time of starvation was determined by mortality rate: approximately 20–24 hr for homozygous lines; 24–30 hr for heterozygous crosses ) . Forty-seven microliters of 4-methylcyclohexanol ( MCH; Sigma ) and 42 µl of 3-octanol ( OCT; Alfa Aesar ) were separately diluted into two bubble humidifiers ( B and F Medical ) each containing 50 ml of mineral oil ( Fisher Scientific ) ; bubble humidifiers were connected in parallel by ¼-inch clear PVC tubing ( VWR ) . 8 cm x 10 cm rectangles of filter paper ( 410 , VWR ) were soaked in water or 1 M sugar solution , and allowed to dry until the paper was damp , then rolled to fit tightly into the training tubes . Groups of 50–70 flies were moved into the t-maze , then into the water tube for 2 min while MCH odor passed through , moved back to the holding chamber in the t-maze for 30 s , then moved to the sugar tube for 2 min while OCT odor was flowing through . The next group of flies was trained reciprocally , where OCT was paired with water and MCH with sugar . Unless otherwise specified , after training flies were fed for 4 hr and restarved until testing 24 hr after training . Flies were tested by being given a choice between OCT and MCH in tubes with no filter paper; test duration was 2 min . Short-term memory was assayed 2 min after training . Memory index = [ ( number of flies in reward odor – number of flies in unrewarded odor ) / ( total number of flies ) ] . A memory index was calculated for each of the two reciprocal trials and then averaged; this average constituted an n of 1 . Sucrose was frequently used as a daily standard , thus the large numbers of sucrose trials . For experiments with two or more controls , the experimental line was trained in parallel with one of the controls , and then again trained in parallel with the other control—thus the large n for both ChaGAL80 and ReaChR experiments . The split-GAL4 vectors ( Pfeiffer et al . , 2010 ) were made using the pHD-ScarlessDsRed vector ( Gratz et al . , 2014 ) , DGRC #1364 . To construct the Gr43a-VP16 vector , the 5’ homology arm was inserted into the AarI restriction site by Gibson assembly ( GACTGAACCGTGTAGGGA . . . TCCCGCGTTCTGAATTACT ) , immediately followed by the VP16 sequence ( ATGGATAAAGCGGAATTAATTCC . . . CTGGGCGGCGGCAAGTAA ) ( addgene #26268 ) . The 3’ homology arm was inserted into the SapI site ( AGTAGTGACACTCGGA . . . GAAGACCATATACGTC ) . CRISPR oligos were designed using http://tools . flycrispr . molbio . wisc . edu/targetFinder/ ( target sequences: AGAACTGGGACCTTACAAGT and TACCTACCGCACGGGAATTT ) . To construct the Gr5a-DBD vector , the 5’ homology arm: ACTTCGTTTGGCGTTTC . . . TAGAGCTTGTACACA , followed immediately by the GAL4 DNA-binding domain sequence ( ATGCTGGAGATCCGC . . . ACAGTTGACTGTATCGTAA ) . The 3’ homology arm for the Gr5a-DBD vector: ATGATGCTTTTCTTCGC . . . TCAACGGCCGTGCTCCTCT . CRISPR target sequences for the Gr5a locus were TGATTCCACACACGGGCATT and CGCACATCCAGCACACTGT . CRISPR gRNAs were ligated into the pU6-BbsI-chiRNA ( addgene #45946 ) and pU6 . 2-BbsI-chiRNA vectors ( Gratz et al . , 2013 ) . DNA was mixed at a ratio of pHD-dsRed 500 ng/ul to U6-gRNA 100 ng/ul , and injected by BestGene , Inc . at a final concentration of 250 ng/ul . Optogenetic activation was performed using the same hardware as previously published ( Inagaki et al . , 2014 ) , except that two rows of six LEDs each were aligned parallel to the tube , 2 cm away , at 90o angles to each other . To minimize behavioral artifacts caused by strong visual stimulation , the red ( 627 nm , 161 lm @ 700 mA ) Rebel LEDs were chosen , and the stimulation protocol ( pulse width , intervals , and duration ) was controlled by Arduino board and Arduino computer language . For dTrpA1 experiments , the relevant training tube was preheated to 31°C , and during training was wrapped in a ReptiTherm Under Tank Heater ( RH-4; Zoo Med Laboratories ) ; the temperature was held constant ( at 31°C ) by an electric temperature control with probe placed in between the wrapped layers ( A419; Johnson Controls ) . The heater temperature required to maintain an internal tube temperature of 30°C was determined empirically . All statistical analyses were performed using Graphpad Prism 5 . All the data met the assumption of homogeneity of variance , therefore unpaired two-tailed t-test or one-way analysis of variance ( ANOVA ) was performed with Tukey post-hoc test between pairs of samples . ANOVA tests for significance were performed a probability value of 0 . 05 and more stringent values are listed in each figure where applicable . For all experiments , each n is considered a biological replicate; separate trials used independent samples of genetically identical flies . For two-choice experiments , a single n constitutes a population measure generated from 50 male flies . The preference index indicates the proportion of flies eating the sugar , which was determined by scoring visible color in the abdomen or thorax . For video monitoring , each n constitutes a single fly . For survival curves , each n is a population measure generated by 50 flies placed in a tube with 1M sugar . Percent survival indicates the percentage of flies alive at each timepoint . For olfactory training experiments with sugar , heat , and light: one trial consists of giving a group of approximately 50–70 flies water and 3-octanol for 2 min , waiting 30 s , then giving sugar and 4-methylcyclohexanol for 2 min . Another group is trained with water and 4-methylcyclohexanol , then sugar and 3-octanol . Memory indices are calculated for each of these two trials and averaged . This average constitutes a single n , which is approximately 100–140 flies . Based on the previous and ongoing experimental effect sizes , 8–10 of these double trials were generally judged to be adequate for memory experiments , unless effect sizes were strikingly large or variable . The more dramatic effect sizes and smaller variability of preference assays allowed a smaller number of trials , generally 4 . In all long-term memory experiments , experimental manipulations for which a negative result was plausible or expected were always trained alongside a positive control . This is the reason for conspicuously large numbers of trials with sucrose and L-arabinose compared to other sugars or manipulations . Similarly , for experimental groups needing to be compared to two or more controls , the experimental group was first trained alongside one of the control groups , and then again trained alongside the other control group ( s ) . This is the reason for large numbers of trials in , for example , the ChaGAL80 and ReaChR experiments . Tissues were dissected in PBS ( Sigma ) , and fixed in 4% paraformaldehyde ( Electron Microscopy Sciences ) in PBS-Triton . 3% ( PBST ) ( Sigma ) for 1–2 hr . They were washed in PBS-Triton: 3% five times for 15 min each time , and blocked in PBST with 10% normal goat serum ( Vector Laboratories ) for 2 hr . Rabbit anti-GFP IgG ( MBL International Corporation ) was diluted 1:1000 in the blocking solution and centrifuged at 14 , 000 r/min for 10 min at 4°C . Tissues were incubated with primary antibody overnight at 4°C , then washed again with PBST for 15 min , five times . Anti-rabbit IgG Alexa Fluor 488 ( Life Technologies , now ThermoFisher Scientific , Waltham , MA ) was diluted 1:1000 in blocking solution , and incubated with the tissues overnight . Tissues were again washed five times , and mounted in Vectashield ( Vector Laboratories ) on slides with doubled clear reinforcement labels ( Avery ) ; No . 1 ½ coverslips were used ( VWR ) . Images were acquired on a Zeiss Pascal confocal microscope with a Plan Apochromat 20 × 0 . 8 NA objective . GFP fluorescence was excited at 488 nm and emission was collected through a 505–530 nm bandpass filter . Tissues from Gr43aGAL4 x UAS-GCaMP3 or UAS-GCaMP6med flies were prepared largely as described previously ( Miyamoto et al . , 2012 ) . Two- to 7-day-old flies were used . All tissues were dissected in Ringers solution ( 5 mM HEPES , 130 mM NaCl , 5 mM KCl , 2 mM CaCl2 , 2 mM MgCl2 ) ; legs were removed from the fly , placed on a 50 mm glass-bottomed dish No 1 . 5 ( Mattek ) , and immobilized with a 1 . 5 µl drop of 2-hydroxyethylagarose ( Sigma ) . After the agarose firmed , 20 µl of Ringers was added to cover the leg . In D- vs L-arabinose comparisons , both front legs of the fly were used as matched controls . Brains adhered to the dish without need for agarose when placed into a 30 µL bubble of Ringers . Proboscis imaging was performed with the proboscis upside down on the plate , so that the dorsal proboscis was contacting the dish; the proboscis was immobilized with 1 . 5 µl of agarose and again covered with 30 µl of Ringers solution . Only one sugar was tested per tissue sample . Images were collected at least 40 s before sugar was added; sugar was added at 2x concentration , in the same volume as the Ringers covering the tissues . Because the training paradigm uses high concentrations ( up to 1 M ) of sugar , we used 500 mM sugar concentrations for the leg and proboscis imaging . However , for the brain , 500 mM appeared to cause osmolarity-induced shrinking , so brain imaging used 100 mM sugars . Leg imaging was performed at approximately one stack per 5–7 s; proboscis imaging at approximately one stack per 13 s; brain imaging at approximately one per 14 s . Only tissues that showed a response were used in analysis , although tissues that didn’t respond were checked for viability by adding fructose as a positive control . Images were acquired on a Zeiss Pascal confocal microscope with a Plan Apochromat 20 × 0 . 8 NA objective . GFP fluorescence was excited at 488 nm and emission was collected through a 505–530 nm bandpass filter . For calcium imaging of the leg with ΔGr43a-GAL4 x UAS-GCaMP3 and ΔGr43a-GAL4; UAS-GCaMP6med flies , images were acquired on an Ultraview Vox ( PerkinElmer ) with a Plan Apochromat 20 × 0 . 8 NA objective at approximately one stack per 10 s; GFP fluorescence was excited at 488 nm and collected through a 525–550 nm bandpass filter . Analysis was performed in ImageJ ( NIH ) using in-house plugins: z-stack images were sum-projected and camera background was subtracted by selecting a region of interest away from the tissue . Where needed , the StackReg registration plugin was used to minimize movement artifacts ( Thévenaz al . , 1998 ) . Measurements were always taken by encircling cell bodies . In tissues with more than one neuron visible , the response of each neuron was analyzed separately and then averaged to generate an average response for that single tissue; this average constituted a single n and was used with others to generate average response curves and peak ΔF/Fo . Peak ΔF/Fo measurements were made by taking the first peak value , and dividing by the average of five timepoints immediately preceding the rise . To generate normalized fluorescence curves , individual tissue averages were aligned by the first timepoint of the rise . Curves for leg and proboscis were linearly resampled at 3 s; brain at 5 s . Curves were then min/max normalized , and average trajectories were calculated . Error bars were calculated as standard error in the mean . Average curves were plotted in GraphPad Prism 5 . One-day-old Canton S adult flies ( males and females ) were transferred to fresh standard food medium for 1 day and then starved ( with free access to water ) for 18–22 hr . These flies were then transferred by groups of 20 into plastic boxes ( Sellier et al . , 2011 ) . Each box had a row of five capillary tubes ( 5 µl minicaps , Hirschmann LaborGeräte , Germany ) , filled with a dilution of sugar mixed with a red dye ( erythrosine 0 . 374 mg/ml; Sigma France ) . The concentrations of sugar ( L- and D-arabinose , Sigma , France ) were: 1 M , 100 mM , 10 mM , 1 mM and 0 mM . Each box was monitored with a webcam ( HD Pro C920 or QuickCam Pro 9000 , Logitech ) . The boxes and cameras were housed in a climatic chamber maintaining a temperature of 25°C and 80% H . R . ( DR-36 VL , CLF Plant Climatics GmbH , Germany ) . For each box , images were acquired at a rate of one image/ min for 2 hr using the software VisionGS , Germany . The stack of images was then transferred to ImageJ ( Abramoff et al . , 2004 ) and the liquid level of each capillary was analyzed using a Java plugin , and subsequently transferred to Excel . Results are expressed as the mean of the change of the liquid level in each capillary ( D-arabinose: n = 12; L-arabinose: n = 10 boxes ) . Error bars are computed as the standard error to the mean ( s . e . m . ) . Tip-recording was performed as previously described ( French et al . , 2015 ) . Briefly , adult flies ( 3- to 4-day old ) were anesthetized on ice and immobilized on a putty platform ( UHU stick ) , using thin stripes of tape . They were then disposed under a stereomicroscope ( MZ12 , Leica ) and specific sensilla from the proboscis or from the legs were stimulated and recorded , using a TasteProbe amplifier ( DT-02 , Syntech , Germany; Marion-Poll and Pers , 1996 ) connected to a general purpose amplifier ( CyberAmp 320 , Data Translation , USA ) which further amplified ( x100 ) and filtered the signal ( 10 Hz-2800 Hz ) . The stimulus electrode contained tricholine citrate ( TCC 30 mM ) , in order to allow an electric contact to be established with the sensillum and to inhibit firing activity arising from water-sensitive cells ( Wieczorek and Wolff , 1989 ) . A reference electrode was connected to the abdomen of the fly , using a drop of electrocardiogram gel . Each stimulation lasted 2 s and was digitized at 10 kHz , 16 bits during 2 s ( DT9818 , Data Translation , USA ) . The data acquisition , spike detection and sorting was performed under a program , dbWave . The results were subsequently transferred to Excel , and expressed as the mean ( n = 8–15 measures ) . Error bars were computed as the s . e . m .	We often remember experiences that are rewarding in some way . However , not every rewarding experience is stored in memory , and the particular experiences we remember are not always those we would expect to remember . Why is it that some experiences generate long-term memories whereas others do not ? Fruit flies feed on a variety of different sugars present in rotting fruits . Although the flies find all of these sugars attractive , they form memories of some sugars more readily than others . This distinction is particularly striking in the case of two sugars with similar structures: D-arabinose and L-arabinose . Flies typically prefer D-arabinose over L-arabinose , but are more likely to remember an encounter with L-arabinose than D-arabinose . McGinnis et al . have used fruit flies to explore how the rewarding properties of an experience affect how likely it is to be stored in memory . The experiments show that D-arabinose and L-arabinose generate different patterns of activity in the fly brain , and identify a subset of taste neurons that support the formation of memories specifically about L-arabinose . These neurons enable flies to associate features of their environment – such as odors – with the presence of this one particular sugar . Such memories may help the flies to find a similar food source again in the future . Artificially activating these neurons is also sufficient to trigger the formation of a memory , even in the absence of L-arabinose itself . Taken as a whole , this work demonstrates that the immediate appeal of a reward can be separated from its ability to generate a long-term memory . The fact that activation of taste neurons can trigger memory formation explains how flies can quickly form long-term memories about desirable food sources . Looking ahead , further work will be required to understand the mechanisms that determine what animals like at any given moment , and what they remember over time .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "short", "report", "neuroscience" ]	2016	Immediate perception of a reward is distinct from the reward’s long-term salience
During low arousal states such as drowsiness and sleep , cortical neurons exhibit rhythmic slow wave activity associated with periods of neuronal silence . Slow waves are locally regulated , and local slow wave dynamics are important for memory , cognition , and behaviour . While several brainstem structures for controlling global sleep states have now been well characterized , a mechanism underlying fast and local modulation of cortical slow waves has not been identified . Here , using optogenetics and whole cortex electrophysiology , we show that local tonic activation of thalamic reticular nucleus ( TRN ) rapidly induces slow wave activity in a spatially restricted region of cortex . These slow waves resemble those seen in sleep , as cortical units undergo periods of silence phase-locked to the slow wave . Furthermore , animals exhibit behavioural changes consistent with a decrease in arousal state during TRN stimulation . We conclude that TRN can induce rapid modulation of local cortical state . Modulation of arousal is one of the central aspects of behavior , as sleep plays an essential role in cognitive function and survival . A key marker of decreased arousal is cortical slow wave activity ( 1–4 Hz ) , which occurs both during non-REM sleep ( Vyazovskiy et al . , 2009; Amzica and Steriade , 1998; Buzsaki et al . , 1988 ) and in awake animals during low vigilance states and sleep deprivation ( Vyazovskiy et al . , 2011; Huber et al . , 2000 ) . The slow wave marks rhythmic periods of suppression in cortical neurons ( OFF periods ) lasting hundreds of milliseconds ( Vyazovskiy et al . , 2009; Steriade et al . , 2001 ) . These brief offline periods are a candidate mechanism for decreased arousal , and slow waves in local cortical regions are associated with behavioral deficits on sub-second timescales ( Vyazovskiy et al . , 2009 ) . Slow waves are thus correlated with both behavioral decreases in arousal and disruption of cortical activity . However , while several brainstem structures for global control of sleep states have been well characterized ( Giber et al . , 2015; Tsunematsu et al . , 2011; Adamantidis et al . , 2007; Anaclet et al . , 2014 ) , no mechanism has been identified that generates the spatially isolated slow waves that occur during drowsiness , known as ‘local sleep’ . Slow wave activity is locally regulated both during sleep , where it plays a role in sleep-dependent memory consolidation ( Huber et al . , 2004 ) , and in the awake state , where it reflects a shift in cortical processing modes ( Wang et al . , 2010 ) . Local modulation of slow waves is therefore an important element of cortical function , but the underlying mechanism is not well understood . We sought to identify a forebrain structure that modulates local cortical slow wave activity . A central modulator of corticothalamic feedback that could initiate these dynamics is the thalamic reticular nucleus ( TRN ) , a subcortical structure that provides powerful inhibition to dorsal thalamic nuclei . The TRN is a thin sheath of GABAergic neurons that surrounds the thalamus and inhibits thalamic relay cells ( Pinault , 2004; Guillery and Harting , 2003 ) . TRN has been implicated in sensory processing ( Hartings et al . , 2003; Deleuze and Huguenard , 2006 ) , attentional gating ( McAlonan et al . , 2008; Crick , 1984; Halassa et al . , 2014; Wimmer et al . , 2015 ) , and sleep state modulation ( McCormick and Bal , 1997; Steriade , 2000 ) - and is uniquely positioned to selectively and rapidly modulate cortical state . TRN has a causal role in initiating sleep spindles ( Halassa et al . , 2011; Barthó et al . , 2014; Bazhenov et al . , 2000; Steriade et al . , 1987 ) , and molecular genetic manipulation of TRN conductances reduces EEG sleep rhythms ( Cueni et al . , 2008; Espinosa et al . , 2008 ) , indicating a role for thalamocortical feedback in cortical sleep oscillations . However , direct manipulations of thalamic activity have yielded conflicting results . Nonspecific activation of multiple thalamic nuclei ( including TRN ) increases time spent in sleep ( Kim et al . , 2012 ) , whereas selectively stimulating thalamus induces a desynchronized cortical state ( Poulet et al . , 2012 ) , suggesting a role for thalamus in controlling arousal states . On the other hand , directly disrupting thalamic activity does not induce slow waves or sleep states ( Constantinople and Bruno , 2011; Steriade et al . , 1993; David et al . , 2013 ) . These mixed findings suggest a complex involvement of thalamus in regulating behavioral arousal , which could be mediated through the TRN . In addition , many sedative and anesthetic drugs act to enhance GABAergic synaptic transmission and thus potentiate the effects of TRN activity ( Brown et al . , 2011 ) , further suggesting that it could be a component of the mechanism by which these drugs induce an unconscious state ( Franks , 2008 ) . Neuronal activity in TRN is known to correlate with arousal ( Halassa et al . , 2014; Barrionuevo et al . , 1981; Steriade et al . , 1986 ) , but it remains unclear whether these firing patterns are a consequence of low arousal or a cause . In particular , the role of TRN in generating the low-frequency oscillatory dynamics characteristic of low arousal states is not known , and the behavioral significance of such cortical dynamics has not been causally tested . Here , we optogenetically activated TRN , and found that this manipulation rapidly induces local sleep-like thalamocortical slow waves . Tonic activation of TRN in awake animals produced slow wave activity in the associated cortical region , together with phase-locked periods of silence in cortical neurons ( OFF periods ) . This manipulation also produced a progressive decrease in arousal state: awake animals exhibited less motor activity and spent more time in non-REM sleep , and anesthetized animals exhibited a decrease in cortical activity and a shift in dynamics favoring OFF periods . We find that the net effect of TRN stimulation is to decrease thalamic firing , suggesting that TRN may modulate arousal state through selective inhibition of thalamic activity , facilitating the onset of slow waves . Furthermore , TRN and other thalamic neurons are phase-locked to the induced oscillations , suggesting that TRN , thalamus , and cortex are all engaged in the rhythm . We conclude that tonic depolarization of TRN rapidly modulates cortical state and controls the animals’ arousal , by inducing suppression and rhythmic spiking in thalamus . The spatial characteristics and rapid timescale ( <50 ms ) of these effects show that local oscillatory dynamics between thalamus and cortex are a central mechanism for modulation of arousal . We first tested how tonic activation of TRN affected neural dynamics in the cortex of awake head-fixed mice . We implanted four mice with stereotrodes distributed across cortex and an optical fiber targeting the somatosensory sector of TRN ( Figure 1a ) . To examine the intrinsic cortical dynamics that emerge when no specific oscillation frequency is imposed , we used tonic rather than phasic stimulation , activating TRN using constant light for 30 s . Tonic TRN activation produced an immediate and substantial increase in low-frequency power in the local field potential ( LFP ) of ipsilateral somatosensory cortex ( Figure 1b–d ) . This power increase was specific to the delta ( 1-–4 Hz ) band , which increased by 2 . 56 dB ( 95% confidence interval [CI] = [2 . 13 2 . 97] ) during laser stimulation . In contrast , beta and gamma ( 15–50 Hz ) power decreased slightly ( Figure 1c , median = -1 . 03 dB , CI = [-1 . 24 -0 . 84] ) . The increase in delta power was rapid and robust: delta waves were already evident in the first second of TRN activation ( change = 1 . 12 dB , CI = [0 . 48 1 . 76] ) and persisted throughout the stimulation period ( Figure 1b ) . When individual slow wave events were detected automatically by thresholding filtered LFPs ( see Materials and methods ) , 0 . 3 slow waves per second were detected during TRN stimulation , significantly more than baseline ( increase = 0 . 13 events/s , CI = [0 . 10 0 . 17] ) . The amplitude of the negative-going peak was smaller during stimulation ( change = -148 μV , CI = [-244 -54] ) , whereas the amplitude of the positive-going peak was larger during stimulation ( change = 43 μV , CI = [8 . 6 76 . 7] ) , similar to the asymmetric waveforms typically seen during sleep slow waves ( Vyazovskiy et al . , 2009 ) . The precise frequency and amplitude of slow wave events depend on the detection criteria being used , but these statistics nevertheless indicate a substantial increase in slow waves during TRN stimulation . To test whether this effect was TRN-specific rather than due to long-range GABAergic projections to thalamus , we next studied VGAT-Cre mice injected with AAV-EF1a-DIO-ChR2-EYFP specifically into the TRN and replicated the increase in delta ( Figure 1—figure supplement 3–5 ) . No such effect was observed in littermate mice that were negative for ChR2 ( Figure 1—figure supplement 6 ) , indicating that the slow waves were not due to nonspecific light or heating effects . Cortical slow waves are observed locally in awake sleep-deprived animals , and this ‘local sleep’ correlates with decreased performance on cognitive tasks ( Vyazovskiy et al . , 2011 ) . Given that TRN establishes topographical connections with its cortical inputs and thalamic outputs , we hypothesized that TRN could support local slow wave generation . We manipulated the extent of TRN activation by varying laser power , stimulating at low ( <2 mW ) or high ( >2 mW ) power while simultaneously recording local field potentials across cortex in individual mice ( Figure 1a ) to investigate the spatial spread of induced slow waves . We recorded in four awake head-fixed mice with fibers targeting the somatosensory sector of TRN . This stimulation protocol is expected to stimulate local somatosensory TRN at low laser power , and stimulate broader regions of TRN at higher laser power ( Figure 1—figure supplement 7 , ( Yizhar et al . , 2011 ) ) . We found that low laser power consistently enhanced local delta power in ipsilateral S1 ( Figure 1e ) . Across all electrodes in the ipsilateral posterior quadrant ( Figure 1e , red circle ) , 9/20 recording sites ( 45% ) showed a significant increase in delta power during tonic activation ( p<0 . 05 , signed-rank test with Bonferroni correction ) . In contrast , only 2/32 recording sites ( 6% ) in other cortical regions ( e . g . contralateral or frontal ) showed a significant increase in delta power , a significantly lower proportion than in the ipsilateral posterior quadrant ( diff . = 0 . 37 , CI = [0 . 15 0 . 6] , binomial bootstrap [see Materials and methods] ) , demonstrating that slow waves were selectively induced in a local ipsilateral cortical region ( Figure 1e , g ) . Trials using high laser power ( i . e . with light spreading to larger regions of TRN ) induced slow waves across a large cortical area: 10/20 ( 50% ) of electrodes in the associated cortex and 11/32 ( 34% ) of distant electrodes showed a significant increase in delta power ( Figure 1f , h ) . The proportion of distant electrodes showing increased delta power was significantly higher than in the low laser power condition ( diff . = 0 . 26 , CI = [0 . 08 0 . 44] , binomial bootstrap [see Materials and methods] ) . These data suggest that weak tonic activation of a small population of TRN neurons produces slow waves in a local ipsilateral cortical region , and that the strength of TRN activation controls the spatial spread of cortical slow waves . Local activation of TRN thus controls an aligned region of cortex , and could support the spatially restricted slow waves that occur in local sleep . Global cortical slow waves could be caused by broad thalamic inhibition , or by traveling waves across cortex spreading from the local site . To investigate these possibilities , we analyzed the phase relationships between different cortical sites during global induction of slow wave activity . We selected all channels with a significant increase in delta power during the high laser power stimulation , filtered the LFP between 1-–4 Hz , and quantified each electrode’s phase relationship relative to the electrode closest to the optical fiber using the phase-locking value ( PLV , [Lachaux et al . , 1999] ) . At baseline , all channels in both the ipsilateral posterior quadrant channels and the more distant channels were significantly phase-locked to the reference site ( ipsilateral: mean PLV = 0 . 52 , s . d . = 0 . 13; distant: mean PLV = 0 . 43 , s . d . = 0 . 15 ) . During TRN stimulation , the PLV decreased slightly across all electrodes ( p=0 . 0065 , signed rank test ) but remained significantly larger than chance ( ipsilateral: mean PLV = 0 . 48 , s . d = 0 . 15; distant: mean PLV = 0 . 39 , s . d . = 0 . 17 , every channel significant in permutation test ) . The mean phase offset at baseline was 0 . 10 radians ( std . = 0 . 22 rad ) , and did not change significantly during TRN stimulation ( mean = 0 . 15 rad , std . = 0 . 26 rad , p=0 . 45 , signed rank test ) . The mean phase offsets across channels were clustered around zero , indicating that the induced slow wave activity was generally synchronized across cortex and phase lags were relatively short . Within individual electrodes , the phase lag was strongly correlated across the baseline and stimulation conditions ( R = 0 . 93 , CI = [0 . 82 0 . 98] , Figure 1—figure supplement 8–9 ) . These results suggest that TRN stimulation did not strongly affect cortical synchronization , but rather that the induced slow waves had similar phase relationships to the baseline dynamics across cortical regions . This result is consistent with previous findings that anesthesia-induced slow waves exhibit similar phase offsets to awake cortical dynamics ( Lewis et al . , 2012 ) . This evidence supports the idea that TRN can induce slow waves in local or global cortical regions , with different thalamocortical loops supporting oscillations with different phase offsets across cortical sites . Cortical slow waves during local sleep and NREM sleep mark an alternation of cortical spiking between activated ( ON ) and inactivated ( OFF ) states ( Vyazovskiy et al . , 2009; Steriade et al . , 1993 ) . To investigate whether the TRN-induced slow waves reproduced this pattern , we identified 31 single units ( putative single neurons ) across cortex and tested whether they were modulated by local slow waves . While TRN stimulation did not significantly change firing rates in cortical units ( Figure 2a , median = -0 . 05 Hz , CI = [-0 . 17 0 . 09] ) , units from electrodes with induced slow waves became strongly phase-locked , similar to cortical activity during NREM sleep and local sleep ( Figure 2b , c , median change = 0 . 044 bits , CI = [0 . 0012 0 . 112] , n = 13 units ) . High gamma ( 70-–100 Hz ) power , which correlates with multi-unit spiking ( Ray and Maunsell 1993 ) , also rapidly became phase-locked to slow waves during TRN activation ( Figure 2d , median = 0 . 0015 , CI = [0 . 0009 0 . 0022] ) , indicating that local neuronal activity was broadly locked to the induced slow waves . In contrast , increased phase-locking was not observed in units from electrodes with no induced slow waves ( Figure 2b , median = 0 . 0002 bits , CI = [-0 . 004 0 . 007] , n = 18 units ) . Across all units , the increase in phase-locking was correlated with the increase in LFP delta power ( R = 0 . 77 , CI = [0 . 57 0 . 88] ) . 10 . 7554/eLife . 08760 . 013Figure 2 . Cortical units undergo OFF periods that are phase-locked to the slow waves during TRN activation . ( a ) Rate effect across all cortical units , categorized by strength of delta power increase in that channel . There is no significant change in spike rate for either group . Error bars show interquartile range . ( b ) Phase-locking effects across all cortical units show that units on channels with induced slow waves become phase-locked to the slow waves during TRN stimulation . Error bars show interquartile range . Each dot is one unit ( sites with slow waves: 13 units , 4 mice; sites without slow waves: 18 units , 4 mice ) . ( c ) Phase distribution of spikes from an example cortical unit recorded on a channel with a 3 . 4 dB delta power increase during TRN activation: unit becomes phase-locked to the slow wave . ( d ) Phase distribution of normalized high gamma ( 70–100 Hz ) power shows that high gamma power becomes rapidly phase-locked to slow waves during TRN stimulation . Gamma power is normalized to have a mean of 1 at each time point , so brightness indicates the strength of phase-locking . ( e ) Phase distribution of all OFF periods shows that they occur during the trough of the slow waves . ( f ) Example trace from somatosensory cortex: optogenetic TRN stimulation rapidly induces slow waves that are associated with OFF periods in cortical activity ( gray shaded regions mark automatically detected OFF periods ) . ( g ) Mean spike rate and LFP locked to laser onset in channels with induced delta: the induced slow wave trough and phase-locked cortical inhibition are observed within 100 ms of laser onset . Stars indicate timing of significant ( α = 0 . 05 ) decrease in LFP voltage and mean spike rate; the decrease persists throughout the first 100 ms . Triggered LFP and units are averaged across cortical electrodes with a delta power increase ( n = 14 channels , 4 mice ) , shaded region is std . err . DOI: http://dx . doi . org/10 . 7554/eLife . 08760 . 013 Phase-locking analysis does not explicitly investigate OFF states , so we next automatically detected OFF states in electrodes that contained both MUA activity and TRN-induced slow waves ( Figure 2f ) . During TRN activation , cortical neurons spent 13 . 1% of the time in OFF periods , significantly more than in the awake baseline state ( 7 . 04% , p<0 . 001 in each mouse ) and significantly more than would be expected to occur randomly ( 3 . 09% , CI = [0 . 04 6 . 15] ) . In addition , these OFF periods occurred predominantly during the negative deflection of the slow wave ( p<0 . 001 in each mouse , Pearson’s chi-square test , Figure 2e ) . Their average duration was 122 ms ( quartiles = [69 146] ms ) , and their mean frequency was 0 . 998/second , similar to natural sleep ( Vyazovskiy et al . , 2009 ) . We concluded that the induced slow waves are sleeplike , marking an oscillatory pattern in which cortical neurons undergo periods of silence lasting tens or hundreds of milliseconds . To determine the timescale of the shift into sleeplike dynamics , we computed the mean LFP and spike rate locked to laser onset , across all cortical units with local slow waves . The LFP underwent a negative-going deflection for the first 100 ms of laser stimulation ( Figure 2g , top ) , and spike rates significantly decreased ( Figure 2g , bottom ) . The reduction in cortical spiking was significant within 20 ms , and the LFP effect by 35 ms . The effect of TRN activation was therefore rapid , driving a slow wave and cortical suppression in tens of milliseconds , and thereby inducing an abrupt transition into a new cortical state in which neurons undergo rhythmic OFF periods . Tonic TRN stimulation produced striking cortical effects that were locally defined , suggesting that the key circuit mechanism was through thalamus , which is the main target of TRN outputs and has corticotopic projections that could support local control of cortex . To investigate the impact of TRN stimulation on thalamic activity , we recorded from TRN and nearby thalamus ( targeting the ventral posteromedial nucleus ) . We isolated 28 single units from five mice and used spike waveforms to distinguish between putative TRN ( ‘Narrow’ ) and putative thalamocortical ( TC , ‘Wide’ ) neurons ( Figure 3a ) . The waveform distribution was bimodal ( Figure 3a ) , with ‘Narrow’ units ( peak-to-trough time under 200 μs ) , and ‘Wide’ units ( peak-to-trough time above 200 μs ) . Narrow waveforms are typically characteristic of TRN GABA-ergic fast-spiking inhibitory neurons ( Zhao et al . , 2011; Yizhar et al . , 2011 ) , so we used these units ( n = 17 ) to infer the activity of TRN , and the ‘Wide’ units to infer TC neuron activity ( Wang et al . , 2010 ) . Spike rates in nearly all putative TC units ( 9/11 , 81 . 8% ) significantly decreased during laser stimulation , whereas no units significantly increased their spike rate ( Figure 3b ) . Thalamic activity was therefore consistently suppressed by TRN activation . 10 . 7554/eLife . 08760 . 014Figure 3 . Optical stimulation strongly activates a subset of TRN neurons and induces periodic suppression of thalamic firing . ( a ) Histogram of waveform parameters from single units recorded in freely behaving mice show a bimodal distribution of peak-to-trough time across subcortical units ( n = 28 units , 5 mice ) . Units with peak-to-trough times under 200 μs were categorized as Narrow ( putative TRN ) , and units over 200 μs were categorized as Wide ( putative thalamocortical [TC] ) . ( b ) Putative thalamocortical ( Wide ) units consistently decrease their firing rates during laser stimulation . Mean firing rate in 500 ms bins , shaded region is std . err . across units . ( c–e ) Heterogeneous firing rates in TRN during stimulation: 4 units strongly increase their firing rates , whereas 10 units decrease their firing rates . The modulation in firing rate is strongly time-locked to laser onset and offset . Shaded regions are std . err . across units . ( f ) Phase-locking effects across all subcortical units show that most become phase-locked to the slow waves during TRN stimulation . Circles mark the change in phase-locking for each unit; error bars show median change with 25th and 75th quartiles . ( g ) The phase distribution of putative TRN neurons is broad , with different neurons exhibiting different preferred phases . ( h ) Peak phase-locking values of putative TC neurons show a tight distribution ( Kurtosis = 3 . 99 , n = 11 units ) , indicating that nearly all putative TC neurons show similar phase-locking to the LFP . Putative TC phase-locking is more consistent across units than putative TRN phase-locking ( in b; Kurtosis = 0 . 24 , n = 17 units , group difference = 3 . 74 , significant at α = 0 . 05 from bootstrap resampling ) . ( i ) Example spike rasters around laser onset from 3 single units . Units were not recorded simultaneously; each raster is an independent example . ( j–l ) Example ISI histograms in single units . ( m ) Example of parameter estimates from generalized linear model for one unit shows the contribution of recent ( <10 ms ) spike history increases during stimulation . Shaded regions are std . err . DOI: http://dx . doi . org/10 . 7554/eLife . 08760 . 01410 . 7554/eLife . 08760 . 015Figure 3—figure supplement 1 . Example waveforms for putative TC and TRN neurons . A representative set of waveform shapes is presented . The putative TRN units have a narrower trough than the putative TC units . DOI: http://dx . doi . org/10 . 7554/eLife . 08760 . 015 Putative TRN units exhibited heterogeneous changes in firing rates , as expected due to the local stimulation induced by limited light spread ( Figure 3c–e ) . A subset of units increased their spike rates as predicted ( 4/17 units , 23 . 5% ) , while other units had no significant change ( 17 . 7% ) , or decreased their firing rate significantly ( 58 . 8% ) . The magnitude of the increase in firing rates ( Figure 3c ) suggested that stimulation induced strong local excitation of TRN , and possibly led to downstream inhibition of neurons located farther from the optical fiber through either intra-TRN inhibition or through suppression of thalamic drive to TRN . Alternatively , the heterogeneous effects of stimulation on TRN neurons could reflect variable expression levels or heterogeneous cell types within the TRN with different functional properties ( Halassa et al . , 2014; Barrionuevo et al . , 1981; Lee et al . , 2014 ) , leading to increased spike rates in a specific subset of TRN cells due to their cell type and role in the local circuit structure . In either scenario , high firing rates in a subset of TRN neurons is sufficient to consistently inhibit thalamocortical cells . These results demonstrate that optogenetic stimulation of TRN strongly drives only a local subpopulation of TRN neurons , but nevertheless causes consistent inhibition of thalamic activity . Whether and how thalamic inhibition can generate sleep states has been debated: although thalamic activation induces wake states ( Poulet et al . , 2012 ) , lesioning thalamus does not produce sleep states ( Constantinople and Bruno , 2011 ) . Similarly , while direct inhibition of thalamus does not induce slow waves ( Lemieux et al . , 2014 ) , activating inhibitory brainstem projections to thalamus does ( Giber et al . , 2015 ) , and thalamic stimulation can entrain cortical slow waves ( David et al . , 2013 ) . We therefore hypothesized that thalamus participates in generating slow waves , and tested whether these neurons engaged in the induced rhythm . We indeed observed that subcortical units increased their phase-locking to the thalamic LFP slow waves ( Figure 3f ) . Putative TRN unit phase-locking was diverse: laser stimulation increased overall phase-locking ( Figure 3c , increase = 0 . 027 bits , CI = [0 . 005 0 . 041] ) , but the preferred phase varied substantially across units ( Figure 3g ) . In contrast , putative TC neurons consistently increased their phase-locking during stimulation ( Figure 3f , h , increase = 0 . 011 bits , CI = [0 . 001 0 . 022] ) . Stimulation of TRN thus causes thalamic neurons to oscillate in a slow wave pattern rather than undergoing a simple decrease in activity . Thalamic entrainment to slow waves can be a combination of intrinsic mechanisms ( Wang , 1994; McCormick and Pape , 1990 ) and cortical entrainment Steriade et al . , 1991 To investigate the contribution of intrinsic oscillatory mechanisms , we examined thalamic spike properties , as thalamic cells can burst at delta frequencies during hyperpolarization ( McCormick and Pape , 1990 ) . To test whether our manipulation affected bursting , we fit generalized linear models to the spike trains of each unit . We tested whether spike history 2-–4 ms prior predicted an increased likelihood of spiking during TRN stimulation as compared to baseline ( Figure 3m ) . We found a significant change in 7/11 ( 63% ) of putative TC cells , suggesting that TRN stimulation increased the likelihood of thalamic bursting ( Figure 3j , m ) . In addition , 5/10 of the TRN units that decreased firing rates during stimulation increased their 2–4 ms history dependence , whereas 0/4 of the TRN units with increased firing did . These results suggest that the laser-driven TRN units fire tonically ( Figure 3i , l ) , and lead to bursting and phase-locking in neighbouring TRN cells and in thalamus ( Figure 3i–k ) . Slow wave activity is associated with drowsiness and sleep ( Pace-Schott and Hobson , 2002 ) , and thalamic activity plays an important role in awake states ( Alkire et al . , 2000; Schiff , 2008 ) , so we next investigated whether strong TRN activation produced behavioral signs of decreased arousal , by recording electromyography ( EMG ) and frontal electroencephalography ( EEG ) in freely behaving mice . EMG power decreased significantly during TRN stimulation ( Figure 4a , mean = -0 . 06 , CI = [-0 . 08 -0 . 04] ) , indicating that stimulation caused the animals to become less active . The decrease was significant within 1 s of laser onset , demonstrating rapid modulation of behavioral state . In addition , the EEG and EMG effects were significantly negatively correlated on the single trial level ( Figure 4c , correlation coefficient = -0 . 43 , CI = [-0 . 53 -0 . 33] ) . This correlation was significantly stronger than at randomly shuffled times , ( p < 0 . 05 , bootstrap ) demonstrating that the decrease in arousal was specifically associated with the optogenetically induced slow waves . Control experiments in ChR2 negative littermates showed no EMG effect ( Figure 4—figure supplement 1 ) . To test a more general measure of arousal , we recorded videos of behaving mice and used an automatic video scoring algorithm to quantify their motion . Motion decreased significantly during TRN activation ( Figure 4a , decrease in 58 . 0% of trials , CI = [53 . 1 62 . 7] ) . These results demonstrated that TRN activation causes a rapid decrease in arousal state , evident by a decline in motor activity . 10 . 7554/eLife . 08760 . 016Figure 4 . TRN modulates arousal state in a bidirectional and state-dependent manner . ( a ) Top panel: Mean EMG power locked to laser onset shows that EMG power decreases significantly during unilateral TRN stimulation in freely behaving mice ( n = 315 trials , 8 sessions , 2 mice ) . Bottom panel: Mean smoothed motion ( 6 . 67 s moving average ) detected in video: animals’ motion decreases significantly during optogenetic stimulation ( n = 421 trials , 7 mice ) . ( b ) Mean change in arousal state during TRN activation: mice spend significantly more time in non-REM sleep and significantly less time in the awake state ( n = 560 trials , 3 mice ) . Stars indicate significant effects at α = 0 . 05 . ( c ) Individual trial correlation shows that the decrease in EMG power is correlated with the TRN-induced increase in EEG delta power ( n = 315 trials , 2 mice ) . ( d ) Delta power increases in VGAT-Cre mice expressing ChR2 during TRN stimulation , whether awake or in NREM at time of stimulation . In VGAT-Cre mice expressing halorhodopsin , TRN inhibition has no effect in awake mice , whereas it decreases the delta power that is present in sleeping mice . N = 3 mice expressing ChR2 ( 160 wake trials; 192 NREM trials ) , n = 3 mice expressing Halo ( 459 wake trials; 211 NREM trials ) , stim . duration = 5 s . All recordings were in freely behaving mice . Dots show mean power +/- std . err; stars indicate a significant effect of the laser on the median power , computed with the Wilcoxon signed-rank test . ( e ) Cortical recordings in VGAT-ChR2 mice ( n = 186 trials , 3 mice ) . During isoflurane anesthesia , the slow waves appear to be saturated and are not increased by TRN stimulation . Instead , broadband power decreases , suggesting a shift in dynamics that favours the inactivated state . DOI: http://dx . doi . org/10 . 7554/eLife . 08760 . 01610 . 7554/eLife . 08760 . 017Figure 4—figure supplement 1 . Laser-induced behavioural decreases in arousal depend on ChR2 expression . EMG power does not decrease during laser stimulation in control mice that are negative for ChR2 , confirming that the behavioral effect is not due to a nonspecific effect of light ( n = 494 trials , 3 mice ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08760 . 01710 . 7554/eLife . 08760 . 018Figure 4—figure supplement 2 . Halorhodopsin expresses in TRN . Example of histology at 10x , large-scale and zoomed-in , from a VGAT-Cre mouse with NpHR viral injections . Blue channel is DAPI and green channel is EYFP , showing selective TRN expression . DOI: http://dx . doi . org/10 . 7554/eLife . 08760 . 01810 . 7554/eLife . 08760 . 019Figure 4—figure supplement 3 . Halorhodopsin expresses in most cell bodies within the locally injected region of TRN , and not in thalamic cell bodies outside TRN . ( a ) Higher-resolution image of expression within TRN in a VGAT-Cre mouse with NpHR viral injections . The striped fluorescence pattern is due to the anatomical structure of TRN , which is a netlike , reticulated structure . Dense rings of fluorescence appear around the TRN cell bodies . Right panels: Zoomed-in images demonstrate that TRN neuronal cell bodies are encircled by bright fluorescence from membrane-bound EYFP , indicating NpHR expression . Bottom panels: Zoomed-in images in thalamus demonstrate that expression is only in projections from TRN , and is not in the thalamic cell bodies ( no ring of fluorescence surrounds the cells ) . ( ) Cell counting in dorsal and ventral TRN shows that the majority of cells in both regions were positive for EYFP expression . Error bars are 95% confidence intervals . DOI: http://dx . doi . org/10 . 7554/eLife . 08760 . 01910 . 7554/eLife . 08760 . 020Figure 4—figure supplement 4 . TRN stimulation further increases cortical neuronal phase modulation during isoflurane anesthesia . Phase-locking effects across all cortical units during isoflurane anesthesia: units become significantly more modulated by slow waves when TRN is activated . Error bars are st . dev , ( n = 15 units , 4 mice ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08760 . 02010 . 7554/eLife . 08760 . 021Figure 4—figure supplement 5 . Example of state-dependent increases in cortical phase-locking during isoflurane anesthesia . TRN activation in an awake mouse causes a cortical single unit to become phase-locked to the induced slow waves . When the mouse is under isoflurane anesthesia , the cortical unit is already phase-locked to slow waves at baseline , and TRN activation causes the phase-locking to become even sharper , with some phases associated with complete suppression of firing . DOI: http://dx . doi . org/10 . 7554/eLife . 08760 . 02110 . 7554/eLife . 08760 . 022Figure 4—figure supplement 6 . TRN stimulation deepens thalamic neuronal suppression during isoflurane anesthesia . Putative thalamic units in the awake mouse are inhibited during TRN stimulation . Putative thalamic units in the anesthetized mouse have a baseline firing rate slightly lower than the awake , TRN-stimulated mouse . TRN stimulation induces an even larger suppression of thalamic activity . DOI: http://dx . doi . org/10 . 7554/eLife . 08760 . 022 We next investigated whether the behavioral effect was due to a decrease in motion during the awake state , or whether the mice were also sleeping more during TRN activation . We performed semi-automated sleep scoring using EMG and frontal EEG recordings and found that TRN stimulation reduced awake time ( median = -2 . 1 percentage points , CI = [-4 . 2 -0 . 47] ) and increased NREM sleep ( median = 3 . 5 percentage points , CI = [1 . 68 5 . 44] ) ( Figure 4b ) . Tonic TRN activation thus shifted sleep dynamics , biasing animals towards NREM sleep . The change in behavioural state was subtle , corresponding to a decrease in motor activity and a small increase in the probability of NREM sleep , similar to the awake but drowsy behaviour reported during local sleep . Given that stimulating TRN could rapidly and locally induce cortical slow waves , we asked whether inhibiting TRN in a sleeping animal could reduce its cortical slow wave activity . We expressed halorhodopsin in TRN neurons using local viral injections ( Figure 4—figure supplement 2 ) . resulting in widespread expression within a local region of TRN ( Figure 4—figure supplement 3 ) , and recorded cortical LFPs during partial TRN inhibition . We found that TRN inhibition reduced slow waves in mice during NREM sleep ( change = -0 . 45 dB , CI = [-0 . 77 , -0 . 13] , Figure 4d ) . To ensure that this effect was not due to spontaneous awakenings , we shuffled the laser onset times and did not observe any effect ( shuffled change = 0 . 01 , CI = [-0 . 39 , 0 . 32] ) , suggesting that the decrease in slow wave activity was specifically due to TRN inhibition . We did not observe behavioural effects of TRN inhibition ( e . g . , Video 1 ) , which likely reflects that multiple powerful pathways including brainstem are acting to suppress motor activity during NREM sleep rather than TRN alone ( Lydic and Baghdoyan , 2005 ) , but could also indicate that more extensive suppression of TRN is needed to modulate behaviour than can be achieved in this preparation . We therefore concluded that TRN can bidirectionally modulate cortical slow wave activity . For direct comparison to the halorhodopsin experiments , we also tonically stimulated TRN in VGAT-Cre mice expressing ChR2 during natural NREM sleep . Tonic TRN stimulation applied during NREM sleep increased delta power by 0 . 62 dB ( CI = [0 . 27 0 . 97] , Figure 4d ) , indicating a further induction of cortical slow waves even during sleep states when slow waves are already present . In contrast , spindle ( 9–15 Hz ) power decreased significantly ( median = -0 . 80 dB , CI = [-1 . 17 -0 . 42] ) , likely due to increased number and prolongation of OFF periods . These dynamics are similar to those observed during transitions into deeper stages of sleep , as spindles subside and slow waves increase , suggesting that TRN stimulation can shift cortical dynamics into deeper stages of NREM . We next examined whether TRN can also decrease arousal in anesthetized mice; would tonic TRN activation induce slow waves when the animal is already in a state of decreased arousal and exhibits global slow waves ? We recorded EEG during isoflurane anesthesia and found that the baseline delta power was high , and there was no further increase during TRN activation ( Figure 4e ) , suggesting that the ability of TRN to generate slow waves was saturated . When individual slow wave events were detected , they also showed no change in frequency during TRN stimulation ( baseline = 0 . 30 events/s , stimulated = 0 . 29 events/s ) . Instead the EEG showed a broadband ( 0 . 5-–50 Hz ) decrease in power ( -0 . 53 dB , CI = [-0 . 69 -0 . 37] ) , demonstrating a generalized quieting of cortical activity . Furthermore , the fraction of time spent in OFF periods increased by 4 . 02 percentage points ( CI = [1 . 9 6 . 2] ) and the amplitude of the positive-going LFP slow wave peak increased by 45 μV ( CI = [16 75] ) Cortical units increased their phase-locking to slow waves ( Figure 4—figure supplement 4–5 , median = 0 . 06 bits , CI = [0 . 018 0 . 189] ) , while their firing rates decreased ( median = -0 . 09 Hz [-5 . 5%] , CI = [-0 . 22 -0 . 02] ) , suggesting that cortical activity became more strongly suppressed by the existing slow waves . Similarly , firing rates in putative thalamocortical neurons were suppressed to even lower levels by TRN stimulation during anesthesia ( Figure 4—figure supplement 6 ) . We concluded that the anesthetized cortex is shifted into an even deeper state by TRN activation: not by inducing slow waves , but rather by modulating the dynamics of a slow wave that is already present and thereby prolonging the duration of the periodic suppressions . We find that TRN can selectively induce slow waves in local cortical regions . This result reinforces recent findings suggesting that sleep contains dynamics that are differentiated across cortex rather than a globally homogeneous cortical state ( Krueger et al . , 2008 ) . Awake sleep-deprived rats also exhibit slow waves and OFF periods in local cortical areas ( Vyazovskiy et al . , 2011 ) , and these local dynamics are correlated with behavioural deficits . Our results show that localized depolarization in TRN can produce such local oscillations , and could therefore underlie the fragmented cortical slow waves observed during sleep as well as drowsy awake states . In addition , local cortical OFF states have been observed during sleep ( Nir et al . , 2011 ) and general anesthesia ( Lewis et al . , 2012 ) in human subjects , demonstrating that OFF periods frequently occur locally even when slow-wave activity is present throughout cortex . The observed asynchronous slow waves in these unconscious states could be due to a global activation of TRN , producing slow waves throughout cortex , but different cortical regions are associated with specific thalamocortical circuits that enable them to undergo separate and asynchronous oscillations . Finally , the local control that TRN exerts over cortex provides evidence for how TRN could modulate attention across sensory modalities , by suppressing arousal in specific cortical regions . This finding thus supports the theory that TRN could function to modulate attention , not only by gating thalamic transmission of sensory information to cortex ( Crick , 1984 ) , but also by modulating non-sensory-driven thalamic activity , which controls the ongoing state in local cortical regions and thereby influences the structure of functional networks in cortex . The finding that TRN can independently control limited corticothalamic circuits therefore suggests it could serve as a central circuit mechanism to regulate specific cortical regions , modulating both attention and arousal . In natural behavior , animals can rapidly transition between arousal states . We find that the slow waves induced by depolarization of TRN are initiated abruptly , suggesting it could play a role in rapid state modulation . Cortical activity is suppressed within 20 ms of laser onset , and the deflection in the LFP can be detected within 35 ms . This pattern suggests that tens of milliseconds of TRN activation are sufficient to inhibit thalamic input to cortex and produce a cortical OFF state . The dynamics at laser offset are similarly abrupt , with slow waves vanishing within a second . TRN can therefore serve as a rapid modulator of arousal state . This finding is also compatible with established neuromodulatory sleep circuits ( Pace-Schott and Hobson , 2002 ) , such as monoaminergic arousal pathways ( Saper et al . , 2005 ) , as these neuromodulators affect TRN activity as one component of arousal regulation . TRN thus engages a fast-acting circuit for arousal control , demonstrating that thalamocortical loops can rapidly control cortical arousal state . Here we used tonic and low-power activation of TRN , leading to decreased thalamic firing rates without complete suppression . This tonic paradigm induced slow waves and modulated cortical state in awake mice without affecting power in the spindle band ( 7–15 Hz ) . In sleeping mice , tonic activation further increased slow waves and decreased spindle power , similar to the dynamics that occur during transitions into deeper stages of sleep . Interestingly , strong phasic activation of TRN induces spindles during non-REM sleep but not in the awake state ( Halassa et al . , 2011; Barthó et al . , 2014 ) . Phasic and tonic modulation of TRN activity therefore produce qualitatively different sleep oscillations , suggesting that changes in the dynamics of inputs to TRN could underlie shifts between different stages of sleep . Brief activation of TRN may lead to a thalamic burst that entrains a cortical spindle , whereas prolonged activation hyperpolarizes thalamocortical cells and allows intrinsic slow waves to emerge . Interestingly , a recent study demonstrated that high-frequency vs . low-frequency corticothalamic input produces qualitatively different effects in thalamus ( Crandall et al . , 2015 ) , consistent with the idea that modulating the temporal dynamics of input to the thalamocortical circuit can lead to different arousal states . The circuit mechanism that generates slow wave activity during sleep and anesthesia remains a topic of debate ( McCormick and Bal , 1997; Crunelli and Hughes , 2010; Destexhe and Contreras , 2011 ) . Our results show that slow waves can be produced by depolarizing TRN , and suggest they may be generated through overall inhibition of thalamic input to cortex . Our manipulation activated a local population of TRN neurons that inhibit the associated region of thalamus . In the absence of this thalamic input to cortex , which drives the desynchronized cortical state ( Poulet et al . , 2012 ) , cortex and thalamus jointly enter an oscillation in which activity is periodically suppressed . Previous studies have demonstrated that cortex can maintain an awake state even when thalamus is lesioned or inactivated ( Constantinople and Bruno , 2011; Zagha et al . , 2013 ) ; our results therefore suggest that slow waves in the intact brain require the involvement of cortical , TRN , and TC neurons in a coordinated rhythm . Direct inhibition or lesioning of TC cells may disrupt the coordination of this rhythm , whereas physiological levels of inhibition from TRN may allow the emergence of intrinsic thalamic delta . Furthermore , TRN stimulation did not induce slow waves in the anesthetized animal , when thalamic T-type calcium channels are blocked ( Todorovic and Lingle , 1998 ) , suggesting that thalamus may contribute to slow wave generation . This theory is also consistent with studies showing that anesthetic infusions directly into thalamus can induce slow waves ( Zhang et al . , 2012 ) , and that manipulations of thalamus affect the frequency of slow waves ( David et al . , 2013 ) . Taken together , these findings suggest that TRN-mediated inhibition of thalamus is a robust driver of local slow wave activity , and that slow waves in the intact brain may require both cortical and TC neurons to fire in a coordinated rhythm . Hyperpolarization but not complete suppression of thalamus may be key to generating slow waves , as TRN induces partial suppression and bursting in TC neurons ( Figure 3b , j ) , as opposed to the stronger suppression achieved through direct manipulation of thalamus . However , TRN , thalamus , and cortex are independently capable of generating low-frequency rhythms ( Amzica and Steriade , 1998; Zhang et al . , 2009; Beltramo et al . , 2013; Dossi et al . , 1992 ) and our results could be consistent with any or all of these areas acting as the slow wave pacemaker . Slow waves could arise through thalamic oscillations , could be generated in cortex due to withdrawal of thalamic excitatory drive , or could be jointly driven by both structures . In each scenario , TRN may act as a local regulator that can shift the thalamocortical circuit between desynchronized and oscillatory regimes . Due to the technical challenges in recording from TRN , only a small number of previous studies have reported single unit recordings in TRN across arousal states . Interestingly , several reports have observed heterogeneous firing properties during sleep , and have suggested the possibility of multiple types of TRN neurons that play different roles in arousal state ( Halassa et al . , 2014; Barrionuevo et al . , 1981 ) . Such heterogeneity could explain the variable firing properties observed in different studies . While many TRN neurons decrease their firing rates during NREM sleep , a subset maintain or increase their firing rates ( Barrionuevo et al . , 1981; Steriade et al . , 1986 ) . In addition , most TRN neurons exhibit bursting properties during sleep , with brief periods of activity locked to slow wave rhythms ( Halassa et al . , 2014; Barrionuevo et al . , 1981; Steriade et al . , 1986; Marks and Roffwarg , 1993 ) . These observations are consistent with our results , in which we observe heterogeneous firing rates in TRN , but nearly all units exhibit phase-locking to the induced slow waves during stimulation . It may be that during natural sleep , high firing rates in TRN inhibit thalamic activity and thereby induce slow waves , but those high rates are limited to only certain phases of the slow wave ( rather than tonic continuous firing ) due to synchronized delta-range input from thalamus and cortex . Experiments using closed-loop control to stimulate at specific phases of slow wave activity could explore whether tonic or phase-locked activity in TRN is most effective at driving cortical slow waves . It may also be that a specific subtype of TRN neuron induces slow wave activity , and that the microcircuitry of TRN enables this subtype to fire more while suppressing other TRN neurons during optogenetic stimulation . Future studies could also examine the effect of stimulation across multiple regions of TRN , as there are distinct subnetworks within TRN that may play different functional roles in regulating arousal ( Halassa et al . , 2014; Lee et al . , 2014 ) . The finding that TRN activation induces slow waves and decreases arousal could contribute to a subset of the effects of GABAergic drugs used for general anesthesia , such as propofol . In human subjects , propofol induces a large increase in low-frequency ( 0 . 1–4 Hz ) power ( Murphy et al . , 2011 ) , and this slow wave induction has been suggested as a potential mechanism for unconsciousness ( Lewis et al . , 2012; Massimini et al . , 2009 ) . Propofol is a GABA-A agonist ( O'Shea et al . , 2000 ) , suggesting that it could increase low-frequency EEG power by increasing the inhibitory effects of both TRN and brainstem structures on thalamus ( Alkire et al . , 2000 ) . Decreased thalamic activity has also been implicated in disorders of consciousness ( Lutkenhoff et al . , 2013 ) , and may be a potent mechanism for inducing decreased arousal ( Schiff , 2008 ) . Modulation of thalamic activity may therefore be an important component of general anesthesia . Partial inhibition of TRN during NREM sleep caused a reduction in slow wave activity , suggesting that TRN plays a role in slow waves observed during natural sleep . However , the decrease in power was modest . This small effect size could be partially due to incomplete inhibition of TRN due to local light delivery and incomplete expression , as little is known about the structure of intra-TRN circuits , and inhibiting only a subset of TRN cells may have different effects than inhibiting all of them . However , the small effect size likely also reflects the fact that multiple arousal centers , including many brainstem nuclei , are modulated during NREM sleep ( Pace-Schott and Hobson , 2002; Saper et al . , 2010 ) , leading to broad thalamic inhibition . Suppressing TRN would thus only moderately reduce inhibitory input to the thalamus as other sources of inhibition persist , leading a reduction in slow wave activity rather than complete suppression . Similarly , stimulating TRN led to robust local cortical slow waves and a relatively small decrease in behavioural arousal , suggesting TRN activity drove local sleep and drowsiness more often than a complete transition into global sleep . Our results , in combination with previous studies , suggest that TRN acts as only one element of a redundant circuit for arousal control . Brainstem structures modulate global arousal state , whereas TRN may serve as a spatially selective circuit for fine-tuning arousal state across local cortical regions , allowing flexible modulation of slow wave activity . TRN may thus play a role in the local slow waves that subserve sleep-dependent memory consolidation , whereas brainstem would regulate the presence of sleep vs . wake states at a global scale . We conclude that TRN can selectively induce slow waves in local cortical regions . Taken together , our results demonstrate that TRN can control oscillatory dynamics in local thalamocortical circuits and suggest it could serve as a spatially selective circuit mechanism to rapidly and independently modulate cortical arousal . All experimental procedures were approved by the MIT Committee on Animal Care . ChR2 expression was achieved through use of either viral injections targeted at TRN in VGAT-Cre mice ( n = 3 mice , Figure 1—figure supplement 3 , 4 , Figure 3d ) or through expression in VGAT-ChR2 mice ( n = 11 mice , all remaining figures ) . VGAT-ChR2 mice were obtained from Prof . Guoping Feng’s laboratory and VGAT-Cre mice were obtained commercially ( Jackson Laboratory , stock number 106962 , Slc32a1 ) . For viral injections , an AAV-EF1a-DIO-ChR2-EYFP virus was injected into two sites bilaterally ( A/P 0 . 6 mm , M/L 1 mm , D 3 . 75/3 . 25 mm; A/P 1 . 58 mm , M/L 1 . 9 mm , D 3 mm ) . Halorhodopsin experiments were done through injections of AAV-EF1a-DIO-eNpHR3 . 0-EYFP into the same sites as described above , again using VGAT-Cre mice ( n = 3 mice ) . These viruses were produced by the vector core at University of North Carolina , Chapel Hill , with titers around 1012 VG/ml . A volume of 100–200 nL per injection site was used . Viral injections were immediately followed by implant of electrodes and optical fibers as described below . Mice with viral injections were implanted at least 3 weeks prior to beginning experiments to allow time for viral expression to develop . In order to deliver light to TRN , all VGAT-ChR2 mice were implanted with a 0 . 21 NA fiber of 200 micron diameter targeting left TRN ( 1 . 8 mm lateral , -0 . 8 to -1 . 7 mm posterior relative to bregma; 2 . 1 mm deep ) . VGAT-Cre mice received implants of 2 to 4 optical fibers to allow for simultaneous manipulation of two sites in TRN . Two types of electrode implant were performed: either a cortical implant with stereotrodes ( McNaughton et al . , 1983 ) distributed across different cortical sites; or a subcortical implant , with moveable stereotrodes targeted to TRN . Mice did not receive both implant types; an individual mouse in which units were recorded would receive either a cortical or subcortical implant . For the cortical implants , stereotrodes were made from pairs of 12 . 5 micron nichrome wire gold plated to ∼300 kOhm ( California Fine Wire , Grover Beach CA ) . Electrodes were attached to small sections of plastic tubing cut to defined depth offsets and inserted by hand in 11 recording sites distributed across the cortex ( Figure 1a ) at depths of 400 , 500 , 600 , or 1300 microns , as defined by the length of the electrode extending from the plastic tubing . In one mouse with the cortical implant , subcortical recordings were also acquired simultaneously by gluing a stereotrode to the optical fiber , with the stereotrode extending 200 microns beyond the optical fiber . This allowed acquisition of single thalamocortical units . To calculate laser power within the brain , the laser power was first measured outside of the brain , and then this value was scaled to account for diminished power after passing through the fiber . For surgery , mice were anesthetized with 1% isoflurane and individual holes were drilled for electrode and optical fiber insertion . Electrodes were inserted by hand and the optical fiber was placed using a stereotaxic arm . Hyperdrive bodies were designed in 3D CAD software ( SolidWorks , Concord , MA ) and stereolithographically printed in Accura 55 plastic ( American Precision Prototyping , Tulsa , OK ) . Each hyperdrive was loaded with 6–8 individual , independently movable microdrives made of a titanium screw cemented to a 21-gauge cannula . Each microdrive was loaded with 1–3 , 12 . 5 micron nichrome stereotrodes ( California Fine Wire Company , Grover Beach , CA ) , which were pinned to a custom-designed electrode interface board ( EIB ) ( Sunstone Circuits , Mulino , OR ) . Two EMG wires , two EEG wires and one ground wire ( A-M systems , Carlsborg , WA ) , were also affixed to the EIB . An optical fiber targeting TRN ( Doric Lenses , Quebec , Canada ) was glued to the EIB . TRN targeting was achieved by guiding stereotrodes and optical fiber through a linear array ( dimensions ∼1 . 1 x× 1 . 8 mm ) secured to the bottom of the hyperdrive by cyanoacrylate . For surgery , mice were anesthetized with 1% isoflurane and placed in a stereotaxic frame . For each animal , five stainless-steel screws were implanted in the skull to provide EEG contacts ( a prefrontal site and a cerebellar reference ) , ground ( cerebellar ) , and mechanical support for the hyperdrive . A craniotomy of size ∼3 × 2 mm was drilled with a center coordinate of ( M/L 2 . 5 mm , A/P -0 . 5 mm ) . The implant was attached to a custom-designed stereotaxic arm , rotated 15 degrees about the median and lowered to the craniotomy . Stereotrodes were lowered slightly at the time of implantation ( <500 microns ) and implanted into the brain . Electrophysiology was performed in a total of eight VGAT-ChR2 positive mice , six VGAT-Cre mice with viral injections , and three mice negative for ChR2 ( total = 17 mice ) . Electrophysiology data was acquired on a Neuralynx ( Neuralynx , Bozeman MT ) system with a 32 kHz sampling rate . Full sampling was used to record spikes , detected with a manually set voltage threshold . LFPs were collected with a highpass filter between 0 . 1 and 0 . 3 Hz and a lowpass between 2000 and 9000 Hz . EMG was collected with a highpass filter of 10 Hz to prevent data saturation . All electrophysiology data was exported to MATLAB ( Mathworks , Natick MA ) , and LFPs and EMGs were then lowpass filtered offline at 500 Hz and downsampled to 1000 Hz sampling rate . Spike sorting was performed with custom software ( Simpleclust , http://github . com/open-ephys/simpleclust ) , using standard waveform features to classify spikes . Spikes that could not be assigned to a well-defined cluster were labeled as multi-unit activity , and triphasic waveforms were excluded as fibers of passage . Awake recordings were carried out in either a head-fixed setup or in a clear plastic bowl . Anesthetized recordings were performed with isoflurane in 100% oxygen , in which drug levels were increased if the animal showed any signs of motion , and decreased when the EEG showed burst suppression , for an average range of 0 . 6% to 1% isoflurane . Experiments in anesthetized trials were performed only after anesthesia was induced with at least 1 . 5% isoflurane and mice had lost the righting reflex , and isoflurane was maintained at at least 0 . 5% isoflurane throughout the stimulation period . Anesthetic levels were varied manually to stay within a lightly anesthetized range , by decreasing levels if the EEG showed burst suppression and increasing levels if mice showed any sign of movement . In sessions with automated motion quantification , two video cameras were mounted at two orthogonal angles to enable automated motion capture . ChR2 expressing neurons were activated with a DPSS laser with a wavelength of 473 nm . Halorhodopsin expressing neurons were activated with a DPSS laser with a wavelength of 579 nm . In VGAT-ChR2 mice , light was delivered as 30 s stimulation periods using steady light levels ( DC stimulation ) , followed by at least 30 s ( typically 60-–90 s ) with no stimulation . Light was maintained at constant levels throughout a single 30 s period . For experiments comparing different laser strengths , the laser output was varied within a single session , but not within a single 30 s stimulation period . Simulations for the transmission of light through tissue at these different laser strengths were performed using the calculator developed by the Deisseroth lab ( http://web . stanford . edu/group/dlab/cgi-bin/graph/chart . php ) . In VGAT-Cre mice , two sites were stimulated simultaneously by using a splitter ( Doric Lenses ) to generate two matched light sources . Light levels were kept below 4 mW for all recordings except those mice with viral injections ( Figure 1—figure supplement 3–4 , Figure 4d ) , in which power was increased to between 4–5 mW to compensate for the lower expression levels . Most recording sessions in VGAT-Cre mice with viral injections used a 5 s DC stimulation period instead of 30 s as a precaution to avoid any tissue heating from the increased laser power , but the subset of sessions with 30 s stimulation periods showed similar results . Recording sessions were limited to no more than 60 stimulation trials to prevent habituation effects , although none were observed in the data , with typical sessions lasting approximately 2 hr . Animals were perfused using 4% paraformaldehyde ( PFA ) in phosphate-buffered saline ( PBS ) and brains were extracted and fixed in PFA-PBST . A vibratome was used to collect 60 micron coronal slices stored in PBS . Images show DAPI staining in blue and EYFP in green . 2x and 10x images were taken on a Zeiss Axio M2 microscope . In addition to the histology from mice in which we performed electrophysiological recordings ( Figure 1—figure supplement 1 , 2 , 5; Figure 4—figure supplement 2 ) , we also injected an additional VGAT-Cre mouse with the same halorhodopsin virus and performed histology at higher resolution . The same histological methods were used , except that slices were 65 microns thick . These images were taken on a Zeiss 750 confocal microscope ( Figure 4—figure supplement 3 ) . Cell counting was performed manually , and the proportion of cells that were positive ( i . e . , were surrounded by a fluorescent ring ) was reported with error bars indicating the 95% confidence intervals calculated theoretically from the binomial distribution . Spectra were computed with the Chronux toolbox using 19 tapers over 30 s windows . Spectrograms were computed with 5 tapers in 5 s sliding windows every 1 s . Normalized spectrograms were computed by first taking the median power across all trials , and then dividing the power in each frequency by the mean of the power at that frequency during the 30 s pre-stimulus window . Error bars across multiple sessions ( e . g . , Figure 1d ) show the standard error of the mean across all trials . Statistical testing was done by taking the sum of power within a band of interest on individual trials , and then comparing power during the 30 s stimulation against power during the 30 s immediately preceding TRN stimulation . Change in power is reported as the median effect size and 95% confidence intervals , computed by inverting the Wilcoxon signed-rank test . Automatic slow wave event detection was performed by bandpassing the LFP between 0 . 1–5 Hz using a finite impulse response filter , detecting local minima with magnitude >100 μV , and computing the difference between the negative trough and subsequent peak . The top 3% percentile of peak-to-peak amplitudes within each session were selected as slow wave events . Any peak within a 400 ms window with standard deviation > 2000 was rejected as artifact . The difference in the number of peaks was statistically tested using a Wilcoxon signed rank test to compute the median difference in slow wave event numbers in the stimulated versus baseline periods for each trial . For analyses across electrodes , the change in power was computed for each electrode and the Bonferroni correction was applied for multiple comparisons across electrodes . To compare the number of activated electrodes in local versus global electrodes , and across laser power conditions , we treated the number of significant electrodes as a binomial distribution . We assumed a uniform prior for the binomial parameter , obtaining a beta density as the posterior distribution for each proportion . We estimated the difference between two conditions by sampling 1000 times from the posterior distributions in each condition , and calculating the median and 95% confidence intervals as the 2 . 5th and 97 . 5th percentiles of the difference between each resampled datapoint ( i . e . , a Monte Carlo bootstrap for the difference between two groups ) . To compute the PLV , each channel was filtered between 1-–4 Hz and Hilbert transformed to extract the instantaneous phase . The PLV was then taken as the circular mean of the difference in phase for each electrode relative to the electrode closest to the optical fiber ( Lachaux et al . , 1999 ) . To test whether the magnitude of the PLV was greater than expected by chance , the trial labels were shuffled 500 times and the true PLV was compared to permuted PLV values from the shuffled trials , at alpha = 0 . 05 . To report the confidence interval for the angle of the PLV , the trials were resampled with a bootstrap procedure and the PLV angle across the resampled data was calculated 500 times . All trials used for behavioral analysis were collected while mice behaved freely in a clear plastic bowl . Recording sessions lasted 1–2 hr and were performed during the day . Two video cameras were mounted at two orthogonal angles to enable automated motion capture . EMG effects were calculated using the Chronux toolbox to determine power in the 10–200 Hz band in non-overlapping windows of 1 s width . Power was summed across all frequencies within the band to obtain a single measure of EMG power . Statistical testing was performed with the Wilcoxon signed-rank test , comparing EMG power within each laser trial with the EMG power in the associated pre-stimulus period . The rapid onset of the EMG effect was assessed by comparing EEG power in the second prior to laser onset with power in the second following laser onset . Overall changes in behavior were calculated by comparing the [-30 0] baseline period to the [0 30] laser-induced period . The correlation between EEG and EMG power was calculated by computing the correlation coefficient between the change in delta ( 1-–4 Hz ) power in the EEG , calculated as the difference in the [-30 0] and [0 30] periods relative to laser onset , and correlating it with the change in EMG power across those same periods . Statistical significance was tested by performing a bootstrap with 1000 iterations on the paired power data , taking the 97 . 5% percentile of these resampled values , and testing whether its absolute value was larger than the correlation computed on a randomly shuffled set of times . This provided a test at significance level α = 0 . 05 of whether the correlation between EEG and EMG effects was significantly higher during laser trials than would be expected during baseline conditions . Automated motion scoring was computed using automated custom software written in Matlab ( http://github . com/jvoigts/optical_flow_analysis ) that calculated the optical flow for each frame in the video via the Horn–Schunck method ( Horn and Schunck , 1981 ) . The points of maximal motion in each camera view were used to compute a motion vector in the horizontal plane . The motion vector was normalized by its mean to avoid artifact due to variations in lighting conditions and camera placement . The magnitude of the motion vector for each frame was then smoothed using a moving average filter ( sigma = 200 frames/6 . 67 s ) and used as a proxy for the magnitude of animal's overall motion . In order to ensure objective assessment of sleep , semi-automated sleep scoring was performed using an algorithm that first detects wake states as periods with heightened muscle tone by computing the instantaneous amplitude between 60–200 Hz of the EMG or cranial screw recording motor activity , smoothing with a Gaussian filter of 50 ms , and then using a manually entered threshold to identify wake states as those with at least 5 s of data above the threshold . Second , it computes the ratio of <4 Hz and 4–16 Hz power in the EEG , smooths with a Gaussian filter , and uses a manually entered threshold to segment non-REM and REM states . Sessions where the automated algorithm could not achieve a separation of sleep and wake states were not included in the analysis ( 1 session out of 13 total was excluded ) . The user selecting the EEG and EMG thresholds was blind to the timing of laser stimulation during the sleep scoring procedure . As with spectral effects , changes were reported as the median power change in dB , and 95% confidence intervals were computed by inverting the Wilcoxon signed-rank test . The shuffled control to test whether halorhodopsin-driven decreases in slow wave activity were larger than would be expected by chance was performed by pseudorandomly selecting an equivalent number of laser onset times during spontaneous NREM sleep and recalculating the change in slow wave power . This shuffling procedure was repeated 400 times to produce confidence intervals . Statistical comparisons of firing rates were performed using confidence intervals derived from the Wilcoxon signed-rank test to quantify the median difference between each neuron’s pre-stimulus ( [-30 0] s ) and stimulus-induced ( [0 30] s ) spike rates . To compute phase modulation , the instantaneous phase was calculated from the local LFP channel by bandpass filtering the LFP between 1–4 Hz with a finite impulse response filter , taking the Hilbert transform , and then extracting the angle . The phase distribution of individual units relative to the delta phase was quantified using the modulation index ( MI ) , adapted from phase-amplitude measurements ( Tort et al . , 2010 ) , which measures the Kullback-Liebler distance between the observed phase distribution and a uniform distribution . The MI for spikes was computed over 10 phase bins as ∑i=110 pi log2 pi + log2 10 , where pi is the proportion of spikes falling within a given phase bin . The MI for gamma power was computed over 100 phase bins using a sinusoid fit to model the amplitude of the gamma oscillation . The effect of TRN activation on unit phase modulation was assessed as in the firing rate case , with median effect sizes and 95% confidence intervals derived from a Wilcoxon signed-rank test comparing each neuron’s MI values in the [-30 0] and [0 30] periods . When comparing cortical units on channels with and without a slow wave effect , normalized delta power changes were computed by dividing delta ( 1–4 Hz ) power by total 0–50 Hz power in the [-30 0] and [0 30] periods . Electrodes with a normalized delta power increase of at least 2% during TRN activation were labeled as having a delta effect . Subcortical units were divided into two categories based on the time between the peak and trough of the waveform . Narrow ( <200 ms peak-to-trough ) units were further subdivided into three categories based on their spike rate response to the laser , computed by taking the difference of their spike rates in the [0 30] s period vs . the [-30 0] period . Phase modulation was computed relative to the local LFP for each unit . To account for sign reversals due to electrode placement or referencing and ensure consistent phase measurements across units , thalamic LFPs were flipped such that the laser-induced deflection was negative across all channels . The fraction change in phase-locking strength for each unit was calculated by subtracting the MI during the stimulated period from the MI during baseline , and then normalizing by the MI during baseline . The peak phase was selected by dividing spikes into 10 phase bins and identifying the bin containing the most spikes . The narrowness of the phase distribution was tested by computing the kurtosis separately for putative TC and putative RE units using the CircStat toolbox ( Berens , 2009 ) . The difference between the two unit types was then calculated . To test whether this difference was significant , the difference in kurtosis was bootstrapped 1000 times with random resampling of units , shuffling the unit type assignment , and then the original difference in kurtosis was compared to the 95% confidence interval derived from the 2 . 5th and 97 . 5th percentile of the resampled differences . To test the timing of the rate decrease relative to laser onset , 10 ms windows in the 2 s pre-stimulus period were used to create a distribution of baseline values , which was bootstrapped 1000 times to determine a threshold for significant change ( the 0 . 25th percentile , α = 0 . 005 ) . 10 ms windows after laser onset were then compared to this threshold to assess timing of a significant change . Triggered LFP analysis was done analogously , averaging the mean LFP value in 10 ms bins . For LFP and spike timing analyses , the alpha level was set at 0 . 005 to correct for multiple comparisons across time bins ( 10 bins of 10 ms width to span the 100 ms deflection interval ) . To detect OFF periods , we combined all multi-unit and single-unit activity on a single channel into a point process representation , and then smoothed with a Gaussian kernel with a standard deviation of 20 ms to approximate an instantaneous firing rate in the units surrounding that electrode . OFF periods were labeled as any period of at least 50 ms with a firing rate of zero . To verify that OFF periods were occurring at a greater rate than would happen by random chance , we also computed OFF periods on simulated data with the same mean firing rate as the experimental data . The simulated data was generated by taking the interspike intervals throughout the recording period , fitting a gamma distribution to these intervals , and then generating a new spike train from that gamma distribution with the same number of spikes as the original dataset . The OFF periods were then calculated with the same method for the simulated data . Statistical testing for OFF periods was performed across trials within each session: the percent of time spent in an OFF period during laser stimulation was compared to the percent of time spent in an OFF period in the 30 s preceding laser stimulation with the Wilcoxon signed-rank test . The significance of this difference within each animal is reported . The percent of time in OFF periods was compared to simulated data by running the simulation 1000 times and testing whether the experimental value was greater than the 97 . 5th percentile of the simulated value . Statistical testing for the phase distribution of OFF periods was computed by splitting the data into 10 phase bins and testing for uniform distribution of OFF periods using Pearson’s chi-square test – a significant result indicated that OFF periods were not uniformly distributed across the LFP slow wave phases , but rather appeared predominantly at specific phases . Temporal firing rate patterns were quantified using a generalized linear model , in which a unit’s spike rate over time was modeled as a point process with rate as a function of previous spike history ( Truccolo et al . , 2005 ) . The model covariates consisted of either a 1 or a 0 to indicate whether a spike was observed in any given preceding time bin . The model used 50 bins of 2 millisecond width each . GLMs were fit in Matlab and confidence intervals were calculated using ‘glmfit’ , which were then used to determine for each cell whether the parameter estimate for the [2 4] ms was significantly different during TRN stimulation as compared to baseline , with α = 0 . 05 . The proportion of cells with a significant change in model parameter estimates is reported .	We usually think of sleep as a global state: that the entire brain is either asleep or awake . However , recent evidence has suggested that smaller regions of the brain can show sleep-like activity while the rest of the brain remains awake . It is not clear why or how these sleep-like patterns of brain activity appear , and whether they are related to the drowsy behaviour that occurs when one is about to fall asleep . Lewis , Voigts et al . investigated how this process works in mice using a technique called optogenetics . This technique makes it possible to genetically engineer mice so that the activity of particular areas of the brain can be switched on or off by light . Lewis , Voigts et al . used light to stimulate different regions of the brain and tracked the resulting brain activity using tiny electrodes that are capable of detecting the activity of individual neurons . The experiments show that stimulating one part of a deep brain structure called the thalamic reticular nucleus causes just one small part of the brain to switch from being awake to producing sleep-like brain wave patterns . When a larger area is stimulated , the whole brain switches into this sleep-like pattern . Stimulation of the thalamic reticular nucleus also caused the animals to become drowsy and they were more likely to fall asleep , which suggests that sleep-like activity in small parts of the brain may contribute to drowsiness . Lewis , Voigts et al . ’s findings identify a brain switch that can influence whether an animal is awake or asleep . Importantly , they show that sleep can be independently controlled in small brain regions , and that the thalamic reticular nucleus contains a ‘map’ that allows it to induce sleep in just one region , or across the whole brain . Memories are strengthened during sleep , so the next challenge is to study whether the thalamic reticular nucleus influences memory formation . The findings also suggest that further study of this brain region may be useful for understanding how the sleep and awake states are controlled by particular neurons .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2015	Thalamic reticular nucleus induces fast and local modulation of arousal state
Influenza virus infections have a significant impact on global human health . Individuals with suppressed immunity , or suffering from chronic inflammatory conditions such as COPD , are particularly susceptible to influenza . Here we show that suppressor of cytokine signaling ( SOCS ) five has a pivotal role in restricting influenza A virus in the airway epithelium , through the regulation of epidermal growth factor receptor ( EGFR ) . Socs5-deficient mice exhibit heightened disease severity , with increased viral titres and weight loss . Socs5 levels were differentially regulated in response to distinct influenza viruses ( H1N1 , H3N2 , H5N1 and H11N9 ) and were reduced in primary epithelial cells from COPD patients , again correlating with increased susceptibility to influenza . Importantly , restoration of SOCS5 levels restricted influenza virus infection , suggesting that manipulating SOCS5 expression and/or SOCS5 targets might be a novel therapeutic approach to influenza . Influenza A virus is a single stranded RNA virus that infects the upper respiratory tract and has a major impact on global health . In most instances the effects are largely socioeconomic , with infected individuals requiring bed-rest and recovering at home . However , for the very young , pregnant women , the elderly and the infirm , severe influenza can result in hospitalisation and even death , with seasonal strains accounting for 500 , 000 deaths annually ( Hsu et al . , 2012b; Vanders et al . , 2015 ) . Those that succumb to the disease can die as a result of an unrestrained inflammatory response ( often referred to as a ‘cytokine storm’ ) , which together with cell death irretrievably damages the airways releasing fluid into the alveolar spaces ( Short et al . , 2014 ) . Individuals who suffer from pre-existing respiratory conditions such as asthma or chronic obstructive pulmonary disease ( COPD ) are at increased risk of influenza and suffer more severe clinical symptoms ( Glezen et al . , 2000; Griffin et al . , 2002 ) . The global community is constantly on the alert for novel avian influenza strains ( Hansbro et al . , 2010 ) that might acquire the ability to spill over from birds to humans and against which we have no pre-existing antigenic immunity , thus raising the very real threat of a global pandemic ( as occurred during the 1918 Spanish Flu ) . For example , the virulent H5N1 virus , when transmitted from birds to humans has a 50% mortality rate ( Fauci , 2006 ) and since 2013 , bird-to-human transmission of the H7N9 virus has resulted in over 600 confirmed cases with a mortality rate of approximately 38% , triggering an emergency response in China ( Hsu et al . , 2011; Xu et al . , 2013 ) . If such a virus evolved the capacity to transmit efficiently from human-to-human , the effects would be devastating ( Fauci , 2006 ) . It is not clear why only some individuals respond to infection with exacerbated inflammatory responses . Similarly , the aetiology surrounding sufferers of chronic respiratory disease and their susceptibility to influenza remains poorly understood ( Hsu et al . , 2012b ) . We are however , beginning to understand the innate immune sensors that initiate the immune defence to viral pathogens and the ability of the influenza virus to evade and indeed hijack the host defence to facilitate viral entry and replication ( Hsu et al . , 2015 ) . Encapsulated within the influenza viral envelope are eight RNA segments that encode 11 proteins . Viral entry occurs via attachment of the surface glycoprotein hemagglutinin ( HA ) to sialic acid residues on the surface of host respiratory epithelial cells . Endocytosis of the viral particles is accompanied by clustering of adjacent receptor tyrosine kinases into lipid rafts . The EGF receptor ( EGFR ) is one such example , and virus-dependent clustering of the receptor results in activation of EGFR kinase activity and downstream phosphoinositide-3-kinase ( PI3K ) signaling , which not only facilitates viral entry , but has been shown to suppress interferon regulatory factor ( IRF ) -induced interferon ( IFN ) production ( Eierhoff et al . , 2010; Hsu et al . , 2015; Ueki et al . , 2013 ) . Viral RNA generated during replication is detected by host pattern-recognition receptors , specifically by the retinoic-acid induced gene I ( RIG-I ) -like RNA sensors RIG-I and Melanoma Differentiation-Associated gene 5 ( MDA-5 ) in the cytoplasm and by Toll-like receptor ( TLR ) s 3 , 7 and 8 in the late endosomes ( Guillot et al . , 2005; Hsu et al . , 2012b ) . Activation of these pathways induces production of type I ( IFNα/β ) and type III ( IFNλ ) interferons and subsequently , the transcription of interferon response genes , which are critical for generating an anti-viral state ( Crotta et al . , 2013; Seth et al . , 2005 ) . The constitutive production of type I IFNs also contributes to anti-viral immunity ( Hsu et al . , 2012a ) . Ultimately , TLR and RIG-I signaling pathways converge to induce pro-inflammatory chemokines and cytokines , and recruit a wave of infiltrating immune cells . Several influenza viral proteins thwart the host response , most notably the multi-functional non-structural 1 ( NS1 ) protein , which interacts with numerous host proteins to inhibit mRNA processing and export , prevent IFN production and alter the intracellular environment ( Marc , 2014; Samji , 2009 ) . For instance , NS1 interacts with the p85 subunit of PI3K to stabilise the kinase complex and promote AKT phosphorylation , enhancing viral entry and inhibiting protective apoptosis ( Ehrhardt and Ludwig , 2009 ) . The suppressor of cytokine signaling proteins ( CIS , SOCS1-7 ) are an important family of small intracellular proteins which act to limit the duration of signaling responses . They primarily act as adaptor proteins to recruit an E3 ubiquitin ligase complex and facilitate the ubiquitination of substrates bound to the SOCS-SH2 domain or N-terminal region , marking them for proteasomal degradation ( Linossi and Nicholson , 2012 ) . While the physiological role and mechanism of action of SOCS1 and particularly SOCS3 , are well understood , much less is known about the other family members ( SOCS4-7 ) , which in addition to the central phosphotyrosine-binding SH2 domain and C-terminal SOCS box , contain an extended N-terminal region ( Feng et al . , 2012 ) . Although SOCS5 has been suggested to regulate EGF ( Kario et al . , 2005; Nicholson et al . , 2005 ) and IL-4 signaling ( Seki et al . , 2002 ) , there is a paucity of data to support a role for SOCS5 as a physiological regulator of these pathways in mammalian cells . Here we show for the first time that SOCS5 has a unique role in restraining the early phase of influenza A infection in airway epithelial cells . We highlight a novel role for SOCS5 in the regulation of PI3K signaling and demonstrate that SOCS5 primarily protects against viral infection by inhibiting EGFR activity . Given the reduced SOCS5 levels in epithelial cells from COPD patients , this provides a hitherto unknown link that may explain the increased susceptibility of COPD patients to influenza virus infection . To examine the role of SOCS5 during an infectious challenge , we infected wild-type and Socs5−/− BALB/c mice with the influenza virus strain A/Puerto Rico/8/32 ( H1N1; PR8 ) . Mice lacking Socs5 lost significantly more body weight following PR8 infection compared to wild-type BALB/c mice ( Figure 1A ) . This was evident at day 3 of infection and correlated with a significantly increased viral load in the lung ( Figure 1B ) . Interestingly , elevated levels of virus were present in Socs5−/− lungs from day 1 , prior to infiltration of immune cells and suggesting that Socs5−/− mice had a reduced innate ability to restrain early viral replication . This difference was comparable to the increased levels of virus observed in SirpA-deficient mice which lack natural killer ( NK ) cells , T and B cells and innate lymphoid cells ( ILCs ) ( Legrand et al . , 2011 ) , but not as great as that observed in Socs4−/− mice challenged with PR8 ( Figure 1—figure supplement 1 ) . 10 . 7554/eLife . 20444 . 003Figure 1 . Socs5−/− mice show increased susceptibility to influenza A virus infection . ( A ) Mice were infected i . n . with 25 pfu influenza virus H1N1 PR8 and weight loss monitored for 7 days . Mice that lost more than 20% of their initial body weight were euthanized . Mean ± S . E . M . ; n = 9 . ( B ) Comparison of viral titres in lung homogenates on days 1 , 2 , 3 and 5 post-PR8 infection . ( C , D ) Lethally irradiated Socs5+/+ Thy1 . 1 mice were reconstituted with Socs5−/− Thy1 . 2 bone marrow ( n = 8 ) or the reciprocal transplantation was performed ( n = 9 ) , following which mice were infected with H1N1 PR8 virus . Weight loss ( C ) was monitored for 7 days , at which time lungs were harvested for ( D ) viral titre estimation . p<0 . 05 , <0 . 005 , *<0 . 001; mean ± S . E . M . ( E ) Lungs from wild-type and Socs5−/− mice were harvested at day ( D ) two post-infection and analyzed for Socs5 and Socs4 mRNA expression by Q-PCR . The results were normalized to 18SrRNA levels . Mean ± S . E . M . ; N . D . =Not detected; n = 3 uninfected ( U/I ) wild-type mice , n = 6 infected wild-type mice , n = 6 infected Socs5−/− mice . ( F ) Lungs were lysed at day 1 and 2 post-infection and SOCS5 protein levels analyzed by immunoprecipitation and immunoblotting . ( G ) Lung immunohistochemistry showing SOCS5 expression ( brown staining ) in wild-type ( +/+ ) airway epithelium at day three post-infection . The following figure supplement is available for Figure 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 00310 . 7554/eLife . 20444 . 004Figure 1—figure supplement 1 . Comparison of viral titres . Wild-type ( WT ) , Socs4−/− , Socs5−/− or Sirpa−/− mice were infected i . n . with 35 pfu influenza virus H1N1 PR8 and viral titres in lung homogenates on day one post-inoculation determined by plaque assay . Mean ± S . E . M . ; n = 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 004 We had previously shown that SOCS4 restrains viral infection via the hematopoietic compartment , most likely through regulating CD8+ T cell function ( Kedzierski et al . , 2014 ) . We therefore investigated the contribution of the hematopoietic compartment to the increased susceptibility to influenza virus observed in the Socs5−/− mice . Chimeric mice were generated by bone marrow transplantation into irradiated , congenic-recipient mice , which were then challenged with PR8 virus . Transplantation of wild-type bone marrow into Socs5−/− hosts resulted in greater weight loss and elevated viral titres , when compared to transplantation of Socs5−/− bone marrow into irradiated wild-type hosts ( Figure 1C , D ) . This strongly suggested that the Socs5−/− defect occurred predominately in non-hematopoietic tissues . Socs5 mRNA was expressed in uninfected mouse lungs and was significantly upregulated at day two post-infection; by comparison , Socs4 was expressed at very low levels even during infection ( Figure 1E ) . These data were confirmed at the protein level by immunoprecipitation and immunoblotting with anti-SOCS5 antibodies , which detected a prominent band migrating at ~67 kDa in wild-type , but not Socs5−/− lungs ( Figure 1F ) . Immunohistochemistry demonstrated specific staining in wild-type lungs , which was increased during infection and was predominately localized to the airway epithelial cells lining the bronchioles ( Figure 1G ) . Pro-inflammatory cytokines and chemokines were elevated in the bronchoalveolar lavage ( BAL ) from Socs5−/− mice , day two post-infection . In particular , the cytokines interleukin ( IL ) -6 and G-CSF , and the chemokines KC , MCP-1 and MIP-1β were elevated compared to controls ( Figure 2A ) . In contrast , type I and type III IFNs were not increased in Socs5−/− lung homogenates , whilst the levels of IFNα , β and λ appeared to be modestly decreased at day one post-infection ( Figure 2B ) . 10 . 7554/eLife . 20444 . 005Figure 2 . Socs5−/− mice have an exaggerated inflammatory response in the lungs to influenza A virus infection . ( A ) Cytokine and chemokine levels were analyzed by Bioplex in bronchoalveolar lavage ( BAL ) fluid recovered from lungs at day two post-infection with 25 pfu influenza virus H1N1 PR8 . p<0 . 05 , *<0 . 005; Mean ± S . E . M . ; n = 7 for Socs5+/+ , n = 8 for Socs5−/− . ( B ) Expression of type I and type III interferon ( IFN ) in the lungs at day 1 and day two post-infection was measured by ELISA . p<0 . 05; Mean ± S . D . ; day 1: n = 5 for Socs5+/+ , n = 6 for Socs5−/−; day 2: n = 8 . ( C ) Flow cytometric analysis was performed on cells in BAL . Cells were gated on CD11b+ and subdivided into CD11c+ APC ( dendritic cells and alveolar macrophages ) , Ly6G+ neutrophils and Ly6C+ inflammatory monocytes . Gated lymphocytes were subdivided into B220+ B cells and CD4+ and CD8+ T cells . p<0 . 05 , <0 . 005 , ns = not significant; Individual and mean values; n = 7 for Socs5+/+ , n = 8 for Socs5−/− . ( D , E ) Mice were infected and lungs lysed at day two post-infection for label-free quantification of global protein expression . Volcano plot ( D ) shows the Log2 protein ratios following the quantitative pipeline analysis ( Socs5+/+ vs Socs5−/− ) . The red and yellow lines represent a 2-fold change in protein expression ( log2 ratio of 1 ) , while blue and green lines represent a 4-fold change ( log2 ratio of 2 ) ; dots are coloured accordingly and represent individual proteins . Proteins with a -log10 p-value of 1 . 3 or greater ( corresponding to a p-value of ≤0 . 05 ) were deemed differentially abundant . ( E ) Heat map displaying Log2-transformed summed peptide intensities ( non-imputed ) for proteins with significantly differential expression in ( D ) . Data from individual biological replicates are shown ( n = 4 ) . Green to red indicates increasing expression levels . ( F ) Schematic showing the relationship between increasing viral load and inflammation . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 00510 . 7554/eLife . 20444 . 006Figure 2—figure supplement 1 . Gating strategy for flow cytometric analysis . ( A ) Cells were initially gated on Forward ( FSC ) and Side Scatter ( SSC ) and divided into CD11b+ and CD11c+ ( dendritic cells and alveolar macrophages ) cells . CD11b+ cells were further subdivided into Ly6G+ neutrophils and Ly6C+ inflammatory monocytes . ( B ) Lymphocytes were gated on FSC and SSC and subdivided into B220+ B cells , CD4+ and CD8+ T cells . Values shown indicate the % of gated or positive cells . FITC: Fluorescein isothiocyanate , APC: allophycocyanin , PerCP: Peridinin Chlorophyll Protein Complex . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 00610 . 7554/eLife . 20444 . 007Figure 2—figure supplement 2 . Increased NETosis in Socs5−/− lungs during influenza virus infection . Bronchoalveolar fluid was harvested from the lungs of wild-type ( +/+ ) and Socs5−/− mice ( −/− ) at day two post-infection with 25 pfu influenza virus PR8 , and assayed for the presence of DNA . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 007 There was also an increase in the total number of cells infiltrating into the airways ( Figure 2C ) . This was accounted for by an increase in neutrophils and is consistent with the elevated cytokine/chemokine levels , in particular the known roles of IL−6 and G-CSF in neutrophil activation and survival , and of KC and MCP-1 in neutrophil recruitment ( Soehnlein and Lindbom , 2010 ) . There were no differences observed in infiltrating monocytic cells , T or B cells ( Figure 2C and Figure 2—figure supplement 1 ) . At day two post-infection , these changes were apparent at a global level in Socs5−/− lungs , with quantitative proteomic analysis showing increased expression of neutrophil proteins and neutrophil chemotactic proteins , in addition to detection of viral NS1 , HA and NP proteins ( Figure 2D , E and Table 1 ) . A total of 1907 unique mouse proteins were identified , with 23 differentially regulated in Socs5−/− lungs . Interestingly , a number of histones were also upregulated in Socs5−/− lungs . Together with increased Hmgb2 and various neutrophil effector proteins , this signature is strongly reminiscent of neutrophil extracellular traps ( NETs ) ( Khandpur et al . , 2013; Urban et al . , 2009 ) , a mechanism whereby dying neutrophils extrude DNA ‘nets’ coated with nuclear and granular proteins , to trap and kill the invading microorganisms ( Rohrbach et al . , 2012 ) ( Figure 2D , E and Table 1 ) . This was further supported by the increased amount of extracellular DNA found in Socs5−/− lungs ( Figure 2—figure supplement 2 ) and by the detection of citrullinated modifications on the differentially expressed proteins ( lactotransferrin , neutrophil elastase ) ( Table 1 ) ( Wang et al . , 2009 ) . 10 . 7554/eLife . 20444 . 008Table 1 . Quantitative proteomic analysis showing differentially expressed proteins in lung lysates at day two post-infection . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 008Accession numberProtein namesGene nameLog2 protein ratio ( −/− ) : ( +/+ ) Protein P-Value ( −/− ) : ( +/+ ) #unique peptides ( −/− ) #unique peptides ( +/+ ) Q3UP42Protein S100-A9S100a92 . 902 . 94E-0222NET-associated proteinP62806Histone H4Hist1h4a2 . 537 . 82E-0343NET-associated proteinQ3UP87Neutrophil elastaseElane2 . 432 . 66E-0221NET-associated proteinQ53 × 15Protein S100-A8S100a82 . 303 . 38E-0433NET-associated proteinQ91XL1Leucine-rich alpha-2-glycoproteinLrg12 . 071 . 14E-0230Neutrophil differentiationQ61646HaptoglobinHp2 . 051 . 03E-04139Produced by neutrophilsQ542I8Integrin beta 2 ( Mac-1 ) Itgb22 . 007 . 85E-0361Induces Net formationQ7TMS4MyeloperoxidaseMpo1 . 974 . 18E-06142NET-associated proteinP51437Cathelin-related antimicrobial peptideCamp1 . 901 . 10E-0231Highly expressed in neutrophilsQ4FJR3LactotransferrinLtf1 . 874 . 43E-112613NET-associated proteinO08692Neutrophilic granule proteinNgp1 . 761 . 10E-0264In neutrophil granulesQ99K94Signal transducer and activator of transcription 1Stat11 . 685 . 80E-05114Low in neutrophils – could be upregulated in response to IFN or EGFR ? P20152VimentinVim1 . 671 . 99E-05143NET-associated proteinQ3UV87Chitinase-3-like protein 3 ( YM1 ) Chi3l31 . 651 . 53E-0696Produced by neutrophilsA0JLV3Histone H2BHist1h2bj1 . 329 . 49E-0433NET-associated proteinG3 × 9V0Proteasome activator complex subunit 2Psme21 . 631 . 36E-0275Q5RKN9F-actin-capping protein subunit alpha-1Capza11 . 651 . 48E-0242Neutrophil protein which inhibits actin polymerisationQ8VCC115-hydroxyprostaglandin dehydrogenase [NAD ( + ) ]Hpgd1 . 631 . 87E-0331Not in neutrophils ? Q3UAZ7High mobility group protein B2Hmgb21 . 623 . 03E-0442Hmgb1: component of neutrophil NET filaments/induces NET formationQ8BH61Erythrocyte membrane protein band 4 . 2Epb4 . 21 . 517 . 57E-0351Neutrophil proteinF8WIX8Histone H2AHist1h2al1 . 441 . 73E-0222NET-associated proteinQ6PEN2Protein Gbp6Gbp61 . 411 . 36E-0331Not in neutrophils ? Q9Z2I8-2Succinyl-CoA ligase subunit betaSuclg2−1 . 364 . 19E-0313 indicates the detection of citrullinated peptides . Note: Given that trypsin cleaves at arginine and lysine residues and citrullination is a modification of arginine , it is likely that citrullination impacts on the efficiency of the tryptic digest . Hence , we may be underestimating the number of citrillinated proteins present . Collectively , these data suggest that the heightened susceptibility to influenza observed in Socs5−/− mice results from a defect in the lung epithelium , with the increased viral titre in the Socs5−/− lungs initiating an inflammatory cascade that is driven primarily by neutrophil infiltration ( Figure 2F ) . To determine whether the enhanced susceptibility to infection was intrinsic to the pulmonary epithelial cells , lungs were harvested from wild-type and Socs5−/− mice and purified mAECs cultured for 7 days prior to infection with influenza virus PR8 strain ( Figure 3A ) . At low levels of virus ( MOI 1 . 25 and 2 . 5 ) , there was a higher percentage of infected Socs5−/− mAECs in comparison to wild-type cells ( Figure 3B ) , suggesting that Socs5−/− mAECs may be more permissive for viral infection . However , this difference was lost once the initial inoculum was increased ( MOI 5 and 10; >74% infected cells ) . When a low MOI was used in the presence of trypsin ( to ensure multiple rounds of replication and release from the cells ) , Socs5−/− mAECs showed elevated viral titres 24 and 48 h post-infection , confirming the reduced capacity of Socs5−/− mAECs to restrain viral replication ( Figure 3C ) . Consistent with our in vivo results , there was increased cytokine and chemokine production , with elevated IL-6 , G-CSF , MCP-1 , MIP-1α and RANTES at 24 h post-infection ( MOI 5 ) . Of these , G-CSF and MCP-1 showed the greatest differential expression ( Figure 3D ) . 10 . 7554/eLife . 20444 . 009Figure 3 . Socs5−/− airway epithelial cells have increased intrinsic susceptibility to influenza virus infection . ( A ) Primary mouse airway epithelial cells ( mAEC ) from wild-type and Socs5−/− mice were purified and cultured in vitro for 7 days prior to infection with influenza virus H1N1 PR8 . ( B ) 8 h post-infection cell monolayers were fixed and stained by immunofluorescence for detection of viral nuclear protein . Results are shown as the mean % of infected cells from four technical replicates and are representative of 2 experiments . ( C ) mAEC were infected with PR8 ( MOI 1 ) and then incubated in the presence of trypsin for 2 , 8 , 24 and 48 h . Culture supernatants were analyzed for infectious virus by plaque assay . Results are shown as the fold increase over the level of virus present at 2 h post-infection . Mean ± S . E . M . are shown for three technical replicates and are representative of 2 experiments . ( D ) mAECs were infected ( MOI 5 ) and cytokine levels in culture supernatants measured by Bioplex at 24 h post-infection . Individual and mean values are shown for technical replicates derived from purified cells pooled from five mice and are representative of 2 experiments . p<0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 009 These results provide evidence for an intrinsic epithelial cell defect , which manifests in increased levels of virus and is likely to be , at least in part , responsible for the phenotype observed in Socs5−/− mice ( Figure 1 ) . To investigate which pathways might be perturbed in the absence of Socs5 , whole lung lysates were analyzed by immunoblotting for the activation of various anti- and pro-viral signaling intermediates . Given that exogenous expression of SOCS5 has been shown to downregulate EGFR levels ( Nicholson et al . , 2005 ) , we included analysis of EGFR expression . Lungs from uninfected Socs5−/− mice showed increased expression of the p110α catalytic subunit of PI3K , and apart from slightly reduced levels of phosphorylated ( p ) AKT and PI3K p85 , other signaling proteins remaining unchanged ( Figure 4A , B ) . At day one post-infection , the expression of EGFR and PI3K p85 and p110α subunits were increased in Socs5−/− lungs . Further , there was also an increase in pAKT and pSTAT3 . In contrast , the levels of pMAPK and total RIG-I were comparable in wild-type and Socs5−/− lungs ( Figure 4A , B ) . 10 . 7554/eLife . 20444 . 010Figure 4 . EGF-PI3K signaling is enhanced in Socs5−/− lungs . ( A ) Whole lungs were harvested from uninfected wild-type ( +/+ ) and Socs5−/− ( −/− ) mice or from mice at 24 h post-infection with 50 pfu influenza virus H1N1 PR8 . Following tissue lysis 100 μg protein from each mouse was analyzed by immunoblotting with antibodies to the indicated proteins . p: phosphorylated . Note that as they are analyzed on separate gels , uninfected samples cannot be compared to infected samples . ( B ) Densitometric values derived from ( A ) were analyzed as follows: Data from uninfected mice were normalized to MAPK values , whilst data from infected mice were normalized to STAT3 . p<0 . 05 , *p<0 . 005; Mean ± S . D . ; n = 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 010 To determine whether SOCS5 existed in the same protein complexes as PI3K , Flag-tagged SOCS5 was expressed in 293T cells , immunoprecipitated and mass spectrometry used to interrogate protein complex composition ( Figure 5A and Table 2 ) . SOCS box components ( Elongins B and C , Rbx2 and Cullin5 ) were specifically enriched in SOCS5 immunoprecipitates , but not from cells transfected with vector alone . Similarly , the PI3K subunits p85 ( PIK3R1 ) , p85β ( PIK3R2 ) and p110β ( PIK3CB ) were enriched in SOCS5 complexes ( Figure 5A and Table 2 ) . We confirmed these results by immunoblotting SOCS5 complexes . The SOCS5:PI3K ( p85; p110α/β ) complex was enriched in cells pre-treated with vanadyl hydroperoxide ( pervanadate ) ( to block phosphatase activity ) , indicating that SOCS5 interaction with the PI3K complex was dependent on phosphorylation of one of the components ( Figure 5B ) . Further , mutation of the SOCS5-SH2 domain ( R406K ) or deletion of the N-terminal 349 residues ( ΔNT ) reduced SOCS5 interaction with the PI3K complex , whereas mutation of the SOCS box ( L484P , C488F ) had no effect on binding . Importantly , we utilised the lung epithelial A549 cell line to confirm , by co-immunoprecipitation , that endogenous SOCS5 and PI3K co-existed in a protein complex ( Figure 5D ) . 10 . 7554/eLife . 20444 . 011Figure 5 . SOCS5 interacts with a PI3K complex . ( A–C ) 293T cells were transiently transfected with constructs expressing Flag-tagged SOCS5 , SOCS5 mutants or vector alone , lysed and SOCS5-containing complexes immunoprecipitated ( IP ) using anti-Flag antibodies . ( A ) Protein complexes were analyzed by tryptic digest and mass spectrometry . Mean ± S . D . of summed peptide intensities from three replicates . The PI3K components , and SOCS5 and SOCS box components , were specifically enriched relative to immunoprecipitates from cells transfected with vector alone . ( B , C ) SOCS5-containing protein complexes were analyzed by immunoblotting with the indicated antibodies . Lysates were probed with antibodies to Erk1/2 to show equal protein input ( lower panels ) . PV indicates pre-treatment of cells with pervanadate to inhibit phosphatase activity . mSH2: SOCS5-R406K; mSB: SOCS5-L484P , C488F; ΔNT: residues 350–536 . ( D ) A549 cells were pre-treated with the proteasomal inhibitor MG132 ( 6 h ) and pervandate ( 20 min ) prior to lysis . Proteins were immunoprecipitated with either isotype control antibodies ( Con ) or SOCS5-specific antibodies ( S5 ) and analyzed by immunoblotting with the indicated antibodies . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 01110 . 7554/eLife . 20444 . 012Table 2 . Proteomic analysis of SOCS5 immunoprecipitates . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 012Summed peptide intensity#unique peptidesAccession numberProtein namesGene namesSOCS5VectorSOCS5VectorP27986PI3K p85 βPIK3R25 . 63E + 072 . 31E + 076 . 18E + 07000627000O00459PI3K p85 αPIK3R15 . 57E + 072 . 60E + 077 . 02E + 07000726000P42338PI3K p110 βPIK3CB8 . 07E + 061 . 00E + 001 . 18E + 07000304000O54928SOCS5SOCS51 . 06E + 101 . 20E + 102 . 00E + 10000323037000Q93034Cullin-5CUL51 . 08E + 107 . 01E + 091 . 31E + 10000544651000Q9UBF6RBX2RNF77 . 23E + 084 . 36E + 081 . 10E + 09000434000Q15369ElonginBTCEB11 . 41E + 099 . 89E + 088 . 69E + 08004 . 78E + 06555001Q15370ElonginCTCEB22 . 92E + 091 . 40E + 093 . 30E + 090001078000* Data are shown from replicate samples . This suggests that the EGFR and PI3K signaling complexes might be direct targets for SOCS5 regulation and that recruitment of the PI3K complex is mediated , at least in part , via a canonical SOCS5-SH2:phosphotyrosine interaction . Notably , the difference in PI3K p110α expression in lungs was detected prior to infection , suggesting that exaggerated activation of this pathway might underlie the increased viral susceptibility in the Socs5−/− mice . In contrast , the changes in EGFR levels and AKT/STAT3 phosphorylation appeared to be influenza virus-induced effects , albeit dependent on SOCS5 . It was important to ascertain the role of SOCS5 in human disease and understand the consequences of changes in SOCS5 expression . This was particularly relevant given the previous finding that PI3K activity was elevated in AECs from COPD patients ( Hsu et al . , 2015 ) . Primary human airway epithelial cells ( hAECs ) were obtained by endobronchial brushing from healthy individuals , smokers with no evidence of lung disease ( smokers ) and ex-smokers suffering from COPD , and examined by Q-PCR for Socs5 expression following infection with various human ( H3N2 , H1N1; MOI 5 ) and avian ( H11N9; MOI 5 ) influenza strains . In cells from healthy individuals , SOCS5 was elevated in response to influenza virus infection with all three strains . However , in cells obtained from individuals with COPD , Socs5 barely increased above basal levels and was significantly lower than in hAECs from healthy individuals ( non-smokers or smokers ) ( Figure 6A ) . In contrast to infection with H3N2 , H1N1 and H11N9 , infection with the highly pathogenic avian virus H5N1 dramatically suppressed Socs5 expression relative to the media control ( Figure 6B ) . 10 . 7554/eLife . 20444 . 013Figure 6 . Socs5 expression in primary human airway epithelial cells regulates EGFR-PI3K signaling and restrains influenza virus infection . Primary human airway epithelial cells ( hAECs ) from healthy individuals , smokers with COPD and smokers without lung disease , were cultured as described and infected with H3N2 , H1N1 and H11N9 influenza virus strains ( MOI 5 ) . ( A ) 24 h post-infection Socs5 mRNA was measured by Q-PCR and is shown as the fold-change from a media only control . Mean ± S . E . M . ; n = 3 . ( B ) Data for healthy controls shown in ( A ) is plotted against the expression of Socs5 mRNA following infection with H5N1 ( MOI 0 . 005 ) . p<0 . 05 Infection vs Media , +p<0 . 05 healthy vs COPD and H5N1 vs other subtypes/strains; Mean ± S . E . M . ; n = 3 . ( C–F ) hAECs from healthy individuals and COPD patients were cultured and SOCS5 depleted using siRNA ( C , E ) . Alternatively , SOCS5 expression was increased in hAECs by transfection with SOCS5 cDNA ( SOCS5 vector ) ( D , F ) prior to infection with H1N1 ( MOI 5 ) . Cells were lysed at 24 h ( C , D ) or 2 h ( E , F ) post-infection and analyzed by immunoblotting with the indicated antibodies . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 01310 . 7554/eLife . 20444 . 014Figure 6—figure supplement 1 . Socs5 expression in human primary airway epithelial cells regulates EGFR-PI3K signalling and restrains influenza infection . ( A ) Human primary airway epithelial cells ( hAECs ) from healthy individuals ( Healthy ) , smokers with COPD and smokers without lung disease ( Smoker ) , were cultured and infected with influenza virus H1N1 ( MOI 5 ) . ( A–D ) Cells were lysed at 2 h post-infection and analyzed by Western blotting with the indicated antibodies . ( B ) Densitometric values were derived by first normalizing to GAPDH loading controls and are expressed as fold-change from healthy untreated controls ( Media ) . p<0 . 05 H1N1 vs media control , +p<0 . 05 COPD vs healthy; Mean ± S . E . M . ; n = 5 . ( C ) hAECs from healthy individuals were cultured and transfected with either siRNA to deplete Socs5 ( SOCS5-si ) , control siRNA ( scrambled ) or vehicle alone . ( D ) Alternatively , SOCS5 expression was increased in hAECs by transfection with cDNA encoding SOCS5 ( SOCS5 ) , empty vector or vehicle alone , prior to infection . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 01410 . 7554/eLife . 20444 . 015Figure 6—figure supplement 2 . Socs5 expression in human primary airway epithelial cells regulates EGFR-PI3K signalling and restrains influenza infection . hAECs from healthy individuals or smokers with COPD were cultured as described and transfected with siRNA to deplete Socs5 , or alternatively , with cDNA encoding SOCS5 to increase SOCS5 expression ( Socs5 vector ) , prior to infection with influenza virus H1N1 ( MOI 5 ) . Densitometric values were derived by first normalizing to GAPDH loading controls and are expressed as fold-change from healthy untreated controls ( Media ) . p<0 . 05 H1N1 vs media control , +p<0 . 05 Socs5 siRNA or Vector vs media control; Mean ± S . E . M . n = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 01510 . 7554/eLife . 20444 . 016Figure 6—figure supplement 3 . Socs5 expression in human primary airway epithelial cells regulates early events in influenza infection . ( A–D ) hAECs from healthy individuals were cultured as described and transfected with control siRNA or siRNA to deplete Socs5 , prior to addition of PI3K ( Wortmannin ) or EGFR ( Erlotinib ) , inhibitor . Cells were then infected with influenza virus H1N1 ( MOI 5 ) and lysed 2 h post-infection . ( A and C ) Immunoblot analysis with the indicated antibodies . ( B and D ) Densitometric values for HA levels were derived by first normalizing to GAPDH loading controls and are expressed as fold-change from uninfected , untreated controls ( H1N1 ) . p<0 . 05; Mean ± S . E . M . n = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 01610 . 7554/eLife . 20444 . 017Figure 6—figure supplement 4 . PI3K p110α is not targeted by SOCS5 for proteasomal degradation . The minimally immortalised bronchial epithelial cell line ( BCi-NS1 . 1 ) was cultured in BEBM complete media ( Lonza ) and transfected with control siRNA or siRNA to deplete Socs5 , prior to addition of the proteasomal inhibitor MG132 ( 10 μM; 4 h ) . Cells were then infected with influenza virus H1N1 ( MOI 5 ) and lysed 2 h post-infection . Immunoblot analysis with the indicated antibodies . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 01710 . 7554/eLife . 20444 . 018Figure 6—figure supplement 5 . SOCS5 does not ubiquitinate PI3K p110α in vitro . In vitro ubiquitination assays . ( A ) Recombinant PI3K p110α was incubated with GST alone or GST-SOCS5-SH2-SOCS box in complex with Elongin B/C ( GST-SOCS5-B/C ) , together with Cullin 5 , Rbx2 , ubiquitin and E2 enzyme at 37°C . Reactions were initiated by the addition of E1 enzyme and samples collected at the indicated times , prior to immunoblot analysis with anti-PI3K antibodies . The lack of higher molecular weight species in the presence of SOCS5 indicates that no SOCS5-mediated ubiquitination of PI3K p110α was observed . Exp: exposure . ( B ) Coomassie stained gel showing all components of the ubiquitination reaction . CTD: C-terminal domain , NTD: N-terminal domain , EloB: Elongin B , EloC: Elongin C . ( C ) Flag-tagged , full-length SOCS5 ( F-SOCS5 ) was enriched from 293T cells and incubated at 37°C for 1 h with the components of the E3 ligase complex in the presence or absence of recombinant PI3K p110α . Immunoblot analysis of PI3K p110α indicates no SOCS5-mediated ubiquitination of PI3K ( WT: wild-type; mSB: SOCS5-L484P , C488F , a non-functional SOCS box ) . ( D ) Immunoblot analysis with anti-Flag antibodies showing expression of SOCS5 prior to enrichment . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 018 We next sought to test the hypothesis that SOCS5 might be regulating viral infection via negative regulation of EGFR and/or PI3K signaling . As an initial step , we examined levels of SOCS5 protein , phosphorylated EGFR , PI3K p110α/p85 and phosphorylated AKT in hAECs from healthy individuals , and COPD patients ( Figure 6C–F , and Figure 6—figure supplement 1 ) . SOCS5 protein was induced in healthy cells 24 h post-H1N1 infection ( Figure 6C , D ) and was consistently reduced in cells from COPD patients , both at a basal level and with limited induction in response to virus ( Figure 6C–F and Figure 6—figure supplement 1A , B ) . SOCS5 expression inversely correlated with p110α levels and virus-induced EGFR phosphorylation , AKT phosphorylation and viral protein , which was elevated in COPD hAECs as indicated by detection of intracellular viral HA protein ( Figure 6—figure supplement 1A , B ) . To determine whether SOCS5 expression -was functionally linked to pathogenicity , hAECs were cultured from healthy individuals or COPD patients and SOCS5 either reduced using siRNA ( SOCS5-si ) or increased by transient transfection ( SOCS5 ) ( Figure 6C–F and Figure 6—figure supplements 1 and 2 ) . Depletion of SOCS5 resulted in enhanced levels of the PI3K p110α catalytic subunit in both healthy and COPD hAECs and this occurred independently of virus . Depletion of SOCS5 also resulted in increased viral-induction of EGFR phosphorylation , and the changes in EGFR and PI3K again correlated with increased viral HA levels 2 h post-infection . Phosphorylation of AKT was also enhanced in some experiments ( Figure 6—figure supplements 1C , 2 and 3 ) . Conversely , forced SOCS5 expression resulted in reduced p110α and EGFR levels and reduced EGFR phosphorylation . Consistent with reduced PI3K p110α , SOCS5 also suppressed AKT phosphorylation in both healthy and COPD hAECs ( Figure 6F and Figure 6—figure supplements 1D and 2 ) . Viral HA levels were also significantly reduced ( Figure 6F and Figure 6—figure supplements 1D and 2 ) . The changes in HA levels ( within 2 h; Figure 6E and Figure 6—figure supplements 1C , 2 and 3 ) are occurring prior to viral replication , suggesting that SOCS5 impacts on very early events in the viral life cycle . To explore this further , the experiment was repeated in the presence of PI3K ( Wortmannin ) and EGFR ( Erlotinib ) inhibitors . Both inhibitors normalized the Socs5-si difference ( Figure 6—figure supplement 3 ) , suggesting that the increase in viral HA protein at this early timepoint derives from EGFR-mediated PI3K activity . We then investigated whether increases in EGFR or PI3K signaling caused the increased susceptibility to influenza infection in the absence of Socs5 . Wild-type and Socs5−/− mice were treated with either vehicle control ( captisol ) , PI3K inhibitor ( BKM-120 ) or EGFR inhibitor ( Erlotinib ) followed by infection with H1N1 PR8 virus . Socs5−/− mice again exhibited elevated levels of virus in the lungs at 24 h post-inoculation , compared to controls . Treatment with the PI3K inhibitor significantly reduced the amount of virus in both wild-type and Socs5−/− mice , with levels in Socs5−/− mice remaining higher than that of wild-type mice . In contrast , treatment with the EGFR inhibitor Erlotinib selectively reduced levels of virus in Socs5−/− mice , to the extent that they dropped below levels in wild-type mice ( Figure 7A ) . 10 . 7554/eLife . 20444 . 019Figure 7 . SOCS5 protects against influenza virus infection by suppressing virus-induced EGFR activity . ( A ) Mice were infected i . n . with 35 pfu influenza virus H1N1 PR8 and viral titres determined in lung homogenates 24 h post-PR8 infection , by plaque assay . Mice were treated with vehicle ( captisol ) , PI3K inhibitor ( BKM-120 ) or EGFR inhibitor ( Erlotinib ) 4 h post-PR8 inoculation . ( B ) Primary mouse airway epithelial cells ( mAEC ) from wild-type and Socs5−/− mice were purified and cultured in vitro for 7 days prior to infection with influenza virus H1N1 PR8 ( MOI 1 ) and then incubated in the presence of trypsin for 24 h . PI3K ( BKM-120 ) and EGFR inhibitors ( AG1478 ) were included in the culture media during and post-infection . Culture supernatants were analyzed for infectious virus by plaque assay . ( C ) Primary human airway epithelial cells ( hAECs ) from healthy individuals were cultured as described and infected with H1N1 strain ( A/Auckland/1/2009; MOI 5 ) . PI3K inhibitor ( Wortmannin ) and EGFR inhibitor ( Erlotinib ) were added 3 h prior to virus inoculation . Culture supernatants were analyzed for infectious virus by plaque assay 24 h post-infection . p<0 . 05 , <0 . 005 . ( A–C; Mean ± S . E . M of indicated n ) . p values were determined by ( A , B ) unpaired student’s t-test or ( C ) a Mann-Whitney U test . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 019 Remarkably , the key observations were reproduced in both primary mouse and human epithelial cell cultures following infection with H1N1 virus . PI3K inhibition ( BKM-120 or Wortmannin ) reduced viral titres in primary mouse and human epithelial cells , with SOCS5-deficient cells retaining a significantly higher level of infection compared to wild-type cells ( Figures 7B and C ) . In contrast , inhibition of EGFR activity ( using either AG1478 or Erlotinib ) reduced and normalized viral titres in wild-type and Socs5−/− cells ( Figures 7B and C ) . Several key conclusions can be drawn from the inhibitor studies . Firstly , consistent with published data , PI3K signaling is important for viral fitness . Secondly , despite the validity of PI3K as a potential SOCS5 target , the increased susceptibility of Socs5−/− mice to influenza is due to altered EGFR signaling . In this context the requirement for EGFR kinase activity predominates over EGFR-mediated PI3K activity . In summary , these data encapsulate several important findings . SOCS5 expression is upregulated in response to influenza A virus infection in hAECs , suggesting that it has an important role in modulating innate host defense . SOCS5 levels are reduced in hAECs from COPD patients and a highly pathogenic avian virus ( H5N1 ) actively reduced SOCS5 expression , thus linking SOCS5 to influenza pathogenesis . Finally , we have shown in vivo and in primary human cells , that SOCS5 acts to negatively regulate EGFR and PI3K signaling and while both are important positive mediators of influenza virus infection , it is SOCS5 restriction of EGFR activity , which limits viral infection in lung epithelium . Our data position SOCS5 as a pivotal regulator of influenza , a major global disease . Mortality related to infection with highly pathogenic influenza strains is associated with the production of high levels of inflammatory cytokines in the airways ( in particular IL-6 ) : the ‘cytokine storm’ ( de Jong et al . , 2006 ) . COPD is a progressive inflammatory disease of the airways that results from smoke exposure ( cigarette smoking and biomass smoke ) and individuals suffering from COPD are at risk of severe complications following infection with influenza and other respiratory viruses . We suggest that SOCS5 acts as a central switch point , with changes in SOCS5 levels and/or activity impacting disease outcome in individuals such as those with COPD , who exhibit increased susceptibility to influenza . The Socs5−/− mice present a novel model of exacerbated human influenza , displaying increased viral load , exaggerated cytokine production in the early phase of the infection and increased neutrophil trafficking into the airways ( Figures 1 and 2 ) . Although total depletion of neutrophils results in worsened disease ( Tate et al . , 2011 ) , their role is more complex . Neutrophils are strongly associated with the inflammatory pathology observed in severe infections ( Brandes et al . , 2013; Narasaraju et al . , 2011; Perrone et al . , 2008 ) , and the process of NETosis has recently been shown to induce inflammatory responses ( McIlroy et al . , 2014 ) . Our results suggest that in Socs5−/− mice , amplification of the inflammatory cascade occurs as the neutrophils die via NETosis , releasing pro-inflammatory molecules such as HMBG1 and 2 . Influenza virus-induced EGFR , PI3K and AKT activity have each separately been shown to increase viral entry and replication ( Ehrhardt and Ludwig , 2009; Eierhoff et al . , 2010; Hsu et al . , 2015 ) . Here we link all three to the susceptibility and pathology associated with COPD patients and identify SOCS5 as a key negative regulator of these pathways . While the individual differences observed in AECs may appear modest , the defects in EGFR , PI3K and AKT need to be considered in combination and importantly , in the context of the entire lung . If these small differences occur across a large part of the whole airways in COPD , then the airways will become more susceptible to influenza , as will the patient . There is a fine balance between resistance and susceptibility to infection and these changes could alter the balance towards infection , especially when combined with other immune defects in COPD . Another example is the modest difference seen in IFN signaling in SOCS1-deficient cells ( Brysha et al . , 2001; Wormald et al . , 2006 ) . While these differences appear small , the consequences in vivo are devastating , SOCS1-deficient mice die of an IFN-driven inflammatory disease shortly after birth ( Alexander et al . , 1999 ) and similarly Socs1−/− Ifng−/− mice die rapidly when injected with IFNγ ( Brysha et al . , 2001 ) . Influenza virus binding to sialic acids on the epithelial cell surface , results in clustering of lipid rafts and crosslinking of surface receptors such as c-Met , PDGF and EGFR ( Eierhoff et al . , 2010 ) . The Class I PI3Ks consist of a heterodimer between a regulatory ( p85 ) and a catalytic ( p110 ) subunit of which there are multiple variants ( Cantley , 2002 ) . Phosphorylated EGFR recruits p85 PI3K to the receptor and activation of PI3K results in phosphorylation of phospholipids at the cell membrane , facilitating endocytosis of the virus , presumably through reorganisation of the actin cytoskeleton ( Ehrhardt et al . , 2006 ) . In addition to this important role for PI3K signaling at the early stages of infection , at later stages direct binding of the viral NS1 protein to PI3K enhances its stability and activation , reducing cell death and maintaining a replicative niche for the virus ( Hale et al . , 2006 ) . EGFR signaling can also suppress the host anti-viral response , inhibiting RIG-I-driven type I interferon production by an as yet , unknown mechanism ( Ueki et al . , 2013 ) . Although PI3K is often activated downstream of EGFR , our data also suggest that PI3K signaling might be targeted by SOCS5 independently of the EGFR . Firstly , PI3K p110α levels were elevated in the lungs of uninfected mice with no apparent corresponding increase in EGFR levels ( Figure 4A ) . Secondly , we captured a specific SOCS5/PI3K p85/p110 complex in 293T cells , in the absence of an EGFR interaction ( Figure 5A , B and Table 2 ) . Although the PI3K p110α catalytic subunit was constitutively elevated in Socs5−/− mouse lungs , expression of the p85 regulatory PI3K subunit appeared to vary with infection ( Figure 4A , B ) . A change in p110:p85 ratio in uninfected cells may prime for enhanced PI3K catalytic activity ( Ueki et al . , 2002 ) following receptor tyrosine kinase activity or direct activation by the influenza virus NS1 protein . This is further supported by the increased phosphorylation of the PI3K downstream substrate AKT in the lungs of Socs5-deficient mice during infection ( Figures 4 and 6E ) . Reduced SOCS5 expression in primary human epithelial cells also resulted in increased viral HA protein within 2 h of infection , indicating that early events in the viral life cycle such as viral entry and trafficking are enhanced . This appears to result from EGFR-driven PI3K activity , as inhibition of either kinase ablated the SOCS5-dependent effects on HA levels ( Figure 6—figure supplement 3 ) . Activation of the viral detection pathways results in the cleavage and shedding of EGFR ligands such as TGFα ( Ito et al . , 2015 ) . If this were the case , EGFR activity may be enhanced in the absence of SOCS5 . Ligand-independent EGFR clustering is thought to preferentially result in autophosphorylation of the PI3K docking site ( Tyr 992 ) , but not that of other more distal sites ( Huang et al . , 2016 ) . We speculate that SOCS5 may inhibit virus-induced receptor clustering and autophosphorylation and when SOCS5 is reduced , selective activation of the PI3K sites . Coupled with increased PI3K p110 levels , this would give a net increase in PI3K activity at the membrane , resulting in increased receptor endocytosis ( enhanced entry ) . Interestingly and somewhat surprisingly , the increase in viral titres observed in Socs5−/− mice at 24 h post-inoculation did not resolve with the inhibition of PI3K activity , despite the importance of PI3K activity in this system and the evidence that SOCS5 can independently target PI3K ( as discussed above ) . This suggests that in vivo , SOCS5 regulates PI3K-independent EGFR effects that are required for restraint of the infection . There are many consequences of EGFR signaling , not least of which is a Ras/MAPK-driven transcriptional response , which may explain the difference between the in vivo results and the short-term infections in vitro in primary human cells . The SOCS proteins are known to function as adaptors for E3 ubiquitin ligases . If SOCS5 regulated phosphorylated EGFR or PI3K p110α by ubiquitination and proteasomal degradation , pEGFR/PI3K levels should increase in the presence of a proteasomal inhibitor such as MG132 and the differences observed with reduced or increased SOCS5 expression should be equalized . However , incubation with MG132 did not change the levels of either target ( Figure 6—figure supplement 4 ) . Similarly , the differences consistently observed with altered SOCS5 levels , were retained in the presence of MG132 ( Figure 6—figure supplement 4 ) . Additionally , we were unable to demonstrate SOCS5-mediated ubiquitination of PI3K p110α in an in vitro assay using recombinant proteins ( Figure 6—figure supplement 5 ) . Collectively , this suggests that SOCS5 does not regulate either pEGFR or PI3K through proteasomal degradation . While proteasomal degradation would have been a reasonable explanation for the elevated pEGFR and PI3K p110α levels in SOCS5-deficient lungs and cells , these results suggest that SOCS5 has alternate ways to modulate protein function and that its regulation of EGFR signaling is more complex than earlier suppositions ( Nicholson et al . , 2005 ) . SOCS1 , SOCS3 and cytokine-inducible SH2-containing ( CIS ) protein can interact with and inhibit the enzymatic activity of the JAK kinases ( Delconte et al . , 2016; Linossi et al . , 2013a ) . SOCS5 belongs to the sub-group of SOCS proteins with an extended N-terminal region ( 369 residues ) . It is likely that the N-terminal region acts as a scaffold to regulate multiple proteins , in addition to its activity as an E3 ligase . Whilst we don’t fully understand the mechanism , SOCS5 may inhibit the ability of EGFR to suppress RIG-I-IFN I production ( Ueki et al . , 2013 ) , possibly through scaffolding larger complexes or targeting an unknown substrate for ubiquitination . Alternatively , SOCS5 may effect a switch in EGFR signaling the result of which is greater viral dependence on EGFR . This is apparent in the presence of the PI3K inhibitor , where even larger differences are observed between SOCS5 wild-type and depleted cells ( Figure 7 ) , and where the levels of virus in Socs5−/− cells drop below that of wild-type cells in the presence of EGFR inhibitor ( Figure 7A ) . Although overexpression of SOCS5 in mammalian cells has been shown to downregulate EGF signaling ( Nicholson et al . , 2005 ) , Socs5−/− mice have no gross EGF-related phenotype ( Brender et al . , 2004 ) . Further , we have cultured Socs5−/− embryonic fibroblasts and found no differences in EGF-induced signaling ( data not shown ) . Thus there is a unique role for SOCS5 in regulating viral activation of the EGFR in lung epithelial cells and this study confirms an important role for endogenous EGFR activity in facilitating viral infections . Identifying SOCS5 as a protective regulator of influenza infection and the subsequent hyper-inflammatory response in airway epithelium , offers the potential to target either SOCS5 itself ( enhancing SOCS5 expression or using mimetics ) or SOCS5 interacting proteins such as PI3K , to reduce the damaging inflammation that is characteristic of severe influenza and chronic respiratory diseases . Similarly , this study also raises the possibility that short-term use of EGFR inhibitors in severe influenza may be beneficial , particularly when coupled with direct delivery to the lung; facilitating rapid action , whilst reducing systemic toxicity . We have shown SOCS5 to be a critical regulator of the intracellular pathways that control influenza virus infection . Mice lacking SOCS5 have higher viral loads and exhibit a neutrophilic inflammatory response associated with increased morbidity . Importantly , restoring SOCS5 expression in bronchial epithelial cells from COPD patients reduced viral entry , suggesting that targeting SOCS5 and/or its interacting partners may be valid intervention strategies to alleviate the severe pathology associated with influenza virus infection in COPD patients , immunocompromised individuals or infection with highly pathogenic influenza strains . Future studies will attempt to understand why SOCS5 levels are reduced in epithelial cells from COPD patients . Human influenza A/Puerto Rico/8/1934 ( H1N1; PR8 ) , A/Auckland/1/2009 ( H1N1 ) A/Wellington/43/2006 ( H3N2 ) and A/Vietnam/1203/04 ( H5N1 ) strains and the avian influenza A/sharp-tailed sandpiper/Australia/6/2004 ( H11N9 ) ( Hurt et al . , 2006 ) strain were obtained from the WHO Collaborating Centre for Reference and Research on Influenza ( Parkville , Victoria , Australia ) . All work with H5N1 was performed in the Level 4 Containment Facility at the Commonwealth Scientific and Industrial Research Organization ( CSIRO ) , Australian Animal Health laboratory , Geelong , Victoria , Australia . Viral titres were determined by plaque assay on monolayers of Madin Derby canine kidney ( MDCK; RRID:CVCL_0422 ) cells ( Huprikar and Rabinowitz , 1980 ) . SOCS5-deficient mice ( Socs5−/− ) have been described previously ( Brender et al . , 2004 ) and were backcrossed for 10 generations onto a BALB/c background . Socs4−/− and Sirpa−/− Balb/c mice have been described previously ( Kedzierski et al . , 2014; Legrand et al . , 2011 ) . All mice were bred at the Walter and Eliza Hall Institute animal facility . Animal experiments followed the NHMRC Code of Practice for the Care and Use of Animals for Scientific Purposes guidelines and were approved by the Walter and Eliza Hall Institute’s Animal Ethics Committee . Mice were lightly anaesthetized by inhalation of methoxyflurane and infected intranasally ( i . n . ) with 25–50 plaque-forming units ( pfu ) of PR8 influenza virus . Kinase inhibitors or vehicle were administered 3 h prior to infection with PR8 influenza virus . The EGFR inhibitor Erlotinib ( MedKoo Biosciences ) was administered by oral gavage ( 20 mg/kg in 6% Captisol ) , the PI3K inhibitor BKM-120 ( Active Biochem ) by I . P . injection ( 50 mg/kg in 12% Captisol ) . Control mice received 12% Captisol ( Ligand Pharmaceuticals ) by I . P . injection . Weight was monitored daily as early as from day one post-infection . Mice were sacrificed at various time points and bronchoalveolar lavage ( BAL ) samples and entire lungs collected for analysis . Lungs were mechanically homogenised using a Polytron System PT 1200 ( Kinematica ) , centrifuged at 836 g for 10 min and the supernatant harvested for detection of infectious virus by plaque assay or for cytokine analysis . Alternatively , lungs were collected in TRIZOL ( Invitrogen ) and homogenised for Q-PCR analysis or snap-frozen in liquid nitrogen prior to protein analysis . Cells recovered from BAL samples were stained with antibodies to CD4-PE , B220-APC , CD8-PerCP , CD11b-FITC , CD11c-APC , Ly6C-PerCP ( BD Biosciences or BioLegend ) and analyzed by flow cytometry on a FACS Canto ( BD Biosciences ) , using FlowJo software ( Tree Star ) . Different combinations of antibodies were used as indicated in the text . Bone marrow chimeras were established as previously described ( Kedzierski et al . , 2014 ) . Briefly , irradiated recipient mice ( BALB/c Thy1 . 1 ) were reconstituted by I . V . injection with 3 × 106 T cell-depleted bone marrow cells from donor mice ( Thy1 . 2 Socs5−/−or BALB/c ) . Following injection , the mice were allowed to reconstitute for at least eight weeks prior to use , blood samples were collected ( by submandibular bleeding ) , and reconstitution assessed by FACS analysis of Thy 1 . 2/Thy 1 . 1 T cells . On average , wild-type mice showed >80% reconstitution with Socs5−/− bone marrow , while Socs5−/− mice showed >70% reconstitution with wild-type bone marrow . BALB/c and Socs5−/− control mice were inoculated with 50 pfu influenza virus PR8 and lungs harvested at day three post-infection . Uninfected lungs were collected as controls . Sections were incubated with SOCS5 polyclonal rabbit antibody ( Abcam ) , followed by biotinylated secondary antibody , and antibody binding visualized using ABC reagent ( Fuzhou Maixin Biotechnology ) as per the manufacturer’s instructions . Slides were counterstained with hematoxylin . See also Supplementary information . Cytokine levels in BAL were analyzed using the BioPlex Pro Assay ( BioRad ) . IFNα and β were detected in lung homogenates by sandwich ELISA using the following mouse monoclonal capture antibodies , IFNα ( clone F18; Thermo Scientific ) , IFNβ ( clone 7F-D3; Abcam ) , together with biotinylated detection antibodies ( PBL Interferon Source ) . IFNγ2 was detected by ELISA ( R and D Systems ) . Mouse AEC cultures were prepared as described ( Thomas et al . , 2014 ) , with minor modifications . Briefly , lungs were digested in 1 . 5 mg/mL Pronase ( Roche ) and 0 . 1 mg/ml DNase I ( Sigma-Aldrich ) for 60 min at 37°C in 5% CO2 . Single cell suspensions were incubated with purified rat anti-mouse CD45 antibody ( BD Biosciences ) and epithelial cells negatively enriched using BioMag goat anti-rat Ig-coupled magnetic beads ( Qiagen ) . Flow cytometry was used to confirm cell purity , which was approximately 95% with mouse anti-EpCAM antibodies ( BioLegend ) and cultures were on average 20% positive for podoplanin ( AEC type I ) and 70% for CD74 ( AEC type II ) . Monolayers of mAEC at approximately 80% confluency , were incubated with the influenza strain PR8 ( MOI as indicated ) in serum free media for 1 h , washed and then placed in media supplemented with 2% FCS . To examine virus infection , cells were fixed with 80% acetone at 8 h post-infection and stained with anti-nuclear viral protein antibodies , as described ( Tate et al . , 2010 ) . The percentage of infected cells was determined in a minimum of four random fields with at least 200 cells counted per sample . To examine virus replication , monolayers of mAEC were incubated with PR8 ( MOI 1 ) in serum free media for 1 h and then incubated in media supplemented with 2% FCS and 4 μg/mL trypsin . Levels of infectious virus in cell supernatants were determined by plaque assay on MDCK cells . Real-time Q-PCR was performed essentially as described ( Lee et al . , 2009 ) . Additional primers sequences are as follows: hIFNλ Forward ( F ) : ACAGCTTCAGGCCACAGCAGAGC; Reverse ( R ) : ccaggagtctccttgctctggg , hSOCS5 F: GCCACAGAAATCCCTCAAATTG; R:ggagcatgtcgagagtaggaatct , hRIG-I F: GCCCTCATTATCAGTGAGCA; R: ATCTCATCGAATCCTGCTGC . Relative expression was determined by normalizing the amount of each gene to the housekeeping gene mouse 18s rRNA using the following primer sequences: F: ACGGACCAGAGCGAAAGCAT and R: CGGCATCGTTTATGGTCGGA or to human hypoxanthine phosphoribosyltransferase ( HPRT ) . The mouse monoclonal antibody to SOCS5 was generated in-house and recognises an epitope in the SOCS5 N-terminal region . Antibodies to phospho-EGFR ( Tyr 1068 ) , EGFR , PI3K p85 , PI3K p110α , RIG-I , phospho-MAPK ( Thr202/204 ) , MAPK , phospho-AKT1 ( Ser473 ) , AKT , phospho-STAT3 and STAT3 were obtained from Cell Signaling Technology . Anti-human SOCS5 , anti-GAPDH and anti-avian Influenza A hemagglutinin antibodies were obtained from Abcam . Whole lungs were dounce homogenized in KALB lysis buffer ( Linossi et al . , 2013b ) . Protein concentrations were determined by the BCA method ( Pierce ) . A549 cells ( RRID:CVCL_0023 ) were treated with 10 μM MG132 ( 6 h ) and pervanadate solution ( H2O2/25 μM Na3VO4 ) ( 20 min ) prior to cell lysis in 1% NP-40 buffer ( 1% v/v NP-40 , 50 mM HEPES , pH 7 . 4 , 150 mM NaCl , 1 mM EDTA , 1 mM NaF , 1 mM Na3VO4 ) . SOCS5 was immunoprecipitated using the in-house anti-SOCS5 antibody conjugated to Sepharose . Alternatively , 293 T cells ( RRID:CVCL_0063 ) were transiently transfected with vector alone or cDNA expressing Flag-tagged mouse SOCS5 , using FuGene6 ( Promega ) . 48 h post-transfection , cells were pre-treated with pervanadate solution for 30 min and lysed in 1% NP-40 buffer . Flag-SOCS5 was immunoprecipitated using M2-beads ( Sigma ) and proteins eluted with 0 . 5% sodium dodecyl sulfate ( SDS ) and 5 mM dithiothreitol ( DTT ) , prior to tryptic digest and mass spectrometry . Endogenous SOCS5 was immunoprecipitated using 5 μg in-house antibody and protein-A Sepharose . Wild-type and mutant SOCS5 expression vectors , immunoprecipitation , gel electrophoresis and immunoblotting have been described previously ( Linossi et al . , 2013b ) . Cell lines were tested as mycoplasma free . Equal amounts of lung lysates ( ~200 μg ) or eluates from SOCS5 immunoprecipitates were subjected to tryptic digest using the FASP protein digestion kit ( Protein Discovery ) ( Wiśniewski et al . , 2009 ) , with the following modifications . Protein material was reduced with TCEP ( 5 mM final ) and digested overnight with 2 μg sequence-grade modified trypsin Gold ( Promega ) in 50 mM NH4HCO3 at 37°C . Peptides were eluted with 50 mM NH4HCO3 in two 40 μL sequential washes and acidified in 1% formic acid ( final ) . Mass spectrometric analysis was performed as described in Appendix 1 . Primary human airway epithelial cells ( hAECs ) were obtained by endobronchial brushing during fibre-optic bronchoscopy from healthy individuals , individuals who smoked but had no history of lung disease , or individuals who smoked and had chronic obstructive pulmonary disease ( COPD ) . Healthy subjects had no history of smoking or lung disease and had normal lung function . See Table 3 for patient details . All subjects gave written informed consent and all procedures were performed according to approval from the University of Newcastle Human Ethics Committee . hAEC were cultured as described and used at passage two ( Hsu et al . , 2015 ) . H1N1 ( A/Auckland/1/2009 ) , H3N2 , H11N9 and H5N1 were diluted in the appropriate serum free media and added to cells at an MOI of 5 ( H3N2 , H1N1 , and H11N9 ) and 0 . 005 ( H5N1 ) . After 1 h the virus was removed and replaced with serum-free media . Cells were lysed 24 h post-infection and analyzed by Q-PCR or immunoblotting . Socs5 was depleted using Silencer Select pre-designed siRNAs ( Life Technologies ) , which were reverse transfected into hAECs using siPORT NeoFX transfection agent ( Life Technologies ) , 24 h prior to infection . Wortmannin ( 100 nM; Sigma-Aldrich ) or Erlotinib ( 100 nM; Selleckchem ) was added to hAECs 24 h before infection or 1 h after virus inoculation . 10 . 7554/eLife . 20444 . 020Table 3 . Subject characteristics . DOI: http://dx . doi . org/10 . 7554/eLife . 20444 . 020HealthyCOPDSmokerP – valueNumber555NASex ( % Female ) 60%60%40%p=0 . 6Mean Age ( S . D . ) 61 ( 16 . 29 ) 68 ( 12 . 35 ) 64 . 33 ( 12 . 82 ) p=0 . 07Mean FEV1 ( S . D . ) 98% ( 8 . 61 ) 43% ( 10 . 63 ) 97 . 5% ( 10 . 30 ) p<0 . 001Cigarette ( Packs/year; S . D . ) 043 ( 10 . 66 ) 23 ( 11 . 54 ) p<0 . 001Years abstinent ( S . D . ) 012 . 35 ( 5 . 28 ) 0NAICS ( percent treated ) 0Seretide/Tiotropium/Salbutamol ( 60% ) Tiotropium ( 40% ) 0NA* FEV1 refers to the forced expiratory volume in 1s expressed as a percentage of the predicated value . The statistical analysis used for this table is ANOVA for multiple groups . NA = Not applicable . Statistical analysis was performed using an unpaired t-test with a 95% confidence level .	Influenza , commonly referred to as the flu , is a highly contagious disease caused by a virus . When an infected person coughs or sneezes , droplets containing the virus are released into the air . Other individuals nearby may breathe in the virus , which then enters the cells lining the lungs and multiplies . Some people are more susceptible to the influenza virus than others . In particular , individuals with chronic obstructive pulmonary disease ( COPD ) often suffer much worse flu symptoms and are more likely to be admitted to hospital . COPD results from smoke exposure , including cigarette smoke and , in developing countries , the smoke from cooking fires , but it is not clear why individuals with COPD are more susceptible to the influenza virus . The influenza virus gains entry to lung cells by manipulating receptors on the cell surface . A protein called SOCS5 is present inside these cells and has been suggested as a potential regulator of these receptors . Here , Kedzierski et al . reveal that SOCS5 plays a critical role in protecting lung cells in mice and humans from the virus . The experiments show that mice lacking the gene that encodes SOCS5 were more susceptible to infection by the influenza virus , had more severe symptoms of disease and increased amounts of virus in their lungs . Further experiments in lung cells collected from human volunteers show that SOCS5 levels increased in both healthy smokers and non-smokers in response to influenza infection . Conversely , SOCS5 levels in lung cells of smokers with COPD remained low after infection . This suggests that SOCS5 might be an important factor in the susceptibility of these patients to influenza . The next is step is to understand exactly how SOCS5 works , which may make it possible to develop new treatments that boost SOCS5 activity in influenza patients .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology", "microbiology", "and", "infectious", "disease" ]	2017	Suppressor of cytokine signaling (SOCS)5 ameliorates influenza infection via inhibition of EGFR signaling
The T cell receptor ( TCR ) repertoire encodes immune exposure history through the dynamic formation of immunological memory . Statistical analysis of repertoire sequencing data has the potential to decode disease associations from large cohorts with measured phenotypes . However , the repertoire perturbation induced by a given immunological challenge is conditioned on genetic background via major histocompatibility complex ( MHC ) polymorphism . We explore associations between MHC alleles , immune exposures , and shared TCRs in a large human cohort . Using a previously published repertoire sequencing dataset augmented with high-resolution MHC genotyping , our analysis reveals rich structure: striking imprints of common pathogens , clusters of co-occurring TCRs that may represent markers of shared immune exposures , and substantial variations in TCR-MHC association strength across MHC loci . Guided by atomic contacts in solved TCR:peptide-MHC structures , we identify sequence covariation between TCR and MHC . These insights and our analysis framework lay the groundwork for further explorations into TCR diversity . T cells are the effectors of cell-mediated adaptive immunity in jawed vertebrates . To control a broad array of pathogens , massive genetic diversity in loci encoding the T cell receptor ( TCR ) is generated somatically throughout an individual’s life via a process called V ( D ) J recombination . All nucleated cells regularly process and present internal peptide antigens on cell surface molecules called major histocompatibility complex ( MHC ) . Through the interface of TCR and MHC , a T cell with a TCR having affinity for a peptide antigen complexed with MHC ( pMHC ) is stimulated to initiate an immune response to an infected ( or cancerous ) cell . The responding T cell proliferates clonally , and its progeny inherit the same antigen-specific TCR , constituting long-term immunological memory of the antigen . The diverse population of TCR clones in an individual ( the TCR repertoire ) thus dynamically encodes a history of immunological challenges . Advances in high-throughput TCR sequencing have shown the potential of the TCR repertoire as a personalized diagnostic of pathogen exposure history , cancer , and autoimmunity ( Thomas et al . , 2014; Kirsch et al . , 2015; Friedensohn et al . , 2017; Ostmeyer et al . , 2017 ) . Public TCRs—defined as TCR sequences seen in multiple individuals and perhaps associated with a shared disease phenotype—have been found in a range of infectious and autoimmune diseases and cancers including influenza , Epstein-Barr virus , and cytomegalovirus infections , type I diabetes , rheumatoid arthritis , and melanoma ( Venturi et al . , 2008; Li et al . , 2012; Madi et al . , 2017; Pogorelyy et al . , 2017; Dash et al . , 2017; Glanville et al . , 2017; Chu et al . , 2018; Pogorelyy et al . , 2018 ) . By correlating occurrence patterns of public TCRβ chains with cytomegalovirus ( CMV ) serostatus across a large cohort of healthy individuals , Emerson et al . identified a set of CMV-associated TCR chains whose aggregate occurrence was highly predictive of CMV seropositivity ( Emerson et al . , 2017 ) . Staining with multimerized pMHC followed by flow cytometry has been used to isolate and characterize large populations of T cells that bind to defined pMHC epitopes ( Dash et al . , 2017; Glanville et al . , 2017 ) , providing valuable data on the mapping between TCR sequence and epitope specificity . We and others have leveraged these data to develop learning-based models of TCR:pMHC interactions , using TCR distance measures ( Dash et al . , 2017 ) , CDR3 sequence motifs ( Glanville et al . , 2017 ) and k-mer frequencies ( Cinelli et al . , 2017 ) , and other techniques . MHC proteins in humans are encoded by the human leukocyte antigen ( HLA ) loci and are among the most polymorphic in the human genome ( Robinson et al . , 2015 ) . Within an individual , six major antigen-presenting proteins are each encoded by polymorphic alleles . The set of these alleles comprise the individual’s HLA type , which is unlikely to be shared with an unrelated individual and which determines the subset of peptide epitopes presented to T cells for immune surveillance . Specificity of a given TCR for a given antigen is biophysically modulated by MHC structure: MHC binding specificity determines the specific antigenic peptide that is presented , and the TCR binds to a hybrid molecular surface composed of peptide- and MHC-derived residues . Thus , population-level studies of TCR-disease association are severely complicated by a dependence on individual HLA type . Here we report an analysis of the occurrence patterns of public TCRs in a cohort of 666 healthy volunteer donors , in which information on only TCR sequence and HLA association guide us to inferences concerning disease history . To complement deep TCRβ repertoire sequencing available from a previous study ( Emerson et al . , 2017 ) , we have assembled high-resolution HLA typing data at the major class I and class II HLA loci on the same cohort , as well as information on age , sex , ethnicity , and CMV serostatus . We focus on statistical association of TCR occurrence with HLA type , and show that many of the most highly HLA-associated TCRs are likely responsive to common pathogens: for example , eight of the ten TCRβ chains most highly associated with the HLA-A02:01 allele are likely responsive to one of two viral epitopes ( influenza M158 and Epstein-Barr virus BMLF1280 ) . We introduce new approaches to cluster TCRs by primary sequence and by the pattern of occurrences among individuals in the cohort , and we identify highly significant TCR clusters that may indicate markers of immunological memory . Four of the top five most significant clusters appear linked with common pathogens ( parvovirus B19 , influenza virus , CMV , and Epstein-Barr virus ) , again highlighting the impact of viral pathogens on the public repertoire . We also find HLA-unrestricted TCR clusters , some likely to be mucosal-associated invariant T ( MAIT ) cells , which recognize bacterial metabolites presented by non-polymorphic MR1 proteins , rather than pMHC ( Kjer-Nielsen et al . , 2012 ) . Our global analysis of TCR-HLA association identifies striking variation in association strength across HLA loci and highlights trends in V ( D ) J generation probability and degree of clonal expansion that illuminate selection processes in cellular immunity . Guided by structural analysis , we used our large dataset of HLA-associated TCRβ chains to identify statistically significant sequence covaration between the TCR CDR3 loop and the DRB1 allele sequence that preserves charge complementarity at the TCR:pMHC interface . These analyses help elucidate the complex dependence of TCR sharing on HLA type and immune exposure , and will inform the growing number of studies seeking to identify TCR-based disease diagnostics . Of the 80 million unique TCRβ chains ( defined by V-gene family and CDR3 sequence ) in the 666 cohort repertoires , about 11 million chains are found in at least two individuals and referred to here as public chains ( for a more nuanced examination of TCR chain sharing see [Elhanati et al . , 2018] ) . The occurrence patterns of these public TCRβs—the subset of subjects in which each distinct chain occurs—can be thought of as forming a very large binary matrix M with about 11 million rows and 666 columns . Entry Mi , j contains a one or a zero indicating presence or absence , respectively , of TCR i in the repertoire of subject j ( ignoring for the moment the abundance of TCR i in repertoire j; Figure 1 depicts two illustrative sub-matrices of M ) . ( Emerson et al . , 2017 ) demonstrated that this binary occurrence matrix M encodes information on subject genotype and immune history: they were able to successfully predict HLA-A and HLA-B allele type and CMV serostatus by learning sets of public TCRβ chains with occurrence patterns that were predictive of these features . Specifically , each feature—such as the presence of a given HLA allele ( e . g . HLA-A02:01 ) or CMV seropositivity—defines a subset of the cohort members positive for that feature , and can be encoded as a vector of 666 binary digits . This phenotype occurrence pattern of zeros and ones can be compared to the occurrence patterns of all the public TCRβ chains to identify similar patterns , as quantified by a p-value for significance of co-occurrence across the 666 subjects; thresholding on this p-value produces a subset of significantly associated TCRβ chains whose collective occurrence in a repertoire was found by Emerson et al . to be predictive of the feature of interest ( in cross-validation and , for CMV , on an independent cohort ) . Generalizing from these results , it is reasonable to expect that other common immune exposures may be encoded in the occurrence matrix M , and that these encodings could be discovered if we had additional phenotypic data to correlate with TCR occurrence patterns . In this study , we set out to discover these encoded exposures de novo , without additional phenotypic correlates , by learning directly from the structure of the occurrence matrix M and using as well the sequences of the TCRβ chains ( both their similarities to one another and to TCR sequences characterized in the literature ) . We hypothesized that patterns of TCR co-occurrence ( correlations between rows in the matrix M ) might indicate shared responses to unknown immune exposures , that co-occurrence between TCR chains and HLA alleles ( correlations between rows in M and rows in the HLA allele occurrence matrix ) could be used to help identify functional TCR chains , and that clustering TCRβ chains by co-occurrence and sequence could highlight functional associations ( Figure 1 ) . To support this effort we assembled additional HLA typing data for the subjects , now at 4-digit resolution ( e . g . , A02:01 rather than A02 ) and including MHC class II alleles , and we compiled a dataset of annotated TCRβ chains by combining online TCR sequence databases , structurally characterized TCRs , and published studies ( see Materials and methods; [Shugay et al . , 2018; Tickotsky et al . , 2017; Berman et al . , 2000; Dash et al . , 2017; Glanville et al . , 2017; Song et al . , 2017; Kasprowicz et al . , 2006] ) . Here we describe the outcome of this discovery process , and we report a number of intriguing general observations about the role of HLA in shaping the T cell repertoire . The results of our analysis are organized in the remaining five sections as follows . We begin with an examination of TCR co-occurrence patterns across the full cohort ( first section , Figures 2–3 ) . In the next section we examine patterns of TCR-HLA association ( Table 1 and Figures 4–5 ) . In the third section we analyze TCR co-occurrence within subsets of the cohort positive for specific HLA alleles , and we identify TCR clusters that may be reflective of shared immune exposures ( Figures 6–8 ) . In the fourth section we use our dataset of HLA-associated TCRβ chains to identify covariation between HLA and the TCRβ CDR3 sequence ( Table 2 and Figure 9 ) . In the final section we focus on CMV-responsive TCRβ chains , examining their degree of HLA-restriction and the extent to which they may be responding to other antigens ( Figure 10 ) . Figure 1 provides a graphical overview of the co-occurrence analysis . We hypothesized that we could identify unknown immune exposures encoded in the public repertoire by comparing the occurrence patterns of individual TCRβ chains to one another . A subset of TCRβ chains that strongly co-occur across the 666 cohort subjects might correspond to an unmeasured immune exposure that is common to a subset of subjects . Since shared HLA restriction could represent an alternative explanation for significant TCR co-occurrence , we also compared the TCR occurrence patterns to the occurrence patterns for class I and class II HLA alleles . We began by analyzing TCR occurrence patterns over the full set of cohort members . For each pair of public TCRβ chains t1 and t2 we computed a co-occurrence p-value PCO ( t1 , t2 ) that reflects the probability of seeing an equal or greater overlap of shared subjects ( i . e . , subjects in whose repertoires both t1 and t2 are found ) if the occurrence patterns of the two TCRs had been chosen randomly ( for details , see Materials and methods and Figure 12 ) . In a similar manner we computed , for each HLA allele a and TCR t , an association p-value PHLA ( a , t ) that measures the degree to which TCR t tends to occur in subjects positive for allele a . Finally , for each pair of strongly co-occurring ( PCO<1×10−8 ) TCRβ chains t1 and t2 , we looked for a mutual HLA association that might explain their co-occurrence , by finding the allele having the strongest association with both t1 and t2 , and noting its association p-value:PHLA ( t1 , t2 ) =mina∈Amaxt∈{t1 , t2}PHLA ( a , t ) , where A denotes the set of all HLA alleles . In words , we take the p-value of the strongest HLA allele association with the TCR pair , where the association of an HLA allele with a TCR pair is defined by the weakest association of the allele among the individual TCRs . Based on this analysis , we identified two broad classes of strongly co-occurring TCR pairs ( Figure 2 ) : those with a highly significant shared HLA association , where the co-occurrence of the two TCRs can be explained by a shared HLA allele association ( i . e . a common HLA restriction ) , and those with only modest shared HLA-association p-value , for which another explanation of co-occurrence must be sought . Points above the dashed y=x line correspond to pairs of TCRs for which there exists an HLA allele whose co-occurrence with each of the TCRs is stronger than their mutual co-occurrence , while for points below the line no such HLA allele was present in the dataset . We used a neighbor-based clustering algorithm , DBSCAN ( Ester et al . , 1996 ) , to link strongly co-occurring TCR pairs together to form larger correlated clusters ( see Materials and methods ) , and then investigated phenotype associations with these clusters . At an approximate family-wise error rate of 0 . 05 ( see Materials and methods ) , we identified 28 clusters of co-occurring TCRs , with sizes ranging from 7 to 386 TCRs ( Figure 3 ) . Given one of these clusters of co-occurring TCRs , we can count the number of cluster member TCRs found in each subject’s repertoire . The aggregate occurrence pattern of the cluster can be visualized as a rank plot of this cluster TCR count over the subjects ( the black curves in Figure 3C–D ) . This ranking can also be compared with other phenotypic or genotypic features of the same subjects . In particular , by comparing this aggregate occurrence pattern to a control pattern generated by repeatedly choosing equal numbers of subjects independently at random ( dotted green lines in Figure 3C–D ) , we can identify a subset of the cohort with an apparent enrichment of cluster member TCRs and look for overlap between this subset and other defined cohort features . Performing this comparison against the occurrence patterns of class I and class II HLA alleles revealed that the majority of the TCR clusters were strongly associated with at least one HLA allele ( as depicted for a DRB115:01-associated cluster in Figure 3C and summarized in Figure 3B ) . In addition , there were two large clusters of TCRs which were not strongly associated with any of the typed HLA alleles ( clusters 6 and 7 in Figure 3 ) . Visual inspection of the CDR3 regions of TCRs in one of these clusters revealed a distinctive ‘YV’ C-terminal motif that is characteristic of the TRBJ2-702 allele ( Figure 3—figure supplement 1 ) , and indeed the 41 subjects whose repertoires indicated the presence of this genetic variant were exactly the 41 subjects enriched for members of this TCR cluster ( Figure 3D ) . This demonstrated that population diversity in germline allele sets manifests as occurrence pattern clustering . The other large , non-HLA associated TCR cluster had a number of distinctive features as well: strong preference for the TRBV06 family , followed by TRBV20 and TRBV04 ( Figure 3—figure supplement 2 ) ; low numbers of inserted ‘N’ nucleotides; and a skewed age distribution biased toward younger subjects ( Figure 3—figure supplement 3 ) . These features , together with the lack of apparent HLA restriction , suggested that this cluster represented an invariant T cell subset , specifically MAIT ( mucosal-associated invariant T ) cells ( Kjer-Nielsen et al . , 2012; Venturi et al . , 2013; Pogorelyy et al . , 2017 ) . Since MAIT cells are defined primarily by their alpha chain sequences , we searched in a recently published paired dataset ( Howie et al . , 2015 ) for partner chains of the clustered TCRβ chain sequences , and found a striking number that matched the MAIT consensus ( TRAV1-2 paired with TRAJ20/TRAJ33 and a 12 residue CDR3 , Figure 3—figure supplement 3D ) . We also looked for these clustered TCRs in a recently published MAIT cell sequence dataset ( Howson et al . , 2018 ) and found that 93 of the 138 cluster member TCRs occurred among the 31 , 654 unique TCRs from this dataset; of these 93 TCRβ chains , 27 were found among the 78 most commonly occurring TCRs in the dataset ( the TCRs occurring in at least 7 of the 24 sequenced repertoires ) , a highly significant overlap ( P<2×10−52 in a one-sided hypergeometric test ) . These concordances indicate that our untargeted approach has detected a well-studied T cell subset de novo through analysis of occurrence patterns . These analyses suggested to us that TCR co-occurrence patterns across the full cohort of subjects are strongly influenced by the distribution of the HLA alleles , in accordance with the expectation that the majority of αβ TCRs are HLA-restricted . Covariation between TCRs responding to the same HLA-restricted epitopes would only be expected in subjects positive for the restricting alleles , with TCR presence and absence outside these subjects likely introducing noise into the co-occurrence analysis . We therefore decided to analyze patterns of TCR co-occurrence within subsets of the cohort positive for specific HLA alleles , and to restrict our co-occurrence analysis to TCRs having a statistically significant association with the specific allele defining the cohort subset . To begin , we performed a comprehensive analysis of TCR-HLA association . At a false discovery rate of 0 . 05 ( estimated from shuffling experiments; see Materials and methods ) , we were able to assign 16 , 951 TCRβ sequences to an HLA allele ( or alleles: DQ and DP alleles were analyzed as αβ pairs , and there were 5 DR/DQ haplotypes whose component alleles were so highly correlated across our cohort that we could not assign TCR associations to individual DR or DQ components; see Materials and methods ) . Table 1 lists the top 50 HLA-associated TCR sequences by association p-value and top 10 associated TCRs for the well-studied A02:01 allele . We find that 8 of the top 10 A02:01-associated TCRs have been previously reported and annotated as being responsive to viral epitopes , specifically influenza M158 and Epstein-Barr virus ( EBV ) BMLF1280 ( Shugay et al . , 2018; Tickotsky et al . , 2017 ) . Moreover , each of these 8 TCRβ chains is present in a recent experimental dataset ( Dash et al . , 2017 ) that included tetramer-sorted TCRs positive for these two epitopes; each TCR has a clear similarity to one of the consensus epitope-specific repertoire clusters identified in that work , with the EBV TRBV20 , TRBV29 , and TRBV14 TCRs , respectively , matching the three largest branches of the BMLF1280 TCR tree , and the three influenza M158 TCRs all matching the dominant TRBV19 ‘RS’ motif consensus ( Figure 4—figure supplement 2 ) . TCRs with annotation matches are sparser in the top 50 across all other alleles , which is likely due in part to a paucity of experimentally characterized non-A02 TCRs , however we again see EBV-epitope responsive TCRs ( with B08:01 and B35:01 restriction ) . A global comparison of TCR feature distributions for HLA-associated versus non-HLA-associated TCRs provides further evidence of functional selection . As shown in Figure 4A , HLA-associated TCRs are on average more clonally expanded than a set of background , non-HLA associated TCRs with matching frequencies in the cohort . They also have lower generation probabilities—are harder to make under a simple random model of the VDJ rearrangement process—which suggests that their observed cohort frequencies may be elevated by selection ( Figure 4B , see Materials and methods for further details on the calculation of clonal expansion indices and generation probabilities; also see ( Pogorelyy et al . , 2018 ) ) . Examination of two-dimensional feature distributions suggests that these shifts are correlated , with HLA-associated TCRs showing an excess of lower-probability , clonally expanded TCRs ( Figure 4C ) ; this trend appears stronger for class-I associated TCRs than for class II-associated TCRs ( Figure 4—figure supplement 1 ) . To give a global picture of TCR-HLA association , we counted the number of significant TCR associations found for each HLA allele in the dataset , and plotted this number against the number of subjects in the cohort with that allele ( Figure 5 ) . As expected , the more common HLA alleles have on average greater numbers of associated TCRs ( since greater numbers of subjects permit the identification of more public TCRs , and the statistical significance assigned to an observed association of fixed strength grows as the number of subjects increases ) . What was somewhat more surprising is that the slope of the correlation between cohort frequency and number of associated TCRs varied dramatically among the HLA loci , with HLA-DRB1 alleles having the largest number of associated TCRs for a given allele frequency and HLA-C alleles having the smallest . The best-fit slope for the five DR/DQ haplotypes ( 12 . 2 ) was roughly the sum of the DR ( 7 . 99 ) and DQ ( 3 . 39 ) slopes , suggesting as expected that these haplotypes were capturing TCRs associated with both the DR and DQ component alleles . The smaller rate of TCR association observed at the HLA-C locus could be explained by a relatively lower level of cell surface expression of HLA-C alleles as well as their greater tendency to interact with killer cell immunoglobulin-like receptors ( KIR ) on natural killer ( NK ) cells ( Kaur et al . , 2017 ) . We assessed the accuracy of our TCR:HLA associations in two ways . First , we compared our HLA allele assignments to those given in the VDJdb database ( which provides the peptide:MHC target and hence a putative HLA restriction for all entries; [Shugay et al . , 2018] ) and found that 90% of the VDJdb assignments for TCRβ chains present in both sets matched our associations . This agreement increases to 96% after filtering for the highest level of supporting evidence ( VDJdb score of 3 ) . Interestingly , two of the mismatches with VDJdb score three were from the protein structural database: the allo-complex between the B08-restricted LC13 TCR and HLA-B44:05 ( Macdonald et al . , 2009 ) , and the structure of the A02-restricted JM22 TCR bridged to a class II allele by a staphylococcal superantigen ( Saline et al . , 2010 ) . In both of these cases , our data predict the canonical association: B08 for the LC13 TCRβ chain and A02 for the JM22 TCRβ chain . Second , we looked for HLA-associated public TCRβ chains in sequenced repertoires from T cell populations that were sorted for the presence of CD4/CD8 surface markers . One would expect that TCRβ chains associated with class I MHC molecules should be preferentially found in CD8+ populations , while class II-associated TCRs should be found in CD4+ populations . We selected four repertoire datasets ( Emerson et al . , 2013; Rubelt et al . , 2016; Li et al . , 2016; Oakes et al . , 2017 ) with matched CD4+ and CD8+ repertoires from a total of 63 individuals , and we analyzed the occurrence patterns of our HLA-associated TCRβ chains in these sequence datasets , producing for each TCRβ counts of the number of CD4+ and CD8+ repertoires it was observed in . Figure 5—figure supplement 1 shows that if we assign each TCRβ to the class ( CD4+ or CD8+ ) with the higher count , these assignments are largely concordant with the MHC class of its associated HLA allele , and moreover this agreement increases as we increase either the stringency of HLA association or the stringency of the CD4/CD8 assignment ( i . e . , the minimum absolute difference between the CD4 and CD8 repertoire counts; see Materials and methods ) . Having identified a set of HLA-associated TCRβ chains , we next sought to identify TCR clusters that might represent HLA-restricted responses to shared immune exposures . We performed this analysis for each HLA allele individually , restricting our clustering to the set of TCR chains significantly-associated with that allele and comparing occurrence patterns only over the subset of subjects positive for that allele . To reduce spurious co-occurrence signals driven by the presence/absence of other HLA alleles , we excluded TCR chains that were more strongly associated with a different HLA allele ( i . e . , not the one defining the cohort subset ) . The smaller size of many of these allele-positive cohort subsets reduces our statistical power to detect significant clusters using co-occurrence information . To counter this effect , we used the TCRdist similarity measure ( Dash et al . , 2017 ) to leverage the TCR sequence similarity which is often present within epitope-specific responses ( Dash et al . , 2017; Glanville et al . , 2017 ) ( see for example the A02:01 TCRs in Table 1 and Figure 4—figure supplement 2 ) . We augmented the probabilistic similarity measure used to define neighbors for DBSCAN clustering to incorporate information about TCR sequence similarity ( as measured by TCRdist ) , in addition to cohort co-occurrence ( see Materials and methods ) . We independently clustered each allele’s associated TCRs and merged the clustering results from all alleles; using the Holm multiple testing criterion ( Holm , 1979 ) to limit the approximate family-wise error rate to 0 . 05 , we found a total of 78 significant TCR clusters . We analyzed the sequences and occurrence patterns of the TCRs belonging to these 78 clusters in order to assess their potential biological significance and prioritize them for further study ( Table 3 ) . Each cluster was assigned two scores ( Figure 6 ) : a size score ( Ssize , x-axis ) , reflecting the significance of seeing a cluster of that size given the total number of TCRs clustered for its associated allele , and a co-occurrence score ( ZCO , y-axis ) , reflecting the degree to which the TCRs in that cluster co-occur within its allele-positive cohort subset ( see Materials and methods ) . In computing the co-occurrence score , we defined a subset of individuals with an apparent enrichment for the member TCRs in each cluster; the size of this enriched subset of subjects is given in the ‘Subjects’ column in Table 3 . We rank ordered the 78 clusters based on the sum of their size and co-occurrence scores ( weighted to equalize dynamic range ) ; the top five clusters are presented in greater detail in Figure 7 and Figure 8 . HLA associations , member TCR and enriched subject counts , cluster center TCR sequences , scores , and annotations for all 78 clusters are given in Table 3 . We found that a surprising number of the most significant HLA-restricted clusters had links to common viral pathogens . For example , the top cluster by both size and co-occurrence ( Figure 7 , upper panels ) is an A24:02-associated group of highly similar TCRβ chains , five of which can be found in a set of 12 TCRβ sequences reported to respond to the parvovirus B19 epitope FYTPLADQF as part of a highly focused CD8+ response to acute B19 infection ( Kasprowicz et al . , 2006 ) . The subject TCR-counts curve for this cluster ( Figure 7 , top right panel ) shows a strong enrichment of member TCRs in roughly 30% of the A24:02 repertoires , which is on the low end of prevalence estimates for this pathogen ( Heegaard and Brown , 2002 ) and may suggest that , if cluster enrichment does correlate with B19 exposure , there are likely to be other genetic or epidemiologic factors that determine which B19-exposed individuals show enrichment . The second most significant cluster by both measures is an A02:01-associated group of TRBV19 TCRs with a high frequency of matches to the influenza M158 response ( 41/43 TCRs , labeled ‘INF-pGIL’ for the first three letters of the GILGFVFTL epitope ) . Notably , the cluster member sequences recapitulate many of the core features of the tree of experimentally identified M158 TCRs ( Figure 4—figure supplement 2 ) : a dominant group of length 13 CDR3 sequences with an ‘RS’ sequence motif together with a smaller group of length 12 CDR3s with the consensus CASSIG . YGYTF . Rounding out the top five , the third and fifth most significant clusters also appear to be pathogen-associated . Cluster #3 brings together a diverse set of DRB107:01-associated TCRβ chains ( Figure 8 , top dendrogram ) , none of which matched our annotation database . However , it was strongly associated with CMV serostatus: As is evident in the subject TCR-counts panel for this cluster ( Figure 8 , top right ) , there is a highly significant ( P<3×10−19 ) association between CMV seropositivity ( blue dots at the bottom of the panel ) and cluster enrichment ( here defined as a subject TCR count ≥3 ) . Finally , the B08:01-associated cluster #5 ( bottom panels in Figure 8 ) appears to be EBV-associated: four of the TCRβ chains in this cluster match TCRs annotated as binding to EBV epitopes ( two matches for the B08:01-restricted FLRGRAYGL epitope and two for the B08:01-restricted RAKFKQLL epitope ) . The fact that this cluster brings together sequence-dissimilar TCRs that recognize different epitopes from the same pathogen supports the hypothesis that at least some of the observed co-occurrence may be driven by a shared exposure . As a preliminary validation of the clusters identified here , we examined the occurrence patterns of cluster member TCRs in two independent cohorts: a set of 120 individuals ( ‘Keck120’ ) that formed the validation cohort for the original Emerson et al . study , and a set of 86 individuals ( ‘Brit86’ ) taken from the aging study of ( Britanova et al . , 2016 ) . Whereas the Keck120 repertoires were generated using the same platform as our 666-member discovery cohort , the Brit86 repertoires were sequenced from cDNA libraries using 5’-template switching and unique molecular identifiers . In the absence of HLA typing information for these subjects , we simply evaluated the degree to which each cluster’s member TCRs co-occurred over the entirety of each of these validation cohorts , using the co-occurrence score described above ( ZCOKeck120 and ZCOBrit86 columns in Table 3 ) . Although rare alleles and cluster-associated exposures may not occur with sufficient frequency in these smaller cohorts to generate co-occurrence signal , co-occurrence scores support the validity of the clusterings identified on the discovery cohort: 94% of the Keck120 scores and 92% of the Brit86 scores are greater than 0 , indicating a tendency of the clustered TCRs to co-occur ( smoothed score distributions are shown in Figure 6—figure supplement 1 ) . Given our large dataset of HLA-associated TCRβ sequences , we set out to look for correlations between CDR3 sequence and HLA allele sequence . Previous studies have identified correlations between TCR V-gene usage and HLA alleles ( Sharon et al . , 2016; Blevins et al . , 2016 ) ; these correlations are consistent with a picture of TCR:peptide:MHC interactions in which the CDR1 and CDR2 loops ( whose sequence is determined by the V gene ) primarily contact the MHC while the CDR3 loops contact the peptide . To complement these studies and leverage our large set of HLA-associated sequences , we set out to look for correlations between the CDR3 sequence itself and the HLA allele . In our previous work on epitope-specific TCRs ( Dash et al . , 2017 ) , we identified a significant negative correlation between CDR3 charge and peptide charge , suggesting a tendency toward preserving charge complementarity across the TCR:pMHC interface . Although the CDR3 loop primarily contacts the MHC-bound peptide , computational analysis of solved TCR:peptide:MHC structures in the Protein Data Bank ( Berman et al . , 2000 ) ( see Materials and methods ) identified a number of HLA sequence positions that are frequently contacted by CDR3 amino acids ( Table 2 ) . For each frequently-contacted HLA position with charge variability among alleles we computed the covariation between HLA allele charge at that position and average CDR3 charge for allele-associated TCRs . Since portions of the CDR3 sequence are contributed by the V- and J-gene germline sequences , and covariations are known to exist between HLA and V-gene usage , we also performed a covariation analysis restricting to ‘non-germline’ CDR3 sequence positions whose coding sequence is determined by at least one non-templated insertion base ( based on the most parsimonious VDJ reconstruction; see Materials and methods ) . We found a significant negative correlation ( R=−0 . 47 , P<4×10−4 for the full CDR3 sequence; R=−0 . 52 , P<7×10−5 for the non-germline CDR3 sequence ) between CDR3 charge and the charge at position 70 of the class II beta chain ( correcting these p-values for the fact that we considered 7 positions yields 2 . 3×10−3 and 4 . 3×10−4 ) . We did not see a significant correlation for the frequently contacted position on the class II alpha chain , perhaps due to the lack of sequence variation at the DRα locus and/or the more limited number of DQα and DPα alleles . None of the five class I positions showed significant correlations , which could be due to their lower contact frequencies , a smaller average number of associated TCRs ( 51 for class I versus 309 for class II ) , bias toward A02 in the structural database , or noise introduced from multiple contacted positions varying simultaneously . Further analysis of the class II correlation suggested that it was driven largely by HLA-DRB1 alleles: position 70 correlations were −0 . 56 versus −0 . 10 for DR and DQ , respectively , over the full CDR3 and −0 . 64 vs −0 . 38 for the non-germline CDR3 . Figure 9 provides further detail on this DRB1-TCR charge anti-correlation , including a structural superposition showing the proximity of position 70 to the TCRβ CDR3 loop . We analyzed the HLA associations of strongly CMV-associated TCRβ chains to gain insight into their predictive power across genetically diverse individuals . Here we change perspective somewhat from earlier sections , in that we select TCRs based on their CMV association and then evaluate HLA association , rather than the other way around . In their original study , Emerson et al . identified a set of TCRβ chains that were enriched in CMV seropositive individuals and showed that by counting these CMV-associated TCRβ chains in a query repertoire they could successfully predict CMV serostatus both in cross-validation and on an independent test cohort . The success of this prediction strategy across a diverse cohort of individuals raises the intriguing question of whether these TCRβs are primarily HLA-restricted in their occurrence and in their association with CMV , or whether they span multiple HLA types . To shed light on this question we focused on a set of 68 CMV-associated TCRβ chains whose co-occurrence with CMV seropositivity was significant at a p-value threshold of 1 . 5e-5 ( corresponding to an FDR of 0 . 05; see Materials and methods ) . For each CMV-associated TCRβ chain , we identified its most strongly associated HLA allele and compared the p-value of this association to the p-value of its association with CMV ( Figure 10A ) . From this plot we can see that the majority of the CMV-associated chains do appear to be HLA-associated , having p-values that exceed the FDR 0 . 05 threshold for HLA association . The excess of highly significant HLA-association p-values for these CMV-associated TCRβs can be seen in Figure 10B , which compares the observed p-value distribution to a background distribution of HLA association p-values for randomly selected frequency-matched public TCRβs . As a next step we looked to see whether these HLA associations fully explained the CMV association , in the sense that the CMV association was only present in subjects positive for the associated allele . For each of the 68 CMV-associated TCRs , we divided the cohort into subjects positive for its most strongly associated HLA allele and subjects negative for that allele . Here we considered both 2- and 4-digit resolution alleles when defining the most strongly associated allele , to allow for TCRs whose association extends beyond a single 4-digit allele . We computed association p-values between TCR occurrence and CMV seropositivity over these two cohort subsets independently and compared them ( Figure 10C ) . We see that the majority of the points lie below the y=x line—indicating a stronger CMV-association on the subset of the cohort positive for the associated allele—and also below the line corresponding to the expected minimum of 68 uniform random variables ( i . e . the expected upper significance limit in the absence of CMV association on the allele-negative cohort subsets ) . There are however a few TCRβs which do not appear strongly HLA-associated and for which the CMV-association remains strong in the absence of their associated allele ( the points above the line y=x in Figure 10C ) . For example , the public TCRβ chain defined by TRBV07 and the CDR3 sequence CASSSDSGGTDTQYF ( which corresponds to the highest point in Figure 10C ) is strongly CMV-associated ( 22/23 subjects with this chain are CMV positive; P<3×10−7 ) but does not show evidence of HLA association in our dataset . TCRs with HLA promiscuity may be especially interesting from a diagnostic perspective , since their phenotype associations may be more robust to differences in genetic background . Finally , we looked to see whether CMV association completely explained the observed HLA associations , in the sense that a response to one or more CMV epitopes was likely the only driver of HLA association , or whether there might be evidence for other epitope-specific responses by these TCRβ chains or a more general affinity for the associated allele , perhaps driven by common self antigens . Put another way , do we see evidence for pre-existing enrichment of any of these TCRβ chains when their associated allele is present , even in the absence of CMV , which might suggest that the CMV response recruits from a pre-selected pool enriched for TCRs with intrinsic affinity for the restricting allele ? To approach this question we split the cohort into CMV seropositive and seronegative subjects and computed , for each of the 68 CMV-associated TCRs , the strength of its association with its preferred allele over these two subsets separately . Figure 10D compares these HLA-association p-values computed over the subsets of the cohort positive ( 289 individuals , x-axis ) and negative ( 352 individuals , y-axis ) for CMV . We can see in this case that all of the associations on the CMV-positive subset are stronger than those on the CMV-negative subset , and indeed the CMV-negative p-values do not appear to exceed random expectation given the number of comparisons performed . Thus , the apparent lack of any significant HLA-association on the CMV-negative cohort subset suggests that the HLA associations of these CMV-predictive chains are largely driven by CMV exposure . A limitation of this analysis is that , although the CMV-negative subset of the cohort is larger than the CMV-positive subset , the number of TCR occurrences in the CMV-negative subset is likely lower than in the CMV-positive subset for these CMV-associated chains , which will limit the strength of the HLA associations that can be detected . Each individual’s repertoire of circulating immune receptors encodes information on their past and present exposures to infectious and autoimmune diseases , to antigenic stimuli in the environment , and to tumor-derived epitopes . Decoding this exposure information requires an ability to map from amino acid sequences of rearranged receptors to their eliciting antigens , either individually or collectively . One approach to developing such an antigen-mapping capability would involve collecting deep repertoire datasets and detailed phenotypic information on immune exposures for large cohorts of genetically diverse individuals . Correlation between immune exposure and receptor occurrence across such datasets could then be used to train statistical predictors of exposure , as demonstrated by Emerson et al . for CMV serostatus . The main difficulty with such an approach , beyond the cost of repertoire sequencing , is likely to be the challenge of assembling accurate and complete immune exposure information . For this reason , we set out to discover potential signatures of immune exposures de novo , in the absence of phenotypic information , using only the structure of the public repertoire—its receptor sequences and their occurrence patterns . By analyzing co-occurrence between pairs of public TCRβ chains and between individual TCRβ chains and HLA alleles , we were able to identify statistically significant clusters of co-occurring TCRs across a large cohort of individuals and in a variety of HLA backgrounds . Indirect evidence from sequence matches to experimentally-characterized receptors suggests that some of these TCR clusters may reflect hidden immune exposures shared among subsets of the cohort members; indeed , several of the most significant clusters appear linked to common viral pathogens ( parvovirus B19 , influenza , CMV , and EBV ) . The results of this paper demonstrate the potential for a productive dialog between statistical analysis of TCR repertoires and immune exposure analysis . Specifically , sequences from the statistically-inferred clusters defined here could be tested for antigen reactivity or combined with immune exposure data to infer the driver of TCR expansion , as was done here for the handful of CMV-associated clusters based on CMV serostatus information . In either case our clustering approach will reduce the amount of independent data required , since the immune phenotype data is used for annotation of a modest number of defined TCR groupings rather than direct discovery of predictive TCRs from the entire public repertoire . We can also look for the presence of specific TCRs and TCR clusters identified here in other repertoire datasets , for example from studies of specific autoimmune diseases or pathogens , as a means of assigning putative functions . However the answer may not be entirely straightforward: it remains possible that enrichment for other cluster TCRs , rather than being associated with an exposure per se , is instead associated with some subject-specific genetic or epigenetic factor that determines whether a specific TCR response will be elicited by a given exposure . The finding by Emerson et al . —now replicated and extended in this work—that there are large numbers of TCRβ chains whose occurrence patterns ( independent of potential TCRα partners ) are strongly associated with specific HLA alleles , raises the question of what selective forces drive these biased occurrence patterns . Our observations point to a potential role for responses to common pathogens in selecting some of these chains in an HLA-restricted manner . Self-antigens ( presented in the thymus and/or the periphery ) may also play a role in enriching for specific chains , as suggested by ( Madi et al . , 2017 ) in their work on TCR similarity networks formed by the most frequent CDR3 sequences . Our conclusions diverge somewhat from this previous work , which may be explained by the following factors: our use of HLA-association rather than intra-individual frequency as a filter for selecting TCRs , our inclusion of information on the V-gene family in addition to the CDR3 sequence when defining TCR sharing and computing TCR similarity , and our use of TCR occurrence patterns , rather than CDR3 edit distance , to discover TCR clusters . We also find it interesting that class II loci appear on average to have greater numbers of associated TCRβ chains than class I loci ( Figure 5 ) : presumably this reflects differences in selection and/or abundance between the CD4+ and CD8+ T cell compartments ( Sinclair et al . , 2013 ) , but the underlying explanation for this trend is unclear , although a similar bias was observed by Sharon et al . , 2016 . One caveat is that it can be difficult to reliably assign TCR associations to individual members of groups of highly correlated HLA alleles; perfectly correlated alleles have been collapsed into haplotypes in our analysis , but there remain allele pairs ( particularly between the HLA-DR and HLA-DQ loci ) that strongly co-occur across the cohort . In addition , TCRβ chains associated with multiple HLA alleles ( for example , because they recognize the same peptide presented by several different alleles ) might be missed in our approach; although our analysis of HLA-association for CMV-associated TCR chains did not detect a substantial degree of HLA promiscuity , it remains to be seen whether this extends to other classes of functional TCRs . Alternative approaches that focus on other features , such as clonal abundance , to select TCR chains for clustering and downstream analysis are worth pursuing . It is also worth pointing out that our primary focus on presence/absence of TCRβ chains ( rather than abundance ) assumes relatively uniform sampling depths across the cohort; in the limit of very deep repertoire sequencing , pathogen-associated chains may be found ( presumably in the naive pool ) even in the absence of the associated immune challenge , while shallow sampling reliably picks out only the most expanded T cell clones . Here the use of clusters of responsive TCRs rather than individual chains lessens stochastic fluctuations in TCR occurrence patterns , providing some measure of robustness . We look forward to the accumulation of new data sets , which will enable future researchers to move beyond the limitations of the study presented here . An ideal study would perform discovery on repertoire data from multiple large cohorts , rather than the single large cohort generated with a single sequencing platform . Although we do validate TCR clusters on two independent datasets , with one from a different immune profiling technology , performing discovery on multiple large cohorts would presumably give more robust results . Future analyses of independent , HLA-typed cohorts will provide additional validation of trends seen here . The lack of sequenced TCRα or paired α/β repertoires for this cohort limits the features we can detect and may introduce bias into some of our conclusions . Certain T cell subsets , such as MAIT and invariant natural killer T cells , are more easily recognized from α chain sequence data . It is likely that many TCRs that are associated with specific immune exposures when considered as paired TCR chains are not detectably associated with those exposures ( or with other TCRs responding to those exposures ) when analyzing only the α or β chain alone: indeed it is somewhat surprising that we find as many apparent associations and co-occurring clusters as we do given that we are considering only the TCRβ chain . Greater sequencing depth and/or analysis of sorted T cell populations will likely be required of future studies that aim to examine the impact of HLA on the composition of the naive T cell repertoire . We also hope that future studies will have rich immune exposure data beyond CMV serostatus: although the cohort members were all nominally healthy at the time of sampling , it is likely that there are a variety of immune exposures , some presaging future pathologies , that can be observed in a diverse collection of 650+ individuals . As an example , two of our EBV-annotated clusters contain TCRβ chains also seen in the context of rheumatoid arthritis: cross-reactivity between pathogen and autoimmune epitopes may mean that TCR clusters discovered on the basis of common infections also provide information relevant in the context of autoimmunity . TCRβ repertoire sequence data for the 666 members of the discovery cohort was downloaded from the Adaptive biotechnologies website using the link provided in the original ( Emerson et al . , 2017 ) publication ( https://clients . adaptivebiotech . com/pub/Emerson-2017-NatGen ) . The repertoire sequence data for the 120 individuals in the ‘Keck120’ validation set was included in the same download . Repertoire sequence data for the 86 individuals in the ‘Brit86’ validation set was downloaded from the NCBI SRA archive using the Bioproject accession PRJNA316572 ( Britanova et al . , 2016 ) and processed using scripts and data supplied by the authors ( https://github . com/mikessh/aging-study ) in order to demultiplex the samples and remove technical replicates . Repertoire sequence data for TCRβ chains from MAIT cells was downloaded from the NCBI SRA archive using the Bioproject accession PRJNA412739 ( Howson et al . , 2018 ) . Repertoire sequence data for TCRβ chains from T cells sorted for CD4/CD8 surface markers were taken from the following studies: ( Emerson et al . , 2013 ) , available for download at https://clients . adaptivebiotech . com/pub/emerson-2013-jim; ( Rubelt et al . , 2016 ) , downloaded from the NCBI SRA archive using the Bioproject accession PRJNA300878; ( Li et al . , 2016 ) , downloaded from the NCBI SRA archive using the Bioproject accession PRJNA348095; and ( Oakes et al . , 2017 ) , downloaded from the NCBI SRA archive using the Bioproject accession PRJNA390125 . V and J genes were assigned by comparing the TCR nucleotide sequences to the IMGT/GENE-DB ( Giudicelli et al . , 2005 ) nucleotide sequences of the human TR genes ( sequence data downloaded on 9/6/2017 from http://www . imgt . org/genedb/ ) . CDR3 nucleotide and amino acid sequences and most-parsimonious VDJ recombination scenarios were assigned by the TCRdist pipeline ( Dash et al . , 2017 ) ( the most parsimonious recombination scenario , used for identifying non-germline CDR3 amino acids , is the one requiring the fewest non-templated nucleotide insertions ) . To define the occurrence matrix of public TCRs and assess TCR-TCR , TCR-HLA and TCR-CMV association , a TCRβ chain was identified by its CDR3 amino acid sequence and its V-gene family ( e . g . , TRBV6-401 was reduced to TRBV06 ) . TCR sequence reads for which a unique V-gene family could not be determined ( due to equally well-matched V genes from different families , a rare occurrence in this dataset ) were excluded from the analysis . The matrix M of public TCRβ occurrences across the discovery cohort , HLA allele occurrence patterns , and other associated data needed to reproduce the findings of this study have been deposited in the Zenodo database ( doi:10 . 5281/zenodo . 1248193 ) . A preliminary analysis of TCR sharing at the nucleotide level was conducted to identify potential cross-contamination in the discovery cohort repertoires . Each TCRβ nucleotide sequence that was found in multiple repertoires was assigned a generation probability ( Pgen , see below ) in order to identify nucleotide sequences with suspiciously high sharing rates among repertoires . Visual comparison of the sharing rate ( the number of repertoires in which each TCRβ nucleotide sequence was found ) to the generation probability ( Figure 11 ) showed that the majority of highly-shared TCRs had correspondingly high generation probabilities; it also revealed a cluster of TCR chains with unexpectedly high sharing rates . Examination of the sequences of these highly-shared TCRs revealed them to be variants of the consensus sequence CFFKQKTAYEQYF ( coding sequence: tgttttttcaagcagaagacggcatacgagcagtacttc ) . Consultation with scientists at Adaptive Biotechnologies confirmed that these sequences were likely to represent a technical artifact of the sequencing pipeline . We elected to remove all TCRβ nucleotide sequences whose sharing rates put them outside the decision boundary indicated by the black line in Figure 11 , which eliminated the vast majority of the artifactual variants as well as a handful of other highly shared , low-probability sequences ( 592 nucleotide sequences in total were removed ) . Each public TCRβ chain was assigned a clonal expansion index ( Iexp ) determined by its frequencies in the repertoires in which it was found . First , the unique TCRβ chains present in each repertoire were ordered based on their inferred nucleic acid template count ( Carlson et al . , 2013 ) , and assigned a rank ranging from 0 ( lowest template count ) to S−1 ( highest template count ) , where S is the total number of chains present in the repertoire . TCRs with the same template count were assigned the same tied rank equal to the midpoint of the tied group . In order to compare across repertoires , the ranks for each repertoire were then normalized by dividing by the number of unique sequences in the repertoire . The clonal expansion index for a given public TCR t was taken to be its average normalized rank for the repertoires in which it occurred:Iexp ( t ) =1m∑i=1m riSi−1 , where the sum is taken over the m repertoires in which t is found , ri is the template-count rank of TCR t in repertoire i , and Si is the total size of repertoire i . HLA genotyping was performed and confirmed by molecular means , including sequence specific oligonucleotide probe typing ( SSOP ) , Sanger sequencing ( SBT ) or next generation sequencing ( NGS ) ( Smith et al . , 2014 ) . Independently , HLA alleles were imputed using data generated by high density single-nucleotide polymorphism arrays as previously described ( Martin et al . , 2017 ) . Imputed alleles were compared with HLA typing data from SBT and NGS , and used to resolve ambiguous HLA codes generated by SSOP and provide a uniform set of four digit allele assignments . HLA typing data availability varied across loci as follows: HLA-A ( 629 subjects ) , HLA-B ( 630 subjects ) , HLA-C ( 629 subjects ) , HLA-DRB1 ( 630 subjects ) , HLA-DQA1 ( 522 subjects ) , HLA-DQB1 ( 630 subjects ) , HLA-DPA1 ( 606 subjects ) , and HLA-DPB1 ( 472 subjects ) . When calculating the association p-values between TCRβ chains and HLA alleles reported in Table 1 , the cohort was restricted to the subset of subjects with available HLA typing at the relevant locus . For comparing TCR association rates across loci in Figure 5 , associations were calculated over the cohort subset ( 522 subjects ) with typing data at all compared loci ( A , B , C , DRB1 , DQA1 , and DQB1 ) in order to avoid spurious differences in association strengths arising from differential data availability among the loci . Due to their very strong linkage on our cohort , five DR-DQ haplotypes were treated as single allele units for association calculations and clustering: DRB103:01-DQA105:01-DQB102:01 , DRB115:01-DQA101:02-DQB106:02 , DRB113:01-DQA101:03-DQB106:03 , DRB110:01-DQA101:05-DQB105:01 , and DRB109:01-DQA103:02-DQB103:03 . We implemented a version of the probabilistic model proposed by Walczak and co-workers ( Murugan et al . , 2012 ) in order to assign to each public TCRβ chain ( defined by a V-gene family and a CDR3 amino acid sequence ) a generation probability , Pgen , which captures the probability of seeing that TCRβ in the preselection repertoire . Pgen is calculated by summing the probabilities of the possible VDJ rearrangements that could have produced the observed TCR:Pgen ( Vfamily , CDR3aa ) =∑s∈S P ( s ) where S represents the set of possible VDJ recombination scenarios capable of producing the observed TCR V family and CDR3 amino acid sequence . To compute the probability of a given recombination scenario s , we use the factorization proposed by Marcou et al . ( 2018 ) , which captures observed dependencies of V- , D- , and J-gene trimming on the identity of the trimmed gene and of inserted nucleotide identity on the identity of the preceding nucleotide:P ( s ) =P ( Vs ) P ( Ds\|Js ) P ( Js ) ×P ( delsV\|Vs ) P ( delsD5′ , delsD3′\|Ds ) P ( delsJ\|Js ) ×P ( InssVD ) ∏iInssVD P ( ni\|ni−1 ) ×P ( InssDJ ) ∏iInssDJ P ( mi\|mi−1 ) Here the recombination scenario s consists of a choice of V gene ( Vs ) , D gene ( Ds ) , J gene ( Js ) , number of nucleotides trimmed back from the end of the V gene ( delsV ) or J gene ( delsJ ) or D gene ( delsD5′ and delsD3′ ) , number of nucleotides inserted between the V and D genes ( InssVD ) and between the D and J genes ( InssDJ ) and the identities of the inserted nucleotides ( {ni} and {mi} respectively ) . At the start of the calculation , the CDR3 amino acid sequence is converted to a list of potential degenerate coding nucleotide sequences ( here degenerate means that nucleotide class symbols such as W ( for A and T ) and R ( for A and G ) are allowed ) . Since each amino acid other than Leucine , Serine , and Arginine has a single degenerate codon ( P=CCN , N = AAY , K = AAR , etc . ) and these three amino acids have two such codons ( S={TCN , AGY} , R={CGN , AGR} , L={CTN , TTR} ) , this list of nucleotide coding sequences is generally not too long . The generation probability is then taken to be the sum of the probabilities of these degenerate nucleotide sequences . Since the total number of possible recombination scenarios is in principle quite large , we make a number of approximations to speed the calculation: we limit excess trimming of genes to at most three nucleotides , where excess trimming is defined to be trimming back a germline gene nucleotide which matches the target CDR3 nucleotide ( therefore requiring non-templated reinsertion of the same nucleotide ) ; at most two palindromic nucleotides are allowed; sub-optimal D gene alignments are only considered up to a score gap of 2 matched nucleotides relative to the best match . The parameters of the probability model are fit by a simple iterative procedure in which we generate rearranged sequences using an initial model , compare the statistics of those sequences to statistics derived from observed out-of-frame rearrangements in the dataset , and adjust the probability model parameters to iteratively improve agreement . We compared the nucleotide sequence generation probabilities computed using our software with those computed using the published tool IGoR ( Marcou et al . , 2018 ) and found good overall agreement: a linear regression analysis of the log10⁡ ( Pgen ) values gives a correlation coefficient R=0 . 97 with slope of 0 . 98 and an intercept of 0 . 22 for a set of 800 randomly selected TCRβ chains . We performed an analysis of covariation across the cohort for pairs of TCR chains and for TCR chains and HLA alleles ( Figure 12 ) . We used the hypergeometric distribution to assess the significance of an observed overlap between two subsets of the cohort ( for example , the subset of subjects positive for a given HLA allele and the subset of subjects with a given TCRβ chain in their repertoires ) , taking our significance p-value to be the probability of seeing an equal or greater overlap if the two subsets had been chosen at random:Poverlap ( k , N1 , N2 , N ) =∑j≥k ( N1j ) ( N−N1N2−j ) ( NN2 ) where k is the size of the overlap , N1 and N2 are the sizes of the two subsets , and N is the total cohort size ( i . e . , the number of individuals in the cohort ) . We use Poverlap to assess the significance of an overlap Ca∩Ct between an HLA allele a found in the cohort subset Ca and a TCRβ chain t found in the cohort subset Ct as follows:PHLA ( a , t ) =Poverlap ( \|Ca∩Ct\| , \|Ca\| , \|Ct\| , N ) where \|C\| denotes the cardinality of the set C . A complication arises when assessing TCR-TCR co-occurrence in the presence of variable-sized repertoires: TCRs are more likely to come from the larger repertoires than the smaller ones , which violates the assumptions of the hypergeometric distribution and leads to inflated significance scores . In particular , when we use the hypergeometric distribution to model the overlap between the sets of subjects in which two TCR chains are found , we implicitly assume that all subjects are equally likely to belong to a TCR chain’s subject set . If the subject repertoires vary in size , this assumption will not hold . For example , in the limit of a subject with an empty repertoire , no TCR subject sets will contain that subject , which will inflate all the overlap p-values since we are effectively overstating the size N of the cohort by 1 . On the other hand , if one of the subject repertoires contains all the public TCR chains , then each TCR-TCR overlap will automatically contain that subject , again inflating the p-values since we are artificially adding 1 to each of k , N1 , N2 , and N . We developed a simple heuristic to correct for this effect using a per-subject bias factor by definingbi=SiN∑j=1N Sj , where Si is the size of repertoire i and N is the cohort size . To score an overlap between the occurrence patterns of two TCRβ chains t and t′ , where t is found in the subset Ct of the cohort , t′ is found in the subset Ct′ , and their overlap Ct∩Ct′ contains the k subjects s1 , . . . , sk , we adjust the overlap p-value ( Poverlap ) by the product of the bias factors of the subjects in the overlap:PCO ( t , t′ ) = ( ∏j=1k bsj ) Poverlap ( \|Ct∩Ct′\| , \|Ct\| , \|Ct′\| , N ) Here we are multiplying the hypergeometric p-value ( Poverlap ) by a term that corrects for the fact that not all overlaps of size k are equally likely ( the product of the k bias factors captures the relative bias toward the observed overlap ) . This has the effect of decreasing the significance assigned to overlaps involving larger repertoires , yet remains fast to evaluate , an important consideration given that the all-vs-all TCR co-occurrence calculation involves about 1014 pairwise comparisons ( and this calculation is repeated multiple times with shuffled occurrence patterns to estimate false-discovery rates ) . When clustering by co-occurrence , we augmented this heuristic p-value correction by also eliminating repertoires with very low ( fewer than 30 , 000 ) or very high ( more than 120 , 000 ) numbers of public TCRβ chains ( nonzero entries in the occurrence matrix M ) , as well as five additional repertoires which showed anomalously high levels of TCR nucleotide sharing with another repertoire—all with the goal of reducing potential sources of spurious TCR-TCR co-occurrence signal . We used the approach of ( Storey and Tibshirani , 2003 ) to estimate false-discovery rates for detecting associations between TCRs and HLA alleles and between TCRs and CMV seropositivity . Briefly , for a fixed significance threshold P we estimate the false-discovery rate ( FDR ) by randomly permuting the HLA allele or CMV seropositivity assignments 20 times and computing the average number of significant associations discovered at the threshold P in these shuffled datasets . The estimated FDR is then the ratio of this average shuffled association number to the number of significant associations discovered in the true dataset at the same threshold . In order to estimate a false-discovery rate for TCR-TCR co-occurrence over the full cohort , we performed 20 co-occurrence calculations on shuffled occurrence matrices , preserving the per-subject bias factors during shuffling by resampling each TCR’s occurrence pattern with the bias distribution {bi} determined by the subject repertoire sizes . We assessed the accuracy of our TCR:HLA associations by looking for HLA-associated public TCRβ chains in sequenced repertoires from T cell populations that were sorted for the presence of CD4/CD8 surface markers . We selected four repertoire datasets with matched CD4+ and CD8+ repertoires from a total of 63 individuals ( see the section Datasets for access details; [Emerson et al . , 2013; Rubelt et al . , 2016; Li et al . , 2016; Oakes et al . , 2017] ) . We analyzed the occurrence patterns of HLA-associated TCRβ chains in these sequence datasets , producing for each TCRβ counts of the number of CD4+ and CD8+ repertoires it was observed in ( NCD4 and NCD8 ) . TCRβ abundance levels within the individual repertoires were ignored; each occurrence in a repertoire contributed a single count to the respective CD4 or CD8 total ( which therefore range between 0 and 63 ) . Given a threshold δ on the CD4/CD8 counts difference , we assign to the CD4 compartment all TCRs for which NCD4−NCD8≥δ , and we assign to the CD8 compartment all TCRs for which NCD8−NCD4≥δ . Figure 5—figure supplement 1 shows the concordance between these assignments and inferences based on the HLA class of the most strongly associated HLA allele , for all significantly associated TCRβ chains and for various threholds δ . We used the DBSCAN ( Ester et al . , 1996 ) algorithm to cluster public TCRβ chains by their occurrence patterns . DBSCAN is a simple and robust clustering procedure that requires two input parameters: a similarity/distance threshold ( Tsim ) at which two points in the dataset are considered to be neighbors , and a minimum number of neighbors ( Ncore ) for a point to be considered a core , as opposed to a border , point . DBSCAN clusters consist of the connected components of the neighbor-graph over the core points , together with any border point neighbors the core cluster members have . To prevent the discovery of fictitious clusters , Tsim and Ncore can be selected so that core points ( points with at least Ncore neighbors ) are unlikely to occur by chance . There is a trade-off between the two parameter settings: as Tsim is relaxed , points will tend to have more neighbors on average and thus Ncore should be increased , which biases toward discovery of larger clusters; conversely , more stringent settings of Tsim are compatible with smaller values for Ncore which permits the discovery of smaller , more tightly linked clusters . For clustering TCRs by co-occurrence over the full cohort , we used a threshold of Tsim=10−8 and chose a value for Ncore ( 6 ) such that no core points were found in any of the 20 shuffled datasets . In other words , two TCRs t1 and t2 were considered to be neighbors for DBSCAN clustering if PCO ( t1 , t2 ) <10−8; a TCR was considered a core point if it had at least 6 neighbors . Choosing parameters for HLA-restricted TCR clustering was slightly more involved due to the variable number of clustered TCRs for different alleles , and the more complex nature of the similarity metric , whose dependence on TCR sequence makes shuffling-based approaches more challenging . To begin , we transformed the TCRdist sequence-similarity measure into a significance score PTCRdist which captures the probability of seeing an observed or smaller TCRdist score for two randomly selected TCRβ chains . Since public TCRβ chains are on average shorter and closer to germline than private TCRs , we derived the PTCRdist CDF by performing TCRdist calculations on randomly selected public TCRs seen in at least 5 repertoires . We identified neighbors for DBSCAN clustering using a similarity score Psim that combines co-occurrence and TCR sequence similarity:Psim ( t1 , t2 ) =f ( PTCRdist ( t1 , t2 ) ⋅PCO ( t1 , t2 ) ) where the transformation by f ( x ) =x−xlog⁡ ( x ) corrects for taking the product of two p-values because f ( x ) is the cumulative distribution function of the product of two uniform random variables . Thus , if PTCRdist and PCO are independent and uniformly distributed , the same will be true of Psim . For HLA-restricted clustering using this combined similarity measure we set a fixed value of Tsim=10−4 and adjusted the Ncore parameter as a function of the total number of TCRs clustered for each allele . As in global clustering , our goal was to choose Ncore such that core points were unlikely to occur by chance ( more precisely , had a per-allele probability less than 0 . 05 ) . We estimated the probability of seeing core points by modeling neighbor number using the binomial distribution , assuming that the observed neighbor number of a given TCR during clustering is determined by M−1 independent Bernoulli-distributed neighborness tests with rate r , where M is the number of clustered TCRs . Rather than assuming a fixed neighbor-rate r across TCRs , we captured the observed variability in neighbor-rate ( due , for example , to unequal V-gene frequencies and variable CDR3 lengths ) by using a mixture of 20 rates {rj} estimated from similarity comparisons on randomly chosen public TCRs . More precisely , we choose the smallest value of Ncore for which the following inequality holds ( where M is the number of clustered TCRs for the allele in question ) :M20∑j=120 ∑i=NcoreM−1 ( M−1i ) rji ( 1−rj ) M−1−i< 0 . 05 We also used this neighbor-number model to assign a p-value ( Psize ) to each cluster reflecting the likelihood of seeing the observed degree of clustering by chance . Since DBSCAN clusters are effectively single-linkage-style partitionings of the core points ( together with any neighboring border points ) , they can have a variety of shapes , ranging from densely interconnected graphs , to extended clusters held together by local neighbor relationships ( Ester et al . , 1996 ) . Modeling the total size of these arbitrary groupings is challenging , so we took the simpler and more conservative approach of assigning p-values based on the size of the largest TCR neighborhood ( set of neighbors for a single TCR ) contained within each cluster . We identified the member TCR with the greatest number of neighbors in each cluster ( the cluster center ) and computed the likelihood of seeing an equal or greater neighbor-number under the mixture model described above . This significance estimate is conservative in that it neglects clustering contributions from TCRs outside the neighborhood of the cluster center , however in practice we observed that the majority of TCR clusters were dominated by a single dense region of repertoire space and therefore reasonably well-captured by a single neighborhood . To control false discovery when combining DBSCAN clusters from independent clustering runs for different HLA alleles , we used the Holm method ( Holm , 1979 ) applied to the sorted list of cluster Psize values , with a target family-wise error rate ( FWER ) of 0 . 05 ( i . e . , we attempted to limit the overall probability of seeing a false cluster to 0 . 05 ) . In the Holm FWER calculation we set the total number of hypotheses equal to the total number of TCRs clustered across all alleles minus the cumulative neighbor-count of the cluster centers ( we exclude cluster center neighbors since their neighbor counts are not independent of the neighbor count of the cluster center ) . When performing HLA-restricted clustering , each TCRβ chain was assigned to its most strongly associated HLA allele . Where two alleles had identical or nearly identical ( within a factor of 1 . 25 ) association p-values , the TCR chain was included in the clustering analysis for both alleles . For each ( global or HLA-restricted ) TCR cluster , we analyzed the occurrence patterns of the member TCRs in order to identify a subset of the ( full or allele-positive ) cohort enriched for those TCRs . We counted the number of cluster member TCRs found in each subject’s repertoire and sorted the subjects by this TCR count ( rank plots in Figure 3B–C and in the right panels of Figure 7 ) . For comparison , we generated control TCR count plots by independently resampling the subjects for each member TCR , preserving the frequency of each TCR and biasing by subject repertoire size . Each complete resampling of the cluster member TCR occurrence patterns produced a subject TCR rank plot; we repeated this resampling process 1000 times and averaged the rank plots to yield the green ( ‘randomized’ ) curves in Figure 3B–C and Figure 7 . To compare the observed and randomized curves , we took a signed differenceDCO=max1≤i≤N ( ∑j≤i ( Cj−Rj ) +∑j>i ( Rj−Cj ) ) between the observed counts Cj and the randomized counts Rj , where the value of the subject index i=imax that maximizes the right-hand side in the equation above represents a switchpoint below which the observed counts generally exceed the randomized counts and above which the reverse is true ( both sets of counts are sorted in decreasing order ) . We take this switchpoint imax as an estimate of the number of enriched subjects for the given cluster ( this is the value given in the ‘Subjects’ column in Table 3 ) . Since the raw DCO values are not comparable between clusters of different sizes and for different alleles , we transformed these values to a Z-score ( ZCO ) by generating , for each cluster , 1000 additional random TCR count curves and computing the mean ( μD ) and standard deviation ( σD ) of their DCOrand score distribution:ZCO=DCO−μDσD We used this co-occurrence score ZCO together with a log-transformed version of the cluster size p-value , Ssize=−log10⁡ ( Psize ) for visualizing clustering results in Figure 6 ( Ssize on the x-axis and ZCO on the y-axis ) and prioritizing individual clusters for detailed follow-up . We annotated public TCRs in our dataset by matching their sequences against two publicly available datasets: VDJdb ( Shugay et al . , 2018 ) , a curated database of TCR sequences with known antigen specificities ( downloaded on 3/29/18; about 17 , 000 human TCRβ entries ) and McPAS-TCR ( Tickotsky et al . , 2017 ) , a curated database of pathogen-associated TCR sequences ( downloaded on 3/29/18; about 9 , 000 human TCRβ entries ) . VDJdb entries are associated with a specific MHC-presented epitope , whereas McPAS-TCR also includes sequences of TCRs isolated from diseased tissues whose epitope specificity is not defined . We added to this merged annotation database the sequences of structurally characterized TCRs of known specificity ( see below ) , as well as literature-derived TCRs from a handful of primary studies ( Dash et al . , 2017; Glanville et al . , 2017; Song et al . , 2017; Kasprowicz et al . , 2006 ) . For matches between HLA-associated TCRs and database TCRs of known specificity , we filtered for agreement ( at 2-digit resolution ) between the associated HLA allele in our dataset and the presenting allele from the database . In other words , TCRs belonging to B08:01-restricted clusters were not annotated with matches to database TCRs that bind to A02:01-presented peptides . We analyzed a set of experimentally determined TCR:peptide-MHC structures to find MHC positions frequently contacted by the CDR3β loop . Crystal structures of complexes involving human TCRs and human class I or class II HLA alleles ( Table 4 ) were identified using BLAST ( Altschul et al . , 1997 ) searches against the RCSB PDB ( Berman et al . , 2000 ) sequence database ( ftp://ftp . wwpdb . org/pub/pdb/derived_data/pdb_seqres . txt ) . Structural coverage of HLA loci and alleles is sparse and highly biased toward well studied alleles such as HLA-A02 . Given the high degree of structural similarity among class I and among class II MHC structures solved to date , we elected to share contact information across loci using trans-locus sequence alignments . For class I we used the merged alignment ( ClassI_prot . txt ) available from the IPD-IMGT/HLA ( Robinson et al . , 2015 ) database . Starting with multiple sequence alignments for individual class II loci from the IPD-IMGT/HLA database , we inserted gaps as needed in order to created merged alignments for the class II α and β chains . These alignments provided a common reference frame in which to combine residue-residue contacts from the TCR:peptide-MHC structures . We considered two amino acid residues to be in contact if they had a side chain heavyatom contact distance less than or equal to 4 . 5Å . The CDR3β contact frequency for an alignment position ( class I , class II-α , or class II-β ) was defined to be the total number of contacted CDR3β amino acids observed for that position , divided by the total number of structures analyzed . Redundancy in the structural database was assessed at the level of TCR and HLA sequence , ignoring the sequence of the peptide . Contacts from a set of n structures all containing the same TCR and HLA were given a weight of 1/n when computing the residue contact frequencies . The statistical significance of correlations between HLA allele charge and average HLA-associated TCR CDR3 charge were computed using a 2-sided test as implemented in the function scipy . stats . linregress . C++ source code implementing the clustering , generation probability , and correlation algorithms described here is available at https://github . com/phbradley/pubtcrs ( copy archived at https://github . com/elifesciences-publications/pubtcrs [Bradley , 2018] ) .	The immune system has two major ways of clearing up an infection . A rapid , first line of defense buys time while the second ‘adaptive’ response disposes of the threat with precision . The adaptive response takes longer to develop but once it has dealt with a disease , it remembers: the next time the body encounters the same threat , the immune system can respond much faster . When cells are infected by a disease-causing microbe , like a bacterium or a virus , they start carrying fragments of that microbe on their surface . Immune cells known as T cells then recognize these fragments using proteins called T cell receptors . Each T cell has a different receptor , which is specific to a precise fragment of a particular microbe . After successfully clearing an infection , some of the T cells that were mobilized remain in the blood . These memory T cells , and their specific receptors , are a lasting trace of the infections a person has encountered in the past . The exact portion of the microbial fragments that the T cells receptors can ‘see’ depends on another set of proteins , called MHC . These hold the fragments at the surface of the infected cells . The genes that code for MHCs are incredibly diverse , to the point that the exact combination of MHCs carried by a cell can be specific to an individual . However , different MHCs present different microbial fragments , and this changes which receptor can recognize the infection . At the level of a population , this mechanism makes it difficult to use T cell receptors to know exactly which diseases people had to face . Here , DeWitt et al . look at the T cell receptor sequences of 666 healthy participants , as well as their MHC variants , to try to reconstruct their disease history . This revealed that many people have clusters of similar T cells receptors sequences that occur together; these could be linked to exposure to common viruses such as parvovirus , influenza , cytomegalovirus and Epstein-Barr virus . Furthermore , examining 3D structures of T cell receptors binding to fragments carried by MHCs helps to identify how changes in the sequence of the MHC can influence which receptor will be able to attach to the complex . These results show that , despite the diversity and complexity of T cell receptors and MHCs , it is possible to spot patterns across people , and to start understanding how those patterns emerge . In addition to fighting body invaders , T cells can also use their receptors to recognize certain protein fragments carried by tumor cells . Improving our knowledge of T cell receptors and MHCs could give new insights to fight cancer .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "computational", "and", "systems", "biology", "immunology", "and", "inflammation" ]	2018	Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity
We read jubmled wrods effortlessly , but the neural correlates of this remarkable ability remain poorly understood . We hypothesized that viewing a jumbled word activates a visual representation that is compared to known words . To test this hypothesis , we devised a purely visual model in which neurons tuned to letter shape respond to longer strings in a compositional manner by linearly summing letter responses . We found that dissimilarities between letter strings in this model can explain human performance on visual search , and responses to jumbled words in word reading tasks . Brain imaging revealed that viewing a string activates this letter-based code in the lateral occipital ( LO ) region and that subsequent comparisons to stored words are consistent with activations of the visual word form area ( VWFA ) . Thus , a compositional neural code potentially contributes to efficient reading . Reading is a recent cultural invention , yet we are remarkably efficient at reading words and even jmulbed wrods ( Figure 1A ) . What makes a jumbled word easy or hard to read ? This question has captured the popular imagination through demonstrations such as the Cambridge University effect ( Rawlinson , 1976; Grainger and Whitney , 2004 ) , depicted in Figure 1A . Reading a word or a jumbled word can be influenced by a variety of factors such as visual , phonological and linguistic processing ( Norris , 2013; Grainger , 2018 ) . At the visual level , word reading is easy when similar shapes are substituted ( Perea et al . , 2008; Perea and Panadero , 2014 ) , when the first and last letters are preserved ( Rayner et al . , 2006 ) , when there are fewer transpositions ( Gomez et al . , 2008 ) , when word shape is preserved ( Norris , 2013; Grainger , 2018 ) . At the linguistic level , it is easier to read frequent words , words with frequent bigrams or trigrams as well as shuffled words that preserve intermediate units such as consonant clusters or morphemes ( Norris , 2013; Grainger , 2018 ) . Despite these insights , it is not clear how these factors combine , what their distinct contributions are , and more generally , how word representations relate to letter representations . Here , we hypothesized that , viewing a string of letters activates a visual representation that is compared with the representation of stored words . To probe visual processing , we devised a visual search task in which subjects had to find an oddball target string among distractor strings . This task does not require any explicit reading and is driven by shape representations in visual cortex ( Sripati and Olson , 2010a; Zhivago and Arun , 2014 ) . An example visual search array containing two oddball targets is shown in Figure 1B . It can be seen that finding OFRGET is easy among FORGET , whereas finding FOGRET is hard ( Figure 1B ) , showing that FOGRET is more visually similar to FORGET . This makes FOGRET easy to recognize as FORGET , whereas OFRGET is harder . Thus , the visual similarity of the jumbled words FOGRET and OFRGET to the original word FORGET ( Figure 1C ) potentially explains why transposing the middle letters renders a word easier to read than transposing its edge letters . This example suggests that orthographic processing can potentially be explained by purely visual processing ( as indexed by visual search ) without invoking any linguistic factors . However , one must be careful since subjects may have been reading during visual search , thereby activating non-visual lexical or linguistic factors . To overcome this confound , we asked whether visual search involving letter strings can be explained using a neurally plausible model containing only visual factors . We drew upon two well-established principles of object representations in high-level visual cortex . First , perceptually similar images elicit similar activity in single neurons ( Op de Beeck et al . , 2001; Sripati and Olson , 2010a; Zhivago and Arun , 2014 ) . Accordingly , we used visual search for single letters to create artificial neurons tuned for letters . Second , the neural response to multiple objects is an average of the individual object responses ( Zoccolan et al . , 2005; Ghose and Maunsell , 2008; Zhivago and Arun , 2014 ) . Accordingly , we created neural responses to letter strings as a linear sum of single letter responses . We define such responses as compositional because the response to wholes is explained by the parts . This stands in contrast to proposals for open bigram detectors ( Grainger and Whitney , 2004 ) and for local combination detectors ( Dehaene et al . , 2005; Dehaene et al . , 2010 ) according to which reading is enabled by neurons tuned for higher order combinations of letters . Our model only assumes neurons tuned for letter shape and retinal position , as observed in high-level visual cortex ( Lehky and Tanaka , 2016 ) . It does not capture any information about bigram or higher order detectors , or about other lexical or linguistic factors . We used this model to explain human performance on visual search as well as word recognition tasks . Finally , using brain imaging , we identified the neural substrates for both the letter code as well as subsequent lexical decisions . In Experiment 1 , subjects had to perform an oddball visual search task involving uppercase letters ( n = 26 ) , lowercase letters ( n = 26 ) and digits ( n = 10 ) . An example search with two oddball targets is shown in Figure 2A , illustrating how finding W is harder compared to finding T in an array of Ns . In the actual experiment , search arrays consisted of only one oddball target among 15 distractors , and subjects had to indicate the side of the screen ( let/right ) containing the target ( see Materials and methods ) . Subjects were highly consistent in their responses ( split-half correlation between average search times of odd- and even-numbered subjects: r = 0 . 87 , p<0 . 00005 ) . We calculated the reciprocal of search times for each letter pair which is a measure of distance between them ( Arun , 2012 ) . These letter dissimilarities were significantly correlated with previously reported subjective dissimilarity ratings ( Appendix 1 ) . Since shape dissimilarity in visual search matches closely with neural dissimilarity in visual cortex ( Sripati and Olson , 2010a; Zhivago and Arun , 2014 ) , we asked whether these letter distances can be used to reconstruct the underlying neural responses to single letters . To do so , we performed a multidimensional scaling ( MDS ) analysis , which finds the n-dimensional coordinates of all letters such that their distances match the observed visual search distances . In the resulting plot for two dimensions for uppercase letters ( Figure 2B ) , nearby letters correspond to small distances that is long search times . The coordinates of letters along a particular dimension can then be taken as the putative response of a single neuron . For example , the first dimension represents the activity of a neuron that responds strongest to the letter O and weakest to X ( Figure 2C ) . Likewise the second dimension corresponds to a neuron that responds strongest to L and weakest to E ( Figure 2C ) . We note that the same set of distances can be obtained from a different set of neural responses: a simple coordinate axis rotation would result in another set of neural responses with an equivalent match to the observed distances . Thus , the estimated activity from MDS represents one possible solution to how neurons should respond to individual letters so as to collectively produce behavior . As expected , increasing the number of MDS dimensions led to increased match to the observed letter dissimilarities ( Figure 2D ) . Taking 10 MDS dimensions , which explain nearly 95% of the variance , we obtained the single letter responses of 10 such artificial neurons . We used these single letter responses to predict their response to longer letter strings in all the experiments . Varying this choice yielded qualitatively similar results . Analogous results for all letters and numbers are shown in Appendix 1 . Next , we proceeded to ask whether searches for longer strings can be explained using single letter responses . In Experiment 2 , we asked subjects to perform oddball searches involving bigrams . We chose seven uppercase letters ( A , D , H , I , M , N , T ) and combined them in all possible ways to obtain 49 bigram stimuli . Subjects performed all possible pairs of 49C2 searches with one bigram as target and another as distractor ( see Materials and methods ) . An example search is depicted in Figure 3A . It can be seen that , finding TA among AT is harder than finding UT among AT . Thus , letter transpositions are more similar compared to letter substitutions , consistent with the classic results on reading ( Norris , 2013; Grainger , 2018 ) . To characterize the effect of bigram frequency , we included both frequent bigrams ( e . g . IN , TH ) and infrequent bigrams ( e . g . MH , HH ) . As before , subjects were highly consistent in their performance ( split-half correlation between odd and even-numbered subjects across all bigrams: r = 0 . 82 , p<0 . 00005 ) . Next , we asked whether bigram search performance can be explained using neurons tuned to single letters estimated from Experiment 1 . The essential principle for constructing bigram responses is depicted in Figure 3B . In monkey visual cortex , the response of single neurons to two simultaneously presented objects is an average of the single object responses ( Zoccolan et al . , 2005; Zhivago and Arun , 2014; Pramod and Arun , 2018 ) . This averaging can easily be biased through changes in divisive normalization ( Ghose and Maunsell , 2008 ) . Therefore , we took the response of each neuron to a bigram to be a weighted sum of its responses to the constituent letters ( Figure 3B ) . Specifically , the response of a neuron to the bigram AB is given by rAB = w1rA + w2rB , where rAB is the response to AB , rA and rB are its responses to the constituent letters A and B , and w1 , w2 are the summation weights reflecting the importance of letters A and B in the summation . Note that the model also does not incorporate any information specific to a particular bigram and is purely based on combining single letters . Note also that if w1 = w2 , the bigram response to AB and BA will be identical . Thus , discriminating letter transpositions necessarily requires asymmetric summation in at least one of the neurons . To summarize , the letter model for bigrams has two unknown spatial weighting parameters for each of the 10 neurons , resulting in 2 × 10 = 20 free parameters . To calculate dissimilarities between a pair of bigrams , we calculated the Euclidean distance between the 10-dimensional response vectors corresponding to the two bigrams . The data collected in the experiment comprised dissimilarities ( 1/RT ) from 1176 ( 49C2 ) searches involving all possible pairs of 49 bigrams . To estimate the model parameters , we optimized them to match the observed bigram dissimilarities using standard nonlinear fitting algorithms ( see Materials and methods ) . This letter model yielded excellent fits to the observed data ( r = 0 . 85 , p<0 . 00005; Figure 3C ) . To assess whether the model explains all the systematic variance in the data , we calculated an upper bound estimated from the inter-subject consistency ( see Materials and methods ) . This consistency measure ( rdata = 0 . 90 ) was close to the model fit , suggesting that the model captured nearly all the systematic variance in the data . As predicted in the schematic figure ( Figure 3B ) , the estimated spatial summation weights were unequal ( absolute difference between w1 and w2 , mean ± sd: 0 . 07 ± 0 . 04 ) . To assess whether this difference is statistically significant , we randomly shuffled the observed dissimilarities and estimated these weights . The absolute difference between shuffled weights was significantly smaller than for the original weights ( average absolute difference: 0 . 03 ± 0 . 02; p<0 . 005 , sign-rank test across 10 neurons ) . According to an influential account of word reading , specialized detectors are formed for frequently occurring combinations of letters ( Dehaene et al . , 2005 ) . If this were the case , searches involving frequent bigrams ( e . g . TH , ND ) or two letter words ( e . g . AN , AM ) should produce larger model errors compared to infrequent bigrams , since our model does not incorporate any bigram-selective units . Alternatively , if bigram discrimination was driven entirely by single letters , we should find no difference in errors . In keeping with this latter prediction , we observed no visually obvious difference in model fits for frequent bigram pairs or word-word pairs compared to other bigram pairs ( Figure 3C ) . To quantify this observation , we compared the model error ( absolute difference between observed and predicted dissimilarity ) for the 20 bigram pairs with the largest mean bigram frequency with the model error of the 20 pairs with the lowest mean bigram frequency . This too revealed no systematic difference ( mean ± sd of residual error: 0 . 10 ± 0 . 08 for the 20 most frequent bigrams and words; 0 . 11 ± 0 . 09 for 20 least frequent bigrams; p=0 . 80 , rank-sum test ) . Thus , model errors are not systematically different for frequent compared to infrequent bigram pairs . We conclude that bigram search can be explained entirely using single neurons tuned to single letters . In the letter model described above , the response to bigrams is a weighted sum of the single letter responses . As detailed earlier , a critical prediction of this model is that the response to transposed bigrams such as AB and BA will be different only if the summation weights are unequal . By contrast , repeated letter bigrams such as AA and BB will remain discriminable regardless of the nature of summation , since their response will be proportional to the respective single letter responses . Since reading expertise can modulate sensitivity to letter transpositions , we reasoned that familiarity might modulate the summation to make it more asymmetric . We therefore predicted that this would make transposed letter searches ( with AB as target and BA as distractor , or vice-versa ) easier to discriminate in a familiar upright orientation compared to the ( unfamiliar ) inverted orientation . By contrast , searches involving repeated letter bigrams ( with AA as target and BB as distractor ) , which also have a change in two letters , will remain equally easy in both upright and inverted orientations . We tested this prediction in Experiment 3 by asking subjects to perform searches involving upright and inverted bigrams ( see Materials and methods ) . The essential findings are summarized in Figure 3D . As predicted , subjects discriminated repeated letter bigrams ( AA-BB searches ) equally well at both upright and inverted orientations , but were substantially faster at discriminating transposed letter pairs ( AB-BA searches ) in the upright orientation ( Figure 3D; for detailed analyses see Appendix 2 ) . We obtained similar results on comparing upright and inverted trigrams as well ( Appendix 2 ) . Correspondingly , we observed a larger difference in the model summation weights for upright compared to inverted bigrams ( Figure 3E ) . We conclude that familiarity leads to asymmetric spatial summation . We note , however , that this familiarity could be due to purely visual familiarity of the letters or due to linguistic factors , which we cannot distinguish in our study . The above analyses show that the letter-based model explains dissimilarities in visual search between bigrams , which rarely contain valid words . We therefore wondered whether these results would extend to longer strings which form words . In Experiment 4 , subjects performed visual search involving six-letter strings that were either valid compound words ( e . g . FORGET , TEAPOT ) or pseudowords ( FORPOT , TEAGET ) . The single letter model yielded excellent fits to the data ( Figure 3F ) . These fits were superior to a widely used measure of string similarity , the Orthographic Levenshtein Distance ( OLD ) model ( Figure 3G ) . Importantly , the letter model fits were equivalent for both word-word pairs and nonword-nonword pairs ( Figure 3H ) . These and other analyses are described in Appendix 3 . We performed several experiments to investigate this for other string lengths . Again , the letter model yielded excellent fits across all string lengths ( Appendix 4 ) . We also tested lowercase and mixed-case strings because word shape is thought to play a role when letters vary in size or have upward and downward deflections ( Pelli and Tillman , 2007 ) . Even here , the letter model , without any explicit representation of overall word shape , was able to accurately predict most of the search performance . These results are detailed in Appendix 4 . The letter model described is neurally plausible and compositional , but is based on dissimilarities between letters presented in isolation . It could be that the representation of a letter within a bigram , although compositional , differs from its representation when seen in isolation . To explore these possibilities we developed an alternate model in which bigram dissimilarities can be predicted using a sum of ( unknown ) part dissimilarities at different locations . The resulting model , which we denote as the part sum model , yielded comparable fits to the data . It is completely equivalent to the letter model under certain conditions . Unlike the letter model which is nonlinear and could suffer from multiple local minima , the part sum model is linear and its parameters can be estimated uniquely using standard linear regression . Its complexity can be drastically reduced using simplifying assumptions without affecting model fits . These results are detailed in Appendix 5 . The above experiments show that discrimination of strings in visual search can be explained by neurons tuned for single letter shape with letter responses that combine linearly . Could the same shape representation drive reading behavior ? We evaluated this possibility through two separate word recognition experiments . In Experiment 5 , we used a widely used paradigm for word recognition , a lexical decision task ( Norris , 2013; Grainger , 2018 ) , in which subjects have to indicate whether a string of letters is a word or not using a keypress . To develop a quantitative model of lexical decision times , we drew from models of lexical decision in which responses are thought to be based on accumulation of evidence toward or against word status ( Ratcliff et al . , 2004; Ratcliff and McKoon , 2008 ) . Consider what happens when we view the string ‘PENICL’ , as opposed to the string ‘EPNCIL’ ( Figure 4A ) . Since PENICL is visually more similar to the stored word ‘PENCIL’ , it is more likely to be confused with a real word and will take longer to be adjudged a nonword . By contrast , the string ‘EPNCIL’ will take much less time to respond , since it is far away from any stored word ( Dufau et al . , 2012; Yap et al . , 2015 ) . Thus , we predict that the response time for a nonword will be inversely proportional to its distance to the nearest word ( Figure 4A ) . We also predict that this comparison will be affected by the strength of the stored word representation , such that matches to frequent words are easier . In other words , we predict that response times for nonwords will be inversely proportional to word frequency . Finally , by the same account , when we view the string ‘PENCIL’ , the match to the stored word PENCIL takes no time ( the distance being negligible ) and the response is therefore dominated by word frequency . We tested these two predictions on the observed lexical decision times . In this experiment , the words comprised four , five or six-letter words and the nonwords consisted of random strings and jumbled versions of the words ( see Materials and methods ) . Subjects were highly accurate in responding to both words and nonwords ( mean ± sd: 96 ± 2% for words , 95 ± 3% for nonwords ) . Importantly , their response times across words and nonwords were consistent between subjects as evidenced by a significant split-half correlation ( correlation between odd- and even-numbered subjects: r = 0 . 59 for words , r = 0 . 73 for nonwords , p<0 . 00005 ) . We started by characterizing response times for words . To depict the systematic variation in word response times , we plotted them in descending order ( Figure 4B ) . Subjects took longer to respond to infrequent words like MALICE compared to frequent words like MUSIC . As predicted , response times for words showed a negative correlation with log word frequency ( r = −0 . 5 , p<0 . 00005 across 450 words ) . We also estimated other lexical factors such as the logarithm of the letter frequency ( averaged across letters of the string ) , logarithm of the bigram frequency ( averaged across all bigrams in the string ) , and the number of orthographic neighbors ( i . e . number of nearby words in the lexicon ) , which are standard measures in linguistic corpora ( see Materials and methods ) . To avoid overfitting , we trained a model based on each factor on one half of the subjects and tested it on the other half . This cross-validated performance is shown for all lexical factors in Figure 4C . It can be seen that the word frequency is the best predictor of word response times ( Figure 4C ) . To assess whether all lexical factors together predict word response times any better , we fit a combined model in which the word response times are modeled as a linear sum of the four factors . The combined model performance was slightly better than the performance of the word frequency model alone ( Figure 4C ) . To assess the statistical significance of these results , we performed a bootstrap analysis . On each trial , we trained all models on the response times obtained from considering only one randomly chosen half of subjects . We calculated the correlation between each model’s predictions on the other half of the data , and repeated this procedure 1000 times . Across these samples , the word frequency model performance rarely fell below all other individual models ( p<0 . 005 ) , but was slightly worse than the combined model ( p<0 . 05 ) . We conclude that word response times are determined primarily by word frequency and to a lesser degree by letter frequency . We note that the dependence of word response times on word frequency is non-compositional , since it cannot be explained by letter frequency . Next we characterized the nonword response times . The nonword responses are plotted in descending order in Figure 4D . Subjects took longer to respond to jumbled words like PENICL ( original word: PENCIL ) with fewer transpositions compared to VTAOCE ( original word: OCTAVE ) with more transpositions . To test whether nonword to word dissimilarity can predict nonword response times , we took the letter model with 10 neurons ( with single letter tuning from visual seach ) and its spatial summation weights to match the reciprocal of the nonword responses for each word length . We optimized the spatial summation weights based on our observation that summation weights varied across visual search experiments , and that this could reflect differing attentional resources across letter positions as required for each experiment . This model yielded excellent fits to the data ( r = 0 . 70 , p<0 . 00005; Figure 4E ) that were comparable to the data consistency ( rdata = 0 . 84 ) . Importantly , this model was able to explain many classic phenomena in orthographic processing . Specifically , subjects took longer to respond to nonwords obtained by transposing a letter of a word , compared to nonwords obtained through letter substitution – these trends were present in the model predictions as well ( Figure 4F ) . Likewise , subjects took longer when the middle letters were transposed compared to when the edge letters were transposed – as did the model predictions ( Figure 4F ) . These effects replicate the classic orthographic processing effects reported across many studies ( Grainger et al . , 2012; Norris , 2013; Ziegler et al . , 2013; Grainger , 2018 ) . Next we asked whether a widely used measure of orthographic distance could explain the same data . We selected the Orthographic Levenshtein Distance ( OLD ) , in which the net distance between two strings is calculated as the minimum number of letter additions , transpositions and deletions required to transform one string into another . The OLD model yielded relatively poorer predictions of the data ( r = 0 . 36 , p<0 . 00005; Figure 4G ) . We compared the letter model with two alternate models: the OLD model and a model based on lexical factors . The OLD model is as described above . In the lexical model , the nonword response time is modeled as a linear sum of log word frequency , log mean bigram frequency of words , log mean bigram frequency of nonwords , # orthographic neighbors , log letter frequency . Since all three models have different numbers of free parameters , we compared their performance using cross-validation: we trained each model on one-half of the subjects and evaluated it on the other half of the subjects . The resulting cross-validated model fits are shown in Figure 4H . The letter model outperformed both the OLD model and the lexical model ( model correlations: r = 0 . 56 ± 0 . 02 , 0 . 33 ± 0 . 01 and 0 . 35 ± 0 . 01 for the neural , OLD and lexical models; fraction of bootstrap samples with neural <other models: p<0 . 005; Figure 4H ) . To be absolutely certain that the superior fit of the letter model was not simply due to having more free parameters , we compared the lexical model with a reduced version of the letter model with only five free parameters ( SID model; Appendix 5 ) . Even this reduced model yielded fits were better than the lexical model ( SID model correlation: r = 0 . 48 ± . 02 ) . To assess whether the model trained on visual search data would also be able to predict nonword response times , we took the model trained on the visual search data in Experiment 4 , and calculated the word-nonword distances using this model . This too yielded a significant positive correlation ( r = 0 . 39 , p<0 . 00005 ) that was better than the OLD and lexical models . Finally , a combined model – in which the neural and lexical model predictions were linearly combined – proved to explain more variance than either model ( Figure 4H ) . In sum , we conclude that word response times are explained primarily by word frequency and nonword response times are explained primarily by the distance between the nonword and the nearest word calculated using the compositional neural code . As a further test of the ability of this compositional code to explain word reading , we performed an additional experiment in which subjects had to recognize the identity of a jumbled word . Here too , response times were explained best by the letter model compared to lexical and OLD models ( Appendix 6 ) . The above results show that visual discrimination of strings can be explained using a letter-based compositional neural code , and that dissimilarities calculated using this code can explain human performance on nonwords during lexical decision tasks . Here , we sought to uncover the brain regions that represent this code and guide eventual lexical decisions . In Experiment 6 , we recorded BOLD responses using fMRI while subjects performed a lexical decision task . Since lexical decision times for nonwords can be predicted using perceptual dissimilarity , we performed a separate experiment to directly estimate perceptual dissimilarities using visual search ( Experiment 7; see Materials and methods ) . Additionally , to compare semantic representations in different ROIs , we estimated the semantic dissimilarity by calculating the cosine distance between GloVe ( Pennington et al . , 2014 ) feature vectors between word pair ( see Materials and methods ) . Importantly , the perceptual and semantic dissimilarities were uncorrelated ( r = 0 . 03 , p=0 . 55 ) , thereby allowing us to identify regions with distinct or overlapping perceptual/semantic representations . The perceptual and semantic representations are visualized in Appendix 7 . We identified several possible regions of interest ( ROIs ) using a combination of functional localizers and anatomical considerations ( see Materials and methods ) . These included the early and mid-level visual areas ( V1-V3 and V4 ) , the object-selective lateral occipital region ( LO ) , and two language areas: the visual word form area ( VWFA ) which selectively responds to words and a broad region in the temporal gyrus reading network ( TG ) . Except for VWFA , all other ROIs were bilateral . The inflated brain map of a representative subject with these ROIs is shown in Figure 5A . In the event-related runs , subjects had to make a response on each trial to indicate whether a string displayed on the screen was a word or not . A total of 64 five-letter strings ( 32 words and 32 nonwords formed using 10 single letters ) were shown . Subjects also viewed the 10 single letters , to which they had to make no response . Subjects were highly accurate ( mean ±std of accuracy: 94 ± 4% ) and showed consistent response time variations ( split-half correlation between odd and even subjects: rsh = 0 . 54 and 0 . 79 for words and nonwords , p<0 . 00005 ) . As before , the lexical decision time for words was negatively correlated with word frequency ( r = −0 . 42 , p<0 . 05 ) . Likewise , the lexical decision times for nonwords were strongly correlated with the word-nonword dissimilarity measured in visual search in Experiment 7 ( r = −0 . 68 , p<0 . 00005 ) . These results reconfirm the findings of the previous experiment performed outside the scanner . We then compared the overall brain activation levels for words , nonwords and letters in each ROI . While V4 showed greater activation for words compared to nonwords , VWFA and TG regions showed greater activation to nonwords compared to words , presumably reflecting greater engagement to discriminate nonwords that are highly similar to words ( Appendix 7 ) . Although the visual regions did not show differential overall activations , there could still be differential activation at the population level for words and nonwords . This revealed above-chance decoding in all ROIs , and better separation between words and substituted compared to transposed nonwords , matching the trend observed in behavior ( Appendix 7 ) . Next , we sought to compare the neural representations in each ROI with perceptual and semantic representations . The perceptual and semantic representations can be quite distinct , as depicted in Figure 5B: in perceptual space , TRAIL and TRIAL can be quite similar since one is obtained from the other by transposing letters , but the word PATH is distinct . By contrast , in semantic space , TRAIL and PATH have similar meanings and usage whereas TRIAL is distinct . Indeed , perceptual and semantic dissimilarities across words were uncorrelated for the words used in this experiment ( r = 0 . 03 , p=0 . 55 ) . To investigate these issues , we calculated the neural dissimilarity for each ROI between a given pair of stimuli as the cross-validated Mahalanobis distance between the voxel-wise activations evoked by the two stimuli . We selected this distance metric because it prioritizes the more reliable voxels . The cross-validation procedure calculates Euclidean distances by multiplying activations across runs to avoid bias due to noise . We then averaged this dissimilarity across subjects to get an average neural dissimilarity for that ROI . We then compared this neural dissimilarity in each ROI with perceptual dissimilarities estimated from visual search . This match to perceptual dissimilarity is shown in Figure 5C . Among the ROIs tested , only the LO dissimilarities showed a significant correlation ( correlation between 1024 pairwise dissimilarities involving 32C2 words , 32C2 nonwords , and 32 word-nonword pairs: r = 0 . 16 , p<0 . 00005; Figure 5C ) . A searchlight analysis confirmed that the match to perceptual dissimilarities was strongest in a region centred around the bilateral LO region ( Appendix 7 ) . Thus , neural dissimilarity in the LO region match best with the perceptual dissimilarities observed in visual search . We therefore conclude that LO is the likely neural substrate for the compositional letter code . To further investigate the link between the compositional letter code and the LO representation , we performed several additional analyses . First , we asked whether the neural activation of each voxel in LO could be explained using a linear sum of the single letter activations . Indeed , model fits were comparable for words and nonwords ( Appendix 7 ) . This parallels our finding that dissimilarity in visual search was predicted equally well for word-word and nonword-nonword pairs ( Figure 3H ) . Second , we confirmed that both the neural tuning for single letters , and the summation weights estimated from the behavioral data in the letter model were qualitatively similar to their counterparts estimated from voxel activations in LO ( Appendix 7 ) . In sum , we conclude that the LO region is the likely neural substrate for the compositional letter code predicted from behavior . Next we compared neural representations in each ROI to semantic space . The match to semantic space was significant only in the LO and TG regions ( correlation between 496 pairwise dissimilarities between words: r = 0 . 18 ± 0 . 05 for LO , 0 . 22 ± 0 . 04 for TG; Figure 5D ) . A searchlight analysis confirmed that semantic dissimilarities were best correlated with the TG region with additional peaks in prefrontal and motor regions ( Appendix 7 ) . The above analysis shows that neural activations in LO are correlated with both perceptual and semantic dissimilarities , but these correlations cannot be directly compared since they are based on different pairs of stimuli . To investigate whether the neural representation in LO matches better with perceptual or semantic space , we compared the match for word-word pairs alone . This revealed no significant difference between the two correlations ( r = 0 . 16 ± . 04 for LO with visual search , r = 0 . 16 ± 0 . 05 for LO with semantic dissimilarites; p=0 . 49 across 1000 bootstrap samples ) . To confirm that there is no shared variance between the perceptual and semantic space correlation , we calculated the partial correlation between neural dissimilarities in LO for word-word pairs and the perceptual dissimilarities after factoring out the dependence on semantic dissimilarities ( or vice-versa ) . As expected , both partial correlations were significant ( partial correlations: r = 0 . 13 , p<0 . 005 with perceptual space; r = 0 . 17 , p<0 . 0005 with semantic space ) . We conclude that both LO and TG regions represent semantic space . If the LO region represents each string ( word or nonword ) using a compositional code , then according to the preceding experiments , lexical decisions for words and nonwords must involve some comparison with stored word representations . Recall that lexical decision times for words are correlated with word frequency , and lexical decision times for nonwords are correlated with word-nonword dissimilarity . We therefore asked whether these lexical decision times are correlated with the average activity ( across voxels and subjects ) in a given ROI . The resulting correlations are shown in Figure 5F . Across the ROIs , only the VWFA showed a consistently positive correlation with lexical decision times for both words and nonwords ( r = 0 . 52 , p<0 . 005 for words; r = 0 . 47 , p<0 . 05 for nonwords , Figure 5E ) . A searchlight analysis confirmed that there was indeed a peak in the correlation with lexical decision times centred on the VWFA , with additional peaks in the parietal and frontal regions ( Appendix 7 ) . Interestingly , VWFA activations were larger for nonwords compared to words ( mean ± std of VWFA activations across subjects: 1 . 46 ± 0 . 22 for words , 2 . 03 ± 0 . 28 for nonwords; p<0 . 005 , signed-rank test across 17 subject activations ) . However , activations were similar for transposed nonwords compared to substituted words ( mean ±std VWFA activations across subjects: 1 . 42 ± 0 . 33 for transposed nonwords , 1 . 38 ± 0 . 33 for substituted nonwords; p=0 . 62 , signed-rank test ) . We conclude that lexical decisions are driven by the VWFA . Our compositional letter code stands in stark contrast to existing models of reading . Existing models of reading assume explicit encoding of letter position and do not account for letter shape ( Gomez et al . , 2008; Davis , 2010; Norris and Kinoshita , 2012; Norris , 2013 ) . By contrast , our model encodes letter shape explicitly and position implicitly through asymmetric spatial summation . The implicit coding of letter position avoids the complication of counting transpositions ( Yarkoni et al . , 2008; Yap et al . , 2015 ) . Our model can thus easily be extended to any language by simply estimating letter dissimilarities using visual search and then estimating the unknown summation weights from visual search for longer strings . Unlike existing models of reading , our compositional letter code is neurally plausible and grounded in well-known principles of object representations . The first principle is that images that elicit similar activity across neurons in high-level visual cortex will appear perceptually similar ( Op de Beeck et al . , 2001; Sripati and Olson , 2010a; Zhivago and Arun , 2014 ) . This is non-trivial because it is not necessarily true in lower visual areas or in image pixels ( Ratan Murty and Arun , 2015 ) . We have turned this principle around to construct artificial neurons whose shape tuning matches visual search . The second principle is that the neural response to multiple objects is typically the average of the individual object responses ( Zoccolan et al . , 2005; Sripati and Olson , 2010b ) that can be biased toward a weighted sum ( Ghose and Maunsell , 2008; Bao and Tsao , 2018 ) . Finally , we note that our letter code assumes no explicit calculations of letter position in a word , since the neurons in our model only need to be tuned for retinal position . We speculate that these neurons may be tuned not only to retinal position but also to the relative size and position of letters , as observed in high-level visual cortex ( Sripati and Olson , 2010a; Vighneshvel and Arun , 2015 ) . We have found that lexical decisions for nonwords are driven by the dissimilarity between the viewed string and the nearest word . This idea is consistent with the well-known Interactive Activation model ( McClelland and Rumelhart , 1981; Rumelhart and McClelland , 1982 ) , where viewing a string activates the nearest word representation . However , the Interactive Activation model does not explain lexical decisions or scrambled word reading , and also does not integrate letter shape and position into a unified code . Our findings are consistent with previous work showing that nonword responses are influenced by the number of orthographic neighbors ( Yap et al . , 2015 ) . Likewise , we found word frequency to be a major factor influencing lexical decisions , in keeping with previous work ( Ratcliff et al . , 2004; Dufau et al . , 2012; Yap et al . , 2015 ) . We note also that personal familiarity with words , as opposed to the word frequency estimated from text corpora , might also influence lexical decisions ( Colombo et al . , 2006; Kuperman and Van Dyke , 2013 ) . We have gone further to demonstrate a unified letter-based code that integrates letter shape and position , and localized the underlying neural substrates of the letter code to the LO region , and the comparison process to the VWFA . We propose that the compositional shape code provides a quick match to unscramble a word , failing which subjects may initiate more detailed symbolic manipulation . The success of our letter code challenges the widely held belief that efficient visual processing of letter strings requires higher-order detectors for letter combinations ( Grainger and Whitney , 2004; Dehaene et al . , 2005; Dehaene et al . , 2015; Grainger , 2018 ) . The presence of these specialized detectors should have caused larger model errors for valid words and frequent n-grams , but we observed no such trend ( Figure 3 ) . However , it is possible that there are combination detectors in subsequent stages where multiple letters have to activate single syllables . So what happens to visual letter representations upon expertise with reading ? Our comparison of upright and inverted bigrams suggests that reading should increase letter discrimination and increase the asymmetry of spatial summation ( Figure 3D , E ) . This is consistent with our recent finding that reading makes words more predictable from letters ( Agrawal et al . , 2019 ) . It is also consistent with differences in letter position effects for symbols and letters ( Chanceaux and Grainger , 2012; Scaltritti et al . , 2018 ) . We propose that both processes may be driven by visual exposure: repeated viewing of letters makes them more discriminable ( Mruczek and Sheinberg , 2005 ) , while viewing letter combinations induces asymmetric spatial weighting or increased separability . Whether these effects require active discrimination such as letter-sound association training or can be induced even by passive viewing will require comparing letter string discrimination under these paradigms . Our results elucidate the neural representations that guide lexical decision in several ways . First , we found that perceptual dissimilarities between strings , regardless of word/nonword status , matched best with neural representations in the LO region ( Figure 5C ) . This is consistent with similar findings using letters ( Agrawal et al . , 2019 ) and natural objects ( Khaligh-Razavi and Kriegeskorte , 2014 ) . Second , we have found that semantic dissimilarities between words matched both with temporal gyrus regions as well as with LO ( Figure 5D ) . The former finding is consistent with temporal gyrus regions participating in the reading network ( Friederici and Gierhan , 2013 ) , while the latter is concordant with other semantic properties such as animacy encoded in LO ( Bracci and Op de Beeck , 2016; Proklova et al . , 2016; Thorat et al . , 2019 ) . Whether these semantic properties are encoded directly by LO or are a consequence of feedback from language/semantic areas can be distinguished using methods with higher temporal resolution such as MEG or intracranial recordings . Third , our results confirm and extend our understanding of the VWFA . We found a striking correlation between lexical decision times for words as well as nonwords in the VWFA ( Figure 5E ) , suggesting that it is involved in comparing the viewed string with stored words . The finding that VWFA activity is positively correlated with word response times ( which reflect word frequency as shown in Figure 4C ) is consistent with previous studies showing that VWFA activity shows weak activity for frequent words ( Kronbichler et al . , 2004; Vinckier et al . , 2007 ) . The finding that VWFA activity is correlated with nonword response times ( which reflect perceptual distance to the corresponding word , as shown in Figure 5E ) , is consistent with observations that VWFA is modulated by orthographic similarity to words ( Vinckier et al . , 2007; Baeck et al . , 2015 ) . Finally , our finding that VWFA activations were stronger for nonwords compared to words ( Figure 5E ) , has also been observed recently ( Bouhali et al . , 2019 ) . While this might seem paradoxical considering its status as a word form area , the higher activity for nonwords is likely due to many of them being perceptually similar to words , making the lexical decision difficult . That VWFA is activated strongly for hard lexical decisions is also concordant with its higher activation for inverted compared to upright words while making lexical decisions ( Carlos et al . , 2019 ) . Fourth , our results point a way to resolve contradictory findings regarding VWFA in the literature . Some studies have reported equal activity in VWFA for words and nonwords ( Baker et al . , 2007 ) , and others have reported higher activity for word-like stimuli ( Vinckier et al . , 2007; Glezer et al . , 2009 ) – but these observations have been made while subjects performed tasks orthogonal to reading . There have been surprisingly few studies of VWFA activations during word processing tasks ( Baeck et al . , 2015; Sussman et al . , 2018; Bouhali et al . , 2019; Carlos et al . , 2019 ) . By comparing brain activations directly with behavioral responses during a lexical decision task , we found an interesting functional dissociation whereby orthographic ( perceptual ) similarity between strings was encoded not by VWFA but by LO ( Figure 5C ) and lexical decisions were encoded by VWFA and not LO ( Figure 5F ) . This finding implies that most orthographic processing phenomena are driven by compositional neural representations in LO , rather than by the VWFA . These findings are consistent with recent intracranial EEG recordings that report a progression from early to late , or letter-level to word-level representations along the ventral occipitotemporal cortex regions ( Thesen et al . , 2012; Hirshorn et al . , 2016; Lochy et al . , 2018 ) . We suggest that fine-grained comparisons between brain activations and behavior will elucidate the roles of the many cortical areas involved in reading . Our letter code explains many orthographic processing phenomena reported in the literature . Its integrated representation of both letter shape and position explains both letter transposition and substitution effects and their relative importance ( Figure 4F ) . Its asymmetric spatial weighting favoring the first letter ( Appendix 3 ) , explains the first-letter advantage observed previously ( Scaltritti et al . , 2018 ) . It also explains why increasing letter spacing can benefit reading in poor readers , presumably because it increases asymmetry in spatial summation ( Zorzi et al . , 2012 ) . To elucidate how various jumbled versions of a word are represented according to this neural code , we calculated responses of the letter model trained on data from Experiment 4 , and visualized the distances using multidimensional scaling ( Figure 6A ) . It can be seen transposing the edge letters ( OFRGET ) results in a bigger change than transposing the middle letters ( FOGRET ) , thus explaining many transposed letter effects ( Norris , 2013 ) . Likewise , it can be seen that substituting a dissimilar letter ( FORXET ) leads to a large change compared to substituting a similar letter ( FORCET ) . Replacing G with C in FORGET leads to a smaller change than replacing with X , thus explaining how priming is stronger when similar letters are substituted ( Marcet and Perea , 2017 ) . Finally , the letter subset FRGT is closer to FORGET than the same letters reversed ( TGRF ) , thereby explaining subset priming ( Grainger and Whitney , 2004; Dehaene et al . , 2005 ) . Finally , as a powerful demonstration of this code , we used it to arbitrarily manipulate reading difficulty along a sentence ( Figure 6B ) , or across multiple transpositions and even number substitutions ( Figure 6C ) . We propose that this compositional neural code can serve as a powerful baseline for the purely visual shape-based representation triggered by viewing words , thereby enabling the study of higher order linguistic influences on reading processes . Our results constitute an important first step in understanding how we read single words , but reading sentences is much more complex , with potentially many words sampled with each eye movement ( Rayner , 1998 ) . Our ability to sample multiple letters or words at a single glance is limited by two factors . The first is our visual acuity , which reduces with eccentricity . The second is crowding , by which letters become unrecognizable when flanked by other letters – this effect increases with eccentricity ( Pelli and Tillman , 2008 ) . The visual search experiments in our study involved searching for an oddball target ( consisting of multiple letters ) among multiple distractors . This would most certainly have involved detecting and making saccades to peripheral targets . By contrast , the word recognition tasks in our study involved subjects looking at words presented at the fovea . Our finding that visual search dissimilarity explains word recognition then implies that shape representations are qualitatively similar in the fovea and periphery . Furthermore , the structure of the letter model suggests a possible mechanistic explanation for crowding . Neural responses might show greater sensitivity to spatial location at the fovea compared to the periphery , leading to more discriminable representations of multiple letters . Alternatively , neural responses to multiple letters might be more predictable from single letters at the fovea but not in the periphery . Both possibilities would predict reduced recognition with closely spaced flankers . Distinguishing these possibilities will require testing neural responses in higher visual areas to single letters and multi-letter strings of both familiar and unfamiliar scripts . Ultimately understanding reading fully will require not only asking how letters combine to form words but also how words combine to form larger units of meaning ( Pallier et al . , 2011; Nelson et al . , 2017 ) . To estimate neural responses to single letters from the visual search data , we used a multidimensional scaling ( MDS ) analysis . We first calculated the average search time for each letter pair by averaging across subjects and trials . We then converted this search time ( RT ) into a distance measure by taking its reciprocal ( 1/RT ) . This is a meaningful measure because it represents the underlying rate of evidence accumulation in visual search ( Sunder and Arun , 2016 ) , behaves like a mathematical distance metric ( Arun , 2012 ) and combines linearly with a variety of factors ( Pramod and Arun , 2014; Pramod and Arun , 2016; Sunder and Arun , 2016 ) . Next , we took all pairwise distances between letters and performed MDS to embed letters into n dimensions , where we varied n from 1 to 15 . This yielded n-dimensional coordinates corresponding to each letter , whose distances matched best with the observed distances . We then took the activation of each letter along a given dimension as the response of a single neuron . Throughout we performed MDS embedding into 10 dimensions , resulting in single letter responses of 10 neurons . We obtained qualitatively similar results on varying this number of dimensions . To obtain upper bounds on model performance , we reasoned that any model can predict the data as well as the consistency of the data itself . Thus , a model trained on one half of the subjects can only predict the other half as well as the split-half correlation rsh . This process was repeated 100 times to obtain the mean and standard deviation of the split-half correlation . However , when a model is trained on all the data , the upper bound will be larger than the split-half correlation . We obtained this upper bound , which represents the reliability of the entire data ( rdata ) by applying a Spearman-Brown correction on the split-half correlation , as given by rdata = 2rsh/ ( rsh+1 ) . A total of eight subjects ( five male , aged 25 . 6 ± 2 . 9 years ) took part in this experiment . We chose seven uppercase letters ( A , D , H , I , M , N , T ) and combined them in all possible ways to obtain 49 bigram stimuli . These letters were chosen to maximize the number of two-letter words for example HI , IT , IN , AN , AM , AT , AD , AH , and HA . Letters measured 3° along the longer dimension . Subjects completed 2352 correct trials ( 49C2 search pairs x two repetitions ) . All other details were identical to Experiment 1 . Letter/Bigram frequencies were obtained from an online database ( http://norvig . com/mayzner . html ) . The total dissimilarity between two bigrams in the letter model is calculated by calculating the average dissimilarity across all neurons . For each neuron , the dissimilarity between bigrams AB and CD is given by:dAB , CD=rAB-rCD=\|w1rA+w2rB-w1rC+w2rD\|where rA , rB , rC and rD are the responses of the neuron to individual letters A , B , C and D respectively ( derived from single letter dissimilarities ) , and w1 , w2 are the spatial summation weights for the first and second letters of the bigram . Note that w1 , w2 are the only free parameters for each neuron . To estimate the spatial weights of each neuron , we adjusted them so as to minimize the squared error between the observed and predicted dissimilarity . This adjustment was done using standard gradient descent methods starting from randomly initialized weights ( nlinfit function , MATLAB R2018a ) . We followed a similar approach for experiments involving longer strings . A total of eight subjects ( four female , aged 25 ± 2 . 5 years ) participated . Twelve three-letter words were chosen: ANY , FOR , TAR , KEY , SUN , TEA , ONE , MAT , GET , PAD , DAY , POT . Each word was jumbled to obtain 12 three-letter nonwords containing the same letters . The 12 words were combined to form 36 compound words ( shown in Appendix 3 ) , such that they appeared equally on the left and right half of the compound words . Detailed analyses for this experiment are included in Appendix 3 . A total of 17 subjects ( 10 males , 25 ± 4 . 2 years ) participated in this experiment . All subjects were screened for safety and comfort beforehand to avoid adverse outcomes in the scanner . A total of 11 subjects ( six males , 26 ± 2 . 7 years ) participated in this experiment , of which seven also participated in Experiment 6 . Stimuli were identical to Experiment 6 , except that they were scaled down to a height of 1° to allow placement in a visual search array . Subjects performed a total of 2048 correct trials ( 32C2 search pairs x two conditions ( words and nonwords ) + 32 word-nonword pairs x two repetitions ) . All trials were interleaved , and incorrect/missed trials appeared randomly later in the task but were not analyzed . All other details were identical to Experiment 1 .	“Aoccdrnig to a rseearch at Cmabridge Uinervtisy , it deos not mttaer in what oredr the ltteers in a wrod are , the olny iprmoatnt tihng is taht the frist and lsat ltteer be in the rghit pclae . ” The above text is an example of the so-called “Cambridge University effect” , a meme that often circulates on the Internet . While the statement has several caveats , people do seem able to read jumbled words – at least short ones – with remarkable ease . But how is this possible ? Answering this question has proven difficult because reading involves many different processes . These include analyzing the visual appearance of a word as well as recalling its pronunciation and meaning . To find out why people are so good at reading jumbled words , Agrawal et al . tested healthy volunteers on a word recognition task . The volunteers viewed strings of letters and had to decide whether each was a word or a non-word . The more closely a jumbled non-word resembled a real word , the longer the volunteers took to categorize it . PENICL took longer than EPNCIL , for example . Words in which some of the original letters had been replaced were easier to categorize than words in which letters had only been swapped . And , as shown by the 'Cambridge University effect' , swapping the first and last letters had a greater effect than swapping the middle ones . Agrawal et al . proposed that this is because when people view a string of letters , visual areas of the brain become active in a pattern representing those letters . The brain then compares this pattern to stored representations of known words . To test this idea , Agrawal et al . developed a computer model consisting of a group of artificial neurons . Each neuron responded more to some letters than others , and the response of the model to a word could be obtained by adding together its responses to all of the letters . This model , based only on processing information using sight , predicted how long volunteers took to process jumbled words . This finding in turn suggests that sound , pronunciation or meaning of the word do not contribute as much to jumbled word reading as previously believed . Finally , the volunteers performed the same task inside a brain scanner . This revealed the brain regions responsible for processing letter strings and for comparing them to stored words . By identifying the brain circuitry that supports reading – of both intact and jumbled words – these findings could ultimately prove useful in diagnosing and treating reading disorders .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2020	A compositional neural code in high-level visual cortex can explain jumbled word reading
Maintenance of epigenetic modifiers is of utmost importance to preserve the epigenome and consequently appropriate cellular functioning . Here , we analyzed Polycomb group protein ( PcG ) complex integrity in response to heat shock ( HS ) . Upon HS , various Polycomb Repressive Complex ( PRC ) 1 and PRC2 subunits , including CBX proteins , but also other chromatin regulators , are found to accumulate in the nucleolus . In parallel , binding of PRC1/2 to target genes is strongly reduced , coinciding with a dramatic loss of H2AK119ub and H3K27me3 marks . Nucleolar-accumulated CBX proteins are immobile , but remarkably both CBX protein accumulation and loss of PRC1/2 epigenetic marks are reversible . This post-heat shock recovery of pan-nuclear CBX protein localization and reinstallation of epigenetic marks is HSP70 dependent . Our findings demonstrate that the nucleolus is an essential protein quality control center , which is indispensable for recovery of epigenetic regulators and maintenance of the epigenome after heat shock . The epigenetic landscape of a cell is fundamentally important for various DNA metabolic processes including gene transcription , DNA replication and DNA repair ( Tessarz and Kouzarides , 2014 ) . Proper maintenance of the epigenome is essential for cell viability , and increasing evidence suggests that changes in the chromatin landscape are causally related to aging-associated functional decline of a cell and ultimately cell death ( Booth and Brunet , 2016 ) . Maintenance of the epigenetic landscape can only be guaranteed by correct positioning and activity of epigenetic modifiers across the genome , and may be threatened by proteotoxic stress . Importantly , misregulation of epigenetic modifiers ( i . e . mutations , misexpression ) is frequently observed in various cancer types , underlining that regulation of the epigenetic landscape is essential for appropriate cellular functioning ( Dawson and Kouzarides , 2012 ) . The Polycomb group ( PcG ) protein family of epigenetic modifiers warrants proper regulation of stem cell self-renewal and cell lineage specification . PcG proteins reside in the canonical Polycomb repressive complexes 1 ( PRC1 ) and 2 ( PRC2 ) ( Simon and Kingston , 2013 ) . The PRC2 complex contains EZH1/2-dependent methyltransferase activity toward histone H3 at lysine 27 ( H3K27me3 ) ( Cao et al . , 2002; Ezhkova et al . , 2011; Kirmizis et al . , 2004; Kuzmichev et al . , 2002; Shen et al . , 2008 ) . PRC1 can ubiquitinate histone H2A at lysine 119 ( H2AK119 ) , by means of its E3 ligase subunit RING1A/B ( de Napoles et al . , 2004; Wang et al . , 2004 ) . PRC1 and PRC2 frequently colocalize at target genes and initially a hierarchical model was proposed for PRC1/2 function , where the CBX subunit of PRC1 recognizes the PRC2 mark H3K27me3 , placing PRC1 function downstream of PRC2 . However , recent studies have shown that PRC1 and PRC2 recruitment to chromatin , and associated histone modifying activities , can also be independent of each other ( for a review see Blackledge et al . , 2015 ) . Alternatively , non-canonical PRC1-deposited H2AK119ub was shown to independently sequester PRC2 complexes . Work from many labs , including ours , has underlined the importance of ( non- ) canonical PRC1 complexes for regulation of cellular identity of normal hematopoietic stem cells and leukemic stem cells ( Iwama et al . , 2004; Lessard and Sauvageau , 2003; Park et al . , 2003; Rizo et al . , 2008; Rizo et al . , 2010; Rizo et al . , 2009; van den Boom et al . , 2016; van den Boom et al . , 2013 ) . It is therefore evident that preservation of Polycomb-mediated epigenetic regulation is essential to maintain cell identity and prevent cellular transformation and that , in case of cellular stress induced proteotoxicity , the functionality of the epigenetic machinery is guaranteed . In this study , we investigated the stability of the epigenetic machinery in response to heat shock ( HS ) . HS is known to lead to a general shutdown of transcription . Cellular stressors like HS and proteasome inhibition induce a quick depletion of the free ubiquitin pool and this coincides with a quick reduction of ubiquitinated histone H2A ( H2AK119ub ) in the cell ( Carlson and Rechsteiner , 1987; Dantuma et al . , 2006; Mimnaugh et al . , 1997 ) . These data suggest that the epigenome may be affected by HS . A study in Drosophila cells indeed showed that HS leads to dramatic alterations of the 3D chromatin architecture as a consequence of weakening insulators between topologically associating domains ( TADs ) and newly formed architectural protein binding sites ( Li et al . , 2015 ) . In addition , Polycomb complexes were redistributed to active promoters/enhancers and formed inter-TAD interactions , likely resulting in transcriptional silencing . For a subset of genes , however , in particular the genes encoding the heat-shock proteins ( HSPs ) , HS does not cause a decrease but rather an increase in gene transcription . This response is referred to as the Heat Shock Response and mediated largely by the so-called Heat Shock Transcription factor-1 ( HSF-1 ) ( Akerfelt et al . , 2010 ) . HSPs function as molecular chaperones , not only guiding co-translational folding under normal conditions but also serving to refold heat-unfolded proteins . If proteins cannot be correctly refolded , they can be poly-ubiquitinated and degraded by the proteasome . Importantly , the intracellular pool of ‘free’ ubiquitin that is used for poly-ubiquitination of proteins is limited ( Carlson and Rechsteiner , 1987 ) . As such , HSPs prevent protein dysfunction and aggregation , a hallmark of various age-related neurodegenerative diseases like Alzheimer’s disease and Parkinson’s disease ( Hartl et al . , 2011; Kampinga and Bergink , 2016; Morimoto , 2008 ) . In this study , we specifically investigated the effects of HS on the epigenetic machinery and how this is restored upon return to physiological temperatures . We observed that PRC1 and PRC2 subunits and various other chromatin modifiers accumulate in the nucleolus upon HS . Various labs have reported on reversible accumulation of reporter-proteins in the nucleus upon heat shock ( Miller et al . , 2015; Nollen et al . , 2001; Park et al . , 2013 ) , but whether this also holds true for endogenous proteins , and what could be the physiological relevance of this process , has remained unclear . We find that the nucleolar accumulation of these epigenetic regulators coincides with a displacement of PRC1 and PRC2 from their target genes and a dramatic loss of H2AK119ub and H3K27me3 . Most importantly , the nucleolar accumulation is reversible in an HSP70-dependent manner allowing epigenetic recovery . Our data demonstrate that the nucleolus is an essential protein quality control ( PQC ) center that serves to restore the epigenomic landscape after conditions of proteotoxic stress in an HSP-dependent manner . To investigate the effects of thermal stress on the epigenetic machinery , we analyzed the localization of PRC1 subunits in response to heat shock ( HS ) . We transduced cord blood CD34+ stem/progenitor cells using a GFP-CBX2 lentiviral vector ( Figure 1A ) . Importantly , GFP pull out experiments in K562 GFP-CBX2 cells confirmed that GFP-CBX2 was properly incorporated in the PRC1 complex ( Figure 1—figure supplement 1A ) and ChIP-seq experiments in K562 GFP-CBX2 and K562 wild-type cells showed that GFP-CBX2 target genes largely overlapped with endogenous CBX2 target genes ( based on ENCODE/Broad Institute data ) and H2AK119ub enriched genes ( van den Boom et al . , 2016 ) . These data underline that the GFP-CBX2 fusion protein is incorporated into a fully functional PRC1 complex . Next , we studied the localization of GFP-CBX2 in untreated and heat shocked cord blood CD34+ cells . Whereas GFP-CBX2 was homogenously distributed throughout the nucleus in untreated cells , cells that received a HS ( 30 min , 44°C ) displayed strong accumulations of GFP-CBX2 in subnuclear domains , both in cells that were fixed after HS ( Figure 1B ) and living cells ( Figure 1C ) . Similarly , K562 leukemic cells also showed HS-induced relocalization of GFP-CBX2 to subnuclear domains ( Figure 1—figure supplement 2A ) . Transmission images suggested that HS induces relocalization of GFP-CBX2 to nucleoli , which was confirmed by immunofluorescence analyses using antibodies against the nucleolar proteins NPM1 and Fibrillarin ( Figure 1D and Figure 1—figure supplement 2B–D ) . GFP-CBX2 localized directly around Fibrillarin , which is confined to the dense fibrillar component ( DFC ) of the nucleolus , and partially colocalized with the granular component ( GC ) protein NPM1 . Taken together , these data suggest that GFP-CBX2 is most enriched in the granular component of the nucleolus ( Boisvert et al . , 2007 ) . The kinetics of HS-induced nucleolar accumulation of GFP-CBX2 were both dependent on the duration and temperature of the HS ( Figure 1E and Figure 1—figure supplement 2E–F ) . Cells exposed to a temperature of 42°C also displayed nucleolar localization of GFP-CBX2 albeit with slower kinetics . To investigate whether HS-induced nucleolar relocalization is common to all PRC1-associated CBX paralogs , K562 cell lines were generated expressing GFP-CBX4 , GFP-CBX6 , GFP-CBX7 or GFP-CBX8 . Indeed , all these CBX paralogs displayed HS-induced nucleolar accumulation ( Figure 1F and G ) . Importantly , using immunofluorescence , we also observed nucleolar accumulation of endogenous CBX4 upon HS , both in K562 and HL60 cells ( Figure 1H and Figure 1—figure supplement 2G ) . Based on these data , we hypothesized that a common domain in these proteins is sufficient for HS-induced nucleolar accumulation . Since the homology between these various CBX proteins is confined to the chromodomain ( N-terminus ) and Pc box ( C-terminus ) , we generated GFP-CBX2 and GFP-CBX8 fusions only containing the chromobox ( GFP-CBX2 [2-63] ) or the chromobox and AT hook ( GFP-CBX2 [2-96] ) ( Figure 1—figure supplement 3A ) . Strikingly , all generated truncated GFP-CBX fusion proteins displayed nucleolar localization after HS , suggesting the presence of the chromodomain is sufficient to induce nucleolar localization after HS ( Figure 1—figure supplement 3B ) . The kinetics of HS-induced relocalization of truncated CBX proteins were slightly slower , suggesting that also non-homologous peptide stretches in CBX proteins contribute to nucleolar relocalization . To verify the HS-induced accumulation of PcG proteins in the nucleolus , we isolated nucleoli from heat shocked and untreated GFP-CBX8 expressing K562 cells ( Figure 2A ) . Microscopic analysis of unfixed isolated nucleoli followed by image analysis showed a robust increase in GFP-CBX8 signal in nucleoli isolated for cells directly after HS ( Figure 2B and C ) . This observation was confirmed by fixing nucleoli and subsequent counterstaining with DAPI ( Figure 2D ) . Next , we performed western blot analysis on isolated cytoplasmic , nucleoplasmic and nucleoli fractions from GFP-CBX8 cells , which confirmed the presence of the nucleolar marker Fibrillarin in the nucleoli fraction and clearly showed an increase of GFP-CBX8 in the nucleolar fraction after HS ( Figure 2E ) . To analyze changes in the localization of endogenous PRC1 subunits , we isolated nucleoli from wild-type K562 cells and similarly performed western analysis of the cytoplasmic , nucleoplasmic and nucleoli fractions ( Figure 2F ) . Also here Fibrillarin was prominently found in the nucleolar fraction and beta-actin was confined to the cytoplasmic fraction . Clearly , both endogenous CBX4 and CBX8 were enriched in the nucleolar fraction after HS . In addition , also endogenous RING1B levels were slightly elevated in the nucleolar fraction after HS . To validate these results in an independent cell line , we analyzed cellular fractions isolated from HL60 cells ( Figure 2G ) . Also here , we observed a robust shift of endogenous CBX4 , CBX8 and RING1B to the nucleolus after HS . What could be the physiological relevance of such a shift of proteins regulating DNA-dependent processes to the nucleolus ? It has been known that one of the most dramatic morphological changes in heat treated nuclei is the swelling of nucleoli ( Welch and Suhan , 1986 ) . Whereas initially considered as heat-induced damage , several lines of independent observations have suggested that this might rather reflect a regulated , HSP-dependent process in which the nucleolus serves as a temporal storage site for unfolded proteins during proteotoxic stress ( Nollen et al . , 2001; Ohtsuka et al . , 1986; Welch and Feramisco , 1984 ) . In line with this hypothesis , we also found that both HSP70 and DNAJB1 are accumulating in the nucleolus after HS ( Figure 2G ) , which is in agreement with earlier observations that DNAJB1 and HSP70s can translocate to the nucleolus after HS ( Ohtsuka et al . , 1986; Welch and Feramisco , 1984 ) . Next , we performed label-free quantification on the nucleolar proteome in untreated and heat shocked K562 cells by LC/MS-MS analyses . In total , we identified 1279 proteins , and the nucleolar proteins NPM1 and Fibrillarin were among the most abundant proteins ( Figure 3—figure supplement 1A ) . GO term analysis of the top ten percent identified proteins based of LFQ values showed strong enrichment for GO terms related to ribosome biogenesis and rRNA processing ( Figure 3—figure supplement 1B ) . Subsequently , we analyzed proteins that were up or down in the nucleolus after HS . MaxQuant-based label-free quantification of two independent experiments measured in triplicate resulted in the identification of 153 significantly enriched proteins in the nucleolus after HS and six depleted proteins ( Figure 3A , Figure 3—figure supplement 2A , Supplementary files 1–2 ) . Nucleolar proteins like Fibrillarin and NPM1 were not affected by HS ( Figure 3—figure supplement 2A ) . Interestingly , proteins enriched in the nucleolus after HS associated with GO terms related to chromatin modification , gene expression , DNA repair , histone ubiquitination and protein refolding ( Figure 3B ) . Consistent with our IF and biochemical fractionation data , various PRC1 subunits , including CBX2 , RING1A , RING1B and PHC2 were significantly enriched in the nucleolus upon HS ( Figure 3C and Figure 3—figure supplement 2B ) . Intriguingly , we also found that PRC2 subunits EZH2 , SUZ12 and EED were enriched ( Figure 3C and Figure 3—figure supplement 2B ) and this HS-induced nucleolar accumulation of EZH2 and SUZ12 was confirmed using western blot analysis on nucleolar fractions isolated from K562 or HL60 cells ( Figure 3D ) . HS-induced nucleolar accumulation of EZH2 was independently confirmed using immunofluorescence analyses in K562 cells , HL60 cells , and primary non-transformed CD34+mobilized peripheral blood stem cells ( mPBSCs ) ( Figure 3—figure supplement 3A–C ) . Intra-nucleolar levels of H3K27me3 and H2AK119ub were not increased in heat shocked cells versus untreated cells , suggesting that PcG proteins are not involved in Polycomb-mediated silencing of nucleolar chromatin ( Figure 3E ) . In addition to these PcG proteins , many other chromatin and transcription regulating proteins were found to be enriched in the nucleolus after HS , including members of the chromodomain helicase DNA-binding ( CHD ) family and the FACT chromatin remodeling complex that both can remodel chromatin ( Marfella and Imbalzano , 2007; Winkler and Luger , 2011 ) , and the PAF complex , which regulates release of RNAPII into processive elongation ( Van Oss et al . , 2017 ) ( Figure 3F and Figure 3—figure supplement 2C ) . In addition , accumulation of BRD proteins and JMJD6 was observed in the nucleolus after HS ( Figure 3G and Figure 3—figure supplement 2D ) . BRD4 and JMJD6 are co-bound to enhancers and regulate promoter-proximal pause-release of RNAPII ( Liu et al . , 2013 ) . Taken together , these data show that HS induces strong shifts of various chromatin and transcription regulators toward the nucleolus . In line with our western analysis of cellular fractions , our proteomic analyses showed several chaperone proteins to be enriched in the nucleolus after HS . These included members of the HSP70 chaperone family ( HSPA1A/B ) , DNAJB1 , DNAJC7 and the small heat shock protein HSPB1 ( Figure 3H and Figure 3—figure supplement 2E ) . Strikingly , many other chaperones , including members of the HSP90 family were not or weakly enriched in the nucleolus after HS showing the response is specific and suggesting that this subset of HSPs may somehow have functional implications in this response . In addition to the HSPs , we also observed a strong accumulation of 26S proteasome subunits in the nucleolus after HS ( Figure 3I and Figure 3—figure supplement 2F ) . It is possible that post-HS proteasomal degradation of damaged proteins occurs in the nucleolus . This is consistent with observations using model proteins , which are targeted to the nucleolus for post-stress degradation ( Park et al . , 2013 ) . Independent LC-MS/MS analysis with K562 GFP-CBX8 cells confirmed these findings , implying that HS-induced nucleolar accumulation of various chromatin regulators , protein chaperones and proteasomal subunits is a conserved biological phenomenon ( Figure 3—figure supplement 4A–E , Supplementary file 3 ) . Our data suggests that upon HS many chromatin remodelers and transcriptional regulators accumulate in the nucleolus which may become a hot spot for protein quality control . In Drosophila , nucleolar accumulation of Pc has been suggested to contribute to the generalized silencing of most of the genome observed in heat shocked cells ( Li et al . , 2015 ) . Combined with our observations that many chromatin remodeling and transcription regulatory proteins accumulate in the nucleolus after HS , these data prompted us to speculate that such HS-induced redistributions may severely impact on the chromatin bound fraction of various epigenetic regulators . To analyze changes in PRC1 complex chromatin binding upon HS we performed ChIP-qPCRs in K562 cells expressing PcG GFP-fusion proteins and validated PRC1 binding to PcG target genes . Indeed , upon HS ( 1 hr , 44°C ) we observed a strong reduction in chromatin binding of GFP-CBX2 , BMI1-GFP , MEL18-GFP and , to a lesser extent , GFP-RING1B ( Figure 4A ) . To investigate how HS impacts on chromatin binding of endogenous PRC1 subunits , we performed ChIPs using an antibody directed against endogenous CBX8 , and also here we observed a quick reduction in CBX8 binding to Polycomb target genes after HS ( Figure 4B ) . Moreover , in line with our LC-MS/MS data , ChIP analysis using an antibody directed against the PRC2 subunit EZH2 also clearly showed a loss of endogenous EZH2 binding from target genes after HS , confirming that both PRC1 and PRC2 show strongly reduced chromatin binding after HS ( Figure 4C ) . To investigate how this may functionally impact on PRC1/2-deposited epigenetic marks , we analyzed H2AK119ub and H3K27me3 levels at PcG target genes . Previous studies have shown that HS induces a rapid but reversible loss of ubiquitinated histones ( Carlson et al . , 1987; Mimnaugh et al . , 1997 ) . Indeed , we also observed a strong decrease in H2AK119ub levels after HS ( Figure 4D ) . In addition , H3K27me3 levels were also significantly reduced in heat-shocked cells vs . control cells ( Figure 4E ) . These data show that HS not only causes displacement of PRC1/2 complexes but also leads to a reduction of their respective epimarks . To assay HS-induced loss of PRC1 chromatin binding in a genome-wide manner , we performed ChIP-seq of both endogenous CBX8 ( K562 cells ) and GFP-CBX2 ( K562 GFP-CBX2 cells ) in untreated cells or after HS . Heat maps and band plots of CBX8 and GFP-CBX2 peaks clearly showed a loss of CBX8 and GFP-CBX2 chromatin binding after HS ( Figure 4F and G , Supplementary files 4–5 ) . In line with our previous observations concerning PRC1 chromatin occupancy , many CBX8 and GFP-CBX2 peaks were found to be intergenic ( van den Boom et al . , 2016 ) ( Figure 4—figure supplement 1A ) . To investigate HS-induced reduction of CBX8 and GFP-CBX2 at gene promoters , we generated heat maps and band plots of transcription start site ( TSS ) -associated peaks ( Figure 4—figure supplement 1B–C ) . Clearly , also here a loss of CBX8 and GFP-CBX2 chromatin association is observed upon HS . TSS-localized peaks are associated to genes enriched for development-related GO terms , confirming that these are Polycomb target genes ( Figure 4H ) . Typical examples of chromatin regions that show a reduction of CBX8 or GFP-CBX2 chromatin binding are shown in Figure 4I . Next , we analyzed HS-induced changes of other epigenetic modifications . Here we find that , in addition to loss of H2AK119ub and H3K27me3 , after HS ( 1 hr , 44°C ) also H3K4me3 levels are reduced , and , likely as a consequence of H3K4me3 loss , H3K4me1 levels are increased ( Figure 4J ) . The increase in H3K4me1 levels argues that loss of epimarks is not a mere consequence of decreased nucleosome occupancy but truly a consequence of changes in the epigenetic marking of the chromatin . Finally , we investigated whether the HS-induced loss of PRC1/2-associated epimarks also resulted in loss of silencing of Polycomb target genes . Indeed , we found that the expression of various Polycomb target genes was increased 3 hr after HS , and recovered to pre-HS levels afterwards ( Figure 4K ) . Taken together , these data show that , in parallel to the reallocation of chromatin remodelers and transcriptional regulators to the nucleolus , HS induces a quick reduction in PRC1 and PRC2 chromatin binding and changes in the epigenetic profile of PcG target genes with consequences for the transcriptional state of these genes . Next , we aimed to determine the physical-dynamic properties of PcG in the nucleoli of heat shocked cells . To achieve this , we stably expressed GFP-CBX2 in HeLa cells and determined GFP-CBX2 dynamics between the nucleolus and the nucleoplasm by photobleaching GFP-CBX2 in the nucleoli and analyzing the fluorescent recovery after photobleaching ( FRAP ) in time ( Figure 5A ) . Strikingly , whereas the kinetics of nucleolar GFP-CBX2 in untreated cells were very quick , nucleolar GFP-CBX2 was highly immobile in heat shocked cells ( Figure 5B and C ) with limited to no dynamic exchange between the nucleolus and the nucleoplasm . The nucleolus is a membrane-less nuclear body with liquid-like properties; its formation depends on liquid-liquid phase transition ( Brangwynne et al . , 2011; Marko , 2012 ) . Analysis of the intra-nucleolar dynamics of GFP-CBX2 in heat shocked cells using FRAP/FLIP ( fluorescence loss in photobleaching ) analysis ( Figure 5A ) , revealed that there was very little fluorescence recovery in the bleached region and fluorescence loss in the adjacent nucleolar region ( Figure 5D and E ) , indicating that GFP-CBX2 might be present in these nucleoli in a more solid-like state . Proteins in similar solid states have been shown to be aggregated ( Patel et al . , 2015 ) . To discriminate between a liquid-like or solid state of CBX protein accumulations , we exposed heat shocked K562 GFP-CBX2 and K562 GFP-CBX8 cells to 1 , 6-hexanediol , an aliphatic alcohol that disturbs weak hydrophobic interactions ( Kroschwald et al . , 2015; Patel et al . , 2007 ) . Clearly , both GFP-CBX2 and GFP-CBX8 nucleolar accumulations were 1 , 6-hexanediol insensitive ( Figure 5F and G ) , in line with a more solid , aggregation-like state . We next asked whether the HS-induced allocation of PcG proteins to nucleoli serves to allow for a quick recovery of the epigenetic modifiers to restore chromatin binding of epigenetic regulators and associated changes in the epigenetic landscape after heat shock . In addition , we argued that the co-appearance of HSPs in these nucleoli may be required to recover these regulators from their solid-like , aggregated state . Certain mammalian HSPs have been demonstrated to be able to disentangle protein aggregates ( Mogk et al . , 2018; Nillegoda et al . , 2018 ) , including the HSPs ( DNAJB1 and HSP70 ) that we identified as part of the nucleolar proteome after heat shock ( Figure 3H ) . To test the reversibility of nucleolar GFP-CBX2 accumulations after HS , we treated K562 GFP-CBX2 cells with a HS ( 30 min , 44°C ) and monitored GFP-CBX2 localization at 37°C afterwards . Clearly , within 3 hr after HS the GFP-CBX2 nucleolar accumulations dispersed and GFP-CBX2 regained its original pan-nuclear distribution ( Figure 6A ) , showing that nucleolar accumulations of PcG proteins are readily reversible . Similarly , and in line with our cellular fraction data and LC-MS/MS data , we also observed a reversible HS-induced nucleolar accumulation of DNAJB1 ( Figure 6—figure supplement 1A ) . In addition , also HSP70 showed post-HS nuclear translocation , and localized to the nucleolus in a reversible fashion ( Figure 6—figure supplement 1B ) . Accumulation of HSP70 in the nucleolus was not as prominent as observed for DNAJB1 , which is in line with our LC-MS/MS data , and may be a consequence of other nuclear activities of HSP70 . Next , we investigated whether drug-induced inhibition of the HSP70 machinery using the HSP70 inhibitor VER-155008 would impact on the relocalization of PcG proteins to the nucleolus or would delay recovery of GFP-CBX2 from the nucleolus after HS . Clearly , post-HS nucleolar accumulation was not impaired upon HSP70 inhibition , suggesting that the HSP70 machinery is not involved in chaperoning PcG proteins to the nucleolus after HS ( Figure 6B ) . However , HSP70 inhibition led to a clear delay in recovery of GFP-CBX2 from the nucleoli suggesting that HSP70 activity is required to resolve the nucleolar accumulation of GFP-CBX2 ( Figure 6B and C ) . Similarly , a partial knockdown of HSPA1A , an abundant heat-inducible HSP70 family member in human cells and identical to HSPA1B at the protein level , resulted in a significant delay of GFP-CBX2 recovery from the nucleoli in HEK293T GFP-CBX2 cells ( Figure 6—figure supplement 1C–E ) . Next , we investigated whether induction of endogenous HSPs accelerates GFP-CBX2 recovery after HS . K562 GFP-CBX2 cells were treated with two consecutive HSs with a 3-hr interval ( Figure 6D ) . HS is known to induce HSP expression ( including several HSP70s ) , resulting in a period of increased thermotolerance . This cellular property allowed us to compare the kinetics of GFP-CBX2 recovery in the presence of basal or HS-induced HSP levels . Indeed , whereas recovery of GFP-CBX2 after the first HS required almost 3 hr , recovery after the second HS was almost completed within 30 min ( Figure 6E and F ) . Importantly , the amount of GFP-CBX2 that initially accumulated during the first or second HS did not change dramatically ( Figure 6E and F ) . This implies that increased HSP levels specifically affect the recovery of GFP-CBX2 proteins from the solid phase within the nucleolus . Next , we tested whether the kinetics of recovery of GFP-CBX2 from the nucleolus and epigenetic recovery after HS were similar . Indeed , we found that H2AK119ub was completely recovered at 4 hr after HS , whereas H3K27me3 recovery was slightly delayed ( Figure 6G ) . At later time points after HS we observed a complete recovery of GFP-CBX2 and various epigenetic modifications ( Figure 6—figure supplement 2A ) . Interestingly , H2AK119ub recovery was strongly dependent on HSP protein expression . Thermotolerant cells that received a second HS displayed a much faster recovery of H2AK119ub compared to cells after the first HS ( Figure 6H ) . In line with our ChIP-qPCR data , H3K27me3 was also reduced albeit with slower kinetics . Both HSP70 and DNAJB1 were strongly induced upon the first HS , whereas EZH2 and CBX8 protein levels were rather stable or slightly reduced . Next , we investigated whether post-HS H2AK119ub recovery is dependent on Polycomb proteins that are recovered from the nucleolus and not merely on newly translated Polycomb proteins . Therefore , we pre-treated K562 cells for 1 hr with cycloheximide or DMSO and studied H2AK119ub recovery after HS . Importantly , H2AK119ub recovery did not depend on de novo protein synthesis as H2AK119ub levels also recovered in cycloheximide-treated cells ( Figure 6—figure supplement 2B ) . Taken together , these data show that molecular chaperones are crucial for recovery of GFP-CBX2 from the nucleolus and that this activity is essential for epigenetic recovery after HS ( Figure 6I ) . Spatial separation of proteins in the cytosol and nucleus upon heat stress has been repeatedly suggested to prevent interference of un- or misfolded proteins with essential cellular processes ( Escusa-Toret et al . , 2013; Kaganovich et al . , 2008; Miller et al . , 2015 ) . At the same time , this ‘storage’ may allow for rapid recovery of proteins to re-initiate the crucial processes they are normally engaged in . For the nucleolus , these experiments have been done with reporter proteins , without any direct connection to physiological cellular processes . Despite this drawback , these studies revealed that HS-induced redistribution to the nucleolus is important for both refolding ( Nollen et al . , 2001 ) or degradation ( Park et al . , 2013 ) of these reporters . Our data are the first to show that numerous endogenous chromatin regulators temporarily accumulate in the nucleolus upon a HS , and , in an HSP70-dependent manner , fully and functionally recover to the chromatin upon return to physiological temperatures . At this stage , we do not know what drives the association of the various PcG proteins to the nucleolus upon HS . Given the protein denaturation effects of HS , ( partial ) protein unfolding is likely a key driver of protein relocalization to the nucleolus . Whether it is unfolding of nucleolar proteins that cause retention of PcG proteins or actually partial unfolding of PcG proteins ( or both ) remains to be elucidated . Data from Audas and colleagues showed that a ncRNA , arising from intergenic stretches in between ribosomal DNA repeats , is capable of sequestering and immobilizing various proteins in the nucleolus upon acidosis or HS ( Audas et al . , 2012 ) . Alternatively or in parallel , heat-unfolded nuclear reporter proteins ( Nollen et al . , 2001 ) or cytosolic proteins ( Park et al . , 2013 ) have been reported to accumulate in the nucleolus via chaperoned transport . Whilst we cannot exclude this possibility here , the appearance of PcG proteins in the nucleolus were independent of the HSP70 machinery . However , like in other studies ( Nollen et al . , 2001; Welch and Feramisco , 1984 ) , we did find significant enrichment of HSP70 family members , and co-chaperones such as DNAJB1 and HSPB1 , in the nucleolus after HS . We also show that both HSPA1A/HSP70 knockdown and inhibiting HSP70 activity result in a significant delay of GFP-CBX2 recovery from the nucleolus , whereas elevated HSP70 expression accelerates the reallocation of PcG proteins to the chromatin . Whereas the nucleolus is a membrane-less organelle with liquid-like properties ( Brangwynne et al . , 2011; Marko , 2012 ) , our finding that post-HS GFP-CBX2 accumulations are 1 , 6-hexanediol insensitive , suggest that it is present in the nucleolus in a more solid phase . This could relate to the requirement of an active HSP70 machinery for its re-solubilization upon recovery . In fact , both HSP70 and DNAJB1 that accumulate in the nucleolus are crucial components of chaperone machines with protein disaggregation power capable of functionally solubilizing proteins ( Mogk et al . , 2018; Nillegoda et al . , 2018 ) . It is important to note that the functional recovery of epigenetic control was not dependent on de novo synthesis of the PcG proteins . Translation inhibition did not interfere with this H2AK119ub recovery , suggesting that a least a large fraction of nucleolar PcG proteins are re-solubilized and functionally refolded . In addition , a fraction of the nucleolar accumulated proteins may be targeted for proteasomal degradation supported by the HS-induced nucleolar enrichment of the 26S proteasome that we found . In line with these data , a link between the proteasome and the nucleolus was previously suggested and proteasome inhibition leads to nucleolar accumulation of the proteasome ( Arabi et al . , 2003; Fátyol and Grummt , 2008; Latonen et al . , 2011 ) . Interestingly , also other types of stress such as transcription inhibition , DNA damage induction and viral infection , have been shown to cause major changes in the protein composition of the nucleolus ( Andersen et al . , 2005; Boisvert et al . , 2010; Emmott et al . , 2010; Lam et al . , 2010 ) . However , whereas HS mainly induced accumulation of proteins in the nucleolus , transcription inhibition using actinomycin D resulted in a release of ribosomal proteins and RNA processing factors and an increase of snRNP proteins ( Andersen et al . , 2005 ) . In contrast , treatment of cells with the proteasome inhibitor MG132 , which similarly to HS leads to proteotoxic stress , led to an increase in ribosomal proteins in the nucleolus ( Andersen et al . , 2005 ) . Together , these data suggest that the nucleolus could be an important protein quality control center serving under many different stress conditions . Proteotoxic stress-induced loss of H2AK119ub has previously been observed by other groups ( Carlson and Rechsteiner , 1987; Dantuma et al . , 2006; Mimnaugh et al . , 1997 ) , and it has been proposed that the reason for this loss is the urgent need for ‘free’ ubiquitin in cells post-HS . Our data suggests that HS-induced redistribution of PcG proteins to the nucleolus has direct implications for histone marking and is not limited to H2AK119ub , but also affects H3K27me3 , H3K4me3 and H3K4me1 levels at PcG target genes . Other studies have also shown HS-induced loss of H3K27Ac from HS-repressed enhancers ( Chen et al . , 2017 ) and changes in the 3D chromatin structure and epigenetic landscape in Drosophila cells ( Li et al . , 2015 ) . In this latter study , the authors observed a moderate localization of the Drosophila Polycomb ( Pc ) protein to the nucleolus upon HS . Although ribosomal DNA transcription in the nucleolus was strongly reduced upon HS , Pc binding to ribosomal DNA repeats was not increased , suggesting that Pc is not involved in repressing ribosomal DNA transcription ( Li et al . , 2015 ) . Whereas our HS-induced CBX protein accumulation in the nucleolus is more robust , we did not observe an increase in rDNA binding by CBX8 ( data not shown ) . We also did not find increased H3K27me3 and H2AK119ub levels in the nucleolus after HS suggesting that accumulating PRC1 and PRC2 subunits are functionally inactive . Together these findings contradict a hypothetical chromatin regulatory activity of PRC1/2 in the nucleolus after HS but rather suggest that their respective subunits are undergoing protein quality control . Taken together , our data shows that HS directly affects chromatin binding of PcG proteins and results in a decrease of PcG-related epigenetic modifications . Importantly , HSP70-dependent protein disaggregation and refolding enables PcG proteins to quickly re-initiate their epigenetic functions at target genes . It is evident that quick re-installation of PcG epimarks is key to maintain proper epigenetic regulation of PcG target genes . The question remains how often cells will erroneously ‘repair’ the epigenetic profile after stress-induced epigenetic instability . Despite the fact that the majority of epimarks may be properly reinstalled , mistakes will result in epigenetic scars , which may contribute to cellular transformation , loss of cell function and ultimately cell death . Various studies have shown that alterations in transcriptional and epigenetic regulation are major contributors to aging-associated loss of cellular function ( Booth and Brunet , 2016 ) . Based on our data , it is tempting to speculate that cellular-stress-induced epigenetic changes may contribute to age-associated epigenetic alterations . Importantly , it has been shown that the molecular chaperone system of a cell displays age-associated functional decline ( Klaips et al . , 2018; Labbadia and Morimoto , 2015 ) . This may well trigger age- or disease-associated reduction in protein quality control of epigenetic regulators , including PcG proteins , leading to alterations in the epigenome . We propose a model where HS leads to loss of chromatin binding and nucleolar accumulation of PcG proteins and various other epigenetic regulators , likely as a consequence of protein unfolding . Loss of PcG chromatin binding leads to a loss of PRC1/2-related epigenetic modifications which recovery depends on HSP70 activity in the nucleolus . Cord blood ( CB ) and mobilized peripheral blood stem cells ( mPBSCs ) were obtained from healthy full-term pregnancies and allogeneic blood stem cell donors respectively after informed consent in accordance with the Declaration of Helsinki at the obstetrics departments at the Martini Hospital and University Medical Center Groningen . This study was approved by the UMCG Medical Ethical Committee . CB CD34+ cells were isolated as previously described ( Schuringa et al . , 2004 ) . CB CD34+ cells , K562 , HeLa , and HEK293T cells were transduced as described previously ( Horton et al . , 2013; Schuringa et al . , 2004; van den Boom et al . , 2013 ) . One round of lentiviral transduction was performed and cells were harvested at day 2 after transduction . For retroviral transductions virus was produced transiently in HEK293T cells by transfection of the appropriate PC182 GFP-fusion vector and pCL-Ampho at day 1 . At day 2 the medium on the HEK293T cells was changed to RPMI ( incl . 10% FCS and 1% P/S ) and at day 3 the supernatant was harvested , filtered and used for infection of cells . To generate stable cell lines GFP-positive cells were sorted out 3 days after transduction . The ( human ) erythromyeloblastoid leukemia cell line K562 and HL60 cells were cultured in RPMI 1640 ( containing L-glutamine ) supplemented with 10% FCS and 1% penicillin/streptomycin ( PAA Laboratories ) . CB CD34+ cells were cultured in IMDM , supplemented with 20% FCS , 1% penicillin/streptomycin , 20 ng/ml SCF , and 20 ng/ml IL-3 . HeLa cells and HEK293T cells were cultured in DMEM supplemented with 10% FCS and 1% penicillin/streptomycin . Cell lines were all tested mycoplasma-free using a PCR-based assay . For VER-155008 treatment cells were pre-treated with VER-155008 at a concentration of 5 μM for 48 hr . Cycloheximide treatments were performed at a concentration of 10 μg/ml . The lentiviral pRRL SFFV GFP-fusion vector for CBX2 was generated as described previously ( van den Boom et al . , 2013 ) . Other GFP-CBX fusion proteins ( PC182 GFP-CBX4 , GFP-CBX6 , GFP-CBX7 and GFP-CBX8 ) were expressed from retroviral vectors that were previously described ( Vandamme et al . , 2011 ) . pRRL SFFV GFP-CBX2 ( aa2-63 ) , pRRL SFFV GFP-CBX2 ( aa2-96 ) , pRRL SFFV GFP-CBX8 ( aa2-62 ) , and pRRL SFFV GFP-CBX8 ( aa2-96 ) were generated by PCR amplification of the indicated fragment of the CBX2/8 protein using pRRL SFFV GFP-CBX2 and PC182 GFP-CBX8 as templates , followed by subcloning into the pJet1 . 2/blunt vector ( Thermofisher ) . After sequence validation , these fragments were excised using BsrGI and subcloned into pRRL SFFV GFP-CBX2 where CBX2 was excised using BsrGI . For immunofluorescence microscopy , cells were cytospinned on glass slides and subsequently fixed using 4% paraformaldehyde in PBS . Subsequently , cells were permeabilized using PBS containing 0 . 1% Triton X-100 . Primary antibodies include anti-Fibrillarin ( ab5821 , Abcam ) , anti-NPM1 ( FC-61991 , Thermo Fisher Scientific ) , anti-CBX4 ( E6L7X , Cell Signalling Technology ) and anti-EZH2 ( D2C9 , Cell Signalling Technology ) followed by secondary antibody staining using Alexa Fluor 488 goat-anti-rabbit ( Thermo Fisher Scientific , A-11008 ) , Alexa Fluor 488 goat-anti-mouse ( Thermo Fisher Scientific , A-11001 ) , Alexa Fluor 647 goat-anti-rabbit ( Thermo Fisher Scientific , A-21244 ) , or Alexa Fluor 647 goat-anti-mouse ( Thermo Fisher Scientific , A-21235 ) . Images were acquired on a Leica DM6000B microscope using a 40x dry objective ( HCX PL FLUOTAR , numerical aperture: 0 . 75 ) or a 63x immersion objective ( PL S-APO , numerical aperture: 1 . 30 ) using LAS-AF software ( Leica ) . Confocal images were acquired on a Leica TCS SP8 confocal laser-scanning microscope using a HC PL APO CS2 63x/1 . 4 oil objective , and excitation with 488 nm ( 20 mW ) and 633 nm ( 30 mW ) laser lines . For FRAP experiments , a confocal laser-scanning microscope ( Zeiss LSM780 NLO; Carl Zeiss Microcopy ) was used . HeLa GFP-CBX2 cells were seeded in a 35 mm dishes , no . 15 coverslip , 14 mm diameter ( MatTek ) . To measure protein mobility after HS , cells were first heat shocked ( 30 min , 44°C ) , followed by FRAP analysis at 37°C . To perform FRAP experiments first the subnuclear location of nucleoli was identified using a transmission image . Next , a region of interest covering the whole nucleolus ( in the case of FRAP experiments ) , or half the nucleolus ( FRAP/FLIP ) was defined . For FRAP/FLIP experiments the opposite side of the nucleolus was selected to measure the FLIP signal . The FRAP region was bleached for five iterations at the highest intensity of the 488 nm line of a 25 mW argon laser focused by a EC Plan-Neofluar 40x/1 . 30 Oil DIC M27 lens ( Carl Zeiss Microcopy ) . Recovery of fluorescence was monitored at 1 s intervals at 0 . 5% of the laser intensity used for bleaching . For generation of FRAP and FLIP curves the background signal was subtracted from the measured fluorescent intensities and subsequently normalized to prebleach levels . Finally , the mean and standard deviation were plotted . For HSPA1A/HSP70 knockdown , HEK293T GFP-CBX2 cells were seeded on poly-L-lysine coated coverslips , and the next day mock or HSPA1A/HSP70 siRNAs were transfected using Lipofectamine 2000 ( Thermo Fisher Scientific ) . Two days after transfection , cells were heat shocked and fixed at the indicated time points . Western blot analysis was performed as published previously ( van den Boom et al . , 2013 ) . The following antibodies were used: anti-GFP ( ab290 , Abcam ) , anti-EZH2 ( D2C9 , Cell Signaling Technology ) , anti-SUZ12 , anti-CBX4 ( 09–029 , Merck ) , anti-CBX8 ( C15410333 , Diagenode ) , anti-RING1B ( ab181140 , Abcam ) , anti-BMI1 ( F6 , Merck ) , anti-DNAJB1 ( SPA-400 , Enzo Life Sciences ) , anti-HSP70 ( SPA-810 , Enzo Life Sciences ) , anti-Fibrillarin ( ab5821 , Abcam ) , anti-H3K27me3 ( 07–449 , Merck ) , anti-H2AK119ub ( D27C4 , Cell Signaling Technology ) , anti-H3K4me3 ( ab8580 , Abcam ) , and anti-β-Actin ( C4 , Santa Cruz ) . Cellular fractionation and nucleoli isolation was essentially performed as described ( Andersen et al . , 2002 and http://www . lamondlab . com ) . Briefly , 1 × 108 K562 or HL60 cells were spun down and washed using PBS . Subsequently , cell pellets were resuspended in 2 ml ice-cold buffer A ( 10 mM Hepes pH 7 . 9 , 1 . 5 mM MgCl2 , 10 mM KCl , 0 . 5 mM DTT , and protease inhibitors . Cells were incubated on ice for five minutes and subsequently broken open using a 2 ml dounce homogenizer ( 10 strokes using a tight pestle ) . Dounced cells were spun down at 4°C and the supernatant ( cytoplasmic fraction ) was stored . The pellet was resuspended in 1 . 2 ml buffer S1 ( 0 . 25 M Sucrose , 10 mM MgCl2 , and protease inhibitors ) and layered on 1 . 2 ml buffer S2 ( 0 . 35M Sucrose , 0 . 5 mM MgCl2 , and protease inhibitors ) . The nuclei were spun though the sucrose cushion for 5 min at 1475 x g . Next , the nuclei were resuspended in 1 . 2 ml buffer S2 and sonicated 6 × 10 s on ice using a probe sonicator ( Soniprep 150 , MSE ) . The sonicated nuclei were layered on 1 . 2 ml buffer S3 ( 0 . 88M Sucrose , 0 . 5 mM MgCl2 , and protease inhibitors ) and centrifuged for 10 min at 2889 x g . The supernatant ( nucleoplasmic fraction ) was stored and the pellet was washed using 0 . 5 ml buffer S2 and finally resuspended in 80 microliter buffer S2 . Subsequently , 20 microliter 5x Laemmli sample buffer was added and samples were boiled for 5 min . Nucleoli samples were loaded on a 4–12% pre-cast NuPAGE gel ( Invitrogen ) , and shortly ran into the gel . Gel staining was performed using Coomassie dye R-250 ( Thermo Scientific ) followed by destaining with ultrapure water . Coomassie-stained samples were excised in one gel slice that were further cut into small pieces and destained using 70% 50 mM NH4HCO3 and 30% acetonitrile . Reduction was performed using 10 mM DTT dissolved in 50 mM NH4HCO3 for 30 min at 55°C . Next the samples were alkylated using 55 mM iodoacetamide in 50 mM NH4HCO3 for 30 min at room temperature and protected from light . Subsequently , samples were washed for 10 min with 50 mM NH4HCO3 and for 30 min with 100% acetonitrile . Remaining fluid was removed and gel pieces were dried for 15 min . at 55°C . Tryptic digest was performed by addition of sequencing-grade modified trypsin ( 10 ng/µl in 50 mM NH4HCO3 ) and overnight incubation at 37°C . Peptides were extracted using 5% formic acid followed by a second elution with 5% formic acid in 75% acetonitrile . Samples were dried in a SpeedVac centrifuge and dissolved in 5% formic acid . Online chromatography of peptides was performed with an Ultimate 3000 nano-HPLC system ( Thermo Fisher Scientific ) coupled online to a Q-Exactive-Plus mass spectrometer with a NanoFlex source ( Thermo Fisher Scientific ) equipped with a stainless steel emitter . Tryptic digests were loaded onto a 5 mm × 300 μm i . d . trapping micro column packed with PepMAP100 5 μm particles ( Dionex ) in 0 . 1% FA at the flow rate of 20 μL/min . After loading and washing for 3 min , peptides were forward-flush eluted onto a 50 cm × 75 μm i . d . nanocolumn , packed with Acclaim C18 PepMAP100 2 μm particles ( Dionex ) . The following mobile phase gradient was delivered at the flow rate of 300 nL/min: 3–50% of solvent B in 90 min; 50–80% B in 1 min; 80% B during 9 min , and back to 3% B in 1 min and held at 3% B for 19 min . Solvent A was 100:0 H2O/acetonitrile ( v/v ) with 0 . 1% formic acid and solvent B was 0:100 H2O/acetonitrile ( v/v ) with 0 . 1% formic acid . MS data were acquired using a data-dependent top-10 method dynamically choosing the most abundant not-yet-sequenced precursor ions from the survey scans ( 300–1650 Th ) with a dynamic exclusion of 20 s . Sequencing was performed via higher energy collisional dissociation fragmentation with a target value of 1e5 ions determined with predictive automatic gain control . Isolation of precursors was performed with a window of 1 . 8 Da . Survey scans were acquired at a resolution of 70 , 000 at m/z 200 . Resolution for HCD spectra was set to 17 , 500 at m/z 200 with a maximum ion injection time of 50 ms . Normalized collision energy was set at 28 . Furthermore , the S-lens RF level was set at 60 and the capillary temperature was set at 250degr . C . Precursor ions with single , unassigned , or six and higher charge states were excluded from fragmentation selection . Raw mass spectrometry data were analyzed using MaxQuant version , 1 . 5 . 2 . 8 ( Cox and Mann , 2008 ) , using default settings and LFQ/iBAQ enabled , and searched against the Human Uniprot/Swissprot database ( downloaded June 26 , 2016 , 20197 entries ) . The data was further processed using Perseus software , version 1 . 5 . 8 . 5 ( Tyanova et al . , 2016 ) . Total RNA was isolated using the RNeasy Mini Kit ( QIAGEN ) , and cDNA was generated using the iScript cDNA synthesis kit ( Bio-Rad ) . For quantitative RT-PCR , cDNA was amplified using SsoAdvanced SYBR Green Supermix ( Bio-Rad ) on a MyIQ thermocycler ( Bio-Rad ) . Primer sequences can be found in Supplementary file 6 . ChIP analysis was essentially performed as described previously ( Frank et al . , 2001 ) . ChIP reactions were performed using the following antibodies: anti-GFP ( ab290 , Abcam ) , anti-CBX8 ( C15410333 , Diagenode ) , anti-EZH2 ( D2C9 , Cell Signaling ) , anti-H2AK119ub ( D27C4 , Cell Signaling ) , anti-H3K27me3 ( C15410195 , Diagenode ) , anti-H3K27Ac ( C15410196 , Diagenode ) , anti-H3K4me1 ( C15410194 , Diagenode ) , and anti-H3K4me3 ( C15410003 , Diagenode ) . ChIP efficiencies were assessed using qPCR . Primer sequences can be found in Supplementary file 6 . Sequencing samples were prepared according to the manufacturer's protocol ( Illumina ) . End repair was performed using the precipitated DNA using Klenow and T4 PNK . A 3’ protruding A base was generated using Taq polymerase and adapters were ligated . The DNA was loaded on gel and a band corresponding to ~ 300 bp ( ChIP fragment + adapters ) was excised . The DNA was isolated , amplified by PCR and used for cluster generation on the Illumina NextSeq 500 genome analyzer . The 50 bp tags were mapped to the human genome HG19 using BWA ( Li and Durbin , 2009 ) . For processing and manipulation of SAM/BAM files SAMtools was used ( Li et al . , 2009 ) . For each base pair in the genome , the number of overlapping sequence reads was determined and averaged over a 10 bp window and visualized in the UCSC genome browser ( Kent et al . , 2002 ) . Peak calling algorithm MACS was used to detect the binding sites at a q-value cut off for peak detection of 0 . 01 . ChIP-seq tracks were visualized using UCSC genome browser ( Kent et al . , 2002 ) . Identification of genes associated to detected peaks was performed using GREAT ( McLean et al . , 2010 ) . The accession number the ChIP-seq data in this paper is GEO: GSE121182 . Tags within a given region were counted and adjusted to represent the number of tags within a 1 kb region . Subsequently , the percentage of these tags as a measure of the total number of sequenced tags of the sample was calculated . Heatmaps and bandplot profiles were generated using fluff ( Georgiou and van Heeringen , 2016 ) . Gene ontology ( GO ) analysis was performed using BiNGO ( Maere et al . , 2005 ) .	All cells in our bodies contain the same sequence of DNA , hence the same genes , in a compartment called the nucleus . Yet different sets of genes are switched on in different types of cells . Cells achieve this by a process called epigenetic regulation . Proteins known as epigenetic regulators modify DNA and its associated proteins in ways that can turn genes on or off . Different types of cells contain different epigenetic regulators , and so express different genes . The Polycomb group proteins ( or PcG for short ) turn their target genes off and are important to maintain the identity of a cell . When the target genes of PcG proteins are inadvertently switched on , this may lead to changes in the fate of cells , potentially resulting in diseases such as cancer . So , it is important that cells keep the PcG proteins active where necessary , even in the face of stress . Cellular stresses come in several forms but often interfere with the normal activities of proteins . If cells experience high temperatures , they can experience a stress known as heat shock . This can cause proteins , including PcG proteins , to unfold . Azkanaz et al . have now investigated what happens to PcG proteins in cells experiencing heat shock , and how these cells try to limit the damage this causes . Azkanaz et al . conducted their experiments on healthy and cancerous human blood cells . After exposing the cells to half an hour of high temperature the PcG proteins disappeared from the genes they were switching off . This means that cells exposed to heat shock lose their epigenetic control machinery , which may lead to permanent changes to epigenetic modifications found across the genome when not quickly reinstalled . PcG proteins , and another group of proteins called the heat shock proteins , were found to move to a compartment within the nucleus called the nucleolus . While the cells had returned to body temperature and were recovering from the heat shock , the heat shock proteins helped the PcG proteins fold back into their proper shapes . The PcG proteins then left the nucleolus and returned to their target genes , where they reinstalled the epigenetic marks . These experiments show that heat shock causes a temporary loss of epigenetic regulators from their target genes and that the nucleolus acts as a protein quality control center . Future experiments might explore how PcG proteins get to the nucleolus after heat shock and how impaired protein quality control ( i . e . upon aging ) may lead to alterations of the epigenetic landscape in a cell . Deeper knowledge of this process could help us to understand how cells can recover from stress .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "chromosomes", "and", "gene", "expression", "biochemistry", "and", "chemical", "biology" ]	2019	Protein quality control in the nucleolus safeguards recovery of epigenetic regulators after heat shock
Polycomb Repressive Complex 2 ( PRC2 ) is essential for gene silencing , establishing transcriptional repression of specific genes by tri-methylating Lysine 27 of histone H3 , a process mediated by cofactors such as AEBP2 . In spite of its biological importance , little is known about PRC2 architecture and subunit organization . Here , we present the first three-dimensional electron microscopy structure of the human PRC2 complex bound to its cofactor AEBP2 . Using a novel internal protein tagging-method , in combination with isotopic chemical cross-linking and mass spectrometry , we have localized all the PRC2 subunits and their functional domains and generated a detailed map of interactions . The position and stabilization effect of AEBP2 suggests an allosteric role of this cofactor in regulating gene silencing . Regions in PRC2 that interact with modified histone tails are localized near the methyltransferase site , suggesting a molecular mechanism for the chromatin-based regulation of PRC2 activity . The combined antagonistic activities of the Polycomb group ( PcG ) and the Trithorax group ( TrxG ) protein complexes contribute to correct homeotic gene expression and accurate cell fate maintenance . PcG and TrxG complexes are evolutionarily conserved in eukaryotes and regulate chromatin structure through recruitment to Polycomb or Trithorax Responsive Elements ( PREs , TREs ) for gene silencing or activation , respectively ( Schuettengruber et al . , 2007 ) . Within the PcG protein complexes , Polycomb Repressive Complex 2 ( PRC2 ) plays an essential role in chromatin modification by di- and tri-methylating Lys 27 of Histone H3 ( H3K27me2/3 ) ( Cao et al . , 2002; Czermin et al . , 2002; Muller et al . , 2002; Kirmizis et al . , 2004 ) . In higher eukaryotes , high levels of H3K27me3 are typically associated with transcriptional repression and gene silencing . The importance of the PcG protein in this process is emphasized by the observation that deletion of any of its components in mice results in early embryonic lethality or severe defects during development ( O'Carroll et al . , 2001; Pasini et al . , 2004 ) . The PRC2 core , conserved from Drosophila to humans , is composed of four proteins that add up to about 230 kDa ( Figure 1A ) ( see Margueron and Reinberg , 2010 for a recent review ) : EED ( present in different isoforms ) , either one of the two methyltranferases Ezh1 or Ezh2 ( Ezh1/2 ) , Suz12 , and either RbAp46 or RbAp48 ( RbAp46/48 ) . Both Ezh1 and Ezh2 contain enzymatic methyl-transferase activity within a C-terminal SET ( Su ( var ) 3-9 , Enhancer-of-zeste , Trithorax ) domain . PRC2 complexes containing Ezh1 have lower enzymatic activity than those containing Ezh2 , and target a subset of Ezh2 genes ( Margueron et al . , 2008 ) . Ezh2 activity appears to depend on interaction with both Suz12 and the WD40 domain in EED ( Cao and Zhang , 2004a; Pasini et al . , 2004; Yamamoto et al . , 2004; Ketel et al . , 2005 ) . EED's WD40 beta propeller , in turn , interacts with H3K27me3 repressive marks . This interaction is proposed to promote the allosteric activation of PRC2 methyltransferase activity ( Margueron et al . , 2009; Xu et al . , 2010 ) . RbAp48 also contains a WD40 propeller required for interaction with both Suz12 and the first 10 residues of unmodified Histone H3 peptides ( Nowak et al . , 2011; Schmitges et al . , 2011 ) . 10 . 7554/eLife . 00005 . 003Figure 1 . Reconstitution of the human PRC2-AEBP2 Complex . ( A ) Schematic representation of the components of the human PRC2 Complex and AEBP2 . ( B ) Size-exclusion chromatography of recombinant PRC2-AEBP2 complex and corresponding SDS-PAGE separation stained with Coomassie brilliant blue . The molecular mass of the recombinant complex is ∼275 kDa . Arrows and numbers indicate elution markers in the size-exclusion chromatography experiments and their molecular masses ( in kilodaltons ) , respectively ( a . u . : arbitrary unit ) . ( C ) Negative-stain EM of the recombinant PRC2-AEBP2 complex . Individual particles have an elongated shape with a length of ∼16 nm and a thickness of ∼7 nm . Bar: 200 nm . ( D ) Comparison between representative reference-free 2D class averages from non cross-linked ( 0% ) and mildly cross-linked ( 0 . 015% glutaraldehyde ) particles of the PRC2-AEBP2 complex . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 003 Biochemical studies have shown that PRC2 co-purifies with the protein AEBP2 and it has been proposed that this interaction aids in targeting of PRC2 to specific DNA sites and enhances its methyl-transferase activity ( Cao and Zhang , 2004b; Kim et al . , 2009 ) . This important cofactor is an evolutionarily conserved protein present in two isoforms in humans , an adult-specific larger form ( 51 kDa ) and an embryo-specific smaller form ( 32 kDa ) , both containing three Gli-Krüppel ( Cys2-His2 ) -type zinc fingers ( He et al . , 1999 ) . The PRC2 complex has been the focus of a significant number of biochemical and molecular studies ( for a recent review see Margueron and Reinberg , 2011 ) , and atomic structures of the EED and RbAp48 subunits have been reported ( Margueron et al . , 2009; Nowak et al . , 2011; Schmitges et al . , 2011; Xu et al . , 2010 ) . A comprehensive picture , however , of the PRC2 complex and the manner in which its different components interact to coordinate the regulated methyl-transferase activity remains elusive . In this study we present the first structure of the entire PRC2 holoenzyme in complex with AEBP2 , describing its subunit architecture and the important interactions between their domains . These results contribute significantly to our structural understanding of this chromatin regulator , allowing us to suggest a possible molecular mechanism for PRC2's role in gene silencing . In preliminary experiments , we reconstituted the tetrameric PRC2 complex ( Ezh2/EED/Suz12/RbAp48 ) in an insect cell expression system , following previous protocols established to reconstitute a functional PRC2 complex ( Pasini et al . , 2004; Ketel et al . , 2005; Margueron et al . , 2008 ) . When analyzed by SDS page the purified complex appeared to be stoichiometric and biochemically homogeneous ( data not shown ) . However , further analysis to generate reference-free 2D class averages and a 3D reconstruction were limited in resolution and lacked clear structural details ( data not shown ) , indicating the presence of extreme conformational flexibility and hampering further structural studies of this tetrameric PRC2 . Since the AEBP2 cofactor is known to stably interact with the PRC2 complex and is required for optimal enzymatic activity ( Cao and Zhang , 2004b ) , we added AEBP2 to the expression system , with the hope of locking the PRC2 complex in a conformation , both more active and more stable . We overexpressed the five-component PRC2-AEBP2 complex using the shorter isoform of AEBP2 protein , which is missing an extended unstructured region present in the longest isoform . Stoichiometric and biochemically homogeneous PRC2-AEPB2 complex was purified from insect cell lysate by affinity chromatography using a StrepII tag on the AEBP2 subunit , followed by size exclusion chromatography ( Figure 1B ) . The subunits of the purified complex appeared to be stoichiometric by SDS page , although we could not rule out the presence of a small excess of AEBP2 , as this subunit was utilized for tag-based purification . In our EM images we observed a small percentage of very small particles , which could be attributed to views of the complex oriented perpendicular to the plane of the grid , or to the presence of partial complexes ( even AEBP2 by itself ) . These smaller particles were not considered in the generation of the 3D reconstruction ( see ‘Materials and methods’ ) . While inclusion of the cofactor significantly improved the quality of the 2D class averages in EM analysis ( Figure 1D , top row ) , both detailed inspection and attempts to pursue a 3D reconstruction ( not shown ) indicated that the stability of the complex remained limiting . In order to preserve complex stability for further structural analysis , we mildly cross-linked the PRC2-AEBP2 complexes using 0 . 015% glutaraldehyde . Under these conditions we saw no apparent aggregation of the complex ( Figure 1C ) . Importantly , comparison of class averages of the non cross-linked and the cross-linked sample ( Figure 1D , bottom row ) , shows that the cross-linking had a positive effect on the stability of the complex , as judged by the increased detail in the class averages ( for similar number of particles and classes ) . Class averages show PRC2 having an elongated shape , with top and bottom density regions that are joined at a narrow central point ( Figure 1D ) . Given the lack of structural information for the PRC2 complex , we carried out ab initio structural determination using the Random Conical Tilt ( RCT ) method ( Radermacher et al . , 1987 ) ( Figure 2 ) . Tilted and untilted image pairs were collected ( Figure 2A ) , and 20 reference-free 2D class averages were calculated from the untilted classes ( Figure 2B ) . RCT reconstructions were generated for each class average using the corresponding tilted images ( Figure 2B , C ) . A four-lobed organization was evident in all the resulting models , indicating that the complex likely assumes one major architectural state . Concurrently , we collected a large dataset ( ∼40 , 000 particles ) of untilted images , which were used to generate 1000 reference-free 2D class averages . Euler angles were assigned to these class averages , using each of the 20 RCT reconstructions as reference models . The results showed that the class averages were aligned in 3D in a self-consistent fashion , producing a similar complex architecture at the low resolution expected at this stage , irrespective of the RCT structure used as a reference ( data not shown ) . Not surprisingly , Euler assignment using the RCT model from the most populated class gave a reconstruction that had the most structural detail ( Figure 2C , class 4 ) . This reconstruction was then used as an initial model ( Figure 3A ) for multiple rounds of projection-based angular refinement for single particles of the full 0° dataset ( Penczek et al . , 1994 ) . The final refined structure has a resolution of 21 Å , based on the 0 . 5 Fourier shell correlation criteria ( Figure 3B ) ( van Heel and Schatz , 2005 ) . Due to the shape of the complex and its tendency to lie flat on the grid , we do not have an even distribution of views . However , views around the longitudinal axis of the model ( vertical axis in the Euler plot ) are well represented ( Figure 3C ) . The quality of our model in representing the experimental images was confirmed by comparing the re-projected 3D reconstruction of the PRC2-AEBP2 complex with reference-free class averages ( Figure 3D ) . 10 . 7554/eLife . 00005 . 004Figure 2 . Ab initio random conical tilt reconstruction of the human PRC2-AEBP2 complex . ( A ) Representative untilted and 60° tilt-pair micrographs ( 80 , 000× magnification ) . Corresponding particles pairs indicated by yellow circles . ( B ) RCT Volumes aligned to each of the 20 corresponding reference free class averages ( from 6075 particles in the 0° micrographs , each class containing between 150 and 800 particles , as indicated in parentheses ) . ( C ) Alignment of the RCT volumes with respect to each other . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 00410 . 7554/eLife . 00005 . 005Figure 3 . Refinement and final statistics for the reconstruction of the human PRC2-AEBP2 complex . ( A ) 3D reconstruction of the ab initio model used for iterative projection-matching ( corresponds to reconstruction 4 in Figure 2C ) . ( B ) Final resolution was estimated as 21 Å using the Fourier shell correlation criterion with a cutoff of 0 . 5 . ( C ) Euler distribution plot of the PRC2-AEBP2 particles , where the size of the circle corresponds to the relative number of views included for that projection . ( D ) Comparison between re-projection of the final model ( even numbers ) and reference free 2D class averages ( odd numbers ) . The projections were generated at 12° increments around the PRC2 long axis , including out-of-plane tilting going up to 30° . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 005 Figure 4 shows four orthogonal views of the PCR2-AEBP2 structure , displayed as an isosurface that corresponds to the estimated molecular mass for the complex ( 275 kDa ) . The structure ( roughly 160 Å by 120 Å by 90 Å ) consists of four large lobes: A , B , C and D ( each approximately 55 Å in diameter ) , interconnected by two narrower arms , Arm 1 at the top , and Arm 2 in the center . In the upper part of the structure , Arm 1 spans the width of the complex horizontally , connecting lobes A and B . These lobes have additional interactions closer to the center of the particle . Arm 2 runs in a more vertical direction , starting at Lobe A and continuing vertically to the lower half of the structure , where it merges with Lobe C and D . Lobe A and D both have distinct ring-like shapes that are consistent with the propeller-like WD40 motifs of EED and RbAp48 ( Figure 4 , blue ) . These two WD40 domains are the only regions in the PRC2 complex for which atomic structures are presently available . 10 . 7554/eLife . 00005 . 006Figure 4 . Structure of the human PRC2-AEBP2 complex . ( A ) The PRC2-AEBP2 complex consists of 4 different lobes , each about 55 Å in diameter ( A , B , C , D ) , interconnected by two narrow arms ( Arm 1 , Arm 2 ) . The two WD40 domains of EED and RbAp48 ( indicated in blue ) are located at opposite ends . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 006 To identify regions of interaction within the PRC2-AEBP2 complex , we carried out chemical cross-linking using isotope labeled cross-linkers and mass spectrometric identification of the cross-linked peptides , on recombinant complexes identical to those used for structure determination . This technique has been successfully utilized in several laboratories during recent years to aid in characterizing the organization of multi-subunit protein complexes ( Gingras et al . , 2007; Maiolica et al . , 2007; Ciferri et al . , 2008; Rappsilber , 2011 ) . For cross-linking analysis , we used DSSG , a homo-bifunctional cross-linker reacting with primary amines in lysine side chains or protein N-termini spaced ∼7 Å apart . We identified about 60 intra- or inter-molecular sites of interaction within the PRC2-AEBP2 complex ( Figure 5; Supplementary file 1 ) . While some of these interactions confirm previous biochemical data , others had never been described before and thus significantly expanded our knowledge of the PRC2 organization . In this report we include all the identified cross-linking interactions ( Supplementary file 1 ) . None of them proved in contradictions with previous reports or published crystal structures . Salient cross-linked Lysines are described in the following section , indicated as [Kn] , where n represents the residue number of the protein . 10 . 7554/eLife . 00005 . 007Figure 5 . MS-coupled cross-linking analysis of the PRC2-AEBP2 complex . Cross-link map for PRC2 in complex with AEBP2 . Observed inter-molecular cross-links ( straight dashed lines ) are colored in grey . Intra-molecular cross-links are color coded by the respective PRC2-AEBP2 subunit ( See also Supplementary file 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 007 Previous crystallographic analysis of the EED-Ezh2 complex described the interaction of the C-terminal WD40 propeller of EED with residues 40–68 of Ezh2 ( Han et al . , 2007 ) . In agreement with these data , we identified a cross-link between EED [K423] and Ezh2 [K39] , as well as an internal cross-link between EED [K79/83] and EED [K433] ( Figures 5 and 6A ) . We found two additional interactions between EED and other PRC2 components ( Figure 5 ) , one connecting the WD40 domain of EED [K433] with a loop between the second and the third Zn fingers of AEBP2 [K118/121] , and the other linking the N-terminal region of EED [K19/20] with a region downstream of the first SANT domain of Ezh2 [K241/243] ( Figure 5 ) . 10 . 7554/eLife . 00005 . 008Figure 6 . Isotopic crosslinking within the WD40 regions of EED and RbAp48 . ( A ) Crystal structure of EED in complex with Ezh2 ( PDB: 2QXV ) . EED is colored in green and Ezh2 in blue . Both intermolecular and intra-molecular crosslinking data fit very well with the inter-residue distance in the structure . Scale bar of 7 . 7 Å represent the length of DSSG molecule used for the cross-linking experiment . ( B ) Structure of the subunit RbAp46 ( PDB: 3CFS; 89% identity to RbAp48 ) . Distance between residues K120 and K160 ( ∼5 Å ) is within the range of the cross-linker . The larger distance observed between cross-linked residues K160 and K220 ( ∼10 Å ) could be justified if the length of both lysine residues is also included in the calculation . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 008 We found several interactions involving this first SANT domain of Ezh2 . The peptide centered at residues [K210/217/222] binds the second SANT domain of Ezh2 [K510/514/515] , associates with the VEFS domain of Suz12 [K650] , and is also cross-linked to the same region of AEBP2 [K118/121] that interacts with EED . Interestingly , this region of AEBP2 , which contains the last two Zn finger domains [K95–K166] , represents an intricate hub of interaction within the PRC2 complex . In addition to the interactions described above , cross-linking data show that AEBP2 binds to Ezh2's methyltransferase ( SET ) -containing C-terminus [K568–K740] , as well as Suz12's Zn finger domain [K414–K534] . Furthermore , this region of Suz12 [K465/468–K483] also interacts with the methyltransferase domain of Ezh2 [K568–K740] , suggesting a trimeric subcomplex that includes the SET domain of Ezh2 and the Zn fingers of Suz12 and AEBP2 . Based on the observations that the Zn finger of AEBP2 and the C-terminal region of Suz12 together interact with both the N-terminal SANT and C-terminal SET domains of Ezh2 , we propose that these two terminal regions of Ezh2 are within proximity to , or directly interacting with each other . Two recently published crystal structures of the Drosophila Nurf55-Suz12 complex identified a small region of interaction between the WD40 domain of Nurf55 ( an ortholog of RbAp48 ) and an alpha helix of Suz12 ( residues 77–93 , corresponding to 105–121 in human ) ( Nowak et al . , 2011; Schmitges et al . , 2011 ) . In addition , Schmitges and colleagues identified a region downstream of this Suz12 helix that plays an important role in stabilizing this interaction . Due to the absence of exposed lysines , we did not observe any cross-linking between the regions of RbAp48 and Suz12 shown to interact in the crystal structure . We did identify internal cross-linking within the WD40 propeller of RbAp48 [K114/120–K160–K215/220] , consistent with the reported crystal structure of the isoform RbAp46 ( PDB: 3CFS; Figure 6B ) . Additionally , we detect the presence of a set of three interactions between the WD40 domain of RbAp48 [K307/309] and the central portion of Suz12 [K167–K403] ( Figure 5 ) . This result suggests that the stabilizing downstream region described by Schmitges and colleagues might actually involve a larger portion of Suz12 . In addition to this interaction with RbAp48 , we also observe cross-linking between this region of Suz12 [K304–K403] and AEBP2 [K229–K274] . These residues of AEBP2 also interact with AEBP2's own Zn finger domain [K95–K166] ( Figure 5 ) . Taken together , these findings suggest that RbAp48 , the central domain of Suz12 , and AEBP2 undergo extensive interactions , in agreement with previous biochemical data ( Cao and Zhang , 2004b ) . In summary , our cross-linking data provides a detailed overview of the interactions among the components of the PRC2-AEBP2 complex . Interestingly , we find that AEBP2 plays a major role in coordinating different PRC2 subunits , explaining its stabilizing effect on the PRC2 complex . AEBP2's central region is involved in interactions with the N- and C-termini of Ezh2 , EED , and the C-terminal region of Suz12 , while its C-terminal region interacts with both the central portion of Suz12 and RbAp48 . In order to provide a better-defined spatial context to all our cross-linking data we used EM analysis to localize the different PRC2 subunits within the EM structure of the AEBP2-bound complex . To determine the position of individual components , we incorporated an N-terminal MBP ( maltose-binding protein ) or an internal or C-terminal GFP ( green fluorescence protein ) into each of the subunits within the PRC2-AEBP2 complex , visualizing the resulting assembly by EM . We used reference-free 2D classification to identify the position of the specific tags when compared to the corresponding class averages of the untagged complex ( Figures 7–10 ) . We concentrated our analysis on a specific view that we define as ‘canonical’ ( corresponding to the first panel in Figure 4 ) , which is both well represented in the EM images ( for most , although not all of the tagged samples ) and where the structural features of the complex are clear and well separated . 10 . 7554/eLife . 00005 . 009Figure 7 . Localization and arrangement of the subunit EED within the PRC2 electron density map . Three different internal GFP tags ( EED-EGFP128 , EED-EGFP370 and MBP-Ezh2 ) were used to determine the correct position of the EED-Ezh2 crystal structure ( PDB: 2QXV ) within the EM reconstruction . ( A and B ) For each study , a cartoon indicates the position of the tag within the protein . MBP tags are indicated in red , GFP tags in green . The far left panel represents the reference-free 2D class obtained from the labeled sample . The middle left panel shows the corresponding class for the unlabeled sample . The middle right panel was calculated by subtracting the labeled reference-free class from the unlabeled average ( labeled and unlabeled ) . Only differences with a standard deviation greater than 3 , are considered significant . The far right panel includes a representative 3D view of the complex , the localized density color-coded and the assigned Lobe indicated . ( C ) Top panel: 2D class averages for each mutant indicates the position of the specific tag on the molecule . Bottom panel: fitting of the X-ray structure within the EM density based on the specific position of the tags . Docking of the Histone H3 based on its crystal structure in complex with the subunit EED ( PDB: 3IIW ) is also indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 00910 . 7554/eLife . 00005 . 010Figure 8 . Localization of the subunit RbAp48 within the PRC2-AEBP2 electron density map . ( A ) Reference-free 2D class of the labeled , unlabeled sample , difference map and 3D view of the complex with assigned localization . ( B ) Fitting of the Nurf55-Suz12 crystal structure within the density of Lobe D . The density has been automatically placed using the local localization algorithm implemented in the UCSF Chimera software ( Pettersen et al . , 2004 ) . Docking of the Histone H3 based on its crystal structure in complex with the RbAp48 homologue subunit Nurf55 ( PDB: 2YBA ) is also indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 01010 . 7554/eLife . 00005 . 011Figure 9 . Localization and arrangement of the subunit EZH2 within the PRC2-AEBP2 electron density map . ( A ) Reference-free 2D class of the labeled , unlabeled sample , difference map and 3D view of the complex with assigned localization for different MBP/GFP fusion mutants of EZH2 . ( B ) Assignment of EZH2 subunit domains to electron density map based on the specific position of the tags . The crosslink between K210 and K510 is shown . The EZH2 protein chain path within the PRC2-AEBP2 complex is indicated in the inset ( See also Figure 9—figure supplement 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 01110 . 7554/eLife . 00005 . 012Figure 9—figure supplement 1 . Subclassification of particles from labeled complexes illustrate the flexibility of the tag . ( A ) Subclassification of the canonical view for the EZH2 1-594-GFP-595-751 complex showed all the classes to contain an extra tag density . The class average selected for Figure 9AIV is indicated with a green box . Notice that the GFP density is clearer in any of the subclasses than in the overall average ( top right panel , green circle ) ( See also Movie 1 ) . ( B ) Subclassification of a well-represented , canonical view of the EZH2 1-303-GFP-304-751 labeled complex shows some subclasses without clear extra density ( left panels ) and other with clear , but flexible tag ( right panel ) . The class shown in Figure 9AII is indicated with the green box . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 012 We started by assigning the position of EED , which consists of a WD40 domain and an 80-residue N-terminus that is predicted to be unstructured ( Han et al . , 2007 ) . Neither N-terminal MBP nor C-terminal GFP fusions assembled into stoichiometric or stable complexes . To overcome this limitation , we took advantage of the fact that the GFP N- and C-termini are in close spatial proximity and designed an expression vector to generate protein chimeras containing an internal GFP , connected by a short loop , at desired sites along the main protein chain . Using the crystal structure of EED ( PDB: 2QXV ) , we designed four constructs , each containing an internal GFP at a distinct site within the WD40 domain . The two constructs containing an internal GFP at either residue 115 or 370 ( EED-GFP115 and EED-GFP370 ) assembled into stoichiometric and stable complexes that were used for EM analysis ( Figure 7A , panels I and II , respectively ) . Comparing the internally GFP-tagged EED sample with the untagged form allowed us to identify the additional GFP density protruding from the WD40-shaped Lobe A ( Figure 7A ) . To further confirm this localization , we reconstituted and analyzed a complex containing an N-terminal MBP on Ezh2 . According to the known crystal structure ( Han et al . , 2007 ) , the EED WD40 domain interacts with the N-terminal region of Ezh2 . Therefore , an MBP in this region should localize similarly to Lobe A . Consistent with this prediction , 2D analysis of this construct showed additional density corresponding to an MBP tag protruding from Lobe A ( Figure 7B ) . Based on the positions of the two internal GFP tags for EED and the MBP tag for Ezh2 , we were able to accurately orient the EED-Ezh2 crystal structure within the 3D EM density . This information , together with the crystal structure of histone H3-bound EED ( Margueron et al . , 2009 ) , suggests that the canonical histone H3-binding site is located in a cavity at the intersection of Lobe A and Arm 2 ( Figure 7C ) . With the EED's WD40 domain assigned to Lobe A , we predicted that Lobe D would correspond to the WD40 propeller of RbAp48 . To test this idea , we reconstituted complexes carrying an N-terminal MBP or and internal or C-terminal GFP tag on RbAp48 , but none of these fusion constructs produced a stable and stoichiometric complex . To overcome this limitation , we again took advantage on the known interaction between Nurf55 ( Drosophila ortholog of RbAp48 ) and Suz12 that has been structurally characterized ( Nowak et al . , 2011; Schmitges et al . , 2011 ) . We generated a complex bearing an internal GFP tag immediately after the alpha helix of Suz12 that interacts with RbAp48 ( Suz12-GFP123 ) . In this particular case , the canonical view was not as well represented and the label was more clearly seen in a different orientation ( Figure 8A ) . The GFP can be seen localizing to Lobe D , confirming that Suz12 interacts with the RbAp48 propeller at this position . Using the local rigid-body fitting algorithm implemented in the UCSF Chimera software package ( Pettersen et al . , 2004 ) , we docked the crystal structures of the Drosophila RbAp48-Suz12 complex ( PDB: 2YB8 ) into our density map ( Figure 8B ) . The WD40 propeller and protruding N-terminal alpha-helix of RbAp48 were accommodated within this region with high fidelity . The docked crystal structure of the histone H3-bound RbAp48 ( Nowak et al . , 2011; Schmitges et al . , 2011 ) provides further insight into histone binding . Attached to RbAp48 at Lobe D , the histone would face Lobe C ( Figure 8B ) , suggesting that the histone H3 tail is situated in a head-to-tail orientation , running vertically from the bottom end of the complex in Lobe D towards Lobe B . Next , and in order to locate within PRC2 the different domains of the catalytic Ezh2 subunit , we again used an internal GFP labeling strategy in addition to our MBP N-terminal label ( Figure 9A , panel I ) . To localize the two SANT domains , we reconstituted PRC2-AEBP2 complexes containing either a GFP label downstream of the first SANT domain ( Ezh2-GFP303 , Figure 9A , panel II; see also Figure 9—figure supplement 1 ) or immediately upstream of the second SANT domain ( Ezh2-GFP436 , Figure 9A , panel III ) . The location of the GFP extra density suggests that the first SANT domain is positioned at the intersection of Lobe A and Arm 1 , and the second at the intersection of Arm 1 and Lobe B . The proximity of these two SANT domains is in very good agreement with our cross-linking analysis ( Figure 5 ) . The methyltransferase SET domain of Ezh2 was located by reconstituting two complexes , one bearing an internal GFP tag immediately upstream of this domain ( Ezh2-GFP595 , Figure 9A , panel IV; see also Figure 9—figure supplement 1 , Movie 1 ) and another with a C-terminal GFP tag ( Ezh2-GFP , Figure 9A , panel V ) . Analysis of the 2D class averages indicates that the SET domain begins at Lobe B and terminates above EED in Lobe A . Figure 9B summarized our Ezh2 labeling results ( proposed chain path in inset ) : the two SANT domains span Arm 1 and Lobe B , while the C-terminal SET domain is located between Lobes A and B . Our identification of a cross-link between the first Ezh2 SANT domain and the N-terminus of EED ( Figure 5 ) further suggest that the N-terminal region of EED localizes to Arm1 ( Figure 11 ) . In agreement with our tagged-based localization , crystal structures for homologous SANT and domains and SET domains ( PDBs: 3HM5 and 3H6L ) fit well within the assigned lobes in the EM structure ( Figure 11 and Movie 2 ) . 10 . 7554/eLife . 00005 . 013Movie 1 . Movement of GFP tag . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 01310 . 7554/eLife . 00005 . 014Figure 10 . Localization and Arrangement of the subunits Suz12 and AEBP2 in the PRC2-AEBP2 electron-density map . ( A ) Reference-free 2D class of the labeled , unlable sample , difference map and 3D view of the complex with assigned localization for different GFP fusion mutants of Suz12 and AEBP2 . ( B ) Assignment of Suz12 and AEBP2 subunit domains to electron density map based on the specific position of the tags . Suz12 and AEBP2 protein chains paths within the PRC2-AEBP2 complex are indicated in the inset . Aminoacidic numbers indicate cross-linked residues in the contest of the electron density map . ( C ) Isotopic cross-linking of PRC2 complex . Connections in red indicate the cross-linked aminoacids referred in the text . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 01410 . 7554/eLife . 00005 . 015Figure 11 . Overall Architecture of the PRC2 Complex . Model of the Prc2 complex from a combination of biochemical data , mass spectrometry , and structural biology data . Docking of crystal structure for EED and RbAp48 WD40s ( PDB: 2QXV; 2YB8 ) are indicated respectively in green and red . Docking of crystal structures of homologue SANT , SET and Zn finger domains ( PDB: 3HM5 , 3H6L and 2VY5 ) are shown in blue and purple . Approximate positions of crosslinking sites are also indicated . See also Movie 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 01510 . 7554/eLife . 00005 . 016Movie 2 . 3D reconstruction of the human PRC2-AEBP2 complex . Docking of crystal structure for EED and RbAp48 WD40s ( PDB: 2QXV; 2YB8 ) are indicated respectively in green and red . Docking of crystal structures of homologue SANT , SET and Zn finger domains ( PDB: 3HM5 , 3H6L and 2VY5 ) are shown in blue and purple . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 01610 . 7554/eLife . 00005 . 017Movie 2—source code 1 . Overall architecture of the PRC2 complex . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 017 In order to localize Suz12 we generated complexes containing tags at either the N- or C-termini of this subunit . However , neither of them resulted in clearly identifiable densities by 2D analysis , probably because these two regions are predicted to be unstructured and the tags were too mobile . While mutant complexes carrying internal GFP tags at the Zn finger and VEFS domains resulted in unstable complexes , two others were amenable for structural studies . The first was a mutant complex containing a GFP tag in the central portion of Suz12 ( Suz12-GFP269 ) that exhibited additional density near Lobe C ( Figure 10A , panel I ) . The second complex contained a C-terminal truncation of Suz12 after the VEFS domain , with a GFP tag at the truncated terminus ( Suz12-ΔC-GFP ) . This complex showed extra density extending from Lobe B ( Figure 10A , panel II ) . Together , our labeling data suggest that Suz12's N-terminal region is located at Lobe C , and terminates at Lobe B near the SET domain of Ezh2 , most likely passing through Arm2 ( Figure 10B ) . Finally , to determine the spatial organization of the cofactor AEBP2 as it binds to PRC2 , we reconstituted different complexes carrying protein tags in AEBP2 at the N- and C-termini ( MBP-AEBP2; AEBP2-EGFP ) and in the two loops connecting the Zn finger domains ( AEBP2-GFP84 and AEBP2-GFP115 ) . With the exception of MBP-AEBP2 , which failed to form a stable complex , all the resulting complexes were suitable for EM analysis . AEBP2-GFP84 ( Figure 10A , panel III ) and AEBP2-GFP115 ( Figure 10A , panel IV ) both localized near Lobe A and Arm 1 . This location , in the vicinity of EED and the first SANT domain of Ezh2 ( Figure 10B ) , agrees with our cross-linking data ( Figures 5 and 10C ) . 2D analysis of the AEBP2-EGFP–containing complex shows that the C-terminal region of AEBP2 is located between Lobes A and D , possibly within Arm 2 ( Figure 10A , panel V ) . Importantly , our cross-linking data also indicate that the AEBP2 C-terminus makes extensive interactions with the central portion of Suz12 ( 304–416 ) , suggesting that these two segments might co-localize to Arm 2 and into Lobe D ( Figures 5 and 10C ) . Thus , AEBP2 runs in the opposite direction of Suz12 , with its central portion located near Arm 1 and Lobe A , from where it continues along the PRC2 structure , terminating near Arm 2 and the top of Lobe D ( Figure 10B ) . The inset in Figure 10B summarizes the proposed chain paths for AEBP2 , and SUZ12 . Our labeling studies indicate that the PRC2-AEBP2 complex contains a single copy of each subunit , as we observed only one additional density within each of the individually tagged complexes . These data also confirm that the determined structure contains all the purified components . The proposed overall architecture for the PRC2-AEBP2 complex is summarized in Figure 11 . The available crystal structures for EED and RbAp48 WD40 domains , as well as those for homologues of the SANT , SET and Zn finger domains are included . In conclusion , using a combination of redundant and consistent data from protein biochemistry , chemical cross-linking and mass spectrometry , electron microscopy , and crystal structure docking , our study provides a robust model of the overall architecture and domain organization of the PRC2-AEBP2 complex . The unprecedented detail in our architectural map of the complex should serve as an invaluable structural framework for past and future functional studies of this essential silencing complex . In higher eukaryotes , the Trithorax ( TrxG ) and Polycomb groups ( PcG ) of protein complexes play a fundamental role in orchestrating the transcriptional activation or silencing of the genome . These genomic ‘on’ and ‘off’ switches are primarily controlled through the trimethylation of H3K4 ( H3K4-me3 ) , H3K36 ( H3K36-me2/3 ) , and H3K27 ( H3K27-me3 ) . The PRC2 complex is an important member of the PcG family , and a large number of biochemical studies have suggested models for its role in mediating gene repression ( reviewed in Margueron and Reinberg , 2011 ) . Characterizing the spatial arrangement of the PRC2 components is a fundamental step towards understanding the molecular mechanism underlying PRC2's regulated histone methylation and its role in gene silencing . However , structural studies so far have been limited to two isolated domains out of the four PRC2 protein subunits , severely restricting our understanding of the molecular mechanism of this fundamental complex . Here , by using EM in conjunction with chemical cross-linking and mass spectrometry , we provide the complete PRC2 architecture , describing the structure of this complex bound to its co-factor AEBP2 . We present a detailed map of localizations and interactions of its constitutive subunits , integrating previous structural and biochemical studies into a more comprehensive molecular model of PRC2 activity . Previous reports have shown that the PRC2 subunit EED is able to bind the chromatin repressive mark H3K27me3 in trans , via its WD40 domain , thus enhancing PRC2 mediated methylation of H3K27 in oligonucleosomes ( Margueron et al . , 2009; Xu et al . , 2010 ) . This activation results in propagation of the repressive chromatin state to neighboring nucleosomes containing newly incorporated unmodified histone H3 ( Hansen et al . , 2008; Margueron et al . , 2009 ) . On the other hand , the studies of Schmitges and colleagues suggested that binding of the chromatin activation marks H3K4me3 and H3K36me2-3 , inhibits PRC2 activity if placed in cis on the same tail containing the target lysine H3K27 ( Schmitges et al . , 2011 ) . Additionally , it has been proposed that the subunit Suz12 , and in particular its VEFS domain , is responsible in mediating this inhibition ( Schmitges et al . , 2011 ) . Since this inhibitory effect can be reversed by the addition of H3K27me3 containing peptides , it has been thought that PRC2 can simultaneously integrate inhibitory and activating chromatin marks tuning its enzymatic activity based on the surrounding chromatin state ( Schmitges et al . , 2011 ) . The results we present here make sense of these previous biochemical findings within the context of the functionally identified architecture of the PRC2 complex , allowing us to propose models that serve as hypotheses for further testing . Our structure shows that the Ezh2's methyltransferase domain forms a structural core with the two activity-controlling elements of PRC2 , the WD40 domain of EED and the VEFS domain of Suz12 ( Margueron et al . , 2009; Schmitges et al . , 2011 ) . This suggests that the proposed regulatory mechanisms of these domains on PRC2 activity could be structurally supported by their physical interaction with the SET domain . SANT domains have been shown to be important in coupling histone-tail binding to enzymatic activity in many chromatin regulating complexes ( Boyer et al . , 2004 ) . Additionally , it is known that histone H3 binds to the N-terminal tail of EED ( Tie et al . , 2007 ) . Our structure and cross-linking studies show an intricate network of interaction between the N-terminal and WD40 regions of EED and SET and SANT domains of Ezh2 . These SANT domains may couple EED-mediated binding of methylated histone H3 to the methyltransferase activity of Ezh2 ( Figure 12A ) , suggesting that complex activity could be influenced through incorporation of EED isoforms that differ in their N-terminal regions . 10 . 7554/eLife . 00005 . 018Figure 12 . Mechanism and allosteric regulation of PRC2 during gene silencing . ( A ) At loci of compact and repressed chromatin , H3K27-me3 marks are recognized by EED . This binding is signaled via the SANT domains to the SET domain increasing the methyl-transferase activity of Ezh2 , strengthening the chromatin compaction . ( B ) At loci of open and actively transcribed chromatin , H3K4me3 and H3K36me2 , 3 are recognized by the VEFS domain of Suz12 and transferred to Ezh2 , with an allosteric regulation that blocks Ezh2's enzymatic activity . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 018 At the opposite end of the core module , the VEFS domain of Suz12 and the SANT and SET domains of Ezh2 together interact with two Zn fingers from AEBP2 and Suz12 ( Figure 12B ) . The position of the Suz12 VEFS domain with respect to the Ezh2 SET domain suggests how the H3K4me3 and H3K36me2/3 inhibition via Suz12 could be transferred to Ezh2 , regulating its methyltransferase activity . The close proximity of these elements could also explain why the gene activation marks need to be on the tail being modified in order to block methyltransferase activity . In summary , our structure offers a three dimensional framework showing how various elements of the PRC2 complex involved in regulated histone-binding can act either independently or synergistically in response to different chromatin environments . Importantly , our PRC2 subunit organization is consistent with previously proposed models of PRC2 activity ( Margueron et al . , 2009; Schmitges et al . , 2011 ) : when chromatin domains are targeted for repression , PRC2 is recruited to deposit the repressive H3K27me3 histone mark at nucleosome-associated regions of DNA . Existing H3K27me3 marks are recognized by EED and , possibly in cooperation with Ezh2's SANT domains , Ezh2 methyltransferase activity is increased ( Figure 12A ) . To ensure that H3K27 trimethylation is limited only to the repressed target , PRC2 is capable of repressing its own enzymatic activity . When PRC2 comes into contact with the boundary of an active gene , the interaction between H3K4me3 , H3K36me2 , and H3K36me3 with the VEFS domain of Suz12 , could be propagated to the Ezh2 subunit , blocking PRC2 activity ( Figure 12B ) . Previous studies have shown that PRC2 favors di- and oligonucleosome substrates over mononucleosomes , octamers , or histone H3 peptides ( Cao and Zhang , 2004a; Kuzmichev et al . , 2004; Martin et al . , 2006 ) . Molecular explanations for this substrate preference have been largely hypothetical in the absence of any structural information . The positioning of the different subunits within the PRC2 structure suggests a model of how PRC2 could interact with a dinucleosome , by placing the regions interacting with histone tails in opposite sides of the complex , thus allowing interaction with two nucleosomes simultaneously , without any steric hindrance . Figure 13 suggests a possible arrangement illustrating this point that also agrees with the proposed binding of AEBP2 to nucleosomal DNA ( Kim et al . , 2009 ) . In such arrangement , EED binding to one nucleosome would position the histone H3 tail from the second nucleosome in close proximity to the Ezh2 SET domain ( Figure 13 ) . 10 . 7554/eLife . 00005 . 019Figure 13 . A proposed possible model for the binding of the PRC2-AEBP2 complex to a di-nucleosome . DOI: http://dx . doi . org/10 . 7554/eLife . 00005 . 019 In conclusion , the human PRC2 structure presented here provides us with the first full picture of the molecular organization of this fundamental complex and offers an invaluable structural context to understand previous biochemical data . Furthermore , the functional mapping of different activities within the physical shape of the complex leads to novel , testable hypotheses on how PRC2 interacts with chromatin that should inspire future research of PRC2 function and regulation . Given the similarity in sequence between PRC2 components from different species , we expect the molecular architecture that we described for human PRC2 to be conserved throughout higher eukaryotes . Sequences corresponding to the full-length EED , RbAp48 , Ezh2 , Suz12 and AEBP2 ( see Figure 1A for domain schematics for these proteins ) were amplified from human full-length cDNA clones . AEBP2 was subcloned in frame with a N-terminal StrepII tag into the pFastBac1 shuttle vector . All the other components were subcloned in the same vector in frame with a N-terminal hexahistidine tag . For PRC2 component localization , all the subunits were sub-cloned into a modified pFastBac1 vector in frame with either an N-terminal maltose binding protein or an internal or C-terminal GFP . All clones were verified by DNA sequencing . Recombinant baculoviruses were generated in Sf9 cells using the Bac-to-Bac kit ( Invitrogen , Grand Island , NY , USA ) according to manufacturer's recommendations . For protein expression , High5 cells were grown in suspension culture in ESF921 serum-free medium ( Expression Systems , Davis , CA , USA ) and infected with the right combination of viruses at a density of 1 . 0 × 106 ml−1 . Cells were harvested 60–72 hr post-infection and lysed by sonication in 25 mM Hepes pH 7 . 5 , 138 mM NaCl , 10% Glycerol , 0 . 05% Nonidet-P40 ( NP40 ) and 1 mM Dithiothreitol ( DTT ) ( supplemented with protease inhibitors ) . All samples were affinity-purified on Strep-Tactin Superflow Plus resin ( Qiagen , Valencia , CA , USA ) using an N-terminal Strep-tag II tag on the AEBP2 subunit . The complex was eluted from the resin by competition with lysis buffer containing 10 mM Desthiobiotin . The complex was then immediately subjected to size exclusion chromatography ( SEC ) on a calibrated Superose 6 PC 3 . 2/30 column ( GE Healthcare , Uppsala , Sweden ) at 4°C equilibrated in SEC buffer ( 25 mM Hepes pH 7 . 5 , 138 mM NaCl , 10% Glycerol , 0 . 05% NP40 , 1 mM Tris ( 2-carboxyethyl ) phosphine hydrochloride [TCEP] ) . To preserve protein stability for structure determination , Prc2 protein complexes were cross-linked with 0 . 015% Glutaraldehyde for 10 minutes and the reaction stopped with 1M Tris , pH 8 . 0 . Protein elution was monitored at 280 nm and protein fractions were collected and analyzed by SDS-PAGE and Coomassie brilliant blue staining . Human PRC2 Complex ( 10 μg of protein equivalent to 36 pmol ) was mixed with a 600X excess of isotope-labeled cross-linker di- ( sulfosuccinimidyl ) -glutarate ( 1:1 mixture of light DSSG-D0 and heavy DSSG-D6 ) ( Creative Molecules , Andover , MA , USA ) in a final volume of 50 μl of 10 mM Hepes , pH 7 . 5 , 138 mM NaCl at room temperature . The reaction was stopped after 30 min by adding 5 μl of 1 M ammonium bicarbonate . Cross-linked proteins were reduced with 5 mM TCEP ( tris ( 2-carboxyethyl ) phosphine; Thermo Scientific , Rockford , IL , USA ) at 37°C for 15 min and alkylated with 10 mM iodoacetamide ( Sigma-Aldrich , St . Louis , MO , USA ) for 30 min in the dark . Proteins were digested with trypsin ( Promega , Madison , WI , USA ) at an enzyme to substrate ratio of 1:50 ( wt/wt ) at 37°C for 2 hr and , after a second addition of trypsin ( 1:50 wt/wt ) , over night . Peptides were acidified with 1% trifluoroacetic acid ( TFA; Sigma-Aldrich , St . Louis , MO , USA ) and purified by solid-phase extraction ( SPE ) using C18 cartridges ( Sep-Pak; Waters , Milford , MA , USA ) . The SPE eluate was evaporated to dryness and reconstituted in 20 µl of SEC mobile phase ( water/acetonitrile/TFA , 70:30:0 . 1 ) . 15 µl were injected on a GE Healthcare ( Uppsala , Sweden ) Äkta micro system . Peptides were separated on a Superdex Peptide PC 3 . 2/30 column ( 300 × 3 . 2 mm ) at a flow rate of 50 µl min−1 using the SEC mobile phase . Two-minute fractions ( 100 µl ) were collected into 96-well plates . LC-MS/MS analysis was carried out on an Eksigent 1D-NanoLC-Ultra system connected to a Thermo LTQ Orbitrap XL mass spectrometer equipped with a standard nanoelectrospray source . SEC fractions were reconstituted in mobile phase A ( water/acetonitrile/formic acid , 97:3:0 . 1 ) . A fraction corresponding to an estimated 1 µg was injected onto a 11 cm × 0 . 075 mm I . D . column packed in house with Michrom Magic C18 material ( 3 µm particle size , 200 Å pore size ) . Peptides were separated at a flow rate of 300 nl min−1 ramping a gradient from 5% to 35% mobile phase B ( water/acetonitrile/formic acid , 3:97:0 . 1 ) . Cross-linked peptides were identified using an in-house version of the dedicated search engine , xQuest ( Rinner et al . , 2008 ) . Tandem mass spectra of precursors differing in their mass by 6 . 037660475 Da ( difference between DSSG-d0 and DSS-d6 ) were paired if they had a charge state of 3+ to 8+ and were triggered within 2 . 5 min of each other . These spectra were then searched against a pre-processed . fasta database . A valid identification of the cross-linked peptides required at least four bond cleavages in total or three in a series for each peptide and a minimum peptide length of six amino acids . A 4 µl aliquot of purified sample was immediately placed onto a continuous carbon grid that had been plasma cleaned in a 75% Ar/25% O2 atmosphere for 20 s in a Solarus plasma cleaner ( Gatan , Inc , Pleasanton , CA , USA ) . After 30 s of incubation on the grid at room temperature , the sample was negatively-stained with a solution of 2% uranyl formate and blotted dry . Samples were imaged using a Tecnai F20 Twin-transmission electron microscope operating at 120 keV at a nominal magnification of 80 , 000× ( 1 . 52 Å/pixel at the detector level ) using a defocus range of −0 . 6 to −1 . 3 μm . Images were automatically recorded with an electron dose of 20e-/Å2 using the Leginon data collection software ( Suloway et al . , 2005 ) on a Gatan 4096 × 4096 pixel CCD camera ( 15 μm pixel size ) . For the random conical tilt ( RCT ) dataset , images were collected at −60° and 0° . All image pre-processing and two-dimensional classification was performed in the Appion image-processing environment ( Lander et al . , 2009 ) . A general scheme was utilized for image analysis to deal with the large number of datasets acquired , as well as to minimize user bias . Particles were initially selected from the 0° tilt micrographs of the PRC2-AEBP2 complex using a difference of Gaussians ( DoG ) transform-based automated picker ( Voss et al . , 2009 ) , and extracted using a 224 × 224-pixel box size . Iterative multivariate statistical analysis ( MSA ) and multi-reference alignment ( MRA ) of the extracted particles provided representative 2D views of the PRC2 complex , which served as templates for all subsequent automated particle selection . The generalized processing scheme utilized for processing was as follows . The ACE2 and CTFFind programs ( Mindell and Grigorieff , 2003; Mallick et al . , 2005 ) ran concurrently with data collection to estimate the contrast transfer function ( CTF ) of the micrographs , as well as to provide a quantitative measurement of the quality of the imaging . At the same time , a template-based particle picker using the representative views of PRC2 ( described above ) located particles on the micrograph . At the end of data collection , the phases of the micrograph were corrected with ACE2 , and individual particles were extracted using a box size of 224 × 224 pixels and decimated by a factor of 2 . Any pixels whose values were above or below 4 . 5 sigma of the mean pixel value were removed using the XMIPP normalization function ( Scheres et al . , 2008 ) . In order to remove inappropriately selected protein aggregates , stain crystals , carbon edges , or other forms of contamination , particles whose mean or standard deviation deviated significantly from the norm were removed . The remaining particles were subjected to a single round of MSA and classification ( Ogura et al . , 2003 ) , in which thousands of class averages were generated with ∼20 particles per class . The resulting class averages were manually inspected to remove remaining aggregation , contamination , false positive particle selections , and very small particles potentially corresponding to incomplete PRC2 complexes . Following these particle-cleaning steps , the remaining 39 , 527 particles were subjected to several rounds of MSA and MRA using the IMAGIC software package ( van Heel et al . , 1996 ) to produce detailed views of the PRC2 complex with high signal-to-noise ratios . For subunit localization , reference-free 2D class averages from each tagged dataset were generated . We concentrated any further efforts on classes corresponding to the canonical view . The full set of tagged particles corresponding to this projection view gives rise to a class average with very good signal for the complex , as well as some extra density , corresponding to the tag , which is distinctive but typically smeared to different extents for different samples due to the flexibility of the tag . For a clearer visualization of the tag , we subclassified particles within this view . Two examples of the subclassification are shown for GFP-tagged complexes in Figure 9—figure supplement 1 . The pivoting of the tag density can be clearly seen in these classes and in Movie 1 . The percentage of subclasses clearly showing the tag ranges from 100% for a significant number of labels to 57% for the least favorable case ( likely due to proteolysis ) . For the generation of the figures in the main text of the paper we chose to show a single subclass average with a larger number of particles that accurately resembles the full ensemble , but that more clearly shows the density of the tag , nicely corresponding to the mass expected for proteins of their size . These classes were then compared to the wild-type PRC2 class averages through cross-correlation using the SPIDER ‘AP SH’ command . After alignment , the matching class averages for the untagged and tagged complexes were individually normalized and then the PRC2 class averages were subtracted from the PRC2-tagged class averages . The densities seen in the difference maps were at least 3-standard deviations above the mean , showing the location of tagged-PRC2 subunits . In order to generate an initial three dimensional model of the negatively-stained PRC2 complex , 190 tilt-pair images ( 60° and 0° ) were collected using the same imaging conditions as described in the previous section . These data were collected in an automated fashion using the RCT-Raster application in Leginon ( Yoshioka et al . , 2007 ) . Particles were automatically selected using the DoG particle picker ( Voss et al . , 2009 ) , and the tilt axis , as well as the particle-pair assignments , were calculated automatically with the TiltPicker module of Appion TiltPicker ( Voss et al . , 2009 ) . Particles were extracted in the same manner as described in the previous section , resulting in 6075 particle pairs . The XMIPP ML2D program was used to generate 20 reference-free 2D averages from the 0° micrographs , each class containing between 150 and 800 particles ( Scheres et al . , 2008 ) . Generation of more than 20 classes did not reveal conformers that were notably different from the reconstructions resulting from the initial classes , and suffered in resolution due to the decreased number of particles attributed to each class . SPIDER routines integrated into Appion were used to generate 3D RCT reconstructions for each of these class averages . In order to minimize missing-cone artifacts inherent to the RCT methodology from influencing subsequent projection-matching-based reconstructions , and assess the homogeneity of the PRC2 conformation represented in the class averages , all the RCT reconstructions were used as references to assign Euler angles to 1000 high signal-to-noise reference-free class averages from the 39 , 527-particle untilted dataset using projection-matching , in lieu of merging the RCT reconstructions into a single density . The class averages were then back-projected according to their assigned Euler angles to provide a three-dimensional model based solely on 0° particles data . The resulting 3D reconstructions , albeit at low resolution , were highly consistent , indicating that there is one major architectural state of the complex . We used the reconstruction obtained from the more populated RCT class as the initial model for refinement against single particles of the full 0° data set . Since potential end-on views of the complex were not considered during structure determination , as they could not be distinguished from partial complexes , we relied on ‘tomographic’ coverage of the views along the long axis of the structure . This refinement was performed using an iterative projection-matching script that makes use of libraries from the SPARX and EMAN2 image processing packages ( Baldwin and Penczek , 2007; Tang et al . , 2007 ) .	Protein complexes—stable structures that contain two or more proteins—have an important role in the biochemical processes that are associated with the expression of genes . Some help to silence genes , whereas others are involved in the activation of genes . The importance of such complexes is emphasized by the fact that mice die as embryos , or are born with serious defects , if they do not possess the protein complex known as Polycomb Repressive Complex 2 , or PRC2 for short . It is known that the core of this complex , which is found in species that range from Drosophila to humans , is composed of four different proteins , and that the structures of two of these have been determined with atomic precision . It is also known that PRC2 requires a particular protein co-factor ( called AEBP2 ) to perform this function . Moreover , it has been established that PRC2 silences genes by adding two or three methyl ( CH3 ) groups to a particular amino acid ( Lysine 27 ) in one of the proteins ( histone H3 ) that DNA strands wrap around in the nucleus of cells . However , despite its biological importance , little is known about the detailed architecture of PRC2 . Ciferri et al . shed new light on the structure of this complex by using electron microscopy to produce the first three-dimensional image of the human PRC2 complex bound to its cofactor . By incorporating various protein tags into the co-factor and the four subunits of the PRC2 , and by employing mass spectrometry and other techniques , Ciferri et al . were able to identify 60 or so interaction sites within the PRC2-cofactor system , and to determine their locations within the overall structure . The results show that the cofactor stabilizes the architecture of the complex by binding to it at a central hinge point . In particular , the protein domains within the PRC2 that interact with the histone markers are close to the site that transfer the methyl groups , which helps to explain how the gene silencing activity of the PRC2 complex is regulated . The results should pave the way to a more complete understanding of how PRC2 and its cofactor are able to silence genes .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "structural", "biology", "and", "molecular", "biophysics" ]	2012	Molecular architecture of human polycomb repressive complex 2
Most glucose is processed in muscle , for energy or glycogen stores . Malignant Hyperthermia Susceptibility ( MHS ) exemplifies muscle conditions that increase [Ca2+]cytosol . 42% of MHS patients have hyperglycemia . We show that phosphorylated glycogen phosphorylase ( GPa ) , glycogen synthase ( GSa ) – respectively activated and inactivated by phosphorylation – and their Ca2+-dependent kinase ( PhK ) , are elevated in microsomal extracts from MHS patients’ muscle . Glycogen and glucose transporter GLUT4 are decreased . [Ca2+]cytosol , increased to MHS levels , promoted GP phosphorylation . Imaging at ~100 nm resolution located GPa at sarcoplasmic reticulum ( SR ) junctional cisternae , and apo-GP at Z disk . MHS muscle therefore has a wide-ranging alteration in glucose metabolism: high [Ca2+]cytosol activates PhK , which inhibits GS , activates GP and moves it toward the SR , favoring glycogenolysis . The alterations probably cause these patients’ hyperglycemia . For basic studies , MHS emerges as a variable stressor , which forces glucose pathways from the normal to the diseased range , thereby exposing novel metabolic links . Skeletal muscle is the major processing site for dietary glucose , consuming it at a high rate during exercise and storing it as glycogen more than any other tissue ( Baron et al . , 1988; Mizgier et al . , 2014 ) . Muscle is critical to glucose homeostasis , to the point that its failure to take up glucose heralds the eventual onset of type 2 diabetes ( DeFronzo and Tripathy , 2009 ) . Reciprocally , glucose is required to provide fuel necessary for muscle contraction and thermogenesis . This relationship consists of a two-way mutual control process: postprandial secretion of insulin increases muscle uptake of glucose , and multiple muscle-generated signals increase glucose uptake during exercise or thermogenesis . Changes in cytosolic Ca2+ concentration ( [Ca2+]cyto ) play a central —albeit not fully understood— role in these interactions . Insulin induces intracellular Ca2+ release sufficient for a modest , transient increase in [Ca2+]cyto ( e . g . Contreras-Ferrat et al . , 2014; Lanner et al . , 2008; Park et al . , 2015 ) ; reciprocally , signaling from contractile activity mediated by increased [Ca2+]cyto results in increased glucose uptake by insulin-dependent and independent mechanisms ( Jessen and Goodyear , 2005; Lanner et al . , 2009; Holloszy and Narahara , 1967; Holloszy et al . , 1986 ) . Once inside the myofiber , whether glucose is processed for direct energy production or stored as glycogen is tightly dictated by contractile activity and energy demand . Because proper function of muscle requires maintenance of ATP concentrations within a narrow range , the myofiber maintains spatially distributed glycogen deposits , together with the enzymatic machinery needed to rapidly process glycogen towards glycolysis ( Adeva-Andany et al . , 2016; Ørtenblad and Nielsen , 2015 ) . The mechanisms that integrate muscle activity and glucose processing rely on changes in [Ca2+]cyto during excitation-contraction coupling . The present study addresses the Ca2+-dependent control of the balance between glycogen synthesis and glycogenolysis ( Ozawa , 2011 ) . The close interactions between glucose metabolism and calcium signaling become relevant in type 2 diabetes ( Contreras-Ferrat et al . , 2014; Park et al . , 2015; Guerrero-Hernandez and Verkhratsky , 2014 ) . The diabetes-induced failure in mechanisms that regulate cell Ca2+ ( Levy , 1999 ) usually results in an increase in [Ca2+]cyto . Because of many possible mutual interactions between Ca2+ and glucose transport , it is not clear whether the increase in [Ca2+]cyto is a contributor to the origin of the disease ( Park et al . , 2015; Zarain-Herzberg et al . , 2014 ) , nor whether it is a cause or consequence of hyperglycemia ( Lanner et al . , 2008 ) . In any case , the alteration in [Ca2+]cyto will in turn modify the pathogenic processes of diabetes ( Contreras-Ferrat et al . , 2014 ) . Malignant Hyperthermia Susceptibility ( MHS ) ( Litman et al . , 2018 ) is a condition usually linked to mutations in RYR1 , which encodes the Ca2+ release channel of the sarcoplasmic reticulum ( SR ) , or in other genes that encode proteins of the skeletal muscle couplon ( Stern et al . , 1997; Franzini-Armstrong et al . , 1999 ) . The primary defect underlying this condition is believed to be a ‘Ca2+ leak’ from the SR , due to an excessive propensity of the RyR channel to open , which in turn leads to increased cytosolic [Ca2+] . Large increases in [Ca2+]cyto have been found in muscle from MHS individuals ( López Padrino , 1994; Lopez et al . , 1992 ) , MH-susceptible pigs ( Lopez et al . , 1986 ) and in myotubes derived from MHS patients ( Figueroa et al . , 2019 ) . An MHS-like phenotype occurs in multiple muscle diseases; the estimates of its prevalence vary widely depending on methods and assumptions ( with results as high as 1/200 [Bachand et al . , 1997] and as low as 1/50000 [Ording , 1985] ) . MHS is clinically diagnosed through measurements of the magnitude of contractile forces ( FH and FC ) developed by freshly biopsied fiber bundles upon exposure to stimulants halothane and caffeine , standardized as the Caffeine Halothane Contracture Test ( CHCT Larach , 1989 ) and the In Vitro Contracture Test ( Hopkins et al . , 2015 ) . Recently Altamirano et al . observed a high prevalence of hyperglycemia in a large group with positive CHCT in our clinic ( Altamirano et al . , 2019 ) , attributing this association to the increase in [Ca2+]cyto previously found in MHS patients ( López Padrino , 1994 ) . These observations are indicators of strong underlying connections between excitation-contraction signaling and glucose metabolism . This study was undertaken to understand these connections and derive consequences for patient management , through the investigation of the quantity , phosphorylation and cellular location of the enzymes that control glycogen storage and utilization , in muscle cells from MHS patients . The findings establish Malignant Hyperthermia Susceptibility and the diseases presenting this condition as prodromes and contributors to the development of hyperglycemia and eventually type 2 diabetes ( e . g . Diabetes Canada Clinical Practice Guidelines Expert Committee , 2020 ) . Additionally , they provide new insights into the normal glucose processing pathways of skeletal muscle . 560 patients ( named the ‘legacy’ cohort ) were subjected to the CHCT and studied clinically in our Malignant Hyperthermia Investigation Unit ( MHIU ) from 1994 to 2013; 329 of them were diagnosed as MHS ( FH >0 . 6 g and/or FC >0 . 2 g ) and 231 as MHN . Their levels of fasting glucose ( measured within the last 3 years ) are plotted in Figure 1 vs . the increase in force of their stretched muscle bundles under exposure to 3% halothane . As reported ( Altamirano et al . , 2019 ) , and shown in the box plot ( 1B ) , 40 . 0% ( 132/329 ) had elevated fasting blood sugar ( FBS , here used as synonymous with ‘glycemia’ ) , which is more than double the expected prevalence in the age-matched general population . Additionally , plot 1A shows a positive and highly significant correlation between FBS and FH ( r = 0 . 21 , p of no correlation <0 . 001 ) . Consistent with the correlation , the median FBS ( illustrated in 1B ) was significantly higher in MHS ( 5 . 4 mM , Inter Quartile Range 4 . 9–6 . 9 ) than in MHN ( 5 . 1 mM , IQR 4 . 8–5 . 7 ) . FBS was not significantly different between women and men but it was significantly correlated with age ( Panel 1C ) and Body Mass Index ( BMI , 1D ) . The 1st order regression lines of FH and age are nearly parallel for MHN and MHS , separated by ~0 . 5 mM , which indicates that the MHS condition associates with elevated FBS in an age-independent manner . Panel E graphs the numbers of patients in the four age bins delimited by the green lines . Black segments represent numbers of MHN subjects with normal glycemia; white segments those with hyperglycemia . The red-tone bars correspondingly represent the MHS numbers . In 1F the numbers are represented as fractions of the total in each bin . The graphs show that the prevalence of hyperglycemia increases with age and is higher in MHS than in MHN at any age . Moreover , the Diabetes Canada Clinical Practice Guidelines Expert Committee stipulates four criteria , any one of which is sufficient for the diagnosis of diabetes ( Diabetes Canada Clinical Practice Guidelines Expert Committee , 2020 ) . According to criterion #1 ( FBS ≥7 . 0 mmol/L ) , 22% of the patients in the ‘legacy’ cohort are diabetic , which more than doubles the prevalence in the general age-matched Canadian population . The balance of comparisons answers in the affirmative the question posed by Altamirano et al . , 2019 , establishing MHS as a prodrome of hyperglycemia and a path to diabetes . Our study seeks cell-level mechanisms that may explain the high prevalence of hyperglycemia in the ‘legacy’ cohort . It focuses on a ‘recent’ group of subjects diagnosed since 2014 to date , whose biopsies are systematically subjected to studies in the lab at Rush University . By contrast with the ‘legacy’ group , in the ‘recent’ cohort the incidence of hyperglycemia and the correlation between FBS and FH are lower ( 3 . 5% , r = 0 . 04 , Figure 1—figure supplement 1 ) ; the differences could be ascribed to an average age 24 years less than that of the ‘legacy’ cohort . ( Within the ‘recent’ cohort , the MHN group is considered a suitable substitute for a sample of healthy individuals . These are subjects tested only because of family history , who additionally have proven negative to relevant gene mutations . None of them has signs of muscle disease . ) The muscles of MHS patients had major changes in protein endowment . This is illustrated in Figure 2B , in a Ponceau-stained gel of the microsomal fraction of biopsies of 12 MHS and 13 MHN patients selected as described in Materials and methods . Greater density of two bands near 100 and 180 kDa was visible in the microsomal fraction ( arrows ) , but not in whole muscle lysates from the same patients ( Figure 2J , see also Figure 2—figure supplement 1 ) . Studied by mass spectrometry ( Appendix 1 ) , the 100 kDa and 180 kDa bands contain respectively and in high abundance glycogen phosphorylase ( GP ) and glycogen debranching enzyme ( GDE ) . The quantity of GP and other proteins was determined by Western blotting as illustrated in Figure 2 . All blots were quantified by a custom application , described in Materials and methods and demonstrated with a recorded analysis session presented as Video 1 ( see also Materials and methods for a formal expression of the linear relationship between band signal and protein content and Figure 2—figure supplement 2 for its validation ) . The antibody used in Figure 2A reacts with both the phosphorylated GP ( GPa ) and its apo form ( GPb ) . The protein detected is referred to as all-forms GP , or just GP . The content of GP derived from this blot , equal to the sum of GPa and GPb contents , was greater by 119% in the MHS group ( p < 0 . 001; panel 2F and Table 1 ) . Glycogenolysis requires GP operating together with glycogen debranching enzyme ( GDE ) . Both proteins are found associated with glycogen granules , which share the intermyofibrillar space with the SR and are extracted with the microsomal fraction ( Ørtenblad and Nielsen , 2015; Fridén et al . , 1989 ) . GDE , the main component of the band at 180 kDa , is visibly increased in the gels of MHS samples . The GDE blot ( 2C ) derived from the gel in 2B confirms the impression , showing a mean content increased by 94% ( p = 0 . 002 ) . Patients that had higher GP had a commensurate elevation in GDE ( r = 0 . 94 , panel 2H ) . The procedure to derive Western blots for multiple proteins from the same electrophoresis gel is illustrated with Figure 2—figure supplement 3 . The differences in GP and GDE can also be quantified directly on the gels stained nonspecifically , without resorting to Western blots . A comparison of the GP band for 13 MHN and 12 MHS muscles in one gel yields an excess of 73% in MHS ( p = 0 . 005 ) and a similar difference between the GDE bands ( p = 0 . 002 , Table 1 ) . In the gel of Figure 2B the band at 100 kDa has the greatest signal mass in the whole gel; the excess protein in this band of the MHS ( 73% ) is comparable with that of GP measured with protein-specific antibodies . The excellent correlation ( r = 0 . 92 ) between the band signal in Western blots and in the stained gel is demonstrated in Figure 2—figure supplement 4 . Taken together , the observations identify GP as the main constituent of the band and the most abundant protein in the microsomal fraction . The differences in these and other glycogen processing enzymes described later are in contrast with the invariance of two couplon proteins tested ( calsequestrin one or Casq1 , and FKBP12 , lower panels in Figure 2 ) and a third calcium handling SR protein , SERCA1 ( p=0 . 65; Table 1 and Figure 2—figure supplement 5 ) . Remarkably therefore , in these subjects bearing a primary calcium handling defect , the expression levels are altered more for two glycogen breakdown enzymes than any of the major calcium management proteins studied here . GP is phosphorylated by phosphorylase kinase ( PhK ) ( Newgard et al . , 2000 ) . PhK , GP , GDE and glycogen synthase ( GS ) , together with GP phosphatase PP1 , and initiator and scaffolding proteins ( Crosson et al . , 2003 ) , associate with glycogen into glycogen granules ( Prats et al . , 2018 ) . Phosphorylation at Ser14 converts GP from the apo or b form into GPa , which promotes the active conformation of the enzyme ( Nagy , 2017 ) . In human and murine muscle , glycogen granules are largely inter-myofibrillar ( Ørtenblad and Nielsen , 2015 ) , close to the SR . Given that PhK is activated by Ca2+ ( Brushia and Walsh , 1999 ) and assuming that MHS patients’ [Ca2+]cyto is elevated ( see Introduction ) , we hypothesize that the excess [Ca2+]cyto , via promotion of PhK , leads to an increase in GPa . To justify the observation of excess GP in microsomes , we also hypothesize that GPa ( rather than GPb ) migrates with microsomes , presumably incorporating into glycogen granules . The quantities of all-forms GP ( i . e . GPa and GPb ) and GPa were measured in immunoblots from different gels loaded with aliquots of the same extracts ( Figure 3 ) . In the whole tissue , GP was greater in the MHS by 29% ( p=0 . 07 ) , GPa by 68% ( p = 4 10-4 , Figure 3A , B ) and the ratio GPa/GP by 42% ( p = 0 . 005 , Figure 3D ) . In the microsomal fraction GPa content was 70% higher in the MHS ( p = 0 . 05 ) ; because all-forms GP also increased ( Figure 2 ) , the ratio of contents GPa/GP did not change significantly ( p = 0 . 28 , Figure 3H ) . If all the GP in microsomes were phosphorylated , an increase in GPa would not change the ratio GPa/GP . This possibility is supported by results that follow . To recapitulate , the MHS have more GPa in their muscle and elevated GP in microsomes . As shown in Figure 3E , the two variables are positively correlated regardless of MH status ( r = 0 . 72 , p = 0 . 01 , Table 2 ) — patients who have high phosphorylation of GP in the whole muscle generally have high GP content in microsomes . These observations suggest a simple explanation: GPa is largely in the intermyofibrillar space , associated with the SR , hence in biochemical analyses it will appear largely with the microsomal fraction . Adding to this assumption the hypothesis raised previously , that all GP in microsomes is phosphorylated , it follows that the greater content of GP in the microsomal fraction of MHS muscle is due to the increase in GPa . The combined hypotheses ( GPa is extracted together with the microsomal fraction and all GP in microsomes is GPa ) predict that GPa will be located at or near the SR . The prediction — which does not distinguish between locations of GPa in intermyofibrillar glycogen granules or bound to the SR — was tested in the patients’ tissue by immunohistochemistry . GP in its two forms was imaged in muscles of known content of all-forms GP and GPa . Thin , slightly stretched bundles were fixed in paraformaldehyde and differentially stained with two antibodies , one specific for GPa and one reactive to both forms . The immunofluorescence was acquired as z-stacks and visualized after deblurring ( Materials and methods ) either as individual images from the stacks ( a . k . a . sections F ( x , y ) ) or as 3-D reconstructions of the corrected stack . The image FGP ( x , y ) of the all-forms fluorescence was variable among different individuals and different myofibers from the same subject . As shown in Figure 4 , GP ( panel Aa ) was sometimes present at highest density near the Z disk , where it colocalized with actinin-1 ( Ab ) , but in most cases ( panels 4 B ) it occupied a slightly wider region , always in the interior of the sarcomeric I band , where most mitochondria are located ( Bb , see inset for structural guidance ) . As is visually apparent and was confirmed by quantifying colocalization , GP and the mitochondrial marker formed I-band patches that remained largely separate . An illustrative study on living cells is in Panels C: images of an intact muscle fiber of an adult mouse expressing GFP-tagged GP , co-stained with TMRE , a marker of polarized mitochondria . All-forms GP ( panel Ca ) is distributed similarly as in the fixed human cells , largely sharing the I band with mitochondria ( Cb ) but without actual overlap ( Cc ) . Confirming the separation , Western blots failed to find GP in mitochondrial fractions derived from human biopsies ( Figure 4—figure supplement 1 ) . Also against association with mitochondria are the variable distribution of GP ( all-forms ) , which seems inconsistent with any systematic association , and the tentative placement of GPb , described below . To test whether the Ca2+ activated kinase PhK provides the mechanistic link between the primary defect in MHS and the observed increased phosphorylation of GP , the PhK content of microsomes and whole cell extracts was compared between MHN and MHS subjects . Blots are in Figure 6 ( A , B ) ; originating gels in Figure 3—figure supplement 1 . The antibody , raised against the 130 kDa α1 subunit of this protein , consistently stained two bands , one at the expected 130 kDa and another at between 110 and 115 kDa . The densities of the two bands are positively correlated ( Figure 6—figure supplement 1 ) , which suggests that the 115 kDa band contains a large fragment of the subunit . The distribution of the 2-band average is plotted in panel 6E . The mean signal in microsomes was almost 100% higher in the MHS ( p = 4 10−4 ) . In the whole muscle fraction , by contrast , the mean signal increased slightly and not significantly ( Table 1 ) . The rate of glycogen synthesis in muscle is determined by the activity of glycogen synthase ( GS ) . GS and GP are phosphorylated by PhK ( Krebs et al . , 1959 ) . If the increase in GPa were due to the greater activity of PhK , it would be associated with an increase in GSa . The ratio GSa/GS was determined in the microsomal fraction of all patients that had PhK evaluated . The GS blots , in panels 6C and D , consistently show two bands . The lighter band might correspond to a GS fragment or to the GS liver isoform . Here they are identified as bands 1 and 2 . The median ratio GSa/GS for band one was higher by 80% in MHS patients ( Figure 6G ) but the difference was not significant ( p = 0 . 11 , Table 1 ) . Band two was not substantially different . Although the increase of GSa/GS in MHS cells was not significant by conventional criteria , it was significantly positively correlated with the PhK content in microsomes , as well as with the level of phosphorylation of GP in whole muscle ( Panels 5K , L , and Table 2 ) . The correlations reduce the likelihood that the changes be due to random variability , revealing instead an ample remodeling of glucose metabolic pathways . Put together , the present observations support a specific causal chain for the observed changes in metabolic enzymes , leading to a reduction in glycogen content and eventually hyperglycemia . To place this chain within the pathophysiological context of our patients , we probed a possible link between the MHS condition and the observed changes in GP phosphorylation . The conversion of GPb to GPa by PhK ( the original ‘converting enzyme’; Krebs and Fischer , 1956 ) , was the first phosphorylation process known to cause functional changes in the substrate . The regulation of PhK is complex ( rev . Brushia and Walsh , 1999 ) . Ca2+ promotes its activation by direct allosteric action and indirectly by enhancing its phosphorylation by PKA . Myofibers from animal models , patients with MHS and myotubes developed from MHS patients’ muscle have resting [Ca2+]cyto elevated to between 150 and 500 nM ( López Padrino , 1994; Lopez et al . , 1986; Figueroa et al . , 2019 ) . We tested whether changes in this range evoke an increase in phosphorylation . Biopsied muscle from four individuals , 3 MHN and 1 MHS , provided sufficient material for a stringent test . Two groups of 10 bundles of 10–40 fibers dissected from the same biopsy were pinned slightly stretched in two Sylgard-bottom chambers , exposed to saponin for permeabilization and then to two [Ca2+] , 100 and 500 nM . One of the biopsies provided enough tissue for an additional [Ca2+] , 300 nM . After 10 min , the muscles exposed to different [Ca2+] were processed separately to whole tissue extracts . As many extracts as applied Ca2+ concentrations were produced for every patient and were aliquoted for quantitative blotting of GP and GPa in separate gels . Blots and gels are shown in Figure 7 . The ratio of signals for GPa and GP evolved upon change in [Ca2+] as plotted in Figure 7C , always increasing at the higher [Ca2+] ( p < 0 . 001 in a t test of paired differences ) . Correlated increases in phosphorylation of GP and GS suggest a concerted control process that , by promoting activation of GP and inactivation of GS , tilts the glucose ←→ glycogen balance towards glycogenolysis and away from glucose storage . In mammalian and especially in human muscle , glycogen is found largely in intermyofibrillar regions ( Ørtenblad and Nielsen , 2015; Entman et al . , 1980; Cuenda et al . , 1994; Lees et al . , 2001 ) , and partitions with the microsomal fraction in biochemical analyses . The Glycogen content of microsomes was compared in the 13 MHN and 12 MHS patients studied for glycogen processing enzymes . ( A test that showed minor not significant differences between samples shipped at 4°C and others kept frozen is described in Materials and methods ) . The results , presented in Figure 8D and Table 1 , were expected from the enzymatic changes: the median glycogen content was reduced in the microsomes of MHS patients by 43% ( p = 0 . 01 ) . Glycogen was negatively correlated with PhK and GPa contents of the microsomal and whole muscle fractions , as illustrated for whole muscle GPa in Figure 8B ( r = -0 . 47 , p = 0 . 02 ) . Glycogen was also negatively correlated with the microsomal content of GDE and phosphorylated GS , but with lower significance ( parameters listed in Table 2 ) . The uniformly negative correlations suggest that the changes in glycogen are driven by the reciprocal changes in activity of GP and GS due to the increased activity of their kinase . Together with the observation of a greater incidence of hyperglycemia in MHS , Altamirano et al . , 2019 found a deficit in phosphorylation of Akt , a kinase that mediates the muscle response to insulin . This response involves recruitment of the glucose transporter GLUT4 to the plasma membrane ( Beg et al . , 2017 ) . A comparison of the GLUT4 content of the microsomal fraction in MHN and MHS patients is documented in Figure 8C–E . The content was lower in the MHS by more than 60% ( p = 0 . 01 , Table 1 ) . There were negative correlations between GLUT4 and PhK and GPa contents of the microsomal and whole muscle fractions , illustrated for whole muscle GPa in Figure 8E ( r = -0 . 46 , p = 0 . 02 ) . This result is consistent with the proposed association of the MHS condition with increased insulin resistance ( Altamirano et al . , 2019 ) , mediated by a reduced availability of GLUT4 . Samples from a different group of patients were subjected earlier to a more limited study of its proteins . The results are described in Appendix 2; they are consistent with those presented in the main text . Our study reveals that Malignant Hyperthermia Susceptibility entails a wide-ranging , pathogenic change in glucose metabolism . The present quantification of glucose processing enzymes in muscle of human subjects , combined with high resolution imaging of tagged proteins , demonstrated altered amounts of many of these molecules in patients susceptible to Malignant Hyperthermia . Multiple features of these changes indicate that they result from the primary defect in Ca2+ management that underlies the MHS condition . Susceptibility to Malignant Hyperthermia , diagnosed by the CHCT , is an inheritable condition with variable disease phenotype and multiple causes . Some susceptible individuals have pathogenic mutations in the couplon proteins , including RyR1 , the Ca2+ release channel of the SR ( Rosenberg et al . , 2015; Robinson et al . , 2006 ) , its voltage sensor CaV1 . 1 ( Robinson et al . , 1997; Carpenter et al . , 2009 ) and the ‘connector’ protein STAC3 ( Webb et al . , 1993; Horstick et al . , 2013 ) . Other MHS patients do not have a known diagnostic mutation , but the commonality in clinical features and cellular alterations in MHS animal models ( Lopez et al . , 1986; MacLennan and Zvaritch , 2011 ) and human cells ( Figueroa et al . , 2019 ) indicates that all have a primary defect originated in the couplon ( Stern et al . , 1997; Franzini-Armstrong et al . , 1999 ) , generally causing excessive release of Ca2+ from the SR . The consequence is partial depletion of SR calcium , which activates compensatory calcium entry into the cell ( SOCE; Michelucci et al . , 2018 ) resulting in elevation of [Ca2+]cyto . The present studies sought mechanistic explanations for the high incidence of hyperglycemia found in MHS subjects ( Altamirano et al . , 2019 ) . Starting from changes visible even without specific staining in gels of the microsomal fraction , the main observations will be summarized with references to Tables 1 and 2 . As listed in Table 1 , where significant changes are coded in color , the MHS samples had substantial increases — in microsomes and whole tissue lysates — of GP , its phosphorylated form GPa , the phosphorylated fraction GPa/GP , phosphorylation of glycogen synthase ( GSa/GS ) , and content of the GP- and GS-kinase PhK . As expected from these changes , microsomal glycogen was substantially reduced . Additionally , we found less GLUT4 in microsomes , a change expected in view of the reduced phosphorylation of Akt in response to insulin in MHS mouse myotubes described by Altamirano et al . , 2019 . Patients who had a marked increase in PhK were found to have substantial changes in the other variables as well . This convergence , quantified by high correlation coefficients between variables ( Table 2 ) , indicates that the changes in PhK content cause the increases in phosphorylation of GP and GS and the decrease in glycogen content . The correlations bolster the statistical significance of the changes — if patients who have high GPa also have high PhK or low glycogen , it is unlikely that the high values be due to random variability or measurement error . Multivariate analysis documented in Appendix 3 , of correlations between more than two variables , strengthen the conclusion that the changes in GP , GPa and glycogen content are caused by activation of PhK . These studies also attribute high significance to the changes in content of GDE and GLUT4 . Elevated GPa in MHS was first reported in Willner et al . , 1980 , but later questioned due to the large number of MHN patients with high GPa ( Traynor et al . , 1983 ) . While we also found false positives among our patients , there was a clear association between the MHS condition and a wide-ranging change in glucose processing enzymes , which included the excess GPa . ( A different validation , based on the estimation of probable error , is presented in the section Materials and methods/Statistics/Replicates and Probable Error ) . In sum , the results demonstrate in MHS individuals an activation of the pathway of glycogen breakdown to glucose , which shares many features with the program activated during exercise , including a dependence on [Ca2+]cyto , parallel phosphorylation of GP and GS , and decrease in glycogen ( rev . Mul et al . , 2015 ) . However , the changes in MHS muscle differ by including an increase in GDE and a decrease in GLUT4 in microsomes . The changes in GDE and GLUT4 complete a wide-ranging metabolic alteration that promotes glycogen breakdown and may hamper glucose uptake and utilization inside muscle , thus promoting hyperglycemia . The observed decrease in GLUT4 in microsomes is not immediately relatable to the steady increase in [Ca2+]cyto that we propose as primary mediator of the metabolic changes in MHS patients . On the contrary , the increase in [Ca2+]cyto that mediates EC coupling ( Valant and Erlij , 1983; Youn et al . , 1991 ) , plus that caused by Ca2+ entry in response to insulin ( Lanner et al . , 2009 ) are believed to promote GLUT4 expression and its translocation to the plasma and transverse tubular membranes . The physiological increase in GLUT4 activity in response to insulin is preceded by activation of phosphatidyl-inositol 3-kinase , subsequent recruitment of Akt to the plasma membrane and its phosphorylation by 3-PI-dependent protein kinase 1 ( PDK1 ) . Akt phosphorylation follows Ca2+ entry , in muscle and also in cultured neurons , where it is associated with electrical activity ( Nicholson-Fish et al . , 2016 ) and in osteogenic cells , where Ca2+entry follows mechanical stimulation ( Danciu et al . , 2003 ) . These observations favor a positive effect of cytosolic Ca2+ on GLUT4 activity , by increasing its expression or deployment to the cell boundaries . On the other hand , Park et al . , 2009 demonstrated that GLUT4 expression is inhibited by chronic elevation of [Ca2+]cyto , in agreement with negative effects of prolonged exposure of muscle to ionomycin ( Lee et al . , 1995 ) . These interventions specifically abolished the activation of GLUT4 expression by AMP-activated protein kinase ( AMPK ) , an insulin-independent effect mediated by [Ca2+]cyto ( Ihlemann et al . , 1999 ) . The time course of [Ca2+]cyto determined the sign of the effect: promotion of GLUT4 required Ca2+ transients , while a sustained increase in the ion concentration had the opposite effect . In conclusion , the chronic increase in [Ca2+]cyto that occurs in MHS could justify , via distinct but entangled mechanisms , both the increased phosphorylation of PhK , GP and GS and the reduced content of GLUT4 observed in these patients . In this view , the reduction in glycogen content would be caused by both the imbalance in the pathway to and from glucose , and a reduction in uptake and availability of glucose . The main elements of this interpretation are diagrammed in Figure 9 . Because the alterations in MHS patients include reduction in the main insulin-dependent glucose transporter , the condition may lead to insulin resistance . Elevated glycogen breakdown and lower flux of glucose into storage inside muscle suggest that the cytosolic concentration of glucose would increase , which would lower the gradient and further impair the response to insulin in MHS subjects . The metabolic alteration in muscle metabolism is associated with hyperglycemia . This is demonstrated by the relationship between FBS and measures of phosphorylation in the present study . As shown in Figure 10 , a positive correlation exists between FBS and the ratio GPa/GP of whole tissue ( r = 0 . 58 ) , which implies that the metabolic alteration detected among the MHS has systemic consequences that favor high FBS . A rough quantitative measure of the relevance of the metabolic change as a determinant of hyperglycemia was derived from the slope ( b = 2 . 5 mM ) of the linear fit in the figure . In the sample studied , the abscissa ( GPa/GP ) increased by 0 . 15 , from a median value of 0 . 36 in the MHN to 0 . 51 in the MHS ( Table I ) . An increment of 0 . 15 in the abscissa corresponds on the regression line to a difference in FBS of 2 . 5 mM ×0 . 15 , or 0 . 38 mM higher sugar . The analysis of ‘legacy’ patients ( illustrated with Figure 1 ) showed an excess FBS averaging ~0 . 5 mM in MHS at all ages . Extrapolation of the present study to the ‘legacy’ cohort suggests that a major part of the excess FBS ( 0 . 38/0 . 5 mM ) is related to and potentially explained by the measured changes in glycogen processing enzymes . The remainder could be explained by other alterations that increase blood sugar , not defined , manifested in the separate correlations of hyperglycemia with age and BMI demonstrated in the ‘legacy’ cohort ( Figure 1 ) . Jointly with the observations of Altamirano et al . , 2019 , the present results identify MHS as a prodrome and cause of hyperglycemia . The alterations justify the increase of the incidence of type 2 diabetes observed in the MHS sample group ( 22% ) over that in the age-matched Canadian population ( 10% ) . In view of this analysis , it was surprising that neither the difference between FBS averaged over all MHS and MHN patients of the ‘recent’ cohort ( Figure 1—figure supplement 1 ) nor that between the 13 MHN and 12 MHS patients fully studied ( Figure 10 and row 18 in Table 1 ) reached statistical significance . A much greater difference was found between groups defined by metabolic variables , namely the MHS patients that had the highest content of PhK and GP in microsomes and patients with low content of these proteins . While the selection was based on PhK and GP only , the top six also had the highest GPa content and GPa/GP ratio in whole muscle lysate , the highest GDE and GSa/GS , and the lowest GLUT4 in microsomes; we call them MC , for metabolically challenged . MC had an average FBS of 5 . 90 mM ( Table 1 , row 19 ) . The metabolically normal or MN ( six patients with low values of PhK and GP , all of whom were MHN ) had FBS = 5 . 05 mM ( Table 1 ) . The difference between MC and MN was highly significant . The weaker association found between FBS and FH ( the measure on which MHS diagnosis is based ) might reflect links between calcium homeostasis and glycogen metabolism that remain unknown and uncorrelated with FH , or just the challenging nature of the CHCT . As a second outcome , the present work promotes the use of samples from MHS subjects as ‘tools’ to explore normal physiology , specifically harboring a perturbation that stresses otherwise normal pathways . Note first that MHN and MHS patients are classified based on a threshold value of the halothane-induced force FH . FH has a unimodal distribution among individuals ( inset in Figure 1A ) , with probability density varying monotonically near the MHS threshold . This implies that there is no unique way of setting this threshold; the classification is conventional rather than clear-cut . Because the MHS-defining variable FH is graded , the associated deviations from normalcy in metabolic properties should also be graded . In agreement , none of the variables measured showed any clustering that would define a boundary between normalcy and disease . An absence of clustering was also the case in the cell-level study of calcium signals of the ‘recent’ cohort ( Figueroa et al . , 2019 ) . Within this context of graded variation , the quantitative correlations linking changes in the measured variables suggest that MH susceptibility alters continuously a cellular parameter critical for the processing of glucose towards glycogen . The graph in Figure 2H provides an example: the good correlation between the quantity of GP and GDE in microsomes extends smoothly from MHN to MHS patients , without obvious change –in regression coefficient or other properties—between the two groups . The MHS condition — presumably through its higher [Ca2+]cyto — appears to impose changes , such as forcing more of the glycogenolytic enzymes towards the SR , without changing essential properties . The stress provided by the MHS condition affords multiple insights into normal pathways: first it reveals a close relationship between abundance of PhK and phosphorylation of GP , which in turn leads to migration of the enzyme to the microsomal fraction . Imaging at unprecedented resolution locates the phosphorylated GP at or near the SR , predominantly at terminal cisternae . An association between GP and the SR has been reported since 1972 ( Wanson and Drochmans , 1972 ) but the effect of GP phosphorylation on this linkage remained obscure . We now find that the content of GP in microsomes is strictly correlated with its degree of phosphorylation in the whole tissue extract , but not with that in microsomes . This is possible if all GP in microsomes is in the GPa form . This inference , derived from biochemical data in lysates ( Figure 3 ) , is consistent with the distribution of GPa imaged in muscle biopsies ( Figure 5 ) and of GPb derived tentatively by linearly combining GP and GPa images ( Figure 4Dc , E ) . The present evidence contradicts previous reports that found mostly GPb in a rabbit SR fraction , the phosphorylation of which caused its detachment from the SR ( Cuenda et al . , 1994; Cuenda et al . , 1995 ) . Glycogen granules 10–40 nm in diameter ( Prats et al . , 2018 ) share the intermyofibrillar space with the SR ( Ørtenblad and Nielsen , 2015; Fridén et al . , 1989 ) and are part of the microsomal fraction . They contain multiple proteins ( including GP , GDE , GS , PhK , PP1 , PKA and glycogen branching enzyme ) . While GP detected in the microsomal fraction might in principle reside in its glycogen granules , the irregular distribution of the granules in the inter-myofibrillar space ( e . g . Ørtenblad and Nielsen , 2015 ) is inconsistent with the detailed delineation of terminal cisternae by the specific GPa antibody ( Figure 5C , D and its supplements ) . The present images suggest instead a detailed and extensive binding of GPa to the SR , perhaps mediated by glycogen as proposed in early studies of GP that distinguished direct and granule-mediated association ( Wanson and Drochmans , 1972; Hirata et al . , 2003 ) . The changes in MHS include enrichment of GDE in the microsomal fraction . GDE , however , diminishes together with glycogen in microsomes of exercised rats ( Lees et al . , 2004 ) . Again , the observations can be reconciled assuming that GDE has means to associate with the SR other than via glycogen granules . While PhK promotes phosphorylation of GP and GS ( Brushia and Walsh , 1999; Walsh et al . , 1979 ) , the changes in GS observed here differ from those of GP: GSa/GS increases in the microsomal fraction while its total content does not ( Table 1 ) . The observations imply that GSa and GSb coexist in the membrane system and their relative amounts are regulated in response to changes in [Ca2+] . As already noted , PhK increases in the microsomes but remains constant in the whole tissue extract . The change thus appears to be a migration of the kinase towards the membrane fraction . The functional implications of PhK migration are intriguing , considering that GPb — its substrate — remains at the I band , near the Z disk ( Figure 4 and Maruyama et al . , 1985; Chowrashi et al . , 2002 ) , while GPa appears near the SR . The strong heterogeneity in distribution of PhK and GPa might be related to the standing gradient of [Ca2+] between triadic space and bulk cytosol proposed to emerge from the operation of SOCE channels preassembled at T-SR junctions ( Cully et al . , 2018 ) . This conundrum and other questions about mechanism left unanswered by the present study , are stressed by question marks in the functional diagram of Figure 9 . The present findings have implications for clinical management . Follow-up of the six patients identified here as metabolically challenged encountered a disease phenotype , quantified by the Clinical Index defined in Figueroa et al . , 2019 , greater than average for the MHS class . The observation suggests that the alterations in glycogen metabolism cause additional health impairment . GP Inhibitors decrease FBS ( Nagy , 2017; Docsa et al . , 2011; Pałasz et al . , 2019 ) and increase insulin sensitivity ( Docsa et al . , 2015 ) . The finding of tight links between calcium signaling and glycogen metabolism suggests the use of GP inhibitors not just to prevent hyperglycemia , but also to treat the MHS condition . Conversely , treating the causes of abnormal Ca2+ handling in MHS should moderate the changes in glycogen metabolism , as already reported by Altamirano et al . , 2019 in their study of an MHS animal model . The present observations prescribe follow-up of all MHS subjects , watchful for the development of diabetes . In animal models , the presence of GP in extracellular circulating vesicles is an early marker of cardiac injury ( Yarana et al . , 2018 ) ; in MHS , the increase of GPa and other proteins precedes hyperglycemia , therefore the potential of circulating glucose metabolism enzymes as its early markers should be evaluated . The present study has limitations: the resting [Ca2+]cyto in muscle —putative proximate cause of the metabolic alterations — is not known for these individuals . As an approximation , [Ca2+]cyto is being measured in myotubes derived from biopsied muscle ( a systematic elevation was found in cells from MHS patients; Figueroa et al . , 2019 ) . To what extent these alterations result in diabetes will be assessed by the response of MHS and MHN myocytes to insulin and glucose tolerance tests on patients . The imaging of GP and GPa presented here will serve as blueprint for studies of other molecules , including PhK — to reveal where phosphorylation of its targets occurs — and mediators of the insulin response in healthy and diseased individuals . The visualization of exogenous GP in living myocytes ( Figure 4C ) demonstrates the feasibility of a different approach: imaging the movements of glucose regulatory proteins during normal function , under imposed stress and upon interventions of potential therapeutic value . Criteria for recruitment of subjects included one or more of the following: a previous adverse anesthetic reaction , family history of MH without a diagnostic MH mutation ( www . emhg . org ) , a variant of unknown significance ( VUS ) in RYR1 or CACNA1S , recurrent exercise- or heat-induced rhabdomyolysis and idiopathic elevation of serum creatine kinase . We distinguish two groups: a ‘legacy’ cohort of 560 individuals diagnosed at the Malignant Hyperthermia Investigation Unit ( MHIU ) of Toronto General Hospital ( TGH ) in the years 1994 to 2013 , who underwent clinical studies and CHCT , and a ‘recent’ cohort of individuals diagnosed since 2014 , which were studied jointly at the MHIU and the Rush University laboratory . The studies communicated here include the analysis of 11 protein species and glycogen in microsomal and whole muscle fractions of 25 patients of the ‘recent’ cohort , 12 MHS and 13 MHN , chosen because the muscle specimens had a regular striation pattern of > 2 µm per sarcomere consistent with a relaxed state and provided sufficient tissue for biochemistry and imaging . An alternate group of 26 samples , 14 MHN and 12 MHS , underwent a less complete study , yielding similar results summarized in Appendix 2 . All specimens from patients of the recent cohort were de-identified and assigned a unique number before arrival to the Rush laboratory; their identity was known only to the attending personnel at the MHIU . 6–10 wk-old mice , Mus musculus , of the Black Swiss strain , sourced at Charles River Laboratories ( Boston MA , USA ) , were used to define the localization of GP in living cells . Hind paws were transfected with plasmid vector for GP-GFP as described in Pouvreau et al . , 2007 . Animals were euthanized and muscles collected and processed for imaging as in Manno et al . , 2017 . Susceptibility to MH was diagnosed following the North American CHCT protocol ( Larach , 1989 ) . Increases in baseline force in response to caffeine and halothane ( FC and FH ) were measured on freshly excised biopsies of Gracilis muscle with initial twitch responses that met viability criteria . Three muscle bundles were exposed successively to 0 . 5 , 1 , 2 , 4 , 8 and 32 mM caffeine; three separate bundles were exposed to 3% halothane . The threshold response for a positive diagnosis was either FH ≥0 . 7 g or FC ( 2 mM caffeine ) ≥0 . 3 g . Patients were diagnosed as ‘MH-negative’ ( MHN ) if the increase in force was below threshold for both agonists , and ‘MH-susceptible’ ( MHS ) if at least one exposure exceeded the threshold . Biopsied segments were shipped from the MHIU to Rush University ( Chicago , USA ) at 4°C overnight in relaxing solution ( Figueroa et al . , 2019 ) . Upon receipt , thin bundles were dissected for immunohistochemistry or physiological study , small pieces were separated for generation of cell cultures and the remaining tissue was quick-frozen for biochemical studies and storage . For measuring total content of proteins in muscle , the tissue was chopped into small pieces in RIPA lysis buffer ( Santa Cruz Biotechnology , Dallas , TX , USA ) containing protease and phosphatase inhibitors , and homogenized using a Polytron disrupter . The homogenate was centrifuged at 13000 g for 10 min and supernatant aliquots were stored in liquid nitrogen . The microsomal fraction was prepared as described in Perez et al . , 2005 . Protein content was quantified by the BCA assay ( Thermo-Fisher Scientific , Waltham , MA , USA ) . Proteins were separated by SDS–polyacrylamide gel electrophoresis , using 26-well pre cast gels ( Criterion TGX , Bio-Rad , Hercules , CA , USA ) , which enable separation in a broad range of molecular weights , and transferred to a nitrocellulose membrane ( Bio-Rad ) . Membranes were blocked at 4 . 5% blotting grade ( Bio-Rad ) in PBS and incubated with the primary antibody overnight at 4°C . Thereafter they were washed in PBS containing 0 . 1% Tween 20 and incubated in horseradish peroxidase–conjugated anti-mouse , anti-rabbit or anti-sheep secondary ( Invitrogen , Carlsbad , CA , USA ) for 1 hr at room temperature . The blot signals were developed with chemiluminescent substrate ( Millipore , Burlington , MA , USA ) and detected using the Syngene PXi system ( Syngene USA Inc , Frederick , Md , USA ) . The figures represent ‘positive’ images ( light intensity ) in units of convenience on a 12 bit scale . Immunofluorescence examinations used thin myofiber bundles dissected from muscle biopsies . Bundles were mounted moderately stretched in relaxing solution , on Sylgard-coated dishes . Relaxing solution was replaced by fixative containing 4% PFA for 20 min . Bundles were transferred to a 24-well plate and washed three times for 10 min in PBS , then permeabilized with 0 . 1% Triton X-100 ( Sigma ) for 30 min at room temperature and blocked in 5% goat serum ( Sigma ) with slow agitation for 1 hr . The primary antibody was applied overnight at 4°C with agitation , followed by 3 PBS washes for 10 min . Fluorescent secondary antibody was applied for 2 hr at room temperature . Dehydrated bundles were mounted with Prolong Diamond anti-fade medium ( Thermo-Fisher ) . The stacks of dual-stained images were first corrected for device ‘bleed through’ and spectral overlap . After background subtraction the fluorescence in dual measurements ( F1 and F2 ) is ( 1 ) F1=A1+b21A2F2=A2+b12A1 Where Ai is the contribution from marker i ( 1 or 2 ) , b21 is the ratio between the fluorescence F1 measured in the absence of marker 1 , divided by the simultaneously measured F2 and b12 is the cross-coefficient determined in the opposite single-marker situation . From Equation 1: ( 2 ) A1= ( F1−b21F2 ) / ( 1−b21b12 ) A2= ( F2−b12F1 ) / ( 1−b21b12 ) Corrected image stacks were then deblurred by a constrained iterative deconvolution algorithm that used all images in the stack ( Agard et al . , 1989; Voort and Strasters , 1995 ) with a point spread function determined in our microscopes . After deblurring , the separation effectively resolved in the x-y plane was approximately 0 . 25 µm for the FluoView and SP2 imagers , and approximately 0 . 1 μm for the SP8 ( Figure 5—figure supplement 2 ) . Representation or ‘rendering’ in 3-D ( Figure 5 ) used the ‘Simulated Fluorescence Process’ ( Messerli et al . , 1993 ) applied to the full deblurred stack . Immunofluorescence images of phosphorylated glycogen phosphorylase , F'GPa , and all-forms GP , FGP , were used to derive a putative image of the apo form , FGPb , as follows . FGP is proportional to the quantity of protein . Assuming that the antibody is insensitive to phosphorylation of GP: ( 3 ) FGP=B ( GPa+GPb ) ≡FGPa+FGPb GPa and GPb represent local densities of the respective proteins , B is the proportionality constant that links the fluorescence reported by the all-forms antibody to these densities . The second equality separates two additive contributions to the fluorescence . The fluorescence of GPa , reported by its specific antibody , satisfies a similar formula , with a different proportionality factor: ( 4 ) FGPa′=C GPa Therefore the fluorescence of the apo form is: ( 5 ) FGPb=FGP−FGPa=FGP− ( B/C ) F'GPa Therefore , the fluorescence of GPb is the difference between the all-GP fluorescence and that reported by the anti-GPa antibody multiplied by a constant . Equation 5 will be applied to images of various dimensions . Was carried out with custom cross-correlation algorithms implemented in the IDL programming environment ( Harris Geospatiale , Paris , France ) , complemented with analysis by JACoP ( Bolte and Cordelières , 2006 ) , a plugin of ImageJ . Both are available at https://imagej . nih . gov/ij/ . The concentration of glycogen in muscle microsomal fractions was determined with colorimetric assay kit MAK016 ( Sigma ) , using a microplate reader at 570 nm . The method provides for adding variable volumes of extract for a final content of 1 µg of protein per well . The readout , an absorbance , is then corrected for the variable quantity of sucrose added with the extraction buffer . Final content is evaluated as µg/µg of protein . We tested whether the temperature at which the tissue was kept in the 18–24 hr interval between biopsy — in Toronto — and analysis — in Chicago — affected the results . Six samples from each of two patients , three of each kept frozen and the others kept at the standard 4°C , were analyzed . The frozen samples from one patient had on average 13% more glycogen in microsomes ( p = 0 . 07 ) . The difference in the second individual was 4% , in the same direction ( p = 0 . 27 ) . Differences between two categories , the MHN and MHS , were tested for significance by the t-test when there was evidence of normality and equal variance in both groups . The non-parametric Mann-Whitney u test was used otherwise . Differences were considered significant at p ≤ 0 . 05 in the two tails of the null effect distribution . To evaluate hypotheses of causation we quantified pairwise correlations , using the Pearson correlation coefficient r . The significance of the r value was calculated with the variable ( 6 ) t=rn/ ( 1−r2 ) which in the null hypothesis has a Student’s t distribution with n−2 degrees of freedom ( p . 466 of Cramér , 1946 ) . An alternative variable appropriate for r > 0 . 5 ( p . 467 of Cramér , 1946 ) , is ( 7 ) v=n−32 log1+r1−r In the null hypothesis , v has a normal distribution with mean 0 and standard error 1 . Following approval by the institutional Research Ethics Board of Toronto General Hospital ( TGH ) , informed consents were obtained from all patients who underwent the CHCT . The consent , also approved by the Institutional Review Board of Rush University , included use of biopsies for functional studies , imaging and cell culture . Ethical aspects of the animal studies were approved by the IACUC of Rush University .	Animals and humans move by contracting the skeletal muscles attached to their bones . These muscles take up a type of sugar called glucose from food and use it to fuel contractions or store it for later in the form of glycogen . If muscles fail to use glucose it can lead to excessive sugar levels in the blood and a condition called diabetes . Within muscle cells are stores of calcium that signal the muscle to contract . Changes in calcium levels enhance the uptake of glucose that fuel these contractions . However , variations in calcium have also been linked to diabetes , and it remained unclear when and how these ‘signals’ become harmful . People with a condition called malignant hyperthermia susceptibility ( MHS for short ) have genetic mutations that allow calcium to leak out from these stores . This condition may result in excessive contractions causing the muscle to over-heat , become rigid and break down , which can lead to death if left untreated . A clinical study in 2019 found that out of hundreds of patients who had MHS , nearly half had high blood sugar and were likely to develop diabetes . Now , Tammineni et al . – including some of the researchers involved in the 2019 study – have set out to find why calcium leaks lead to elevated blood sugar levels . The experiments showed that enzymes that help convert glycogen to glucose are more active in patients with MHS , and found in different locations inside muscle cells . Whereas the enzymes that change glucose into glycogen are less active . This slows down the conversion of glucose into glycogen for storage and speeds up the breakdown of glycogen into glucose . Patients with MHS also had fewer molecules that transport glucose into muscle cells and stored less glycogen . These changes imply that less glucose is being removed from the blood . Next , Tammineni et al . used a microscopy technique that is able to distinguish finely separated objects with a precision not reached before in living muscle . This revealed that when the activity of the enzyme that breaks down glycogen increased , it moved next to the calcium store . This effect was also observed in the muscle cells of MHS patients that leaked calcium from their stores . Taken together , these observations may explain why patients with MHS have high levels of sugar in their blood . These findings suggest that MHS may start decades before developing diabetes and blood sugar levels in these patients should be regularly monitored . Future studies should investigate whether drugs that block calcium from leaking may help prevent high blood sugar in patients with MHS or other conditions that cause a similar calcium leak .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "structural", "biology", "and", "molecular", "biophysics" ]	2020	Intracellular calcium leak lowers glucose storage in human muscle, promoting hyperglycemia and diabetes
GIRK channels control spike frequency in atrial pacemaker cells and inhibitory potentials in neurons . By directly responding to G proteins , PIP2 and Na+ , GIRK is under the control of multiple signaling pathways . In this study , the mammalian GIRK2 channel has been purified and reconstituted in planar lipid membranes and effects of Gα , Gβγ , PIP2 and Na+ analyzed . Gβγ and PIP2 must be present simultaneously to activate GIRK2 . Na+ is not essential but modulates the effect of Gβγ and PIP2 over physiological concentrations . Gαi1 ( GTPγS ) has no effect , whereas Gαi1 ( GDP ) closes the channel through removal of Gβγ . In the presence of Gβγ , GIRK2 opens as a function of PIP2 mole fraction with Hill coefficient 2 . 5 and an affinity that poises GIRK2 to respond to natural variations of PIP2 concentration . The dual requirement for Gβγ and PIP2 can help to explain why GIRK2 is activated by Gi/o , but not Gq coupled GPCRs . G protein gated inward rectifier K+ ( GIRK ) channels are inward rectifier K+ ( Kir ) channels whose activity is regulated by GTP binding proteins ( G proteins ) ( Brown and Birnbaumer , 1988; Yamada et al . , 1998 ) . They are present in many different cell types including electrically excitable cells in the cardiovascular and nervous systems ( Dascal et al . , 1993; Lesage et al . , 1994; Krapivinsky et al . , 1995; Lesage et al . , 1995; Karschin et al . , 1996 ) . GIRK channels regulate cellular electrical activity by modulating K+ conductance near the resting membrane potential . Specifically , stimulation of certain G protein coupled receptors ( GPCRs ) on a cell's surface leads to the opening of GIRK channels , which increases K+ conductance and opposes the initiation of action potentials . This inhibitory effect on excitation is best understood in the cardiovascular system , where vagus nerve stimulation slows heart rate through acetylcholine action on GPCRs in atrial pacemaker cells ( Loewi and Navratil , 1926; Giles and Noble , 1976; Osterrieder et al . , 1981 ) . The ensuing activation of GIRK channels prolongs the interval between pacemaker action potentials , thus causing the heart rate to slow . GIRK channels are also abundant in the nervous system , where they modulate slow inhibitory postsynaptic currents in response to neurotransmitter stimulation ( Luscher et al . , 1997; Lujan et al . , 2009 ) . Cellular electrophysiology experiments and biochemical analysis have yielded a rich understanding of GIRK channel function . As background to the questions addressed in the present study , the following conclusions have been established over the past several decades of study . First , the G protein ‘subunit’ called Gβγ ( a tightly bound complex of β and γ protein polypeptides ) is the major mediator through which GPCR stimulation opens GIRK channels ( Pfaffinger et al . , 1985; Logothetis et al . , 1987; Reuveny et al . , 1994; Wickman et al . , 1994; Huang et al . , 1995; Inanobe et al . , 1995; Kofuji et al . , 1995; Krapivinsky et al . , 1995 ) . When a GPCR is stimulated through the binding of a neurotransmitter ( or drug ) on the outside of the cell membrane it catalyzes guanine nucleotide exchange–GTP replaces GDP on the Gα subunit–and release of Gα ( GTP ) and Gβγ subunits on the cytoplasmic side ( Gilman , 1987 ) . These subunits are then free to activate their ‘target’ proteins: Gβγ activates GIRK . Second , the signaling lipid phosphatidylinositol 4 , 5-bisphosphate ( PIP2 ) also regulates GIRK channel opening ( Huang et al . , 1998; Sui et al . , 1998; Logothetis and Zhang , 1999 ) . This property means that GIRK channels respond to more than one ligand and thus are under the control of more than one distinct signaling pathway . Third , the activity of certain GIRK channels , for example the neuronal GIRK2 channel , is also regulated by the concentration of intracellular Na+ ( Petit-Jacques et al . , 1999; Ho and Murrell-Lagnado , 1999a , 1999b ) . Intracellular Na+ concentration increases during excessive electrical excitation . Activation of GIRK2 under this circumstance may serve as a cell protective mechanism through action potential suppression . Finally , x-ray crystallography has recently provided atomic level descriptions of a neuronal GIRK channel , GIRK2 , in the absence and presence of Gβγ , PIP2 and Na+ ( Whorton and MacKinnon , 2011 , 2013 ) . The present study addresses the following still unanswered questions . First , does the Gα ( GTP ) subunit also regulate GIRK function ? The Gα subunit co-precipitates with GIRK in biochemical ‘pull-down’ experiments ( Huang et al . , 1995; Peleg et al . , 2002; Ivanina et al . , 2004; Rubinstein et al . , 2009; Berlin et al . , 2010 , 2011 ) and also exhibits interaction with the channel's cytoplasmic domain as detected by solution NMR spectroscopy ( Mase et al . , 2012 ) . Do these interactions underlie channel regulation by Gα ? Second , how does GIRK channel activity depend on the concentration of PIP2 in the membrane ? PIP2 is a signaling lipid whose concentration in the cell membrane changes ( Lemmon , 2008; Balla , 2013 ) . To know whether GIRK activity is responsive to these changes , a quantitative description of the channel's PIP2 concentration dependence is needed . Until now , no method has existed to change PIP2 in a known , quantitative and controlled manner in membranes in which GIRK channel activity is measured . Third , do Gβγ , PIP2 and Na+ , act on the channel independently or do they function together ? The issue of multi-ligand dependence is relevant to understanding the molecular biophysics of channel regulation and to understanding how multiple signaling pathways control membrane potential . At the level of molecular biophysics we wish to understand how multiple allosteric inputs regulate channel opening . At the level of cellular control of membrane potential we wish to understand GIRK's AND/OR relationship with respect to multiple signaling inputs . The real difficulty in answering the above questions lies in the compositional complexity and uncertainty of living cell membranes—the natural system in which nearly all past experiments have been carried out . In this study we use the planar lipid bilayer technique to characterize the regulation of GIRK channels by G-proteins , PIP2 and Na+ ions . The bilayer technique is a total reconstitution method in which the channel and its various regulatory components are first purified to homogeneity and then combined . Every component of the system is known to within a small error in composition and amount , including the membrane , which is produced from either purified or synthetic lipids . A schematic of the bilayer system depicts two chambers separated by a partition with a hole onto which lipid membranes are painted ( Montal and Mueller , 1972; Miller and Racker , 1976; Figure 1B ) . Components are added by fusing lipid vesicles that contain channels and membrane-anchored G proteins and by adding Na+ or soluble short chain PIP2 molecules to either chamber . The chambers are connected electrically through a voltage-clamp circuit to record K+ currents across the membrane . 10 . 7554/eLife . 03671 . 003Figure 1 . Structural depiction of GIRK2 regulation and horizontal planar bilayer configuration . ( A ) View from the membrane plane of a GIRK2 channel ( gray ) bound to its activating ligands PIP2 ( purple ) , Na+ ( brown ) and the heterodimeric G protein subunit Gβγ ( blue , with the postulated geranylgeranyl group on the Gγ subunit drawn as blue lines ) in surface representation ( PDBID: 4KFM ) . There are four binding sites for each of these ligands on each GIRK2 homotetramer . ( B ) A schematic of the horizontal planar bilayer system used to characterize GIRK2 channel activity . Two solution-filled chambers in a polyoxymethylene block are separated by a piece of transparency film with a small hole ( ∼100 μm diameter ) . Lipid bilayers are formed across the hole by spontaneous thinning of a painted solution of lipid in decane . Membrane proteins are incorporated into the lipid bilayer by fusion of applied proteoliposomes . Soluble reagents are either directly applied to either chamber followed by thorough mixing or added via a local perfusion system . The two chambers are electrically voltage-clamped as indicated for current recordings . DOI: http://dx . doi . org/10 . 7554/eLife . 03671 . 003 The current trace in Figure 2A shows K+ currents in bilayer membranes in which GIRK2 channel-containing vesicles were fused in the absence of PIP2 and Gβγ . Brief channel openings appear as downward current spikes because the membrane voltage was held positive relative to ground ( top chamber , Figure 1B ) and the sign of current was inverted to conform to eletrophysiological convention . When PIP2 was added to the top chamber half way through the trace , channel activity remained essentially unchanged ( Figure 2B ) . Similarly , addition of Gβγ in the absence of PIP2 did not perceptibly alter baseline channel activity ( Figure 2C ) . Only when both PIP2 and Gβγ were present , independent of the order in which they were added , did we observe large K+ currents due to robust GIRK2 channel opening ( Figure 2D , E ) . We note that the baseline current prior to the addition of the second ligand in Figure 2D , E , shown on an expanded current axis scale , is similar in magnitude to the currents in Figure 2B , C . It is thus clear in the bilayer assay that PIP2 or Gβγ alone are insufficient to elicit robust channel activation . Both of these ligands together are necessary . While past studies had reached the conclusion that both ligands are important—that one ligand enhances the affinity of the other ( Huang et al . , 1998; Yamada et al . , 1998; Logothetis and Zhang , 1999 ) —the complexity of cell membranes obscured the simple conclusion so obvious here that both ligands are necessary to open the channel . 10 . 7554/eLife . 03671 . 004Figure 2 . GIRK2 activation requires both Gβγ and PIP2 . Currents are plotted according to electrophysiology convention such that negative values represent inward current with respect to channel orientation . The same buffer containing 150 mM KCl was used in both chambers and the membranes were held at −50 mV ( eliciting inward channel current ) . Initial compositions of each bilayer are indicated above each panel . Fusion of lipid vesicles was done manually and induced significant noise during application . This noise did not interfere with subsequent recording of current change . ( A ) Current spikes were observed after fusing GIRK2 vesicles into the lipid bilayer . ( B and D ) Gβγ is necessary for PIP2 activation of GIRK2 . Currents were recorded from a bilayer containing either GIRK2 alone ( B ) or GIRK2+Gβγ ( D ) before and after application of 32 μM C8-PIP2 ( indicated with arrow ) . Only when Gβγ was present ( D ) did application of PIP2 increase current through GIRK2 . ( C and E ) PIP2 is necessary for Gβγ activation of GIRK2 . Currents were recorded from a bilayer containing either GIRK2 alone ( C ) or GIRK2 and 2% brain PIP2 ( E ) before and after application of Gβγ vesicles ( indicated with arrow ) . The amplifier gain was adjusted during the gap in the recording in ( E ) . Insets in ( D and E ) show spontaneous openings of GIRK2 before application of Gβγ ( compare to B and C ) . ( F ) Current recorded from a bilayer with GIRK2 and PIP2 before and after application of non-lipidated Gβγ ( left side of recording ) followed by application of lipidated Gβγ vesicles ( right side of recording ) . Lipidated Gβγ ( ∼15 μM ) robustly activated GIRK2 while high concentrations ( ∼116 μM ) of non-lipidated Gβγ only slightly increased channel activity . DOI: http://dx . doi . org/10 . 7554/eLife . 03671 . 004 Figure 2F addresses the importance of the lipid anchor on the Gβγ subunit . The recording on the left side shows that when mutant Gβγ subunits devoid of the Gγ-linked geranylgeranyl lipid moiety ( C68S ) were added to solution ( to ∼116 μM concentration ) channels did not open robustly . When Gβγ subunits containing the lipid moiety ( ∼15 μM in lipid vesicles ) were subsequently applied to the same membrane , large currents were elicited ( Figure 2F , right side recording ) . This experiment shows that the lipid anchor is essential . The likely explanation is , by mediating membrane partitioning , the lipid anchor is required to achieve sufficiently high enough concentrations of Gβγ on the membrane surface to activate GIRK2 . Cellular electrophysiologists have referred to G protein activation of GIRK channels as ‘membrane delimited’ ( Soejima and Noma , 1984; Breitwieser and Szabo , 1985; Pfaffinger et al . , 1985; Logothetis et al . , 1987; Reuveny et al . , 1994; Wickman et al . , 1994 ) , meaning Gβγ stays stuck to the membrane when it is released from a GPCR and diffuses to its target . Here , this property of membrane targeting mediated by a lipid anchor is nicely replicated in the bilayer assay . The importance of the lipid anchor is also consistent with the orientation of Gβγ in the crystal structure , which shows the geranylgeranylated C-terminus of Gγ pointed toward the membrane ( Figure 1A; Whorton and MacKinnon , 2013 ) . In contrast to the membranes of cells , in which channels are inserted with one orientation , in the planar bilayer system channels and Gβγ subunits insert randomly and thus are oriented in both directions . The experiments in Figure 3 show , despite the dual orientation of proteins in the bilayer membrane , that short chain C8-PIP2 activates only one orientation of GIRK2 channels—corresponding to those channels with their physiologically intracellular surface pointed toward the chamber to which PIP2 was added . Demonstration of this fact made use of the GIRK channel inhibitor tertiapin-Q ( TPNQ ) , a derivative of the peptide toxin tertiapin isolated from honeybee venom ( Jin and Lu , 1999 ) . TPNQ inhibits GIRK channels by binding to a specific receptor site on the extracellular surface of the channel . In the first experiment GIRK2 channels and Gβγ subunits were reconstituted into bilayer membranes randomly , C8-PIP2 was added to one side ( bottom chamber ) , and an I-V curve was recorded ( Figure 3A , squares ) . The solutions on both sides of the membrane contained equal concentrations of K+ and thus the I-V curve reverses at zero mV , but still exhibits the characteristic inward rectification of the GIRK channel . Next , TPNQ was added to the opposite side ( top chamber ) and essentially all of the currents were inhibited ( Figure 3A , circles ) . The second experiment is similar to the first except C8-PIP2 and then TPNQ were added to the same side of the membrane ( top chamber ) ( Figure 3C ) . In this case TPNQ did not block the K+ currents . 10 . 7554/eLife . 03671 . 005Figure 3 . Activation of GIRK2 by C8-PIP2 is sided . ( A ) Representative current–voltage relationship recorded from a bilayer containing GIRK2 and Gβγ . Robust current was recorded upon addition of C8-PIP2 to the bottom side of the bilayer ( black squares ) that is blocked by application of TPNQ to the top side ( white circles ) . ( B ) Schematic of molecular interactions that explain the data in ( A ) . ( C and D ) Analagous to ( A and B ) , except that C8-PIP2 and TPNQ were applied to the same side of the bilayer . GIRK2 channels that are activated by C8-PIP2 ‘cytoplasmically’ can only be blocked by ‘extracellular’ TPNQ . DOI: http://dx . doi . org/10 . 7554/eLife . 03671 . 005 The interpretation of each experiment is shown pictorially ( Figure 3B , D ) . C8-PIP2 ( and Gβγ ) activate a uniformly oriented population of channels from their intracellular surface , whereas oppositely oriented channels are not activated because they are not exposed to PIP2 on their physiologically intracellular side . This conclusion neatly accounts for complete inhibition of currents when TPNQ is added to the side opposite PIP2 ( Figure 3A , B ) and no inhibition when it is added to the same side ( Figure 3C , D ) . The Gα and Gβγ subunits act on specific target proteins to regulate their function . With GDP bound to the catalytic site of Gα , Gα binds to Gβγ , rendering both ( Gα and Gβγ ) unavailable to their targets . The role of G protein coupled receptors ( GPCRs ) is , upon stimulus , to catalyze GDP/GTP exchange on Gα and release of both Gα ( GTP ) and Gβγ to action . After a period of time , being a slow GTPase , Gα catalyzes the conversion of GTP back to GDP , allowing Gα ( GDP ) to again form a tight complex with Gβγ , thus terminating action ( Gilman , 1987 ) . Gβγ is known to activate GIRK , but as stated in the introduction , several lines of evidence indicate that Gα can also interact directly with the GIRK channel ( Huang et al . , 1995; Rebois et al . , 2006; Rubinstein et al . , 2007; Schreibmayer , 2009; Berlin et al . , 2010 , 2011 ) . The bilayer assay , in which every component is defined , allows a simple approach to address whether Gα plays a direct role in GIRK channel gating . When Gαi1 containing the non-hydrolyzable GTP analog GTPγS is applied to membranes containing GIRK channels there is no activation ( Figure 4A ) . In this experiment GIRK2 channels were pre-fused with bilayer membranes containing 1% brain PIP2 ( Balla , 2013 ) . The characteristic brief baseline channel openings are observed , demonstrating that channels are present in the membrane , but no change in current is observed upon subsequent fusion of vesicles containing Gαi1 ( GTPγS ) . To confirm the presence of abundant channels , Gβγ-containing vesicles were subsequently fused with the same bilayer membrane and large K+ currents were evoked ( Figure 4A , right side of trace ) . From this experiment it is clear that GIRK2 channels are activated by Gβγ but not by Gαi1 ( GTPγS ) , as summarized pictorially ( Figure 4B ) . 10 . 7554/eLife . 03671 . 006Figure 4 . Gαi1 ( GTPγS ) does not activate GIRK2 . Recordings were performed as in Figure 2 . The increased signal during application of vesicles resulted from opening the chamber for electromagnetic shielding . ( A ) Current recorded from a bilayer with GIRK2 and 1% brain PIP2 before and after application of Gαi1 ( GTPγS ) ( left ) followed by application of Gβγ ( right ) . ( B ) Schematic of molecular interactions that explain the data in ( A ) . Activation by Gβγ but not Gαi1 ( GTPγS ) confirmed the presence of GIRK2 channels in the bilayer and their insensitivity to applied Gαi1 . DOI: http://dx . doi . org/10 . 7554/eLife . 03671 . 006 While Gαi1 ( GTPγS ) fails to affect GIRK2 channel gating in the absence of Gβγ , there remains the possibility that Gαi1 ( GTPγS ) can function together with Gβγ to influence gating . To test this possibility we first fused GIRK2 channels and Gβγ into the bilayer and supplemented the top chamber with 32 μM C8-PIP2 , followed by fusion of vesicles containing Gαi1 ( GTPγS ) . C8-PIP2 was used instead of brain PIP2 to ensure that all the activated channels would be oriented with their cytoplasmic side facing the top chamber . To facilitate fusion ( Fisher and Parker , 1984 ) , vesicles were applied in a solution containing 750 mM KCl . This concentration of KCl near the membrane surface produced a transient reduction of K+ current–until diffusion or mixing restored the local ion composition to bulk values–as shown in the empty vesicle control on the left side of the trace ( Figure 5A ) . Fusion of Gαi1 ( GTPγS ) -containing vesicles appeared essentially identical to empty vesicles ( Figure 5B ) . Thus , GIRK2 channels are unresponsive to Gαi1 ( GTPγS ) even after they have been activated by PIP2 and Gβγ . 10 . 7554/eLife . 03671 . 007Figure 5 . Gαi1 ( GDP ) but not Gα i1 ( GTPγS ) deactivates GIRK2 by sequestering Gβγ . Recordings were performed as in Figure 2 . ( A and B ) Current recording from a bilayer with GIRK2 and Gβγ activated by 32 μM C8-PIP2 before and after ( A ) addition of empty vesicles followed by ( B ) addition of Gαi1 ( GTPγS ) vesicles ( vesicle additions indicated by arrows ) . Note that the high salt concentration of the vesicle solution ( 750 mM KCl ) used to facilitate fusion to the bilayer caused a similar transient reduction in K+ current in both ( A ) and ( B ) . In both cases , current level returned to near pre-vesicle application levels after thorough mixing . ( C ) Current recording from a bilayer with GIRK2 and Gβγ activated by C8-PIP2 before and after application of vesicles containing Gαi1 ( GDP ) ( indicated by arrow ) . ( D ) Comparison of the effects of non-lipidated ( soluble ) Gαi1 ( GDP ) and Gαi1 ( GTPγS ) on activated GIRK2 . Current at −50 mV holding potential ( normalized to the value before addition of Gαi1 ) recorded from bilayers ( ±SEM , n = 3 bilayers ) with GIRK2 and Gβγ activated by C8-PIP2 vs concentration of added Gαi1 species is plotted . The lines connecting data points have no theoretical meaning . ( E ) Illustration of molecular interactions that explain data in ( A–D ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03671 . 007 In contrast , fusion of vesicles containing Gαi1 ( GDP ) caused rapid disappearance of activated GIRK2 currents ( Figure 5C ) . We also studied the effect of Gαi1 ( GDP ) and Gαi1 ( GTPγS ) using soluble Gαi1 subunits ( produced in Escherichia coli ) that did not contain a covalent membrane lipid anchor . At micromolar concentrations , soluble Gαi1 ( GDP ) closed GIRK2 channels , whereas soluble Gαi1 ( GTPγS ) had only a small effect most likely due to small amounts of Gαi1 ( GDP ) present in the Gαi1 ( GTPγS ) preparation ( Figure 5D ) . Apparently Gαi1 ( GDP ) in solution binds to membrane anchored Gβγ , rendering it unavailable to activate GIRK2 , as depicted ( Figure 5E ) . It is interesting to contrast the ability of soluble Gαi1 ( GDP ) to close GIRK2 channels ( Figure 5D ) with the inability of soluble ( lipid anchor-removed ) Gβγ to open GIRK2 channels ( Figure 2F ) . This observation suggests differences in the relative affinities of Gβγ for GIRK2 vs Gαi1 ( GDP ) ( The equilibrium dissociation constant for Gαi1 ( GDP ) binding to Gβγ is ∼3 nM in Lubrol and ∼10 nM in lipids [Sarvazyan et al . , 1998] ) . These differential affinities are likely important for G protein regulation of channel activity . The importance of the signaling lipid PIP2 to GIRK channel gating has been well documented through studies in cells ( Huang et al . , 1998; Sui et al . , 1998; Rohacs et al . , 2002; Gamper and Rohacs , 2012 ) . Previous studies augmented PIP2 levels in cell membranes through the addition of PIP2 to undetermined baseline levels or reduced PIP2 levels through the use of PIP2-sequestering antibodies ( Huang et al . , 1998; Logothetis and Zhang , 1999; Rohacs et al . , 2002; Inanobe et al . , 2010 ) . These approaches yielded qualitative data on the relationship between GIRK channel activity and PIP2 concentration . Here we aim to describe the quantitative relationship between GIRK channel activity and PIP2 levels . The bilayer allows quantitative control of lipid composition . In a first experiment GIRK2 channels and Gβγ were reconstituted into planar lipid membranes without PIP2 . Soluble C8-PIP2 was then perfused with a microperfusion pipette aimed directly at the membrane to activate channels: during perfusion of C8-PIP2 channels opened and during perfusion of buffer they closed as soluble C8-PIP2 diffused away from the membrane ( Figure 6A ) . Repeating this experiment with different C8-PIP2 concentrations , or applying different concentrations of C8-PIP2 to the bath solution , gave the titration data shown ( Figure 6B ) . In the graph , in order to compare current levels from different membranes , which in general contain different channel numbers , data were normalized to the current value measured in the presence of 28 μM C8-PIP2 . The activation curve follows a sigmoid shaped concentration dependence , consistent with activation by multiple PIP2 molecules . The atomic structure shows four binding sites per channel ( Figure 1A ) . The solid and dashed curves correspond to two different models of channel activation: the solid curve to the Hill equation , which assumes cooperativity between sites , and the dashed curve to a model in which all four binding sites must be occupied but binding is independent ( non-cooperative ) . Both models conform to the data reasonably well . The most important point is that the shape of the titration curve is consistent with multiple binding events being required for channel activation . 10 . 7554/eLife . 03671 . 008Figure 6 . Concentration dependence of GIRK2 activation by C8- and brain-PIP2 . ( A ) Current recorded from a bilayer containing GIRK2 and Gβγ during local perfusion of C8-PIP2 ( black bars above recording ) or buffer ( white bars above recording ) . ( B ) Plot of current recorded from bilayers with GIRK2 and Gβγ ( normalized to current at 28 μM C8-PIP2 , mean ± SEM , n = 3 bilayers ) vs concentration of C8-PIP2 . Hill fit ( solid line ) gives an apparent dissociation constant of ∼15 μM and a Hill coefficient of ∼3 . 1 . Fit to a non-cooperative model ( ‘Materials and methods’ ) in which simultaneous binding of 4 PIP2 molecules to one channel is required for channel opening is also shown ( dotted line ) . ( C ) Current recorded from a bilayer with GIRK2 , Gβγ and 1 . 38% ( mole fraction ) brain PIP2 during local perfusion of 32 μM C8-PIP2 ( black bars above recording ) or buffer ( white bars above recording ) . Channels oriented with their extracellular side facing the PIP2 perfusion chamber were blocked with 100 nM TPNQ in perfusion buffers . As a way to normalize channel numbers in different membranes , the current value during perfusion of buffer was normalized to the current level during perfusion of 32 μM C8-PIP2 . ( D ) Plot of current recorded from bilayers with GIRK2 and Gβγ ( mean ± SEM , n = 3 bilayers ) vs concentration of brain PIP2 in the membrane . Regression to Hill equation ( solid line ) resulted in an apparent dissociation constant of ∼0 . 8% mole fraction brain PIP2 and a Hill coefficient of ∼2 . 4 . The dashed line shows regression to the same non-cooperative four sites model as in ( B ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03671 . 008 C8-PIP2 is a soluble form of PIP2 that partitions into the membrane . Given that the ( unknown ) partition coefficient is likely independent of C8-PIP2 concentration , the titration in Figure 6B is valid for assessing the shape of the titration curve . But what we really want to know is the activity dependence of GIRK2 as a function of the mole fraction of PIP2 inside the membrane . The practical difficulty in determining this is each measurement for a given mole fraction has to come from a different membrane; and each membrane has a different number of channels . In other words , to compare membranes we have to normalize with respect to the number of channels . To do this we produced membranes with a desired mole fraction of full-length PIP2 in the lipid mixture , recorded GIRK2 currents , and then applied C8-PIP2 to maximally activate ( or nearly so ) , as shown ( Figure 6C ) . Data from different bilayers could then simply be normalized with respect to their maximally activated current . The graph shows GIRK2 channel activation as a function of PIP2 mole fraction ( Figure 6D ) . Here , as in the case of soluble C8-PIP2 , activation is a sigmoid shaped function of PIP2 mole fraction , consistent with a requirement of multiple PIP2 molecules in channel activation . Moreover , the steepest portion of the graph ( corresponding to a few tenths to one percent PIP2 ) is in agreement the known concentration range of PIP2 in the inner leaflet of cell membranes ( Heck et al . , 2007; van Meer et al . , 2008; Balla , 2013 ) . This mole fraction dependence suggests that naturally occurring variations of PIP2 in cell membranes should influence GIRK2 activity level . Intracellular Na+ was shown previously to be an activator of GIRK2 channels ( Petit-Jacques et al . , 1999; Ho and Murrell-Lagnado , 1999a , 1999b; Inanobe et al . , 2013 ) . Here we aim to replicate Na+ dependence in the bilayer through quantitative measurements and to further our understanding of Na+ activation by investigating the relationship between Na+ and PIP2 activation . Figure 7 graphs GIRK2 channel activity as a function of internal Na+ or C8-PIP2 concentration ( Figure 7A–C ) . In these experiments GIRK2 channels and twofold excess Gβγ ( per channel subunit ) were reconstituted into bilayer membranes . The 3 dimensional plot shows how these ligands' effects are interdependent ( Figure 7A ) . As shown earlier , robust channel opening is absolutely dependent on the presence of both PIP2 and Gβγ ( Figure 7C ) . By contrast , GIRK2 channels open in the absence of internal Na+ , but opening increases as the Na+ concentration is increased . Viewed along the axis of PIP2 activation , the presence of internal Na+ steepens the dependence of channel opening on PIP2 concentration ( Figure 7B ) . Thus , Na+ is not essential but augments channel opening . The effect of Na+ depends on the degree of activation by PIP2 . In the presence of ample PIP2 , channel activity increases as a function of Na+ concentration most steeply in the range 0–10 mM . This dependence seems well matched to the concentration range over which cytoplasmic Na+ levels change in response to excessive electrical excitation ( Muller and Somjen , 2000; Somjen , 2002 ) . 10 . 7554/eLife . 03671 . 009Figure 7 . Interdependence of PIP2 and Na+ activation . Channel activity is normalized to that at the condition in which 28 μM of PIP2 and 32 mM of Na+ is present . ( A ) A 3-D representation of the activation landscape of PIP2 and Na+ . The surface is gradient colored according to activation . ( B ) Plot of percentage activity of GIRK2 channels in membranes with saturating Gβγ vs added C8-PIP2 concentration in the absence ( red , n = 3 ) and presence ( black , n = 3 ) of 32 mM Na+ . PIP2 activated GIRK2 currents were ∼2 . 5-fold higher ( at 28 μM ) in the presence of 32 mM Na+ . Hill fitting resulted in apparent dissociation constants of ∼15 μM and ∼12 μM and Hill coefficients of ∼3 . 1 and ∼1 . 5 in the absence and presence of Na+ , respectively . ( C ) Plot of percentage activity of GIRK2 channels in membranes with saturating Gβγ vs concentration of added Na+ in the absence ( red , n = 3 ) and presence ( black , n = 3 ) of 28 μM C8-PIP2 . The titration data were fit to a simple 1:1 dose response model with an apparent dissociation constant of ∼9 mM . DOI: http://dx . doi . org/10 . 7554/eLife . 03671 . 009 One question addressed in this study , motivated by past work showing that Gα can interact physically with at least some GIRK channels ( Yatani et al . , 1988; Huang et al . , 1995; Peleg et al . , 2002; Ivanina et al . , 2004; Berlin et al . , 2011 ) , concerns the role of Gα vs Gβγ in GIRK channel activation . Using bilayer reconstitution we find the following . Gβγ activates GIRK2 , as was previously shown ( Logothetis et al . , 1987; Reuveny et al . , 1994; Wickman et al . , 1994; Kofuji et al . , 1995 ) . Gαi1 ( GTP ) does not activate GIRK2 and moreover does not appear to influence the activation of GIRK2 by Gβγ . These findings suggest , at least for the case of the activated form of Gαi1 ( Gαi1 ( GTP ) ) and GIRK2 , that physical interactions between these proteins , if they indeed exist , are without direct functional consequence . Gαi1 inhibits the GIRK2 channel activity only when it is in its GDP bound form , consistent with the canonical view that GIRK is activated by Gβγ and that GDP-bound Gαi1 sequesters Gβγ ( Gilman , 1987; Wickman et al . , 1994; Peleg et al . , 2002 ) . In the bilayer experiments the action of Gαi1 ( GDP ) is so rapid ( Figure 5C ) as to suggest that it might bind to the channel and cause Gβγ to dissociate . We think , however , that such a displacement mechanism need not be invoked to account for the rapid inhibitory effect of Gαi1 ( GDP ) . In drawing this conclusion we appeal to the inefficiency of Gβγ to activate when it does not contain a lipid anchor . Presumably Gβγ fails under this circumstance because , without membrane partitioning , its local concentration near the channel is never high enough to achieve sufficient occupancy to open the channel . Thus , Gβγ apparently binds to the channel with low affinity , compatible with a rapid dissociation rate . Comparatively , Gβγ appears to bind Gαi1 ( GDP ) with high affinity , as this would account for sequestration of Gβγ even when no lipid is present on Gαi1 ( GDP ) ( Figure 5D ) . Therefore , it seems likely that Gαi1 ( GDP ) turns off GIRK by simply binding to the free ( channel unbound ) form of Gβγ and shifting the equilibrium away from channel occupancy by Gβγ . Several further lines of evidence support the notion that Gβγ binds with low affinity to the GIRK channel . During biochemical purification we have never detected a complex between these two proteins on gel filtration even when both proteins are at concentrations in the 100 µM range ( Whorton and MacKinnon , 2013 ) . Using solution NMR spectroscopy , Shimada et al . estimated the affinity between Gβγ and the GIRK1 cytoplasmic domain ( which accounts for the full protein–protein interaction surface ) to be about 250 μM ( Yokogawa et al . , 2011 ) . Using this value , if we conservatively assume an association rate of 105 M−1s−1 ( well below the expected diffusion limit in solution ) then the dwell time for a single Gβγ on the channel is calculated to be about 100 ms , a duration much shorter than the wash out time in our experiments . Finally , a low affinity interaction matches the relatively small surface area ( ∼700 Å2 ) of interaction between Gβγ and its binding site on GIRK2 in the crystal structure ( Whorton and MacKinnon , 2013 ) . Therefore , all observations are consistent with the idea that Gβγ is in rapid exchange with its binding site on the GIRK channel . Given the apparent low affinity between Gβγ and the GIRK channel , the high affinity of Gβγ for Gα ( GDP ) , and the absence of an effect of Gα on channel function , we think the following mechanism is most likely . GPCR activation generates Gβγ and Gα ( GTP ) , then Gβγ activates GIRK , but once GTP is hydrolyzed the resultant Gα ( GDP ) removes Gβγ from the channel by equilibrium-mass action . This picture is simple and in good agreement with the accepted view , but it raises the following question . If the affinity of Gβγ for GIRK is so low and the interaction so transient , how does GPCR activation ever achieve sufficiently high concentrations of Gβγ in the membrane to activate the channel ? It seems unlikely that a single GPCR located near a GIRK channel would be able to generate a high enough local concentration of subunits to activate it , especially if multiple bound Gβγ subunits are required , which we suspect is the case on the basis of the crystal structure ( Whorton and MacKinnon , 2013 ) . One way to explain this dilemma is to imagine that GIRK channels and GPCRs might be co-localized in relatively large patches on the membrane . Such an organization would exploit a fundamental property of 2-dimensional diffusion: a point source in an infinite space has no steady state solution . In other words , the transition between local and bulk Gβγ concentration will grow radially with activation time . Thus , in a patch of multiple GPCRs ( and channels ) a sufficiently high concentration of Gβγ on the membrane could be reached . The possibility of co-localization of GPCRs and GIRK channels raises an interesting challenge to the replication of cell-like signaling in the bilayer . It could be that additional cellular elements ( e . g . , cytoskeleton ) are required to perfectly replicate the signaling behavior observed in cells . Another question addressed in this study concerns the quantitative dependence of GIRK2 channel activity on the concentration of PIP2 in the membrane . In the presence of Gβγ , GIRK2 channel activity shows a sigmoid dependence on PIP2 mole fraction . A Hill plot yields a coefficient around 2 . 5 . We cannot tell from our data whether PIP2 binding is cooperative ( as the Hill model assumes ) or non-cooperative ( e . g . , if multiple PIP2 molecules must bind , but independently , to open the channel ) . Cooperative or not , the sigmoid functional relationship points to multiple PIP2 molecules required to open the channel . This result is consistent with the crystal structures , which show four PIP2 molecules bound to the channel ( Whorton and MacKinnon , 2011 ) . The apparent binding constant for PIP2 is 0 . 8% mole fraction ( Hill model ) , which , together with the Hill coefficient , describes the position of the steepest part of the sigmoid curve along the mole fraction axis . We see that GIRK2 activity is most responsive to PIP2 in the mole fraction range 0 . 1–1 . 0% . This is precisely the range over which PIP2 is known to vary in the inner membrane leaflet of cells ( Balla , 2013 ) . In other words , GIRK2 is poised to be under control by PIP2 membrane concentration . Finally this study addressed the interdependence of multiple ligands known to activate GIRK channels . Robust channel opening requires the presence of both Gβγ and PIP2 . Earlier electrophysiological studies concluded that activation can occur at high concentrations of a single ligand and that the role of Gβγ , for example , was to enhance the stimulation brought about by PIP2 ( Huang et al . , 1998; Sui et al . , 1998 ) . Those studies were carried out in cells , where the composition of the membrane is not so certain . The planar bilayer experiments support a simple conclusion: Gβγ and PIP2 are simultaneously required to activate GIRK2 . This conclusion is beautifully compatible with structural studies of GIRK2 , which show that when PIP2 alone is bound the channel's inner gate remains closed ( it exhibits the same closed conformation as when PIP2 is not bound ) ( Whorton and MacKinnon , 2011 ) . But when both Gβγ and PIP2 are bound the cytoplasmic domain rotates with respect to the transmembrane domain and causes the inner helix gate to begin opening ( Whorton and MacKinnon , 2013 ) . PIP2 binds near the interface between the cytoplasmic domain and the transmembrane domain . We hypothesize that PIP2 strengthens allosteric communication between these two domains of the channel . Such a strengthening of interaction would allow conformational changes in the cytoplasmic domain , induced by Gβγ binding , to open the gate in the transmembrane domain . Na+ is not sufficient and not necessary for GIRK2 channel activation . Na+ increases channel opening in the presence of Gβγ and PIP2 and thus Na+ is best described as a modulator of the other ligands' effects ( Figure 7C ) . This conclusion is easily appreciated through inspection of the C8-PIP2 activation curve in the presence and absence of Na+ ( Figure 7B ) . Not only does Na+ increase the current level at any given concentration of PIP2 ( and a fixed concentration of Gβγ ) , but the shape of the PIP2 dependence curve is affected by Na+ . In other words , Na+ does not simply scale the PIP2 dependence curve . This is explicable if the binding affinity/effect of PIP2 is coupled to the presence of Na+ ( Ho and Murrell-Lagnado , 1999a , 1999b ) . In the crystal structure Na+ binds at a location in between Gβγ and PIP2 . It is therefore reasonable to think that conformational changes induced by the binding of one ligand , for example Na+ , would have consequences for the binding/effect of another ligand , for example Gβγ or PIP2 . Although Na+ plays a modulatory rather than obligatory role in gating , its effect is substantial and occurs over the range in which cytoplasmic Na+ concentration is known to change when a cell is experiencing excessive electrical excitation ( Muller and Somjen , 2000 ) . These findings are in good agreement with the hypothesis that Na+ modulation renders GIRK2 a negative feedback safety element ( Ho and Murrell-Lagnado , 1999a ) . The dual requirement for both Gβγ and PIP2 may have important consequences for the control of cellular electrical excitability by different signaling pathways . Consider that Gβγ is released upon stimulation of all GPCRs , but GIRK channels only open specifically in response to Gi/o coupled GPCRs ( Gi/o refers to G protein trimers containing i/o class Gα subunits ) ( Breitwieser and Szabo , 1985; Pfaffinger et al . , 1985 ) . It is easy to understand a possible origin of specificity when comparing Gi/o coupled GPCRs ( which activate GIRK ) and Gq coupled GPCRs ( which do not activate GIRK ) ( Lei et al . , 2001; Cho et al . , 2005 ) . Gq coupled GPCRs release Gβγ , but at the same time the Gαq subunit activates PLC , which depletes PIP2 through hydrolysis ( Cho et al . , 2005; Keselman et al . , 2007; Sohn et al . , 2007 ) . In the absence of sufficient PIP2 , Gβγ cannot activate GIRK . Indeed , recent experiments have shown in both atrial pacemaker cells ( Cho et al . , 2005 ) and certain neurons ( Sohn et al . , 2007; Yamamoto et al . , 2014 ) that Gq coupled GPCR stimulation inhibits GIRK activation by Gi/o coupled GPCR stimulation . It is thus reasonable to think that absence of PIP2 accounts at least in part for the inability of Gq coupled GPCRs to activate GIRK . Gi/o coupled GPCRs on the other hand generate Gβγ without degrading PIP2 so both necessary ligands are available to activate GIRK . This kind of GPCR selectivity results because GIRK is an AND gate with respect to Gβγ and PIP2 activation . In addition to the more widespread ( but not yet proved ) idea of GPCR/target protein co-localization ( Fowler et al . , 2007; Labouebe et al . , 2007; Cui et al . , 2010; Luscher and Slesinger , 2010 ) , AND gating of GPCR targets by multiple ligands provides a mechanism for selectivity in G protein signaling . Mouse GIRK2 ( amino acid residues 52–380 ) was expressed in Pichia pastoris cells , extracted with decyl-β-D-maltopyranoside ( DM ) and purified as previously described ( Whorton and MacKinnon , 2011 ) . The protein was concentrated to ∼15 mg/ml after purification and used immediately for reconstitution . Human G protein subunits β1 and γ2 were expressed in High Five ( Invitrogen , Carlsbad , CA ) insect cells by co-infection as previously described ( Whorton and MacKinnon , 2013 ) . Cell membranes were then prepared and G proteins were extracted from the membranes with Na-cholate ( Sigma , St . Louis , MO ) and purified as described ( Whorton and MacKinnon , 2013 ) with the following modifications: 1% ( wt/vol ) of Na-Cholate was used in Talon resin ( Clontech , Mountain View , CA ) purification instead of 0 . 5% ( wt/vol ) of Anzergent 3–12 ( Anatrace , Maumee , OH ) . After elution from Talon resin and digestion with PreScission protease , the protein was diluted to an imidazole concentration of 10 mM . The diluted solution was passed through a column packed with Talon resin pre-equilibrated with the same buffer without imidazole . The flow-through from this step was concentrated and loaded onto a Superdex 200 10/300 ( GE Healthcare , Pittsburgh , PA ) gel filtration column in the buffer 20 mM HEPES pH 8 . 0 , 150 mM KCl , 5 mM DTT , 1 mM EDTA , 0 . 2% DM . The hetero-dimer Gβγ protein eluted as a major peak at ∼12 ml . Fractions containing this peak were pooled and concentrated to ∼30 mg/ml and were either used immediately or frozen in aliquots at −80°C . The non-lipidated form of Gβγ was expressed by co-infection of High Five cells with two baculoviruses each containing Gβ1 and Gγ2 C68S mutant genes . The purification methods are the same as wild-type Gβγ except that no detergent was used . For the preparation of Gαi1 protein , cDNA of human Gαi1 was cloned into pFastBac ( Invitrogen ) vector without any affinity purification tag . High Five insect cells were co-infected with three baculoviruses each bearing one of the following proteins: Gαi1 , Gβ1 and Gγ2 . Gαi1 was purified by first binding the Gαi1βγ heterotrimer to Talon resin and then eluting just the Gαi1 subunit by dissociating it from the heterotrimer with aluminum tetrafluoride AIF4− as previously described ( Kozasa , 2004 ) . Briefly , cell membranes containing all three G protein subunits were extracted with Na-cholate in the buffer system used for Gβγ purification supplemented with 3 mM MgCl2 and 10 µM GDP . After binding the extracted protein to Talon resin and washing with 10 mM imidazole , Gαi1 was eluted in the same buffer supplemented with 50 mM MgCl2 , 30 µM AlCl3 , 10 mM NaF , and 30 µM GDP . Since this elution step specifically disrupts the Gαi1-Gβγ interaction , the eluted fraction is fairly pure . The eluent is then concentrated and loaded onto a Superdex 200 10/300 gel filtration column in the buffer 20 mM HEPES pH 8 . 0 , 150 mM KCl , 5 mM DTT , 2 mM MgCl2 , 0 . 2% DM . Gαi1 eluted as a major peak at ∼14 . 5 ml . Fractions containing this peak were pooled and concentrated to ∼10 mg/ml . For exchange of nucleotide on Gαi1 , either 2 mM GDP or GTPγS were added to the protein , then incubated at 37°C for 30 min . Slight precipitation usually occurs , but can be clarified by centrifugation at 4°C . The binding status of Gαi1 was assayed by a controlled trypsin digestion assay ( Mazzoni et al . , 1991; Marin et al . , 2001 ) in which the activated form of Gαi1 ( GTPγS ) is protected from digestion by N-tosyl-L-phenylalanyl chloromethyl ketone ( TPCK ) treated trypsin . Purified Gαi1 proteins were put on ice until used . To obtain non-lipidated Gαi1 protein , Gαi1 cDNA was cloned into pET-28a ( + ) vector ( Novagen , Billerica , MA ) with an N-terminal hexahistidine tag and transformed into BL21 E . coli for protein expression . In order to obtain non-lipidated protein , we did not co-express with N-myristoyltransferase ( NMT ) ( Mumby and Linder , 1994 ) . At an OD600 of ∼0 . 9 . 1 mM IPTG was used to induce expression , which was continued at 37°C for 8 hr . The same buffers were used for non-lipidated Gαi1 purification as for non-lipidated Gβγ except that MgCl2 was added to 2 mM and GDP to 10 µM . The purification steps are essentially the same as non-lipidated Gβγ . After gel filtration , protein was concentrated and nucleotide exchange ( GDP or GTPγS ) was performed as described above for lipidated Gαi1 . The function of the proteins were tested with a histidine tag based pull down assay using Gβγ . A lipid mixture composed of 3:1 ( wt:wt ) 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine ( POPE ) : 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho- ( 1′-rac-glycerol ) ( POPG ) was used for reconstitution of GIRK2 channels and G proteins into lipid vesicles . The reconstitution procedure is similar to that previously reported ( Heginbotham et al . , 1999; Long et al . , 2007; Tao and MacKinnon , 2008; Brohawn et al . , 2012 ) with some modifications . Briefly , 20 mg/ml of the above lipid mixture was dispersed by sonication and then solubilized with 20 mM DM . GIRK2 and Gβγ were diluted with reconstitution buffer ( 10 mM potassium phosphate pH 7 . 4 , 150 mM KCl , 1 mM EDTA and 3 mM DTT ) supplemented with 0 . 2% DM to 2 mg/ml ( ∼51 µM ) and 4 . 8 mg/ml ( ∼106 µM ) , respectively . Equal volumes of 20 mg/ml solubilized lipid mixture was combined with each of these protein solutions to make protein: lipid ( wt:wt ) ratios of 1:10 and 1:4 . 2 with a final lipid concentration of 10 mg/ml . For co-reconstitution of GIRK2 with Gβγ , the two proteins were mixed , resulting in final concentrations of 2 mg/ml and 4 . 8 mg/ml respectively . Subsequently , this solution was mixed with an equal volume of 20 mg/ml solubilized lipid mixture . Detergent was removed by dialysis against reconstitution buffer at 4°C for 4–6 days . For reconstitution of Gαi1 ( GTPγS ) and Gαi1 ( GDP ) , the proteins were diluted to 4 mg/ml ( ∼100 µM ) with reconstitution buffer supplemented with 0 . 2% DM and 2 mM of respective nucleotides . After mixing the diluted Gαi1 protein solutions with equal volumes of 20 mg/ml solubilized lipid mixture , the detergent was removed by dialysis against reconstitution buffer supplemented with either 10 μM GTPγS or GDP , at 4°C for 4–6 days . The resulting proteoliposomes were flash-frozen with liquid nitrogen in 20 μl aliquots and stored at −80°C until needed . Bilayer experiments were performed as previously described ( Ruta et al . , 2003 ) with the following modifications: 20 mg/ml of a lipid solution in decane composed of 2:1:1 ( wt:wt:wt ) of 1 , 2-dioleoyl-sn-glycero-3-phosphoethanolamine ( DOPE ) : 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( POPC ) : 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine ( POPS ) was painted over a ∼100 μm hole on a piece of polyethylene terephthalate transparency film that separates two chambers in a polyoxymethylene block ( Figure 1B ) . In some experiments , 1–4% ( wt% ) of brain PIP2 ( Avanti Polar Lipids , Alabaster , AL ) was added into the lipid mixture . In most experiments the same buffer ( 10 mM potassium phosphate pH 7 . 4 , 150 mM KCl , 1 mM EDTA ) was used in both chambers . Voltage across the lipid bilayer was clamped with an Axopatch 200B amplifier in whole-cell mode . The analog current signal was low-pass filtered at 1 kHz ( Bessel ) and digitized at 20 kHz with a Digidata 1322A digitizer . Digitized data were recorded on a computer using the software pClamp ( Molecular Devices , Sunnyvale , CA ) . For local perfusion , a SmartSquirt8 ValveLink Micro-Perfusion System ( Automate Scientific , Berkeley , CA ) was used with a 250 μm diameter perfusion pencil positioned just above the lipid bilayer . Recordings were performed at room temperature . The Hill equation used for fitting titration data is: ( 1 ) I=I0+ ( Imax−I0 ) xnkn+xn , where I is the observed current , I0 is the basal current ( spontaneous opening current and leak , very close to 0 ) , Imax is the maximum current when channels are fully activated , x is ligand concentration and n is the Hill coefficient . Under the assumption that binding of each PIP2 to GIRK2 is non-cooperative ( independent ) , the fraction of PIP2 binding sites occupied was expressed as: ( 2 ) q=xkd+x , where q is the fraction of occupied PIP2 binding sites , x is ligand concentration and kd is the apparent dissociation constant . For a channel with four PIP2 binding sites the fraction of channels with all four sites occupied is: ( 3 ) p= ( 44 ) ×q4× ( 1−q ) 0=q4 . If channel opening corresponds ( is equivalent ) to occupancy by four PIP2 molecules then the total current is: ( 4 ) I=Imax×p , where Imax is the maximum current when all channels are activated . Substituting Equation 2 and 3 into Equation 4 gives ( 5 ) I=Imax× ( xkd+x ) 4 . We used Equation 5 in fitting titration data points . Although likely overly-simplified , the purpose of using this model is to emphasize that a sigmoidal dose–response does not necessarily indicate cooperativity among binding sites .	Though every cell in the body is surrounded by a membrane , there are a number of ways that molecules can pass through this membrane to either enter or leave the cell . Proteins from the GIRK family form channels in the membranes of mammalian cells , and when open these channels allow potassium ions to flow through the membrane to control the membrane's voltage . GIRK channels are found in the heart and in the central nervous system , and can be activated in a variety of ways . Sodium ions and molecules called ‘signaling lipids’ can regulate the activation of GIRK channels . These channels can also be caused to open by G proteins: proteins that are found inside cells and that help to transmit signals from the outside of a cell to the inside . Three G proteins—called Gα , Gβ , and Gγ—work together in a complex that functions a bit like a switch . When switched on , the Gα subunit is separated from the other two subunits ( called Gβγ ) ; and both parts can then activate different signaling pathways inside the cell . The Gβγ subunits and a signaling lipid have been known to regulate the opening of GIRK channels for a number of years , but these events have only been studied in the context of living cells . The specific role of each molecule , and whether the Gα subunit can also regulate the GIRK channels , remains unknown . Now Wang et al . have produced one type of mouse GIRK channel , called GIRK2 , in yeast cells , purified this protein , and added it into an artificial membrane . This ‘reconstituted system’ allowed the regulation of a GIRK channel to be investigated under more controlled conditions than in previous experiments . Wang et al . found that the Gβγ subunits and the signaling lipid both need to be present to activate the GIRK2 channel . Sodium ions were not essential , but promoted further opening when Gβγ and the signaling lipid were already present . When locked in its ‘on’ state , the Gα subunit had no effect on GIRK2 , but adding Gα locked in the ‘off’ state closed these channels by removing the Gβγ proteins . The findings of Wang et al . suggest that it should be possible to use a similar reconstituted system to investigate what allows different G proteins to activate specific signaling pathways .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "biochemistry", "and", "chemical", "biology", "structural", "biology", "and", "molecular", "biophysics" ]	2014	Quantitative analysis of mammalian GIRK2 channel regulation by G proteins, the signaling lipid PIP2 and Na+ in a reconstituted system
In many mouse models of skin cancer , only a few tumors typically form even though many cells competent for tumorigenesis receive the same oncogenic stimuli . These observations suggest an active selection process for tumor-initiating cells . Here , we use quantitative mRNA- and miR-Seq to determine the impact of HrasG12V on the transcriptome of keratinocytes . We discover that microRNA-203 is downregulated by HrasG12V . Using a knockout mouse model , we demonstrate that loss of microRNA-203 promotes selection and expansion of tumor-initiating cells . Conversely , restoration of microRNA-203 using an inducible model potently inhibits proliferation of these cells . We comprehensively identify microRNA-203 targets required for Hras-initiated tumorigenesis . These targets include critical regulators of the Ras pathway and essential genes required for cell division . This study establishes a role for the loss of microRNA-203 in promoting selection and expansion of Hras mutated cells and identifies a mechanism through which microRNA-203 antagonizes Hras-mediated tumorigenesis . Recent efforts in comprehensively sequencing human cancer genomes have confirmed ∼140 protein-coding genes that , when mutated , can drive tumorigenesis ( Vogelstein et al . , 2013 ) . When genome sequencing data were utilized to construct the history of cancer cells in breast cancer , it was revealed that a considerable amount of ‘molecular time’ exists between the common ancestors that harbor the great majority of driver mutations and the phenotypically identified cancer cells that compose the bulk of the tumor ( Nik-Zainal et al . , 2012 ) . In support of these observations , lineage tracing experiments conducted in genetically engineered mouse models revealed that only a few clones give rise to tumors whereas a vast majority of mutated cells are unable to sustain tumorigenesis ( Driessens et al . , 2012; Schepers et al . , 2012 ) . These results suggest that even after the acquisition of key driver mutations in the nascent cancer cells , these cells must still undergo continuous evolution and likely clonal selection before developing into clinically apparent tumors . To begin to understand the molecular basis underlying such selection , we examined papilloma formation driven by oncogenic Hras in the skin , a well-characterized model where Hras has been shown to initiate the formation of tumors that clonally evolve ( Brown et al . , 1986; Driessens et al . , 2012; Beck and Blanpain , 2013 ) . Oncogenic Ras mutations are some of the most frequently detected driver mutations in human cancer . Among the three Ras genes ( H- , K- , and N-ras ) , Hras is commonly mutated in tumors originated from stratified epithelial tissues including squamous cell carcinoma in the skin , head , and neck cancer as well as bladder cancer ( Bos , 1989; Agrawal et al . , 2011; Stransky et al . , 2011 ) . Experimental and genomic sequencing studies have revealed that the vast majority of Ras mutations are missense , point mutations at amino acid residues glycine 12 ( G12 ) , glycine 13 ( G13 ) , or glutamine 61 ( Q61 ) ( Bos , 1989 ) . Structural and biochemical studies have further confirmed that all of these mutations generally interfere with the GTP binding pocket and compromise the GTPase activity of Ras proteins . In turn , these mutations lead to uncontrolled activation of downstream effectors including Raf/MEK/ERK and PI ( 3 ) K pathways , resulting in sustained cell survival and proliferation observed in human cancers . Because of the prominent role of Ras mutations in human cancer , extensive efforts have been devoted to uncover and subsequently target downstream pathways that are regulated by Ras mutations . However , the immediate impact of Ras mutations on the transcriptome , in particular , with regards to microRNAs ( miRNAs ) remains unclear . miRNAs are a class of small , noncoding RNA species that are involved in virtually all biological processes examined in mammals including mouse and human . These regulatory RNA molecules function by repressing the protein producing ability of mRNA targets through destabilization of mRNAs and inhibition of translation ( Bartel , 2009 ) . miRNAs typically target a large number of mRNAs in a dosage- and cell context-dependent manner ( Mukherji et al . , 2011 ) . As prominent proto-oncogenes , Ras mutations have long been recognized to interact with the miRNA pathway . Indeed , Hras , Kras , and Nras all harbor multiple binding sites for the let-7 miRNA , a founding member of miRNAs , in their 3′UTRs ( Johnson et al . , 2005 ) . Additionally , impaired miRNA biogenesis in the form of Dicer1 disruption has been shown to be a tumor-suppressing mechanism for the development of Kras-induced lung cancer in a mouse model ( Kumar et al . , 2007 ) . A number of individual miRNAs were also found to function as modifiers for Ras-induced tumorigenesis that include miR-21 , -29 , and miR-17∼92 as tumor-promoting miRNAs and miR-34 , -15/16 , and miR-143/145 as tumor-suppressing miRNAs ( Kasinski and Slack , 2010; Iorio and Croce , 2012; Mendell and Olson , 2012 ) . Collectively , these seminal studies demonstrate unequivocally that the miRNA pathway and individual miRNAs play important roles in Ras-induced tumorigenesis . However , it is unclear how Ras mutations , usually the tumor-initiating drivers , directly alter the landscape of miRNA expression during tumorigenesis . Importantly , it is also unknown whether the changes in miRNA expression play a role in the selection of oncogenic Ras-transformed cells during tumor initiation . Finally , the lack of a comprehensive survey of high-confidence miRNA targets that may play a role downstream of Ras mutations hinders our mechanistic understanding and limits the potential to develop miRNA-based therapeutics . In this study , we utilized our recently improved quantitative miR-Seq techniques to examine the impact of an oncogenic Hras mutation ( HrasG12V ) on both mRNA and miRNA expression . We discovered that miR-203 , the most highly expressed miRNA in the skin ( Yi et al . , 2008; Jackson et al . , 2013 ) , is downregulated by HrasG12V . Using both knockout ( KO ) and inducible models , we provide evidence for an important role of miR-203 in restricting expansion of oncogenic Hras-transformed cells in vitro and in vivo . We comprehensively surveyed skin-specific targets of miR-203 and identified a number of novel targets that have important implications for Hras-mediated tumorigenesis . Our results suggest that miR-203 plays a tumor-suppressing role in inhibiting selection and expansion of tumor-initiating cells early in tumor development . Oncogenic mutation of the Hras gene is one of the initiating drivers in the development of benign papillomas and malignant squamous cell carcinomas in murine skin chemical carcinogenesis . However , the molecular consequences defining the cellular changes that accompany expansion of oncogenic Hras-transformed keratinocytes to initiate papillomas remain elusive . We first investigated the consequences of HrasG12V activation on the mRNA transcriptome using a modified form of PolyA+ RNA-Seq , known as 3P-Seq , or 3seq ( Figure 1A–C ) . Compared to traditional RNA-Seq , 3Seq allows both quantification of mRNA transcripts and detection of changes in alternative 3′ UTR formation ( Wang et al . , 2013 ) . To examine the immediate impact of HrasG12V on primary skin cells , we used primary keratinocytes isolated from newborn skin and performed 3Seq after HrasG12V transduction . We did not observe widespread shortening or alternative formation of 3′UTRs , which are often ascribed to oncogenic transformation when comparing tumor cell lines to normal cells ( data not shown ) . This is similar to our previous observation that alternative 3′UTR usage is infrequent within the skin lineages ( Wang et al . , 2013 ) . Over 1100 transcripts were differently expressed ( two-fold change and FDR <0 . 05 ) in keratinocytes expressing HrasG12V , compared to the control ( Figure 1C and Figure 1—source data 1 ) . Gene ontology functional analysis revealed profound deregulation in three core processes by HrasG12V: activation of cellular migration , upregulation of pro-angiogenic pathways , and suppression of the terminal differentiation program ( Figure 1D ) . All of these three processes are identified as hallmarks of human cancer ( Hanahan and Weinberg , 2011 ) . The observed widespread changes in the transcriptome also endorse the driver role of HrasG12V in skin tumorigenesis . Importantly , transcripts upregulated by HrasG12V in our primary keratinocytes strongly and significantly overlapped with the putative cancer stem cell signatures obtained from murine squamous cell carcinoma ( SCC ) models ( Schober and Fuchs , 2011 ) . In addition , transcripts upregulated by HrasG12V significantly overlapped with transcripts known to be targets of the c-Fos transcription factor in a genetic model of SCC ( Durchdewald et al . , 2008 ) . Furthermore , known core components of the Hras signaling pathway were also among the differentially detected genes ( Bild et al . , 2006 ) ( Figure 1E ) . These transcriptome data indicate that we have captured the initiating changes induced by oncogenic Hras in the keratinocytes . 10 . 7554/eLife . 07004 . 003Figure 1 . Genome-wide profiling of the oncogenic HrasG12V-transformed miRNA and mRNA transcriptome in primary keratinocytes . ( A ) Schematic of experimental approach to identify deregulated mRNA and miRNA networks driven by oncogenic HrasG12V using small-RNA Seq and 3Seq . The 3seq library preparation allows quantitative definition of poly-A+ RNA 3′ends and expression levels . ( B ) 3Seq reproducibly detects mRNA expression levels over 4 orders of magnitude . Pearson correlation coefficient displayed ( C ) unsupervised hierarchical clustering of log-transformed mean-centered mRNA expression levels for all transcripts deregulated twofold by oncogenic HrasG12V ( n = 2 libraries per condition ) ( D ) Gene Ontology analysis of transcripts up and downregulated by HrasG12V ( twofold change FDR <0 . 05 ) indicates enrichment for migratory and angiogenic processes , and suppression of keratinocyte differentiation . ( E ) GSEA analysis of selected genesets relevant to skin carcinogenesis . ( F ) Unsupervised hierarchical clustering of log-transformed mean-centered miRNA expression levels for all transcripts deregulated twofold by oncogenic HrasG12V ( n = 2 libraries per condition ) ( G , H ) Abundant miRNAs such as miR-203 , miR-205 , and miR-21 are strongly deregulated by oncogenic Ras . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 00310 . 7554/eLife . 07004 . 004Figure 1—source data 1 . Log2 fold changes for transcripts up or down regulated twofold with FDR <0 . 05 in HrasG12V-transformed keratinocytes . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 004 To define the impact of the oncogenic Hras on the landscape of miRNA , we applied our recently developed , quantitative miRNA-Seq ( Zhang et al . , 2013 ) to HrasG12V-transformed keratinocytes . Overall , we detected 15 differentially expressed miRNAs upon HrasG12V expression ( FDR <0 . 05 , two-fold change ) ( Figure 1F–H ) . Two key patterns emerged in these profiles . First , the epithelial tissue-specific miRNAs , miR-203 and miR-205 , which represent the most abundant miRNAs expressed in murine skin and primary keratinocytes were strongly suppressed by HrasG12V . Additionally , members of the abundantly expressed , miR-23/24/27 miRNA cluster were also downregulated by HrasG12V . Secondly , miR-21 was strongly induced becoming the most highly expressed miRNA , consistent with its direct activation by oncogenic Ras reported in other systems ( Talotta et al . , 2009 ) . The upregulation of miR-21 is also consistent with its well-appreciated oncogenic function in skin cancer ( Darido et al . , 2011 ) . miR-146 was also induced by HrasG12V . However , this miRNA is expressed 2-orders of magnitude lower than miR-21 , suggesting that its upregulation may have limited contribution to Hras-initiated tumorigenesis at this early stage . We further measured mature miR-21 , miR-203 , and miR-205 RNAs by qPCR . In support of the quantitative performance of our miR-Seq , the differential expression of all three miRNAs measured by qPCR was nearly identical to the quantification by our miR-Seq ( Figure 2A , B ) . To initially probe the mechanism through which HRASG12V suppresses miR-203 expression , we examined the level of miR-203 primary transcripts . We previously characterized the transcribed genomic region of miR-203 including the promoter region and transcription start site ( Jackson et al . , 2013 ) . Because the primary transcript of miR-203 harbors a polyadenylation [Poly ( A ) ] signal and generates a Poly ( A ) tail , we directly quantified the abundance of the primary transcripts by counting the 3′end reads of the primary miRNA obtained by 3seq ( Figure 2C ) . This result was further confirmed by qPCR measurement specific to the pri-miR-203 ( Figure 2D ) . The degree of downregulation for both mature and primary miR-203 transcripts was similar as judged by these two independent assays . Collectively , we conclude that the repression of miR-203 by HrasG12V is most likely mediated by suppressing the production of primary miR-203 transcripts at an early stage of oncogenic cellular transformation . 10 . 7554/eLife . 07004 . 005Figure 2 . miR-203 is strongly suppressed in mouse and human SCCs . ( A , B ) qPCR and small-RNA-Seq independently validate downregulation of miR-203 and upregulation of miR-21 driven by oncogenic HrasG12V ( n = 3 biological rep . qPCR , n = 2 small-RNA-Seq , mean ± SEM displayed , p < 0 . 05 , Student's t-test two-sided ) . ( C ) Gene track and quantification the 3′end of the miR-203 primary transcript based on 3Seq . ( D ) miR-203 primary transcript detection by qPCR ( n = 3 biological replicates , Mean ± SEM displayed , p < 0 . 05 , ns = non-significant , Student's t-test two-sided ) . ( E ) miR-203 is downregulated in DMBA/TPA produced papillomas compared to normal adjacent tissue . Epi = epidermis , Der = dermis , T = tumor , and S = stroma . The black lines denote the epidermal/dermal and tumor/stroma boundary ( F ) miR-203 is downregulated in malignant SCCs derived from KrasG12D/Smad4cKO and passaged in immunocompromised mice . ( G–O ) Reduced miR-203 expression is correlated with increasing malignancy in human skin SCC cancers . Panels G , J , M were taken from regions with more histologically normal regions to demonstrate successful miR-203 hybridization . ( P ) miRNA-Seq quantification from patient matched normal and tumor tissue obtained from the TCGA consortium data ( bar indicates mean value , Student's t-test two-sided ) . Scale bar = 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 005 Our data revealed the early silencing of miR-203 by oncogenic Hras in an in vitro model . We further investigated miR-203 expression with in situ hybridization during skin tumorigenesis in vivo . In classic chemical carcinogenesis models initiated by DMBA/TPA treatment of mouse skin , Hras is preferentially mutated , which compromises the GTPase activity and results in constitutive Hras activation . Mutating Hras at Q61 leads to papilloma development and infrequent malignant transformation to SCCs ( Abel et al . , 2009 ) . We first examined miR-203 expression in benign papillomas . Consistent with our in vitro results , miR-203 expression was absent from epithelial compartments adjacent to tumor stroma , the region where putative cancer stem cells reside . Although we also observed moderately expressed miR-203 in tumor regions with evidence of cellular differentiation ( keratin pearls and large differentiated morphology ) , the levels of miR-203 was considerably lower in these regions compared to the suprabasal cells of adjacent normal skin , where miR-203 is normally expressed ( Figure 2E ) . Overall , miR-203 expression levels were strongly reduced in tumor tissues compared to adjacent epidermal regions . In an independent mouse model of SCC , we also found that miR-203 expression was gradually lost in a KrasG12D/Smad4cKO model where skin tumors progressed to invasive SCC through serial passages of tumors ( White et al . , 2013 ) ( Figure 2F ) . Taken together , these results demonstrate that miR-203 is significantly downregulated at both early and late stages of papilloma and SCC formation in mouse models . To evaluate the relevance of the loss of miR-203 in human skin cancer , we examined 9 tumor samples obtained from patients with the early , middle , and late stages of skin cancer . In these human skin SCC samples , miR-203 was already downregulated at the onset of tumorigenesis and progressively lost during the course of tumor progression , similar to the pattern observed in the mouse models . In the poorly differentiated SCC samples , the miR-203 signal was completely absent , yet readily detectable in surrounding hyperplastic or normal epidermal regions ( Figure 2G–O ) . In addition , we mined publically available data sets from The Cancer Genome Atlas ( TCGA ) and detected a significant reduction in miR-203 and an elevation of miR-21 in head and neck SCC samples compared to patient-matched normal tissues ( Figure 2P ) . Altogether , these results corroborate our observations in mouse models and validate the strong correlation between the loss of miR-203 and the development of skin cancer at multiple stages of tumorigenesis . These expression analyses suggest that the loss of miR-203 coincides with the tumor-initiating events and miR-203 might function as a tumor-suppressing mechanism in skin cancer . We then generated a conditional KO mouse model to assess the function of miR-203 in murine skin development and carcinogenesis in vivo . We have characterized the genomic locus of miR-203 and determined that miR-203 is located in an intergenic region , 3 . 3 kbp downstream from the Asp gene and 15 . 3 kbp upstream of the Kif26a gene ( Figure 3—figure supplement 1 ) . Two loxP sites were inserted to flank the miR-203 hairpin ( Figure 3A ) . miR-203 was deleted by first mating miR-203fl/fl mice with Actb-Flpe mice to remove the Neo cassette , followed by breeding with EIIa-Cre or Krt14-Cre mice , resulting in complete miR-203 loss from all tissues or only from skin tissues , respectively . In both cases , ablation of miR-203 was confirmed by qPCR on isolated epidermis and in situ hybridization ( Figure 3B , C ) . We previously demonstrated that expression of miR-203 was largely restricted to stratified epithelial tissues . Within the skin , the differentiated skin lineages express miR-203 ∼10-fold higher than the stem/progenitor lineages . Consistent with this observation , there are no discernible differences between constitutive miR-203 null ( miR-203−/− ) mice and skin-specific cKO ( Krt14-Cre/miR-203fl/fl ) mice . Both strains were born at the expected Mendelian ratio . They also showed no signs of gross developmental defects as adults ( Figure 3D ) . For all subsequent studies , we used miR-203−/− , which was maintained in the C57BL/6 background . miR-203 is highly expressed primarily in stratified epithelial tissues , such as the epidermis , tongue , esophagus , and cervix , yet poorly expressed or absent in other tissues such as the small intestine , bladder , lung , kidney , liver , or brain ( Figure 3—figure supplement 2 ) . Because miR-203 begins to be expressed by E13 when the epidermis begins to stratify , we examined the proliferation rate and thickness of embryonic skin from E16 to P4 . At E16 , we observed a mild increase in cell proliferation in the KO , as measured by BrdU incorporation ( Figure 3E , F ) . Although the difference in BrdU incorporation did not achieve statistical significance ( p = 0 . 07 ) , the thickness of the KO epidermis was significantly increased , compared to WT littermates ( p = 0 . 03 ) . Interestingly , the increase in epidermal thickness was most prominent at early stages ( E16 and E17 ) and waned as skin development progressed ( P4 ) ( Figure 3G ) . At P4 , miR-203−/− mice displayed normal histological development of the epidermis and hair follicles , and the difference in proliferation and epidermal thickness between KO and WT became indistinguishable ( Figure 3G ) . In addition , we found no evidence of perturbed differentiation in miR-203−/− mice based upon analysis of early and late epidermal differentiation markers , Keratin 1 and Loricrin , for the spinous and granular layers , respectively ( Figure 3H , I ) . Together , these results provide evidence that miR-203 limits cell division when the proliferation rate is high during early embryonic skin development but not at later stages when the proliferation rate wanes . 10 . 7554/eLife . 07004 . 006Figure 3 . Loss of miR-203 modestly impairs embryonic epidermal development . ( A ) Schematic of miR-203 conditional allele generation and knockout strategy . ( B ) Validation of miR-203 ablation by qPCR from isolated epidermal samples ( n = 3 biological replicates , * p < 0 . 05 , ns = non-significant , Student's t-test two-sided ) . ( C ) Validation of miR-203 ablation within the epidermis by in situ hybridization ( Scale bar = 50 μm ) . ( D ) miR-203 knockout mice are visibly indistinguishable from wild-type counterparts . ( E–G ) miR-203 ablation results in mild epidermal hyperplasia during embryonic development . ( n = 3 E16 , n = 4 E17 , and n = 3 p4 animals , p-value provided in figure , Student's t-test one-sided ) . ( H ) Representative hematotoxylin and eosin image from p4 . 5 animals , demonstrating restored normal skin morphology in neonatal animals . ( Scale bars = 50 μm for inset and 100 μm for main images ) ( I ) Epidermal differentiation is not compromised by loss of miR-203 . ( Scale bars = 50 μm ) ( J ) miR-203−/− primary keratinocytes are more clonogenic than wild-type counterparts ( representative results from 3 experiments , p < 0 . 05 , Student's t-test two-sided ) . ( K ) Conditional ablation of miR-203 from passaged miR-203fl/fl keratinocytes results in higher clonogenicity ( representative results from n = 3 independent experiments , mean ± standard deviation displayed , p < 0 . 05 , Student's t-test , two-sided ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 00610 . 7554/eLife . 07004 . 007Figure 3—figure supplement 1 . Generation of a miR-203 conditional knockout mouse . ( A ) RNA-Seq and 3Seq detection of the miR-203 primary transcript ( B ) Schematic of the miR-203 conditional allele . H3 = HindIII ( C ) Southern blot confirmation of founder miR-203 conditional knockout mice ( D ) Small-RNA-seq confirmation of miR-203 loss . Blue lines indicate twofold change ( E ) qPCR quantification of miR-203 and miR-205 , demonstrating that the miR-203floxed allele does not alter microRNA levels . Error bars represent S . E . M from reactions performed from n = 2 mice in technical triplicate , ns = non-significant . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 00710 . 7554/eLife . 07004 . 008Figure 3—figure supplement 2 . miR-203 expression in diverse mouse tissues . qPCR detection of mature miR-203 in various adult mouse organ tissues . Error bars represent S . E . M from reactions performed in technical duplicate . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 008 We noted that the impact of miR-203 loss was correlated with the rate of cell proliferation . To further test whether miR-203 functions to restrict the expansion of highly proliferative cells , we investigated the roles for miR-203 in regulation of primary and established keratinocyte cell lines , respectively . We observed ∼two-fold higher colony-forming capacity of miR-203−/− keratinocytes , compared to the WT controls ( Figure 3J ) . To further confirm the ability of miR-203 to suppress cell proliferation cell-autonomously , we generated a miR-203fl/fl keratinocyte cell line . By treating these cells with an adenoviral vector to express Cre ( Ad-Cre ) , we determined that within 48 hr of Ad-Cre exposure miR-203 was completely depleted ( <1% remaining as measured by qPCR ) . We again observed a similar , ∼two-fold higher colony-forming capacity by the Ad-Cre-treated miR-203fl/fl cells compared to the Ad-GFP-treated control cells ( Figure 3K ) . In both cases , although we detected some slightly larger clones with more cells formed by the miR-203 null keratinocytes , the biggest differences were the significantly increased number of clones formed by the miR-203 null cells . These results suggest that miR-203 limits the clonogenicity of normal keratinocytes . When miR-203 is deleted , more cells are likely to become clonogenic . We determined that the loss of miR-203 is an early event in the initiation and development of both mouse and human skin SCCs . Furthermore , genetic deletion of miR-203 confers ∼two-fold higher colony-forming ability on primary keratinocytes . Because oncogenic Hras is a potent driver for tumorigenesis in the skin and our miR-seq data revealed a rapid and strong repression of miR-203 by HrasG12V , these observations prompted us to investigate whether the loss of miR-203 plays a role in skin carcinogenesis . We subjected WT and miR-203 null mice to two-stage chemical DMBA/TPA carcinogenesis ( Figure 4A ) . The chemical carcinogenesis experiments were terminated at week 21 when tumor burden had reached a maximum . Because our mice were generated in the C57BL/6 background and these mice are known to be resistant to two-stage chemical carcinogenesis ( Abel et al . , 2009 ) , all tumors generated in our mice were papillomas with no evidence for malignant conversion to squamous cell carcinomas during the time frame of our study . Nevertheless , we examined the temporal and numeric characteristics of tumor formation in our mice for the role of miR-203 in tumor initiation . During the course of carcinogenesis , we observed a slightly earlier tumor formation pattern on the backskin of miR-203 null animals ( Figure 4B ) . Furthermore , miR-203 null animals developed ∼2 . 5-fold more tumors , when compared to WT control animals ( Figure 4B ) . Measurement of tumor sizes at week 17 showed no statistically significant difference in tumor sizes between genotypes although miR-203 null animals were more susceptible to tumor formation ( Figure 4C ) . These results suggest that loss of miR-203 increases the number of tumor-initiating cells but does not significantly alter the type of tumors for example , conferring tumors with more aggressive phenotypes . 10 . 7554/eLife . 07004 . 009Figure 4 . Loss of miR-203 sensitizes mice to DMBA/TPA skin carcinogenesis . ( A ) Representative images of tumors that were formed in the skin of WT and miR-203 null mice treated with DMBA/TPA . ( B ) miR-203−/− mice have a larger tumor burden than miR-203+/+ counterparts ( n = 6 and 7 miR-203+/+ and miR-203−/− animals respectively , mean ± SEM displayed , £ = p < 0 . 05 , Whitney–Mann U-test one-sided ) . ( C ) miR-203−/− tumor size distribution is similar to wild-type animals ( ns = non-significant , Student's t-test two sided , median displayed as bar ) . ( D , E ) miR-203+/+ and miR-203−/− papillomas display similar morphologies and histology . ( F ) Proliferation and differentiation dynamics are similar between miR-203+/+ and miR-203−/− tumors . ( Scale bars = 50 μm ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 00910 . 7554/eLife . 07004 . 010Figure 4—figure supplement 1 . The HrasQ61L mutation is common in both miR-203+/+ and miR-203−/− tumors . ( A ) Representative end-point PCR followed by XbaI digestion . The HrasQ61L allele produces 120 bp and 87 bp product , whereas wild-type produces a 207-bp product ( B ) Table with quantification of genotyping results . non-significant p > 0 . 05 , chi-squared test . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 010 Hematoxylin and Eosin staining revealed that the papillomas produced had similar morphology , displaying exophytic lesions with evidence for squamous differentiation ( Figure 4D , E ) . Assessment of proliferation ( Ki67 ) , differentiation markers ( Krt1 , Lor ) revealed similar dynamics between miR-203+/+ and miR-203−/− tumors ( Figure 4F ) . To further probe the mechanistic differences between miR-203 WT and KO tumors , we assessed the mutation spectra of these tumors and found that they possess the canonical HrasQ61L mutation at similar frequencies , 80% and 85 . 7% for WT and KO tumors , respectively ( Figure 4—figure supplement 1 ) . Taken together , these results provide direct evidence for miR-203's tumor suppressing roles at the stage of tumor initiation in the classic DMBA/TPA tumorigenic model . To further probe miR-203's role in clonal selection , we infected miR-203 WT and null primary keratinocytes with pBabe-HrasG12V . At the passage 1 , the loss of miR-203 led to ∼40% increase in colony-forming capacity immediately after the initial plating ( Figure 5A ) . When we passaged the HrasG12V transduced cells , we began to observe reduced numbers of colonies that were formed by both the WT and null cells ( Figure 5A ) . This was likely due to oncogenic stress caused by HrasG12V induction . Strikingly , in contrast to the WT cells that generally formed smaller colonies in subsequent passages , the miR-203 null cells generated more and bigger clones compared to WT control cells at each passage ( Figure 5A ) . Collectively , the serial passage assay supported the enhanced selection of tumor-initiating cells in the absence of miR-203 upon oncogenic Hras induction . 10 . 7554/eLife . 07004 . 011Figure 5 . miR-203 antagonizes HrasG12V-driven keratinocyte proliferation . ( A ) HrasG12V transduced miR-203−/− primary cultures are more colonogenic upon serial passage than wild-type controls ( representative of n = 2 independent experiments , mean ± standard deviation displayed . p < 0 . 05 , ns = non-significant , Student's t-test two-sided ) . ( B ) qPCR of miR-203 induction upon addition of doxycycline in vector and HrasG12V transduced cells ( mean ± SEM displayed , n = 3 biological replicates ) . ( C ) Restoration of miR-203 using a doxycycline-inducible transgene results in suppression of colony formation ability in HrasG12V transduced and control keratinocytes . miR-203 was induced with doxycycline ( 5 μg/ml ) 24 hr after plating ( representative of n = 3 independent experiments , mean ± standard deviation displayed , p ≤ 0 . 05 , , ns = non-significant ) . ( D ) miR-203 restoration suppresses HrasG12V-driven S-Phase entry . miR-203 was induced for 24 hr prior to harvesting for flow cytometry . ( n = 3 , mean ± standard deviation displayed , p ≤ 0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 011 To further characterize the ability of miR-203 to suppress the growth of oncogenic Hras-transformed cells , we used our previously established Krt14-rtTA/pTre2-miR-203-inducible model ( Jackson et al . , 2013 ) . After infecting the inducible keratinocytes with either the pBabe vector control or pBabe-HrasG12V , we treated the cells with 5 µg/ml doxycycline to induce ∼4- to 7-fold increase in miR-203 expression , a physiologically relevant level of miR-203 typically observed during epidermal differentiation ( Figure 5B ) . Introduction of miR-203 resulted in suppression of keratinocyte proliferation and colony formation ability ( Figure 5C , D ) , as noted previously ( Yi et al . , 2008; Jackson et al . , 2013; Benaich et al . , 2014 ) . Furthermore , whereas oncogenic Hras-enhanced S-phase entry , short-term ( ∼24 hr ) induction of miR-203 completely abolished the gain of S-phase entry ( Figure 5D ) . Over a longer term , continuous induction of miR-203 severely compromised the colony-forming capacity of the transduced cells ( Figure 5C ) . We did not detect any evidence for enhanced apoptosis caused by miR-203 , as measured by the absence of sub-G1 keratinocytes . Taken together , our results suggest that the loss of miR-203 is critical for the initial selection and expansion of primary keratinocytes harboring the oncogenic Hras mutation and the gain of miR-203 can effectively suppress the growth of these cells . Our data so far have suggested a role of miR-203 in suppressing the expansion of tumor-initiating cells driven by oncogenic Hras mutations . To decode the underlying mechanism , we carried out comprehensive analyses to identify miR-203 targets in the skin . Recent studies demonstrated that miRNAs' impact on gene expression could be largely captured by measuring the changes of mRNA levels upon manipulation of miRNA expression ( Guo et al . , 2010; Eichhorn et al . , 2014 ) . In parallel , the high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation ( HITS-CLIP ) approach directly crosslinks miRISC to their targets and identifies miRNA targets through physical interaction ( Chi et al . , 2009 ) . Therefore , we took a combinatorial approach integrating multiple data sets obtained from our KO and inducible mouse models with Ago2 HITS-CLIP analysis . We applied several different profiling techniques including ribosome profiling , RNA-Seq ( 3Seq ) and microarray methods to determine upregulated genes in miR-203 KO samples and downregulated genes in miR-203-induced samples . For ribosome profiling and 3Seq , we used primary keratinocytes , which are amenable to effective cycloheximide treatment and therefore also allowed us to investigate miR-203's impact on translation efficiency ( Figure 6A ) . For microarray analysis , we used freshly isolated epidermis obtained from miR-203 WT and KO animals at P4 . 5 . To interrogate the impact of miR-203 gain-of-function , we used the Krt14-rtTA/pTre2-miR-203-inducible mouse model that allows us to control the duration and dosage of miR-203 expression ( Jackson et al . , 2013 ) . We applied microarray profiling to determine downregulated genes in FACS-purified neonatal epidermal and hair follicle progenitors with a short-term ( 24 hr ) induction of miR-203 ( Figure 6A ) . Altogether , we accrued 20 genome-wide expression data sets from miR-203 WT , KO and inducible samples . This comprehensive collection of functional genomic data allowed us to characterize the action of miR-203 on the transcriptome . 10 . 7554/eLife . 07004 . 012Figure 6 . Comprehensive identification of miR-203 targets using genome-wide expression analyses and Ago2 HITS-CLIP . ( A ) Schematic of genome-wide expression profiling data sets used in meta-analysis to identify bono-fide miR-203 targets . ( B ) Genes upregulated in all three miR-203 loss-of-function data sets ( 786 genes , no fold-change or p-value cut-off ) were compared to genes downregulated in both miR-203 gain-of-function data sets ( 1704 genes , no fold-change or p-value cut-off ) to identify a subset of genes with a strong inverse correlation to miR-203 expression ( 294 genes ) of which 100 genes contained miR-203 7mer or 8mer seed sequence matches in their 3′UTRs . ( C ) Table demonstrating top 20 genes identified in meta-analysis ranked by negative-correlation to miR-203 expression . Genes colored in red contain 3′ UTR miR-203 7 or 8mer seed matches . ( D ) De novo motif searching identified an 8mer miR-203 seed motif , complementary to the miR-203 seed sequence enriched in the 3′UTR of candidate miR-203 target genes identified in the meta-analysis ( 294 genes ) . Table demonstrating enrichment for 7 or 8mer seed matches in the 3′UTR of candidate miR-203 target genes ( 294 ) over the background seed distribution in primary keratinocytes , which is not seen for randomly selected 294 genes expressed in primary keratinocytes or a negative control gene set of genes upregulated in miR-203 gain-of-function and downregulated miR-203 loss-of-function ( 353 genes ) . ( E ) Schematic of Ago2 HITS-CLIP and the identified miR-203 seed motif . ( F ) Diagram of genes detected by expression meta-analysis and Ago2-HITS-CLIP . Table of 21 high confidence miR-203 targets identified through expression meta-analysis and that have Ago2-HITS-CLIP 3′UTR peaks with miR-203 seed matches . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 01210 . 7554/eLife . 07004 . 013Figure 6—source data 1 . GO-analysis of selected miR-203 data sets . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 01310 . 7554/eLife . 07004 . 014Figure 6—source data 2 . Putative miR-203 targets detected in the expression meta-analysis . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 01410 . 7554/eLife . 07004 . 015Figure 6—figure supplement 1 . Transcripts containing 3′UTR miR-203 seed matches are regulated by miR-203 . ( A ) Genes containing miR-203 seed matches are more likely to be downregulated upon miR-203 overexpression or ( B ) upregulated upon miR-203 ablation ( panel B ) ( p-value <0 . 05 for comparison of 8mer match to no-match , K–S test , one-sided ) . ( C ) Transcripts are ranked based upon aggregate fold-changes consistent with miR-203 regulation to produce a ranked expression correlation metric . Transcripts with 7 or 8mer miR-203 seed matches are more likely to be regulated by miR-203 modulation . ( p < 0 . 05 ) ( see ‘Materials and methods’ ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 01510 . 7554/eLife . 07004 . 016Figure 6—figure supplement 2 . Ago2-HITS-CLIP in primary keratinocytes . ( A ) Example autoradiogram of isolated Ago2-RNA complexes , red box indicates region excised for sequencing . ( B ) Proportion of miRNAs detected by Ago2-HITS-CLIP . ( C ) Number of 3′UTR Ago2 peak containing seed sequences from miRNA families accounting for 90% of all miRNAs in p4 epidermis . ( D ) Genome-wide distribution of Ago2 peaks and reads . ( E ) Comparison of miRNAs detected by Ago2-HITS-CLIP and small-RNA-Seq . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 01610 . 7554/eLife . 07004 . 017Figure 6—figure supplement 3 . Ago2 HITS-CLIP 3′UTR peaks are enriched in keratinocyte miRNA seed matches , including miR-203 . ( A ) Position of miRNA seed matches for miRNAs highly expressed in total epidermal samples . The peak summit represents nucleotide position 0 ( B ) De novo motif searching identifies the most enriched 8mer motifs in 3′UTR peaks . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 01710 . 7554/eLife . 07004 . 018Figure 6—figure supplement 4 . Predicted miR-203 targets based on HITS-CLIP are regulated by miR-203 . ( A ) Cumulative distributions of miR-203 targets or not targeted transcripts based on HITS-CLIP in miR-203 overexpression data sets . ( B ) Cumulative distributions of miR-203 targets or not targeted transcripts based on HITS-CLIP in miR-203 knockout data sets ( C ) Ranked analysis of miR-203 targets detected by HITS-CLIP , based on 6 , 7 , 8mer seed only , or through meta-analysis ( p value displayed on plots ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 01810 . 7554/eLife . 07004 . 019Figure 6—figure supplement 5 . miR-203 targets do not display translation efficiency changes upon miR-203 ablation . ( A ) Comparison of translation efficiency for miR-203 targets ( red ) and non-targeted transcripts ( blue ) identified by expression meta-analysis . ( B ) Quantification of the change in translation efficiency in miR-203 KO samples for miR-203 targets identified by expression meta-analysis . ( C ) Comparison of translation efficiency for miR-203 targets identified by Ago2-HITS-CLIP ( red ) and non-targeted transcripts ( blue ) . ( D ) Quantification of the change in translation efficiency in miR-203 KO samples for miR-203 targets based on Ago2-HITS-CLIP . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 019 We first analyzed the miR-203 overexpression samples . GO-analysis demonstrated that genes involved in regulation of mitotic cell cycle , DNA synthesis , and metabolism were prominently downregulated upon miR-203 induction ( Figure 6—source data 1 ) . miR-203 upregulation in both epidermal and hair follicle progenitors resulted in strong suppression of transcripts harboring perfect seed matches to miR-203 in their 3′UTRs ( Figure 6—figure supplement 1 ) . Among them , transcripts containing the 8-mer matches were strongly suppressed , compared to transcripts without a miR-203 seed match ( p = 1 . 5 × 10−23 for epidermis and 2 . 6 × 10−25 for hair follicle , respectively ) ( Figure 6—figure supplement 1A ) . In contrast to miR-203 overexpression , deletion of miR-203 did not perturb transcript levels genome-wide as strongly as observed under the induced conditions and offered little insight into miR-203's function based upon GO analysis ( Figure 6—source data 1 ) . However , despite the relatively mild changes in the transcript levels in the KO skin and primary keratinocytes , transcripts that contain 8-mer 3′UTR matches were still more significantly upregulated than transcripts without any miR-203 seed matches ( p < 0 . 05 ) ( Figure 6—figure supplement 1B ) . Lastly , a ranked sum analysis that ranked genes based on expression changes correlated with the levels of miR-203 showed that genes with the 8-mer or 7-mer seed ranked significantly higher than those transcripts without seed matches ( Figure 6—figure supplement 1C ) . By demanding all potential targets be upregulated in all the miR-203 KO samples and downregulated in all the miR-203-induced samples , we identified 294 transcripts as our candidates for miR-203 targets ( Figure 6B ) . Among the top 20 most differentially expressed transcripts when miR-203 was deleted or induced , 15 of them contain at least one 7-mer or 8-mer match in their 3′UTRs ( Figure 6C and Figure 6—source data 2 ) . In support of this approach , an unbiased de novo motif search using the 3′UTRs of these 294 transcripts revealed that the most enriched motif ( ACAUUUCA , p = 1 × 10−13 ) perfectly matched to nucleotide position 2–9 of miR-203 , the 8-mer seed sequences of miR-203 ( Figure 6D ) . Additional statistical analysis of the number of genes containing perfect 3′UTR matches to the 7-mer or 8-mer seed among these candidates revealed significant enrichment over the background distribution among mRNAs expressed in the skin ( Figure 6D ) . Therefore , we selected genes containing 7-mer or 8-mer seed matched 3′UTR sites as miR-203 target candidates and obtained a collection of 100 potential miR-203 targets ( Figure 6B ) . We then applied HITS-CLIP to interrogate the direct interaction between miR-203 and its mRNA targets . We generated four Ago2 HITS-CLIP libraries from primary WT keratinocytes , which abundantly express miR-203 ( Figure 1G ) . Ago2-RNA complexes were isolated from a region extending from approximately 110 kd–130 kd as expected ( Figure 6—figure supplement 2A ) . Sequencing libraries from Ago2 HITS-CLIP samples were generated and analyzed using previously described methods ( Chi et al . , 2009; Moore et al . , 2014 ) . The HITS-CLIP faithfully captured miRNA species expressed in keratinocytes , including miR-203 ( Figure 6—figure supplement 2B , E ) . Overall our HITS-CLIP reads and clusters alignment were similar to previous published results with a significant portion aligned to 3′UTRs ( Figure 6—figure supplement 2D ) . To further validate our approach , we analyzed the positional enrichment of miRNA seed sequences within the 3′UTR HITS-CLIP clusters and detected strong enrichment over dinucleotide shuffled cluster sequences or randomly distributed 3′UTR regions ( Figure 6—figure supplement 3A ) . Additionally , de novo motif searching for 8mer motifs identified the seed sequences of miRNAs that are highly expressed in keratinocytes ( Figure 6—figure supplement 3B ) . The motif corresponding to miR-203 detected by the HITS-CLIP was also ACAUUUCA ( p = 1 × 10−19 ) , identical to the motif detected by our transcriptome analysis ( Figure 6E and Figure 6—figure supplement 3B ) . A total of 113 mRNAs were detected to have miR-203 seed containing Ago2 binding sites . We next examined the ranked sum expression of these targets and determined that transcripts with Ago2 HITS-CLIP miR-203 binding sites were ranked significantly higher compared to non-targeted mRNAs , indicating that many of these targets are functional ( Figure 6—figure supplement 4 ) . Finally , we did not detect any evidence for global regulation at the translation level for miR-203 targets based on our analysis of translational efficiency changes upon miR-203 deletion ( Figure 6—figure supplement 5 ) . Together , these HITS-CLIP data independently validated that miR-203 uses its seed sequences for mRNA targeting and predicted 113 miR-203 targets based on Ago2 binding . By combining the targets detected by our differential expression data sets and by the HITS-CLIP , we identified a list of high-confidence targets for miR-203 ( Figure 6F ) . Importantly , we found a number of key regulators in the Ras signaling pathway and important genes involved in regulation of cell division including Hbegf , Ccnd1 , Snai2 , Met , and Pola1 in this list ( Figure 6F ) . This collection of miR-203 targets suggests that miR-203 targets multiple pathways , including several components of the Ras signaling pathway , to suppress cell proliferation . Our genome-wide analyses identified a number of novel targets of miR-203 . Given our findings that the loss of miR-203 promotes the selection and expansion of oncogenic Hras-transformed cells both in vivo and in vitro , we were interested in understanding the underlying mechanism . Overall , miR-203 targets identified in the meta-analysis were enriched in the upregulated transcripts in HrasG12V-transformed keratinocytes , compared to non-targeted transcripts , consistent with the downregulation of miR-203 in these cells ( Figure 7—figure supplement 1A ) . Among these targets , we were intrigued by the observation that multiple regulators of the Ras signaling pathway and critical factors for DNA replication and cell cycle progression were among our high confidence targets and additionally were among the most upregulated miR-203 targets in HrasG12V-transformed keratinocytes ( Figure 7—figure supplement 1B ) . To begin to validate the high-confidence set of miR-203 targets , we selected Pola1 and Hbegf for further study . Pola1 is the catalytic subunit of the DNA-POL-alpha holoenzyme , which is required in initiation of DNA replication during S-phase ( Lehman and Kaguni , 1989 ) . Hbegf is an Egf-like ligand that activates MAPK signaling through activation of EGF-receptors , Erbb1 and Erbb4 . In keratinocytes , Hbegf is mitogenic and promotes keratinocyte migration ( Stoll et al . , 2012 ) . In an epithelial cancer cell line , Hbegf acts as an oncogene promoting cell proliferation ( Miyamoto et al . , 2004 ) . Pola1 and Hbegf contain 3′ UTR miR-203 target sites ( 9-mer and 8-mer respectively ) that are targeted by miR-203 , validated by luciferase assay ( Figure 7A ) . Furthermore , in miR-203 null epidermis , both Pola1 and Hbegf mRNAs were elevated ( Figure 7B ) . In addition to mRNA levels , Pola1 protein levels were also elevated in the absence of miR-203 . It was further elevated in the presence of HrasG12V and repressed by miR-203 induction ( Figure 7C , D ) . We were unable to measure the protein level of Hbegf due to poor antibody performance . Additionally , we observed that the expression of Ccnd1 , an essential cell cycle regulator that is often induced or amplified by oncogenic Ras ( Downward , 2003; Stransky et al . , 2011 ) , showed a strong negative correlation to miR-203 ( Figure 7C , D ) . This suggested that loss of miR-203 increases the levels of Ccnd1 and contributes to the observed upregulation of this critical gene . 10 . 7554/eLife . 07004 . 020Figure 7 . Hbegf and Pola1 are direct miR-203 target genes critical for keratinocyte proliferation . ( A ) 3′UTR luciferase reporter assays demonstrate that miR-203 directly targets Trp63 ( positive control ) , Pola1 , and Hbegf in keratinocytes ( representative of n = 3 independent experiments , mean ± propagated standard deviation displayed , p < 0 . 05 , ns = non-significant , Student's t-test two-sided ) ( B ) Hbegf and Pola1 are upregulated in miR-203−/− isolated epidermis ( p4 ) ( n = 8 and n = 10 , miR-203+/+ and miR-203−/− animals respectively , mean ± SEM displayed , p < 0 . 05 ) . ( C , D ) Western blots from lysates with miR-203 overexpression ( 48 hr ) or miR-203 ablation . ( E ) shRNA knockdown of Hbegf or Pola1 impairs keratinocyte colony formation ability ( representative of n = 3 independent experiments , p <0 . 05 , mean ± standard deviation displayed ) . ( F ) Model for the mechanism of miR-203 in restricting Hras-initiated tumorigenesis . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 02010 . 7554/eLife . 07004 . 021Figure 7—figure supplement 1 . A subset of miR-203 targets are upregulated by HrasG12V . ( A ) miR-203 target genes identified through meta-analysis containing miR-203 seed matches are more likely to be upregulated upon HrasG12V expression in primary keratinocytes than non-targeted transcripts ( p-value ≤0 . 05 , K–S test one-sided ) . ( B ) Top 20 miR-203 targets , based on expression meta-analysis , ranked by upregulation by HrasG12V , genes shown in red are also identified by Ago2-HITS-CLIP . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 02110 . 7554/eLife . 07004 . 022Figure 7—figure supplement 2 . Hbegf and Pola1 are required for keratinocyte growth potential in HrasG12V-transformed miR-203+/+ and miR-203−/− cultures . ( A ) Colony formation assays of established miR-203+/+ cultures stably infected with pbabe-HrasG12V-neo , followed by Pola1 and Hbegf knockdown . ( B ) Colony formation assays of established miR-203+/+ cultures stably infected with pbabe-HrasG12V-neo , followed by Pola1 and Hbegf knockdown . ( p-value determined by ANOVA with Tukey HSD correction , n = 3 wells per experiment ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07004 . 022 To assess the functional consequences of Hbegf and Pola1 suppression on keratinocyte proliferation , we knocked down these targets using three independent shRNAs . Suppression of Hbegf and Pola1 strongly suppressed the growth potential of keratinocytes ( Figure 7E ) . Additionally , we knocked down Hbegf and Pola1 in established miR-203+/+ and miR-203−/− keratinocytes transduced with HrasG12V and similarly observed potent suppression of keratinocyte growth potential , demonstrating that these targets are also required in HrasG12V-transformed keratinocytes ( Figure 7—figure supplement 2 ) . Taken together , these results validate Hbegf and Pola1 as direct targets of miR-203 . We hereby propose a model in which miR-203 restricts selection and expansion of Hras-mutated cells by repressing multiple targets , a subset of which are involved in the Ras signaling pathway ( Figure 7F ) . Identification and experimental validation of driver gene mutations have been instrumental for our understanding of cancer biology ( Vogelstein et al . , 2013 ) . Although it is clear that not all cells harboring driver gene mutations develop into tumors , the mechanism of clonal selection within cells that have acquired driver gene mutations remains poorly understood . In this study , we used an oncogenic Hras-induced tumor model to determine the molecular consequences of this oncogenic driver mutation and to study the selection process . We quantitatively measured the impact of oncogenic HrasG12V on the landscape of mRNA and miRNA expression . Whereas our mRNA-Seq data confirmed the profound ability of HrasG12V in promoting cellular transformation , our miR-Seq data revealed new insights into the dynamic changes in highly expressed miRNAs caused by HrasG12V ( Figure 1 ) . In particular , the downregulation of miR-203 , one of the most highly expressed miRNAs in the skin , likely occurs at the transcriptional level ( Figure 2C , D ) . By examining tumor samples obtained from both mouse and human at multiple stages , we showed that the downregulation of miR-203 is associated with tumor initiation and progression ( Figure 2E–P ) . To determine the role of miR-203 in this process , we generated a KO mouse model . Initial insights into the function of miR-203 came from the analysis of miR-203 null epidermis during skin development . Whereas we and others have shown the potent inhibition of epidermal proliferation by miR-203 with gain-of-function approaches ( Lena et al . , 2008; Yi et al . , 2008; Jackson et al . , 2013; Benaich et al . , 2014 ) , it is unknown how the loss of miR-203 affects the skin and mouse development in general . By analyzing the KO mouse , we revealed an early , albeit mild , hyperproliferative phenotype in the embryonic but not postnatal skin ( Figure 3E–I ) . However , when we subjected miR-203 WT and null cells obtained from postnatal skin for their colony-forming ability , we observed an increased ability of the miR-203 null cells to give rise to colony-forming cells ( Figure 3J , K ) . This observation was indicative of a role for miR-203 in restricting the expansion of highly proliferative cells . Of note , we did not observe any defects in the formation of the differentiated layers of the skin in the miR-203 KO mice . This suggests that the primary function of miR-203 is to restrict cell proliferation but not to promote epidermal differentiation per se . When we examined the role of miR-203 during DMBA/TPA-mediated chemical carcinogenesis , we observed strong increase in the number of papillomas formed in the KO skin ( Figure 4A–C ) . In vitro colony formation assays with serial passages further illustrated the enhanced ability for selecting tumor-initiating cells with miR-203 KO cells when subjected to HrasG12V transformation ( Figure 5A ) . However , due to tumor resistance of our C57BL/6 strain , we were unable to determine if the loss of miR-203 also promotes malignant transition . Of note , a recent study showed interesting results that restoration of miR-203 suppresses human SCC metastasis ( Benaich et al . , 2014 ) . Collectively , our results provide experimental evidence for an important role of miR-203 in suppressing clonal selection and expansion of Hras-mutated nascent cancer cells . A major challenge in understanding miRNA functions is to identify their high-confidence targets globally . To this end , we employed two independent approaches: transcriptome analysis with our KO and inducible models and direct miRNA target capture by Ago2 HITS-CLIP ( Figure 6 ) . Importantly , with our expansive data set , we confirmed that miR-203 utilizes the 5′ seed sequences to target perfectly matched sequences located on the 3′UTRs of its target genes . Consistent with recent genome-wide studies for the impact of miRNA on mRNA levels and translation efficiency in mammals ( Guo et al . , 2010; Eichhorn et al . , 2014 ) , we found no evidence for global changes of translational efficiency in the absence of miR-203 , one of the most highly expressed miRNAs in the skin . However , we also noted that Trp63 , a well-established miR-203 target , showed little change at the mRNA levels under all conditions ( not shown ) . Our analysis with differential gene expression did not identify Trp63 as a miR-203 target . In contrast , the recognition of the 3′UTR site of Trp63 by miR-203 was robustly detected by the HITS-CLIP ( Figure 6E ) . Thus , it is likely that our identification of miR-203 targets based on the transcriptome analysis alone was conservative . More studies will be required to further integrate direct miRNA target capture with transcriptome analysis for miRNA target identification . Nevertheless , we still observed strong enrichment of miR-203 targets in genes required for cell proliferation . These high-confidence targets illustrate the broad targeting of cell proliferation by miR-203 and suggest mechanisms by which loss of miR-203 can facilitate the expansion of oncogenic Hras-transformed cells . These new insights point to the potential of utilizing miR-203 to simultaneously target multiple effectors required for cell division in human cancers . We also note that miR-203 is known to antagonize human papillomaviruses ( HPV ) ( Melar-New and Laimins , 2010 ) . Our identification of Pola1 , a critical cellular gene required for HPV genome replication , as a target of miR-203 may provide a potential mechanism for miR-203-mediated antagonism against HPV infection . Finally , the relatively mild requirement for miR-203 in slowly proliferating cells suggests that a reduction or increase in the level of miR-203 may be well tolerated by most normal cells . This unexpected discovery could be further explored to determine miR-203's therapeutic potential in treating certain types of epithelial cancers . 3′seq libraries were constructed using methods described previously ( Wang et al . , 2013 ) . Briefly , 500 ng of total RNA isolated from primary keratinocytes was poly-A purified ( Dynabeads , Thermo Fisher Scientific , Waltham , MA ) and chemically fragmented by treatment 95°C for 8 min in Fragmentation Buffer ( Thermo Fisher Scientific ) . Fragmented RNAs were then oligoDT primed with a P7T20V oligo and reverse transcribed using SuperScript III ( Thermo Fisher Scientific ) ( see Supplementary file 2 for Oligo Sequences ) . Following ethanol precipitation , ligation competent cDNAs were generated via second-strand synthesis using RNase H and DNA Pol I enzymes , end-repaired using a mix of T4 DNA polymerase , Klenow DNA polymerase and T4 Polynucleotide Kinase in the presence of dNTPs ( New England Biolabs , Ipswich , MA ) , and A-Tailed using Klenow 3′ to 5′ exo- in the presence of dATP ( New England Biolabs ) . Ligation was then performed using the P5 Adaptor with T4 DNA Ligase for 1 hr ( New England Biolabs ) . cDNA inserts 80–100 nt in length were isolated from 8% PAGE gels and subject to PCR amplification using RP1 and RT-Primer oligos for 12–16 cycles ( Phusion , New England Biolabs ) . PCR products were isolated from PAGE gels and subject to 6 cycles of secondary PCR to introduce unique indices for multiplexed DNA sequencing on a HiSeq 2000 ( Illumina , San Diego , CA ) . Bioinformatics processing of 3seq data was performed as described previously , including adaptor trimming , read alignments , peak calling , peak filtering to remove internal polyA priming events , and transcript quantification with minor modifications ( Wang et al . , 2013 ) . Following read alignment to the mm10 genome , alignments from every library were grouped together for peak calling and 3′end filtering to create a master set of high confidence 3′ end peaks . From this 3′end data set , reads counts for each library were obtained ( see Supplementary file 1 for mapping statistics ) . To obtain transcript read counts , reads from all peaks that passed our internal priming filter and were 3′UTR localized were summed to obtain an overall transcript count for each transcript . Normalized transcript counts were calculated as reads per million reads mapped ( RPM ) . To determine differential transcript expression , low-abundance transcripts with less than 10 reads in two libraries were first excluded then the remaining transcripts were analyzed with EdgeR with classical analysis parameters . Small RNA-Sequencing libraries were prepared using minor modifications of a previously published protocol that reduces RNA-ligation biases and enables accurate miRNA quantification ( Zhang et al . , 2013 ) . Briefly , two micrograms of total RNA was first ligated to a pre-adenylated 3′ linker ( 10 μM ) using truncated T4 RNA Ligase 2 ( 20 Units New England Biolabs ) in the presence of PEG-8000 ( 10% wt/vol ) RNaseOUT ( 20 Units Thermo Fisher Scientific ) for 4 hr at room temperature . Ligation reactions were then resolved on 7 M Urea-15% PAGE gel to isolate the miRNA-3′ adaptor hybrids . Following overnight elution from the acrylamide gel in HSCB buffer ( 400 mM NaCl , 25 mM Tris–HCl pH 7 . 5 , 0 . 1% SDS ) , ligation products were ethanol precipitated , resuspended in 5′ ligation reaction mixture containing a 5′ linker ( 10 μM ) , 1 mM ATP , Peg-8000 ( 20% wt/vol ) , 1 × T4 RNA Ligase buffer ( New England Biolabs ) , denatured for 5 min at 70°C , after which RNaseOUT ( 20 units ) and T4 RNA Ligase 1 ( 10 units ) were added . Following ligation for 37°C for 2 hr , cDNA was generated via reverse transcription using Superscript III ( Thermo Fisher Scientific ) in the presence of a 3′adaptor specific primer . cDNA products were then subjected to 10–14 PCR cycles and resolved on 8% native PAGE gels . Libraries were then eluted as described above , precipitated , and submitted for sequencing on a HiSeq 2000 ( Illumina ) . Small-RNA sequencing reads were analyzed following previously described methods with the following modifications ( Zhang et al . , 2013 ) . Adaptor sequences were trimmed from reads using CutAdapt using default parameters . Reads were then further trimmed to remove the randomized adaptor dinucleotides on the 5′ and 3′ end . Read were next aligned to a database of mouse miRNA sequences ( miRbase ) using blastn with the following settings ( blastn -word_size 11 -outfmt 6 -strand plus ) . Blast alignments were then parsed with custom python scripts to extract and count the best miRNA alignment for each read with a minimum read alignment of 18 nucleotides . miRNA counts for each library were then filtered to keep only miRNAs with a minimum count of 50 reads in two libraries and analyzed for differential expression using EdgeR , with classical analysis parameters . For the miR-203 epidermal loss-of-function microarray analysis , RNA was isolated from total epidermal samples from two-pairs of miR-203+/+ and miR-203−/− animals at p4 . For the doxycycline-inducible miR-203 over-expression microarray analysis , RNA was isolated from two pairs of doxycline induced or uninduced Krt14-rtTA/ pTre-miR-203 animals using FACS sorting for Krt14-H2B-GFP+ cells as described previously ( Jackson et al . , 2013 ) . The microarray analysis of miR-203 overexpression in basal epidermis was previously published ( Jackson et al . , 2013 ) . Subsequently total RNAs ( 500 ng ) were processed and hybridized to the GeneChip Mouse Genome 430 2 . 0 array ( Affymetrix , Santa Clara , CA ) following the manufacturer's instruction at the MCDB microarray facility . Microarray image files were processed using the R Bioconductor suite and Mas5 normalization . Probesets were then filtered to include only those probes with present or absent calls in at least two arrays . Probesets were then collapsed using the probeset with the maximum averaged probeset intensity to represent each GeneID . Log2 fold changes were then computed using the limma Bioconductor package . Ribosome profiling was performed on primary keratinocyte lysates using the ARTseq Ribosome profiling kit ( Illumina ) . Briefly , lysates from a 10-cm dish of primary keratinocytes were isolated in the presence of cycloheximide ( Sigma-Aldrich , St . Louis , MI , 50 μg/ml ) and subject to limited RNAse digestion ( 10 units ) for 45 min at room temperature . RNase digestion was terminated by addition of 15 μl of SUPERase In ( Thermo Fisher Scientific ) followed by ribosome isolation using illustra MicroSpin S-400 HR Columns ( GE Healthcare , United Kingdom ) . Following RNA extraction and precipitation , rRNA was depleted using the Ribo-Zero Gold kit ( Illumina ) , with the remaining RNA then fractionated through 18% PAGE gels . RNA species 28–32 nt were isolated for adaptor ligation , reverse transcription , circularization , and PCR amplification following the manufacturer's protocol . PAGE gel isolated PCR products were then sequenced on a HiSeq 2000 ( Illumina ) . Raw reads were first trimmed to remove 3′ adaptors using cutAdapt with default parameters . Reads were aligned to mm10 rRNA , tRNA , and ncRNA ( Ensemble annotation ) databases using Bowtie ( default settings ) to exclude reads aligning to abundant rRNA , tRNA , and ncRNA sequences . Unaligned reads were then aligned via Tophat using default settings , with a supplied . gtf annotation file containing combined Refseq and Ensembl gene annotations ( iGenomes Illumina downloaded 9/4/2013 ) . Uniquely aligned read counts were quantified across each CDS using HTSeq Count ( settings: -s yes -m union -t CDS ) using the above-mentioned GTF annotation database . Transcripts with low reads counts were excluded by only keeping transcripts with at least 50 reads in at least two libraries . Filtered transcript reads count data were then analyzed for differential expression using EdgeR with classical analysis parameters . To calculate translation efficiency for each transcript , Reads Per Million Mapped ( RPM ) values from 3Seq were divided by Reads per Million Mapped to coding sequence for the Ribo-Seq . The change in translation efficiency was then computed as the ratio of translation efficiency in the miR-203−/− and miR-203+/+ libraries . Ago2 HITS-CLIP was performed as described with minor modifications ( Chi et al . , 2009 ) . 15-cm2 dishes of primary keratinocytes were irradiated twice at 200 mJ/cm with 254 nm UVC light . Following irradiation , cell lysates were harvested by scraping and stored at −80°C . Following thawing , lysates were further lysed by trituration 3 times through pre-chilled 25- and 30-gauge needles . Lysates were then treated with 10 μl Turbo DNase ( Thermo Fisher Scientific ) and 5 μl RNaseOUT ( Thermo Fisher Scientific ) per ml of lysate . Limited RNase digestion was performed using 10 μl per ml of lysate of a 1:20 dilution of an RnaseA/T1 mix ( Sigma-Aldrich/Thermo Fischer Scientific 1× mix = 3 . 33 μl RnaseA [2 μg/μl] with 6 . 66 μl RnaseT1 [1 U/μl] ) . Crosslinked Ago2 was recovered via immunoprecipitation for 2 hr at 4°C with 3 μg of a monoclonal anti-mouse Ago2 antibody ( Wako Chemicals USA Inc . , Richmond , VA , clone 2D4 ) complexed with Protein-G Dynabeads ( Thermo Fisher Scientific ) . Immunoprecipitates were washed twice with High-Salt Buffer and PNK buffer , then end labeled with 25 μCi 32P y-ATP using PNK 3′ phosphatase minus ( New England Biolabs ) for 5 min at 37°C . After washing the beads as listed above , 5′ adaptor ligation was performed for 2 hr at room temperate using T4 RNA Ligase 1 with 10 μM 5′ RNA Linker , 20% PEG-8000 ( wt/vol final ) , 1 mM ATP , and RNaseOUT ( Thermo Fisher Scientific ) . Beads were again washed twice with PNK buffer then resuspended in a phosphatase reaction with 5 μl FastAP ( Thermo Fisher Scientific ) with RNaseOUT . Following washing twice with PNK buffer , Protein–RNA complexes were eluted from the beads using 1× NuPage Loading buffer supplemented with 50 mM DTT at 70°C for 10 min . Protein–RNA complexes were then resolved on an 8% Bis-Tris Gel and transferred to nitrocellulose . Membranes were exposed to a phosphor screen for 1–2 hr to obtain an autoradiograph . Subsequently , RNA–protein complexes migrating in the 110–130 kD range were excised . RNA was recovered from the nitrocellulose using Proteinase K treatment followed by phenol–chloroform extraction and ethanol precipitation . Isolated RNA was ligated to a 3′linker using the same ligation reaction conditions as for 5′ ligation , and ligated RNA species were fractionated away from adaptor–adaptor products on 10% UREA PAGE gels . The RNA was eluted from the PAGE gel with HSCB buffer overnight at 4°C , then ethanol precipitated and resuspended for reverse transcription with SuperScript III ( Thermo Fisher Scientific ) . cDNA products were then subjected to PCR amplification for 20–24 cycles and fractionated on an 8% native PAGE gel . PCR products representing cDNA inserts of 20–50 nts were recovered and subject to sequencing on a HiSeq 2000 ( Illumina ) . HITS-CLIP reads were analyzed as follows . First , reads were processed to identify miRNA alignments using the same pipeline that we use for Small-RNA-Seq listed above . Reads not mapping to miRNAs were next processed as follows . Reads were trimmed with Cutadapt to remove adaptor sequences using default settings . To avoid PCR duplicates from biasing the analysis , duplicate reads were then collapsed to a single read using Fastx_collapser ( default settings ) . The 5′ and 3′ adaptor sequences contain randomized dinucleotides on their 3′ and 5′ ends respectively , which were next trimmed from the reads . The reads were then aligned to the mm10 genome assembly using NovoAlign requiring a minimum alignment length of 25 nucleotides ( settings –s 1 –t 85 –l 25 ) ( Novocraft , Malaysia ) . All unique alignments from each library were then pooled to identify Ago2 HITS-CLIP clusters . Clusters were defined as two read alignments that overlap by a minimum of one nucleotide . Clusters were next annotated to gene features in a hierarchical manner in which clusters were annotated to protein coding RefSeq 3′ UTRs ( with 5 kbp extension allowed ) , RefSeq CDS regions , RefSeq 5′ UTRs , Ensemble ncRNA regions , and RefSeq intron regions . Clusters not found in these regions were annotated as intergenic clusters . For predicting miRNA target sites , 3′UTR cluster sequences were searched for 6mer seed-sequence matches for miRNA species that accounted for 90% of miRNAs expressed in epidermis based on small-RNA sequencing . From this data set , 117 binding sites in 113 mRNAs were predicted be miR-203 target sites . The miR-203 overexpression data sets used in the meta-analysis included previously published microarrays from sorted H2B-GFP+ epidermal cells with transient miR-203 induction ( GSE45121 ) , and microarrays from sorted H2B-GFPhi+ hair follicle progenitor cells with transient miR-203 induction described in this manuscript . The miR-203 knockout data sets used in the meta-analysis included the microarrays from p4 . 5 total epidermal samples from miR-203+/+ and miR-203−/− animals , ribosome profiling of miR-203+/+ and miR-203−/− primary keratinocytes , and 3seq of miR-203+/+ and miR-203−/− primary cultures . We also performed 3seq on miR-203−/− cells transformed with HrasG12V; however , there was no enrichment for miR-203 seed matches in the upregulated transcripts based on CDF analysis , consistent with the low levels of miR-203 in HrasG12V-transformed keratinocytes , and therefore , this data set was excluded from the expression meta-analysis ( data not shown ) . Gene symbols were used to compare across microarray , 3Seq , and Ribo-Seq data sets . Log2 fold changes were used to assess differential gene expression in each data set . In total 6407 genes were detectable in all the data sets , from which 294 satisfied the criteria of being upregulated in all the miR-203 knockout data sets and downregulated in the miR-203 overexpression data sets . Negative control data sets were constructed to analyze the enrichment of genes containing miR-203 seed matches with the following criteria , randomly selected genes from the list of detected transcripts in the meta-analysis , or genes that have a positive correlation to miR-203 expression ( downregulated in miR-203 knockout data sets and upregulated in miR-203 overexpression data sets ) . In order to compare gain-of-function and loss-of-function data sets in aggregate , a ranked correlation to miR-203 metric was calculated . Transcripts were assigned a rank in each knockout data set with the most upregulated gene given a rank of 1 . Transcripts were next assigned a rank in each overexpression data set with the most downregulated gene given a rank of 1 . The ranked values from each of the 5 data sets were then summed and ranked with the transcript most upregulated upon miR-203 ablation and downregulated upon overexpression being assigned a value of 1 . Normalized transcript abundances ( expressed as Reads Per Million ) were used for mean-centered unsupervised hierarchical clustering using Cluster 3 . 0 software . For 3seq data , only transcripts with at least twofold change were selected and for miRNA data , only miRNAs with at least an expression level of 1000 RPM and at least a twofold change were selected for visualization in JavaTree View . GO-term enrichment analysis was performed by DAVID using Gene-IDs as input , with analysis being performed using GO biological processes data sets . GSEA analysis was performed using ranked expression values for the displayed data set , and genesets were selected from the referenced publications . De novo motif searching was performed using the HOMER package to search for 7 or 8mer motifs in RefSeq 3′UTR sequences for selected genesets . For genes with multiple 3′UTR isoforms , the longest 3′UTR was selected for motif searching . miRNA seed-sequence searches in Ago2-HITS-CLIP clusters were performed with a Python script using regular expressions . Head and Neck SCC miRNA-Seq data were obtained from the TCGA https://tcga-data . nci . nih . gov/tcga/ ( Download date 10/23/2014 ) . Patient matched normal solid tissue and tumor miRNA quantification records were identified using custom R scripts ( regular expression query for tumor and solid tissue normal samples respectively ‘TCGA-[0-9A-Z]{2}-[0-9A-Z]{4}-0’ , ‘TCGA-[0-9A-Z]{2}-[0-9A-Z]{4}-11’ ) . The normalized reads_per_million quantification for miR-203 and miR-21 was then plotted to determine the relative expression in normal and tumor tissue samples . A gene targeting vector was constructed that contained 11 kbp homologous region surrounding the miR-203 locus ( Figure 3—figure supplement 1 ) . LoxP were inserted flanking the pre-miR-203 sequences , with a neomycin selection cassette flanked by Frt sites . The construct was electroporated into Cy2 . 4 ES cells ( B6 ( Cg ) -Tyr<c2J> genetic background ) . Positive clones were identified by Southern blot analysis using a probe complementary to the 3′ end of the targeted homologous region . ES cells were injected into blastocysts and chimeras were screened based on white/black coat color selection . Upon obtaining germline transmission , the neo cassette was excised by breeding the F1 progeny to an Actb-Flpe line maintained on a C57BL/6J background ( obtained from Jackson Labs , Bar Harbor , ME ) . miR-203floxed animals were then bred to a EIIa:Cre line maintained on a C57Bl/6 background ( obtained from Jackson Laboratory ) or a Krt14:Cre line maintained on a mixed background ( obtained from E . Fuchs Laboratory ) , to obtain germline or conditional ablation of miR-203 . The EIIa:Cre transgene was removed from the germline miR-203 deleted line by backcrossing to a C57Bl/6 line and subsequently maintained on a C57Bl/6 background . pTre2-miR-203/Krt14-rtTA mice were generated as described previously and maintained on an FVB background ( Jackson et al . , 2013 ) . Mice were bred and housed according to the guidelines of IACUC at a pathogen-free facility at the University of Colorado ( Boulder , CO , USA ) . Frozen cryostat sections ( 8 μM ) were fixed in 4% paraformaldehyde for 10 min at room temperature , washed thrice with PBS , and blocked for 10 min using Gelatin Block ( 0 . 1% Triton X-100 , 2% gelatin , 2 . 5% normal goat serum , 2 . 5% normal donkey serum , and 1% BSA in PBS ) . Primary antibodies , diluted in gelatin block , were then incubated overnight ( see Supplementary file 2 for antibody references ) . Following three washes with PBS , sections were incubated with appropriate Alexa-Fluor secondary antibodies ( 1:2000 ) for 1–2 hr at room temperature . Following three washes with PBS , sections were stained with Hoescht Dye and mounted in Anti-fade solution . miRNA in situ hybridization for miR-203 was performed on frozen sections as described previously ( Yi et al . , 2008 ) . EdU detection was performed following manufacturer's instructions , with the following parameters . P4 animals were IP injected with 50 μg/g EdU 4 hr prior to tissue harvest . Following EdU detection , the sections were blocked and probed with antibodies as described above . BrdU detection was performed as previously described , with the following parameters . Pregnant female mice were IP injected with 50 μg/g BrdU for 2 hr prior to embryo harvest in OCT compound . miR-203 in situ hybridization on FFPE mouse and human tumor samples was performed with the following modifications , after deparaffinization in xylenes , the tissue was treated with proteinase K ( 20 μg/ml ) for an extended period of 20 min at an elevated temperature ( 37°C ) . Microscopy images were obtained using a Leica DM5500B microscope with either a Leica camera ( brightfield ) or Hamamatsu C10600-10B camera ( fluorescence ) and processed with the Leica image analysis suite , MetaMorph ( MDS Analytical Technologies , Sunnyvale , CA ) and Fiji software . BrdU or EdU image quantifications were performed by counting the number of Krt5+/EdU or BrdU positive cells in randomly chosen microscopy fields . The length of the basement membrane was used to represent the length of the epidermis analyzed and was determined by tracing the basement membrane and calculating line length using Fiji software . Epidermal thickness was assessed by tracing a line tangential to the basement membrane and extending to the beginning of the stratum corneum and calculating the line length . qPCR was performed using the Qiagen ( Germany ) miR-script RT system and a BioRad CFX-384 machine ( Hercules , CA ) . Fold-changes were computed using the ΔΔCt formula normalized to sno25 and Hprt values . In all qPCR figures , error bars denote standard errors of the normalized mean . Western blotting was performed using 20–40 μg of protein lysate run on 8–12% SDS-PAGE gels . Proteins were transferred to PVDF for detection of Pola1 , β-tubulin ( Tubb5 ) , or Ccnd1 . Primary antibodies were incubated in 5% BSA overnight and detected using HRP-conjugated secondary antibodies and Amersam ECL-Plus reagents ( 1:10 , 000 ) ( GE Healthcare ) . See Supplementary file 2 for antibody descriptions and dilutions . X-ray films were scanned and processed with Fiji software to calculate relative protein abundance . Primary keratinocytes were isolated from neonatal mice using previously described methods with the following modifications ( Lichti et al . , 2008 ) . Isolated skin was incubated on a solution of Dispase overnight at 4°C to dissociate the epidermis from the dermis . The following day epidermal sheets were incubated in 37°C Trypsin for 10 min to isolate keratinocytes . Primary keratinocytes were then plated in 6-cm or 10-cm dishes with E-Low media supplemented with 0 . 2 mM calcium chloride for the first 24 hr then switched to E-Low media with 0 . 05 mM Ca++ . Lentiviral particles were produced by transient transfection of pLKO-shRNA constructs , PsPax . 2 , and pVSVG . 24 hr post transfection , the media was changed to E-Low calcium . Retroviral particles were produced by transient transfection of pBabe-vector-puro , pBabe-HrasG12V-puro , pBabe-vector-neo , or pBabe-HrasG12V-neo , with PCL-Eco and pAdvantage packaging plasmids . Viral supernatant was harvested every 12 hr for up to 96 hr , pooled and filtered with 0 . 45-μM filter . Ad-eGFP or Ad-CREeGFP adenoviruses were obtained from the Iowa Gene Transfer Core and used at MOI of 50 . Retroviral and lentiviral infections were performed 3–4 days after plating primary keratinocytes . Keratinocytes were selected with 2 μg/ml puromycin for 48 hr or 50 μg/ml neomycin for 7 days , at which time non-infected cell cultures were non-viable . Spontaneously immortalized miR-203+/+ , miR-203−/− , and miR-203fl/fl mouse keratinocyte cell lines were also generated via serial passage on mitomycin-C treated NIH-3T3 feeder cell culture layer and utilized for assays as noted in the text . Flow cytometry was performed as previously described , with minor modifications ( Jackson et al . , 2013 ) . Cell cultures derived from pTre2-miR-203/Krt14-rtTA animals were treated with 5 μg/ml doxycycline for 24 hr to induce miR-203 expression , pulsed with EdU ( 10 μM ) for 30 min , harvested and analyzed according to the Click-IT EdU Plus instructions on an BD Cyan flow cytometer ( Thermo Fisher Scientific ) . For colony formation assays , 2000 cells were split into individual wells of 6-well plates , cultured for 10–14 days , fixed with 4% PFA , and stained with 0 . 2% crystal violet in 70% ethanol . For induction of miR-203 , 24 hr after plating , cells were supplied with fresh media containing doxycycline at 5 μg/ml . Sigma–Aldrich TRC lentiviral shRNAs against Hbegf and Pola1 were obtained from the Functional Genomic Facility ( University of Colorado at Boulder , sequences listed in Supplementary file 2 ) . 3′UTR reporter constructs were generated by PCR amplification of 3′UTRs from cDNA or gDNA and subcloning of the fragments into pGL3-Control ( Promega , Madison , WI ) ( primer sequences listed in Supplementary file 2 ) . 2 ng renilla luciferase control , 20 ng pGL3-3′UTR reporter , and 380 ng of Krt14 empty vector , or Krt14-miR-203 were transiently cotransfected into keratinocytes in each well of a 12-well plate using Mirus Bio LT1 reagent ( Mirus Bio LLC , Madison , WI ) . 24 hr later cell lysates were collected , and renilla and firefly luciferase activity were measured using Dual-Glo Luciferase Assay system ( Promega ) as described previously ( Yi et al . , 2008 ) . Data are represented as the ratio of firefly to renilla RFU values , normalized to Pgl3-control values . Error bars represent propagated standard deviations . DMBA/TPA carcinogenesis was performed as described previously ( Abel et al . , 2009 ) . The backskin of 7- to 9-week-old miR-203+/+ and miR-203−/− mice was shaved . 48 hr later the backskin was painted with a single dose of 25 μg of DMBA in 200 μl of Acetone . 2 weeks following DMBA treatment the mice began receiving bi-weekly treatments of 4 μg of TPA in acetone . The number of palpable tumors of at least 1 mm in diameter , persisting for at least 2 weeks , was recorded weekly . Tumor diameters were measured using a digital caliper . Following 21 weeks of TPA treatment , mice were euthanized and tumors were collected for HrasQ61l genotyping , OCT embedding , and paraffin embedding . Tumor DNA was isolated by incubating the tissue in a DNA Lysis Buffer ( 400 mM NaCl , 0 . 1% SDS , 1 mM EDTA , 1 μg/ml Proteinase K ) at 55°C for 4 hr . Lysates were then vortexed and lightly centrifuged to liberate DNA from the partially digest tumor tissue . The supernatant was then removed and subject to Phenol–chloroform extraction , followed by isopropanol precipitation . Isolated DNA pellets were then resuspended in TE buffer and quantified by UV spectrophotometry ( 10 mM Tris pH 8 . 0 , 1 mM EDTA ) . The Hras gene was PCR amplified using primers that flank exon 2 . Following amplification , the PCR reactions were digested with 5 units of XbaI restriction enzyme at 37°C . The reaction products were then resolved and visualized on a 3% agarose gel . DNA isolated from the tails of animals in the DMBA/TPA experiment was treated in parallel as a negative control for detection of the HrasQ61L mutation . Statistical analysis was performed using either R or Microsoft Excel . Statistical methods employed are indicated in the figure legends . Unpaired two-sided Student’s t-tests were used to assess statistical significance unless indicated otherwise in the figure legends . For comparisons with multiple categories , ANOVA was used with Tukey's HSD post-hoc test . Non-parametric Whitney–Mann U-tests were used to assess significance for the tumor multiplicity measurements ( Abel et al . , 2009 ) . The hypergeometric test was used to assess the enrichment of gene lists in genome-wide studies . The Kolmogorov–Smirnov test was used to assess differences in cumulative distributions functions . All sequencing and microarray data are deposited in the Gene Expression Omnibus ( GSE66056 ) .	DNA mutations occur and accumulate during an individual's lifetime . Often these changes are harmless . But some mutations—called driver mutations—can trigger the formation of tumors . This is often because these mutations allow the cells to grow faster than normal cells . Mutations in genes in the Ras gene family are among the most common driver mutations found in human cancers . These common mutations lead to the uncontrolled activation of genes that are normally tightly controlled , which in turn allows the cells to divide more and live for longer: these are two key features of cancer cells . So , how are Ras genes and the genes that they control regulated to prevent such dangerous over activation ? One mechanism rests on binding sites in their messenger RNA sequence that are recognized by smaller RNA molecules called microRNAs . RNA molecules are created when genes are transcribed . Some RNAs , called messenger RNAs , are then decoded to create proteins . Many other RNAs , including microRNAs , do not code for proteins , but instead bind to many messenger RNA targets , and repress their ability to be decoded into proteins . Three genes , called Hras , Kras , and Nras , are regulated in this way by numerous microRNAs , which together act to dampen the normal activities of these genes . Riemondy et al . investigate how a cancer-promoting mutation in the Hras gene affects the activities of microRNAs in mouse skin cells in culture . By measuring RNA levels , the experiments reveal that skin cells carrying this mutation produce significantly lower levels of what is normally the most highly produced microRNA in the skin . This microRNA , called microRNA-203 , acts to limit the proliferation of skin cells when these cells are dividing rapidly . When the gene encoding microRNA-203 was deleted in mice , the skin cells proliferated more . These mice also developed more skin tumors than normal mice when they were exposed to cancer-causing chemicals . When the gene for microRNA-203 was added into skin cells carrying the Hras mutation and then activated , the cells both divided less and , as a results , grew less . This indicates that microRNA-203 could prevent cancerous cells from expanding in number , a key event in the initiation of tumors . Riemondy et al . also used a variety of approaches to identify the molecules targeted by microRNA-203 in the skin , and reveal that it targets multiple signaling pathways , including components of the Ras pathway , to suppress cell proliferation . Together , these findings highlight microRNA-203 as a potential source of new treatments to prevent or slow tumor growth in humans .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology", "cancer", "biology" ]	2015	MicroRNA-203 represses selection and expansion of oncogenic Hras transformed tumor initiating cells
Most mammalian transcription factors ( TFs ) and cofactors occupy thousands of genomic sites and modulate the expression of large gene networks to implement their biological functions . In this study , we describe an exception to this paradigm . TRIM33 is identified here as a lineage dependency in B cell neoplasms and is shown to perform this essential function by associating with a single cis element . ChIP-seq analysis of TRIM33 in murine B cell leukemia revealed a preferential association with two lineage-specific enhancers that harbor an exceptional density of motifs recognized by the PU . 1 TF . TRIM33 is recruited to these elements by PU . 1 , yet acts to antagonize PU . 1 function . One of the PU . 1/TRIM33 co-occupied enhancers is upstream of the pro-apoptotic gene Bim , and deleting this enhancer renders TRIM33 dispensable for leukemia cell survival . These findings reveal an essential role for TRIM33 in preventing apoptosis in B lymphoblastic leukemia by interfering with enhancer-mediated Bim activation . DNA-binding transcription factors ( TFs ) typically recognize short 6–12 base-pair motifs , which exist at a high frequency in mammalian genomes ( Wunderlich and Mirny , 2009; Spitz and Furlong , 2012 ) . While chromatin restricts the accessibility of these sequences , TFs nonetheless occupy thousands of genomic sites in any given cell type ( Spitz and Furlong , 2012 ) . As examples , MyoD has been detected at ∼39 , 000 sites in primary myotubes and Pax5 at ∼20 , 000 sites in B cell progenitors ( Cao et al . , 2010; Revilla et al . , 2012 ) . Furthermore , transcriptional cofactors ( coactivators and corepressors ) are also known to occupy the mammalian genome in a generalized manner ( Ram et al . , 2011 ) . Thus , strong support exists for a model in mammalian cells in which transcriptional control is performed by regulators that exert broad , yet highly coordinated effects on gene expression ( Wunderlich and Mirny , 2009; Spitz and Furlong , 2012 ) . This is in stark contrast to prokaryotic species , in which TFs can occupy as few as one or two sites in an entire genome to implement precise transcriptional responses ( Martínez-Antonio and Collado-Vides , 2003; Wunderlich and Mirny , 2009 ) . Whether such a degree of gene-specificity exists among mammalian transcriptional regulators is currently unknown . TRIM33 , also known as TIF1γ , belongs to the tripartite motif ( TRIM ) -containing protein family of E3 ubiquitin ligases ( Venturini et al . , 1999; Hatakeyama , 2011; Herquel et al . , 2011 ) . TRIM33 and its homologs TRIM24 and TRIM28 belong to a subfamily of ubiquitously expressed TRIM proteins that contain a tandem plant homeodomain ( PHD ) and bromodomain module and function as transcriptional cofactors ( Venturini et al . , 1999 ) . One role of TRIM33 is in the TGFβ signaling pathway , which occurs through regulation of SMAD4 mono-ubiquitination and/or by functioning as a cofactor for the TFs SMAD2 and SMAD3 ( Dupont et al . , 2005 , 2009; He et al . , 2006; Agricola et al . , 2011 ) . TRIM33 is also known to interact with the TF SCL/TAL1 to promote transcription elongation of erythroid-specific genes , which may occur through the recruitment of FACT and P-TEFb complexes ( Bai et al . , 2010 , 2013 ) . However , another study suggests that TRIM33 can form repressive complexes with SCL to block transcriptional activation ( Kusy et al . , 2011 ) . The PHD-bromodomain module of TRIM33 interacts with covalently modified histone tails , which can further stabilize TRIM33 chromatin occupancy and stimulate its E3 ligase activity ( Tsai et al . , 2010; Agricola et al . , 2011; Xi et al . , 2011 ) . While several studies have linked TRIM33 to transcriptional control , a genomewide assessment of TRIM33 occupancy has yet to be performed to define the scope of its regulatory function . In an ongoing effort to identify roles for chromatin regulators as cancer dependencies , we carried out a negative selection RNAi screen in a cell line derived from a mouse model of high risk B cell acute lymphoblastic leukemia ( B-ALL ) , initiated by introducing the BCR-ABL oncogene into Arf-null bone marrow cells ( Williams et al . , 2006 ) . 1 , 126 shRNAs targeting 259 chromatin regulators were assessed individually for effects on the viability of B-ALL cells in culture , which led to the identification of 16 dependencies after applying stringent scoring criteria ( Figure 1A and Figure 1—figure supplement 1 ) . The majority of these factors had been identified in a prior screen that evaluated the requirement of chromatin regulators in MLL-AF9/NrasG12D acute myeloid leukemia ( AML ) ( Zuber et al . , 2011b ) , however TRIM33 was identified here as a unique requirement in B-ALL . The six TRIM33 shRNAs used in the screen exhibited a close correlation between knockdown efficiency and loss of B-ALL viability , suggesting on-target effects ( Figure 1A , B ) . Furthermore , a v-Abl transformed B cell progenitor line , 38B9 ( Alt et al . , 1984 ) , and several human B cell cancer lines were also sensitive to TRIM33 knockdown ( Figure 1C and Figure 1—figure supplement 1 ) . In contrast , TRIM33 knockdown led to negligible effects on the viability of MLL-AF9/NrasG12D AML and Notch-mutant T-cell acute lymphoblastic leukemia ( T-ALL ) ( Figure 1D , E ) . Upon knockdown of TRIM33 , B-ALL cells underwent apoptosis , as shown by Annexin V/DAPI staining ( Figure 1F ) . Taken together , these observations suggest that TRIM33 is essential for the survival of neoplastic B cells . 10 . 7554/eLife . 06377 . 003Figure 1 . RNAi screen identifies TRIM33 as a lineage dependency in cancers of B cell origin . ( A ) Negative selection shRNA screen targeting chromatin regulators in murine B cell acute lymphoblastic leukemia ( B-ALL ) . shRNAs are rank ordered by the fold-change in GFP positivity over 10 days in culture , which represents a competition-based assay in which loss of GFP positivity reflects shRNA-postive cells becoming outcompeted by shRNA-negative cells . ( B–E ) Competition-based assays and Western blotting to evaluate the effect of TRIM33 shRNAs on B-ALL , 38B9 , acute myeloid leukemia ( AML ) , or T-cell acute lymphoblastic leukemia ( T-ALL ) cells . GFP percentages are normalized to day 2 measurements . Results are the average of three biological replicates . ( F ) Annexin V/DAPI staining following transduction of B-ALL cells with the indicated MLS shRNAs on day 3 post-transduction . Representative experiment of three biological replicates is shown . ( G ) shRNA transgenic mouse strategy . TRE: tet ( doxycycline ) response element; rtTA-M2: reverse tet transactivator M2 variant ( tet-on ) . ( H ) Western blotting performed of indicated tissue lysates prepared from mice treated with dox for 4 weeks . Representative experiment of three biological replicates is shown . ( I–J ) Flow cytometry analysis using the indicated antibody stainings of whole bone marrow or spleen . B220 and Cd19: B lymphoid , Ter119: erythroid , Gr1 and Mac1: myeloid , Cd3: T lymphoid . Gating was performed on GFP+/shRNA+ cells prior to quantifying marker positivity . The GFP+ percentage in bone marrow was ∼75% and in the spleen was ∼15% . Mice were administered dox for 1 week or 4 weeks , with both timepoints giving similar results . Results shown are the average 4 or 5 mice . All error bars in this figure represent S . E . M . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 00310 . 7554/eLife . 06377 . 004Figure 1—figure supplement 1 . Hits identified in the shRNA screen and validation experiments in human cell lines . ( A ) B-ALL chromatin regulator dependencies identified in the shRNA screen . 68 shRNAs in the primary screen were depleted >10-fold over 10 days in culture . By setting a criteria of having at least 2 independent shRNAs targeting the same gene exhibiting >10-fold GFP depletion , we identified 16 dependencies , indicated here . An Rpa3 shRNA was included as a positive control . A Renilla luciferase shRNA was included as a negative control . Each horizontal bar represents an independent shRNA , except for Rpa3 and Renilla controls which are independent experiments with the same shRNA . Fold-depletion was capped at 50-fold for visualization purposes . Results were compared with prior screening results and validation experiments performed in MLL-AF9/NrasG12D AML to identify TRIM33 was a unique dependency in B-ALL ( Zuber et al . , 2011b ) . ( B–E ) Competition-based proliferation assays using the indicated TRIM33 or Ren shRNA expressing MLS-E vectors , which were transduced into the indicated human cell lines . All experiments shown represent the average of three biological replicates . Error bars represent S . E . M . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 00410 . 7554/eLife . 06377 . 005Figure 1—figure supplement 2 . Additional analysis of TRIM33 shRNA transgenic mice . ( A ) Immunohistochemical analysis of anti-TRIM33 antibody staining shTRIM33 or shRen adult mice ( heterozygous ) following 30 days of doxycycline administration . All mice were homozygous for the ROSA26-rtTA-M2 transgene . Counterstaining was performed with Hematoxylin . ( B ) Hematoxylin and Eosin staining of indicated tissues . ( C and D ) Analysis of the reversibility of the B cell phenotype in TRIM33 knockdown mice as compared to Renilla shRNA control . Mice were treated for 7 days ON dox or were treated with 7 days ON dox followed by 21 days OFF dox , as indicated . GFP gating was not performed for this analysis , since in the OFF dox condition the GFP reporter is extinguished . n = 4 or 5 . ( E ) Animal weight measurements following dox administration . Error bars represent S . E . M . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 005 To compare the TRIM33 requirement in B-ALL with its role in normal tissues , we derived transgenic mice in which a TRIM33 shRNA was expressed from a doxycycline ( dox ) -regulated promoter ( Figure 1G ) ( Premsrirut et al . , 2011 ) . When crossed with mice that express rtTA-M2 from the ROSA26 promoter , animals exhibited dox-dependent suppression of TRIM33 in multiple tissues ( Figure 1G , H and Figure 1—figure supplement 2 ) . Flow cytometry analysis of the bone marrow and spleen following 1 or 4 weeks of dox administration revealed a selective loss of CD19+ and B220+ B lymphoid cells in TRIM33-deficient animals , while erythroid , myeloid , and T lymphoid populations remained intact or even increased in relative abundance ( Figures 1I , J ) . Taking advantage of the reversibility of the dox-regulated shRNA system , we found that B cell production recovered fully in TRIM33-deficient mice upon restoring TRIM33 expression ( Figure 1—figure supplement 2 ) . Notably , TRIM33 knockdown led to negligible effects on the histological appearance of colon , liver , thymus , and pancreatic tissues and led to no significant changes in animal weight ( Figure 1—figure supplement 2 ) . Collectively , these findings suggest that B lymphoid cells are uniquely sensitive to TRIM33 inhibition , thus implicating TRIM33 as a lineage dependency in cancers of B cell origin . These results are in agreement with the prior characterization of Trim33−/− mice , which were found to exhibit a B lymphocyte deficiency and an expansion of myeloid cells ( Aucagne et al . , 2011; Kusy et al . , 2011; Bai et al . , 2013 ) . We next evaluated the mechanism underlying the essential TRIM33 function in B cell neoplasms . To this end , we performed RNA-seq analysis in B-ALL cells following 3 or 4 days of TRIM33 knockdown . This analysis revealed a distribution of gene expression changes , however , we noted that Bim and Atp1b3 were the two most upregulated genes upon TRIM33 depletion ( Figure 2A ) . To evaluate whether any of these mRNA changes were due to direct regulation , we performed ChIP-seq analysis in B-ALL cells to evaluate the genomic localization of TRIM33 and various histone modifications that annotate active promoter and enhancer regions . Remarkably , the two strongest sites of TRIM33 enrichment in B-ALL were located 117 kb upstream of Bim ( in an intron of a non-expressed gene Acoxl ) and at a site 35 kb upstream of Atp1b ( Figure 2B–D ) . The other gene expression changes incurred upon TRIM33 knockdown did not correlate with its genomic occupancy ( data not shown ) , suggesting they might be an indirect effect of B-ALL cells initiating an apoptotic response . The TRIM33-occupied regions upstream of Bim and Atp1b3 were enriched for H3K27 acetylation but not for H3K4 tri-methylation , suggesting that these elements are active enhancers ( Rada-Iglesias et al . , 2011 ) ( Figure 2C , D ) . We also observed TRIM33 occupancy at these same two regions in 38B9 , AML , and in whole spleen , but not in T-ALL ( Figure 2—figure supplements 1 , 2 ) . A striking attribute of the genomewide pattern of TRIM33 occupancy was its strong bias for a small number of locations , with lower levels of enrichment at other sites across the genome ( Figure 2E , F , and Figure 2—figure supplements 3 , 4 ) . This analysis suggests that TRIM33 is concentrated at a small number of sites in the B-ALL genome , with two of these regions correlating with a repressive effect on the expression of nearby Atp1b3 and Bim genes . 10 . 7554/eLife . 06377 . 006Figure 2 . TRIM33 preferentially associates with two lineage-specific enhancers in B lymphoblastic leukemia cells . ( A ) RNA-seq analysis of B-ALL cells transduced with shTRIM33 . 1271 . shRNA+/GFP+ cells were sorted on day 3 or 4 post-infection . Plotted is the average fold-change in mRNA level of two independent biological replicates . ( B ) Ranking of TRIM33 occupied sites based on average tag counts obtained from B-ALL ChIP-seq analysis . The 31 regions shown represent the significant reproducible peaks identified in two independent biological replicates . ( C–F ) B-ALL ChIP-seq occupancy profiles using the indicated antibodies . The y-axis reflects the number of cumulative tag counts in the vicinity of each region . Validated transcript models from the mm9 genome assembly are depicted below . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 00610 . 7554/eLife . 06377 . 007Figure 2—figure supplement 1 . ( A–B ) TRIM33 ChIP-seq occupancy profiles at the Bim locus ( A ) and the Atp1b3 locus ( B ) in the indicated cell types . Validated transcript models from the mm9 genome assembly are depicted below . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 00710 . 7554/eLife . 06377 . 008Figure 2—figure supplement 2 . Trim33 ChIP-qPCR analysis in various cell lines . ( A–B ) ChIP-qPCR validation of TRIM33 occupancy at the Bim or Atp1b3 loci in the indicated cell lines . qPCR amplicons were designed at the indicated locations of the Bim or Atp1b3 loci . Labels refer to kilobase distance relative to Bim or Atp1b3 transcriptional start site ( TSS ) . Plotted is the average of three biological replicates . Error bars denote S . E . M . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 00810 . 7554/eLife . 06377 . 009Figure 2—figure supplement 3 . ( A–D ) Comparison of two independent TRIM33 ChIP-seq biological replicates in B-ALL . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 00910 . 7554/eLife . 06377 . 010Figure 2—figure supplement 4 . TRIM33 ChIP-seq analysis in 38B9 , AML , and T-ALL . ( A ) Ranking of TRIM33 occupied sites based on average tag counts obtained from ChIP-seq analysis in the indicated cell lines . The regions shown represent the reproducible peaks identified in each of two independent biological replicates . The Bim-117 and Atp1b3-35 regions are as indicated in 38B9 and AML . In T-ALL we did not identify these regions as robust peaks and instead we labeled the top two outlier TRIM33 peaks in this cell type . ( B ) MEME-based motif analysis at 400 bp windows centered on TRIM33 occupied peaks shown in A . The distribution of motifs in this window is indicated on the right . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 010 We next pursued the mechanism underlying the profound accumulation of TRIM33 at the Bim –117 and Atp1b3 –35 regions in B-ALL . Using the FIMO motif analysis tool ( Grant et al . , 2011 ) , we found that both of these regions possessed a high density of sequence motifs recognized by PU . 1 ( 17 instances at Bim –117 and 14 instances at Atp1b3 –35 ) , which is an essential hematopoietic TF expressed in B lymphoid and myeloid lineages ( Scott et al . , 1994 ) . In contrast , we observed a much lower density of motifs for other TFs involved in B cell specific transcriptional regulation ( E2A , EBF1 , or PAX5 ) ( Figure 3A , B ) . Since TRIM33 has been shown previously to interact with PU . 1 ( Kusy et al . , 2011 ) , we investigated whether PU . 1 promotes TRIM33 recruitment to these regions . ChIP-seq analysis of PU . 1 in B-ALL confirmed its association with Bim –117 and Atp1b3 –35 , as well as with more than 2600 other genomic sites ( Figure 2C , D and Figure 3—figure supplement 1 ) . Remarkably , Bim –117 and Atp1b3 –35 regions are outliers when compared to other PU . 1-occupied sites with regard to the overall density of PU . 1 recognition motifs and the overall level of PU . 1 enrichment ( Figure 3C and Figure 3—figure supplement 1 ) . The level of the active enhancer chromatin mark , H3K27ac , is not skewed in a similar manner towards these two locations , indicating that these regions are not super-enhancers that exhibit high levels of all regulators ( Figure 3—figure supplement 1 ) ( Hnisz et al . , 2013 ) . Using ChIP-qPCR , we also confirmed PU . 1 occupancy at these two regions in 38B9 and AML cells , but not in T-ALL ( Figure 3—figure supplement 2 ) . 10 . 7554/eLife . 06377 . 011Figure 3 . TRIM33 is recruited by PU . 1 to select enhancers and antagonizes PU . 1 function to promote B-ALL survival . ( A ) Motif analysis using the FIMO/MEME algorithm . The table indicates the number of motif matches in each sequence interval . ( B ) Schematic diagram of PU . 1 motif ( MA0080 . 3 ) locations across the indicated cis elements . ( C ) Analysis of PU . 1 motifs ( MA0080 . 3 ) counts at all of the 2682 PU . 1 peaks identified by ChIP-seq . ( D–I ) ChIP-qPCR analysis at the indicated locations of the Bim locus in B-ALL . Labels refer to the kilobase distance relative to the Bim TSS . For TRIM33 knockdown experiments ( G–I ) , both shRen and shTRIM33 were introduced into B-ALL cells harboring a Bim shRNA . Plotted is the average of three biological replicates . ( J ) Competition-based assay evaluating effect of a PU . 1 shRNA on the sensitivity of B-ALL cells to TRIM33 knockdown . shRen or shPU1 linked to GFP were introduced first into B-ALL cells and then were subsequently transduced with shTRIM33 or shRen linked to mCherry . The GFP/mCherry double positive population was measured over time . Results are normalized to day 2 measurements . Plotted is the average of three biological replicates . All error bars in this figure represent S . E . M . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 01110 . 7554/eLife . 06377 . 012Figure 3—figure supplement 1 . ( A–B ) Ranked ordering of PU . 1 and H3K27 acetylation peaks identified using ChIP-seq analysis in B-ALL . Peaks were ranked based on sequence tag counts . Positions of Bim-117 and Atp1b3-35 are indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 01210 . 7554/eLife . 06377 . 013Figure 3—figure supplement 2 . ( A–B ) PU . 1 ChIP-qPCR analysis at the indicated locations of Bim ( A ) and Atp1b3 ( B ) loci in 38B9 , AML , and T-ALL . Labels refer to kilobase distance relative to TSS . Plotted is the average of three biological replicates . Error bars denote S . E . M . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 01310 . 7554/eLife . 06377 . 014Figure 3—figure supplement 3 . Additional PU . 1 knockdown experiments . ( A ) Validation of Pu . 1 shRNA by Western blotting . Experiment was performed in MLP-GFP transduced B-ALL cells following puromycin selection . ( B–C ) Effect of PU . 1 knockdown on TRIM33 occupancy at Atp1b3 . ChIP-qPCR analysis at the indicated locations of the Atp1b3 locus in B-ALL . Labels refer to kilobase distance relative to Atp1b3 TSS . Plotted is the average of three biological replicates . Error bars denote S . E . M . ( D ) Effect of PU . 1 knockdown on Bim expression . RNA was prepared from B-ALL cells transduced with indicated shRNAs and used for reverse transcription . Plotted is the average of three biological replicates . Results were normalized to Gapdh . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 01410 . 7554/eLife . 06377 . 015Figure 3—figure supplement 4 . Competition-based assay evaluating effect of a PU . 1 shRNA on the sensitivity of 38B9 cells to TRIM33 knockdown . shRen or shPU1 linked to GFP were introduced first into B-ALL cells and then were subsequently transduced with shTRIM33 or shRen . The GFP/mCherry double positive population was measured over time . Results are normalized to day 2 measurements . Plotted is the average of three biological replicates . All error bars in this figure represent S . E . M . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 015 These findings raised the possibility that an exceptional density of PU . 1 at these two sites might serve as the trigger for TRIM33 recruitment . To evaluate this , we used shRNAs to derive PU . 1-deficient B-ALL cells , which displayed normal proliferation and viability ( Figure 3—figure supplement 3 , data not shown ) . We found that PU . 1 knockdown led to a significant loss of TRIM33 occupancy at Bim and Atp1b3 in proportion to the loss of PU . 1 ( Figure 3D , E , and Figure 3—figure supplement 3 ) . PU . 1-deficient B-ALL cells also harbored reduced H3K27ac at Bim –117 and reduced Bim mRNA levels , suggesting that PU . 1 is responsible for maintaining this enhancer in an active state while , paradoxically , also facilitating TRIM33 recruitment ( Figure 3F and Figure 3—figure supplement 3 ) . Collectively , these results suggest that a high density of PU . 1 underlies the skewed distribution of TRIM33 genomic occupancy observed in B-ALL . Since PU . 1 and TRIM33 appear to have opposing effects on Bim expression ( Figure 2A and Figure 3—figure supplement 3 ) , we next evaluated how TRIM33 regulates the function of PU . 1 at their co-occupied sites . Interestingly , knockdown of TRIM33 led to an increase in PU . 1 occupancy and H3K27 acetylation at Bim –117 , suggesting that TRIM33 antagonizes PU . 1 binding to DNA and suppresses enhancer activity , consistent with prior findings ( Kusy et al . , 2011 ) ( Figure 3G–I ) . This result is significant , since Bim –117 is already heavily enriched for PU . 1 in B-ALL cells , but PU . 1 occupancy becomes even greater when TRIM33 is suppressed . Consistent with the functional importance of PU . 1 antagonism by TRIM33 , we found that knockdown of PU . 1 resulted in a reduced sensitivity of B-ALL and 38B9 cells to the lethal effects of TRIM33 knockdown ( Figure 3J and Figure 3—figure supplement 4 ) . Collectively , these observations suggest that TRIM33 antagonizes PU . 1-dependent enhancer activation as part of its essential function in B lymphoblastic leukemia . Bim encodes a BH3-only domain protein with a well-established pro-apoptotic function ( Willis and Adams , 2005 ) . Therefore , we considered whether repression of Bim is the essential role of TRIM33 in B-ALL . For this purpose we suppressed Bim using shRNAs and evaluated how this influenced the sensitivity of cells to TRIM33 knockdown ( Figure 4A , B ) . Notably , Bim-deficient B-ALL cells were completely resistant to the apoptotic effects of TRIM33 shRNAs , indicating that Bim is the critical downstream repression target of TRIM33 to maintain cell viability ( Figure 4B ) . In contrast , Atp1b3 knockdown did not alter the sensitivity of B-ALL to TRIM33 suppression ( data not shown ) . To further evaluate whether the essential function of TRIM33 is due to its occupancy at the −117 region , we employed CRISPR-Cas9 to delete this element ( Cong et al . , 2013; Mali et al . , 2013 ) ( Figure 4C , D ) . Similar to the effects observed upon Bim knockdown , three independent B-ALL clones harboring a homozygous deletion of the −117 element did not require TRIM33 for viability ( Figure 4E ) . Bim knockdown and the deletion of the Bim –117 region also rendered 38B9 cells resistant to the lethal effects of TRIM33 suppression ( Figure 4—figure supplement 1 ) . This indicates that a single genomic binding site at the Bim locus accounts for the entire TRIM33 requirement in preventing B-ALL apoptosis . 10 . 7554/eLife . 06377 . 016Figure 4 . TRIM33 occupancy at a single Bim enhancer accounts for its essential function in B-ALL . ( A ) Western blot performed on extracts prepared from B-ALL following transduction with the indicated shRNAs . Labeled are two of the known Bim isoforms . TRIM33 . 3905 shRNA was used . ( B ) Competition-based assay evaluating the effect of Bim knockdown on the sensitivity of B-ALL cells to TRIM33 knockdown . shRen or shBim linked to GFP were introduced first into B-ALL cells and then were subsequently transduced with shTRIM33 or shRen linked to mCherry . The GFP/mCherry double positive population was measured over time . Results are normalized to day 2 measurements . Plotted is the average of three biological replicates . ( C ) Experimental design for generating a homozygous deletion of the Bim –117 region using CRISPR-Cas9 . sgRNAs were designed to cut at locations flanking the TRIM33 binding site to delete the intervening 1 . 4 kb . Location of PCR primers used for genotyping are indicated . ( D ) Genotyping PCR to track the CRISPR-based deletion . Parental refers to Cas9+ B-ALL cells . +sgRNAs refers to Cas9+ B-ALL cells co-transduced with two sgRNAs targeting the Bim –117 element linked to mCherry reporters . Three clonal lines were derived by limiting dilution . ( E ) Competition-based assay evaluating the effect of the Bim –117 deletion on the sensitivity to TRIM33 knockdown . B-ALL cells were transduced with indicated shRNAs linked to GFP reporters . TRIM33 . 3905 shRNA was used . Results are normalized to day 2 measurements . Plotted is the average of three biological replicates . All error bars in this figure represent S . E . M . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 01610 . 7554/eLife . 06377 . 017Figure 4—figure supplement 1 . Additional experiments evaluating the role of Bim in 38B9 cells . ( A ) Competition-based assay evaluating the effect of Bim knockdown on the sensitivity of 38B9 cells to TRIM33 knockdown . shRen or shBim linked to GFP were introduced first into 38B9 cells and then were subsequently transduced with shTRIM33 or shRen linked to mCherry . The GFP/mCherry double positive population was measured over time . Results are normalized to day 2 measurements . Plotted is the average of three biological replicates . ( B ) Genotyping PCR to track the CRISPR-based deletion , as described in Figure 4D . Parental refers to Cas9+ 38B9 cells . +sgRNAs refers to Cas9+ 38B9 cells co-transduced with two sgRNAs targeting the Bim-117 element linked to mCherry reporters . Three clonal lines were derived by limiting dilution . ( C ) Competition-based assay evaluating the effect of the Bim-117 deletion on the sensitivity to TRIM33 knockdown . 38B9 cells were transduced with indicated shRNAs linked to GFP reporters . TRIM33 . 3905 shRNA was used . Results are normalized to day 2 measurements . Plotted is the average of three biological replicates . All error bars in this figure represent S . E . M . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 01710 . 7554/eLife . 06377 . 018Figure 4—figure supplement 2 . Additional experiments evaluating the role of TRIM33 in AML and T-ALL . ( A ) RT-qPCR analysis of Bim mRNA levels following TRIM33 shRNA transduction into the indicated cell lines . Plotted is the average of three biological replicates . Results were normalized to Gapdh . ( B ) ChIP-seq occupancy profile of H3K27 acetylation in the indicated cell lines . Arrows indicate the location of the Bim-117 and -66 regions ( see text ) . The H3K27ac ChIP-seq data from AML was derived from ( Shi et al . , 2013 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06377 . 018 While both B lymphoid and myeloid leukemia cells harbor PU . 1 and TRIM33 at the Bim –117 region , it is important to note that knockdown of TRIM33 in AML does not result in apoptosis or Bim upregulation ( Figure 1D and Figure 4—figure supplement 2 ) . ChIP-seq analysis of H3K27ac revealed that AML cells possess a large cluster of active enhancers located 66 kb upstream of Bim , which is not observed in B-ALL ( Figure 4—figure supplement 2 ) . Importantly , the level of H3K27ac at this AML-specific enhancer cluster is much greater than that observed at the −117 region . This leads us to speculate that the AML-specific enhancers at the −66 kb region might be the dominant regulatory elements at the Bim locus in this cell type . The overall strength of these enhancers in AML may diminish the functional significance of the −117 element , thereby rendering TRIM33 dispensable for AML survival . Here we provide evidence that TRIM33 prevents apoptosis in murine B-ALL cells by blocking enhancer-mediated Bim activation . The role of TRIM33 as a negative regulator of enhancer activity is reminiscent of LSD1 , a histone demethylase that has been shown previously to suppress active enhancers during embryonic stem ( ES ) cell differentiation ( Whyte et al . , 2012 ) . However , a key distinction we make here is that LSD1 suppresses thousands of enhancers in ES cells , while TRIM33 preferentially accumulates at a small number of regulatory elements in the B-ALL genome . We trace this unusual enhancer selectivity of TRIM33 in B-ALL to the density of PU . 1 , a TF which has been shown previously to associate with TRIM33 ( Kusy et al . , 2011 ) . This binding interaction is likely to be cooperative on motif-rich DNA elements , which would account for the biased accumulation of TRIM33 at enhancers harboring an exceptional PU . 1 density . Our ChIP-seq analysis of TRIM33 in myeloid , B lymphoid , and T lymphoid leukemias suggests that other TFs are also likely to recruit TRIM33 , such as TCF3/E2A ( Figure 2—figure supplement 4 ) . To our knowledge , TRIM33 is the first example of a mammalian transcriptional regulator that performs an essential function through a single genomic binding site . The selective occupancy of TRIM33 within the active enhancer landscape may account for the minimal phenotypes observed in TRIM33-deficient mice . This is in contrast to the effects of suppressing BRD4 , a more general cofactor that is essential for the maintenance of many cancer types and for the homeostasis of several normal tissues ( Bolden et al . , 2014; Shi and Vakoc , 2014 ) . Remarkably , it is the localization of TRIM33 to one lineage-specific enhancer upstream of Bim that renders B-ALL cells hypersensitive to loss of TRIM33 . The similar sensitivity of normal and transformed B cells to TRIM33 inhibition implies that the regulatory mechanisms defined here in B cell leukemia are likely to have evolved to control normal B cell development . Interestingly , Bim is upregulated as a means to eliminate auto-reactive B cells and to prevent autoimmune disease , suggesting a rationale for precise modulation of enhancer activity at the Bim locus by TRIM33 to regulate adaptive immunity ( Bouillet et al . , 1999; Enders et al . , 2003 ) . In humans , a reversible depletion of normal B cells is a well-tolerated side effect of the anti-CD20 antibody rituximab , which is an efficacious therapy in mature B cell neoplasms and in autoimmune disease ( Coiffier et al . , 2002; Edwards et al . , 2004 ) . Hence , our findings also implicate TRIM33 as a promising therapeutic target , since one of the major phenotypic abnormalities in mice experiencing systemic TRIM33 inhibition is a selective and reversible B lymphocyte deficiency . Targeting of TRIM33 is particularly attractive since other bromodomain modules ( e . g . , BRD4 ) have proven to be amenable to direct chemical inhibition ( Prinjha et al . , 2012 ) . However , we observe a modest expansion of myeloid cells upon short durations of TRIM33 suppression in vivo and Trim33−/− mice have been shown to develop chronic myelomonocytic leukemia ( Aucagne et al . , 2011 ) . Thus , long-term inhibition of TRIM33 might have pro-tumorigenic effects in certain tissues . Nonetheless , our findings justify consideration of TRIM33-inhibition as a therapeutic approach in B cell neoplasms , which would be expected to unleash an apoptosis-promoting enhancer element . For competition-based assays in murine cells , the LMN-GFP or LMN-mCherry shRNA retroviral vector were used ( MSCV-miR30-shRNA-PGKp-NeoR-IRES-GFP/mCherry ) . When drug selection was required , some experiments were performed using MLP-GFP ( MSCV-miR30-shRNA-PGKp-PuroR-IRES-GFP ) . For Annexin V experiments , the MLS vector was used ( MSCV-miR30-shRNA-SV40p-GFP ) to obtain a higher infection efficiency . For competition assays in human cell lines , the MLS-E vector was used ( MSCV-miRE-shRNA-SV40p-GFP ) ( Fellmann et al . , 2013 ) . All of the cloning procedures were performed using the In-Fusion cloning system ( #638909; Clontech , Mountain View , CA ) . Murine B-ALL ( driven by BCR-ABL and p19Arf inactivation ) ( Williams et al . , 2006 ) and 38B9 ( Alt et al . , 1984 ) were cultured in RPMI supplemented with 10% fetal bovine serum ( FBS ) , 1% penicillin/streptomycin , and 0 . 055 mM 2-Mercaptoethanol . Derivation of MLL-AF9/NrasG12D AML cells is described in Zuber et al . ( 2011a ) . The T-ALL cell line was provided by I Aifantis ( NYU ) and was derived from TAL transgenic/HEB heterozygous mice with an acquired truncation of Notch1 . Murine AML and T-ALL cells and human REH , SU-DHL , MM1S , and JURKAT cell lines were cultured in RPMI/10% FBS/1% penicillin/streptomycin . The retroviral and lentiviral packaging cells Ecotropic Plat-E cells ( Morita et al . , 2000 ) and HEK293T cells were cultured in DMEM supplemented with 10% FBS and 1% penicillin/streptomycin . All retroviral packaging was performed according to established protocols ( Morita et al . , 2000 ) . Virus was collected 48–72 hr post-transfection . A previously described LMN shRNA library targeting chromatin regulatory proteins was used ( Zuber et al . , 2011b ) . Additional shRNAs were included in the library to allow comprehensive targeting of all bromodomain-containing proteins . shRNA plasmids from this library were arrayed individually in 96 well plates at 50 ng/µl for retrovirus preparation using Ecotropic Plat-E cells as described ( Morita et al . , 2000 ) . Viral transduction of B-ALL cells was also performed in 96 well plates , followed by flow-cytometry-based measurement of GFP positivity using a Guava Easycyte instrument ( Millipore , Billerica , MA ) . Measurements were performed on day 2 and day 12 post-transduction . Renilla and Rpa3 control shRNAs were included with each 96 well plate as negative and positive controls , respectively . If an shRNA was introduced into B-ALL cells with a day 2 GFP percentage below 5% , this sample was discarded from the screen . The lower limit on GFP% measurements on day 12 was arbitrarily set at 0 . 2% . A single instance of Renilla and Rpa3 control shRNAs are included in the screen histogram shown in Figure 1A . The screen data can be found in Supplementary file 1 . To evaluate the effect of shRNAs on murine cell proliferation ( e . g . , in Figure 1A–E ) , cultures were retrovirally transduced with the indicated LMN-GFP shRNA vectors , followed by measurement of the GFP percentages over a time course post-infection using a Guava Easycyte flow cytometer . For double shRNA competition-based assays ( e . g . , Figures 3J , 4B ) , cells were first transduced with MLP-GFP ( Ren , Bim , or PU . 1 shRNA ) and then were selected with puromycin ( 1 μg/ml ) . Cells were subsequently transduced with LMN-mCherry ( Ren or TRIM33 shRNAs ) and double GFP/mCherry positivity was tracked using a BD LSR2 flow cytometer . For human competition assays in Figure 1—figure supplement 1 , the MLS-E vector was used and GFP was tracked using a Guava Easycyte instrument . shRNA sequences can be found in Supplementary file 2 . In order to quantify the cell death effect induced by Renilla or TRIM33 shRNAs , B-ALL cells were transduced at a high efficiency ( >95% GFP ) using the MLS-GFP vector . After 3 days post-infection , cells were spun down , washed with PBS and resuspended in 1× binding buffer and incubated with APC Annexin V antibody ( as described by manufacturer protocol , #550475; BD Pharmingen , San Diego , CA ) and DAPI , followed by BD LSRII flow cytometry analysis . GFP gating was not performed . The three independent TRIM33 shRNAs revealed similar results . The data analysis was performed using Flowjo software . All experimental mouse procedures were approved by the Cold Spring Harbor Animal Care and Use Committee . Renilla and TRIM33 shRNA targeted ES cells were derived by Mirimus ( Cold Spring Harbor , NY ) as described ( Premsrirut et al . , 2011; Dow et al . , 2012 ) , using site-specific integration of the shRNA cassette into the Col1a1 locus of KH2 ES cells ( Beard et al . , 2006 ) . 8–10 week old mice heterozygous for alleles of shRen . 713 or shTRIM33 and homozygous for ROSA26-rtTA-M2 alleles were treated with dox drinking water ( 2 mg/ml doxycycline and 2% sucrose ) and dox food ( 625 mg/kg ) for 7 or 28 days . Fluorescent markers ( eGFP for shRenilla and TurboGFP for shTRIM33 ) were indicators of shRNA-expressing cells following doxycycline treatment . The animal health and body weight were monitored during the time course of experiments . Primers used for genotyping can be found in Supplementary file 2 . To measure the impact of TRIM33 knockdown on hematopoietic lineages , bone marrow and spleen tissues were harvested from 8–10 week old transgenic mice following the administration of dox for 7 or 28 days . A single cell suspension of harvested samples was treated with ACK buffer ( 150 mM NH4Cl , 10 mM KHCO3 , 0 . 1 mM EDTA ) to lyse the red blood cells . The sample was then stained with hematopoietic lineage marker antibodies ( 1:200 ) in FACS buffer ( 1× PBS with 5% FBS and 0 . 05% NaN3 ) for 30 min and then subjected to BD LSRII flow cytometry analysis . Gating was performed on live cells ( FSC/SSC ) and on GFP+ cells prior to quantifying marker positivity . The GFP percentage in total bone marrow was approximately 75% on dox for shRen and shTRIM33 mice . The GFP percentage in the total spleen was approximately 15% for shTRIM33 and shRen mice on dox . This heterogeneity in GFP expression in these different tissues requires GFP gating to accurately define hematopoietic phenotypes using flow cytometry . The data analysis was performed using Flowjo software . To evaluate the reversibility of the TRIM33 knockdown phenotype in bone marrow B lymphoid cell , animals were placed on dox for 7 days and a second cohort was placed on dox for 7 days followed by removal of dox for 21 additional days . B220 and Cd19 marker staining was performed as described above , but the GFP marker was not gated upon since GFP expression is extinguished upon removing dox . This accounts for the weaker B cell phenotype in this 7 day dox treatment from the findings presented in Figure 1I , in which GFP gating was performed . For immunohistochemistry analysis of TRIM33 expression , transgenic mice were maintained on dox for 28 days . Mouse tissues were fixed overnight in 10% neutral buffered formalin and then washed with PBS . Samples were then processed using Shandon Excelsior Tissue processor and embedded in paraffin , 6 micron sections were then mounted onto VWR Superfrost Plus slides . For western blotting , whole cell lysates were prepared by direct lysis using SDS-PAGE sample buffer and about 50 , 000 cell equivalents were loaded into each lane . Samples were then separated by SDS-PAGE electrophoresis and transferred to nitrocellulose for detection using antibodies . For western blotting of mouse tissues , samples were harvested and lysed in RIPA buffer ( 25 mM Tris-HCl pH 7 . 6 , 150 mM NaCl , 1% NP-40 , 1% sodium deoxycholate and 0 . 1% SDS ) using a dounce homogenizer , then briefly sonicated and incubated on ice for 15 min . Pellets were spun down and the supernatant was collected and quantified using a Bradford assay . 20 μg of protein were loaded into each gel lane . Deparaffinization of samples was done with Xylene ( 2 × 10 min ) followed by ethanol rehydration ( 100%-2 × 5 min , 95% and 75% 2 min each ) and washed in distilled water . 3% Hydrogen Peroxide was used for blocking endogenous peroxidase , followed by another wash . Antigen retrieval was done with Citrate buffer in an Electric Pressure Cooker then washed in TBS . Samples were then blocked with 5%NHS , 1%BSA in TBS for 1 hr at room temperature and incubated with TRIM33 antibody ( 1:500/1:1000 ) overnight at 4°C . Followed by washing with TBS and incubated with Vector mp-7401 anti-rabbit secondary antibody for 30 min and then washed again . Slides were then incubated with Vector ImmPACT DAB , SK-4105 for 3 min and then rinsed in water . Counterstaining was performed with Hematoxylin and coverslipping was performed with Surgipath mounting medium prior to analysis . Cells were crosslinked with 1% formaldehyde for 20 min at room temperature and then quenched with 0 . 125 M glycine . Samples were sonicated and incubated with 5 μg of antibody overnight and then precipitated using Protein A Dynabeads ( cat #10002Dl; Life Technologies , Grand Island , NY ) . For TRIM33/PU . 1 ChIP in B-ALL , 50 million cells were used for each ChIP conditions with 5 μg antibody . For histone modification ChIP experiments in B-ALL , 20 million cells were used for each ChIP conditions with 2 μg antibody . For whole spleen ChIP , 6–8 week old mice were sacrificed and a suspension of 30 million splenocytes was prepared for ChIP with 5 μg antibody . ChIP-qPCR was performed on the reverse crosslinked DNA samples as previously described ( Steger et al . , 2008 ) . All of the results were quantified by qPCR using SYBR green ( ABI , Grand Island , NY ) on an ABI 7900HT . Each IP signal was quantified based on an input standard curve dilution series of the pre-immunoprecipitated genomic DNA ( IP/Input ) to normalize for the differences of total amount of cells subjected for ChIP and for the different amplification efficiencies of various primer sets . Total RNA was extracted from cells using Trizol reagent according to the manufacturer's instructions . Upon isolating RNA , DNase I was treated to eliminate contaminating genomic DNA . For cDNA synthesis , Q-Script cDNA SuperMix ( Quanta BioScience , Gaithersburg , MD ) was used . All results were quantified by qPCR performed using SYBR green ( ABI ) on an ABI 7900HT using the delta Ct method using Gapdh as a control gene . All ChIP and RT-qPCR primers used in this study can be found in Supplementary file 2 . ChIP DNA was purified using a QIAquick Gel Extraction Kit ( Qiagen , Valencia , CA ) and ChIP-Seq libraries were constructed using TruSeq ChIP Sample Prep Kit ( Illumina ) following the manufacturer's instructions . The quality of each library was determined by Bioanalyzer analysis using the High Sensitivity chip ( Agilent ) . Two independent biological replicates were performed for each ChIP-seq experiment . Barcoded libraries were sequenced in a multiplexed fashion with two to six libraries at equal molar ratio , with single end reads of 50 bases . For RNA-seq , total RNA was extracted using Trizol reagent ( Invitrogen , Carlsbad , CA ) . Libraries were constructed using the TruSeq sample Prep Kit V2 ( Illumina , San Diego , CA ) according to the manufacturer's instructions . DNA libraries were sequenced using an Illumina HiSeq 2000 platform . The obtained reads were trimmed into 28 base reads corresponding to 9th to 36th position from the 5′ ends of the reads . These reads were mapped to the mouse genome ( mm9 ) using Tophat software allowing no mismatch , then differentially expressed genes were analyzed by using Cuffdiff software . During this step , structural RNAs ( e . g . , ribosomal or mitochondrial RNA ) were masked . To calculate relative fold change to control , only genes with expression cutoff above 5 reads per million per kilobase ( RPKM ) and OK test status were considered . Average RPKM from two biological replicates of each control ( Ren ) and TRIM33 shRNA expressed samples was then used to calculate fold change with log2 scale . The sequence reads were of 36 or 50 bp in length and mapped to the reference murine genome assembly NCBI37/mm9 using Bowtie . Alignments were performed using the following criteria: -m1 , -v2 . To identify ChIP-Seq peaks , we used the MACS version 1 . 4 . 0 beta ( Model based Analysis of ChIP-Seq ) peak finding algorithm by using a p value threshold of enrichment of 1e-5 as a cut-off . For AML , 38B9 , and T-ALL , we also implemented a 10-fold IP/Input enrichment ratio as a filtering criteria . To identify reproducibly enriched regions of TRIM33 ChIP-Seq from two biological replicates , called peaks from MACS were compared and intersected . If the peaks showed at least 1 bp overlap between replicates , they were considered as reproducible . To calculate average tag counts from reproducible peaks , tag counts were normalized to total mapped reads , and further ranked by tag counts . All ChIP-seq and RNA-seq data from this study can be found at the GEO accession super-series GSE66234 . Sequence coordinates from TRIM33 peak calling using MACS were used to obtain sequences of Bim-117 and Atp1b3 regions . Analysis was performed at the FIMO/MEME website: http://meme . nbcr . net/meme/tools/fimo using a p-value output threshold of 1E-4 and motif file JASPAR_CORE_2014_VERTEBRATES . MEME from the Jaspar database ( Mathelier et al . , 2014 ) . For the unbiased discovery of motifs at TRIM33 enriched regions , 400 bp core sequences centered around reproducible TRIM33 peaks were submitted to the browser-based MEME-ChIP analysis ( http://meme . nbcr . net/meme/tools/meme-chip ) . The MSCV-hCas9-PGK-Puro construct was derived by cloning the N terminal 3× FLAG tag human-codon optimized Cas9 cDNA ( #49535; Addgene ) into the MSCV-PGK-Puro vector ( #634401; Clontech ) . The U6-sgRNA-EFS-mCherry vector was constructed using the lentiviral backbone from lentiCRISPR ( #49535; Addgene ) . All sgRNAs were designed using http://crispr . mit . edu/ and cloned into the U6-sgRNA-EFS-mCherry vector following published protocols ( Ran et al . , 2013 ) . For sgRNA lentivirus production , HEK293T cells were transfected with sgRNA:pVSVg:psPAX2 plasmids in a 4:2:3 ratio by using PEI reagent ( #23966; Polysciences , Warrington , PA ) following standard procedures . To generate CRISPR competent line , parental B-ALL and 38B9 cells were transduced with MSCV-hCas9-PGK-Puro construct followed by puromycin selection ( 1 μg/ml ) . To generate lines with a homozygous deletion of Bim –117 , two sgRNAs were designed to target flanking sequences of the −117 element and were retrovirally co-transduced into B-ALL or 38B9 cells , followed by isolation of clonal lines by limiting dilution . Genomic DNA was prepared from each clone using QiAamp DNA mini kit ( #51304; Qiagen ) following the manufacturer's instructions , followed by PCR-based screening for clones harboring a homozygous deletion , using primers that span the Bim enhancer region . The −117 deletion of individual clones was further verified by Sanger sequencing . sgRNA and genotyping primer sequences can be found in Supplementary file 2 . Anti-TRIM33 antibody ( A301-060A; Bethyl Laboratories , Montgomery , TX ) , Anti-PU . 1 antibody ( #2266; Cell Signaling , Beverly , MA or sc-352; Santa Cruz , Dallas , TX ) , Anti-Bim antibody ( #2819; Cell Signaling ) , Anti-H3K27ac antibody ( ab4729; Abcam , Cambridge , MA ) , Anti-H3K4me3 antibody ( 07-473; Millipore ) , Anti-ß-actin HRP antibody ( #A3854; Sigma , Ronkonkoma , NY ) , APC anti-mouse B220 ( #103212; BioLegend , San Diego , CA ) , APC anti-mouse CD-19 , APC anti-mouse Mac-1/Cd11b ( #101211; BioLegend ) , APC anti-mouse Ly-6G/Gr-1 ( #17-5931; eBioscience , San Diego , CA ) , APC anti-mouse TER-119 ( #116212; BioLegend ) , APC anti-mouse CD-3 ( #100209; BioLegend ) .	The DNA inside every cell in a human body is the same , and yet the activities that occur within different types of cells can vary greatly . White blood cells , for example , are different from skin cells or liver cells because different genes are active in each type of cell . Molecules called transcription factors and transcriptional cofactors associate with specific DNA sequences to control the activity of nearby genes . It is common for a single transcription factor or cofactor to bind to thousands of sites across the DNA of any cell . In humans , our immune systems protect us against infectious diseases and from malfunctioning cells that could become cancerous . White blood cells called B cells provide part of this immune defense . These cells help to identify invading bacteria and viruses , and can also develop into memory cells that help the immune system to rapidly recognize , respond to and eliminate a disease if it is re-encountered . Immature B cells—also known as B lymphoblasts—mature within bone marrow . If any problem occurs in a cell as it matures , that cell is usually programmed to self-destruct in a process called apoptosis . If these cells are not destroyed , they can accumulate in the bone marrow and prevent the production of other immune cells . This leads to a type of cancer called acute lymphoblastic leukemia . Wang et al . now reveal that TRIM33—a protein that B-lymphoid leukemia cells need to survive—is a transcriptional cofactor that prevents apoptosis . Furthermore , unlike other known transcription factors and cofactors in mammals , TRIM33 binds to an exceedingly small number of sites across the DNA of B cells . In fact , the cancer cell's dependency on the protein is due to TRIM33 associating with just a single binding site . The role of TRIM33 in B cell leukemia also has potential therapeutic implications . Although it is found in cells throughout the body , Wang et al . found that inhibiting TRIM33 in mice resulted in lower numbers of B cells being produced , but did not affect other tissues . Developing drugs that prevent TRIM33 from working could therefore provide new options for treating leukemia .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "chromosomes", "and", "gene", "expression", "short", "report" ]	2015	The transcriptional cofactor TRIM33 prevents apoptosis in B lymphoblastic leukemia by deactivating a single enhancer
We recently implemented a bioinformatics pipeline that can uncover novel , but rare , riboswitch candidates as well as other noncoding RNA structures in bacteria . A prominent candidate revealed by our initial search efforts was called the ‘thiS motif’ because of its frequent association with a gene coding for the ThiS protein , which delivers sulfur to form the thiazole moiety of the thiamin precursor HET-P . In the current report , we describe biochemical and genetic data demonstrating that thiS motif RNAs function as sensors of the thiamin precursor HMP-PP , which is fused with HET-P ultimately to form the final active coenzyme thiamin pyrophosphate ( TPP ) . HMP-PP riboswitches exhibit a distinctive architecture wherein an unusually small ligand-sensing aptamer is almost entirely embedded within an otherwise classic intrinsic transcription terminator stem . This arrangement yields remarkably compact genetic switches that bacteria use to tune the levels of thiamin precursors during the biosynthesis of this universally distributed coenzyme . Approximately 40 distinct riboswitch classes that regulate gene expression in various bacterial species have been experimentally validated to date ( McCown et al . , 2017; Serganov and Nudler , 2013; Sherwood and Henkin , 2016; Breaker , 2011 ) . Based on the abundances and distributions of these known riboswitch classes , it has been proposed that many thousands of additional riboswitch classes remain to be discovered in the eubacterial domain of life ( Ames and Breaker , 2010; Breaker , 2012; McCown et al . , 2017 ) . The collection of known riboswitch classes largely sense compounds or ions that are of fundamental importance to organisms from all three domains of life , and these ligands also exhibit a bias in favor of compounds ( enzyme cofactors , RNA nucleotides and their precursors or derivatives ) that are predicted to be of ancient origin ( Breaker , 2012; McCown et al . , 2017; Nelson and Breaker , 2017 ) . If these trends hold , it seems likely that numerous additional riboswitch classes that regulate fundamental biological processes remain to be discovered . Unfortunately , the vast majority of these undiscovered riboswitch classes are predicted to be exceedingly rare , and this characteristic is likely to cause difficulties for researchers who seek to identify them . To address this challenge , we developed a computational pipeline that first identifies the regions of bacterial genomes that are most likely to serve as transcription templates for structured noncoding RNAs ( ncRNAs ) , and then uses comparative sequence and structural analyses to identify novel candidate RNA motifs ( Stav et al . , 2019 ) . Specifically , this approach examines only the putative noncoding regions of a given sequenced bacterial genome , and evaluates each intergenic region ( IGR ) based on two parameters: ( i ) percent guanosine and cytidine ( GC ) nucleotide content and ( ii ) length in nucleotides . For many bacteria , structured ncRNAs are GC-rich compared to other regions of the genome ( Klein et al . , 2002; Schattner , 2002 ) , and the IGRs that serve as synthesis templates for these ncRNAs tend to be much longer than typical bacterial IGRs that contain only an RNA polymerase promoter and/or a protein-specific regulatory domain . Our current bioinformatics pipeline is based on earlier implementations of this search strategy that were used to discover several novel structured ncRNA motifs ( Meyer et al . , 2009 ) , including the SAM-V riboswitch class ( Poiata et al . , 2009 ) . Our updated computational pipeline was employed to comprehensively examine the genomes of five bacterial species , which revealed the existence of as many as 70 novel genetic elements , including 30 candidate ncRNA motifs ( Stav et al . , 2019 ) . Of the eight candidate riboswitch classes uncovered in this search , the most promising was called the ‘thiS motif’ ( Figure 1A ) because representatives most commonly reside immediately upstream of thiS genes , which code for a protein that delivers sulfur to the pathway for the production of the thiamin biosynthetic intermediate 5- ( 2-hydroxyethyl ) −4-methylthiazole phosphate ( HET-P ) ( Begley et al . , 2012 ) . Other genes associated with thiS motif RNAs appear to code for proteins that participate in the production of HET-P or its fusion to 4-amino-5-hydroxymethyl-2-methylpyrimidine diphosphate ( HMP-PP ) , to ultimately produce the bioactive coenzyme thiamin pyrophosphate ( TPP ) ( Jurgenson et al . , 2009 ) . An additional clue regarding the function of thiS motif RNAs was derived from the fact that this novel RNA structure occasionally resides in tandem with TPP riboswitches ( Stav et al . , 2019 ) . Tandem arrangements of other riboswitch classes have been shown to function as two-input Boolean logic gates ( Sudarsan et al . , 2006; Stoddard and Batey , 2006; Lee et al . , 2010; Sherlock et al . , 2018 ) , suggesting that the genes associated with tandem arrangements of TPP riboswitches and thiS motif RNAs likely respond to concentration changes of two distinct ligands . Together , these observations strongly indicate that thiS motif RNAs function as riboswitches that respond to a biochemical intermediate of the TPP biosynthetic pathway . Previously ( Stav et al . , 2019 ) , we created a riboswitch-reporter fusion construct by joining a thiS motif RNA representative to a β-galactosidase gene . Using this construct , we observed robust gene expression in host Bacillus subtilis cells with a deleted thiS gene ( ΔthiS ) . This same reporter-fusion construct yields no reporter gene expression in host B . subtilis cells that naturally carry a thiS gene . These findings were consistent with our hypothesis that thiS motif RNAs function as riboswitches , but the precise ligand sensed by the unusual architecture of this RNA remained unknown . In the current report , we describe a series of bioinformatic , genetic and biochemical analyses that provide conclusive evidence that the ligand for thiS motif RNAs is the TPP biosynthetic intermediate HMP-PP . Furthermore , our findings demonstrate that this unusually small riboswitch employs a distinct architecture to regulate RNA transcription termination . This regulatory RNA provides cells with an efficient mechanism to balance the production of two key biosynthetic intermediates , HMP-PP and HET-P , which are then fused to make the essential coenzyme TPP . Most experimentally validated riboswitch classes are composed of distinct , but partially overlapping aptamer and expression platform domains ( Barrick and Breaker , 2007; Breaker , 2012 ) . In contrast , the thiS motif exhibits an unusual arrangement wherein the predominant secondary structure , derived by thermodynamic modeling , is an extended hairpin structure that maximizes conventional Watson/Crick base pairing ( Figure 1A , left ) . This structure exhibits all the features characteristic of bacterial intrinsic terminator stems , including an uninterrupted and strong base-paired stem followed by a run of six or more uridine ( U ) residues ( Wilson and von Hippel , 1995; Yarnell and Roberts , 1999 ) . As a result , we concluded that transcription termination was certain to be a major function of thiS motif RNAs . We frequently encounter simple terminator stems when using bioinformatics search algorithms to identify novel ncRNA candidates , and we have now begun to assign predicted terminator function to such motifs and then quickly move on to examine other more promising ncRNA candidates . When evaluating the thiS candidate , however , we noted four features that suggested this terminator stem was peculiar . First , the loop of the terminator hairpin is abnormally well conserved compared to the loop sequences of more typical terminator stems , which are usually irrelevant to the mechanism of transcription termination . This unique terminator element is found in species from two phyla and from several classes within Firmicutes ( Supplementary file 1 , Supplementary file 2 ) . Second , another consensus structural model was also consistent with the comparative sequence analysis data ( Figure 1A , right ) . This architecture , including a possible pseudoknot and two major base-paired stems , disrupts the contiguous terminator stem near the run of U residues , suggesting that mutually exclusive and competing structures and functions might exist for thiS motif RNAs . Third , these apparently specialized terminator stems associate exclusively with genes related to the biosynthesis and utilization of HET-P , which is a precursor of the coenzyme TPP ( Figure 1B ) . These sequence , structure , and genomic distribution characteristics suggested to us that each thiS motif RNA might function as a compact ligand sensor and regulator of TPP coenzyme biosynthesis . A fourth feature of thiS motif RNAs is that approximately 30% of the known representatives reside immediately downstream of riboswitches that sense and respond to TPP ( Stav et al . , 2019 ) . Each associated TPP riboswitch appears to use a terminator stem as an expression platform . The tandem arrangement of two terminator stems for a single riboswitch aptamer would be unprecedented , and so this observation also supported our hypothesis that thiS motif RNAs represent an unusual form of riboswitch . Due to these tandem arrangements , we speculated that the natural ligand for this riboswitch candidate would not be TPP . Rather , it seemed more likely that the ligand would be a major precursor of TPP ( either HET-P or HMP-PP ) , and that each tandem TPP-thiS motif system would function as a two-input Boolean logic gate ( Sudarsan et al . , 2006; Stoddard and Batey , 2006; Lee et al . , 2010; Sherlock et al . , 2018 ) to regulate HET-P production in response to the cellular concentrations of both TPP and one of its biosynthetic precursors . Furthermore , if the two secondary structure states called the ‘terminator configuration’ and the ‘aptamer configuration’ ( Figure 1A ) represent the riboswitch ‘OFF’ and ‘ON’ configurations , respectively , then ligand binding by the aptamer in thiS motif RNAs is expected to activate gene expression . The vast majority of riboswitches that sense metabolites and control biosynthetic pathways are OFF switches , and thus the accumulation of the metabolite ligand results in reduced expression of the protein products that otherwise would make ( or import ) more of the desired metabolite . Although thiS motif representatives associate with genes for the production of HET-P ( Figure 1B ) , it does not make sense that HET-P would turn on its own production when it is already abundant . Finally , we recognized that the thiE gene ( coding for thiamin-phosphate synthase ) is occasionally associated with thiS motif representatives , suggesting that the RNA motif determines if cellular conditions are suitable to couple the two key precursors of thiamin monophosphate ( TMP ) to eventually yield TPP . Taken together , all these bioinformatic observations are consistent with the hypothesis that thiS motif RNAs function as ON riboswitches for the TPP precursor HMP-PP . As an initial test of our hypothesis that HMP-PP is the ligand for thiS motif RNAs , we employed a chromosomally-integrated reporter construct ( Stav et al . , 2019 ) carrying a β-galactosidase gene fused downstream of nucleotides encompassing the thiS motif from Clostridium species Maddingley ( Rosewarne et al . , 2013 ) ( Figure 2A ) . Transformed B . subtilis cells carrying a transcriptional fusion of the wild-type ( WT ) riboswitch to a lacZ reporter gene , which was integrated into the amyE locus , exhibited no β-galactosidase activity in response to HMP added to rich ( LB ) liquid culture media ( Figure 2B ) . Perhaps the natural suppression of TPP biosynthesis when cells have sufficient amounts of this coenzyme precludes the formation of excess HMP-PP in this surrogate organism . Specifically , if translation of the thiD gene coding for HMP/HMP-P kinase is largely suppressed under normal cellular conditions , the externally supplied HMP cannot be phosphorylated to generate HMP-PP . In contrast , robust β-galactosidase activity is evident in cells co-transformed with the reporter construct and a vector overexpressing the ThiD protein , but only when growth media is supplemented with HMP ( Figure 2B ) . Reporter gene expression levels are dependent on the concentration of HMP added to the culture medium ( Figure 2C ) , suggesting that cells take up HMP and use the thiD gene product to add two phosphates to generate excess HMP-PP , which activates lacZ reporter expression by binding to the riboswitch . Furthermore , the characteristics of a series of mutant riboswitch-reporter constructs examined in B . subtilis cells likewise indicates that thiS motif RNAs function as riboswitch aptamers for HMP-PP . Constructs carrying mutations M1 ( G26C ) or M2 ( C35G ) ( Figure 2A ) , which alter strictly-conserved nucleotides that are predicted to be part of the pseudoknot structure of the aptamer ( Figure 1A ) , are not activated by HMP addition ( Figure 2C ) . Importantly , construct M3 ( U46A , U47A , U48A ) exhibits a detectable level of reporter gene expression in LB media alone , and this expression is further enhanced by the addition of HMP . This result suggests that these nucleotides are involved in forming a strong terminator stem , but that the identities of the nucleotides at these positions are not critical for ligand binding by the putative HMP-PP aptamer . The WT riboswitch-reporter construct ( Figure 2A ) was also used to assess gene regulation in response to differences in growth media , and to several genetic disruptions of the TPP biosynthetic pathway ( Figure 2—figure supplement 1 ) . B . subtilis cells grown in LB medium are expected to suppress the genes needed to produce both the pyrimidine moiety HMP-PP and the thiazole moiety HET-P . Thus , as expected , expression of the reporter gene fused to the putative HMP-PP riboswitch is off , regardless of the genetic background tested . Likewise , WT cells grown in minimal ( GMM ) medium also do not express the reporter gene , presumably because HMP-PP does not accumulate due to its rapid and efficient conversion to the final product , TPP . By contrast , ΔthiS and ΔthiE cells ( carrying genetic disruptions of the thiS and thiE genes , respectively ) exhibit high levels of reporter gene expression ( Figure 2—figure supplement 1 ) . These two genetic knock-out strains are predicted to accumulate HMP-PP because they either lack the protein ( ThiS ) that initiates the production of HET-P , or they lack the protein ( ThiE ) that fuses HMP-PP to HET-P even when HET-P is available . Additional support for the hypothesis that HMP-PP is the ligand , rather than HMP-P , is provided by the level of expression of the reporter construct when present in host cells carrying a deletion of the gene ( ΔthiD ) required to phosphorylate HMP-P to make HMP-PP . Reporter gene expression is not observed in ΔthiD cells ( Figure 2—figure supplement 1C ) , despite the fact that these cells should accumulate HMP-P . Overall , these genetic results strongly indicate that HMP-PP is the ligand for a riboswitch class represented by thiS motif RNAs that turns on gene expression when this ligand is abundant . The two possible architectures of the thiS motif RNA from C . sp . Maddingley ( Figure 3A ) , also observed in other members of this candidate riboswitch class ( Figure 1A ) , suggest a mechanism for gene regulation involving the mutually exclusive formation of an intrinsic terminator stem and its competing ligand-bound aptamer state . Such transcription control mechanisms for riboswitches have been experimentally validated in the past ( e . g . McDaniel et al . , 2003; Mironov et al . , 2002; Sudarsan et al . , 2003; Wickiser et al . , 2005a ) by using single-round in vitro transcription assays ( Landick et al . , 1996 ) to reveal ligand-dependent modulation of RNA transcript lengths . Therefore , we sought to examine the proposed riboswitch mechanism by conducting in vitro transcription reactions with the expected natural ligand , HMP-PP . Unfortunately , our use of such transcription assays was made more difficult , because HMP-PP is not commercially available and is known to be relatively unstable ( Hanes et al . , 2007 ) . Therefore , we freshly generated HMP-PP from HMP and ATP enzymatically ( Figure 3B ) by using recombinantly produced ThiD protein . HMP-PP production as described previously ( Hanes et al . , 2007 ) was confirmed by mass spectrum analysis ( Figure 3C and Figure 3—figure supplement 1 ) . The enzymatic reactions were deproteinized by filtration , and the resulting mixtures containing HMP-PP were added , without further purification , to various assays as described for each experiment . The DNA template for the WT thiS motif RNA construct ( Figure 3A ) yields only ~14% full-length transcript when transcription reactions are conducted in the presence of HMP alone . In contrast , nearly 40% of the transcripts are full-length ( FL ) when transcription reactions are conducted in the presence of enzyme-prepared HMP-PP ( Figure 3D ) . Furthermore , mutations M1 and M2 , which disrupt possible pseudoknot formation by the aptamer and which eliminate gene expression in vivo ( Figure 2C ) , fail to produce more FL transcripts when HMP-PP is present . These results suggest that HMP-PP binding induces RNA polymerase to transcribe past the intrinsic terminator element to yield fewer terminated ( T ) products and a greater proportion of FL RNAs . From the assay data presented , this switching effect is incomplete in vitro , as evident by the fact that construct M4 , which carries three nucleotide changes that disrupt the otherwise perfect base pairing of the terminator stem , yields nearly 100% ‘FL’ transcripts . Construct M5 yields transcripts that approximate the length of ‘T’ RNAs , which are generated naturally by termination due to the action of the intrinsic terminator stem . Incomplete switching by terminator-regulating riboswitches is typical for such assays conducted in vitro ( e . g . McDaniel et al . , 2003; Mironov et al . , 2002; Sherlock et al . , 2018; Sudarsan et al . , 2003; Wickiser et al . , 2005a ) . However , it seems likely that a larger dynamic range for transcription control is exploited by this riboswitch in cells , as evident by the robust differences in gene expression for the nearly identical reporter constructs used in this study ( Figure 2 and Figure 2—figure supplement 1 ) . The unique architecture of thiS motif RNAs also served as an obstacle for evaluating the ability of these RNAs to directly bind a ligand . We frequently employ in-line probing assays ( Soukup and Breaker , 1999; Regulski and Breaker , 2008 ) to determine if RNAs undergo structural changes in response to ligand binding . For thiS motif RNAs , the terminator stem is expected to dominate over the aptamer configuration ( Figure 1A ) simply due to the different number of base-pairs present in each structural state . Therefore , under thermodynamic equilibrium conditions typically experienced by RNAs subjected to in-line probing reactions , a full-length construct is not expected to bind HMP-PP because the RNA will always favor the terminator configuration ( ‘OFF’ state ) . To address this problem , we reasoned that a shorter construct that weakens the terminator stem might permit the aptamer configuration to be adopted . Such constructs are also likely to better represent the structures naturally adopted by the riboswitch during transcription . An RNA polymerase paused at the run of U nucleotides at the end of the intrinsic terminator element will sequester ~12 nucleotides of the transcript within the protein structure ( Monforte et al . , 1990; Komissarova and Kashlev , 1998; Vassylyev et al . , 2002 ) . Therefore , only the first 50 to 54 nucleotides of the nascent transcript of the natural C . sp . Maddingley thiS motif should be exposed if the RNA polymerase complex is paused within the run of U nucleotides of the intrinsic terminator stem . A series of four thiS motif RNA constructs was prepared to investigate the hypothesis that shorter constructs might permit aptamer formation by weakening the terminator stem . The longest RNA construct carries the full terminator stem ( 66 thiS ) , and three progressively truncated variants ( 54 thiS , 53 thiS and 52 thiS ) represent only the RNA regions that would be exposed when RNA polymerase is stalled at various locations within the run of U nucleotides ( Figure 4A ) . In-line probing assays ( Figure 4B ) reveal that 66 thiS indeed exclusively forms the terminator stem , as evident by robust spontaneous RNA cleavage in the unstructured regions near the 5′ terminus through nucleotide position 9 , and by the unstructured loop region including nucleotides 35 through 39 . This loop region remains unstructured with all constructs tested , demonstrating that the upper base-paired portion is common to all structural configurations . Construct 54 thiS appears to retain all base-pairs of the terminator stem , except those that are disrupted by the deletion of the 3′-terminal nucleotides from position 54 and beyond ( Figure 4B ) . In stark contrast , construct 52 thiS adopts a configuration that matches the consensus model for the aptamer configuration , including the formation of stems P1 and P2 ( Figure 1A ) . Intriguingly , this shortest construct exhibits modest evidence ( Figure 4B , asterisk ) of a weakening of base-pairing within the series of G-U wobble interactions formed between nucleotides 24 to 27 and nucleotides 46 to 49 . This structural flexibility might permit the formation of a pseudoknot or some other tertiary interactions between these highly-conserved G nucleotides and pyrimidines in the loop region upon ligand binding . Also noteworthy is the fact that the intermediate length construct , 53 thiS , exhibits an in-line probing pattern consistent with the formation of both the terminator configuration and the aptamer configuration . These results suggest that the genetic decision whether to form the terminator stem configuration or the ligand-bound aptamer configuration is made within a very narrow window of transcription progression . Given the ability of the 52 thiS construct to exclusively adopt the aptamer configuration , we used this RNA to seek additional evidence for direct binding of HMP-PP by the riboswitch . However , this construct still failed to exhibit ligand-induced changes in the banding pattern resulting from in-line probing reactions . We speculate that nucleotide positions 24–27 and positions 46–49 form base-pairs that trap this portion of the motif in its terminator configuration . To further favor the formation of the desired aptamer configuration , the M3 version of the 52 thiS construct was made ( Figure 5A ) . The same M3 mutations , made in the context of the full-length construct , earlier were observed to retain ligand responsiveness in riboswitch-reporter assays in vivo ( Figure 2B ) . The 52 thiS M3 construct indeed exhibits structural modulation upon introduction of HMP-PP ( Figure 5B ) . Although the main base-paired regions P1 and P2 remain unchanged by HMP-PP addition , nucleotides involved in forming the putative pseudoknot ( sites 1 and 2 ) , and other nucleotides in the loop of P2 ( site 3 ) appear to become more structured . Additional mutations were introduced into construct 52 thiS M3 to evaluate the effects of mutations known or expected to disrupt gene control function . Specifically , previous mutations M1 or M2 ( Figure 2A ) , or M6 ( Figure 5—figure supplement 1A ) were introduced to create constructs , termed M7 , M8 and M9 , which carry alterations to highly-conserved nucleotides that presumably disrupt pseudoknot formation . These additional mutations eliminate structural modulation by HMP-PP ( Figure 5—figure supplement 1B ) , as would be expected if these highly-conserved sequence and structural features are critical for riboswitch aptamer function . We suspect that the ligand concentration is insufficient to saturate all RNAs in the sample . Regardless , by quantifying the band intensities at the three sites of structural modulation and by assuming that these values could become zero ( or background ) upon ligand binding , we generated a partial HMP-PP binding curve for 52 thiS M3 ( Figure 5C ) , which is consistent with a 1-to-1 interaction between the ligand and the RNA aptamer . Given our use of enzymatically prepared HMP-PP samples , we cannot use this data to precisely determine the dissociation constant ( KD ) for this interaction . However , if we assume that all HMP was enzymatically converted into HMP-PP , this product was fully recovered after removal of protein by filtration , and that there was no degradation over the time frame of the assays , the KD value cannot be greater than 500 μM for this variant aptamer construct . A series of shortened constructs carrying the M3 mutations ( Figure 5—figure supplement 2A ) recapitulate the transition between the terminator and aptamer configurations as originally observed for the series of WT truncated RNAs ( Figure 4 ) . Importantly , the 53 thiS M3 construct exhibits evidence of both binding to HMP-PP and of structural switching to favor the aptamer configuration ( ‘ON’ state ) ( Figure 5—figure supplement 2B ) . This finding suggests that it is necessary to prevent the formation of G-U wobble base-pairs between nucleotide positions 25–27 and positions 46–48 for ligand binding to be observed by using in-line probing assays . The time scale of in-line probing reactions ( 24 to 48 hr ) is much longer than the time scale for the natural genetic decision to take place ( probably a few seconds ) . Therefore our biochemical assays permit the RNAs to reach thermodynamic equilibrium ( Wickiser et al . , 2005a; Wickiser et al . , 2005b ) , which should favor the terminator configuration . By mutating the U nucleotides at positions 46–48 , we prevent constructs from becoming thermodynamically trapped in the terminator configuration during in-line probing , which permits HMP-PP binding to be observed . This same effect could be achieved with natural thiS motif sequences by having the U nucleotides at positions 46–48 remain sequestered in the RNA-exit channel ( Vassylyev et al . , 2002; Hein et al . , 2014 ) of an RNA polymerase that is paused within the run of U nucleotides of the intrinsic terminator stem . We speculate that momentary ligand binding during this paused state will be sufficient to prevent terminator formation on the vastly shorter time scale that is relevant to the genetic decision process in cells . Overall , our biochemical data are strongly consistent with the hypothesis that thiS motif RNAs function as riboswitches that directly bind to HMP-PP . As a result , we favor renaming thiS motif RNAs as HMP-PP riboswitches . The pursuit of more precise biochemical and biophysical characteristics , and further evidence for the mechanism proposed above , will require both the judicious use of RNA constructs that form the aptamer configuration , and either the preparation of pure samples of HMP-PP with consideration for its relative instability or the use of more stable analogs that can trigger riboswitch function . As noted above , approximately 30% of the representatives of HMP-PP riboswitches reside in tandem with TPP riboswitches . In these tandem systems , the TPP riboswitch always occurs first , and the TPP aptamer is always associated with its own expression platform , which is routinely a readily recognizable intrinsic terminator stem . The HMP-PP riboswitch follows each complete TPP riboswitch , and by its unique architecture , carries its own intrinsic terminator expression platform . This arrangement , as observed in a representative from the bacterium Clostridium lundense ( Figure 6A ) , makes apparent the genetic decisions made by the host . Specifically , abundant TPP should repress expression of the associated gene , which usually codes for a protein needed to biosynthesize HET-P ( Figure 1B ) . This repression , regardless of the concentration of HMP-PP , makes sense because cells do not need to make more TPP when this enzyme cofactor is already abundant . However , when TPP is in short supply , transcripts should read through the first intrinsic terminator stem to begin transcription of the HMP-PP riboswitch . The HMP-PP aptamer only permits transcription read-through of its co-resident intrinsic terminator stem if the relative abundance of its ligand is high . If HMP-PP is abundant , then the resulting full-length mRNAs will produce proteins that biosynthesize more HET-P . This two-input tandem riboswitch system has a truth table that matches a ‘converse nonimplication’ Boolean logic function ( Figure 6B ) . To confirm that a tandem TPP and HMP-PP riboswitch system functions with this Boolean logic , we created a tandem riboswitch-reporter fusion construct based on the natural C . lundense RNA ( Figure 6A ) , and examined its function in B . subtilis cells . Unfortunately , we cannot easily create cellular conditions to fully examine all four possible ligand states represented in the truth table ( Figure 6B ) . Most obviously , TPP is an essential cofactor , and thus we cannot deplete its concentration to zero while maintaining cell viability . Also , the various TPP biosynthetic intermediates are likely to be present in low concentrations if the cell is continuously making more TPP , even at low flux . As a result , a cell might experience conditions where an individual nascent RNA transcript of the tandem riboswitch system could respond to any of the four possible states , but these responses when summed over multiple nascent transcripts will yield a hybrid gene expression response that generally reflects the state of ligands in the cell . To fully examine the gene expression outputs for all four possible states , despite the complications described above , we only partially manipulated the state of ligands , while also creating artificial representations of the missing states by using riboswitch aptamer mutations . Specifically , by adding HMP to the culture medium , we could change the ligand state from state 2 ( +TPP , ‒ HMP-PP ) to state 4 ( +TPP , +HMP-PP ) . As predicted by the truth table , ligand states 2 and 4 do not yield expression of the reporter gene ( Figure 6C ) . We also artificially created states 1 ( ‒ TPP , ‒ HMP-PP ) and 3 ( ‒ TPP , +HMP-PP ) by employing disruptive mutations in the aptamers for TPP ( M10 ) , HMP-PP ( M1 ) , or both ( M11 ) . Only the artificial state 3 condition , as created by mutating the TPP aptamer and growing cells in the presence of HMP , yields robust reporter gene expression ( Figure 6C , M10 ) . This gene expression effect is lost when disabling mutations are placed in both aptamers ( M11 ) to simulate state 1 . These findings are consistent with our hypothesis that each tandem TPP and HMP-PP riboswitch system functions as a Boolean converse nonimplication logic gate . All of our findings derived from bioinformatic , genetic and biochemical analyses indicate that thiS motif RNAs represent an unusual class of riboswitches that sense and respond to the thiamin pyrophosphate biosynthetic intermediate HMP-PP . Its architecture is unusual because the majority of the nucleotides and structures required for ligand recognition appear to be encompassed by an intrinsic transcription terminator , which forms in a mutually-exclusive manner to regulate gene expression . All other riboswitch classes reported to date ( McCown et al . , 2017 ) that employ an intrinsic terminator structure as an expression platform carry the most-highly conserved nucleotides and structural features largely upstream of , and sometimes only partly overlapping , the terminator structure . This unique overlapping architecture of HMP-PP riboswitches provides bacteria with a highly-compact , yet effective RNA-based gene control system for measuring HMP-PP concentrations and tuning the production of HET-P to more efficiently produce TPP . The embedded nature of the aptamer and expression platform domains , however , complicated the pursuit of experiments typically used to biochemically validate a newly discovered riboswitch class . Most importantly , our experiments required the use of non-native constructs to successfully reveal HMP-PP binding in vitro . Therefore , it is notable that modifications to certain key constructs maintain function in vitro or in vivo . For example , the trimming of nucleotides at the 3′ region of the terminator stem to create RNA construct 52 thiS yields a molecule that is expected to represent the exposed portion of a polymerase-paused nascent RNA transcript ( Figure 4A ) . This truncated construct adopts the predicted P1 stem of the aptamer configuration ( Figure 4B ) . Also , the addition of nucleotide changes to create the M3 variant of 52 thiS yields a construct that binds HMP-PP , whereas this same M3 mutation in a related construct retains ligand-dependent gene-control function in cells ( Figure 2C ) . Our results also reveal a mechanism for gene control involving the stalling of RNA polymerase at a precise location to yield an exposed nascent RNA transcript that is transiently capable of forming an HMP-PP binding pocket . The 52 thiS construct predominantly exists in the aptamer configuration , which is distinct from even modestly longer forms that partially ( 53 thiS ) or exclusively ( 54 thiS ) adopt the terminator configuration ( Figure 4 ) . If HMP-PP is present and docks in the aptamer when the nascent mRNA transcript is exposed up to nucleotide position 52 , terminator stem formation is precluded and transcription proceeds to generate the full-length RNA . Forward movement of RNA polymerase by even a single nucleotide yields a construct that begins to commit to the terminator stem structure , and at this point transcription would be terminated regardless of the presence of HMP-PP . The thiS motif was originally identified as a riboswitch candidate by using an updated computational pipeline ( Stav et al . , 2019 ) that focuses attention on relatively long , GC-rich IGRs . This method of riboswitch discovery was developed to improve our ability to uncover rare and/or small riboswitch candidates . Thousands of riboswitch classes are predicted to remain undiscovered in bacteria ( Ames and Breaker , 2010; McCown et al . , 2017 ) , and many of these are likely to be exceedingly rare and/or difficult to find because of their structural simplicity . Experimental demonstration that thiS motif RNAs are representatives of a compact riboswitch class that responds to HMP-PP provides initial confirmation that the updated computational pipeline can uncover riboswitch classes that have resisted discovery by other bioinformatic or genetic methods . We are hopeful that many more riboswitch classes will be revealed by employing such bioinformatics search algorithms on additional sequenced bacterial genomes . Finally , we note that the discovery of an HMP-PP riboswitch provides yet another example of a modern RNA class that selectively binds a molecule that was likely present in an RNA World ( Benner et al . , 1989; Gilbert , 1986 ) . Many of the common enzyme cofactors are derived from RNA nucleotides or their precursors , including TPP and its precursor HMP-PP , which is a characteristic previously used to support the hypothesis that these molecules predate proteins ( White , 1976 ) . TPP and many other RNA-derived coenzymes might have participated in a complex metabolic state run entirely by RNA enzymes and receptors , and therefore many riboswitches for coenzymes likewise might be molecular descendants from RNAs that utilized these coenzymes long ago ( Breaker , 2012; McCown et al . , 2017 ) . If true , HMP-PP riboswitches provide another opportunity to learn how simple RNA sequences and structures could have selectively bound important nucleotide-derived metabolites in primitive organisms of the RNA World . Chemicals were purchased from Sigma-Aldrich with the exception of 4-amino-5-hydroxymethyl-2-methylpyrimidine , also known as ( 4-amino-2-methylpyrimidin-5-yl ) methanol , which was purchased from Enamine Ltd . The radiolabeled molecules [γ-32P]-ATP and [α-32P]-ATP were purchased from PerkinElmer . All enzymes were purchased from New England BioLabs unless otherwise specified . Synthetic DNA oligonucleotides were purchased from Sigma-Aldrich or Integrated DNA Technologies . A list of oligonucleotides used in this study can be found in Supplementary file 3 . BL21 ( DE3 ) E . coli cells were purchased from New England Biolabs and transformed with the appropriate plasmid for overproduction of the ThiD protein , whose enzymatic function ultimately was confirmed by monitoring the production of HMP-PP by mass spec analysis . The parent B . subtilis 168 ( BGSC 1A1 ) strain , and the corresponding mutant strains ΔthiS ( BGSC 11680 ) , ΔthiE ( BGSC 38290 ) and ΔthiD ( BGSC 11710 ) were obtained from the Bacillus Genetic Stock Center ( BGSC ) at The Ohio State University , and genetically modified as described herein . All bacterial strains were verified by testing for the expected growth , antibiotic resistance , and reporter gene expression phenotypes . Representative thiS motif RNAs used to create the consensus sequence and structure models ( Figure 1A ) were identified using Infernal 1 . 1 ( Nawrocki and Eddy , 2013 ) from RefSeq version 80 and certain environmental microbial databases as described previously ( Weinberg et al . , 2017 ) . A total of 400 unique and complete representatives were used to generate an updated consensus model relative to that published previously ( Stav et al . , 2019 ) . The consensus sequence and structural models were derived by using the R2R algorithm ( Weinberg and Breaker , 2011 ) , which employs weighting . To prevent irrelevant differences between the aptamer and terminator configurations caused by weighting , the consensus model of the aptamer configuration was used to annotate the consensus sequence for the terminator configuration for the overlapping region . Riboswitch-reporter constructs , integrating riboswitch representatives from either C . sp Maddingley or C . lundense DSM 17049 , were prepared as synthetic oligonucleotides , amplified by PCR and cloned into vector pDG1661 upstream of the E . coli lacZ gene as described previously ( Sudarsan et al . , 2003; Nelson et al . , 2017 ) . Transcription initiation of the constructs is driven by the B . subtilis lysC gene promoter . The resulting WT and mutant reporter constructs were integrated into the amyE locus of WT ( 1A1 strain 168 Δtrp ) or thiamin biosynthetic knockout strains ( ΔthiS , ΔthiE or ΔthiD ) as indicated . The resulting transformed strains were verified as previously described ( Sherlock et al . , 2018 ) . The thiD gene construct was generated by amplifying this gene from B . subtilis genomic DNA by PCR and inserted into the StuI site of a modified pDG148 vector using ligation-independent cloning as described previously ( Joseph et al . , 2001 ) . The lacI gene in this vector has been mutated so that the thiD gene is expected to give constitutive expression . The resulting protein expression vector was then transformed into B . subtilis strains containing WT or mutant riboswitch reporter constructs , as indicated for each experiment . Riboswitch-reporter assays were performed by inoculating various B . subtilis strains into Lysogeny Broth ( LB ) with appropriate antibiotics and growing overnight at 37°C . For liquid-culture reporter assays with thiamin biosynthetic knock-out strains , overnight cultures grown in LB were then diluted 1/20 into Spizizen glucose minimal medium ( GMM ) ( Anagnostopoulos and Spizizen , 1961 ) and grown overnight at 37°C . The residual thiamin from LB is sufficient for growth in GMM over the duration of the assay . For riboswitch reporter experiments with ThiD-producing strains , bacteria were diluted directly into LB and grown overnight with or without supplementation with HMP . Liquid media ( LB or GMM ) was supplemented with X-gal ( 200 μg mL−1 ) to allow visual detection of reporter gene expression . Similarly , reporter expression analysis using 4-methylumbelliferyl β-D-galactopyranoside was conducted as described previously ( Nelson et al . , 2015; Atilho et al . , 2019 ) to establish fluorescence units . An N-terminal 6xHis-tagged thiD gene from Salmonella typhimurium was cloned into a pETDuet vector , which was then transformed into E . coli strain BL21 ( DE3 ) . Transformed cells were grown in Terrific Broth medium until the OD600 reached 0 . 8 . The resulting culture was incubated overnight at 16°C for protein expression that was induced by the addition of 0 . 3 mM isopropyl β-D-1-thiogalactopyranoside . Cells were pelleted , resuspended in Buffer A [50 mM Tris ( pH 8 at 23°C ) , 400 mM NaCl , 10 mM imidazole , 5% glycerol , 0 . 1 mM tris ( 2-carboxyethyl ) phosphine ( TCEP ) ] , and lysed with a microfluidizer . The resulting lysate was clarified via centrifugation , applied to NiNTA resin , and washed with 10 column volumes of Buffer A . Tagged protein was eluted from the column with three column volumes of Buffer B [50 mM Tris ( pH 8 at 23°C ) , 400 mM NaCl , 400 mM imidazole , 5% glycerol , 0 . 1 mM TCEP] and applied to a size-exclusion column equilibrated in Buffer C [50 mM Tris ( pH 8 at 23°C ) , 150 mM NaCl , 0 . 1 mM TCEP] . 10% Glycerol ( v/v , final concentration ) was added to the protein sample before storage at −80°C . Pyrophosphorylation of HMP was carried out enzymatically as described previously ( Hanes et al . , 2007 ) . Briefly , the reaction was initiated by adding 10 μL of Salmonella typhimurium HMP-P kinase ( ThiD , 20 mg/mL ) to a 90 μL reaction preparation to yield a final concentration of 5 mM HMP , 20 mM ATP , 50 mM Tris-HCl buffer ( pH 7 . 5 at 23°C ) , 2 mM TCEP , and 5 mM MgCl2 . This 100 μL reaction mixture was allowed to incubate at 23°C overnight , at which time the protein was removed by ultrafiltration using an Amicon Ultra-0 . 5 centrifugal filter unit with a 3 kDa cutoff membrane . The HMP-PP generated in this manner was used without further purification , typically on the same day but no later than 2 weeks after production . HMP-PP stock solutions were stored at −20°C . Control assays for transcription termination and in-line probing conducted without the addition of enzymatically prepared HMP-PP contained an equivalent amount of the enzymatic reaction preparation wherein the ThiD protein was excluded . These control assays therefore contain HMP , whereas test reactions contain HMP-PP . Enzymatically prepared HMP-PP samples were sent to the MS and Proteomics Resource at Yale University for analysis . The presence of HMP-PP was confirmed by data ( Figure 3C—figure supplement 1 ) generated using a Thermo Scientific LTQ Orbitrap ELITE mass spectrometer . Data was acquired and analyzed with Xcalibur ( v2 . 1 ) . Peaks were considered to have the same mass-to-charge ratio as HMP-PP if they were within 10 ppm of the calculated ratio . The protocol used for single-round in vitro transcription assays was adapted from that described previously ( Landick et al . , 1996 ) . DNA constructs were designed to include the promoter sequence of the lysC gene from B . subtilis , the riboswitch aptamer , and the expression platform of the thiS gene from C . sp Maddignly to 33 nucleotides following the terminator stem . Additional non-native nucleotides were added to the 5′ region upstream of the HMP-PP aptamer to increase the amount of [α-32P]-ATP incorporation . To assemble each in vitro transcription reaction , approximately 2 pmol of the purified , PCR amplified DNA template was added to a transcription initiation mixture [final concentration of 20 mM Tris ( pH 8 . 0 at 23°C ) , 20 mM NaCl , 14 mM MgCl2 , 100 µM EDTA , 10 µg mL−1 bovine serum albumin , 130 µM ApA dinucleotide , 1% glycerol , 0 . 04 U µL−1 E . coli RNA polymerase holoenzyme , 2 . 5 µM GTP , 2 . 5 µM UTP , and 1 µM ATP] . Approximately 8 µCi [α-32P]-ATP was added to the 90 µL transcription reaction and transcription was allowed to proceed at 37°C for 30 min , leading to formation of a stalled polymerase complex before the first cytidine of each transcript . The reaction mixture was then distributed in 8 µL aliquots into separate microfuge tubes , which contained 1 µL of an HMP or HMP-PP solution plus 1 µL of 10x elongation buffer ( 200 mM Tris [pH 8 . 0 at 23°C] , 200 mM NaCl , 140 mM MgCl2 , 1 mM EDTA , 1 mg mL−1 heparin , 1 . 5 mM each of ATP , GTP , and CTP , and 0 . 5 mM UTP ) . For transcription termination assays , the maximum concentration of HMP-PP is 1 . 5 mM , assuming 100% enzymatic conversion of HMP and no loss to instability . However , HMP-PP concentration is likely substantially lower due to incomplete conversion of HMP to HMP-PP ( Figure 3—figure supplement 1 ) . Transcription elongation was allowed to proceed for 45 min at 37°C . The transcription products were separated by denaturing ( 8 M urea ) 10% polyacrylamide gel electrophoresis ( PAGE ) then imaged and quantified using a Typhoon Phosphorimager and ImageQuaNT software . The fraction of full length ( FL ) and terminated ( T ) RNA transcripts was calculated by measuring band intensity values and using the equation Fraction FL = ( FL intensity ) / ( FL intensity +T intensity ) . The differences in specific activities between the FL and T products due to [α-32P]-ATP incorporation were considered negligible . RNAs were prepared by in vitro transcription using DNA oligonucleotides containing a T7 RNA polymerase promoter sequence upstream of the desired template sequence . The resulting desired RNA transcripts were purified , enzymatically 5′ 32P-labeled , and repurified as previously described ( Mirihana Arachchilage et al . , 2018; Atilho et al . , 2019 ) . In-line probing assays ( Soukup and Breaker , 1999; Regulski and Breaker , 2008 ) were performed precisely as described previously ( Atilho et al . , 2019; Mirihana Arachchilage et al . , 2018 ) . The maximum HMP-PP concentration ( max ) is achieved by using a 3/10 dilution of enzymatically prepared HMP-PP as described above . The control reaction was performed using the 1/10 dilution of the control sample that lacks HMP-PP .	Many bacteria use small genetic devices called riboswitches to sense molecules that are essential for life and regulate the genes necessary to make , break or move these molecules . Riboswitches are made of molecules of RNA and appear to have ancient origins that predate the evolution of bacteria and other lifeforms made of cells . Inside modern bacteria , chunks of DNA in the genome provide the instructions to make riboswitches and around 40 different types of riboswitch have been identified so far . However , it has been proposed that the instructions for thousands more riboswitches may remain hidden in the DNA of bacteria . All of the currently known riboswitches contain a region called an aptamer that binds to a target molecule . This binding causes another structure in the riboswitch RNA to switch a specific gene on or off . For example , the aptamer binding might cause a hairpin-like structure called a terminator to form , which stops a gene being used to make new RNA molecules . In 2019 a team of researchers reported using a computational approach to identify new riboswitches in bacteria . This approach identified many different chunks of DNA that might code for a riboswitch , including one known as the thiS motif . This potential new riboswitch appeared to be associated with a gene that encodes a protein required to make a vitamin called thiamin ( also known as vitamin B1 ) . To test the new computational approach , Atilho et al . including several of the researchers involved in the earlier work used genetic and biochemical techniques to study the thiS motif . The experiments revealed that the motif binds to a molecule called HMP-PP , which bacteria use to make thiamin . Unexpectedly , the aptamer of the riboswitch was nested within a terminator , rather than being a separate entity . The findings of Atilho et al . reveal that riboswitches can be even more compact than previously thought . Furthermore , these findings reveal new insights into how bacteria use riboswitches to manage their vitamin levels . In the future it may be possible to develop drugs that target such riboswitches to starve bacteria of these essential molecules .	[ "Abstract", "Introduction", "Results", "and", "discussion", "Discussion", "Materials", "and", "methods" ]	[ "biochemistry", "and", "chemical", "biology" ]	2019	A bacterial riboswitch class for the thiamin precursor HMP-PP employs a terminator-embedded aptamer
Human vestibular sensory epithelia in explant culture were incubated in gentamicin to ablate hair cells . Subsequent transduction of supporting cells with ATOH1 using an Ad-2 viral vector resulted in generation of highly significant numbers of cells expressing the hair cell marker protein myosin VIIa . Cells expressing myosin VIIa were also generated after blocking the Notch signalling pathway with TAPI-1 but less efficiently . Transcriptomic analysis following ATOH1 transduction confirmed up-regulation of 335 putative hair cell marker genes , including several downstream targets of ATOH1 . Morphological analysis revealed numerous cells bearing dense clusters of microvilli at the apical surfaces which showed some hair cell-like characteristics confirming a degree of conversion of supporting cells . However , no cells bore organised hair bundles and several expected hair cell markers genes were not expressed suggesting incomplete differentiation . Nevertheless , the results show a potential to induce conversion of supporting cells in the vestibular sensory tissues of humans . Loss of the sensory ‘hair’ cells from the cochlea , the mammalian hearing organ , as a consequence of exposure to ototoxic drugs , excessive noise or through ageing , results in permanent hearing loss . More than 40% of those aged over 50% and 70% of those over 70 have a clinically significant hearing loss ( Action on Hearing Loss; www . actiononhearingloss . org . uk/your-hearing/about-deafness-and-hearing-loss/statistics . aspx ) . Hearing loss has also been reported as a risk factor for dementia ( Livingston et al . , 2017 ) . Loss of hair cells from the vestibular epithelia of the inner ear , results in balance dysfunction causing dizziness and vertigo , significantly under-appreciated disabling conditions . As with hearing loss , the prevalence of vestibular dysfunction increases with age . Dizziness is the most common reason for visits to the GP in those over 75% and 80% of unexplained falls in the elderly are attributable to vestibular dysfunction ( Agrawal et al . , 2009; Baloh et al . , 2001; Department of Health , 1999; Herdman et al . , 2000; Pothula et al . , 2004 ) . Regeneration of hair cells could potentially offer a therapeutic approach to amelioriate these conditions . In the sensory epithelia of the inner ear in all vertebrates each hair cell is surrounded and separated from its neighbours by intervening supporting cells . Hair cells derive their name from the organised bundle of projections from the apical poles . They are mechanotransducers that convert motion into electrical signals . Supporting cells play a role in maintaining the physiological environment necessary for hair cell function and survival , and also repair the lesions in the epithelium when hair cells die . In non-mammalian vertebrates , hair cells lost from the auditory or vestibular sensory epithelia are replaced spontaneously by new ones ( Collado et al . , 2008; Rubel et al . , 2013 ) . These nascent hair cells are derived from supporting cells . Initially , new hair cells arise from direct , non-mitotic transdifferentiation ( phenotypic conversion ) of supporting cells into hair cells ( Cafaro et al . , 2007; Taylor and Forge , 2005 ) . Other supporting cells re-enter the cell cycle , the daughter cells giving rise to hair and supporting cells ( Burns and Stone , 2017; Cafaro et al . , 2007; Collado et al . , 2008; Rubel et al . , 2013 ) . There is no regeneration in the adult mammalian auditory system . However , there is a limited capacity to regenerate hair cells in vivo in the mammalian vestibular system ( Forge et al . , 1993; 1998; Lopez et al . , 1997; Kawamoto et al . , 2009 ) . These hair cells arise by direct phenotypic conversion of supporting cells ( Li and Forge , 1997; Lin et al . , 2011 ) . We recently reported the presence of cells bearing immature hair bundles in the vestibular system of elderly people ( Taylor et al . , 2015 ) . This suggests that a capacity to regenerate hair cells may exist at a very low level throughout life in humans . During development , supporting cells and hair cells are derived from the same homogeneous population of precursor cells following a terminal mitotic event ( Kelley , 2006 ) . The mosaic patterning of hair and supporting cells develops by lateral inhibition mediated by the Notch signalling pathway ( Kiernan , 2013 ) . In cells differentiating as hair cells the basic helix-loop-helix transcription factor Atonal homolog 1 ( Atoh1 ) is transiently expressed and has been shown to be necessary for sensory precursors to differentiate into hair cells ( Bermingham et al . , 1999; Kelley , 2006; Woods et al . , 2004 ) . The nascent hair cells express the Notch ligand Delta1 on their surface which activates the Notch receptor , a transmembrane protein , in adjacent neighbouring cells . The inhibitory activity of Notch prevents the adjacent cell following the same fate , so this cell will not become a hair cell but instead will be a supporting cell . Ligand binding to the Notch receptor triggers the extracellular cleavage of Notch by tumour necrosis factor alpha converting enzyme ( TACE ) ( Kopan and Ilagan , 2009 ) and intracellular cleavage by γ-secretase ( Kiernan , 2013 ) . This releases the Notch intracellular domain which enters the nucleus interacting with several transcription factors to suppress Atoh1 expression thereby inhibiting differentiation as a hair cell and promoting HES/HEY expression , propelling those cells to become supporting cells . Atoh1 is expressed during hair cell regeneration in chick and zebrafish and several studies have shown that ectopic overexpression of Atoh1 in the organ of Corti or vestibular sensory epithelia of mammals is sufficient to induce generation of cells that express hair cell marker proteins ( Zheng and Gao , 2000 ) . These arise by direct transdifferentiation without an intervening mitotic event . Overexpressing Atoh1 or using γ- secretase inhibitors , potentially offer means to induce supporting cells to become hair cells . Both these approaches have been applied to murine vestibular epithelia depleted of hair cells ( Lin et al . , 2011; Staecker et al . , 2007 ) . We have established a consortium of surgeons throughout the UK to harvest human vestibular epithelia from translabyrinthine operations for the removal of acoustic neuromas ( vestibular schwannomas ) ( Taylor et al . , 2015 ) . Here we use the vestibular epithelium collected from such surgeries to examine the capacity of supporting cells to generate new hair cells in adult human inner ear tissue . We have used an adenoviral vector to deliver ATOH1 ( adV2- ATOH ) to transduce cells in human sensory epithelia from which hair cells have been ablated with the ototoxic agent , gentamicin . We find that significant numbers of cells expressing hair cell markers can be generated . We have also exposed tissue to the γ- secretase inhibitor TAPI1 ( TNFα protease inhibitor 1 ) , a TACE inhibitor . Cells expressing hair cell marker proteins are also generated but in fewer numbers that with ATOH1 transduction . However , neither protocol resulted in fully differentiated hair cells . Studies of gene expression following ATOH1 transduction by RNA sequencing confirmed that a significant cascade of downstream effectors is induced by this treatment . However , this induction falls short of complete hair cell conversion but highlights components that may be necessary in completing the conversion events . In utricles fixed and processed immediately following harvesting , hair cells , labelled for myosin VIIa , a hair cell marker , were present across the entire epithelium ( Figure 1A ) , but , as reported previously ( Taylor et al . , 2015 ) , the density of hair cells varied and in some samples , particularly those from older individuals , there were very few . SEM showed the characteristic hair bundles of vestibular hair cells with stereocilia of graded height but there was considerable variability in the morphology of the bundles and the height of the longest stereocilia in each bundle ( Figure 1B ) . Short microvilli covered the apical surfaces of supporting cells . Thin sections of untreated utricles revealed a bilayer of cells with rounded hair cell nuclei in the more apical region closer to the luminal surface , while the more irregularly shaped supporting cell nuclei were located close to the underlying basement membrane ( Figure 1C , D ) . Hair cells survived in vitro in most untreated samples maintained in explant culture for 28 days although in some cultures incubated for this period , condensed , mis-shapen remnants also labelled positively for myosin VIIa suggesting a possible incipient deterioration in the cultures by this time ( Figure 1D ) . This defined a period of 21–22 days for an optimal total time of incubation in subsequent experiments , a period of sufficient length to cover that over which spontaneous regeneration of hair cells occurs in the vestibular organs in vivo in guinea pigs ( Forge et al . , 1993 , 1998 ) , chinchillas ( Lopez et al . , 1997 ) and mice ( Kawamoto et al . , 2009 ) . To ablate hair cells we exposed utricles to the ototoxic aminoglycoside antibiotic gentamicin . At 24 hr following 48 hr exposure to 2 mM gentamicin , few intact hair cells remained . Apoptotic death of hair cells in the body of the epithelium was evident by positive immunolabelling for activated caspase 3 ( not shown ) and in thin sections for TEM by pyknotic nuclei or marginated chromatin , with apoptotic bodies inside supporting cells ( Figure 2A , B ) . Some hair cells were also seen to be extruded from the epithelium ( Figure 2C ) . Both apoptosis , with apoptotic bodies phagocytosed by supporting cells , and extrusion of hair cells from the epithelium have been observed to occur in the vestibular sensory epithelia in vivo in animals treated with aminoglycoside ( Li et al . , 1995 ) . With loss of hair cells , supporting cells closed the lesions ( Figure 2D ) . The number of hair cells in cultures treated with gentamicin ( N = 16 ) was assessed by counting myoVIIa positive cells in two groups: an early timepoint , 2–4 days post-gentamicin ( dpg ) ( N = 5 ) ; and a late timepoint , 11–21 dpg ( N = 11 ) . At the earlier stage there was a mean of 5 . 26 ± 1 . 48 per 10000 µm2 . The hair cell bodies that persisted in the first few days after gentamicin exposure were scattered across the epithelium ( Figure 2E ) . They were always rounded in shape and most contained an actin-rich rod-like inclusion structure ( Figure 2E , F , G ) indicative of pathology; they are reminiscent of the ‘cytocaud’ observed in damaged guinea pig vestibular hair cells ( Kanzaki et al . , 2002 ) , and in mice appear in damaged hair cells destined for phagocytosis by supporting cells ( Bucks et al . , 2017 ) . In tissue cultured for longer periods , up to 21 days post gentamicin treatment , there were very few hair cells ( Figure 2H , ca . 1 . 4 ± 0 . 31 per 10000 µm2 ) . The surfaces of almost all cells across the epithelium were of similar appearance ( Figure 2I ) , with no surface projections or other structural specialisations , except for dispersed short microvilli , and with a polygonal outline , features characteristic of supporting cells following loss of hair cells . There was no evidence of spontaneous regeneration of hair cells . Thus , the prolonged high dose exposure to gentamicin ablated the majority of hair cells resulting in an epithelium composed predominantly of supporting cells . To promote hair cell generation by expression of exogenous ATOH1 transduction we used a second generation multiply-depleted replication-incompetent adenoviral vector , Ad2 , previously shown to transduce human hair cells and supporting cells in vitro ( Kesser et al . , 2007 ) . The vector carried the genes for green fluorescent protein ( GFP ) and atonal homologue 1 ( ATOH1 ) independently driven by CMV promoters . After ablation of hair cells by incubation with gentamicin , utricles ( and some cristae ) were thoroughly rinsed with medium and incubated for up to 24 hr with the viral vector in serum-free medium . Expression of GFP at 4 days after transduction showed that supporting cells in the human tissue could be efficiently transduced , and delineated variable shapes of supporting cells revealing some with thin basally directed projections ( Figure 3A ) . Labelling for SOX2 , which is expressed by supporting cells , showed that GFP was co-expressed in cells with nuclei positively labelled for SOX2 ( Figure 3B ) . Expression of ATOH1 could be detected by immunolabelling in some cells expressing GFP both in utricles ( Figure 3C ) and in cristae ( Figure 3D ) , but not all GFP expressing cells also expressed ATOH1 . Likewise , myosin VIIa was also expressed in some cells expressing GFP but not others ( Figure 3E ) . By 17 days after incubation with the virus ( tissue cultured for a total of 20 days ) many myosin VIIa-positive cells were apparent ( Figure 3F ) . These myosin VIIA-positive cells were often tightly packed together , contacting each other in some regions of the tissue ( Figure 3G ) and of variable shapes , rarely rounded but often elongated and some with thin basally directed projections ( Figure 3H . ) Only some cells expressed both GFP and myosin VIIA ( Figure 3G , H ) but in many that did , GFP was less strongly expressed than in cells expressing GFP alone ( Figure 3G ) , perhaps suggesting that GFP may be downregulated over time as cells differentiate . The number of cells expressing myoVIIa ( 14 . 54 ± 3 . 68 , N = 7 ) was significantly greater than the number in control tissue from which hair cells had been ablated and then maintained for an equivalent period of time without further treatment ( Figure 4 ) . Studies in birds and mammals have shown that replacement hair cells can be derived from the supporting cells that remain after hair cell loss through inhibition of the Notch-signalling pathway ( Daudet et al . , 2009; Lin et al . , 2011; Warchol et al . , 2017 ) . To test the hypothesis that hair cell regeneration in human inner ear tissue could be induced by inhibition of Notch signalling , we maintained gentamicin treated cultures ( N = 7 ) in TAPI-1 for 18 days . TAPI-1 is a potent small molecule inhibitor of matrix metalloproteinases and TACE ( TNF-α convertase/ADAM17 ) . Myosin VIIA+ cells were quantified and numbers compared with damaged utricles cultured for a similar duration ( 3 . 85 ± 1 . 2 vs 1 . 39 ± 0 . 3 , respectively; Figure 4 ) . Statistical analyses revealed a significant difference between gentamicin-only treated tissue and samples subsequently maintained with TAPI-1 ( p<0 . 05 ) but also that the number of myosinVIIA positive cells following incubation with TAPI1 was significantly less than that generated by ATOH1 transduction . To test whether we could further enhance the generation of ‘hair cells’ seen in tissue transduced with ATOH1 , we exposed damaged utricles ( N = 6 ) to Ad2-ATOH1-GFP and then maintained them in medium with TAPI-1 . Unexpectedly , the number of myoVIIA+ cells was lower than tissue transduced with Ad2-ATOH1-GFP and maintained in medium alone ( 8 . 39 ± 02 . 65 ) . However , there were more myosin VIIA+ cells than found in damaged cultures exposed to TAPI-1 alone . Overall , the number of myoVIIa positive cells in all three treatment groups ( ATOH1 transduced; TAPI-1 alone; and ATOH1 transduced +TAPI-1 ) was statistically significantly greater than that in gentamicin-only treated utricles ( Figure 4 ) providing evidence that supporting cells can be manipulated to generate hair cell-like cells in human inner ear sensory epithelia . There was no significant difference in the mean ages of the donors in each treatment group and the age ranges were similar ( control: mean 58 . 1 , range 36–81; ATOH1 transduced: mean 55 . 7 , range 41–67; TAPI1: mean 46 . 8 , range 32–64; ATOH1 +TAPI: mean 46 . 7; range 20–71 ) . This indicated that age is unlikely to be a factor underlying the difference between treatment regimes in the number of myosin VIIa cells generated . In explants exposed to ATOH1 , either with or without TAPI1 , following the ablation of hair cells with gentamicin , SEM ( 3 samples of each condition ) showed the apical surface of many cells across the epithelium had bushy microvillar projections that were noticeably longer than the microvilli of neighbouring cells ( Figure 5A ) . Some cells had a central protrusion within the cluster of microvilli , reminiscent of the position of the kinocilium seen in immature hair bundles during development of the sensory epithelia and hair cell regeneration ( Denman-Johnson and Forge , 1999; Forge et al . , 1998; Tilney et al . , 1992a; Tilney et al . , 1992b ) ( Figure 5B , C ) . The projections were present on cells that expressed GFP and labeled for myosin VIIa and were composed of actin ( Figure 5D ) , but no cells showed organized hair bundles , and in whole mount samples prepared for immunolabelling , very little labelling for espin , a known actin bundling protein expressed along the length of stereocilia ( Zheng et al . , 2000 ) , could be detected . In thin sections , cells with prominent elongated microvilli were identified . They were also evident in samples that had first been examined by SEM ( two samples ) ( Figure 5D , E ) as well in samples which had been embedded after immunolabelling for myosin VIIa ( two samples ) ( Figure 5F , G ) . These cells showed some features resembling hair cells: they were generally cylindrical in shape with a rounded nucleus located towards the luminal pole and , when their profiles were followed through the serial sections , they were not in contact with the basement membrane , although some cells had thinner elongated basal projections similar to that seen with myosin VIIA labelling and expected of a cell converting from a supporting cell ( Li and Forge , 1997; Taylor and Forge , 2005 ) . However , these cells did not possess cuticular plates - the actin meshwork that forms a platform beneath the stereocilia in differentiating hair cells - nor were there any synaptic specializations such as synaptic ribbons ( Taylor et al . , 2015 ) . Thus , it appeared that while cells expressing hair cells markers could be generated , those cells did not differentiate fully as hair cells . To investigate alterations in gene expression after Ad2-ATOH1-GFP transduction of human supporting cells , we measured transcriptome changes by RNA-seq . We compared gentamicin-treated tissue transduced with Ad2-ATOH1-GFP cultured for 5 days , versus control tissues only treated with gentamicin ( cultured for 2 days , 8 days and 14–18 days ) . Three separate comparisons were performed between the Ad2-ATOH1-GFP transduced tissues and the three different controls to limit the variability between control samples and derive a consistent set of gene expression changes . Based upon this stringent comparison , a total of 494 genes were significantly differentially expressed ( −2 >= fold change >= 2 and p<0 . 05 across all three comparisons ) . The expression of 441 out of these 494 genes exhibited the same direction of change in all three comparisons . Of these , 53 were down-regulated by ATOH1 transduction whereas 388 were upregulated . ATOH1 was among the most significantly overexpressed genes in tissues transduced with ATOH1 ( 70 to 286-fold < p < 0 . 05 ) . Within the 441 significantly differentially expressed genes we were interested in identifying key regulatory genes that showed consistent changes in gene expression . Including ATOH1 , a total of 18 transcription factors ( TFs ) and five known chromatin modifiers ( CMs , one gene SATB2 has both activities ) fall into this class . These are shown in Table 1 . To validate results from the RNA-sequencing , quantitative RT-PCR was conducted on five of the genes listed in Table 1 . These were CITED , IRF9 , SNAI1 , EP300 and HDAC9 . In all cases the trends for fold-changes were the same as in RNA-seq and in most cases they were very close in actual values ( Figure 6 ) . It is interesting to note from Table 1 that only four of the TFs and all of the CMs exhibit downregulation after ATOH1 transduction . The vast majority of the TFs are upregulated . Among the upregulated TF genes , six are of particular note ( ATOH1 , POU4F3 , RAX , ZNF837 , GTF2IRD2 AND MEF2B ) since they are induced from close to zero to a significant level of transcript abundance ( abundance levels are shown in Table 1 ) . The remaining TFs appear to already be present at significant abundance levels in gentamicin-treated controls . 19 putative HC markers ( ACTC1 , ATOH1 , CCDC60 , CES2 , ENO2 , EPS8L2 , HIST3H2A , JAG2 , OBSCN , ODF3B , PKN3 , POU4F3 , RABL2A , RAX , SYTL1 , SYT7 , TAS1R1 , UBXN11 , UNC5A ) and six putative downstream targets of ATOH1 ( EPS8L2 , JAG2 , OBSCN , PKN3 , POU4F3 , UNC5A ) ( Cai et al . , 2015 ) , plus 4 markers of HC bundles ( EPS8L2 , GPI , PFKL , UBA7 ) ( Shin et al . , 2013 ) show significant upregulation in the ATOH1-treated samples . MYO7A , is also overexpressed in ATOH1-transduced tissues compared to all three controls but did not reach statistical significance . These markers of HC maturation are listed in Supplementary file 1 . A large number of additional established hair cell markers ( >150 ) exhibit consistent upregulation in all three comparative analyses , but failed to pass the statistical threshold of significance ( see Supplementary file 1 ) . Overall , of the 1375 putative HC markers we searched for within the upregulated gene expression dataset , a total of 335 were detectable and upregulated . These data support the contention that ATOH1 upregulation is driving towards an incomplete program of HC differentiation . Enrichment analysis using ToppGene Suite ( Chen et al . , 2009 ) showed that GO biological processes such as muscle filament sliding and muscle system process were significantly enriched ( FDR < 0 . 01 ) . These processes included actin , myosin and troponin genes which suggests that hair cell bundles might be forming in the ATOH1-transduced tissues . However , most of these genes encoded relatively low abundance transcripts within the total dataset . Interestingly , there was also a significant enrichment of genes with TF binding sites for TCF3 and MEF2A/B transcription factors ( FDR < 0 . 01 ) . TCF3 is a known ATOH1 co-factor ( Masuda et al . , 2012 ) and is overexpressed in the ATOH1-transduced tissues compared to all three controls but failed to reach statistical significance . MEF2A is not differentially expressed within our data but , as noted above , MEF2B is induced three-fold from a basal level in ATOH1-transduced tissues . These may represent possible potentiators of ATOH1 function . This unbiased transcriptomic analysis further supports the premise that manipulation of cell fate with gene modification enhances the conversion of supporting cells to a transcriptional signature that overlaps with known markers of the hair cell phenotype . Here we demonstrate the plasticity of the human vestibular epithelia via manipulation of developmental pathways using a viral vector to transduce supporting cells . The capacity of this vector to incorporate ATOH1 into sufficient supporting cells and subsequently yield large numbers of myosin VIIa+ cells supports our contention that it is possible to regenerate damaged epithelium and offers a therapeutic intervention to balance disorders caused by hair cell loss . Human vestibular tissue was obtained as previously described ( Taylor et al . , 2015 ) . Briefly , utricular maculae ( utricles ) and sometimes cristae were collected anonymously following informed consent from patients undergoing excision of vestibular schwannoma ( acoustic neuromas ) via a trans-labyrinthine approach . Excised tissue was transported from participating hospitals in medium used for long term culture . Each explant culture was incubated at 37°C in a 5% CO2 atmosphere in one well of a 24 well plate , free floating in 0 . 5 ml Minimal Essential Medium ( MEM ) with glutamax ( Gibco ) , 1% HEPES ( N-2-hydroxyethylpiperazine-N-2ethanesulfonic acid ) and 10% fetal bovine serum ( Hyclone ) . The medium was supplemented with ciprofloxacin and amphotericin B to prevent contamination . To ablate hair cells , tissue was exposed to 2 mmol/L gentamicin for 48 hr and subsequently rinsed thoroughly with fresh medium before maintenance for up to 21 days with 50% of the medium changed on alternate days . Replication deficient second generation adenovirus ( Ad2 ) with deleted E1 , E3 , pol , pTP regions was used as vector ( Kesser et al . , 2007; Kesser et al . , 2008 ) . The Ad2 virus contained two expression cassettes each driven by the human cytomegalovirus promoter: CMV-ATOH1 and CMV-GFP . Aliquots of Ad2-ATOH1-GFP viral vector stock were stored at −80°C until required . Following gentamicin treatment , tissue was rinsed three times in serum-free medium containing ciprofloxacin and incubated for up to 24 hr but no less than 18 hr in 200 μL of serum-free medium with a dilution of Ad2-ATOH1-GFP to give 1 × 108 total particles per ml . Tissue was then rinsed five times with MEM with glutamax with serum to halt the transduction and maintained in this medium for a further 17 days . Cultures were rinsed and processed for immunohistochemistry or electron microscopy as described below . The Notch pathway was inhibited using the TACE inhibitor TAPI-1 . Gentamicin-treated samples were maintained in medium +50 μM TAPI-1 ( Sigma-Aldrich , Poole ) throughout the duration of culture . Initially TAPI-1 was dissolved in DMSO and further diluted with medium to give a stock solution of 1 mM that was stored at −20°C . These utricles were rinsed , fixed and processed for immunohistochemistry or electron microscopy . Six lesioned utricles were transduced with Ad2-ATOH1-GFP and then incubated continuously in medium containing TAPI-1 as for the above samples . They were processed for immunohistochemistry or electron microscopy . Tissue for immunohistochemistry was fixed in 4% paraformaldehyde ( PFA ) in phosphate buffered saline solution ( PBS ) for 90 min . The majority of samples were prepared as whole mounts with a small number prepared for cryosectioning . Whole mounts were rinsed in PBS and permeabilized using 0 . 5% Triton X-100 for 20 min and placed in blocking solution ( 10% goat serum , 0 . 15% Triton in PBS ) . Tissue was incubated overnight at 4°C in primary antibody , rinsed thoroughly in PBS and then incubated for 2 hr at room temperature with the appropriate secondary antibody conjugated to a fluorophore . Primary antibodies used were: mouse monoclonal against myosin VIIA ( Developmental Studies Hybridoma Bank; myo7a 138–1 ) used at a dilution of 1:100; a rabbit polyclonal against espin ( a kind gift from J Bartles ) used at 1:50; a rabbit polyclonal against sox 2 ( Abcam , ab 97959 ) at 1:100 and a polyclonal against Atoh1 ( Aviva Systems Biology , ARP 32365_P050 ) at 1:100 . A Tyramide signal amplification kit ( Molecular Probes ) was used according to manufacturer’s protocol to amplify the Atoh1 labelling in tissue . Tissue was incubated in the appropriate secondary antibody ( sheep anti-mouse ( Zymed ) , or goat anti-rabbit ( Sigma ) ) at 1:200 . A fluorescent phalloidin conjugate ( Sigma ) was added at 1 μg/ml to the secondary antibody solution to label filamentous actin . Following staining , utricles were mounted onto slides using Vectashield with DAPI ( Vectorlabs ) to label nuclei . Samples were examined and images captured with a Zeiss LSM 510 confocal microscope . For cryosections , fixed tissue was incubated in 30% sucrose solution overnight at 4°C embedded in low-temperature setting agarose and mounted in the required orientation . Cryosections of 15 μm were cut and collected on polylysine coated slides ( VWR ) . Immunolabelling was performed as for whole mounts . For scanning electron microscopy ( SEM ) and transmission electron microscopy ( TEM ) cultured utricles were rinsed and fixed in 2 . 5% glutaraldehyde in 0 . 1 mol/L cacodylate buffer for 2 hr and subsequently post-fixed in 1% OsO4 for 1 . 5 hr . Utricles for SEM , were then processed following the repeated thiocarbohydrazide-osmium procedure ( Davies and Forge , 1987 ) , dehydrated in an ethanol series and critical point dried . Samples were mounted on support stubs using silver conductive paint and sputter coated with platinum before examination and collection of digital images on a Jeol 6700F instrument . Tissue for thin sectioning was partially dehydrated to 70% ethanol and stained ‘en bloc’ with uranyl acetate in 70% ethanol before completing dehydration and embedding in plastic . Some immunolabelled whole mount samples were removed from the slides after confocal microscopy , fixed in glutaraldehyde and OsO4 and processed for thin sectioning . Some samples examined by SEM also were prepared for thin sectioning . They were removed from the specimen support stubs into acetone , then into 100% ethanol before embedding in plastic . For all plastic embedded samples , serial thin sections across the entire width of each utricle were collected at a minimum of three depths separated by ca . 50 µm . Sections , some mounted on formvar –coated single slot grids , were stained with aqueous uranyl acetate and lead citrate and examined in a Jeol1200EXII instrument . Digital images were acquired with a Gatan camera . Sections on grids were also examined and imaged in the SEM using back-scatter detection to provide uninterrupted views of the entire width of the section . Myosin VIIA+ cells were viewed and quantified from z-stacks of confocal images viewed in Image J . . Assessment was made of at least two different fields on a single utricle viewed with a x20 objective , with a random movement in X and Y planes between each field . In each field intact Myo VIIa positive cells with a distinct nucleus were counted in a delineated , measured area of at least 20 , 000 µm2 enclosing continuous intact epithelium as defined by phalloidin labelling of cell-cell junctions at the luminal surface , and excluding regions where the epithelium was folded over on itself , or was significantly disrupted , which occurred in several samples during prolonged incubation and processing due to the friability of the tissue and detachment of the epithelium from the underlying mesenchyme . At each location , each individual optical section through the entire depth of the epithelium was analysed . Cell counts were normalised to a unit area of 10000 um2 . Using Prism4 GraphPad , discrete comparisons were made between each individual treatment group and the gentamicin only treated cultures maintained for similar lengths of time ( control ) using t-tests . ANOVA with Tukey correction was used to assess whether there were significant differences between the treatment groups . Previous mRNA-Seq data derived by the Lovett group ( Ku et al . , 2014 ) were used to calculate the median coefficient of variation in gene expression across samples when FPKM ( Fragments Per Kilobase Of Exon Per Million Fragments Mapped ) values > 1 were used . Based on the model published by Hart et al . ( 2013 ) , two biological replicates per treatment group are needed to detect a 2-fold difference with 80% power and 0 . 1 type 1 error , if 6 samples are multiplexed per lane ( ~1000 read counts per transcript ) . Based on these calculations , in this study we sequenced 2 replicates for the treatment group and 2 replicates for the control group ( 14–18 days post gentamycin only ) . Two additional control samples ( 2dpg and 8 dpg ) were available and therefore these were also used in the statistical analysis as described in the methods section . Three untreated samples were also sequenced and used in the ANOVA model but no comparisons with these were performed for the purposes of this study . Six whole utricle samples ( as pure as possible ) were used for transcriptome analysis: Ad2-ATOH1-GFP transduced utricles cultured for 5 days after gentamicin treatment ( two biological replicates ) , a utricle cultured for 2 days post gentamicin ( early time-point , first control ) , one utricle cultured for 8 days post gentamicin ( mid time-point , second control ) and two utricles cultured for 14–18 days post gentamicin ( late time-point , these two samples were grouped and used as the third control group ) . Following treatment , all utricles were collected into RNA-later . RNA was extracted using Zymo Research Quick-RNA MicroPrep kit . Libraries were prepared using Illumina Truseq Stranded mRNA Library Prep kit . 75 bp paired-end sequencing was performed on the Illumina HiSeq platform by service provider CNAG , Barcelona , Spain . Raw reads in fastq format were trimmed to remove adapter sequences and low-quality bases ( Q < 30 ) from the 3’ end of reads using Cutadapt v1 . 9 ( Martin , 2011 ) , then aligned against hg38 using Tophat v2 . 1 ( Kim et al . , 2013 ) and expression values ( FPKMs ) were generated using Cufflinks v2 . 1 ( Trapnell et al . , 2010 ) based on ensembl87 annotations . A filtering step was applied with at least one sample to be ≥0 . 5 FPKM . All sequencing data from all of these samples have been deposited in NCBI GEO ( number applied for ) . The utricle samples were not age-matched and due to possible genetic variability , Ad2-ATOH1-GFP transduced samples were compared against different control samples treated with gentamicin only . Three comparisons between Atoh1-transduced utricle samples against gentamicin-treated only samples were performed using one-way Anova on Partek Genomics Suite software: a ) Atoh1-transduced versus 2 days post-gentamicin control sample; b ) Atoh1-transduced versus 8 days post-gentamicin control sample and c ) Atoh1-transduced versus 14–18 days post-gentamicin group of controls ) . Biological replicates and the group of control gentamicin-treated samples for 14–18 days had an average spearman’s rho of 0 . 92 . Statistical significance was considered when p<0 . 05 and −2 ≤ fold change≥2 and fold changes were in the same direction across the three comparisons . Statistically significant differentially expressed genes were uploaded in ToppGene ( Chen et al . , 2009 ) to identify enriched GO biological processes ( FDR < 0 . 05 ) and GeneMANIA ( Warde-Farley et al . , 2010 ) to identify literature-supported interactions . Taqman assays for CITED , IRF9 , SNAI1 , EP300 and HDAC9 ( Hs 00388363 , Hs00196051 , Hs00195591 , Hs00914223 and Hs-1081558 ) were purchased from ABI and were run in technical triplicates on an ABI QuantStudio Real Time PCR System under manufacturer’s standard parameters for comparative CT analysis . Total polyA+ RNA from two ATOH1 utricle transduction experiments ( 5 days post gentamicin ) were separately converted into cDNA . These were then pooled and constituted the ATOH samples . Total polyA+ RNA from three control utricle samples ( 2 days , 8 days and 14 days post gentamicin ) were separately converted into cDNA . These were then pooled and constituted the control samples . Amplifications were normalized to a GAPDH internal control ( ABI Taqman assay Hs99999905 ) .	The inner ear contains our balance system ( the vestibular system ) and our hearing organ ( the cochlea ) . Their sensing units , the hair cells , detect movement or sound waves . A loss of hair cells is a major cause of inner ear disorders , such as dizziness , imbalance and deafness . When hair cells die , supporting cells that surround them close the ‘wound’ to repair the tissue . In fish , amphibians , reptiles and birds , the supporting cells can replace lost hair cells , but in mammals – including humans – hair cells are unable to regenerate in the cochlea , so hearing loss is permanent . However , previous research has shown that in certain mammals , spontaneous replacement of lost hair cells in the vestibular system can occur , but not enough to lead to a full recovery . Scientists have been able to convert supporting cells in the vestibular system of mice into hair cells by using either certain chemicals , or by introducing a specific gene into the supporting cells . In the mouse embryo , this gene , called Atoh1 , switches on a signalling pathway in the inner ear , through which a non-specialised precursor cell becomes a hair cell . Inducing hair cell regeneration could be a therapy for inner ear disorders . Therefore , Taylor et al . wanted to find out if such procedures would work in inner ear tissue from humans . The researchers collected intact tissue samples from the vestibular system of patients who had undergone surgery to have a tumour removed , which would normally destroy the inner ear . All existing hair cells were removed so that mainly supporting cells remained . Then , the tissue was either treated with chemicals that increased the production of hair cells or received the gene ATOH1 . The results showed that the cells containing the gene were able to develop many features characteristic of hair cells . And a smaller number of hair cells treated with the chemicals also started to develop hair cell-like features . A gene analysis after the ATOH1 transfer revealed a number of active genes known to be markers of hair cells , but also several inactive ones . This suggests that additional factors are necessary for generating fully functional hair cells . Dizziness and balance disorders present a major health care burden , particularly in the elderly population . Yet , they are often disregarded and overlooked . This study suggests that hair cell regeneration could be a feasible therapy for some forms of balance disorders linked to loss of vestibular hair cells . More research is needed to identify the other factors at play to test if hair cell regeneration in the cochlea could be used to treat hearing impairment .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2018	Regenerating hair cells in vestibular sensory epithelia from humans
The mammalian target of rapamycin complex 1 ( mTORC1 ) is the key signaling hub that regulates cellular protein homeostasis , growth , and proliferation in health and disease . As a prerequisite for activation of mTORC1 by hormones and mitogens , there first has to be an available pool of intracellular amino acids . Arginine , an amino acid essential during mammalian embryogenesis and early development is one of the key activators of mTORC1 . Herein , we demonstrate that arginine acts independently of its metabolism to allow maximal activation of mTORC1 by growth factors via a mechanism that does not involve regulation of mTORC1 localization to lysosomes . Instead , arginine specifically suppresses lysosomal localization of the TSC complex and interaction with its target small GTPase protein , Rheb . By interfering with TSC-Rheb complex , arginine relieves allosteric inhibition of Rheb by TSC . Arginine cooperates with growth factor signaling which further promotes dissociation of TSC2 from lysosomes and activation of mTORC1 . Arginine is the main amino acid sensed by the mTORC1 pathway in several cell types including human embryonic stem cells ( hESCs ) . Dependence on arginine is maintained once hESCs are differentiated to fibroblasts , neurons , and hepatocytes , highlighting the fundamental importance of arginine-sensing to mTORC1 signaling . Together , our data provide evidence that different growth promoting cues cooperate to a greater extent than previously recognized to achieve tight spatial and temporal regulation of mTORC1 signaling . Cells rely on the ability to appropriately sense and respond to intra- and extracellular stimuli in order to ensure their proper function , maintenance , and growth . Dysregulation of pathways required to control this homeostasis frequently occur in human pathologies such as cancer , metabolic syndrome , and aging . As such , intense efforts have been made to understand the regulatory signaling pathways and underlying mechanisms via which cells sense anabolic and catabolic signals . The mammalian target of rapamycin complex 1 ( mTORC1 ) plays a fundamentally important role in the integration of metabolic , energy , hormonal , and nutritional stimuli to promote cellular biosynthesis and suppress the catabolic process of macroautophagy ( Laplante and Sabatini , 2012; Carroll et al . , 2015 ) . The two most proximal regulators of mTORC1 are small GTPases of the Ras superfamily: Rheb and the Rag GTPases . Both are considered to be resident on the cytoplasmic surface of the lysosome , a site that has in recent years come to the very forefront of mTORC1 signal integration ( Sancak et al . , 2010; Buerger et al . , 2006 ) . The Rag GTPases facilitate signaling of amino acids to mTORC1 ( Kim et al . , 2008; Sancak et al . , 2008 ) , a stimulus that has long been known to be both necessary and sufficient , albeit minimally , for mTORC1 activation ( Long et al . , 2005b; Hara et al . , 1998 ) . The current consensus is that amino acids activate mTORC1 by promoting its localization to the cytoplasmic surface of late endosomes and lysosomes ( for simplicity this mTOR-positive compartment is referred to as lysosomes ) via a heterodimeric complex of Rag GTPases ( Sancak et al . , 2008; Sancak et al . , 2010 ) . There are four mammalian Rag GTPases that show functional redundancy , A or B dimerize with C or D and unlike most small GTPases they rely on another complex , the Ragulator , for tethering them at the membrane . The Ragulator complex further acts as a guanine nucleotide exchange factor ( GEF ) to activate the Rag GTPases and works in opposition to the GTPase-activating protein ( GAP ) complex , GATOR1 ( Bar-Peled et al . , 2012; Bar-Peled et al . , 2013; Panchaud et al . , 2013 ) to ensure the tight control of Rag GTPases and therefore mTORC1 activity . mTORC1 localization to the lysosome is thought promote its activity by bringing it into close proximity to the membrane-associated Rheb . Rheb is the master activator of mTORC1 and , although the mechanism by which GTP-loaded Rheb stimulates mTORC1 kinase activity remains unknown ( Avruch et al . , 2009 ) , it has been demonstrated that the nucleotide and therefore activity status of Rheb is controlled by a wide range of upstream stimuli . Potent mTORC1 regulators , for example energy status via AMPK and growth factors via PI3K and Akt converge upstream of Rheb to positively or negatively control the TSC complex ( Inoki et al . , 2003b; Huang and Manning , 2008 ) . The TSC complex consists of TSC1 , TBC1D7 , and TSC2 , where TSC2 has GAP activity that specifically inactivates Rheb ( Tee et al . , 2003; Inoki et al . , 2003a; Garami et al . , 2003; Zhang et al . , 2003; Dibble et al . , 2012 ) . Phosphorylation of TSC2 by Akt has recently been shown to regulate localization of the TSC complex to the cytoplasm and away from the lysosome thereby preventing Rheb inactivation and promoting mTORC1 activation ( Menon et al . , 2014 ) . The regulation of TSC2 localization to the lysosome has also been associated with the amino acid/Rag GTPase axis ( Demetriades et al . , 2014 ) , indicating that the underlying mechanisms controlling the opposing localization of mTOR and TSC2 to the lysosome involve complex interplay between different upstream stimuli . Leucine and glutamine are the best-studied mTORC1-regulating amino acids and their mechanisms of action have been partially elucidated ( Han et al . , 2012; Sancak et al . , 2010; Durán et al . , 2012; Jewell et al . , 2015 ) . In addition to leucine and glutamine , mTORC1 has also been shown to sense the presence of arginine ( Hara et al . , 1998; Wang et al . , 2015; Ban et al . , 2004 ) , an amino acid essential during embryogenesis ( Wu et al . , 2009 ) . It has recently been suggested that arginine activates mTORC1 via a mechanism similar to that of leucine , involving Rag GTPases where a membrane transporter SLC38A9 acts as a sensor of amino acids ( Jung et al . , 2015 ) , including arginine ( Rebsamen et al . , 2015; Wang et al . , 2015 ) , on lysosomal membranes . Here , we show that arginine has an unexpected role in activating mTORC1 via the TSC/Rheb signaling axis . Most notably , we demonstrate that arginine does not significantly affect mTORC1 localization but rather , it is required for maximal growth factor signaling by preventing TSC2 localization to the lysosome and it’s interaction with Rheb . We demonstrate that TSC2 localization is modulated by arginine together with the classical TSC regulators , growth factors , to tightly control Rheb and ultimately mTORC1 activity in a spatial and temporal manner . Arginine is likely to act as an intact molecule as its metabolism is not required for mTORC1 activation but it remains to be elucidated whether the effect is direct or indirect . Finally , arginine is the only amino acid that mTORC1 activity shows sensitivity to in both undifferentiated stem cells and subsequent differentiated lineages demonstrating the fundamental importance of the mechanism described herein . This report further highlights the complex signaling interplay between different growth-promoting stimuli in regulating mTORC1 at the level of lysosomes . The presence of amino acids is a prerequisite for mTORC1 activity . Leucine in particular has been well documented for its role in the regulation of mTORC1 and its growth-promoting properties . However , the contribution and mechanisms of action of other amino acids are not as well understood . To further explore amino acid signaling , we investigated the sensitivity of mTORC1 to essential and conditionally essential amino acids in a panel of cell lines . Acute deprivation of leucine , arginine , or glutamine alone was observed to significantly perturb mTORC1 activity in multiple cell lines ( Figure 1—figure supplement 1A–G and see , among others [Durán et al . , 2012; Nicklin et al . , 2009; Wang et al . , 2015; Jewell et al . , 2015] ) . Deprivation of other single amino acids , including isoleucine and methionine , did not significantly affect mTORC1 activity in any cell line tested ( Figure 1—figure supplement 1A–G and data not shown ) . Interestingly , mTORC1 was found to be differentially sensitive to amino acids in different cell lines . For example , sensitivity to glutamine , previously associated with leucine-dependent and -independent mechanisms of mTORC1 regulation ( Durán et al . , 2012; Nicklin et al . , 2009; Jewell et al . , 2015 ) , varied significantly . While glutamine was essential for mTORC1 activation in both HeLa and HEK293T cells , it was less important in Mouse Embryonic Fibroblasts ( MEFs ) . Similarly , removal of leucine significantly suppressed mTORC1 activity in most cell lines with an exception of HeLa and U2OS . Of particular interest , we found that mTORC1 activity is dependent on arginine in every cell line tested . Therefore , this amino acid appears to be a fundamentally important regulator of mTORC1 . Simultaneous deprivation of arginine and leucine had an additive effect on mTORC1 inhibition ( Figure 1—figure supplement 1A–F ) , while at the same time they ( alongside glutamine which is known to be required for the transport of leucine into the cell [Nicklin et al . , 2009] ) represent a sufficient signal to activate mTORC1 ( Figure 1—figure supplement 1H ) . No one individual amino acid is sufficient to activate mTORC1 , but this is unlikely to be a result of lower intracellular concentrations . Thus , for example , arginine uptake is in fact increased in cells completely starved of all amino acids compared to the cells incubated with complete amino acids , presumably due to a lack of competition for transport into the cell ( Figure 1—figure supplement 1I ) . mTORC1 activity induced by a combination of arginine , leucine , and glutamine is minimal compared to that of a complete set of amino acids; however , it is not further enhanced by the addition of any other individual amino acid , such as isoleucine ( Figure 1—figure supplement 1H and data not shown ) . Together , these data suggest that arginine , leucine and glutamine represent the major contributors to amino acid-dependent mTORC1 activation . Moreover , the synergistic effect of these amino acids on mTORC1 activity rather than simply concentration-dependent effects suggests they act via multiple mechanisms . We observe that metabolism or cellular utilization of arginine does not participate in its ability to activate mTORC1 ( Figure 1—figure supplement 2A–J ) . Rather , interventions that increase intracellular levels of arginine ( such as knock-down of arginyl-tRNA synthetase ( RARs ) , L-norvaline ( an arginase inhibitor ) , and cycloheximide ( an inhibitor of protein translation ) ) enhanced the activity of mTORC1 ( Figure 1—figure supplement 2D , E , J ) , suggesting that arginine acts as an intact molecule . Indeed , we observed that upon arginine starvation and recovery , arginine uptake is rapid and intracellular concentrations of arginine recover to steady state levels within 15 min of arginine re-addition . Within this same time frame , there were no changes in metabolites associated with arginine metabolism such as ornithine , citrulline , arginosuccinate , or fumarate indicating that arginine remains intact during this period ( Figure 1—figure supplement 2A , B ) . Furthermore , following the addition of stable radiolabeled arginine ( 13C6 , 15N4 ) for 2 hr , we did not observe its incorporation into any other metabolites ( Figure 1—figure supplement 2C ) , suggesting that arginine turnover is slow , at least in HeLa cells . These data suggest that free arginine is an important signal regulating mTORC1 activity . Several observations argue for a mechanism of arginine action different to that of leucine . First , deprivation of arginine but not leucine or isoleucine , significantly perturbed the growth factor-dependent input into mTORC1 . This is evident from suppressed phosphorylation of ribosomal protein S6 kinase 1 ( S6K1 ) , eukaryotic translation initiation factor 4E-binding protein 1 ( 4E-BP1 ) and ULK1 , with a concomitant increase in autophagy ( Figure 1A–C and Figure 1—figure supplement 3A–D ) . Deprivation of arginine in the presence of growth factors limits mTORC1 activation to a level similar to that resulting from serum starvation . Addition of growth factors to leucine or isoleucine-deprived cells , however , permits maximal mTORC1 activation ( Figure 1A–C and Figure 1—figure supplement 3A–D ) . Amino acids , particularly leucine , are known to signal to mTORC1 via the V-ATPase/Ragulator/Rag GTPase protein complexes and have been comprehensively demonstrated to control mTORC1 localization to the lysosome and its activity ( Carroll et al . , 2015 ) . We observed that arginine did not affect mTOR localization in any cell line tested . While complete amino acid starvation and , to a lesser extent , leucine starvation , caused redistribution of mTORC1 to the cytoplasm , arginine deprivation led to a strong suppression of mTORC1 without a significant reduction in co-localization of mTOR with the lysosomal marker Lamp1 in HeLa , MEFs , and HEK293T cells ( Figure 1D ) . Furthermore , overexpression of constitutively active Rag heterodimer ( Sancak et al . , 2008 ) did not completely rescue the effect of arginine starvation in the absence or presence of growth factors , thus suggesting that arginine may affect mTORC1 both via Rag-dependent and Rag-independent mechanisms ( Figure 1—figure supplement 3E , F ) . Finally , the knockdown of membrane transporter , SLC38A9 that has recently been implicated in Rag GTPase-dependent lysosomal recruitment and activation of mTORC1 ( Wang et al . , 2015; Rebsamen et al . , 2015; Jung et al . , 2015 ) did partially perturb the recovery of mTORC1 following arginine starvation; however , there was no effect on the response of mTORC1 to arginine starvation either in the absence or presence of growth factors ( Figure 1—figure supplement 3G ) . Together , these data suggest that , in addition to an effect of arginine on mTORC1 via Rag GTPases/SLC38A9 , this amino acid also plays an important role in regulating mTORC1 via the growth factor-regulated TSC-Rheb signaling axis . Upstream signaling events from growth factors via PI3K-Akt to TSC are not affected by deprivation of arginine ( or complete amino acid or leucine ) , as assessed by Akt phosphorylation at both threonine 308 and serine 473 and downstream phosphorylation of TSC2 at serine 939 ( Figure 1—figure supplement 3H–K ) . Similarly , arginine starvation is unlikely to suppress mTORC1 via activation AMPK , as neither in HeLa cells , where this pathway has low activity ( Corradetti et al . , 2004 ) , nor in MEFs did we observe any increase in AMPK phosphorylation in response to arginine , leucine or complete amino acid withdrawal ( Figure 1—figure supplement 3L , M ) . The loss of TSC2 , however , did render MEFs insensitive to arginine ( Figure 1E and Figure 1—figure supplement 3N , O ) , suggesting that arginine contributes to mTORC1 activity at the level of TSC . At the same time , sensitivity to leucine and complete amino acid starvation was preserved in TSC2-/- MEFs ( Figure 1E and Figure 1—figure supplement 3N , O ) in agreement with the idea that they are activating mTORC1 via other mechanisms such as Rag-dependent mTOR localization ( Figure 1D ) . Dynamic changes in TSC localization to lysosomes have been shown to control mTORC1 activity; however , it remains controversial whether it is regulated by growth factors or amino acids ( Demetriades et al . , 2014; Menon et al . , 2014 ) . Therefore , we next investigated whether arginine could be involved in this process . In normal growth conditions , TSC2 was found in a diffuse pattern within both HeLa and MEF cells ( Figure 2A–D ) . While serum starvation or arginine deprivation in the presence of dialyzed serum moderately promoted TSC2 localization to the lysosomal compartment , simultaneous starvation of arginine and growth factors resulted in an additive effect leading to very strong recruitment of TSC2 to Rab7- and Lamp1-positive late endosomal and lysosomal structures ( Figures 2A–D , Figure 2—figure supplement 1A , B ) , which also correlates with a robust impairment of mTORC1 activity ( Figures 1A ) . Recruitment of TSC2 to lysosomes was confirmed to be specific and no significant localization of TSC2 to other cellular organelles such as mitochondria , Golgi , or peroxisomes was observed ( Figure 2—figure supplement 1C–F; [Zhang et al . , 2013] ) . The effect of arginine is similar to that of all amino acids , while leucine did not influence lysosomal localization of TSC2 ( Figure 2—figure supplement 2A–D ) . Replenishment of amino acids or arginine following starvation caused re-distribution of TSC2 to the cytoplasm ( Figure 2—figure supplement 2E ) . Furthermore , the addition of arginine but not leucine to cells starved of all amino acids was sufficient to cause significant re-distribution of TSC2 to the cytoplasm ( Figure 2—figure supplement 2F ) , demonstrating that arginine acts as a specific inhibitor of TSC2 recruitment to lysosomes . We further confirmed these immunofluorescence observations by lysosomal fractionation ( enrichment of lysosomes was determined by western blot for multiple membrane and soluble proteins [Figure 2—figure supplement 2G–H] ) . Indeed , TSC2 was found to strongly accumulate upon a combination of serum and arginine ( or all amino acid ) starvation and , to a lesser extent , upon removal of a single stimulus ( Figure 2E ) . The amount of Rheb in lysosomal fractions was not significantly affected by our starvation protocols ( Figure 2—figure supplement 2G ) , suggesting that changes in localization of TSC in response to arginine may regulate Rheb function on lysosomes . We next investigated whether arginine contributes to the regulation of TSC localization via either the Rheb or Rag GTPase-dependent mechanisms that have been described previously ( Menon et al . , 2014; Demetriades et al . , 2014 ) . In our system , we confirmed the central importance of Rheb in this process ( Menon et al . , 2014 ) by demonstrating that Rheb knock-down or inhibition of Rheb farnesylation ( and therefore its membrane recruitment ) completely prevented the arginine and growth factor starvation-induced re-localization of TSC2 ( Figures 3A–F and Figure 3—figure supplement 1A ) . However , as reported before ( Demetriades et al . , 2014 ) , we also found that expression of the dominant negative Rag constructs partially reduced lysosomal localization of TSC2 ( Figure 3—figure supplement 1B , C ) . Consistent with its role in regulating mTORC1 via Rag GTPases , knock-down of SLC38A9 had a mild effect on TSC2 re-localization ( Figure 3—figure supplement 1D , E ) . Furthermore , we tested whether mTOR itself is required for TSC recruitment to lysosomes . Knock-down of mTOR suppressed recruitment of TSC2 to lysosomes ( Figure 3—figure supplement 1F–H ) . At the same time , rapamycin did not affect TSC2 recruitment suggesting that the activity of mTORC1 is not required for this translocation event ( Figure 3—figure supplement 1I ) . As knockdown or hyperactivation of Rheb did not affect either total mTOR protein levels or lysosomal localization of mTOR ( Figure 3—figure supplement 2A–D , also see [Sancak et al . , 2010] ) , Rheb-dependent recruitment of TSC2 is unlikely to be indirectly mediated by perturbed mTOR localization . Taken together , these data strongly suggest that interaction with Rheb is the main mechanism of TSC2 recruitment to lysosomes but , in addition , the integrity of a larger lysosomal protein complex , including mTORC1 and Rag GTPases ( Demetriades et al . , 2014 ) , is also important , possibly indirectly by regulating Rheb activity , localization or availability . Small GTPase function is regulated by the GTP-nucleotide binding status of the protein , and indeed GTP-bound Rheb promotes mTORC1 kinase activity ( Long et al . , 2005a ) . By exploiting Rheb mutants that are either constitutively GTP-bound ( R15G and N153T ) or nucleotide-binding deficient ( D60K and N119I ) ( Li et al . , 2004; Land and Tee , 2007; Urano et al . , 2007 and Figure 4—figure supplement 1A , B ) , we went on to investigate whether the status of Rheb nucleotide loading is important for its interaction with TSC2 on lysosomal membranes . A model of the TSC2-Rheb complex indicates that mutations used here are unlikely to affect the Rheb-TSC2 interface other than by modifying nucleotide loading ( Figure 4—figure supplement 1A , B ) . Interestingly , only Rheb·GTP ( and wild-type although this is also predominantly GTP-bound ) promoted TSC2 localization to lysosomes , and this was observed regardless of cellular nutrient status ( Figures 4A and Figure 4—figure supplement 1C–E ) . Lysosomal recruitment of TSC2 by Rheb·GTP in the presence of nutrients is likely to take place because the local concentration of amino acids is insufficient to interfere with overexpressed Rheb , consistent with previous studies of TSC2-Rheb·GTP interaction ( Castro et al . , 2003 ) . Combined with previous data ( Figures 3 and Figure 3—figure supplement 1A ) , these observations allowed us to conclude that Rheb is both necessary and sufficient ( when GTP-bound ) for the recruitment of TSC2 to lysosomes and arginine interferes with process . It was not clear , however , whether this interference was occurring at the surface of the lysosome . To investigate this , TSC2 was constitutively targeted to the cytoplasmic surface of lysosomes by a p18-derived lysosomal targeting signal ( Menon et al . , 2014 ) ( Figure 4B ) . Interestingly , inactivation of mTORC1 signaling by either wild-type or constitutively lysosomal TSC2 was found to be suppressed by arginine ( Figure 4C ) . This suggests that arginine interferes with Rheb-TSC2 interaction irrespective of lysosomal recruitment . To test this , we performed pull-down assays where immunoprecipitated Flag-Rheb was incubated with lysates of cells expressing V5-TSC2 and subjected to serum and amino acid starvation protocols ( Figure 4D ) . In agreement with inhibition of Rheb-TSC2 interaction by arginine , deprivation of arginine but not leucine promoted strong binding of TSC2 to Rheb ( Figure 4D ) . Moreover , an interaction of TSC2 ( immunoprecipitated from mammalian cells ) with recombinant GST-Rheb in vitro was perturbed by the addition of physiological concentrations of arginine ( Figure 4E ) . These findings do not preclude an indirect effect of arginine via a putative sensor co-immunoprecipitated with TSC2; however , it strongly supports the role of arginine in regulating Rheb-TSC2 interaction . In agreement with binding assays , arginine also reduced the ability of TSC2 to promote hydrolysis of Rheb-bound GTP ( Figure 4F , G ) . Interestingly , despite the strong interaction of Rheb with TSC2 in the absence of arginine , a large fraction of Rheb ( ~40% ) remains in GTP-bound active state ( Figure 4D–G ) . This is consistent with data in vivo suggesting that a large fraction of Rheb remains in GTP-bound form even when mTORC1 is inactivated by starvation ( Long et al . , 2005b ) . Therefore , we hypothesized that a stable interaction between GTP-loaded Rheb and TSC2 may lead to steric hindrance or a conformational change thus preventing activation of mTORC1 by Rheb . In agreement with this idea , the increased binding of TSC to Rheb is at the expense of the interaction between Rheb and mTOR ( Figure 4—figure supplement 1F , G ) , which would render mTORC1 inactive and explain severe inhibition of its activity seen in complete starvation conditions . If TSC2 can inactivate Rheb by an additional , GAP-independent mechanism then TSC2 should still be able to inhibit mTORC1 activity driven by constitutively GTP-loaded Rheb mutants . Indeed , overexpression of TSC2 efficiently inhibited activation of mTORC1 by both wild-type and active Rheb mutants ( R15G and N153T ) both in the presence and absence of growth factors , despite resistance of these constructs to GAP activity of TSC2 ( Figures 4H and Figure 4—figure supplement 1A , B , H and [Urano et al . , 2007; Li et al . , 2004; Urano et al . , 2005; Marshall et al . , 2009] ) . Similarly , if binding of TSC2 to Rheb is sufficient to inhibit its activity towards mTORC1 , then GAP activity of TSC2 may be dispensable . This is indeed the case as overexpression of GAP-domain-dead TSC2 mutants ( Zhang et al . , 2013 ) efficiently suppressed activity of endogenous or overexpressed Rheb , and perturbed Rheb-mTOR binding , albeit to a lesser extent when compared to the wild-type construct ( Figure 4I and Figure 4—figure supplement 1I , J ) . Therefore , we propose that one of the mechanisms by which serum and amino acid ( particularly arginine ) starvation suppresses activity of mTORC1 is by increased TSC2-Rheb interaction on lysosomes , which results in a partial hydrolysis of GTP as well as in the shielding of remaining GTP-loaded Rheb from mTORC1 . Finally , we tested the relative contribution of arginine-dependent mTORC1 regulation as compared to branched chain amino acids , sensing of which is believed to be the main function of mTORC1 , in human embryonic stem cells ( hESCs ) and adult human cells differentiated from hESCs ( Figure 5—figure supplement 1A–E ) . Interestingly , in hESCs mTORC1 activity was not suppressed by deprivation of leucine or isoleucine but was sensitive to arginine . This sensitivity to arginine remained an important limiting factor for mTORC1 activation in hESC-derived cells , including fibroblasts , neural precursors , neurons , and hepatocytes ( Figure 5A–E ) . At the same time , sensitivity to leucine was acquired only after differentiation , although its depletion had lesser impact on mTORC1 in neurons and hepatocytes compared to that of arginine ( Figure 5A–E ) . Together , these data suggest that arginine is the primary signal for the mTORC1 pathway in hESC and hESC-derived neurons and hepatocytes , while leucine-dependent regulation is an additional layer of control established only following differentiation . Leucine and arginine represent the major contributors to amino acid-dependent mTORC1 activity . While the role of leucine , together with glutamine , in Rag-dependent activation of mTORC1 has been the subject of intense investigations , the role of arginine in mTORC1 activation has remained relatively understudied . Arginine can contribute to mTORC1 activity via the canonical amino acid sensing , Ragulator/Rag signaling pathway via the recently characterized low-affinity amino acid transporter SLC38A9 ( Wang et al . , 2015; Rebsamen et al . , 2015; Jung et al . , 2015 ) . Our data suggest that arginine also works via the parallel , TSC2-Rheb signaling axis upstream of mTORC1 and stimulation of both inputs is required for the maximal activation of this signaling pathway ( Figure 6 ) . In particular , we show that arginine cooperates with growth factors and acts as a permissive factor for their activation of mTORC1 . Growth factors and arginine interfere with the TSC2-Rheb interaction; while Akt-mediated growth factor signaling phosphorylates TSC2 , arginine does not affect phosphorylation of Akt , at least on the sites tested in this study . Instead , arginine appears to act as an intact molecule at a site more proximal to the TSC2-Rheb interaction , whether acting directly or via a putative sensor molecule . Interestingly , removal of both serum and arginine is required for complete inhibition of mTORC1 signaling , suggesting that either stimulus can support low levels of basal mTORC1 activity ( Figure 1A ) . Our measurements of TSC2 localization and nucleotide loading of Rheb help to explain this mechanistically . We propose that in response to serum or arginine starvation TSC2 transiently interacts with Rheb in a GTP-bound conformation on the surface of lysosomes promoting its GTPase activity . However , due to high basal GTP levels of Rheb ( Li et al . , 2004 ) , the GAP activity of TSC2 is not sufficient for complete GTP hydrolysis to GDP leaving a substantial fraction of Rheb in a GTP-bound state . This residual-active GTP-bound Rheb allows for the low level of mTORC1 activation in conditions of serum or arginine starvation . On the other hand , deprivation of both stimuli results in a complete suppression of mTORC1 activity , which is associated with a stable interaction of TSC with Rheb on lysosomes . This complex formation serves to sequester Rheb and to prevent its interaction with mTORC1 and its activation . By regulating Rheb-TSC2 interaction , arginine acts as a switch for upstream signaling inputs from growth factors . Previous reports describing the spatial regulation of TSC2 as a mechanism controlling mTORC1 activity have demonstrated that various inputs such as mechanical stimulation , growth factors and amino acids can contribute to the tight regulation of the lysosomal localization of TSC2 ( Cai et al . , 2006; Jacobs et al . , 2013; Demetriades et al . , 2014; Menon et al . , 2014 ) . Demetriades et al . ( 2014 ) argue the importance of an amino acid/Rag GTPase-dependent recruitment of TSC2 to lysosomes , while instead Menon et al . ( 2014 ) demonstrate that deprivation of growth factors , the classical regulators of TSC activity , controls its recruitment to lysosomes . Our findings begin to reconcile these differences by identifying that amino acids ( specifically arginine ) cooperate with growth factors to fine tune the spatial and temporal regulation of TSC and therefore mTORC1 . We show that both arginine and growth factors suppress strong recruitment of TSC2 to lysosomes by interfering with stable TSC2-Rheb interaction as described by Menon et al . ( 2014 ) . In agreement with Demetriades et al . ( 2014 ) , we also find a role of Rag GTPases in this process ( Demetriades et al . , 2014 ) , which are likely to be acting via mTORC1 , thus suggesting that the recruitment of TSC to lysosomes is a complex process mediated not only by Rheb but also by its interacting partners . At the same time , by proposing a dual mechanism of Rheb inhibition by TSC ( GTPase activation and sequestration into a stable complex which prevents the interaction of Rheb with mTOR as discussed above , Figure 4 ) , our interpretation of TSC2 localization phenotypes conceptually extends the conclusions of others . The ability of arginine to regulate mTORC1 activity is fundamentally important , and it is the only amino acid essential to both undifferentiated stem cells and differentiated lineages ( Figure 5 ) . The fact that mTORC1 sensitivity to leucine is acquired only after differentiation might reflect the need to integrate an increased complexity of extracellular stimuli to mTORC1 . Arginine is an essential amino acid during embryogenesis which is a period of active growth and proliferation largely driven by mTORC1 ( Hentges et al . , 2001; Wu et al . , 2009; Wu et al . , 2013 ) , and we speculate that this underlies the dependence of mTORC1 on this amino acid . Growth-promoting signals in the absence of arginine would lead to defective protein synthesis , which would explain the need for coordination between growth factor and arginine availability . We acknowledge , however , that other , arginine-independent molecular mechanisms contribute to mTORC1 regulation , and , together , these serve to provide extremely tight spatial and temporal regulation of mTORC1 signaling in response to nutrients . Indeed , while arginine , leucine , and glutamine are sufficient for mTORC1 signaling , the level of activation achieved by these amino acids is very low compared to an entire set of free amino acids . Understanding the mechanisms by which mTORC1 is sensing the entire complement of amino acids provides an exciting challenge for future investigations . HeLa cells , HEK293T , the osteosarcoma U2OS , primary human fibroblasts ( MRC5 ) , TSC2-deficient ( TSC2–/– ) and wild-type ( TSC2+/+ ) mouse embryonic fibroblast cell lines ( MEFs ) ( a kind gift from D . Kwiatkowski , Harvard University , Boston ) and neuroblastoma SK-N-SY were cultured in Dulbecco's Modified Eagle Medium ( DMEM; Sigma D6546 ) with 2 mM L-glutamine , 10% Foetal Bovine Serum ( FBS ) , 100 Units/ml penicillin-streptomycin at 37°C , 5% CO2 . Primary mouse neurons were cultured in Neurobasal medium containing b27 supplement ( 2% ) , glutamine ( 0 . 5 mM ) , and pen/strep ( 1% ) . Human embryonic stem cells ( hESCs ) WIBR3 ( Whitehead Institute Center for Human Stem Cell Research , Cambridge , MA ) were cultured as described previously ( Lengner et al . , 2010 ) . Briefly , hESCs were maintained on mitomycin C -inactivated MEF feeder layers in hESC medium [DMEM/F12 ( Life Technologies , Carlsbad , CA ) supplemented with 15% FBS ( Hyclone , Logan , UT ) , 5% KnockOut Serum Replacement ( Life Technologies ) , 1 mM glutamine ( Life Technologies ) , 100 U/ml penicillin/streptomycin ( Life Technologies ) , 100 U/ml penicillin/streptomycin ( Life Technologies ) , 1% non-essential amino acids ( Life Technologies ) , 0 . 1 mM β-mercaptoethanol , 4 ng/ml FGF2 ( R&D Systems ) ] . Cultures were passaged every 5 to 7 days either manually or enzymatically with 1 mg/ml collagenase type IV ( Life Technologies ) . Differentiation into hepatic-like cells was induced as described previously ( Si-Tayeb et al . , 2010 ) . Briefly , hESCs were plated on matrigel-coated plates as single cells and cultured in the presence of ROCK inhibitor for 24 hr . After reaching 80% confluency , differentiation was initiated following published protocols ( Si-Tayeb et al . , 2010 ) . As described previously , fibroblasts and neural progenitors ( NPs ) were derived using an embryoid body ( EB ) -based protocol ( Xu et al . , 2004; Marchetto et al . , 2010 ) . NPs were differentiated into neurons as described previously ( Soldner et al . , 2009; Chambers et al . , 2009 ) . Cells were transfected using FugeneHD ( Promega , Madison , WI ) or Lipofectamine 2000 ( Life Technologies ) according to the manufacturer’s protocols for 24 hr prior to lysis . The following plasmids were purchased from Addgene ( Cambridge , MA ) : pcDNA3-flag RhebN153T ( #19997 ) ( Urano et al . , 2007 ) and HA-GST p70S6K ( #15511 ) , pRK5-HA-GST RagB Q99L ( # 19303 ) , pRK5-HA-GST RagC S75L ( # 19305 ) . V5-TSC2 was a kind gift from M . Nellist ( Erasmus MC University Medical Center , Rotterdam , The Netherlands ) ( Hoogeveen-Westerveld et al . , 2012 ) . Lyso-TSC2 fused to the lysosomal-targeting sequence of p18 was a kind gift from B . Manning ( Harvard School of Public Health , Boston , MA ) ( Menon et al . , 2014 ) . Flag-tagged TSC2 constructs ( R1743Q and L1624P ) have been described previously ( Zhang et al . , 2013 ) . Myc-mTOR ( Korolchuk et al . , 2011 ) and GFP-Rab7 ( Seaman , 2004 ) have been described previously . pcDNA-flag-Rheb wild-type has been described previously ( Dunlop et al . , 2011 ) . Wild-type Rheb , the active mutant R15G , and the dominant negative mutant D60K were generated by mutagenesis of the threonine in pcDNA3-flag RhebN153T to asparagine followed by sequential mutation of arginine at residue 15 to glycine ( R15G ) or aspartic acid at residue 60 to lysine ( D60K ) using Stratagene Quickchange mutagenesis kit ( Stratagene , San Diego , CA ) as per company instructions . Primers: T153N to generate wild-type Rheb 5’-ccacagcagtctgattttctttagcagaagattccaaaaaag-3’ and 5’-cttttttggaatcttctgctaaagaaaatcagactgctgtgg-3’ . Rheb R15G 5’- ttccccacagacccgtagcccaggatc-3’ and 5’- gatcctgggctacgggtctgtggggaa-3’ . Rheb D60K 5’-tggacaagaatatcatcttcaacttgtaaaaacagccgggcaagat-3’ and 5’-atcttgcccggctgtttttacaagttgaagatgatattcttgtcca-3’ . ON-TARGETplus SMARTpool siRNA against human Rheb ( #6009 ) , mouse Rheb ( #19744 Dharmacon , Lafayette , CO ) , human RARS ( #5917 ) , human SLC38A9 ( #007337 ) and non-targeting SMARTpool siRNA ( D-001810-04 ) were purchased from Dharmacon . Four individual oligos against human mTOR ( Flexitube FRAP1_4–7 , Qiagen , Germany ) were pooled to a final stock concentration of 20 μM . Final siRNA concentrationsof 100 nM were used for 96 hr for silencing and transfections were carried out using Lipofectamine 2000 as per company instructions . Cells were grown in 12- or 6-well plates until 80% confluent . Serum starvation was carried out overnight , amino acid starvations were 1 hr and , where indicated , amino acid starvation in the presence of dialyzed FCS ( dFCS ) was carried out for 1 hr . For amino acid starvation , cells were washed briefly in PBS followed by 60 min incubation at 37° C with RPMI without amino acids ( US Biologicals , Salem , MA ) or in the presence of various amino acid mixtures ( and , where stated , dFCS ) . Recovery experiments were carried out by the addition of amino acids for 30 min before lysis . 100x stocks of amino acid mixtures were prepared from individual powders and diluted to 1x in RPMI without amino acids to the final concentration as found in DMEM media . Dialyzed FCS was purchased as <1 kDa cut-off ( Dundee Cell Products , UK ) and further dialyzed with cassettes of <3 kDa cut-off . Where noted , cells were incubated with insulin at a final concentration of 10 μg/ml and EGF ( Peprotech , Rocky Hill , NJ ) at 100 ng/ml . As a positive control for activation of AMPK , cells were incubated in DMEM without glucose ( Life Technologies; supplemented with 4 mM L-glutamine , 10 mM D-galactose , 10 mM HEPES , 1 mM sodium pyruvate , penicillin and streptomycin and 10% FBS ) for 1 hr . HeLa cells were incubated with 1 μM of the farnesyl-transferase inhibitor ( FTI ) , lonafarnib ( Caymen Chemicals , Ann Arbor , WI; #11746 ) or DMSO control for 24 hr . Cells were then incubated in the presence or absence of amino acids ( in the presence of lonafarnib ) before being fixed and immunostained . HeLa cells were incubated with rapamycin ( 100 nM ) for 1 hr pre-treatment in full nutrient medium . Cells were then incubated in the presence or absence of amino acids in RPMI without amino acids for 1 hr . Cells were lysed for immunoblot to confirm inhibition of mTOR activity or fixed and stained for TSC2 and Lamp1 . Cells were lysed in RIPA Buffer ( 50 mM Tris-HCl pH 7 . 4 , 150 mM NaCl , 1% NP40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS and supplemented with Halt protease and phosphatase inhibitors ( Thermo Scientific , Waltham , MA; #1861280 ) ) on ice . Lysates were centrifuged at 13 , 000 rpm for 10 min at 4°C and protein concentration was measured using DC protein assay ( Bio-Rad , Hercules , CA; #500–0112 ) . Equal amounts of protein ( 20–40 μg ) were subjected to SDS-PAGE and immunoblotted , as previously described ( Korolchuk et al . , 2011 ) and ( Smith et al . , 2005 ) . For immunoblotting of SLC38A9 , samples were prepared as described in ( Rebsamen et al . , 2015 ) , samples were prepared in sample buffer with reducing agent and incubated at room temperature for 15 min rather than boiled at 100°C . The following antibodies were used: rabbit anti-mTOR ( #2972 , 1:1000 ) , rabbit anti-phospho S6KThr389 ( #9205S , 1:1000 ) , rabbit anti-S6K ( #9202 , 1:1000 ) , rabbit anti-phospho S6Ser235/236 ( #4856 , 1:2000 ) , rabbit anti-S6 ( #2217 , 1:2000 ) , rabbit anti-phospho ULK1ser939 ( #3615 , 1:1000 ) , rabbit anti-phospho AktSer473 ( #9271 , 1:1000 ) , rabbit anti-phospho AktThr308 ( #4056 , 1:1000 ) , rabbit anti-phospho 4E-BP1Thr37/46 ( #2855 , 1:1000 ) , rabbit anti-phospho AMPKThr172 ( #2535 , 1:1000 ) , rabbit anti-Na+K+-ATPase ( #3010 . 1:1000 ) , rabbit anti-TSC1 ( #4906 , 1:1000 ) , rabbit anti-TSC2 ( #4308 , 1:1000 ) and rabbit anti-phospho TSC2Ser939 ( #3615S , 1:1000 ) were all purchased from Cell Signaling Technologies ( Danvers , MA ) . Other antibodies used in this study include mouse anti-alpha-tubulin ( 12G10 , DSHB , Iowa City , IA ) , goat anti-Rheb ( clone C-19 #sc-6341 , Santa Cruz , Dallas , TX , 1:500 ) , mouse anti-flag ( clone M2 , #F3165 , Sigma , 1:1000 ) , mouse anti-Lamp1 ( clone H4A3 , Abcam , Cambridge , MA , 1:1000 ) , mouse anti-V5 ( # R960-25 , Life Technologies , 1:5000 ) , mouse anti-myc ( Roche , Switzerland , 1:1000 ) , mouse anti-HA ( Covance , Princeton , NJ , 1:1000 ) , mouse anti-GM130 ( #610822 , BD Bioscience , UK , 1:1000 ) , mouse anti-PMP70 ( #SAB4200181 , Sigma , 1:2000 ) and mouse anti-SLC38A9 ( #HPA043785 , Sigma ) . Secondary antibodies conjugated to horseradish peroxidase ( HRP ) were all used at 1:5000 for 1 hr at room temperature . Clarity western ECL substrate ( Bio-Rad ) was used to visualize chemiluminescence on LAS4000 ( Fujifilm ) . Quantification of blots was carried out using ImageJ ( NIH ) . Where indicated , lysosomes were visualized with CellLight Lysosomes-GFP , BacMam 2 . 0 ( Life Technologies ) and mitochondria were labeled with MitoTracker green ( Life Technologies ) by incubation overnight . Immunofluorescence was carried out essentially as described previously ( Korolchuk et al . , 2011 ) . Briefly , cells were fixed in 4% formaldehyde in PBS for 10 min at room temperature . Cells were permeabilized with 0 . 5% Triton X-100 ( or methanol at −20°C for LC3 staining ) for 5 min at room temperature . Following 1 hr of blocking in 5% normal goat serum/ PBS 0 . 05% Tween-20 ( Tween-20 was omitted for LC3 staining ) , cells were incubated with primary antibodies overnight at 4°C . Primary antibodies used in this study include anti-mTOR ( #2972 Cell Signaling Technologies , 1:200 ) , anti-TSC2 ( #4308 Cell Signaling Technologies , 1:1000 ) , mouse anti-PMP70 ( #SAB4200181 , Sigma , 1:1000 ) , mouse anti-GM130 ( #610822 , BD Bioscience , 1:500 ) , mouse anti-LC3 ( Enzo/Nanotools , Germany; #LC3-2G6 , 1:250 ) , mouse anti-Lamp1 ( Abcam , 1:1000 ) , rat anti-Lamp1 ( #1D4B , DSHB , 1:1000 ) anti-flag ( Sigma , 1:1000 ) , anti-V5 ( Life Technologies , 1:1000 ) , anti-HA ( Covance , 1:1000 ) , Oct4 ( #sc-5279 , Santa Cruz , 1:500 ) , Nanog ( #AF1997 , R&D Systems , Minneapolis , MN , 1:500 ) , SSEA4 ( #MC-813-70 , DSHB , 1:20 ) , Nestin ( #AB5922 , Millipore , Germany , 1:500 ) , Pax6 ( #PRB-278P , Covance , 1:250 ) , Tuj1 ( #MMS-435P , Covance , 1:1000 ) , AFP ( #A8452 Sigma-Aldrich , 1:1000 ) , HNF4α ( #sc-6556 , Santa Cruz , 1:500 ) . Cells were washed three times and incubated with the appropriate secondary antibodies for 1 hr at room temperature ( Life Technologies , 1:1000 ) . Cells were washed and nuclear DNA was stained by incubation with TO-PRO-3 iodide ( Life Technologies , 1:3000 ) for 10 min at room temperature . Coverslips were mounted on slides with Prolong Gold antifade reagent ( Life Technologies ) . HeLa cells were seeded in 10-cm2 plates until 80–90% confluent . Cells were treated with various starvation protocols as indicated before lysis . Lysosomes were isolated with the Lysosomal enrichment kit for tissue and cultured cells as per company instructions ( Thermo Scientific ) . Briefly , cells were scraped into cold Buffer A as provided with the kit , supplemented with protease and phosphatase inhibitors on ice . Lysates were vortexed at maximum speed for 5 s and incubated on ice for 2 min . Cells were lysed by sonication ( lysis efficiency was optimized by visual comparison with non-lysed control ) and equal volume of Buffer B ( provided by kit ) was added . Intact cells were removed by centrifugation at 500 x g for 10 min at 4°C . OptiPrep gradients were prepared with reagents provided in the kit , at the concentrations indicated in the kit . Samples were prepared with OptiPrep Cell Separation Media to a final concentration of 15% and overlayed on the gradient . Samples were centrifuged at 145 , 000 x g for 2 hr at 4°C . The top layer was removed and diluted with PBS before being subject to another centrifugation step at 18 , 000 x g for 30 min at 4°C . The subsequent pellet was washed with PBS and centrifugation was repeated as in the previous step . The lysosome-enriched pellet was resuspended in RIPA buffer , subject to protein quantification and Western blot analysis . HeLa cells were seeded in 10 cm2 plates 24 hr prior to transfection with either pRK5-flag Rheb or V5-TSC2 and/or myc-mTOR . First , Rheb-expressing cells were lysed ( buffer 1 ( mTOR-Rheb ) 20mM Tris pH 8 . 0 , 10 mM MgCl , 0 . 3% CHAPS and 2x Halt protease and phosphatase inhibitors or buffer 2 ( TSC2-Rheb ) 40 mM Tris pH 7 . 4 , 10 mM MgCl , 5 mM EGTA , 25 mM NaCl , 0 . 2% CHAPs , 2x Halt protease and phosphatase inhibitors [Sancak et al . , 2007; Smith et al . , 2005; Long et al . , 2005a; Sun et al . , 2008; Castro et al . , 2003; Rebhun et al . , 2000] ) on ice and centrifuged at 13 , 000 rpm for 10 min . Lysates were incubated with pre-washed and equilibrated anti-flag M2 magnetic beads ( Sigma Aldrich , #M8823 ) for 1 hr at 4°C with constant rotation . The Rheb-loaded beads were washed four times in lysis buffer . TSC2/mTOR transfected cells were incubated in the presence or absence of arginine for 1 hr prior to lysis . Lysates were centrifuged at 13 , 000 rpm for 10 min at 4°C and samples were collected ( 5% ) for subsequent analysis of protein expression levels . Lysates were incubated with Rheb-loaded beads ( 20 μl slurry/assay ) for 1 hr at 4°C with constant rotation . Beads were washed twice with lysis buffer and the pulled-down protein was eluted from the beads by incubation with 25 μl 0 . 2 M glycine-HCl pH 2 . 5 for 10 min at room temperature . Eluent was neutralized by the addition of 2 . 5 μl Tris-HCl pH 8 . 8 . The samples were then mixed with sample buffer and boiled at 100°C for 5 min before being subjected to western blot analysis . LC-MS was carried out as previously described ( Labuschagne et al . , 2014; Maddocks et al . , 2013 ) . Briefly , HeLa cells were seeded ( in triplicate ) in six-well plates and cultured in standard DMEM until 90% confluent . Cells were serum starved overnight and subjected to amino acid starvation protocols as indicated . Alternatively , cells pre-incubated with 10 mM L-norvaline , 100 μM , ADMA , 1 mM L-citrulline , control siRNA or RARs siRNA ( see above for treatment conditions ) were washed twice with PBS and incubated in the presence or absence of labeled arginine ( 13C6 , 15N4 , [#CNLM-539-H , CK gas] ) for 2 hr . Cells were washed once with cold PBS and lysed ( 50% methanol/30% acetronitrile/20% dH20 ) at a concentration of 2 × 106 cells per ml . Samples were vortexed for 45 s and centrifuged at 13 , 000 rpm . LC-MS was carried out as described ( Labuschagne et al . , 2014; Maddocks et al . , 2013 ) . HeLa cells were grown on 10-cm2 plates and were incubated in 5 ml of phosphate-free medium containing 0 . 5 mCi [32P]orthophosphate for 3 hr prior to lysis . Flag-tagged Rheb was immunoprecipitated for 1 hr at 4°C with anti-Flag antibodies bound to protein G-Sepharose . Immunoprecipitates were washed twice each with both buffer A ( 50 mM HEPES ( pH 7 . 4 ) , 100 mM NaCl , 10 mM MgCl2 , 1 mg/ml BSA , 1 mM DTT , 1% Triton ) and buffer B ( 50 mM HEPES [pH 7 . 4] , 100 mM NaCl , 10 mM MgCl2 , 0 . 1% Triton ) in the presence of protease inhibitors . [32P]-radiolabeled GTP and GDP were eluted from Rheb using 20 μl Rheb elution buffer ( 0 . 5 mM GDP , 0 . 5 mM GTP , 5 mM DTT , 5 mM EDTA , 0 . 2% SDS ) at 68°C for 20 min and then resolved by thin layer chromatography on polyethyleneimine cellulose with KH2PO4 . The GTPase-activating protein assays were carried out as previously described ( Tee et al . , 2003 ) . An N-terminal GST fusion protein of full length human Rheb ( GST-Rheb ) was expressed in Rosetta2 ( DE3 ) E . coli ( Novagen , Merck Millipore , Billerica , MA ) using a pGEX-4T-2 vector ( Tee et al . , 2003 ) , grown in 2xYT media and induced with 0 . 5 m IPTG at 37°C for 3 hr . Cells were resuspended in 20 mM Tris pH 8 . 0 , 500 mM NaCl , 2 mM DTT with EDTA-free protease inhibitors ( Pierce , Thermo Fisher Scientific ) and lysed by sonication . Initial purification was achieved by glutathione Sepharose 4B ( GE Healthcare , UK ) affinity chromatography , with elution in 20 mM Tris pH 8 . 0 , 200 mM NaCl , 2 mM DTT , 10 mM reduced glutathione . GST-Rheb was further purified by anion exchange chromatography ( GE Healthcare ) , concentrated ( Millipore Amicon Ultra-4 ) , flash-frozen in liquid nitrogen and stored at −80°C in 20 mM Tris pH 8 . 0 , 300 mM NaCl , 5 mM MgCl2 , 2 mM DTT at 4 mg/ml . Protein samples were analysed by SDS-PAGE using the Bolt Bis-Tris system with SimplyBlue SafeStain ( Thermo Fisher ) . Protein concentrations were measured using a Nanodrop 1000 spectrophotometer ( Thermo Scientific ) with extinction coefficients and molecular weights determined by ExPASy ProtParam ( Bairoch et al . , 2005 ) In this study , 5 × 106 HEK293T cells were transfected in 10 cm dishes overnight before 5 μg each of Flag-tagged TSC1 and V5-tagged TSC2 were transfected for a further 24 hr . Cells were harvested in 1 ml lysis buffer ( 20 mM Tris pH 7 . 4 , 150 mM NaCl , 1 mM MgCl2 , 1% NP-40 , 10% glycerol , 1 mM DTT and 2x protease/phosphatase inhibitors ) . Lysates were incubated on ice and centrifuged at 13 , 000 rpm for 10 min at 4°C . Samples were incubated with 200 μl V5-conjugated agarose ( Sigma ) for 2 hr at 4°C . Beads were washed 2x in pull-down buffer ( 20 mM Hepes pH 7 . 4 , 150 mM NaCl , 1 mM EDTA , 5 mM MgCl2 , 0 . 5% NP-40 , 10 mg/ml BSA , 1 mM DTT and 2x protease/phosphatase inhibitors ) and once in Rheb-loading buffer ( 20 mM Hepes pH 8 . 0 , 200 mM NaCl , 5 mM MgCl2 , 10 mM EDTA ) . Equal volume of beads was aliquoted into separate , experimental tubes . Recombinant GST-Rheb1-184 was defrosted quickly and centrifuged at 13 , 000 rpm for 20 min at 4°C . GST-Rheb was diluted in Rheb-loading buffer and loaded with non-hydrolysable GTP ( GTPγS [Cytoskeleton , Denver , CO] ) by the addition of 0 . 2 mM GTPγS and incubated at 30°C for 10 min , followed by the addition of 20 mM MgCl2 . Reactions were prepared by incubating 2 μg/ml GST-Rheb either with or without 100 μM arginine . The pH of the buffer was adjusted to 7 . 4 before the addition of equal amounts of TSC2 agarose beads . Reactions were incubated for 1 hr at 4°C with rotation and beads were washed three times in TSC2 wash buffer . Immunoprecipitates were eluted by incubation with 0 . 2 M glycine pH 3 for 10 min at room temperature . Sample buffer was added to the eluted samples , boiled and analysed by western blot . The structure of the Rheb-TSC2 complex was predicted using the X-ray crystal structure of the human Rap1-Rap1GAP complex ( PDB: 3BRW ) ( Scrima et al . , 2008 ) as a template . It was assumed that , because of the conservation of mechanism in small G-Protein-GAP systems , the two components of the Rheb-TSC2 complex would be oriented relative to one another in the same way , so an interface of approximately correct structure would be produced by superposing each component on its homologue in the Rap1-Rap1GAP complex . TSC2: The model structure of the TSC2 GAP domain was generated with the SWISS-MODEL server ( Arnold et al . , 2006 ) ( http://swissmodel . expasy . org/ ) . The stoichiometry of Rap1-Rap1GAP in 3BRW is 1:3 , but on examination of the complex it is clear that it is the B Rap1GAP chain that is bound in the correct orientation to assist in catalysis . Accordingly , the B chain was used as the template . The server was used in alignment mode with a manually adjusted alignment; the alignment was based on that automatically generated , and in the C-terminus , that used to build a pre-existing TSC2 model in the SWISS-MODEL repository ( Kiefer et al . , 2009 ) which had used the unbound Rap1GAP ( PDB:1SRQ ) as a template . The sequence ID is 21% , although higher in the N-terminal half . The bound TSC2 model returned by the server contains almost the entire GAP domain ( residues 1525–1756 ) and is already in the coordinate frame in which it is superposed on the template . Rheb and complex: The GTP-bound structure of human Rheb ( PDB:1XTS ) ( Yu et al . , 2005 ) was used , and was superposed on human Rap1 ( PDB:3BRW chain D ) using the PDBeFold structure-based superposition server ( Krissinel and Henrick , 2004 ) ( http://www . ebi . ac . uk/msd-srv/ssm/cgi-bin/ssmserver ) . The two components were then concatenated . The resulting complex was refined by energy minimization with GROMACS ( Van Der Spoel et al . , 2005 ) to remove steric clashes in the interface ( bound GTP was removed to do this and then replaced afterwards ) . It was confirmed that the catalytic asparagine ( Asn ) 1643 of the TSC2 was appropriately positioned to interact with the gamma-phosphate of GTP . The interface residues of the complex were defined based on a simple distance criterion , that one non-hydrogen atom of the side-chain of the residue should be within 4 . 5 Å of any non-hydrogen atom of the other component . Quantification of confocal images was carried out by two separate techniques . First , the percentage of cells with punctate mTOR or TSC2 ( as a proxy for membrane localization of these proteins ) was blindly scored . >300 cells were counted per slide and quantification is based on at least three independent experiments unless otherwise stated . Second , confocal images were collected and the co-localization plug-in in ImageJ was used to measure the co-localization between mTOR or TSC2 and the lysosomal protein Lamp1 . A constant threshold was applied to all the images in the z-stack , and for every image within each experiment . Following application of the co-localization plug-in , all channels were projected ( max ) and quantified using Analyse particle plugin ( particles 5 pixels and above were included ) . The data were expressed as a percentage of mTOR or TSC2 that co-localized with Lamp1 . Quantification was carried out on 20–40 cells per condition from at three independent experiments . Quantification of immunoblots was carried out using ImageJ software ( NIH ) . Two-tailed , unpaired Student’s t-tests were carried out on experimental data from at least three individual experiments .	Cells need to be able to sense and respond to signals from their environment . A group ( or complex ) of conserved proteins called mTORC1 acts a key signaling hub that regulates cell growth and many other processes . This complex can be activated by many different signals from outside the cell . However , mTORC1 can only be activated by these signals if there is also a good supply of amino acids – which are needed to make new proteins – within the cell . The amino acids are thought to be presented to mTORC1 on the outer surface of cellular compartments known as lysosomes . A protein called Rheb on the surface of the lysosomes activates mTORC1 , while a protein complex called TSC inhibits the activity of Rheb to regulate mTORC1 activity . Previous studies have shown that some amino acids influence whether mTORC1 can be activated by affecting whether it is localized to the lysosomes or not . Here , Carroll et al . explored how an amino acid called arginine regulates mTORC1 . The experiments show that arginine is the major amino acid that influences whether mTORC1 can be activated in several different types of human cell . When cells were deprived of arginine , the activity of the complex was strongly suppressed . However , microscopy showed that arginine had no effect on whether mTORC1 was found at the lysosomes or not , which suggests that arginine might be acting in a different way to other amino acids . Further experiments found that a lack of arginine led to an increase in the number of TSC complexes at the lysosomes . This led to the inhibition of Rheb and therefore prevented mTORC1 from being activated . Together , Carroll et al . ’s findings provide evidence that the different signals that regulate mTORC1 signaling cooperate to a greater extent than previously thought . A future challenge will be to understand the molecular details of how the arginine is detected .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology" ]	2016	Control of TSC2-Rheb signaling axis by arginine regulates mTORC1 activity
The origin of animal multicellularity may be reconstructed by comparing animals with one of their closest living relatives , the choanoflagellate Salpingoeca rosetta . Just as animals develop from a single cell–the zygote–multicellular rosettes of S . rosetta develop from a founding cell . To investigate rosette development , we established forward genetics in S . rosetta . We find that the rosette defect of one mutant , named Rosetteless , maps to a predicted C-type lectin , a class of signaling and adhesion genes required for the development and innate immunity in animals . Rosetteless protein is essential for rosette development and forms an extracellular layer that coats and connects the basal poles of each cell in rosettes . This study provides the first link between genotype and phenotype in choanoflagellates and raises the possibility that a protein with C-type lectin-like domains regulated development in the last common ancestor of choanoflagellates and animals . The molecular mechanisms underlying animal multicellularity evolved , in part , through the modification of ancient adhesion and signaling pathways found in the unicellular and colonial progenitors of animals . The evolution of the animal molecular toolkit may be reconstructed through the study of the choanoflagellates , the closest living relatives of animals ( Lang et al . , 2002; Carr et al . , 2008; Ruiz-Trillo et al . , 2008; Philippe et al . , 2009; Paps et al . , 2012 ) . For example , despite the fact that choanoflagellates are not animals , they express diverse genes required for animal multicellularity , including C-type lectins , cadherins , and tyrosine kinases ( Abedin and King , 2008; King et al . , 2008; Manning et al . , 2008; Nichols et al . , 2012; Suga et al . , 2012; Fairclough et al . , 2013 ) , demonstrating that these genes predate the origin of animals . In addition , the architecture of choanoflagellate cells is conserved with animals and helps to illuminate the ancestry of animal cell biology ( Nielsen , 2008; Richter and King , 2013; Alegado and King , 2014 ) . The colony-forming species Salpingoeca rosetta promises to be particularly informative about the origins of cell differentiation , intercellular interactions , and multicellular development in animals . Through a process that resembles the earliest stages of embryogenesis in marine invertebrates , single cells of S . rosetta undergo serial rounds of cell division to develop into spherical rosette colonies ( hereafter , ‘rosettes’; Figure 1 ) ( Fairclough et al . , 2010; Dayel et al . , 2011 ) . Rosette development in choanoflagellates mirrors the transition to multicellularity that is hypothesized to have preceded the origin of animals ( Haeckel , 1874; Nielsen , 2008; Mikhailov et al . , 2009 ) , although its relationship to animal development is unknown . Recent improvements to the phylogeny of choanoflagellates reveal that colony development may have an ancient origin that extends to the first choanoflagellates and possibly to the last common ancestor of choanoflagellates and animals ( Nitsche et al . , 2011 ) . The possibility that choanoflagellate colony development and animal embryogenesis have a common evolutionary history is brought into greater relief when compared with the quite different process of development observed in outgroups of the animal + choanoflagellate clade ( e . g . , Capsaspora owczarzaki; Sebe-Pedros et al . , 2013 ) , in which isolated cells with different genotypes gather into aggregates . 10 . 7554/eLife . 04070 . 003Figure 1 . S . rosetta: an emerging model for studying animal origins and multicellularity . S . rosetta cells are polarized , each having a single apical flagellum encircled by a collar of microvilli ( bracket ) , shown in cross-sectional diagram ( A ) and through DIC imaging of a live cell ( B ) . In rosette colonies ( C ) , each cell is oriented around a central point , with the flagella facing outward . Bacterial prey ( ∼1 µm rods ) attach transiently to the collars of some cells prior to ingestion by phagocytosis . Scale bar = 10 µm . ( D ) S . rosetta transitions between several morphologically differentiated cell types during its life history: rosette colonies ( RC ) , chain colonies ( CC ) , slow swimmers ( SS ) , fast swimmers ( FS ) , and thecate cells ( TC ) . The transition from slow swimmers to rosette colonies ( star ) is induced by lipids from the bacterium Algoriphagus machipongonensis and can be regulated in the laboratory . ( E ) S . rosetta undergoes a sexual cycle in the laboratory . When starved , haploid cultures produce anisogamous gametes that are capable of mating to produce diploids . Diploids undergo meiosis and thereby produce haploids when grown in nutrient-rich media . Haploids and diploids can also reproduce asexually through mitosis . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 003 S . rosetta is also notable for its experimental tractability relative to other choanoflagellate species . Importantly , the switch between the S . rosetta solitary life style and rosette development is regulated by specific lipids produced by the prey bacterium Algoriphagus machipongonensis ( Alegado et al . , 2012 ) . Thus , rosette development can be induced in the laboratory . Moreover , the genome and transcriptome of S . rosetta have been sequenced and analyzed , revealing numerous homologs of diverse animal genes , many of which are up-regulated in colonies ( Fairclough et al . , 2013 ) . However , the roles of animal gene homologs in choanoflagellates have not been determined , and there have not been any published reports of successful disruptions to choanoflagellate gene function ( including gene deletions , RNA interference , or transgene expression ) . Indeed , no direct functional links have yet been drawn between genotype and phenotype for any choanoflagellate gene or trait . We recently found that S . rosetta can be induced to undergo sex and meiosis , suggesting that it may be amenable to mapping crosses ( Levin and King , 2013 ) . Therefore , to determine the genetic basis of rosette development and investigate its relationship to animal development , we set out to establish forward genetics in S . rosetta . To induce mutations in S . rosetta , cultures of haploid cells were exposed either to 0 . 3% EMS or 6300 rems X-rays , which resulted in a 10% or 40% reduction in cell number , respectively , when averaged across multiple trials ( Figure 3—figure supplement 1A ) . We elected to use these relatively light mutagen doses to minimize the number of background mutations in any mutant of interest . After exposing cells to either EMS or X-rays , clonal lines of potential mutants were established by isolating individual cells through limiting dilution ( i . e . , on average , plating less than one cell/well ) into 96-well plates containing rosette-inducing A . machipongonensis conditioned media ( ACM; Figure 2 ) . After 5 to 7 days , each well seeded with a wild-type cell was filled with rosettes , while wells seeded with mutant cells defective in rosette development were expected to produce cultures of solitary cells and/or chain colonies , but few to no rosettes , even in the presence of ACM . 10 . 7554/eLife . 04070 . 004Figure 2 . A screen for rosette defect mutants in S . rosetta . Rosette defect mutants were isolated by exposing S . rosetta haploid cells to either EMS or X-rays and then isolating clones in rosette-inducing Algoriphagus conditioned media ( ACM ) prior to visual screening . The use of limiting dilution to isolate clones resulted in many wells with no cells ( indicated as white circles ) . Wells seeded with a wild-type cell ( gray circles ) produced a culture with abundant rosette colonies , while wells seeded with a rosette defect mutant ( black circle ) produced a culture with chains or single cells , but few to no chain colonies . Candidate rosette defect mutants were validated through repeated rounds of limiting dilution prior to re-screening in ACM . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 004 We screened 15 , 344 clonal cultures for the presence or absence of rosettes ( Figure 2 ) . Nine mutants with validated rosette defects were isolated ( ‘Materials and methods’ ) , each of which showed a significant reduction in rosette development relative to wild type ( Figure 3A ) . The nine rosette defect mutants fell into seven phenotypic classes ( classes A–G , Table 1; Figure 3 ) based upon their ability to form rosette colonies in the presence of ACM or live A . machipongonensis , their swimming behavior as solitary cells , and the morphology of chain colonies produced when grown in the absence of ACM . 10 . 7554/eLife . 04070 . 005Table 1 . Classification of mutant phenotypesDOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 005Mutagen usedObserved rosette inductionOther phenotypesACMLive bacteriaSwimming†Chain morphologyWild typeN/A86%88%Wild typePrimarily linearMutant class A RosettelessEMS00Wild typePrimarily linearMutant class B InsensateX-rays05Wild typePrimarily linearMutant class C SlackerX-rays2042Wild typePrimarily linearMutant class D UptightX-rays3356Wild typeBranchedMutant class E JumbleEMS00Wild typeBranched BranchedX-rays00Wild typeBranchedMutant class F SeafoamX-rays00Wild typeLarge clusters SoapsudsX-rays00Wild typeLarge clustersMutant class G SoloX-rays00Slow , shakingPrimarily solitaryThe percentage of cells in rosettes following induction . †Swimming phenotypes of single cells . 10 . 7554/eLife . 04070 . 006Figure 3 . Phenotypes of diverse rosette defect mutants . ( A ) Cultures of all nine mutants isolated in this study showed a significantly reduced number of cells in rosettes relative to wild type ( one-tailed Mann–Whitney test , p < 0 . 01 ) . Rosette development was measured as the % of cells in rosettes after 48 hr in 20% ACM , shown as mean ± SEM . Ø indicates mutants in which no rosettes were observed ( limit of detection = 0 . 03% ) . ( B ) Wild-type S . rosetta grown without ACM formed flexible , linear chains or single cells ( Figure 3—figure supplement 2 ) . When exposed to ACM , wild-type S . rosetta cultures produced spherical rosettes ( arrowheads ) . Rosetteless cultures did not form rosettes in ACM , but otherwise appeared in wild type , forming normal chain colonies and proliferating at rates indistinguishable from wild-type S . rosetta ( Figures 3—figure supplement 1B , E and 2 ) . ( C ) Unlike Rosetteless , the remaining eight rosette defect mutants showed additional phenotypic aberrations . Although a small percentage of Slacker and Uptight cells were found in bona fide rosettes ( arrowheads ) , most remained as single cells or chain colonies that were easily disrupted when exposed to shear ( Figure 3—figure supplement 1E ) . Seafoam and Soapsuds formed large , disorganized clusters of cells that were easily disrupted when exposed to shear ( Figure 3—figure supplement 1E and 2 ) and were thus not rosettes . Scale bars = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 00610 . 7554/eLife . 04070 . 007Figure 3—figure supplement 1 . Mutagenesis and mutant phenotypes . ( A ) Vertical scatter plot showing the effect of mutagenesis on cell number , shown as the number of mutagenized cells divided by the number of cells in a paired , unmutagenized culture at 24 hr post-mutagenesis . Each dot represents one mutagenesis experiment and the mutagenesis from which the Rosetteless mutant was isolated is highlighted ( red ) . The dotted line at 1 . 0 represents no effect . The mutagen doses used in the screen were 0 . 3% EMS ( vol/vol ) and 6300 rem X-rays , which each resulted , on an average , in a decrease in cell number ( p < 0 . 05 , Wilcoxon signed rank test ) . ( B ) Growth curve of wild-type ( open circles , dotted line ) and Rosetteless mutant ( filled circles , solid line ) cells shows that the Rosetteless phenotype is not due to a growth defect . Error bars show standard deviation . ( C ) Quantification of rosette induction in the presence of live A . machipongonensis , shown as mean ± SEM . Ø represents cultures in which no rosettes were observed ( limit of detection = 0 . 03% ) . Compare to Figure 3B to see rosette induction from A . machipongonensis conditioned media ( ACM ) rather than live bacteria . Notably , the insensate mutant shows a low level of rosette induction when exposed to live bacteria but not when exposed to ACM . ( D ) Single confocal slices through rosettes stained with FM 1–43X dye showed differences in cell packing within wild-type and uptight mutant rosettes . Bottom: inverted images were false colored to mark the space between cells in the center of the rosette . Scale bar = 5 µm . ( E ) Phenotypes of rosette defect mutants after vigorous pipetting . Rosettes ( red arrowheads ) form in the presence of ACM and are robust to pipetting , whereas chain colonies break up into single cells . The uptight mutant occasionally formed rosettes , but none were visible in this field of view . Scale bar = 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 00710 . 7554/eLife . 04070 . 008Figure 3—figure supplement 2 . Chain colony morphologies of diverse mutants . S . rosetta chain colonies from wild-type and mutant cultures grown without exposure to A . machipongonensis signals are shown at low magnification to document the morphologies of numerous chain colonies . Each phase-bright circle is a choanoflagellate cell , while the dark specks are bacteria . To the right of each image , the cells of each in focus chain colony have been false colored blue to identify chains and emphasize chain morphology . Three mutants ( Rosetteless , Insensate , and Slacker ) exhibit essentially wild-type chains . Solo is largely single-celled and rarely forms chains . The five remaining mutants all form chains that are more branched or highly clustered than wild-type chains . Scale bar = 50 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 008 Class A consisted of a single mutant , named Rosetteless , that was isolated after EMS treatment . In the presence of either ACM or live A . machipongonensis , Rosetteless cells failed entirely to develop into rosettes , but their cell morphology and proliferation were otherwise indistinguishable from wild type ( Figure 3B and Figure 3—figure supplement 1B ) . Mutants from classes B–D formed some rosettes , but significantly fewer than wild-type strains , and class D exhibited altered spacing and orientation of cells within the rare rosettes that formed ( Figure 3A , Figure 3—figure supplement 1C and D ) . Rosette development was never observed in mutants from classes E–G ( Figure 3A , Figure 3—figure supplement 1C , E , Table 1 ) . Although S . rosetta was originally isolated as a rosette , wild-type cells can produce linear , ‘chain’ colonies when grown without A . machipongonensis ( Figure 1 ) . Rosettes and chain colonies can be easily distinguished from each other . In addition to the differences in their morphology , the connections among cells in rosettes are robust and resistant to mechanical shear , whereas chain colonies are fragile and readily fall apart into individual cells when exposed to shear forces . Nonetheless , chain colonies and rosettes have some similarities , including the presence of fine intercellular bridges connecting neighboring cells and similar transcriptional profiles ( Dayel et al . , 2011; Fairclough et al . , 2013 ) . We therefore investigated whether the rosette defect mutants had co-occurring defects in their ability to form normal chain colonies with linear morphology . Mutant classes A–C formed apparently wild-type chain colonies . In contrast , classes D–F developed into highly branched chain colonies while cultures of the class G mutant , which rarely formed chains , were instead observed to be predominantly single celled ( Table 1 , Figure 3—figure supplement 2 ) . The ability to isolate mutants with a range of rosette and chain phenotypes demonstrates the potential of forward genetics to illuminate diverse aspects of multicellular development in S . rosetta . The Rosetteless mutant phenotype was highly penetrant and yet the mutant lacked any other obvious defects ( Figure 3 , Figures 3—figure supplement 1B and 2 ) . We thus inferred that the gene ( s ) disrupted in the Rosetteless mutant might have roles specific to rosette development . Therefore , as we set out to establish methods for mapping mutations in S . rosetta , we focused on the Rosetteless mutant . We started by sequencing Rosetteless and two closely related wild-type strains ( the parental strain from which Rosetteless was isolated and C2E5 , a co-isolated wild-type strain ) to identify sequence variants that could serve as genetic markers ( Figure 4—figure supplement 1 , ‘Materials and methods’ ) , with the understanding that one or more of the detected sequence variants might ultimately prove to be the causative mutation ( s ) . After filtering the sequence variants by quality , we identified 25 , 160 potential genetic markers that differed between Rosetteless and the reference genome sequence , only four of which were unique to the Rosetteless genome ( Figure 4—figure supplement 1 ) . Our recent discovery of the sexual cycle of S . rosetta ( Levin and King , 2013 ) suggested that it might be possible to perform a choanoflagellate mapping cross to identify the mutation ( s ) responsible for the Rosetteless phenotype . To this end , Rosetteless was mated with another , previously sequenced S . rosetta strain , Isolate B ( Levin and King , 2013 ) , that carried 39 , 451 putative sequence polymorphisms relative to Rosetteless ( Figure 4—figure supplement 2 ) . Using a combination of serial dilutions and genotyping , we isolated seven outcrossed diploids and established clonal cultures ( Figure 4 , ‘Materials and methods’ ) . In the second phase of the mapping cross , the heterozygous diploid cultures were expanded and divided into multiple flasks , rapidly passaged in rich media to induce meiosis , and subjected to another round of serial dilution to generate clonal cultures . Of 442 clonal cultures genotyped , 182 were haploid progeny of the cross , as evidenced by their homozygosity at three microsatellite markers ( ‘Materials and methods’ ) . 10 . 7554/eLife . 04070 . 009Figure 4 . Rosetteless maps to EGD82922 . ( A ) Design of the mapping cross . Rosetteless cells were mixed with Isolate B , an S . rosetta culture capable of forming rosettes . Mating was induced by starvation . To isolate the products of outcrossed mating , cells then underwent clonal isolation , and clonal populations were genotyped to identify outcrossed , diploid heterozygotes . These heterozygotes were expanded and induced to undergo meiosis , after which clonal isolation and genotyping were repeated . Haploid progeny of the cross were homozygous at all three markers . ( B ) 2 × 2 contingency table shows that the Rosetteless phenotype was tightly linked to the genotype of the supercontig 8: 427 , 804 candidate splice donor mutation . ( C ) Map of the supercontig 8 markers . Top numbers show the genetic distance between the markers and the Rosetteless phenotype in centimorgans ( cM ) . Bottom numbers show marker genomic positions on supercontig 8 . Black lines within the central bar show all sites of predicted polymorphism ( i . e . , possible marker positions ) between Rosetteless and Isolate B . The blue marker is the EGD82922 splice donor mutation . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 00910 . 7554/eLife . 04070 . 010Figure 4—source data 1 . Full genotyping data for all progeny of the Rosetteless x Isolate B cross . For each isolated progeny of the cross , we provide the phenotype when grown in HN media ( chains or rosettes ) , the identity of the heterozygous flask from which the haploid isolate was derived ( ‘Flask isolated from’ ) , and the genotypes at 60 SNV and microsatellite loci . For the microsatellite loci , numbers indicate the approximate size of the amplicon . Missing genotype data is indicated by ‘NA’ . Color-coding shows genotypes and phenotypes that match Isolate B ( white ) or Rosetteless ( yellow ) . Genotyping data was used to construct a linkage map for S . rosetta ( Figure 4—figure supplement 3 ) . For each marker , we list its linkage group and its inferred position within the linkage group . Four of the cross isolates were excluded from further analyses because their genotypes were identical to other cross isolates . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 01010 . 7554/eLife . 04070 . 011Figure 4—figure supplement 1 . Identification of Rosetteless-specific mutations . ( A ) Venn diagram of the high-quality single nucleotide variants ( SNVs ) detected in the genome sequences of Rosetteless , the parental strain , and a co-isolated wild-type strain as compared to the S . rosetta reference genome . The vast majority of detected SNVs ( 25 , 055 ) were shared among all three isolates . Only 17 unique , high-quality SNVs were predicted in Rosetteless . ( B ) The seventeen predicted , Rosetteless-specific SNV calls . Genotyping of Rosetteless and wild-type S . rosetta was used to determine whether the putative unique SNV was verified ( V , present in Rosetteless but absent in wild type ) , a false positive ( FP , absent from both Rosetteless and wild type ) , a false negative ( FN , present in both Rosetteless and wild type ) , or a mistakenly called SNV in a region where the S . rosetta reference genome was misassembled ( GM ) . The SNV on supercontig 8 ( bold ) was the only one predicted to alter a coding region . ( C ) Cloning and Sanger sequencing confirmed that the supercontig 8: 427 , 804 SNV was polymorphic between Rosetteless and the parental strain . Note that the Sanger sequencing shown here was from the + strand , while the gene is encoded on the—strand . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 01110 . 7554/eLife . 04070 . 012Figure 4—figure supplement 2 . Map of polymorphisms and markers used in the cross . Horizontal bars represent the 25 largest supercontigs in the S . rosetta genome . The positions of each predicted polymorphism , based on comparisons of the genome sequences of Rosetteless and Isolate B , are vertical lines mapped semi-transparently in black , such that regions with high variant density are darker than those with lower variant density . Red arrows show positions genotyped in all cross isolates , including the perfectly linked supercontig 8: 427 , 804 mutation ( * , Figure 4—source data 1 ) . Note that the supercontig 8: 427 , 804 mutation lies within a haplotype block with little to no polymorphism between Rosetteless and Isolate B . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 01210 . 7554/eLife . 04070 . 013Figure 4—figure supplement 3 . A linkage map for S . rosetta . Linkage map of S . rosetta , based on the genotypes of the 178 independent Rosetteless–Isolate B cross progeny ( LOD cutoff = 5 ) . The color of each box represents the genetic distance between two markers ( shades of gray; see key ) or the genetic distance between a marker and the Rosetteless phenotype ( shades of blue; see key ) , thresholded such that non-significant distances are white ( one-tailed Fisher's exact test , p > 0 . 05 ) . The boxes representing the four SNVs that were unique to Rosetteless are outlined in red . Gray lines show the boundaries between assembled linkage groups , labeled at the top of the figure . The supercontig locations of each marker are listed , in order , on the left and bottom sides of the figure . Note that the Rosetteless phenotype ( blue ) is tightly linked only to the splice donor mutation and not to any of the other genomic positions or Rosetteless-specific SNVs . Although there was some observed linkage between linkage groups 2 and 3 , this may be an artifact of the segregation distortion and so we conservatively assigned these to separate linkage groups . Figure 4—source data 1 lists the exact genomic and linkage group positions for each marker . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 013 Genotyping of each haploid isolate at 60 polymorphic sites across the genome revealed that most markers followed Mendel's law of Segregation and Independent Assortment ( ‘Materials and methods’ , Figure 4—source data 1 ) ( Mendel , 1866 ) ; thus S . rosetta inheritance appears to follow the rules of classical genetics . Analysis of the genotyping data in haploids also revealed genetic linkage among some of the markers , allowing us to generate a linkage map containing 27 preliminary linkage groups that represent approximately 70% of the S . rosetta genome ( Figure 4—figure supplement 3 , Figure 4—source data 1 ) . Most importantly , the genotype data revealed only one mutation ( supercontig 8 , position 427 , 804 ) that was tightly linked ( <0 . 56 cM ) to the rosette defect phenotype . The presence of the mutation was linked to the presence of the Rosetteless phenotype in all examined haploid progeny from the Rosetteless × Isolate B cross ( 177/177 , Figure 4 ) . Moreover , the mutation was one of the four validated Rosetteless-specific SNVs and , by disrupting a splice donor in the gene EGD82922 , was the only one predicted to cause a coding change ( Figure 4—figure supplement 1B ) . To investigate whether our variant calling method was too restrictive , we also genotyped the heterozygous diploids for 20 additional putative polymorphisms near the EGD82922 marker , which were called below our quality threshold , and none proved to be polymorphic in this cross . Therefore , based on the tight linkage between the EDG82922 mutation and the phenotype , as well as the absence of any other detected polymorphisms in the 5′ end of supercontig 8 , we infer that the candidate splice donor mutation in the gene EGD82922 is responsible for the rosette defect phenotype . We hereafter refer to EGD82922 ( Genbank accession XP_004995286 ) as rosetteless ( rtls ) and the relevant mutation as rtlsl1 . The rtls gene encodes a 119 kDa protein with an N-terminal signal peptide and two C-type lectin-like domains ( CTLDs; Figure 5A ) . CTLD-containing proteins , including the C-type lectins , are found in all animal lineages and play diverse roles , including cell–cell adhesion , cell–extracellular matrix adhesion , cell signaling , and innate immune recognition of pathogens through their binding to carbohydrates , proteoglycans , lipids , and other ligands ( Ruoslahti , 1996; Cambi et al . , 2005; Zelensky and Gready , 2005; Geijtenbeek and Gringhuis , 2009; Švajger et al . , 2010 ) . Similar to CTLDs from animals , the Rtls CTLDs contain four conserved cysteines required for two disulfide bonds , as well as the Glu-Pro-Asn motif ( Figure 5B ) that is required for mannose binding in some C-type lectins ( Drickamer , 1992 ) . Nonetheless , because the CTLDs of Rtls have not yet been shown to bind sugar moieties , we follow the convention of the field and provisionally refer to Rtls as a C-type lectin-like protein . Rtls also contains several low-complexity regions that each consists of as many as 50–60 consecutive threonines and serines and two high-complexity internal repeat regions ( RP1 and RP2 ) of unknown function near the C-terminus of the protein ( Figure 5A , C ) . The serine-threonine-rich regions resemble mucin-like domains found in some animal C-type lectins and are likely sites of O-linked glycosylation ( Carraway and Hull , 1991; Drickamer and Dodd , 1999 ) . The rtlsl1 mutation , a T-to-C mutation in the predicted splice donor of intron 7 , falls 3′ of the sequences encoding the two CTLDs and 5′ of the RP1 and RP2 sequences ( Figure 5A ) . 10 . 7554/eLife . 04070 . 014Figure 5 . Gene structure , domain organization , and expression of rtls . ( A ) The rtls gene ( top ) contains 12 exons ( numbered ) and encodes a protein ( bottom ) with an amino-terminal signal peptide ( green ) , two C-type lectin-like domains ( CTLDs ) , extended stretches of serines and threonines ( wavy lines ) , and two internal repeats of unknown function ( RP1 and RP2 ) . The rtlsl1 SNV interrupts a splice donor in intron 7 ( GT → GC ) . The epitope used to generate the anti-Rtls antibody is shown ( orange bracket ) . ( B ) An alignment of Rtls CTLDs with CTLDs from rat surfactant protein A ( rat SP-A , 1R13_A ) and rat mannose-binding protein ( rat MBP , 2MSB_A ) revealed that residues used in disulfide bonds ( blue ) , mannose-type sugar binding ( red ) , and calcium ion binding ( * ) are conserved . Other conserved or similar residues are highlighted in gray . ( C ) Alignment of the RP1 and RP2 regions . ( D ) RT-PCR of rtls with primers to the exon 5/6 junction and exon 12 showed that wild-type cells produce a single isoform while Rosetteless cells produce diverse splice isoforms . ( E ) Wild-type cDNA yielded the expected splice isoform ( i ) while Rosetteless mutant cDNA yielded isoforms with: ( ii ) intron 7 retention or ( iii–iv ) variants of exon 7 that were longer ( * ) or shorter ( ** ) than wild type . Isoforms ii and iv contained early stop codons ( arrows ) . ( F ) Semi-quantitative analysis of the fluorescent signal observed in Rtls dot blots , normalized to the intensity of the wild-type culture ( WT ) . Rosetteless mutant cells ( rtlsl1 ) showed reduced Rtls signal both with and without A . machipongonensis ( Alg ) relative to WT ( Figure 5—figure supplement 1C ) . Error bars show standard deviation . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 01410 . 7554/eLife . 04070 . 015Figure 5—figure supplement 1 . Rosetteless splicing and protein levels . ( A ) RT-PCR with a primer bridging the exon 7/8 boundary of rtls paired with a primer in exon 12 , amplified the wild-type rtls splice isoform from wild-type and rtlsl1 cDNA . This was in contrast with the diverse alternative rtls splice isoforms amplified from Rosetteless cells when RT-PCR was performed with a primer bridging the exon 5/6 junction and a primer in exon 12 ( Figure 5D ) . ( B ) Purified , recombinant protein corresponding to the anti-Rtls epitope has a predicted size of approximately 38 kDa ( arrowhead ) . The purity of 100 ng of recombinant protein was analyzed by silver stain ( left ) and by western blot with anti-Rtls ( right ) on two separate 4–12% gradient gels . ( C ) Raw data showing the validation of anti-Rtls on dot blots of wild-type cell lysates . Pre-incubation of anti-Rtls with the recombinant Rtls epitope competes away the staining , demonstrating that the majority of the signal is specific to the Rtls protein . Three replicate samples are shown . ( D ) Raw dot blot data showing levels of Rtls in wild-type ( WT ) or Rosetteless mutant ( rtlsl1 ) cultures with or without inoculation with A . machipongonensis ( Alg . ) . Each spot is normalized for total S . rosetta cell number . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 01510 . 7554/eLife . 04070 . 016Figure 5—figure supplement 2 . The diversity of S . rosetta and M . brevicollis CTLD-containing proteins . The protein domain architectures of all CTLD-containing proteins predicted in the genomes of S . rosetta ( blue ) and M . brevicollis ( purple ) are shown . In addition , diagrams of select animal CTLD-containing proteins with similar architectures to Rosetteless are provided ( white ) . Genbank accession numbers are listed at the left . All diagrams are drawn to scale . Although proteins in choanoflagellates are commonly assigned animal orthologs based on their diagnostic domain architecture , the minimal domain features of Rosetteless preclude any clear assignments of orthology . Additionally , within animals , C-type lectins are a family of rapidly evolving genes that exhibit extensive duplications and rearrangements among taxa ( e . g . , [Drickamer and Dodd , 1999] , [Sattler et al . , 2012] ) . It is thus not currently possible to assign clear orthology relationships between many of the animal C-type lectins , much less between animal C-type lectins and more evolutionarily distant CTLD-containing proteins found in choanoflagellates and other eukaryotes ( e . g . , [Wheeler et al . , 2008] ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 016 We hypothesized that the exon 7 splice donor mutation in Rosetteless cells might result in defective splicing of rtls . To test whether proper rtls splicing of exons 7 and 8 can occur in Rosetteless cells , we performed RT-PCR using primers that selectively amplify rtls splice isoforms with the predicted exon 7/8 junction and recovered the expected splice isoform from both wild-type and Rosetteless cells ( Figure 5—figure supplement 1A , Supplementary file 1 ) . However , when using primers that could amplify either the wild-type isoform or variant splice isoforms ( a 5′ primer bridging the exon 5/6 junction paired with a 3′ primer in exon 12 ) , we found that rtls was spliced as predicted in wild-type cells , but produced multiple , variant splice isoforms in Rosetteless cells . The variant isoforms included one isoform in which intron 7 was retained and two smaller isoforms in which an alternative splice donor either 14 bp upstream or 27 bp downstream of the mutation was used instead ( Figure 5D , E ) . Importantly , the wild-type rtls isoform was not detected in Rosetteless cells using this assay . For two of the major splice isoforms in Rosetteless cells , the altered splicing led to frame shifts and early stop codons downstream of the mutation , which may either lead to a truncation of the Rtls protein or to degradation of the transcript by nonsense mediated decay in mutant cells ( Lareau et al . , 2007 ) . To investigate endogenous Rtls protein in S . rosetta cells , we generated an antibody against residues 438–539 , a region of the protein that is unique to Rtls and expected to be present in all wild-type and mutant Rtls isoforms ( Figure 5A , ‘Materials and methods’ ) . Using this antibody , we found that total Rtls protein levels in mutant cells were ∼25% that of wild-type cells ( Figure 5F , Figure 5—figure supplement 1B–D ) . The lack of transgenic approaches in choanoflagellates meant that we could not complement the rtlsl1 mutation nor delete the rtls gene in wild-type cells . Nonetheless , C-type lectins in animals have been functionally perturbed through the use of blocking antibodies ( Tassaneetrithep et al . , 2003; Liu et al . , 2014 ) , and we hypothesized that we could block the function of the extracellular pool of Rtls protein by incubating wild-type cells with an anti-Rtls antibody ( ‘Materials and methods’ ) . Therefore , to test the necessity of Rtls function for rosette development , wild-type S . rosetta cultures were incubated with 0–50 μg/ml anti-Rtls antibody during exposure to A . machipongonensis bacteria . Treatment with anti-Rtls resulted in significant inhibition of rosette formation relative to negative controls ( Figure 6A and Figure 6—figure supplement 1A ) . Specifically , in cultures treated with 50 µg/ml anti-Rtls during rosette induction , only 10 ± 11% of cells were observed in rosettes ( mean ± standard deviation , Figure 6A ) . In contrast , wild-type S . rosetta cultures incubated with an equal volume of rabbit pre-immune serum or an equivalent concentration of normal rabbit IgG or BSA showed normal levels of rosette development , with 91 ± 2% , 90 ± 2% , or 88 ± 1% of cells in rosettes , respectively . Importantly , treatment of wild-type cells with 50 µg/ml anti-Rtls did not result in a loss of cell viability or reduction in cell growth ( Figure 6—figure supplement 1B ) . Therefore , we conclude that the function of secreted Rtls is specific to and essential for rosette development . 10 . 7554/eLife . 04070 . 017Figure 6 . Rtls is required for rosette development and localizes to the center of rosettes . ( A ) Rosette development in wild-type S . rosetta was inhibited in the presence of 50 µg/ml anti-Rtls antibody , leading to a significant reduction in the percentage of cells in rosettes ( one-tailed t test , p < 0 . 05 ) as compared to BSA , pre-immune serum , and IgG negative controls . Error bars show standard deviation . ( B–C ) The localization pattern of cell-associated Rtls differs between wild-type rosettes , chains , and single cells . ( B ) In rosettes , Rtls ( cyan ) was detected as a thick layer associated with the basal poles of the cells . Commonly , a gap was observed in the Rtls staining between one pair of neighboring cells in each rosette ( arrow ) . The collar microvilli and filopodia were stained with phalloidin ( red ) and anti-tubulin staining ( white ) was used to highlight the cell body and flagellum . ( C ) Rtls localization in ( 1 ) wild-type rosettes , ( 2 ) wild-type chains , ( 3 ) wild-type single cells , and ( 4 ) Rosetteless mutant single cells . In single cells and chains imaged as in Figure 6B ( ‘Rtls’ , laser intensity = 2 . 0 , zoom = 2 . 5 , gain = 544 ) , Rtls signal was nearly undetectable . However , when imaged with a higher photomultiplier gain ( ‘Rtls–high gain’ , laser intensity = 2 . 0 , zoom = 2 . 5 , gain = 750 ) , Rtls was detected in membrane-associated patches ( arrowheads ) in wild-type single cells and chains , but not in Rosetteless cells . Wild-type single cells and chains frequently also had immunoreactive material deposited on the slide adjacent to the cells ( asterisk ) . All cell types showed faint , diffuse fluorescence throughout the cell body , but this was likely the result of non-specific staining ( Figure 6—figure supplement 2 ) . Scale bars = 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 01710 . 7554/eLife . 04070 . 018Figure 6—figure supplement 1 . Anti-Rtls blocks rosette development . ( A ) Extended data for Figure 6A , showing that increasing concentrations of anti-Rtls lead to a significant reduction in rosette development relative to BSA , IgG , and pre-immune serum negative controls . ( B ) Cell densities of wild-type S . rosetta incubated with 50 μg/ml BSA , pre-immune serum , 50 μg/ml IgG , or increasing concentrations of anti-Rtls for 24 hr for the experiment in ( A ) , demonstrating that the reduction in rosette development following treatment with anti-Rtls was not due to growth inhibition . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 01810 . 7554/eLife . 04070 . 019Figure 6—figure supplement 2 . Validation of the anti-Rtls antibody in immunofluorescence . To determine the specificity of the anti-Rtls antibody , the antibody was incubated either with ( + ) or without ( − ) the recombinant , purified epitope prior to the staining of wild-type or Rosetteless mutant cells . This pre-incubation of anti-Rtls with its purified epitope was expected to compete away the specific anti-Rtls signal and led to the loss of: the bright staining in the center of rosettes , the enriched patches of staining associated with the cell membranes of wild-type single cells ( arrowhead ) , and immunoreactive material that was often detected on the slide surface adjacent to wild-type single cells ( * ) . Notably , these are also the staining patterns that are missing from the Rosetteless mutant cells . ( The loop at the apical tip of the flagellum was observed in wild-type and mutant cells and is not part of the Rosetteless phenotype . ) In all cells following competition with the epitope there remained faint staining in the cell body; we thus infer that this is non-specific staining that does not reflect the distribution of Rtls protein in the cells . All cells were stained with the same concentration of anti-Rtls antibody and were imaged at the same exposures by confocal microscopy . Co-staining with phalloidin ( red ) and anti-tubulin ( white ) allowed for the visualization of the collar , cell body , and flagellum . Scale bar = 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 04070 . 019 The connection between Rtls function and rosette development was also reflected in its differential localization in wild-type rosettes , chains , and single cells . In wild-type cells , Rtls was highly enriched in the extracellular matrix-filled center of rosettes , where it was observed in a thick layer underlying the basal poles of all cells in the rosette ( Figure 6B , C ) . While Rtls staining sometimes connected all cells in the center of rosettes ( Figure 6C ) , in most instances Rtls was observed to connect all but one pair of neighboring cells ( Figure 6B ) . Because rosettes form through a process of incomplete cytokinesis ( Fairclough et al . , 2010; Dayel et al . , 2011 ) , this discontinuous Rtls staining may reflect the history of cell division during rosette formation , with discontinuities in its distribution indicating adjacent cells that were not sisters . We hypothesize that Rtls regulates rosette development by interacting with components of the extracellular matrix ( ECM ) , which has previously been shown to fill the center of rosettes ( Dayel et al . , 2011 ) . In wild-type single cells and chain colonies , the subcellular localization and apparent abundance of Rtls were notably different than in rosettes . Despite the fact that equivalent levels of Rtls were detected in lysates from rosette-induced and -uninduced cultures ( Figure 5F ) , little to no Rtls signal was detected by immunofluorescence when wild-type single cells and chains were imaged with the settings used for visualizing Rtls in rosettes ( Figure 6C ) . Because Rtls has a predicted secretion signal , it is possible that S . rosetta chain colonies and single cells released Rtls into their aquatic environment , where it may have been washed away during processing of cells for immunofluorescence . After increasing the gain used during confocal imaging , we were able to detect Rtls in cell membrane-associated patches in wild-type single cells and chains , but these patches were absent from Rosetteless mutant cells ( Figure 6C , Figure 6—figure supplement 2 ) . The patches of Rtls localization were most often located near the basal pole of each cell , but were sometimes detected at the apical pole or along the lateral regions of the cell ( Figure 6C ) . In no case was the Rtls staining in single cells or chains as intense as the Rtls staining observed in the cores of rosettes . In summary , three findings demonstrate that Rtls function is necessary for and specific to rosette development: ( 1 ) the localization of Rtls protein is developmentally regulated and most abundant in the core of rosettes , ( 2 ) Rosetteless mutant cells fail to form rosettes but are otherwise wild type , and ( 3 ) secreted Rtls protein is essential for rosette development , while being dispensable during other stages of the S . rosetta life history . The rosetteless gene is the only gene yet known to be required for choanoflagellate multicellular development . The molecular mechanisms by which Rtls regulates rosette formation remain unknown , but the developmentally regulated secretion of Rtls protein into the ECM-filled space in the center of rosettes ( Figure 6B ) likely provides some important clues . Rtls may stabilize the connections between rosette cells by interacting with the ECM in a manner akin to the lecticans , a family of animal C-type lectins that stabilize cartilage and other connective tissues by cross-linking carbohydrates and proteins in the ECM ( Ruoslahti , 1996 ) . Such a role would be consistent with the observation that the Rosetteless mutant produces wild-type chain colonies , as one of the main differences between rosettes and chain colonies lies in the stability and mechanical robustness of rosettes as compared to chain colonies . A second possible hint regarding Rtls function stems from the fact that the Rtls CTLDs most closely resemble animal CTLDs that preferentially bind mannose ( Drickamer , 1992 ) , such as mannose binding protein and pulmonary surfactant protein A , each of which functions in innate immunity as pattern recognition receptors ( Takahashi et al . , 2006 ) . Because rosette development is regulated by bacterial signals ( Alegado et al . , 2012 ) , Rtls may play a role in substrate recognition and cell signaling . Future work on the biochemical and physiological roles of Rtls will enable the discovery of other proteins in the rosette development regulatory pathway , while also potentially providing insights into the ancestral functions of CTLD-containing proteins . The discovery that Rosetteless regulates rosette development provides a starting point for investigating the relationship between animal and choanoflagellate multicellularity . CTLD-containing proteins have previously been shown to regulate cell adhesion and development in animals ( Reidling et al . , 2000; Iba et al . , 2001; Kulkarni et al . , 2008; Chin and Mlodzik , 2013 ) , offering intriguing parallels with the role of the Rosetteless CTLD protein in the control of rosette development . However , the molecular functions of Rtls are currently unknown and it is therefore unclear whether they are conserved in animal CTLD-containing proteins . Moreover , while the genomes of diverse animals encode Rtls-like proteins containing a signal peptide , two C-type lectin-like domains , and serine-threonine-rich low complexity regions ( e . g . , the placozoan Trichoplax adhaerans ( XP_002112548 ) , the cnidarian Hydra vulgaris ( XP_002155329 ) , the nematode Caenorhabditis elegans ( NP_501369 . 1 ) , and the fish Danio rerio ( XP_005158004 ) ; relevant motifs detected by SMART , [Letunic et al . , 2012] ) , it is not clear whether the similarities among these proteins and Rtls are the result of homology or convergent evolution ( Figure 5—figure supplement 2 ) . Although it is not straightforward to reconstruct the evolutionary relationships among S . rosetta and animal CTLD-proteins , the future analysis of additional rosette defect mutants promises to illuminate the remaining rosette regulatory pathway and reveal whether this pathway is conserved in the regulation of animal multicellularity . Forward genetic screens have been vital tools for uncovering fundamental mechanisms driving development in eukaryotic model organisms , including Saccharomyces cerevisiae , Drosophila melanogaster , C . elegans , Mus musculus , D . rerio , and Arabidopsis thaliana ( Hartwell et al . , 1970; Brenner , 1974; Nüsslein-Volhard and Wieschaus , 1980; Mayer et al . , 1991; Haffter et al . , 1996; Kasarskis et al . , 1998 ) , but such approaches have been restricted to a relatively small number of taxa that represent a small fraction of eukaryotic diversity ( Abzhanov et al . , 2008 ) . Expanding the phylogenetic reach of forward genetic approaches will allow for a more rigorous and complete interrogation of the origin and evolution of animal development . The establishment of forward genetics in choanoflagellates has provided the first insights into the genetic underpinnings of development in these evolutionarily relevant organisms and promises to illuminate mechanisms underlying intercellular interactions in the progenitors of animals . Unenriched artificial seawater ( ASW ) , cereal grass media ( CG media ) , and high nutrient ( HN ) media were prepared as described previously ( Levin and King , 2013 ) . HN media ( 250 mg/l peptone , 150 mg/l yeast extract , 150 µl/l glycerol in unenriched sea water ) was made by diluting Sea Water Complete Media ( Atlas , 2004 ) to 5% ( vol/vol ) in ASW . A . machipongonensis conditioned media ( ACM ) was made from the sterile supernatant of the liquid A . machipongonensis culture ( ATCC BAA-2233 [Alegado et al . , 2013] ) grown shaking for 48 hr in HN media at 30°C to an OD600 of 0 . 30–0 . 39 and filtered through a 0 . 2 µm filter to remove bacterial cells and detritus . The above conditions were used for the isolation of all mutants except Rosetteless . For this mutant , ACM was prepared in CG media and was grown for 24 hr to an OD600 of 0 . 1 . Rosetteless clonal isolation steps used a mixture of 20% Algoriphagus conditioned CG media , 5% fresh CG media , and 75% ASW ( vol/vol ) . The parental strain for the screen was SrEpac ( ATCC PRA-390; accession number SRX365844 ) , which contains S . rosetta grown in the presence of Echinicola pacifica bacteria ( Nedashkovskaya et al . , 2006; Levin and King , 2013 ) , previously described as ‘Isolate C’ in Levin and King ( 2013 ) . SrEpac was generated through serial clonal isolation to ensure a genetically homogeneous background for the screen , and frozen stocks of SrEpac were thawed prior to each mutagenesis to limit the accumulation of random mutations . SrEpac cultures were haploid when passaged every 2–3 days in HN media ( Levin and King , 2013 ) . During each mutagenesis treatment , an SrEpac culture was divided into two; one half was mutagenized and the other half underwent all incubations , washes , and clonal isolation steps of the protocol except for the mutagenesis . The Isolate B culture used in the cross ( accession number SRX365839 ) contains S . rosetta grown in the presence of A . machipongonensis bacteria . Isolate B was diploid when passaged with scraping every 3 days in CG media ( Levin and King , 2013 ) . To determine a mutagen dose to be used in the screen , we titrated each mutagen over three orders of magnitude and examined the cell number of mutagenized vs unmutagenized cultures 24 hr later ( Figure 3—figure supplement 1A ) . For both EMS and X-rays , we observed a general decrease in cell number following increased mutagen dose , suggesting that the mutagen was effective . For the screen , we used mutagen doses of 0 . 3% ( vol/vol ) EMS and 6300 rem of X-rays , as both treatments showed an intermediate effect on cell number , but this effect varied considerably among mutagenesis trials . For EMS mutagenesis , approximately 106 cells were washed and resuspended in 1 ml ASW . Liquid EMS ( ethyl methanesulfonate , Sigma , St . Louis , MO ) was added to 0 . 3% ( vol/vol ) and cells were incubated 1 hr at room temperature . The EMS was subsequently removed and neutralized by washing the cells three times in 5% sodium thiosulfate in ASW ( wt/vol ) before returning the cells to their initial media ( HN or CG media ) for 24 hr of recovery . In parallel with the isolation of the Rosetteless mutant , we also co-isolated a wild-type strain ( C2E5 ) that underwent all washing and clonal isolation steps but was not mutagenized . Fox X-ray mutagenesis , approximately 106 cells were transferred into 35 mm diameter tissue culture dishes ( Thomas Scientific , Swedesboro , NJ ) and placed in an X-ray cabinet ( Faxitron 43855C ) 30 . 3 cm from the X-ray source with the lids of the dishes removed . Cultures were irradiated at the 125 V , 3 mA setting for 3 hr , which corresponded to a dose of approximately 6300 rems . Although we observed only mild choanoflagellate death from the mutagenesis treatments ( Figure 3—figure supplement 1A ) , there was significant death and/or growth inhibition of the E . pacifica bacteria following X-ray mutagenesis . Therefore , to avoid S . rosetta starvation , we added 500 µl of an unmutagenized , liquid culture of E . pacifica bacteria to the S . rosetta after X-ray mutagenesis and resuspended the cells in 10 ml HN media before a 24 hr recovery . To measure the X-ray dose delivered under these conditions , we placed ring dosimeters at the same position and exposed them for 1 min , 1 . 5 min , or 1 . 75 min to generate a standard curve . By linear regression , we obtained the following formula with a fit of R2 = 0 . 997: millirems of exposure = 35 , 091 * ( minutes exposure ) —4531 . 1 . Given this equation , we calculated that the X-ray mutagenesis dose corresponded to approximately 6300 rems . SrEpac cells were mutagenized either with 0 . 3% ( vol/vol ) liquid EMS ( ethyl methanesulfonate ) for 1 hr or exposed to 6300 rem of X-rays ( Figure 3—figure supplement 1A ) . 24 hr after mutagenesis , control and mutant clones were isolated by dilution-to-extinction into 150 µl screen media ( 20% ACM , 40% HN media , 40% ASW [vol/vol] ) in 96-well plates . Cells were plated at an approximate density of 1 cell/150 µl ( i . e . , 1 cell/well ) . The probability that each isolate underwent a clonal bottleneck during this step was 0 . 70 to 0 . 89 , calculated using the Poisson distribution and the number of choanoflagellate-free wells per plate ( Levin and King , 2013 ) . After 5–7 days , clonal populations were visually screened for mutants deficient in rosette formation ( Figure 2 ) . Selected controls and rosette defect mutants were expanded in 3 ml 10% ACM in 6-well plates to verify the phenotype . In total , we isolated 19 candidate mutants . Nine were eventually verified as rosette defect mutants through repeated re-isolation and testing of rosette induction . A tenth mutant had a mild growth defect and was thus discarded . Of the remaining candidate mutants , most were isolated as thecate cells , a cell type that is not competent to form rosettes ( Figure 1 ) ( Dayel et al . , 2011 ) , but upon further passaging the cells in these cultures began to form rosettes . We concluded that the rosette defect phenotypes initially detected in these clones were likely a result of epigenetic rather than genetic heritability , and we focused instead on the nine verified rosette defect mutants . To ensure that each mutant and control isolate was truly clonal , a second clonal isolation step was performed into 96-well plates to an average of 1 cell/1500 µl ( i . e . , 1 cell/10 wells ) . The probability that each isolate underwent a clonal bottleneck during this step was 0 . 935 to 0 . 997 , resulting in an overall probability of 0 . 991–0 . 999 that each isolate underwent a clonal bottleneck at least once . S . rosetta cultures were exposed to either ACM or live colony-inducing bacteria . For the live bacteria treatments , A . machipongonensis liquid cultures were grown shaking in HN media at 30°C for 24 hr . To begin the induction , S . rosetta cells were diluted to 104 cells/ml in 3 ml HN media with either 20% ACM or 4 µl/ml of liquid A . machipongonensis culture . 48 hr after induction , we pipetted the culture vigorously and repeatedly to break up chain colonies , concentrated the cells fivefold by centrifugation , fixed an aliquot of the culture with formaldehyde , and assessed rosette formation by counting on a hemacytometer . Thus , our operational definition for rosettes only included those rosettes that were robust to vigorous pipetting . For all experiments to visualize mutant rosette phenotypes , cells were plated at a density of 104 cells/ml in 3 ml of either HN media or 10% ACM in HN media ( vol/vol ) . Cultures were imaged 48 hr after induction . For all non-fluorescent images , cells were visualized live ( Figure 3B , C , and Figure 3—figure supplement 1E ) . For the high magnification DIC images ( Figure 3B , C ) , 96-well µclear flat bottom plates ( Greiner ) were coated with 0 . 1 mg/ml poly-D-lysine ( Sigma ) for 5 min and allowed to air dry for 5 min before gently transferring 100 µl of culture to the well with a cut-off pipet tip . Cells were allowed to settle for 5 min and imaged live at 63× oil immersion with a Leica DMI6000B microscope equipped with a Leica X-Cite 120 camera . To visualize low magnification fields of view of the mutants following pipetting ( Figure 3—figure supplement 1E ) , cells were pipetted rigorously to break up chain colonies and concentrated 30–100-fold by centrifugation . 10 to 20 µl of concentrated cells were imaged live on a slide at 10× on a Leica DMIL LED inverted compound microscope with a Leica DFC 300FX camera . For the confocal slices through rosette colonies ( Figure 3—figure supplement 1D ) , sterile , 8-well µ-slides ( Ibidi , Germany ) were coated with 0 . 1 mg/ml poly-D-lysine ( Sigma ) for 5 min and allowed to air dry for 5 min before gently transferring 250 µl of culture to the well with a cut-off pipet tip . Cells were fluorescently stained with 1 µl of 2 . 5 µg/ml FM 1-43X dye ( Molecular Probes , Eugene , OR ) , fixed with 1 µl 25% glutaraldehyde ( Electron Microscopy Sciences , Hatfield , PA ) , allowed to settle for 5 min , and imaged at 63× using a Zeiss LSM 700 confocal microscope . Single confocal slices are shown . Because chain colonies break up upon pipetting and because some of the mutants formed chains with very large clusters of cells , we attempted to visualize the chain phenotypes while manipulating the cells as little as possible . Cells were diluted at a 1:10 ratio into 10 ml of HN media in 25 cm2 culture flasks ( Corning , NY ) . 24 hr later , we imaged the chain colonies at the bottom of the flask at 10× using a Leica DMIL LED inverted compound microscope with a Leica DFC 300FX camera . Images were manually false colored to highlight the chain colonies that were in focus . We sequenced the genomes of the Rosetteless mutant , the parental strain from which it was derived , and an unmutagenized wild-type strain ( C2E5 ) that was isolated and cultured in parallel with Rosetteless . We prepared genomic DNA from mutant and wild-type S . rosetta cultures by phenol chloroform extraction and used a CsCl gradient to separate S . rosetta and E . pacifica DNA by GC content ( King et al . , 2009 ) . Multiplexed , 100 bp paired-end libraries were sequenced on an Illumina HiSeq 2000 . Raw reads were trimmed with TrimmomaticPE ( Lohse et al . , 2013 ) to remove low quality base calls . Trimmed reads were mapped to the S . rosetta reference genome ( Fairclough et al . , 2013 ) using Burrows-Wheeler Aligner ( Li and Durbin , 2009 ) , and we removed PCR duplicates with Picard ( http://picard . sourceforge . net ) . Rosetteless was sequenced to a median coverage of 71× and over 93% of the reference genome had at least 10× coverage , while the parental strain and C2E5 were each sequenced to a median coverage of 50–60× and over 91% of the genome had at least 10× coverage . We realigned reads surrounding indel calls using GATK ( DePristo et al . , 2011 ) and called variants using SAMtools and bcftools ( Li et al . , 2009 ) . To obtain the high quality variant calls ( Figure 4—figure supplement 1 ) , we removed all variants that were called with a quality score below 100 in addition to all variants that were called as heterozygous , since we expected these haploid genomes to yield homozygous calls . We focused on detecting single nucleotide variants ( SNVs ) , because Rosetteless was isolated following EMS treatment . After filtering the detected SNVs by quality score , we found that Rosetteless contained 25 , 160 high-quality SNVs , 25 , 143 of which ( 99 . 93% ) were shared among Rosetteless and at least one of the wild-type strains ( the parental strain and C2E5 ) , meaning that they were segregating polymorphisms , which were unlikely to contribute to the Rosetteless phenotype . We experimentally validated all of the predicted Rosetteless-specific SNVs ( Figure 4—figure supplement 1B ) . Short regions of genomic DNA flanking SNVs predicted to be unique to Rosetteless were amplified by PCR using a 1:1 mix of Taq ( New England Biosciences , Ipswich , MA ) and Pfu ( Thermo Fisher Scientific , Waltham , MA ) , gel extracted using the GeneClean II kit ( MP Biomedicals , Santa Ana , CA ) , and analyzed by Sanger sequencing . SNVs were considered ‘verified’ if they were present in Rosetteless gDNA but absent from gDNA from the parental strain . The supercontig 8 splice donor mutation ( rtlsl1 ) was the only Rosetteless-specific SNV predicted to alter a coding region , and we confirmed that this mutation was present in Rosetteless and absent from the parental strain by PCR and Sanger sequencing ( Figure 4—figure supplement 1C ) . In contrast , when we attempted to verify the other 16 detected Rosetteless SNVs , only three were verified as polymorphic between Rosetteless and the parental strain . Of the remaining variants , three were false-positive variant calls in Rosetteless , two lay within regions of the reference genome that were misassembled , and eight were false-negative variant calls , where shared , segregating polymorphisms were not identified in the parental strain or the C2E5 wild-type strain . Thus despite the fact that the vast majority of called SNVs were high quality and independently called in all three samples , the enrichment of poor SNV calls in the Rosetteless-specific set meant that of the 17 potentially unique SNVs originally identified in the Rosetteless genome , there remained only four verified SNVs , including a predicted splice donor mutation at supercontig 8: position 427 , 804 . We were initially surprised to find such a small number of unique mutations in Rosetteless . However , it is possible that the EMS mutagenesis was ineffective prior to the isolation of Rosetteless , which is consistent with the fact that the Rosetteless mutagenesis did not result in substantial cell death ( Figure 3—figure supplement 1A ) . Thus , despite the fact that Rosetteless was derived from a culture treated with EMS , it may in fact be a spontaneous mutant . The raw reads for the SrEpac parental strain , the C2E5 wild-type co-isolate , and Rosetteless are publicly available ( accession numbers SRX365844 , SRX476076 , and SRX476075 , respectively ) . All alignments of protein sequences were made using fast statistical alignment ( Bradley et al . , 2009 ) . To investigate whether additional mutants isolated in this screen ( i . e . , mutant classes B–G; Table 1 ) bore mutations in rtls , we used Phusion polymerase ( New England Biosciences ) to amplify the coding region of the rtls from each mutant prior to cloning into the pCR 2 . 1 vector ( Invitrogen ) . The coding region was divided into three regions for each mutant , using the following primer pairs: Rtls_L1/Rtls_R3 , Rtls_L5/Rtls_R4 , and Rtls_L3/Rtls_R2 ( Supplementary file 1 ) . The full insert of each clone was analyzed by Sanger sequencing . No mutations were found in rtls in any of the eight remaining rosette defect mutants . To genotype microsatellites with size polymorphisms larger than 30 bp ( e . g . , the indel1 marker ) , we separated PCR products on a 2% agarose gel . To genotype smaller microsatellites , we fluorescently labeled PCR products ( Schuelke , 2000 ) and analyzed the size polymorphisms by fragment analysis on a 3730XL DNA Analyzer ( Applied Biosystems ) . The gt_indel_2 and gt_indel_7 primer sets included an M13 site on the left primer to enable fluorescent labeling in a 3-primer reaction , while the gt_indel_9 left primer was directly fluorescently labeled ( Supplementary file 1 ) . We sought to investigate whether the Rosetteless phenotype was dominant or recessive by examining the phenotypes of the heterozygotes isolated after mating Rosetteless to Isolate B . All seven heterozygous cultures contained predominantly thecate cells—a cell type that is not competent for rosette formation ( Figure 1 ) ( Dayel et al . , 2011 ) —and three of these cultures also contained a small number of rosettes . The presence of rosettes ( and absence of chain colonies ) suggested that the Rosetteless phenotype might be recessive . However , culture conditions that favor rosette or chain colony development over the production of thecate cells ( i . e . , rapid passaging in nutrient-rich media ) also favor meiosis , meaning that the rare rosette colonies could represent a minority of cells that had already undergone meiosis . Therefore , we were not confident that rosettes were developing from diploid cells and could not definitively determine whether the Rosetteless phenotype was dominant or recessive . From the genome sequences of Rosetteless and Isolate B , we identified 39 , 451 putative polymorphic positions that could be used as markers to genotype the mapping cross . We prioritized markers that were: ( 1 ) on supercontig 8 near to the 427 , 804 rtlsl1 splice donor position , to increase our confidence in the mapping; ( 2 ) validated Rosetteless-specific SNVs ( Figure 4—figure supplement 1B ) , because these were other plausible candidates for causing the Rosetteless phenotype; and ( 3 ) markers located near the ends of supercontigs , as linkage detected between these positions and markers on other supercontigs would allow for an improved linkage map of the S . rosetta genome . We genotyped 182 haploid isolates at 60 markers , for a total of 10 , 920 genotyping reactions . In addition to the three microsatellite markers , the cross progeny were further genotyped at 57 markers using KASP technology ( LGC Genomics , Beverly , MA; Figure 4—source data 1 and Supplementary file 2 ) . We obtained genotype data for 91% of these reactions . Four of the cross isolates were duplicates of other isolates in the set and were excluded from further analysis . The program R/qtl ( Broman et al . , 2003 ) was used to construct a preliminary linkage map from the Rosetteless–Isolate B cross progeny . For each genotyping reaction , Rosetteless alleles were coded as ‘A’ and Isolate B alleles were coded as ‘H’ to emulate a backcross and allow for haploid genetics to be analyzed . We included in the linkage map the 60 markers that were genotyped in over 90 progeny and the data from the 175 non-duplicate individuals that were genotyped in at least 30 markers each . We constructed linkage groups using a maximum recombination fraction of 0 . 4 and a minimum LOD of 5 ( i . e . , 1 in 100 , 000 odds of linkage ) . Following linkage group construction by R/qtl , we used the physical linkage known from the S . rosetta genome assembly to manually link markers on the same supercontig into a single linkage group . The linkage map is shown in Figure 4—figure supplement 3 and the genotype data and linkage group assignments for each marker are available in Figure 4—source data 1 . For 78% of the genotyped markers , we observed that each allele was present in approximately 50% of progeny , as expected from Mendel's Law of Segregation ( Mendel , 1866 ) . However , we observed some segregation distortion for 13 of the markers examined . Four of these markers had missing data for 50–80 of the genotyped progeny , suggesting that an error in calling one of the alleles may be responsible for the apparent distortion . We also observed segregation distortion for four of the supercontig 2 markers and the gt2 splice donor marker that were each genotyped in the majority of the haploid progeny . The Rosetteless phenotype and splice donor mutation were present in 82% of the isolated progeny ( 145/177 ) , while the remaining progeny readily formed rosette colonies , ( Figure 4 ) . We believe this segregation distortion may be related to the Rosetteless phenotype itself . As chain colonies break up into many individual cells , it may be more likely that chain-forming cells will be selected during the clonal isolation process as compared to cells in rosettes . However , regardless of the cause of the segregation distortion , we concluded that the defect underlying the Rosetteless phenotype was tightly linked to the rtlsl1 splice donor mutation . We isolated RNA from the SrEpac parental strain and Rosetteless using the RNAqueous kit ( Life Technologies ) and prepared cDNA using oligo dT primers and Superscript II Reverse Transcriptase ( Invitrogen ) according to the manufacturer's instructions . After first strand synthesis , we performed PCR using primers flanking the candidate splice donor mutation ( Rtls_2L and Rtls_1R; Supplementary file 1 ) and cloned the resulting bands into the pCR 2 . 1 vector ( Invitrogen ) . Splice isoforms were determined by Sanger sequencing the full insert of each clone . From Rosetteless , we sequenced 11 clones that had retained intron 7 ( isoform ii ) , 3 clones that had a late splice donor ( isoform iii ) , and 21 clones that had an early splice donor ( isoform iv ) , but no clones with the wild-type isoform . We repeated the above procedure using a primer specific to the wild-type exon 7/8 boundary ( Rtls_5L and Rtls_1R ) and obtained a single band from both wild-type and Rosetteless cDNA , each of which corresponded to the wild-type product upon sequencing . From both wild-type and Rosetteless samples , no bands were observed from the negative control PCRs , which used RNA that was not reverse transcribed as a template . The anti-Rtls antibody was generated using Genomic Antibody Technology ( SDIX , Newark DE ) ; in which rabbits were immunized with a DNA construct corresponding to this epitope: SSTPQQFPALVLEFPTPISESDVPAIELLLQSAGLPSNNPTGSSITVQLLSSQLVYIQLAGNFEQYAGELALKALNDQLIWQAGIPIAYVPLTSVLDQIQAT . The epitope is unique to Rtls and bears no resemblance to other polypeptide sequences in S . rosetta; when the amino acid sequence of the epitope was used to search the full catalog of S . rosetta proteins ( using blastp ) , no other protein hit the epitope with an e-value less than 20 ( i . e . , the other hits were not statistically significant ) . The antibody was affinity purified against recombinant Rtls generated by SDIX . Separately , we cloned , expressed , and purified recombinant protein corresponding to the epitope from wild-type S . rosetta cDNA that was prepared with oligo dT primers as described above . We amplified the epitope using Pfu ( Finnzymes ) and primers Rtls_epit_L1 and Rtls_epit_R1 ( Supplementary file 1 ) and cloned it into the pGEX-6P GST-fusion expression vector . Protein was expressed in BL21 E . coli grown overnight at 16°C , purified using glutathione Sepharose 4B beads ( GE Life Sciences , Pittsburgh , PA ) , and eluted with 50–100 mM glutathione . Elutions from multiple experiments were pooled and concentrated using a 30K Amicon Ultra-4 filter and the buffer was exchanged for the following protein wash buffer ( 50 mM Tris pH 7 . 4 , 150 mM NaCl , 1 mM EDTA , 1 mM DTT , 0 . 01% Triton X-100 ) . To visualize the purity of the recombinant Rtls epitope , 100 ng of protein was run on a 4–12% gradient SDS-PAGE gel ( Bio-Rad , Hercules , CA ) and silver stained with Fermentas PageSilver Silver Staining Kit ( K0681 ) according to manufacturer's instructions . For western blot analysis , samples were transferred to PVDF membrane ( Immobilon-FL , IPFL00010 ) , blocked with Odyssey Blocking Buffer ( Licor , 927–40 , 010 ) , and probed with anti-Rtls antibody at 1:2500 followed by the secondary antibody anti-rabbit IRDye 800CW ( Licor , 926–322111 ) . The blot was imaged with the Licor Odyssey infrared imaging system . Wild-type and Rosetteless cultures were grown for 24 hr in the presence or absence of live A . machipongonensis bacteria . Following filtration through a 40 µm filter to remove bacterial biofilms , 1 . 5 × 106 cells from each culture were pelleted , resuspended in 10 µl of lysis buffer ( 10 mM Tris HCl pH 8 , 0 . 1 mM EDTA pH 8 , 0 . 5% SDS wt/vol ) , and spotted directly onto a nitrocellulose membrane ( NitroBind , GE Osmonics , Minnetonka , MN ) . Spots were allowed to air dry completely before blocking in 5% milk and treatment with anti-Rtls primary antibody ( 1:500 ) . Primary antibody signal was detected using an IR-dye-conjugated secondary antibody ( Licor Biosciences anti-rabbit 800 nm 1:10 , 000 ) and the Odyssey Infrared Imaging System ( Licor Biosciences , Lincoln , NE ) . To test the specificity of anti-Rtls to Rtls protein on dot blot , the primary antibody was pre-incubated with a 100-fold molar excess of purified epitope at room temperature for 1 hr before application of the primary antibody to the membrane . Images were analyzed in ImageJ ( Schneider et al . , 2012 ) as follows: a box of constant area was placed over each dot to measure the integrated density of the area . The integrated density value for the secondary only control was subtracted from each sample to eliminate the signal due to mild autofluorescence of the membrane , and then each sample was normalized to the wild-type dot . Only dots processed on the same membrane were normalized in this manner . To induce rosette development , HN media was inoculated with a single colony of live A . machipongonensis , vortexed , and aliquoted into a 96-well plate . Anti-Rtls antibody ( 1 . 25 mg/ml stock in PBS ) was added to a final concentration of: 50 , 25 , 12 . 5 , 6 . 25 , 3 . 13 , or 1 . 56 µg/ml and SrEpac wild-type cells were added to each well at a 1:5 dilution . Cells were incubated at room temperature in 100 µl total volume in a 96-well plate for 24 hr to induce rosette development , at which point cultures were vigorously pipetted and counted on a hemacytometer . Three negative control treatments were analyzed in parallel: ( 1 ) 50 µg/ml BSA ( from a 1 . 25 mg/ml stock in PBS ) ; ( 2 ) 14 µl of pre-immune serum ( equivalent to the volume of antibody added in the 50 µg/ml condition ) ; and ( 3 ) 50 µg/ml of a control , rabbit polyclonal antibody ( from a 1 . 25 mg/ml stock in PBS , #A01008 , Genscript , Piscataway , NJ ) . All conditions were tested in triplicate . Live cells were allowed to settle for 30–60 min onto poly-L-lysine coated coverslips ( BD Biosciences ) and fixed in two steps: 5 min in 6% acetone followed by 10–15 min in 4% formaldehyde . Cells were stained with the anti-Rtls genomic antibody at 6 . 25 ng/µl ( 1:200 ) , E7 anti-tubulin antibody ( 1:1000; Developmental Studies Hybridoma Bank ) , Alexa fluor 488 anti-rabbit and Alexa fluor 647 anti-mouse secondary antibodies ( 1:400 each; Molecular Probes ) , and 6 U/ml rhodamine phalloidin ( Molecular Probes ) before mounting in Prolong Gold antifade reagent with DAPI ( Molecular Probes ) . To test the specificity of the antibody staining , 1 µl of anti-Rtls primary antibody was diluted in 190 µl block ( 1% BSA and 0 . 3% Triton X-100 in 100 mM PIPES pH 6 . 9 , 1 mM EGTA , and 0 . 1 mM MgSO4 ) and incubated with 9 µl of either protein wash buffer ( see above ) or purified epitope ( equivalent to approximately 18 µg or a 60-fold molar excess ) for 1 hr at room temperature before application of the primary antibody to the cells . Cells were imaged at 63× using a Zeiss LSM 700 confocal microscope ( laser intensity = 2 . 0 , zoom = 2 . 5 , Rtls low exposure gain = 544 , Rtls high exposure gain = 750 ) . To date , no official rules have been established for naming choanoflagellate mutants or genes . Thus , we outline here proposed guidelines for naming choanoflagellate genes . Any genes with clear homology to named genes in other organisms should be referred to by the pre-existing name ( e . g . , hsp90 ) . Any genes without clear homology to named genes should be given names that allude to the gene's function or the phenotype of the first mutant allele isolated . Before mutant genes are cloned , each mutant is given its own name , but renaming may be necessary once the causative mutation is identified . Gene names should be written in lower case and in italics . Specific mutations should be named with one letter for the last name of the first author of the publication describing the allele and one number for the order of allele isolated . Mutations are presented as an italicized superscript ( e . g . , rtlsl1 ) . Mutant names and protein names are written in upper case and non-italics . Since many three-letter abbreviations have already been used for genes in other organisms , we propose the use of four-letter abbreviations .	All animals descended from a common ancestor that made the leap from living as a single cell to becoming more complicated , with many cells working together . At first , such a creature would likely have been made from clusters of cells that all had the same function . Eventually , different cells took on different roles , and today animals have many organ systems , each made up of specialized cell types . How the ancestors of animals transitioned from being single celled to multicellular , however , is poorly understood . It is now possible to reconstruct key steps in the evolution of ‘multicellularity’ by comparing modern animals with their closest living relatives—the choanoflagellates . These are a group of aquatic microorganisms that can either live as single cells or develop into multicellular colonies . The genes that allow choanoflagellate cells to form colonies are hypothesized to be similar to the genes that the very first animals used to become multicellular . Now , Levin et al . have studied a choanoflagellate called S . rosetta . This species is a good choice , as its genome sequence has been decoded and it is relatively easy to induce S . rosetta cells to switch between living on their own or living in spherical colonies called rosettes . Using a technique known as ‘forward genetics’ , Levin et al . bombarded S . rosetta cells with chemicals and X-rays to introduce genetic mutations into the cells . The mutated cells were then grown in conditions that would normally cause S . rosetta to form rosette colonies; the cells that continued to live in isolation in these conditions were then studied further , as this meant that mutations had occurred in the genes responsible for colony formation . Levin et al . identified several mutant S . rosetta strains that cannot form rosettes . One of these mutant strains had an altered copy of a gene that Levin et al . named rosetteless . The protein produced by the rosetteless gene is similar to proteins that connect animal cells to one another in tissues and organs . Normally in rosettes this protein is found outside of the cells , in a secreted structure that joins the cells of the colony together . In the Rosetteless mutants , the protein is often incorrectly made and typically ends up on the wrong part of the cell . Levin et al . further confirmed the importance of the rosetteless-encoded protein by creating antibodies that stick to the protein and interfere with its function , thereby blocking rosette formation . Unraveling the role of the rosetteless gene is an important step towards understanding which genes made it possible for single-celled organisms to evolve into complex multicellular animals . Future genetic screens in S . rosetta promise to reveal whether rosetteless is part of a network of genes and proteins which regulate animal development and could thus illuminate the molecular machinery behind multicellularity in the long-extinct predecessors of animals .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology", "genetics", "and", "genomics" ]	2014	The rosetteless gene controls development in the choanoflagellate S. rosetta
The mechanisms that underlie directional cell migration are incompletely understood . Eph receptors usually guide migrations of cells by exclusion from regions expressing Ephrin . In sea urchin embryos , pigmented immunocytes are specified in vegetal epithelium , transition to mesenchyme , migrate , and re-enter ectoderm , distributing in dorsal ectoderm and ciliary band , but not ventral ectoderm . Immunocytes express Sp-Eph and Sp-Efn is expressed throughout dorsal and ciliary band ectoderm . Interfering with expression or function of Sp-Eph results in rounded immunocytes entering ectoderm but not adopting a dendritic form . Expressing Sp-Efn throughout embryos permits immunocyte insertion in ventral ectoderm . In mosaic embryos , immunocytes insert preferentially in ectoderm expressing Sp-Efn . We conclude that Sp-Eph signaling is necessary and sufficient for epithelial insertion . As well , we propose that immunocytes disperse when Sp-Eph enhances adhesion , causing haptotactic movement to regions of higher ligand abundance . This is a distinctive example of Eph/Ephrin signaling acting positively to pattern migrating cells . Dispersal of cells from a site of origin to form a predictable pattern throughout an organism is a spectacular demonstration of directed migration , an essential component of the metazoan patterning toolkit . The complex movements of neural crest or the extensions of central nervous system axons are familiar vertebrate examples that are brought about by a relatively small number of cellular mechanisms . Attraction to a chemical source , permissive substrates , and mechanisms of exclusion or repulsion appear to the principal means of controlling even complex cell migrations ( Davies , 2005 ) . In many situations Ephrin and Eph are important components of the signaling that regulates migration of cells ( Pasquale , 2005 , 2008; Xu and Henkemeyer , 2012; Poliakov et al . , 2004 ) . Eph receptors are a family of receptor tyrosine kinases that are activated by cell surface ligands , Ephrins . Ephrins are bound to membranes through a glycosylphosphatidylinositol linkage ( Ephrin A ) or a transmembrane domain ( Ephrin B ) . The receptors and ligands are found throughout metazoans and they are thought to accompany the evolution of multicellularity ( Srivastava et al . , 2010; Tischer et al . , 2013 ) . Vertebrate receptors and ligands are diverse with as many as 16 Eph receptors and 8 Ephrins . The principal developmental functions of Eph/Ephrin signaling include defining tissue domains and guiding migrating cells or growth cones ( Klein , 2012; Cayuso et al . , 2015 ) . In the best-understood models , Eph receptors repulse cells from regions expressing Ephrin , or with reverse signaling , where ligand bearing cells respond to receptor binding , Ephrin-expressing cells are repulsed from cells expressing Eph receptors . Eph and Ephrins regulate cytoskeletal dynamics and often integrate activity of other receptor ligand systems to coordinate adhesive and migratory responses to the environment of the cell ( Bashaw and Klein , 2010; Bush and Soriano , 2012 ) . In vertebrates Eph and Ephrins have complex patterns of expression and interaction , which complicates functional studies . Genomic studies of sea urchins reveal that there is a single Eph receptor ( Sp-Eph ) and a single Ephrin ligand ( Sp-Efn ) , making them an attractive model for studies of function ( Whittaker et al . , 2006 ) . In addition , urchins are basal deuterostomes , so they provide an opportunity to study the range of morphogenetic mechanisms employed by embryos that share a common ancestor with vertebrates ( Drescher , 2002; Mellott and Burke , 2008 ) . In S . purpuratus , embryos express Sp-Eph and Sp-Efn in two overlapping domains of ectoderm and Eph/Ephrin signaling appears to have a role in morphogenesis of the ciliary band ( Krupke and Burke , 2014 ) . Pigmented immunocytes are a well-studied cell lineage in urchin embryos because of their distinctive reddish granules and their early specification within the nested rings of endomesodermal cells in the vegetal plate . Specification of pigmented immunocyte precursors begins in cleavage stages when micromeres express Delta , which initiates Notch-mediated specification of a non-skeletogenic mesoderm domain in an adjacent ring of veg2-derived blastomeres ( Sherwood and McClay , 1999; Sweet et al . , 2002 ) . This domain is first demarcated by expression of the transcription factor glial cells missing ( GCM ) , which is critical to the specification of pigmented immunocytes in the dorsal half of the domain ( Ransick and Davidson , 2006 , 2012; Ruffins and Ettensohn , 1996 ) . During gastrulation , the pigmented immunocytes transition to mesenchyme and migrate to the ectoderm ( Gibson and Burke , 1985 , 1987; Takata and Kominami , 2004 ) . Once in the ectoderm the cells adopt a distinctive dendritic form and mediate innate responses to pathogens ( Solek et al . , 2013 ) . Although many aspects of the lineage have been well studied ( Barsi et al . , 2015; Beeble and Calestani , 2012; Buckley and Rast , 2015 ) , we know little about the dispersal and insertion into epithelium of pigmented immunocytes . Here we report immunocyte precursors express the receptor , Sp-Eph , while migrating over a gradient of Sp-Efn ligand on the basal surfaces of dorsal ectodermal cells . Manipulations of Sp-Eph and Sp-Efn concentrations reveal that suppression of Sp-Eph signaling interferes with insertion of immunocytes into the ectoderm . Altering ligand concentration indicates that pigmented immunocytes are more likely to insert in ectoderm expressing high levels of Sp-Efn . We propose Sp-Eph and Sp-Efn function initially in the dispersion of immunocytes precursors , and subsequently they are necessary for the insertion of mesenchyme into epithelium . Pigmented immunocytes of sea urchin embryos arise in the vegetal mesoderm and begin to transition from epithelium to mesenchyme during the initial phase of archenteron invagination ( Gibson and Burke , 1985; Ruffins and Ettensohn , 1996 ) ( Video 1 ) . The precursors migrate to the ectoderm where they insert between epithelial cells ( Videos 2 , 3 ) . In their migratory phase , immunocytes are spherical and have numerous short , spike-like projections . When immunocytes insert into epithelium , they extend lamellipodia that spread distally and project numerous filopodia ( Video 4 ) . Live imaging indicates that when pigmented immunocytes insert into epithelium , they become stationary or move only small distances . However , they continue to actively extend and retract filopodia ( Videos 2 , 5 ) . As pigmented immunocytes in the ectoderm remain relatively stationary , we concluded that they select a site to insert during their migration from the vegetal plate and that insertion includes a change in immunocyte morphology . 10 . 7554/eLife . 16000 . 003Video 1 . Epithelial-mesenchyme transition ( EMT ) and migration of DGP:GFP labelled pigmented immunocytes . Live imaging of an embryo ( 40 hr ) injected with DGP:GFP ( Ransick and Davidson , 2012 ) . GFP is expressed in pigmented immunocyte precursors undergoing EMT ( first arrow ) . Subsequent to this cell migrating to the ectoderm , another cell ( second arrow ) undergoes EMT and also attaches to the ectoderm . The duration of the sequence is 90 min . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 00310 . 7554/eLife . 16000 . 004Video 2 . Pigmented immunocytes remain at the site of insertion for prolonged periods of time . Live imaging of the pigmented immunocytes in an early pluteus . The cells remain active , but they are not displaced from their location throughout the 1 . 5 hr sequence . As pigmented immunocytes remain relatively stationary , we concluded that they select a site to insert during the phase of migration subsequent to release from the vegetal plate . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 00410 . 7554/eLife . 16000 . 005Video 3 . Pigmented immunocyte inserting into ectoderm . In the insertion sequence a cell first approaches the ectoderm , then inserts between epithelial cells ( 30 min sequence ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 00510 . 7554/eLife . 16000 . 006Video 4 . Pigmented immunocyte inserts into ectoderm and changes from migratory form to a form similar to a dendritic cell . The sequence is 90 min long , beginning with a 48 hr embryo . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 00610 . 7554/eLife . 16000 . 007Video 5 . In this sequence , a pigmented immunocyte that is inserted in the ectoderm extends a single process through the ectoderm to the outside of the embryo ( arrow ) . The process extends and retracts repeatedly throughout this 30 min sequence . Note also the distinctive process extended from the blastocoelar surface of the immunocyte . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 007 The distribution of the pigmented immunocytes is stereotypic in that they do not enter ventral ectoderm and the cells are most dense at the tips of larval arms ( Figure 1A ) . As well , the density of immunocytes is graded in the dorsal ectoderm ( Figure 1F ) . Pigmented immunocytes are most dense at the tip of larval body and along the edge of the ciliary band . Counts of pigmented immunocytes in a series of 5 midline quadrants arrayed along the axis from the animal pole to the vertex indicate a parabolic distribution ( Figure 1G ) . 10 . 7554/eLife . 16000 . 008Figure 1 . The distribution of pigmented immunocytes correlates with the abundance of Sp-Efn . ( A ) Maximum intensity projection of confocal optical sections of an early pluteus larva prepared with Sp1 , a pigmented immunocyte cell surface marker , and pFAK , a marker for apical junctions that are prominent in ciliary band cells ( MeOH fixation ) . Pigmented immunocytes are scattered throughout the dorsal ectoderm ( DE ) , which is the larval surface posterior to the ciliary band ( CB ) . Immunocytes do not normally insert in the ventral ectoderm ( VE ) , which is the larval surface surrounding the mouth ( M ) and encircled by the ciliary band . Immunocytes are most dense at the tips of larval arms , along the edge of the ciliary band , and the posterior tip of the larval body . Bar = 15 µm ( B ) Maximum intensity projection of confocal optical sections of a gastrula prepared with an antibody to Sp-Efn . The ligand is not expressed in the ventral ectoderm ( VE ) . BP , blastopore ( PFA fixation ) Bar = 20 µm ( C ) Single sagittal optical section of a prism larva showing the expression of Sp-Efn only in the dorsal ectoderm and ciliary band and not in the ventral ectoderm ( VE ) ( PEM fixation ) Bar = 20 µm ( D ) Single sagittal optical section of an early pluteus larva showing the expression of Sp-Efn and the ciliary band marker , Hnf6 . Sp-Efn expression is not uniform throughout the dorsal ectoderm , there is an apparent gradation of expression that is highest at the vertex of the larva . ( PFA fixation ) . Bar = 20 µm ( E ) Maximum intensity projection of an early pluteus prepared with anti-Sp-Efn and oriented with the ventral surface foremost . Arrows indicate high levels of immunoreactivity where the postoral larval arms are situated . M , mouth ( PEM fixation ) Bar = 20 µm ( F ) Mid sagittal optical section of an early pluteus prepared with Sp1 to show the distribution of pigmented immunocytes ( MeOh fixation ) . Bar = 20 µm ( G ) The distribution of pigmented immunocytes in the ectoderm between the animal pole domain and the vertex was determined from a set of mid-sagittal images from 6 embryos prepared with Sp1 ( see Figure 1—figure supplement 1 ) . The distance from the vertex of the larva along the surface to the animal pole domain was divided into five zones each 40 µm long and the number of pigmented immunocytes in each zone was counted . Mean and S . E . M . ( H ) The distribution of Sp-Efn in the same region of ectoderm was determined from a set of mid-sagittal images from 6 embryos prepared with anti-Sp-Efn ( See Figure 1—figure supplement 1 ) . Projections of 6-image stacks were prepared and equal-sized rectangles were positioned at 40 µm intervals along the ectoderm . Mean intensity per pixel , normalized to the highest intensity per embryo was determined within each rectangle and Mean and S . E . M . plotted . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 00810 . 7554/eLife . 16000 . 009Figure 1—source data 1 . Source data for Figure 1G and H . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 00910 . 7554/eLife . 16000 . 010Figure 1—figure supplement 1 . Figure 1G Quantification of pigmented immunocytes . The distribution of pigmented immunocytes on the dorsal aspect of the ectoderm was determined from a set of mid-sagittal confocal optical sections from 72 hr embryos fixed with methanol and prepared for immunofluorescence with Sp1 . Embryos in lateral orientations were selected and a set of 8 optical sections centered on an image plane that included the mouth was collected for each specimen . A maximum intensity projection of the 8 optical sections from 6 specimens was prepared . For each of these 6 projected images , the distance from the posterior-most tip of the larva along the dorsal surface was divided into 5 zones , each 40 µm in length the number of pigment cells in each zone was counted , and the mean and S . E . M . were calculated and plotted . A Sketch showing a mid-sagittal , lateral optical section of a pluteus . ( B ) Sketch to demonstrate a projection of 8 optical sections centred mid-sagittally . The number of pigment cells within the volume bounded by the rectangular reticle was determined and plotted in Figure 1G . Figure 1H Quantification of Sp-Efn . The distribution of Sp-Efn along the dorsal aspect of the ectoderm was determined from a set of mid-sagittal confocal optical sections from 72 hr embryos fixed with PEM and prepared for immunofluorescence with anti-Sp-Efn ( 4D2 ) . Embryos in lateral orientations were selected and a set of 8 sections centered on an image plane that included the mouth was collected for each specimen . A median intensity projection of the stack of 8 optical sections from 6 specimens was prepared . Equal-sized reticles were positioned at 40 µm intervals along the dorsal surface . The reticles were positioned so that they included only ectodermal cells and their associated cytonemes . The mean intensity per pixel within each rectangle was determined and the values were normalized to a percentage of the most posterior sample . The mean value for each region and S . E . M . were calculated and plotted . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 010 Sp-Efn is expressed in the dorsal ectoderm and ciliary band ( Krupke and Burke , 2014 ) . A more detailed examination of the ligand indicates an apparent correlation between the relative fluorescence , or immunoreactivity of ectoderm expressing Sp-Efn and the distribution of pigmented immunocytes . Sp-Efn is not expressed in ventral ectoderm , but is expressed in the ciliary band and dorsal ectoderm and it appears to be most abundant at the tips of the larval arms ( Figure 1B–E ) . Measurements of fluorescence intensity per pixel in 5 evenly spaced quadrants along the axis from the animal pole domain to the vertex indicate that there is a gradient of Sp-Efn abundance in the dorsal ectoderm ( Figure 1H ) . These observations show a correlation in the distribution of pigmented immunocytes and the abundance of Sp-Efn , suggesting a role for Sp-Efn in localization of these cells . Fixation with PEM , a buffer designed to stabilize cytoskeletal structure ( Vielkind and Swierenga , 1989 ) results in immunolocalization of Sp-Efn on the basal surface of ectodermal cells ( Figure 2A ) . In addition to small cytoplasmic granules , Sp-Efn is abundant on long , thin projections that radiate into the blastocoel ( Figure 2B ) . The processes are unbranched , up to 15 µm in length and 0 . 1–0 . 2 µm in diameter ( Figure 2C ) . Notably , fixation with ice-cold methanol , or buffered formaldehyde solutions , localizes Sp-Efn to dorsal ectodermal cells , but does not preserve the basal processes . Localization with apical markers , spectrin or actin , and DIC imaging to establish the position of the basal surface , confirms that Sp-Efn is abundant on processes radiating into the blastocoel ( Figure 2D–J ) . Filopodial projections from ectoderm in urchin embryos have been described ( Andrews , 1897; Dan , 1960; Gustafson and Wolpert , 1961; Vacquier , 1968 , Miller et al . , 1995 ) . Co-localization of Sp-Efn with filamentous actin indicates that the processes contain actin ( Figure 2H–J ) . In addition , we prepared embryos for live imaging by expressing a GFP construct containing 80 nucleotides from the C-terminus of mammalian RAS ( membraneGFP ) , which is known to localize fluorescence to membranes ( Moriyoshi et al . , 1996 ) . In sequential images captured at a rate of 4 frames per minute , fluorescent processes emanating from the basal surfaces of ectodermal cells are apparent ( Figure 2K , L , Video 6 ) . Similarly , when embryos express the actin-binding Lifeact-GFP ( Riedl et al . , 2008 ) , thin fluorescent projections emanate from the bases of ectodermal cells ( Figure 2M , N , Video 7 ) . The projections are common on the basal surfaces of dorsal and ventral ectoderm , but not associated with endodermal epithelia . We concluded from these studies that Sp-Efn protein is presented on membrane-delimited , actin-containing , cytonemal processes on the basal surfaces of ectodermal cells . 10 . 7554/eLife . 16000 . 011Figure 2 . Sp-Efn is expressed on membrane-bounded , actin-containing cytonemes on the blastocoelar surface of dorsal ectoderm . ( A ) Fixation that preserves the cytoskeleton ( PEM , Vielkind and Swierenga , 1989 ) reveals that Sp-Efn immunoreactivity localizes to long thin projections from the basal surfaces of dorsal ectoderm ( DE ) . VE , ventral ectoderm Bar = 15 µm . ( B , C ) Higher magnification showing cellular detail of basal surface of dorsal ectodermal cells . PI , pigmented immunocyte . ( B ) Z-projection of 5 optical sections of a 72 hr larva . C Single optical section of dorsal ectodermal cells of 48 hr gastrula stage embryo . ( D–G ) Images demonstrating anti-Sp-Efn immunoreactivity is principally associated with basal cytonemes . ( D ) Spectrin localizes to apical junctional complexes ( arrows ) . ( E ) Sp-Efn localizes to thin filamentous projections ( c ) underlying the dorsal ectoderm . ( F ) DIC image in which the basal surface ( arrowheads ) of the ectodermal cells is bright relative to the rest of the cytoplasm , showing that the cytonemes project into the blastocoel . Note that most of the Sp-Efn immunoreactivity is in the cytonemal layer , not in the cytoplasm of the ectodermal cells . Bar = 15 µm ( H–J ) Sp-Efn localizes to actin containing basal projections of ectoderm . ( H ) F-actin ( Alexa 488-phalloidin ) is abundant in the sub-apical cytoplasm of ectoderm ( open arrowhead ) and junctional complexes ( arrowheads ) . In addition basal projections ( arrows ) are also fluorescent . ( I ) Sp-Efn localizes to the same basal projections that bind phalloidin ( arrows ) . Bar = 15 µm ( K , L ) To determine if there are membrane-bounded cytonemes on the basal surfaces of blastocoelar cells , eggs injected with RNA encoding membrane GFP ( mGFP ) were live imaged at 4 frames per min for intervals of 45–60 min . Individual thin , fluorescent processes appear to extend from the ectodermal cells ( arrows ) . Bar = 4 µm See supplemental data , Video 6 . ( M , N ) To determine if ectodermal cells extend actin containing cytonemal processes , eggs injected with RNA encoding Lifeact-GFP ( Riedl et al . , 2008 ) and live imaged at 4 frames per min for intervals of 45–60 min . In addition to processes emanating from blastocoelar cells , there are thin processes , about 15 µm in length that extend from ectoderm into the blastocoel . Bar = 4 µm See supplemental data , Video 7 . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 01110 . 7554/eLife . 16000 . 012Video 6 . Membrane GFP live imaging of dorsal ectodermal cytonemes . Live imaging of membrane GFP expressing ectodermal cells . There are numerous thin , membrane-bounded processes extending and retracting from the basal surface of dorsal ectodermal cells . ( 30 min sequence ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 01210 . 7554/eLife . 16000 . 013Video 7 . Lifeact-GFP live imaging of dorsal ectodermal cytonemes . Live imaging of ectodermal cells in an embryo expressing Lifeact: GFP . There are numerous thin , actin containing processes extending and retracting from the basal surface of dorsal ectodermal cells . ( 30 min sequence ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 013 In hatched blastulae , cells in the vegetal plate that are immunoreactive with the immunocyte-specific antibody Sp1 ( Gibson and Burke , 1985 ) , are also immunoreactive with anti-Sp-Eph ( Krupke and Burke , 2014 ) ( Figure 3A–C ) . Sp-Efn is present at the same time on the basal surfaces of ectodermal cells ( Figure 3D ) . By 36 hr , presumptive pigmented immunocytes are mesenchyme within the blastocoel and continue to express Sp-Eph ( Figure 3E–G ) . In gastrulae ( 42 hr ) , pigmented immunocytes have migrated to the basal surface of dorsal and ciliary band ectoderm ( Figure 3H–J ) and they extend projections into the ectoderm . The projections can extend beyond the apical ectodermal surface ( insets Figure 3H–J , Video 5 ) . In early plutei ( 72 hr ) , pigmented immunocytes have a typical dendritic morphology in which cells have multiple lamellipodia-like projections between ectodermal cells immediately beneath apical junctions ( Figure 3K–M ) . The pigmented immunocytes continue to express Sp-Eph in early pluteus stages . These findings indicate Sp-Eph is expressed by pigmented immunocyte from the time of their release from the vegetal plate throughout early larval development . 10 . 7554/eLife . 16000 . 014Figure 3 . Pigmented immunocytes express Sp-Eph as they differentiate in the vegetal plate and throughout their migration and insertion into ectoderm . ( A–C ) In blastulae , SpEph can be detected in cells beginning to express the pigmented immunocyte marker Sp1 . The cells are in the vegetal plate and immunoreactivity is strongest in foci adjacent to or overlapping with foci of Sp1 immunoreactivity ( MeOH fixation ) . ( D ) At stages in which cells expressing Sp-Eph are releasing from the vegetal plate , Sp-Efn can be detected on process on the basal surfaces of ectodermal cells . Here a single pigmented immunocyte progenitor is emerging from the vegetal plate ( PEM fixation ) . ( E–G ) Pigmented immunocyte precursors expressing surface Sp1 in the blastocoel also express Sp-Eph ( MeOH fixation ) . ( H–J ) In gastrulae , pigmented immunocytes expressing Sp-Eph have inserted into the ectoderm . Inset images indicate that processes of immunocytes that extend through the ectoderm express Sp-Eph ( MeOH fixation ) . See supplemental data Video 5 . ( K–M ) . In early larvae immunocytes within the ectoderm continue to express Sp-Eph on their surface ( MeOH fixation ) . Bars = 15 µmDOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 014 To determine the function of Eph/Ephrin signaling in pigmented immunocyte development , we perturbed Eph/Ephrin signaling with an Eph kinase inhibitor ( NVP , Martiny-Baron et al . , 2010 ) or antisense morpholinos ( Sp-Eph MO1 ) . Suppressing expression of Sp-Eph produces embryos that gastrulate and have defects in the ciliary band ( Krupke and Burke , 2014 ) ( Figure 4A ) . Immunocyte precursors migrate to the ectoderm and at 40 hr there is no difference in the number of Sp1 immunoreactive cells between control and experimental treatments ( Figure 4A , E ) . However at 72 hr pigmented immunocyte abundance is significantly reduced in NVP treated ( p<0 . 0001 ) or Sp-Eph MO1 injected ( p = 0 . 009 ) embryos ( Figure 4E ) . In 72 hr embryos injected with Sp-EphMO1 , the immunocytes are larger in diameter , with fewer lamellipodia and shorter filopodia than embryos injected with a control morpholino ( Figure 4F ) . As well , in embryos treated with NVP or derived from eggs injected with Sp-Eph MO1 , a significantly larger proportion of immunocytes are within the blastocoel ( Figure 4G ) . Thus , interfering with Eph signaling results in immunocytes that are impaired in their ability to insert into the ectoderm and do not transition to the characteristic dendritic morphology . In addition , immunocytes that remain in the blastocoel of Sp-Eph MO injected embryos commonly have fragmented nuclei and reduced Sp1 intensity . Using an antibody that detects a cleaved or activated form of caspase-3 ( a marker for apoptosis ) , we find that there are a small number of pigmented immunocytes that are cleaved caspase-3 positive in 55 hr Sp-Eph MO injected embryos ( Figure 4B–D , H ) . This supports a model in which pigmented immunocytes with suppressed Eph signaling are less able to insert in the ectoderm , remain rounded in form , and a small number become apoptotic . 10 . 7554/eLife . 16000 . 015Figure 4 . Interfering with expression of Sp-Eph or inhibition of Eph kinase function impedes immunocyte insertion into the ectoderm and they become immunoreactive to anti-Caspase3 . ( A ) Maximum intensity projection of an embryo injected with Sp-Eph MO1 ( MeOH fixation ) . Pigmented immunocytes are dispersed , with some having inserted into the ectoderm . The immunocytes are more rounded and do not have their typical dendritic form . They are commonly within the blastocoel . The ventral ectoderm ( VE ) remains clear of immunocytes . Inset: DIC image of another specimen showing the overall healthy appearance of Sp-Eph morpholino injected embryos ( MeOH fixation ) . ( B , C ) When Sp-EphMO1-injected embryos are prepared with an antibody that recognizes only the cleaved , or activated form of Caspase3 , pigmented immunocyte precursors in the blastocoel are immunoreactive ( MeOH fixation ) . ( D ) Control morpholino injected embryos have almost no pigmented immunocytes that are anti-Caspase3 immunoreactive ( MeOH fixation ) . ( E ) Counts of the number of Sp1 immunoreactive cells indicate that there are no differences in the number of cells among treatments ( NegControlMO , Sp-EphMO1 , Sp-EfnMO1 , DMSO , or NVP ) in prism stages . However , in early plutei there are fewer pigmented immunocytes in Sp-EphMO-injected embryos , or embryos treated with NVP . ( F ) Interfering with expression of Sp-Eph blocks the transition to epithelial-inserted , dendritic morphology . In Sp-EphMO1 injected embryos , immunocytes have larger diameters , shorter filopodia , and fewer lamellipodia than NegControlMO injected embryos . ( G ) There are fewer pigmented immunocytes in the blastocoels of 72 hr embryos injected with control MO , or Sp-Efn MO1 than there are in 72 hr embryos injected with Sp-Eph MO1 . ( H ) Preparations of Morpholino injected embryos with an antibody that recognizes the activated form of Caspase3 show that there are significantly more pigmented immunocytes in the blastocoel expressing activated Caspase3 . ( I–L ) Expressing Sp-Efn throughout the embryo results in mislocalization of immunocytes . ( I ) Lateral view of an embryo injected with 200 ng/µl Sp-Efn RNA . The image is a projection of 8–1 µm optical sections centered on the mouth . Note that Sp1 labelled immunocytes have inserted , or are closely associated with the ventral ectoderm ( VE ) ( MeOH fixation ) . ( J ) Maximum intensity projection of the ventral surface of an embryo injected with RNA encoding full length Sp-Efn . Pigmented immunocytes are inserted into the ventral ectoderm ( MeOH fixation ) . See Figure 4—figure supplement 1 for a set of orthogonal projections at various levels through the image stack used to prepare this projection , which demonstrates that the Sp-1 labelled immunocytes are inserted in , or closely associated with the ventral ectoderm . The position of the oval outlining the ventral ectoderm was determined by the higher nuclear density of the cliary band . M; mouth K . Lateral view of an uninjected , control embryo prepared in the same manner as I ( MeOH fixation ) . The image is a projection of 8–1 µm optical sections centered on the mouth . Note that Sp1 labelled immunocytes have not inserted in ventral ectoderm ( VE ) . ( L ) A single mid-sagittal , optical section of an embryo injected with Sp-Efn RNA , showing immunoreactivity in the ventral ectoderm . Note that at this stage there is almost no expression of Sp-Efn in endoderm and the expression in ventral ectoderm appears graded ( PEM fixation ) . ( M ) Maximum intensity projection of an embryo injected with Sp-Efn RNA showing ectopic expression of Sp-Efn in the ventral ectoderm ( PEM fixation ) . * indicates significantly different outcomes Bars = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 01510 . 7554/eLife . 16000 . 016Figure 4—source data 1 . Source data for Figure 4E–H . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 01610 . 7554/eLife . 16000 . 017Figure 4—figure supplement 1 . Orthogonal views of Figure 4J , a maximum intensity projection of an embryo expressing Sp-Efn throughout the ectoderm . This supplemental figure is comprised of 3 sets of orthogonal views ( YZ and XZ ) of the image stack used to make the Figure 4J projection . ( A–A” ) The thin lines indicate the image plane of the orthogonal view and demonstrates that the pigmented immunocyte positioned immediately below the mouth in Figure 4J is associated with the ventral ectoderm ( arrow ) . Similarly in ( B–B” ) and ( C–C” ) all of the immunocytes that appear to be in ventral ectoderm in the projected image ( Figure 4J ) are within or contacting the ventral ectoderm and do not lie deeper in the embryo . Bar = 15 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 017 To determine the effects of ectopic Ephrin expression on pigment cell distribution we analyzed embryos from eggs injected with Sp-Efn RNA . In 72 hr control larvae ( eggs injected with GFP RNA ) , GFP is expressed in all ectoderm and pigmented immunocytes are distributed as in uninjected embryos . In 72 hr embryos from eggs injected with Sp-Efn RNA , the ventral ectoderm is immunoreactive with anti-Sp-Efn ( Figure 4L , M ) . In these embryos pigmented immunocytes insert in the ventral ectoderm ( Figure 4I–K ) but not endodermal or mesodermal epithelia . These data indicate that ectopic Sp-Efn is sufficient to mislocalize pigmented immunocytes to ventral ectoderm . To further examine the effects of altering the levels of expression of Sp-Efn , we injected one blastomere of a 2-cell embryo to create mosaic embryos in which one half of the embryo is expressing the ectopic gene . The left-right axis is not fixed in S . purpuratus , so embryos produced in this manner have random positioning of the injected half ( Henry et al . , 1992 ) . To track the position of the half injected we co-injected GFP and assessed the number of pigmented immunocytes associated with each half of the embryo . When half of the embryo expresses Sp-Efn ectopically there are more immunocytes inserted in ectoderm on the injected half of the embryo than on the uninjected half or the GFP injected controls ( Figure 5A–C , M , Video 8 ) . When half of the embryo has Sp-Efn expression suppressed with a morpholino ( Sp-Efn MO2 ) , more immunocytes insert on the un-injected half than in the half containing the morpholino , or the GFP control ( Figure 5D–F , M ) . Many of the immunocytes in the half expressing GFP and containing Sp-Efn MO2 insert close to the interface of the two halves and extend processes to the Efn expressing side ( Figure 5E ) . To test the combined effect of the morpholino and the RNA , we injected eggs with Sp-Efn MO2 and one blastomere with Sp-Efn RNA . When expression of Sp-Efn is suppressed throughout the embryo and half of the embryo overexpresses Sp-Efn , nearly all of the pigmented immunocytes insert in ectoderm of the half of the embryo expressing Sp-Efn ( Figure 5G–I , M ) . Control embryos expressed only GFP in half of the embryo and pigmented immunocytes were evenly distributed ( Figure 5J–L , M ) . We concluded from these experiments that pigmented immunocytes are more likely to insert in the regions of ectoderm that express Sp-Efn and that high levels of expression of Sp-Efn enhances pigment cell insertion . 10 . 7554/eLife . 16000 . 018Figure 5 . Altering the abundance of Sp-Efn in half embryos by injecting a single blastomere of 2-cell embryos indicates pigmented immunocytes insert preferentially in ectoderm expressing Sp-Efn and high levels of expression of Sp-Efn enhances pigment cell insertion . ( A–C ) Maximum intensity projection of an embryo in which half of the specimen co-expresses membrane GFP and Sp-Efn ( MeOH fixation ) . Sp1 reveals the distribution of immunocytes . Note that a subset of the immunocytes expresses GFP . Most of the immunocytes are either inserted in the half expressing Sp-Efn or extend contacts to that half of the embryo . ( D–F ) Through focus projection of a living embryo that was co-injected in one blastomere with GFP and SpEfn MO2 . In the DIC image immunocytes can be identified by their pigment and the white line demarks the interface between ectoderm containing morpholino ( anatomical left ) and untreated ectoderm ( anatomical right ) . A subset of the immunocytes express GFP . Several pigmented immunocytes are associated with the interface between the to domains of ectoderm , those marked with * project processes to the untreated , Sp-Efn expressing , ectoderm . ( G–I ) Embryo from an egg that was injected with Sp-EfnMO2 to suppress Sp-Efn expression throughout the embryo ( MeOH fixation ) . Once the egg had cleaved , one blastomere was injected with Sp-Efn RNA . The pigmented immunocytes are almost exclusively inserted in the half of the embryo expressing Sp-Efn . ( J–L ) Control embryos were injected with mGFP RNA only and the distribution of pigmented immunocytes can be seen to be unaffected ( MeOH fixation ) . ( M ) Quantification of the distribution of pigmented immunocytes from the experiments depicted above ( A–L ) . For each treatment ( X axis ) there is an injected half ( green ) and an uninjected half ( blue ) . The number of pigmented immunocytes inserted in ectoderm of the injected half , or the uninjected half was determined for each embryo ( 72 hr ) . Bar = 10 µmDOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 01810 . 7554/eLife . 16000 . 019Figure 5—source data 1 . Source data for Figure 5M . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 01910 . 7554/eLife . 16000 . 020Video 8 . Behavior of pigmented immunocyte in the context of mosaic Sp-Efn expression . In this 90 min sequence half of the embryo is expressing high levels of Sp-Efn , as indicated by the regions co-expressing GFP . Pigmented immunocytes have inserted in the ectoderm adjacent to the interface . Once inserted they do not move toward the region expressing higher levels of Sp-Efn . This sequence supports a model in which pigmented immunocytes migrate and then insert in the ectoderm without making extensive subsequent movements within the ectoderm . DOI: http://dx . doi . org/10 . 7554/eLife . 16000 . 020 Ephrins and Eph receptors are typically expressed on cell surfaces and signaling is mediated by direct cellular contact ( Klein , 2012 ) . Soluble Ephrins have been shown to function as competitive inhibitors and details of receptor activation indicate that clustering is an essential part of the activation process ( Davis et al . , 1994; Himanen et al . , 2010; Seiradake et al . , 2010 ) . Our data show that Sp-Efn is abundant on cytonemal processes on the basal surfaces of dorsal ectodermal cells . This is a distinctive mechanism of Ephrin presentation and may indicate that the epithelial cells could expand their range of Ephrin signaling by distances of up to 15 µm from the cell surface . An implication of this manner of ligand presentation is that cells within the length of cytonemes could potentially activate receptors on a pigmented immunocyte without making direct contact with the cell – a form of paracrine signaling . Live imaging indicates that immunocytes migrate from the vegetal plate and contact the basal surface of the ectoderm as they migrate . Presentation of Ephrin on cytonemes suggests an active presentation of ligand in a context where immunocyte precursors move through a shag carpet of Ephrin . However , at this time there is no experimental evidence supporting a need for Sp-Efn expression on cytonemes in order for it to function . Cytonemes on the ectoderm of sea urchin embryos may have additional roles in presentation of ligands and receptors . Thin filopodia have not previously been termed cytonemes in sea urchin embryos . Andrews ( 1897 ) described filopodia on the basolateral membranes of the epithelial cells of blastulae of sea urchin embryos that extend , retract , branch and fuse . He suggested that the processes are connections between blastomeres and provided cytoplasmic continuity . The existence of these fine processes was confirmed with phase contrast imaging , time-lapse , and transmission EM ( Dan , 1960; Gustafson , 1961; Vacquier , 1968 ) . The filopodia of blastomeres were suggested to function in cell adhesion or to maintain cytoplasmic continuity of blastomeres . Miller et al . ( 1995 ) described ectodermal filopodia using DIC optics and noted that they are shorter and less abundant than filopodia of mesenchyme . McClay ( 1999 ) distinguished thin filopodia from other forms of filopodia and emphasized the potential role they may have as structures detecting patterning information . In Drosophila , thin filopodia have been demonstrated to be involved in signaling; either in presentation of ligands or receptors , and are distinguished by calling them cytonemes ( Ramirez-Weber and Kornberg , 1999 ) . Numerous examples of signaling are now known to involve the subset of filopodia called cytonemes ( Gradilla and Guerrero , 2013; Kornberg , 2014; Kornberg and Roy , 2014 ) . The demonstration that thin ectodermal filopodia of sea urchin embryos present the ligand Sp-Efn indicates that they are cytonemes that function in presentation of patterning information . The expression of Sp-Eph on pigmented immunocyte precursors , the expression of Sp-Efn on basal cytonemes , and the pattern of abundance of Sp-Efn provide a strong correlative basis for the hypothesis that Eph/Ephrin signaling is a component of the mechanisms determining the distribution of pigmented immunocytes . The principal effect of suppressing expression of Sp-Eph or inhibiting Eph kinase function is the pigmented immunocytes not adopting a characteristic dendritic morphology . The treatments result in a small increase the number of pigmented immunocyte precursors in the blastocoel , which suggests attachment to the ectoderm is independent of Eph/Ephrin signaling . Suppressing expression of Sp-Eph or inhibiting Eph kinase function results in a 20% reduction in the number of pigmented immunocytes in 72 hr embryos . There is no difference in the number of pigmented immunocytes at 48 hr when Eph is either knocked down or inhibited and activated caspase-3 and morphological indications of apoptosis occur in a small proportion of the immunocytes . As well , apoptosis appears to occur relatively late in the process of immunocyte migration ( 72 hr ) and is almost completely absent in control embryos . We have concluded that the 20% reduction in the number of pigmented immunocytes is in part due to immunocytes becoming apoptotic . If Eph functioned solely as a survival factor selecting among randomly dispersed immunocyte precursors , apoptosis would be detected in control embryos . As well , using Sp1 as a differentiation marker , it is apparent that immunocyte precursors are distributed throughout the dorsal ectoderm before the end of gastrulation , which indicates patterning arises during migration . Although there is a single ligand for Eph in urchins , morpholinos directed at the receptor result in a reduction of pigmented immunocytes , and morpholinos directed at the ligand ( SpEphrin ) have no effect on cell numbers . This outcome may result from other interactions , both extra- and intra-cellular , with Sp-Eph , or from differences in efficiencies of the morpholinos that target the receptor or the ligand . Complete suppression of either the ligand or the receptor is necessary to fully explain this observation . Our loss-of-function data support an essential role for Eph/Ephrin signaling in the insertion of mesenchyme into epithelium , however we are unable to distinguish the precise step in the transition requiring Eph activation . The morphology of cells changes as they insert into the ectoderm , but the underlying mechanisms are not clear . Loss-of-function data does not distinguish between a sequential process and a process that is directly dependent on Eph/Ephrin . In one situation , insertion and the change in morphology are independent pathways that are sequentially activated , and only one pathway is dependent on Eph/Ephrin signaling . An alternative is that insertion and the change in morphology are independent processes , but they are directly dependent on Eph/Ephrin signaling . By injecting Sp-Efn RNA we are able to express ligand throughout the embryo , including the ventral ectoderm , where this ligand is not normally expressed . The ventral ectoderm does have cytonemes , at similar densities to dorsal ectoderm , but they do not express Sp-Efn . Expression of Sp-Efn in ventral ectoderm is sufficient for pigmented immunocytes to disperse beneath ventral ectoderm and insert in the epithelium . As well , a high level of Sp-Efn expression in half of an embryo enhances immunocyte insertion in dorsal ectoderm . Overall , loss-of-function and gain-of-function outcomes indicate that Sp-Eph and Sp-Efn are components of the pathways that facilitate insertion of immunocytes into epithelium . It is also clear from our data that there are additional factors that influence the distribution of Sp-Efn and the ability of pigmented immunocytes to insert in an epithelium . When Sp-Efn is ectopically expressed , early gastrula stages have uniform expression of Sp-Efn . However , by the completion of gastrulation , Sp-Efn cannot be detected in endoderm ( Figure 4L ) . This may account for pigmented immunocytes not inserting into endoderm or coelomic epithelia , however the mechanism by which Sp-Efn is excluded from the endoderm is not known . The accumulation of pigmented immunocytes in regions of abundant Sp-Efn and experiments in which Sp-Efn levels are perturbed in half of the embryo indicate that pigmented immunocytes move into regions expressing high levels of Sp-Efn . Several mechanisms could potentially account for the apparent attraction of immunocyte precursors to ectoderm expressing Sp-Efn . Given the established role for Eph/Ephrin signaling in regulating cellular adhesion , we propose a mechanism in which Sp-Eph activation leads to a localized increase in adhesion . Although soluble attractants cannot be eliminated as possible co-factors , haptotaxis may be sufficient to account for the observations that we report . Haptotaxis guides by means of a gradient of adhesion and is dependent upon adhesive bonds that are continuously made and broken . Stronger adhesions resist disruption better than weaker adhesions , with the result that random motions will move a cell up an adhesive gradient ( Davies , 2005 ) . Our data indicate that Ephrin abundance is not uniform and pigmented immunocytes continuously extend and retract short processes . If Eph activation leads to a localized enhancement of adhesion , random movements of cells would cause them to disperse and move to regions in which the level of Ephrin is higher . Eph activation has been demonstrated to promote integrin mediated cell adhesion in mammalian cells ( Huynh-Do et al . , 1999 ) and the kinase NIK ( Nck-Interacting Ste20 Kinase ) is activated by Eph receptors , which leads to integrin activation ( Becker et al . , 2000 ) . As well , Eph receptors have been demonstrated to interact with Ig Superfamily receptors , which are also expressed by pigmented immunocytes ( Barsi et al . , 2015 ) . Gradients of Ephrin have been previously demonstrated to be critical to retinotectal and corticospinal patterning . However , the predominant mechanism is for high levels of ligand to suppress projection of axons ( Suetterlin et al . , 2012; Triplett and Feldheim , 2012 ) . Thus , our data suggest that Sp-Eph and Sp-Efn may also function in the dispersion of immunocyte precursors from their site of origin , in addition to their role in insertion into the ectoderm . Phylogenetic analyses suggest that Eph receptors and ephrin ligands diverged into A- and B-types at different points in their evolutionary history , such that primitive chordates likely possessed an ancestral ephrin-A and an ancestral ephrin-B , but only a single Eph receptor ( Drescher , 2002; Mellott and Burke , 2008 ) . No extant groups of deuterostomes are known to have retained this primitive condition . However , urchins have the simplest set: a single receptor and ligand . In urchins , Eph and Ephrin are expressed in broad domains of ectoderm , where they function at the interface to mediate apical contractility ( Krupke and Burke , 2014 ) . We now add to this a role in mediating aspects of dispersal and epithelial transition of migratory cells . Summaries of the diverse functions of Eph and Ephrin in vertebrates emphasize broad categories of functions in establishing domains within epithelia such as we find in developing hindbrains , somites , intestinal villi , and vascular remodelling ( Klein et al . , 2009 , 2012; Pasquale , 2005 , 2008; Poliakov et al . , 2004 ) . In addition , Eph and Ephrin have a second broad set of functions in guiding migrating cells , or axonal outgrowths ( Nievergall et al . , 2012; Klein , 2004; Triplett and Feldheim , 2012: Suetterlin et al . , 2012 ) . This parallel suggests that urchin embryos are a model that reveals a basal range of Eph and Ephrin functions , which has expanded and diversified such that Eph and Ephrin are the principal molecular determinants of cellular positioning and tissue patterning in vertebrates . Eggs and sperm were collected from S . purpuratus adults that had been induced to spawn by shaking . Sperm was diluted 1:100 in filtered seawater prior to fertilization and embryos were grown at 12–14°C . Eggs were prepared for microinjection as described previously ( Krupke et al . , 2014 ) . Injection solutions contained water , 125 mM KCl and either RNA or morpholinos . Ephrin was targeted by injecting 2–4 pL of injection solution containing morpholino antisense oligonucleotides ( GeneTools ) against SpEphrin ( Sp-EfnMO1 , 2 ) at 400 μM . Ectopic expression of Sp-Efn was achieved using synthetic , capped mRNAs derived from the full length S . purpuratus Sp-Efn gene cloned in pCS2+ and transcribed using the SP6 mMessage mMachine kit ( Ambion ) . 2–4 pL of a 0 . 2 μM injection solution was injected into each freshly fertilized egg as previously described ( Krupke and Burke , 2014 ) . Single blastomere injections followed Krupke et al . ( 2014 ) . Eph inhibitor , NVP BHG 712 ( NVP ) was used at 1 . 75 μM ( Cat . No . 4405 , Tocris Biosciences ) ( Martiny-Baron et al . , 2010 ) . Oligonucleotide DNA primers were obtained from Operon . Sequences encoding full-length Ephrin were obtained from Echinobase ( http://www . echinobase . org/Echinobase/ , RRID:SCR_013732 ) and DNA was amplified with high fidelity PCR from cDNA isolated from 72 hr S . purpuratus embryos and cloned using the pGEM-T Easy system ( Promega , RRID:SCR_006724 ) . Morpholino antisense oligonucleotides were obtained from GeneTools . To control for morpholino specificity , we used a control , irrelevant morpholino , which would have no effect on phenotype , as well as two experimental morpholinos against different target sequences that produced the same aberrant phenotype for each gene . Antibodies were used to confirm loss of immunoreactivity and in the case of the Sp-Efn MO rescue experiments are reported in which eggs were injected with an Sp-Efn morpholino and one cell of a 2-cell embryo injected with Sp-Efn RNA . The morpholinos targeting different sites in Sp-Efn and Sp-Eph were used interchangeably in experiments . Morpholino sequences: Sp-EfnMO1: 5’-AAATTTAGTCCTGGAAAGATGAGAC-3’ . Sp-EfnMO2: 5’CTCCAGGGTCAAAGTGCTCAGGTAT-3’ . Sp-EphMO1: 5’ATTGGAAAGAGTAAATCCGAGATGT-3’ . Sp-EphMO2: 5’AAATAAGTCATTCTCTCCTCTCCGT-3’ . NegControlMO: 5’GAATGAAACTGTCCTTATCCATCA-3’ . S . purpuratus embryos were collected at the desired time point and fixed for 20 min in modified PEM buffer , paraformaldehyde sea water ( PFA ) ( Krupke et al . , 2014 ) or 5 min in ice-cold methanol . Embryos were washed with PBS , blocked for 1 hr in SuperBlock ( Thermo ) , probed with primary antibody , and washed three times with PBS . Alexa Fluor fluorescent secondary antibodies ( Invitrogen , Carlsbad , CA ) were used to visualize antibody labeling on a Zeiss 700 LSM ( Carl Zeiss ) confocal microscope . Imaging and analysis was conducted using ZEN software ( Carl Zeiss ) . ImageJ , ZEN Lite or Adobe Photoshop ( RRID:SCR_002078 ) was used to adjust image contrast and brightness and for final editing . Antibodies employed have all been described previously , or are commercially developed . Data on antigens and specificity are available in the cited references . Sp1 , hybridoma supernatant diluted 1:2 ( Gibson and Burke , 1985; Developmental Studies Hybridoma Bank , RRID:SCR_013527 ) Sp-Efn , hybridoma supernatant diluted 1:2 ( Krupke and Burke , 2014 ) Sp-Eph , Rat polyclonal antiserum diluted1:400 ( Krupke and Burke , 2014 ) pFAK , ( Phospho-FAK pTyr397 Antibody #44-624G , RRID:AB_2533701 ) Invitrogen , diluted 1:2000 ( Krupke and Burke , 2014 ) Hnf6 , Rat polyclonal antiserum , diluted 1:600 ( Yaguchi et al . , 2010 ) Cas3 ( Cleaved Caspase-3 ( Asp175 ) ( 5A1E ) Rabbit mAb #9664L . Cell Signaling Technologies , diluted 1:250 ( Wei et al . , 2012 ) . Spectrin diluted 1:500 ( Fishkind et al . 1990 ) Alexa 488 Phalloidin ( Actin Green , GeneCopia Inc . ) diluted 1:250 GFP Goat pAb #ab6673 , AbCam , diluted 1:500 The quantification of the distribution of pigmented immunocytes and Sp-Efn immunoreactivity are described in Figure 1—figure supplement 1 . For experiments in which one blastomere was injected , embryos were fixed and prepared with anti-GFP and Sp1 and a complete confocal , through focus series was prepared . Embryos were selected for cell counting if the GFP occupied approximately half of the ventral ectoderm , so as not to bias the surface area , which was assumed to be equal for the two halves of the embryo . Individual sections were examined sequentially and the number of immunoreactive cells ( Sp1 ) was determined . In some embryos , through focus series were made of live embryos and the number of cells with pigment granules on each half of the embryos was determined . Live images were made using the time series acquisition features of the Zeiss LSM 700 ( Zen 2009 , ver . 6 . 0 . 0 . 303 ) . Embryos were pipetted onto NewSilane Adhesive Coated Slides ( Newcomer Supply Ltd . ) and trapped under a glass coverslip attached along two edges with double-sided adhesive tape ( 3M Inc ) . Paraffin oil was applied to the open edges of the coverslip to reduce evaporation and the room temperature was controlled to 16°C . Embryos could routinely be maintained for 6 to 8 hr before they moved out of the field of view . Stacks were prepared using ImageJ ( RRID:SCR_003070 )	During animal development , numerous cells move around the embryo to form and shape the growing tissues . As these cells move , they are guided to their destination by molecular cues . The embryo’s tissues produce these cues and the cues can either repel or attract migrating cells . Ephrins are a large and well-studied family of proteins that serve as guidance cues and are found on the surface of certain types of cells . Some migrating cells have receptors for Ephrin and are repelled from tissues that contain Ephrin proteins . In these cases , the repulsive interaction between Ephrins and cells with receptors ensures that migrating cells avoid certain locations and reach the correct final destination . The sea urchin is an important model organism for studying how animals develop and in particular how genes control animal development . This is in part because these animals can be easily manipulated in the laboratory and are more closely related to animals with backbones than many other model organisms . Sea urchins also have a relatively simple set of genes; many of which are similar to the human form of the gene . In sea urchin embryos , pigmented cells called immunocytes are known to migrate from one region of the embryo to another where they form part of its immune system . However it was not clear what guides this migration . Sea urchins produce one type of Ephrin protein and its associated receptor , and now Krupke et al . show that immunocytes carry the receptor for Ephrin and migrate to embryonic tissues that produce high levels of this Ephrin . This finding suggested that the Ephrin is actually attracting the immunocytes to their final destination rather than repelling them . Further experiments supported this idea and revealed that immunocytes that lack the Ephrin receptor fail to enter the right tissue . Similarly , altering the pattern of Ephrin in the embryo’s tissues altered immunocyte migration in a predictable way . These findings of Krupke et al . suggest that Ephrin and its receptor have changed their biological functions during evolution of animals . This raises a number of questions for future research including whether the molecular mechanisms used by Ephrin and its receptor to attract immunocytes in sea urchins is the same as that used to repel cells in other species .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[]	2016	Eph and Ephrin function in dispersal and epithelial insertion of pigmented immunocytes in sea urchin embryos
Sex differences and social context independently contribute to the development of stress-related disorders . However , less is known about how their interplay might influence behavior and physiology . Here we focused on social hierarchy status , a major component of the social environment in mice , and whether it influences behavioral adaptation to chronic stress in a sex-specific manner . We used a high-throughput automated behavioral monitoring system to assess social dominance in same-sex , group-living mice . We found that position in the social hierarchy at baseline was a significant predictor of multiple behavioral outcomes following exposure to chronic stress . Crucially , this association carried opposite consequences for the two sexes . This work demonstrates the importance of recognizing the interplay between sex and social factors and enhances our understating of how individual differences shape the stress response . Stress-related psychopathologies , such as mood and anxiety disorders , show a pronounced gender bias in their prevalence , severity , age-of-onset , and most common comorbidities ( Altemus et al . , 2014; Bangasser and Valentino , 2014; Kessler et al . , 2005; Young and Pfaff , 2014 ) . For example , the latest studies estimate the prevalence of major depressive disorder among women as 1 . 5 times higher than in men ( World Health Organization , 2017 ) . In addition , in women major depression is characterized by increased symptom severity ( Martin et al . , 2013 ) and is more commonly comorbid with anxiety disorders , eating disorders , and sleep disturbances , while men with major depression are more prone to develop aggression , alcohol or substance abuse , and suicidal ideation ( Marcus et al . , 2005; Martin et al . , 2013 ) . Despite these observations and the documented examples of sexual dimorphism in human stress response ( Bangasser and Valentino , 2012 ) , the biological mechanisms that give rise to sex differences in stress response are not well understood ( Beery and Zucker , 2011; Joel and McCarthy , 2017 ) . The symptomatology of stress-related pathologies and the biological response to stress span several domains of functioning including energy metabolism , mood , and sociability . Recent studies in rodent models of stress-related psychopathologies have already identified several differences across molecular , behavioral , and metabolic levels ( Bangasser and Wicks , 2017; Brivio et al . , 2020; Hodes , 2018; Hodes and Epperson , 2019; Young and Pfaff , 2014 ) . Very few studies , however , have looked into the interaction between pre-existing differences in social behavior between the sexes and stress . Considering that abnormalities in social functioning are an essential part of the symptomatology of stress-related disorders , differences in social behavior and social cognition prior to disorder onset are likely to contribute to disorder susceptibility . Here , we explored how social context shapes the response to chronic stress . We focused specifically on social dominance , an essential characteristic of rodent social groups . Wild and laboratory rodents form complex and dynamic social structures which typically involve the formation of dominance hierarchies ( Kondrakiewicz et al . , 2019 ) . These have been observed in the lab in group sizes ranging from three to over a dozen individuals ( Horii et al . , 2017; Varholick et al . , 2019; Wang et al . , 2014 ) . Hierarchies are thought to improve social stability and reduce severe conflicts and aggression ( Curley , 2016 ) . As a consequence , an individual’s position in the dominance hierarchy has important consequences , including preferential access to food , shelter , and mates ( Drews , 1993 ) . Social rank within male hierarchies is also known to influence health , hormonal profile , brain function , metabolism , and mortality ( Pallé et al . , 2019; Razzoli et al . , 2018 ) . For instance , subordinate individuals display increased anxiety-like behavior , a suppressed immune response , higher basal corticosterone levels , and reduced life span ( Bartolomucci , 2007 ) . These types of relationships have classically been studied in male animals , as female mice have usually appeared more communal and displayed limited aggression ( König and Lindholm , 2012 ) . Recent work , however , has demonstrated that female laboratory mice also form hierarchies that appear quite similar to those seen in males , accompanied by some of the same dominance-related physiological markers , such as differences in corticosterone levels ( Schuhr , 1987; van den Berg et al . , 2015; Varholick et al . , 2019; Varholick et al . , 2018; Williamson et al . , 2019 ) . Thus , we examined social dominance status as a putative mediator of sex differences in the response to adverse events . To do so , we took advantage of a high-throughput automated behavioral monitoring system ( the Social Box , SB ) to assess and better understand the hierarchies of groups of male or female mice ( Forkosh et al . , 2019; Shemesh et al . , 2013 ) . We then exposed mice to a well-established chronic stress procedure , the chronic mild stress ( CMS ) paradigm , and evaluated its effects using a series of standard behavioral and physiological readouts . Finally , we used social dominance status at baseline to predict behavioral outcomes following CMS . We hypothesized that an individual’s standing in the social hierarchy would be a predictor of behavior upon stress exposure , and that this relationship would differ between the sexes . We first explored the hierarchical structure of grouped CD-1 mice over four days of baseline monitoring as well as the stability of hierarchies following an acute stressor ( 15 min of restraint stress ) . Social dominance was assessed by calculating the David’s Score ( DS ) , an established method for inferring social hierarchies ( David , 1987; Gammell et al . , 2003 ) . We based the DS on the numbers and directionality of chases between each pair of individuals in a group . A cumulative DS for the four baseline days of the SB assessment was used as a final measure of social dominance . In line with previous studies ( Schuhr , 1987; van den Berg et al . , 2015; Varholick et al . , 2019; Varholick et al . , 2018; Williamson et al . , 2019 ) , we were able to detect some stability in the hierarchies of both sexes ( Figure 1a ) . We further calculated several properties of male and female hierarchies to explore potential differences in their characteristics . Namely , we calculated: ( 1 ) steepness – a measure of social distance between each individual in the hierarchy , ( 2 ) despotism – a measure of the extent to which the top-ranking individuals dominate over the rest of the group , ( 3 ) directional consistency – the extent to which the directionality of the interactions follow the expected direction from higher to lower rank , and ( 4 ) Landau’s modified h’ – a measure of hierarchy linearity ( de Vries , 1995; Landau , 1951; Figure 1—figure supplement 1 ) . We found that male hierarchies were steeper , more linear , more despotic , and had higher directional consistency than those of females . Interestingly , mice housed in larger groups show analogous relationships between sexes ( Williamson et al . , 2019 ) . To investigate if social rank carries any sex-specific implications for overall behavior , we analyzed the correlation structure between an individual’s DS and 59 behavioral readouts recorded post-habituation ( days 2–4 ) in the SB within each sex ( briefly described in Methods . For a detailed list of behaviors and how they are computed , see Forkosh et al . , 2019 ) . Thirty of the fifty-nine behavioral readouts tested ( 50 . 84% ) showed significant correlations with cumulative baseline DS in at least one of the sexes ( Figure 1—figure supplement 2 , q < 0 . 05 , Spearman’s rank correlation , Benjamini-Hochberg adjustment within each sex ) . While the overall association pattern was quite similar between male and female mice , there were several correlations seen in males that were absent in females ( Figure 1b and Figure 1—figure supplement 2 ) . These included measures of overall locomotion , such as Distance Outside ( Male rs = 0 . 478 , n = 40 mice , p = 0 . 00355 . Female rs = −0 . 253 , n = 48 mice , p = 0 . 137 ) , and Fraction of Time Outside – the mean proportion of time a mouse spent outside the nest ( Male rs = 0 . 523 , n = 40 mice , p = 0 . 00125 . Female rs = −0 . 144 , n = 48 mice , p = 0 . 402 ) , as well as two related measures of roaming entropy , which assess the predictability of how an individual explores their environment – Entropy and Grid Entropy [6 × 6] ( for brevity we only report the latter , Male rs = 0 . 531 , n = 40 mice , p = 0 . 00123 . Female rs = 0 . 00677 , n = 48 mice , p = 0 . 694 ) . These correlations indicate that overall locomotion and exploration of the home environment may be more strongly connected to social rank in male groups , while being seemingly independent of social status in females . Interestingly , no correlations were present in females but absent in males . Altogether these findings suggest that male and female social dominance hierarchies , despite having a similar structure , have different relationships to overall behavior . Next , we estimated DS stability over time by examining the frequency of rank change events and comparing those to the chance-level expectation . Briefly , normalized daily DS values were ranked for each group to create a four-rank hierarchy: α ( most dominant ) , β , γ , and δ ( most subordinate ) and each mouse was assigned a single rank based on its four-day cumulative DS . For each pair of consecutive days , we observed how many individuals maintained the same rank they had been assigned on the previous day . We then calculated the rank maintenance odds for animals in each final rank category relative to the expected chance-level ( Figure 1c ) . The true probability of rank maintenance in our data was higher than chance in α-females and in all male ranks ( one-tailed binomial tests against the rank maintenance probability of 25% , α-females: 21/36 successes , p = 2 . 1×10−5 , α-males: 22/30 successes , p = 3 . 7×10−8 , β- and γ-males: 13/30 successes each , p = 0 . 0216 , δ-males: 19/30 successes , p = 1 . 02×10−5 ) . These results indicate that the highest rank in a hierarchy is often occupied by the same individual over time in both sexes , while the lower ranks appeared to be stable in males only ( Figure 1c ) . In addition to stability over time during baseline recordings , individual DS also remained stable following acute restraint stress ( Pearson’s correlation between cumulative baseline DS and DS following acute restraint on day 5 , Figure 1d and Figure 1—figure supplement 1 ) . Both males ( r = 0 . 6639 , n = 40 mice , p = 3×10−6 ) and females ( r = 0 . 446 , n = 48 mice , p = 0 . 000149 ) showed significant DS correlations from baseline to acute restraint . Finally , we investigated the possibility of differential effects of acute restraint on the behavior used to produce the DS – numbers of chase events ( Figure 1e ) . Repeated-measures ANOVA on log-transformed chase numbers showed that the number of chases decreased significantly following acute restraint stress ( F ( 1 , 86 ) = 29 . 04 , n = 88 mice , p = 6 . 11×10−7 ) , however the extent of this decrease did not differ between the sexes ( Sex x Stage interaction , F ( 1 , 86 ) = 1 . 053 , n = 88 mice , p = 0 . 301 ) . The apparent robustness of social hierarchies over time and in response to acute stress suggested that predictions from the baseline assessment may carry information that would still be relevant to behavioral outcomes following a long-term intervention . More specifically , we hypothesized that occupancy of the highest-ranking positions in the social hierarchy in both sexes and additionally the lowest in males might be sufficiently stable to allow for long-term predictions . To investigate the effects of pre-existing social dominance status on the behavioral response to chronic stress , we employed a CMS protocol adapted for group-housed animals . In short , groups were exposed to a weekly schedule of two daily randomly combined mild stressors ( e . g . wet bedding , tilted cage , overcrowding ) for a total of three weeks . Six groups of each sex ( n = 24 per sex ) were randomly assigned to receive CMS , while the rest of the groups ( six groups of females and four groups of males ) were assigned to the control condition . The 21-day CMS procedure was followed by a behavioral test battery for both control and CMS mice , which included tests previously shown to capture the effects of chronic stress ( Figure 2a ) . This included , among others , classical tests of locomotion ( open field test , OFT ) , anhedonia ( sucrose preference test , SPT ) , anxiety-like behavior ( elevated plus maze , EPM ) , and stress coping ( tail suspension test , TST ) . Additionally , we assessed several physiological indicators of stress level ( Figure 2b–e ) . All the physiological and behavioral outcome variables following CMS were collected into a single dataset . Since the full experiment was run in two batches , all outcome variables were adjusted for batch effect ( see Methods ) . To improve readability , we report the batch-adjusted values relative to the mean of female control mice . As expected , we found that both bodyweight change and cumulative coat quality were significantly reduced following CMS in both males and females ( Figure 2b–c , Bodyweight: F ( 1 , 82 ) = 7 . 394 , p = 0 . 00798 , Coat quality: KW test , χ2 ( 1 ) = 18 . 586 , p = 1 . 6×10−5 ) , although post-hoc pairwise comparisons indicated a bodyweight difference in females only ( females: t ( 43 . 784 ) = 3 . 9447 , p = 0 . 000285 , males: t ( 36 . 937 ) = 1 . 1064 , p = 0 . 27 ) . Bodyweight-adjusted adrenal weights were increased after CMS in males only ( Figure 2d and -way ANOVA , sex by condition interaction , F ( 1 , 80 ) = 4 . 42 , p = 0 . 039 , followed by pairwise within-sex 2-sided t-tests: males: t ( 27 . 03 ) = −3 . 143 , p = 0 . 004; Females: t ( 41 . 18 ) = 0 . 0726 , p = 0 . 94 ) . For all further analyses , these physiological outcomes were combined with the behavioral ones in a single dataset . To explore how exposure to chronic stress shapes behavior in groups of mice , we investigated the major drivers of variance in the dataset containing all behavioral and physiological readouts following CMS using principal components analysis ( PCA , Figure 3a–d ) . The first principal component ( PC1 ) , explained approximately 21 . 6% of the variance in the outcome data ( Figure 3a ) . To our surprise , neither sex nor condition ( CMS vs controls ) appeared to capture variance contained in PC1 ( Figure 3b , condition effect: F ( 1 , 82 ) = 0 . 608 , p = 0 . 44 ) . Instead , sex and condition were associated with PC2 and PC3 respectively ( Figure 3—figure supplement 1 ) . Since none of the expected variables ( sex , condition , or their interaction ) contributed to the main source of variance in the dataset , we investigated whether social dominance was a contributing factor . We tested the association between PC1 scores and DS ( Figure 3c ) . Remarkably , baseline DS significantly predicted scores on PC1 in CMS individuals only and this association was in opposite directions between the two sexes ( sex by DS interaction: F ( 1 , 43 ) = 6 . 016 , p = 0 . 0183 ) . Thus , the principal source of variation in the outcome dataset contained an interaction between baseline dominance scores and sex in the CMS mice . To better assess the set of behaviors responsible for this association , we correlated PC1 scores with all the input features from the behavioral and physiological readouts ( Figure 3d ) . We found that seventeen readouts were significantly correlated with PC1 scores in this dataset ( Spearman’s rank correlation , Bonferroni-adjusted p < 0 . 05 ) . Among the strongest correlates of PC1 were measures derived from the OFT and EPM , and specifically features related to locomotion and anxiety-like behavior , such as distance traveled and visits to the anxiogenic regions of test chambers . Interestingly , these behaviors do not typically differentiate CMS and control individuals . Instead , CMS exposure appeared to create relationships between dominance and the outcome variables that were not present in controls ( the top examples from the OFT and EPM are depicted in Figure 3e–f , correlations between individual readouts and DS within each sex and condition are available in Figure 3—figure supplement 2 ) . To conclude , we were able to narrow down a portion of the variance in a broad range of behavioral and physiological outcomes following CMS to an interaction between dominance and sex with more subordinate CMS males showing apparent increases in measures of overall activity ( distance/speed in the OFT and EPM ) and more subordinate males and more dominant females showing an apparent reduction in anxiety-like behavior . Thus , we were able to identify a novel role for social rank belonging in modulating behavior following chronic stress in a sexually dimorphic way . Social behavior in general and social dominance in particular are important contributors to individual differences ( Forkosh et al . , 2019 ) . As such , they may also shape how individuals respond to environmental challenges . In humans , different types of social hierarchies coexist in complex structures and they influence an individual’s behavior and health ( Sapolsky , 2005 ) . Mouse social dominance hierarchies are considerably simpler , however parallels between the two species can be drawn in particular with regard to human socioeconomic status ( SES ) . Both objective and perceived SES impact human health , mortality , morbidity , and susceptibility to psychiatric disorders such as depression or anxiety ( Farah , 2017; Freeman et al . , 2016; Hoebel et al . , 2017; McEwen and Gianaros , 2010; Shaked et al . , 2016; Wetherall et al . , 2019 ) . These outcomes resemble findings related to social dominance in mice ( Bartolomucci , 2007; Pallé et al . , 2019; Razzoli et al . , 2018 ) . Intriguingly , some human studies have identified sex differences in the relationship between SES and stress-related psychopathologies ( Kosidou et al . , 2011; Mwinyi et al . , 2017; Peplinski et al . , 2018 ) . All this suggests that social status and the stress and health gradient that characterize human social structures ( McEwen and Gianaros , 2010 ) likely have close analogues in other mammals with well-defined social structures . Here , we have demonstrated that both male and female socially housed mice establish social dominance hierarchies , which are relatively stable over time and resistant to acute perturbations . In agreement with previous work , female hierarchies were less despotic and had lower directional consistency ( Williamson et al . , 2019 ) , suggesting that females may be maintaining a less rigid structure compared to males . This is supported by our finding that only the top rank in females showed significant stability over time , whereas in males both subordinate and dominant ranks appeared stable . Further research is needed to ascertain if this observation is limited to our paradigm and specific set of measurements ( same-sex groups of four individuals ) , or if it represents a true sex difference in social dominance hierarchies . Additionally , our data suggest that an individual’s position in the hierarchy carries different implications for overall behavior in each sex . While we are unable to assess whether social rank belonging should be considered a cause or consequence of these behavioral differences , we have observed an interesting sex difference in this relationship . For groups living in the enriched environment of the SB apparatus , dominance in males but not in females was associated with overall locomotion , proportion of time spent outside the nest , and exploration entropy . These associations likely reflect territorial or patrolling behavior in males , which may be less relevant to female social hierarchies . As hypothesized , occupancy of different positions in the social hierarchy conferred varying levels of responsiveness to the challenges posed by chronic stress . Previous investigations have rarely found associations between social dominance and response to chronic stress ( Larrieu and Sandi , 2018 ) . An important exception is a recent study by Larrieu et al . , 2017 in groups of male mice exposed to chronic social defeat stress . The authors found increased susceptibility to chronic social defeat for dominant males , but in contrast to our results , no real behavioral alterations for subordinates . It is important to note , however , that chronic social defeat and CMS are profoundly different paradigms . Social defeat is strongly tied to social dominance and might be perceived as loss of status more than other stressors ( Larrieu and Sandi , 2018 ) . Our use of CMS allowed us to investigate both sexes under comparable levels of stress . Nevertheless , both the study from Larrieu and colleagues , and ours highlight that social status can influence an individual's response to long-term adverse life events . Importantly , we demonstrated that the effects of preexisting dominance on stress outcomes were sexually divergent , such that the association between dominance and anxiety-like and locomotor behavior following CMS was in opposite directions between males and females . Specifically , subordinate males appeared to display hyperlocomotion , while dominant females displayed increased boldness ( reduced anxiety-like behavior ) compared to non-CMS controls . Overall , our data indicate that an individual’s position within a social structure can influence their behavioral response to chronic stress in a sex-specific fashion . These findings suggest an intriguing possibility . Given that male social hierarchies are likely antagonistic , we speculate that social living carries an especially high cost for subordinate males , who are the recipients of most antagonistic interactions . Conversely , female hierarchies may contribute to more affiliative social interactions , and thus social context may carry a net benefit for females , with the highest benefit gained by the dominant females . We speculate that this positioning as the most advantaged and disadvantaged individuals may confer higher behavioral flexibility and results in the strongest behavioral change upon exposure to environmental challenges . Crucially , since we decided to maintain social context throughout our experimental design , the current work did not allow for the assessment of the effect of group- versus single-housing on CMS outcomes . Given this constraint , we were not able to confidently assess the difference in how CMS was experienced by each sex in groups as opposed to if they had been single-housed . However , since we were interested in the prediction from baseline dominance , we did not wish to remove the salience and thereby the effect of social context . Likewise , in naturalistic conditions , mice are found in mixed-sex groups ( Kondrakiewicz et al . , 2019 ) . Working with same-sex groups provided us with a more controlled environment , preventing confounding by mating behavior and pregnancy . However , this was at the expense of the ethological validity of our findings . Further research is needed to understand if and how mixed-sex social structures may differ in their impact on stress outcomes . Moreover , while CMS produced some of the expected physiological changes ( i . e . , reduction of bodyweight gain , reduced coat quality , adrenal weight increase ) , we did not observe several of the behavioral phenotypes often found using similar protocols ( e . g . , hyperlocomotion , anhedonia , passive coping , Franceschelli et al . , 2014 ) . While we have sufficient evidence that CMS individuals experienced significant amounts of stress , we are not able to determine if the absence of some of these behavioral signatures of CMS is a result of the maintenance of social context throughout the protocol or if it is due to other unknown factors . We are , however , not the first to observe no change in adrenal size or sucrose preference in female CD1 mice ( Dadomo et al . , 2018 ) . Additionally , we did not observe any changes in basal corticosterone levels . This is probably be due to the fact that our blood sampling was performed one week after the end of the CMS paradigm , allowing enough time for corticosterone levels to return to normal . Finally , we employed the David’s Score as a continuous linear indicator of social dominance for the additional statistical power that this approach provides . Dominance hierarchies , however , are more commonly thought of as ordinal , and we lacked sufficient sample sizes per rank and condition to be able to reliably quantify the contribution of each rank to the behavioral outcomes of chronic stress . Further research is needed to replicate and extend these findings to specific social ranks . While were not able to directly compare between single- and group-housed animals , our data suggest that the existence of a social hierarchy in groups of mice might contribute to increased variability in behavioral outcomes after chronic treatment generating rank-specific responses . Moreover , this effect could be especially relevant when studying sex differences . Often , housing conditions ( single vs . group ) are not taken into consideration as a variable of interest . Based on the findings reported here , we speculate that housing conditions might have contributed to discordant behavioral findings in studies of stress and sex ( Franceschelli et al . , 2014 ) . Our results argue for considering group-derived individual differences and , in particular , dominance status , in the design of experiments , especially when investigating the contribution of sex differences to stress response . Taken together , this work suggests that social dominance might influence the perception of and reaction to chronic stress differently for male and female mice . While there has been some work looking into the effects of dominance on stress susceptibility in males ( Larrieu et al . , 2017 ) , very little is known about female social dominance and its contribution to stress coping . Our findings emphasize the need for exploring the stress response in the presence of conspecifics in a more naturalistic manner and the importance of recognizing that the same social factors may carry divergent consequences for the behavior of males and females . Male and female ICR CD-1 mice at 7–9 months old were employed for all experiments ( Charles River , Sulzfeld , Germany ) . Mice were housed in groups of four in the animal facilities of the Max Planck Institute of Psychiatry in Munich , Germany , from weaning and were maintained under standard conditions ( 12L:12D light cycle , lights on at 07:00 AM , temperature 23 ± 2°C ) with food and water available ad libitum . All experiments were approved by and conducted in accordance with the regulations of the local Animal Care and Use Committee ( Government of Upper Bavaria , Munich , Germany ) , under licenses Az . : 55 . 2-1-54-2532-148-2012 , Az . :55 . 2-1-54-2532-32-2016 and ROB-55 . 2–2532 . Vet_02-18-50 . The fur of all mice was marked using four different colors under mild isoflurane anesthesia and mice were left to recover for several days before the start of the experiment . On day 1 , animals were transferred to the SB ( see ‘The ‘Social Box’ paradigm’ section ) , for a total of 5 days ( five light periods and five dark periods ) . On day 6 , animals were removed from the SB and placed in their original cage under standard housing conditions for the rest of the experimental procedure ( see ‘Chronic mild stress protocol’ and ‘Behavioral battery’ sections ) . Before the beginning of the fifth night in the SB , mice were removed from the SB and restrained in a ventilated tube for 15 min . To account for the smaller size of females , we employed a smaller sized ventilated tube to ensure the same degree of movement restriction between sexes . At the end of the acute restraint , groups of mice were put back in their original SB and tracked for additional 12 hr . Two separate batches of mice were exposed to three weeks of CMS prior to the behavioral test battery . A random combination of two stressors per day ( one in the a . m . and one in the p . m . hours ) was chosen among the followings: acute restraint in the dark ( 15 min ) , acute restraint in bright light ( 15 min , ~200 lux ) , acute restraint witnessing ( half of the group at a time was restrained and placed inside the cage , 15 min each ) , removal of nesting material ( 24 hr ) , cage-tilt 30° along the vertical axis ( 6 hr ) , no bedding or nesting material ( 8 hr ) , wet bedding ( 6 hr ) , water avoidance ( 15 min ) , cage change ( fresh cage every 30 min for a total of 4 hr ) , cage switching ( mice are assigned the cage of another group of the same sex ) , overcrowding ( eight mice per cage , 1 hr ) . For the water avoidance stress , an empty rat cage ( 395 × 346 cm ) was filled with room temperature water . Mice were placed on a platform ( 10 × 12 cm ) , 2 cm above the water level , for 15 min . On days 1 , 3 , 7 , 10 , 14 , 17 , and 21 both CMS and control mice were weighed . During the weighing session , their coat state was scored on a scale 0 to 3 according to the following criteria: Cumulative coat state was calculated as the sum of the seven daily scores . Control mice were kept in an adjacent room to the stressed mice and handled twice per week to obtain weight and coat scores . The day after the last stressor , mice started a behavioral test battery consisting of the OFT , 2-hr SPT , grouped SPT , the splash test ( SPL ) , the nest building test ( NBT ) , the EPM , a grouped sucrose preference , and the TST . Throughout the testing period , mice were maintained in their original groups and habituated to the testing room for at least one hour prior the start of the test . Forty-eight hours after the last test , mice were terminally anesthetized in isoflurane and sacrificed . Terminal bodyweight , plasma , adrenal glands , and thymus were collected . Adrenal glands and thymus were cleaned from fat tissue and weighed . Absolute values were adjusted to bodyweight using the bodyweights collected on day 1 . Tissue weighing , corticosterone measurement , and behavioral scoring were performed by an experimenter blind to sex , condition , and social rank . On the day following the last stressors ( day 22 ) , mice locomotor activity and exploratory behavior were assessed in the OFT for 10 min . The apparatus consisted in round arenas ( diameter 38 cm ) made of black polyvinylchloride ( PVC ) under dim illumination ( 3 lux ) . Mice were automatically tracked with ANYmaze Video Tracking System 6 . 13 ( Stoelting , IL , USA ) . The space was virtually divided in an inner zone ( diameter 16 cm ) and an outer zone . Total distance traveled , distance from the center , speed , and turn angle were calculated across the full 10 min . In addition , distance traveled , speed , visits , and time spent in each of the subdivisions were used as parameters . Preference was calculated as follows: outerzonetimeinnerzonetime . Twenty-four hours after the OFT , the anhedonia phenotype was tested with a modified version of the SPT . Each group was assigned a test cage containing one water bottle and one bottle with 2% sucrose . One mouse per group at a time was placed in the test cage for two hours , across three consecutive days during the light phase ( days 23 , 24 , and 25 ) . At the end of each session , the bottles were weighed . At the end of the test the amounts of water and sucrose consumed were summed across the three sessions . Sucrose preference was calculated as sucrosewater+sucrose*100 . On day 27 , sucrose preference was tested at a group level . Each group was given a bottle of water and a bottle of 2% sucrose within their home-cage . Their sucrose preference was calculated after 24 hr as above . A grouped sucrose preference value was obtained for each group . On day 24 , during the dark period , mice were tested in the splash test under dim light ( 3 lux ) . Mice were placed in their test cage for 5 min prior being sprayed on their dorsal coat twice ( approximately 1 ml ) with 10% sucrose solution . Mice were recorded for 5 min and total time spent grooming , and latency to the first grooming bout was manually scored using Solomon Coder 17 . 03 . 32 ( https://solomon . andraspeter . com/ ) . During the third day of the 2-hr sucrose preference , mice in the test cage were given a small square cotton pad of approximately 23 g . The cotton pad was weighed at the beginning of the test and at the end of the two hours and the percentage of intact material was calculated . The built nest was scored from 0 to 4 according to the following criteria: For nests matching only partially the description ( e . g . , identifiable flat nest , but less than 50% of torn material ) , half points were assigned . On day 26 , during the light phase , anxiety phenotype was assessed using the EPM test . An apparatus composed of four arms made of gray polyvinylchloride ( PVC ) , two open without walls , two enclosed by 14 cm walls and a central platform ( 5 × 5 cm ) was used . The apparatus was placed 33 cm from the ground under dim illumination ( 3 lux ) . Mice were placed on the central platform facing the open arms and let free to explore the apparatus for 10 min . Mice were automatically tracked using ANYmaze Video Tracking System 6 . 13 ( Stoelting , IL , USA ) . Number of entries in each arms , time , and distance were calculated . In addition , closed arm preference was calculated as timeinclosedarmstimeinclosed+timeinopenarms . Stress coping behavior was assessed using the TST on day 28 . Mice were hung by their tail 50 cm above the surface and their behavior recorded for 6 min . Immobility was automatically scored using ANYmaze Video Tracking System 6 . 13 ( Stoelting , IL , USA ) and number of immobility episodes and total time immobile were used as parameters . At sacrifice , trunk blood was collected in EDTA-coated tubes . Blood was centrifuged at 1 , 000 g for 15 min at 4°C . Plasma was retrieved and corticosterone levels were measured using [125I] radioimmunoassay kit ( MP Biomedicals ) , according to the manufacturer’s instructions .	Most people experience chronic stress at some point in their life , which may increase their chances of developing depression or anxiety . There is evidence that chronic stress may more negatively impact the well-being of women , placing them as higher risk of developing these mental health conditions . The biological factors that underlie these differences are not well understood , which leaves clinicians and scientists struggling to develop and provide effective treatments . The social environment has a powerful influence on how people experience and cope with stress . For example , a person’s social and socioeconomic status can change their perception of and reaction to everyday stress . Researchers have found differences in how men and women relate to their social standing . One way for scientists to learn more about the biological processes involved is to study the effect of social standing and chronic stress in male and female mice . Now , Karamihalev , Brivio et al . show that social status influences the behavior of stressed mice in a sex-specific way . In the experiments , an automated observation system documented the behavior of mice living in all female or male groups . Karamihalev , Brivio et al . determined where each animal fit into the social structure of their group . Then , they exposed some groups of mice to mild chronic stress and compared their behaviors to groups of mice housed in normal conditions . They found that both the sex and social status of each played a role in how they responded to stress . For example , subordinate males displayed more anxious behavior under stressful circumstances , while dominant females acted bolder and less anxious . More studies in mice are needed to understand the biological basis of these social- and sex-based differences in stress response . Learning more may help scientists understand why some individuals are more susceptible to the effects of stress and lead to the development of personalized prevention or treatment strategies for anxiety and depression .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2020	Social dominance mediates behavioral adaptation to chronic stress in a sex-specific manner
Although cell cycle control is an ancient , conserved , and essential process , some core animal and fungal cell cycle regulators share no more sequence identity than non-homologous proteins . Here , we show that evolution along the fungal lineage was punctuated by the early acquisition and entrainment of the SBF transcription factor through horizontal gene transfer . Cell cycle evolution in the fungal ancestor then proceeded through a hybrid network containing both SBF and its ancestral animal counterpart E2F , which is still maintained in many basal fungi . We hypothesize that a virally-derived SBF may have initially hijacked cell cycle control by activating transcription via the cis-regulatory elements targeted by the ancestral cell cycle regulator E2F , much like extant viral oncogenes . Consistent with this hypothesis , we show that SBF can regulate promoters with E2F binding sites in budding yeast . The networks regulating cell division in yeasts and animals are highly similar in both physiological function and network structure ( Figure 1 ) ( Cross et al . , 2011; Doonan and Kitsios , 2009 ) . For example , the cell cycle controls proliferation in response to a variety of internal and external signals during the G1 phase , between cell division and DNA replication . These input signals , including cell growth , are integrated into a gradual increase in cyclin dependent kinase ( Cdk ) activity , which triggers a feedback loop at the basis of the all-or-none irreversible decision to proliferate ( Bertoli et al . , 2013 ) . 10 . 7554/eLife . 09492 . 003Figure 1 . Topology of G1/S regulatory network in mammals and budding yeast is conserved , yet many regulators exhibit no detectable sequence homology . Schematic diagram illustrating the extensive similarities between ( A ) animal and ( B ) budding yeast G1/S cell cycle control networks . Similar coloring denotes members of a similar family or sub-family . Fungal components colored white denote proteins with no identifiable animal orthologs . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 003 Many of the molecular mechanisms underlying G1 regulation are highly conserved . In animal cells , Cyclin D , in complex with either Cdk4 or Cdk6 , initiates cell cycle entry by phosphorylating the retinoblastoma protein , pRb . This begins the inactivation of pRb and the concomitant activation of the E2F transcription factors that induce transcription of downstream cyclins E and A , which complete the inhibition of pRb thereby forming a positive feedback loop ( Bertoli et al . , 2013 ) . Similarly , in budding yeast , the G1 cyclin Cln3-Cdk1 complex initiates the transition by phosphorylating and partially inactivating Whi5 , an inhibitor of the SBF transcription factor ( Costanzo et al . , 2004; de Bruin et al . , 2004; Nasmyth and Dirick , 1991; Ogas et al . , 1991; Sidorova and Breeden , 1993 ) . This allows for SBF-dependent transcription of the downstream G1 cyclins CLN1 and CLN2 , which also inactivate Whi5 to complete a positive feedback loop ( Skotheim et al . , 2008 ) . Thus , both the biochemical function of G1 regulators and their specific targets are highly conserved ( Figure 1 ) . Many of the individual proteins performing identical roles are unlikely to be true orthologs , i . e . , it cannot be inferred from sequence identity that the proteins evolved from a common ancestral gene . In yeast , a single cyclin-dependent kinase , Cdk1 , binds distinct cyclin partners to perform all the functions of three non-orthologous animal Cdks ( Cdk2 , 4 and 6 ) during cell cycle entry ( Liu and Kipreos , 2000 ) . Furthermore , no member of the transcription factor complex SBF-Whi5 exhibits amino acid sequence identity or structural similarity to any member of the E2F-pRb complex ( Cross et al . , 2011; Hasan et al . , 2013; Taylor et al . , 1997 ) . Finally , Cdk inhibitors such as Sic1 and p27 play analogous roles in yeast and mammals despite a total lack of sequence identity ( Cross et al . , 2011 ) . Taken together , these examples imply significant evolution of cell cycle regulatory proteins in fungi and/or animals while the network topology remains largely intact . Although identification of network topology is restricted to a few model organisms and is not as broad as sequence analysis , the similar network topology in budding yeast and animals suggests that this feature is more conserved than the constituent regulatory proteins ( Cross et al . , 2011; Doonan and Kitsios , 2009 ) . The shared presence of E2F-pRb within plants ( Archaeaplastida ) and animal ( Metazoa ) lineages would suggest that this regulatory complex , rather than the fungal SBF-Whi5 complex , was present in the last eukaryotic common ancestor ( Cao et al . , 2010; Doonan and Kitsios , 2009; Fang et al . , 2006; Harashima et al . , 2013; Hallmann , 2009 ) . The sequence divergence between G1 regulators is surprising because fungi and animals are more closely related to one another than either is to plants . This fungal-metazoan difference raises the question as to where the fungal components came from . Fungal components could either be rapidly evolved ancestral regulators or have a distinct evolutionary history , which would suggest convergent evolution of regulatory networks . To address this question , we examine conserved and divergent features of eukaryotic cell cycle regulation . In contrast to previous work that considered a protein family of cell cycle regulators in isolation ( Cao et al . , 2010; 2014; Eme et al . , 2011; Gunbin et al . , 2011; Ma et al . , 2013; Wang et al . , 2004 ) , we studied the evolutionary history of an entire regulatory network across hundreds of species . We examined a greater number of genomes covering most of eukaryotic diversity , including Excavata , Haptophyta , Cryptophyta , SAR ( Stramenopiles , Alveolata , Rhizaria ) , Archaeplastida ( plants ) , Amoebozoa , Apusozoa and the Opisthokonta ( animals and fungi ) . This survey allowed us to estimate the cell cycle repertoire of the last eukaryotic common ancestor ( LECA ) , a prerequisite to clarifying the evolutionary transitions of the cell cycle components of both animals and fungi . Our results indicate that LECA likely had complex cell cycle regulation involving at least one Cdk , multiple cyclin families , activating and inhibitory E2F transcription factors , and pRb-family pocket proteins . Identifying the LECA repertoire helps establish that the emergence of SBF-Whi5 is abrupt and distinguishes fungi from all other eukaryotes . We show that basal fungi can have both ancestral E2F-pRb and fungal SBF-Whi5 components . Thus , fungal evolution appears to have proceeded through a hybrid network before abruptly losing the ancestral components in the lineage leading to Dikarya . This supports the hypothesis that network structure , rather than the individual components , has been conserved through the transition to fungi and argues against the case of convergent evolution . Our data confirm that SBF shows homology to KilA-N , a poorly characterized domain present in prokaryotic and eukaryotic DNA viruses . Thus , SBF is not derived from E2F ( its functional analog ) and likely emerged through horizontal gene transfer in the fungal ancestor . We show that SBF can regulate promoters with E2F binding sites in budding yeast . We then use high-throughput in vitro binding assay data to elucidate the shared nucleotide preferences of E2F and SBF for DNA binding . These data suggest that a viral SBF may have initially hijacked cell cycle control , activating transcription via the cis-regulatory elements targeted by the ancestral cell cycle regulator E2F , much like extant viral oncogenes . Recent work shows that the last eukaryotic common ancestor ( LECA ) already had a complex repertoire of protein families ( Dacks and Field , 2007; Eichinger et al . , 2005; Merchant et al . , 2007 ) . Indeed , all sequenced eukaryotic lineages have lost entire gene families that were present in LECA ( Fritz-Laylin et al . , 2010 ) . In contrast to the growing consensus that LECA had an extensive repertoire of proteins , the prevailing view of the cell cycle in LECA is that it was based on a simple oscillator constructed with relatively few components ( Coudreuse and Nurse , 2010; Nasmyth , 1995 ) . According to the ‘simple’ LECA cell cycle model , an ancestral oscillation in Cyclin B-Cdk1 activity drove periodic DNA replication and DNA segregation , while other aspects of cell cycle regulation , such as G1 control , may have subsequently evolved in specific lineages . The model was motivated by the fact that Cdk activity of a single Cyclin B is sufficient to drive embryonic cell cycles in frogs ( Murray and Kirschner , 1989 ) and fission yeast ( Stern and Nurse , 1996 ) , and that many yeast G1 regulators have no eukaryotic orthologs ( Figure 1 ) . To determine the complexity of LECA cell cycle regulation , we examined hundreds of diverse eukaryotic genomes . We first built sensitive profile Hidden Markov Models ( Eddy , 2011 ) for each of the gene families of cell cycle regulators from model organisms Arabidopsis thaliana , Homo sapiens , Schizosaccharomyces pombe , and Saccharomyces cerevisiae . These HMMs were then used to query the sequenced eukaryotic genomes for homologs of both fungal and animal cell cycle regulators ( see Materials and methods and Figure 2—figure supplement 1 for a complete list of regulatory families in each genome ) . Phylogenetic analyses were performed on the detected homologs for accurate sub-family assignment of the regulators and inference of their evolutionary history ( see Materials and methods ) . If LECA regulation were simple , we would expect little conservation beyond the Cyclin B-Cdk1 mitotic regulatory module . However , if LECA regulation were more complex , we would expect to see broad conservation of a wider variety of regulators . While we did not find either of the fungal regulators ( SBF and Whi5 ) outside of Fungi , we did find animal-like cell cycle regulators in Archaeplastida , Amoebozoa , SAR , Haptophyta , Cryptophyta , Excavata and Metazoa ( Figure 2 ) . For example , the cyclin sub-families ( A , B , D , and E ) known to regulate the cell cycle in metazoans ( for cyclin phylogeny see Figure 2—figure supplement 2 ) are found across the major branches of eukaryotes . We also found examples of all three sub-families of E2F transcription factors ( E2F1-6 , DP , E2F7/8 ) and the pRb family of pocket proteins ( for E2F/DP and pRb phylogeny see Figure 2—figure supplement 3 and Figure 2—figure supplement 4 ) . Nearly all species contain the APC specificity subunits Cdc20 and Cdh1/Fzr1 , which regulate exit from mitosis and maintain low Cdk activity in G1 ( for Cdc20-family APC phylogeny see Figure 2—figure supplement 5 ) . Taken together , these data indicate that LECA cell cycle regulation was based on multiple cyclin families , as well as regulation by the APC complex and members of the pRb and E2F families . More broadly , our phylogenetic analyses tend to place the fungal regulators as sister groups to the metazoan regulators , as would be expected from the known eukaryotic species tree . These phylogenies are in agreement with the hypothesis that many fungal and metazoan regulators were vertically inherited from an opisthokont ancestor rather than loss of these regulators in fungi followed by secondary acquisition through horizontal gene transfer . 10 . 7554/eLife . 09492 . 004Figure 2 . Animal and plant G1/S regulatory network components were present in the last eukaryotic common ancestor . Distribution of cell cycle regulators across the eukaryotic species tree ( Adl et al . , 2012 ) . Animals ( Metazoa ) and yeasts ( Fungi ) are sister groups ( Opisthokonta ) , and are distantly related to plants ( Charophyta ) , which are members of the Archaeplastida . Check marks indicate the presence of at least one member of a protein family in at least one sequenced species from the corresponding group . We developed profile-HMMs to detect cell division cycle regulators in eukaryotic genomes . For each cell cycle regulatory family ( e . g . , Cyclins ) , we used molecular phylogeny to classify eukaryotic sequences into sub-families ( e . g . , Cyclin B , Cyclin A , Cyclin E/D ) . See Figure 2—figure supplement 1 for complete list of regulatory families in all eukaryotic species , and Figure 2—figure supplement 2 ( Cyclin ) , Figure 2—figure supplement 3 ( E2F/DP ) , Figure 2—figure supplement 4 ( pRb ) , Figure 2—figure supplement 5 ( Cdc20-family ) , and Figure 2—figure supplement 6 ( CDK ) for final phylogenies . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 00410 . 7554/eLife . 09492 . 005Figure 2—source data 1 . Reduced set of eukaryotic cell cycle cyclins for phylogenetic analysis . These files contain the protein sequences used to create molecular phylogeny in Figure 2—figure supplement 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 00510 . 7554/eLife . 09492 . 006Figure 2—source data 2 . Complete set of eukaryotic E2F/DP transcription factors for phylogenetic analysis . These files contain the protein sequences used to create molecular phylogeny in Figure 2—figure supplement 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 00610 . 7554/eLife . 09492 . 007Figure 2—source data 3 . Complete set of eukaryotic Rb inhibitors for phylogenetic analysis . These files contain the protein sequences used to create molecular phylogeny in Figure 2—figure supplement 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 00710 . 7554/eLife . 09492 . 008Figure 2—source data 4 . Reduced set of eukaryotic Cdc20-family APC regulators for phylogenetic analysis . These files contain the protein sequences used to create molecular phylogeny in Figure 2—figure supplement 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 00810 . 7554/eLife . 09492 . 009Figure 2—source data 5 . Reduced set of eukaryotic cyclin-dependent kinases for phylogenetic analysis . These files contain the protein sequences used to create molecular phylogeny in Figure 2—figure supplement 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 00910 . 7554/eLife . 09492 . 010Figure 2—figure supplement 1 . Comparative genomic data of G1/S regulators across eukaryotes . Each entry lists the number of sub-family members ( column ) for each eukaryotic genome ( row ) . Grey rows list the sub-family gene names in H . sapiens and A . thaliana . Additional cyclin sub-family members are listed in parentheses . Protein sequences listed in this table , which were used to create molecular phylogenies , can be found in Figure 2—source data 1 through Figure 2—source data 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 01010 . 7554/eLife . 09492 . 011Figure 2—figure supplement 2 . Reduced phylogeny of eukaryotic cell cycle cyclins . The cell division cycle ( CDC ) cyclin family consists of several sub-families with a well-characterized cyclin box: ( 1 ) CycA ( H . sapiens , A . thaliana ) , ( 2 ) CycB and CLB ( H . sapiens , A . thaliana , S . cerevisiae , S . pombe ) , ( 3 ) CycD ( H . sapiens , A . thaliana ) , ( 4 ) CycE ( H . sapiens ) , and ( 5 ) CLN ( S . cerevisiae , S . pombe ) . We combined CycA , CycB , CycD , CycE , CLB , and CLN sequences from H . sapiens ( 10 cyclins ) , A . thaliana ( 13 cyclins ) , S . cerevisiae ( 9 cyclins ) , and S . pombe ( 5 cyclins ) to create a eukaryotic CDC cyclin profile-HMM ( pCYC . hmm ) following the procedure outlined in the Methods . Our HMM profile was sensitive enough to discover known , but uncharacterized cyclin sub-families ( CycO , CycF , CycG , CycI , CycJ , CycSDS ) as bona fide CDC cyclins . A domain threshold of E-20 was used to identify potential CDC cyclin homologs . We first made a phylogeny of all cyclins to classify them . This dataset was then manually pruned to remove long-branches and problematic lineages . Our reduced CDC cyclin dataset ( Figure 2—source data 1 ) has a total of 499 sequences . Columns with the top 10% Zorro score ( 496 positions ) were used in our alignment . Confidence at nodes was assessed with multiple support metrics using different phylogenetic programs under LG model of evolution ( aBayes and SH-aLRT metrics with PhyML , RBS with RAxML , Bayesian Posterior Probability with Phylobayes ( 23 , 163 sampled trees , meandiff= 0 . 01 , maxdiff= 0 . 5 ) ) . Colored dots in branches indicate corresponding branch supports . Thick branches indicate significant support by at least two metrics , one parametric and one non-parametric; branch support thresholds are shown in the center of the figure; see Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 01110 . 7554/eLife . 09492 . 012Figure 2—figure supplement 3 . Phylogeny of eukaryotic E2F/DP transcription factors . E2F-DP is a winged helix-turn-helix DNA-binding domain that is conserved across eukaryotes ( van den Heuvel and Dyson , 2008 ) . There are three sub-families within the E2F-DP family: ( 1 ) the E2F subfamily , ( 2 ) the E2F7-8/DEL subfamily , and ( 3 ) the DP subfamily . The E2F family consists of E2F1-6 ( H . sapiens ) and E2FA-C ( A . thaliana ) . The E2F7-8/DEL family consists of E2F7-8 ( H . sapiens ) and DEL1-3 ( A . thaliana ) . The DP family consists of DP1-3 ( H . sapiens ) and DPA-B ( A . thaliana ) . The members of E2F form heterodimers with DP , whereas the DEL family has two DNA-binding domains and does not require DP to bind DNA . We used the E2F_TDP . hmm profile from PFAM to uncover members of the E2F/DP family across eukaryotes . A domain threshold of E-10 was used to identify potential E2F/DP homologs . Our E2F/DP dataset ( Figure 2—source data 2 ) has 248 sequences . Columns with the top 8% Zorro score ( 284 positions ) were used in our alignment . Confidence at nodes was assessed with multiple support metrics using different phylogenetic programs under LG model of evolution ( aBayes and SH-aLRT metrics with PhyML , RBS with RAxML , Bayesian Posterior Probability with Phylobayes ( 53 , 009 sampled trees , meandiff=0 . 0064 , maxdiff=0 . 18 ) ) . Colored dots in branches indicate corresponding branch supports . Thick branches indicate significant support by at least two metrics , one parametric and one non-parametric; branch support thresholds are shown in the center of the figure; see Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 01210 . 7554/eLife . 09492 . 013Figure 2—figure supplement 4 . Phylogeny of eukaryotic Rb inhibitors . H . sapiens has Rb1 , RBL1 ( p107 ) , and RBL2 ( p130 ) , and A . thaliana has RBR1 . The model fungi S . cerevisiae and S . pombe do not have any obvious retinoblastoma pocket proteins . We needed more eukaryotic sequences to create a robust HMM profile ( pRb . hmm ) for the pRb family . Based on the pRb sequences collected in Hallmann , ( 2009 ) , we built a profile-HMM using putative pRb homologs from H . sapiens , G . gallus , C . intestinalis , D . melanogaster , C . elegans , N . vectensis , T . adhaerens ( metazoa ) ; B . dendrobatidis ( fungi ) ; D . discoideum , D . purpureum , T . pseudonana , P . tricornutum , N . gruberi , E . huxleyi ( protists ) ; C . merolae , O . tauri , O . lucimarinus , M . pusilla , V . carteri , C . reinhartdii , P . patens , S . moellendorfii , A . thaliana ( plants ) . A domain threshold of E-20 was used to identify pRB homologs . Our pRB dataset ( Figure 2—source data 3 ) has 72 sequences . Columns with the top 15% Zorro score ( 566 positions ) were used in our alignment . Confidence at nodes was assessed with multiple support metrics using different phylogenetic programs under LG model of evolution ( aBayes and SH-aLRT metrics with PhyML , RBS with RAxML , Bayesian Posterior Probability with Phylobayes ( 23 , 219 sampled trees , meandiff=0 . 0035 , maxdiff=0 . 067 ) ) . Colored dots in branches indicate corresponding branch supports . Thick branches indicate significant support by at least two metrics , one parametric and one non-parametric; branch support thresholds are shown in the center of the figure; see Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 01310 . 7554/eLife . 09492 . 014Figure 2—figure supplement 5 . Reduced phylogeny of eukaryotic Cdc20-family APC regulators . We combined CDC20 and CDH1/FZR1 sequences from H . sapiens ( 3 members ) , A . thaliana ( 9 members ) , and S . cerevisiae ( 3 members ) to create a eukaryotic CDC20-family APC regulator profile-HMM ( pCDC20 . hmm ) following the procedure outlined in the Methods . A domain threshold of E-50 was used to identify CDC20 homologs . Our pCDC20 dataset has 350 sequences . This dataset was manually pruned to remove long-branches and problematic lineages . Our reduced CDC20 dataset ( Figure 2—source data 4 ) has a total of 289 sequences . Columns with the top 20% Zorro score ( 608 positions ) were used in our alignment . Confidence at nodes was assessed with multiple support metrics using different phylogenetic programs under LG model of evolution ( aBayes and SH-aLRT metrics with PhyML , RBS with RAxML , Bayesian Posterior Probability with Phylobayes ( 13 , 638 sampled trees , meandiff=0 . 015 , maxdiff=0 . 37 ) ) . Colored dots in branches indicate corresponding branch supports . Thick branches indicate significant support by at least two metrics , one parametric and one non-parametric; branch support thresholds are shown in the center of the figure; see Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 01410 . 7554/eLife . 09492 . 015Figure 2—figure supplement 6 . Reduced phylogeny of eukaryotic cyclin-dependent kinases . To create a profile-HMM ( pCDCCDK . hmm ) for eukaryotic cell cycle CDK , we combined Cdk1-3 , Cdk4 , Cdk6 sequences from H . sapiens , CdkA and CdkB from A . thaliana , Cdc28 from S . cerevisiae , and Cdc2 from S . pombe . A domain threshold of E-20 was used to identify potential CDK homologs , and we only kept cell cycle CDKs ( i . e . Cdk1-3 , Cdk4 , 6 , Cdc28 , CdkA , CdkB ) . Our reduced CDK dataset ( Figure 2—source data 5 ) has 272 sequences . Columns with the top 15% Zorro score ( 473 positions ) were used in our alignment . Confidence at nodes was assessed with multiple support metrics using different phylogenetic programs under LG model of evolution ( aBayes and SH-aLRT metrics with PhyML , RBS with RAxML , Bayesian Posterior Probability with Phylobayes ( 28 , 193 sampled trees , meandiff=0 . 015 , maxdiff=0 . 53 ) ) . Colored dots in branches indicate corresponding branch supports . Thick branches indicate significant support by at least two metrics , one parametric and one non-parametric; branch support thresholds are shown in the center of the figure; see Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 015 Members of the Cdk1-3 family ( i . e . CdkA in plants ) are also broadly conserved across eukaryotes , suggesting they were the primary LECA cell cycle Cdks ( for CDK phylogeny see Figure 2—figure supplement 6 ) . Other cell cycle Cdk families in animals ( Cdk4/6 ) and plants ( CdkB ) are thought to be specific to those lineages . However , we found CdkB in Stramenopiles , which may have arrived via horizontal transfer during an ancient secondary endosymbiosis with algae as previously suggested ( Cavalier-Smith , 1999 ) . We excluded from our analysis other families of cyclin-Cdks , e . g . , Cdk7-9 , which regulate transcription and RNA processing , and Cdk5 ( yeast Pho85 ) , which regulate cell polarity , nutrient regulation , and contribute to cell cycle regulation in yeast ( Cao et al . , 2014; Guo and Stiller , 2004; Ma et al . , 2013; Moffat and Andrews , 2004 ) . While interesting and important , an extensive examination of these cyclin-Cdk families is beyond the scope of this work . To identify the possible origins of fungal SBF and Whi5 , we developed SBF and Whi5 HMMs to query other eukaryotic genomes for homologs of these fungal-specific regulators . We were unable to find any eukaryotic homologs of SBF or Whi5 outside of fungi , with a few exceptions related to DNA viruses that we discuss later . SBF is an important member of a larger family of winged helix-turn-helix transcription factors that includes Xbp1 , Bqt4 , and the APSES family ( Acm1 , Phd1 , Sok2 , Egf1 , StuA ) ; see Materials and methods and Figure 3—figure supplement 1 for a complete list of homologs in each fungal genome . The emergence of the new fungal regulators SBF , which includes the large APSES family , and Whi5 is abrupt and occurs near the split of basal fungi from metazoans ( Figure 3 ) . The precise location remains unclear because we have only 1 Nuclearid genome ( Fonticula alba ) and because Microsporidia are fast-evolving fungal parasites with reduced genomes ( Cuomo et al . , 2012 ) . Interestingly , the new regulators ( SBF and Whi5 ) and ancestral regulators ( E2F and Rb ) co-exist broadly across basal fungi and the lineages formerly known as 'zygomycetes' ( Figure 3 ) . Zoosporic , basal fungi such as Chytrids ( e . g . , Spizellomyces punctatus ) can have both fungal and animal cell cycle regulators , which likely represents the ancestral fungal hybrid network . SBF-Whi5 in budding yeast plays a similar role to E2F-pRb in animals , which suggests that these pathways were functionally redundant in an ancestral hybrid network . This redundancy would lead to the evolutionary instability of the hybrid network and could explain why different constellations of components are present in the extant zygomycetes and basal fungi ( Figure 3—figure supplement 1 ) . For example , the zygomycetes have lost pRb while retaining E2F , which was then abruptly lost in the transition to Dikarya . However , with the possible exception of Microsporidia , all fungi have retained SBF and never completely reverted back to the original ancestral state . 10 . 7554/eLife . 09492 . 016Figure 3 . Fungal ancestor evolved novel G1/S regulators , which eventually replaced ancestral cyclins , transcription factors , and inhibitors in Dikarya . Basal fungi and 'Zygomycota' contain hybrid networks comprised of both ancestral and fungal specific cell cycle regulators . Check marks indicate the presence of at least one member of a protein family in at least one sequenced species from the group; see Figure 3—figure supplement 1 for a complete list of homologs in all fungal species . Cells are omitted ( rather than left unchecked ) when a family is completely absent from a clade . For each fungal regulatory family ( e . g . , SBF/MBF ) , we used molecular phylogeny to classify eukaryotic sequences into sub-families ( e . g . , SBF/MBF , APSES , Xbp1 , Bqt4 ) . See Materials and methods for details and Figure 3—figure supplement 2 ( SBF/MBF only ) , Figure 3—figure supplement 3 ( SBF/MBF+APSES ) , and Figure 3—figure supplement 4 ( Whi5/Nrm1 ) for final phylogenies . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 01610 . 7554/eLife . 09492 . 017Figure 3—source data 1 . Complete set of fungal SBF/MBF transcription factors for phylogenetic analysis . These files contain the protein sequences used to create molecular phylogeny in Figure 3—figure supplement 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 01710 . 7554/eLife . 09492 . 018Figure 3—source data 2 . Complete set of fungal SBF/MBF and APSES transcription factors for phylogenetic analysis . These files contain the protein sequences used to create molecular phylogeny in Figure 3—figure supplement 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 01810 . 7554/eLife . 09492 . 019Figure 3—source data 3 . Complete set of fungal Whi5/Nrm1 inhibitors for phylogenetic analysis . These files contain the protein sequences used to create molecular phylogeny in Figure 3—figure supplement 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 01910 . 7554/eLife . 09492 . 020Figure 3—figure supplement 1 . Comparative genomic data of G1/S regulators across fungi . Grey rows list the sub-family gene names in S . cerevisiae , S . pombe , and H . sapiens . Protein sequences listed in this table , which were used to create new molecular phylogenies not shown in Figure 2 , can be found in Figure 3—source data 1 through Figure 3—source data 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 02010 . 7554/eLife . 09492 . 021Figure 3—figure supplement 2 . Phylogeny of fungal SBF/MBF transcription factors . SBF and MBF are transcription factors that regulate G1/S transcription in budding and fission yeast . To detect SMRC ( Swi4/6 Mbp1 Res1/2 Cdc10 ) across fungi , we built a sensitive profile-HMM ( pSMRC . hmm ) by combining well-characterized SMRC sequences from S . cerevisiae , C . albicans , N . crassa , A . nidulans , and S . pombe . A domain threshold of E-10 was used to identify SMRC homologs . Our SMRC dataset ( Figure 3—source data 1 ) has 147 sequences . Columns with the top 20% Zorro score ( 709 positions ) were used in our alignment . Confidence at nodes was assessed with multiple support metrics using different phylogenetic programs under LG model of evolution ( aBayes and SH-aLRT metrics with PhyML , RBS with RAxML , Bayesian Posterior Probability with Phylobayes ( 19 , 457 sampled trees , meandiff=0 . 0056 , maxdiff=0 . 145 ) ) . Colored dots in branches indicate corresponding branch supports . Thick branches indicate significant support by at least two metrics , one parametric and one non-parametric; branch support thresholds are shown in the center of the figure; see Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 02110 . 7554/eLife . 09492 . 022Figure 3—figure supplement 3 . Phylogeny of fungal SBF/MBF and APSES transcription factors . SBF/MBF and APSES transcription factors ( Asm1 , Phd1 , Sok2 , Efg1 , StuA ) share a common DNA-binding domain ( KilA-N ) , which is derived from DNA viruses . During our search for SBF and APSES homologs , we consistently detected two additional fungal sub-families with homology to KilA-N: XBP1 ( family name taken from S . cerevisiae ) and BQT4 ( family name taken from S . pombe ) . To detect APSES , XBP1 , and BQT4 homologs , we built profile-HMMs ( APSES . hmm , XBP1 . hmm , and BQT4 . hmm ) by combining APSES , XBP1 , and BQT4 homologs from S . cerevisiae , C . albicans , N . crassa , A . nidulans , and S . pombe . A domain threshold of E-10 was used to identify APSES , XBP1 , and BQT4 homologs . Our final dataset ( Figure 3—source data 2 ) contains all fungal KILA sub-families ( SBF/MBF , APSES , XBP1 , BQT4 ) and has a total of 301 sequences . Columns with the top 10% Zorro score ( 447 positions ) were used in our alignment . Confidence at nodes was assessed with multiple support metrics using different phylogenetic programs under LG model of evolution ( aBayes and SH-aLRT metrics with PhyML , RBS with RAxML , Bayesian Posterior Probability with Phylobayes ( 15 , 251 sampled trees , meandiff=0 . 012 , maxdiff=0 . 25 ) ) . Colored dots in branches indicate corresponding branch supports . Thick branches indicate significant support by at least two metrics , one parametric and one non-parametric; branch support thresholds are shown in the center of the figure; see Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 02210 . 7554/eLife . 09492 . 023Figure 3—figure supplement 4 . Phylogeny of fungal Whi5/Nrm1 inhibitors . WHI5 and NRM1 are a yeast-specific protein family that has been identified and functionally characterized across S . cerevisiae , C . albicans , and S . pombe . Both WHI5 and NRM1 are fast evolving proteins . There is a small conserved region of 25 amino-acids ( known as the GTB domain ) that is responsible for interacting with Swi6/Cdc10 ( Travesa et al . , 2013 ) . Unfortunately , the Whi5 . hmm profile from PFAM is unable to detect an SRL3 paralogue in S . cerevisiae or the NRM1 orthologues in A . gossypii or C . albicans . We built a more sensitive profile-HMM ( pWHI5 . hmm ) by combining WHI5/NRM1 sequences across ascomycetes ( including SRL3 from Saccharomyces genomes and NRM1 from Candida genomes ) . A domain threshold of E-05 was used to identify WHI5 homologs . Our WHI5 dataset ( Figure 3—-source data 3 ) has 98 sequences . Columns with the top 15% Zorro score ( 260 positions ) were used in our alignment . Confidence at nodes was assessed with multiple support metrics using different phylogenetic programs under the LG model of evolution ( aBayes and SH-aLRT metrics with PhyML , RBS with RAxML , Bayesian Posterior Probability with Phylobayes ( 77 , 696 sampled trees , meandiff=0 . 0068 , maxdiff=0 . 11 ) ) . Colored dots in branches indicate corresponding branch supports . Thick branches indicate significant support by at least two metrics , one parametric and one non-parametric; branch support thresholds are shown in the center of the figure; see Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 023 A simple scenario to explain the emergence of a hybrid network would be gene duplication of the E2F pathway followed by rapid sequence evolution to create a partially redundant SBF pathway . To this end , we scrutinized the highly conserved E2F and SBF DNA-binding domains to detect any sequence and structural homology . We used the Pfam HMMER model of the E2F/DP DNA-binding domain and SBF+APSES DNA-binding domain ( KilA-N . hmm ) , which is homologous to the KilA-N domain ( Iyer et al . , 2002 ) . Our rationale for using KilA-N . hmm from Pfam for remote homology detection of SBF+APSES stems from the fact that it was trained on a diverse set of KilA DNA-binding domains across bacterial DNA viruses , eukaryotic DNA viruses , and fungal SBF+APSES proteins . Thus , it should be a more sensitive HMM model to detect remote KilA-N homologues in other eukaryotic genomes . Our controls ( H . sapiens genome , Figure 4A and S . cerevisiae genome , Figure 4B ) demonstrate that E2F_TDP . hmm is specific to E2F/DP and that KilA-N . hmm is specific to SBF+APSES . We show that genomes with hybrid network ( S . punctatus , Figure 4C , and other basal fungi with both transcription factors , Figure 4D ) have both E2F/DP and SBF+APSES . E2F_TDP . hmm never hits an SBF+APSES transcription factor and KilA-N . hmm never hits an E2F transcription factor ( i . e . there are no scores on the diagonal of the panels in Figure 4 ) . Thus , there is no misclassification by the Pfam HMM models . These data suggest that SBF and other KilA-N domains have no more sequence identity to E2F than non-homologous proteins . We find that non-fungal genomes only hit E2F/DP ( Figure 4E ) with the notable exception of Trichomonas vaginalis , the only non-fungal genome with E2F/DP and KilA-N homologs ( Figure 4F ) . We will discuss the case of T . vaginalis in the next section . 10 . 7554/eLife . 09492 . 024Figure 4 . SBF and E2F HMM models detect different sequences . We used the Pfam HMMER model of the E2F/DP DNA-binding domain ( E2F_TDP . hmm ) and SBF DNA-binding domain ( KilA-N . hmm ) . Every protein in the query genome ( listed at top ) was scored using hmmsearch with E2F/DP HMM ( x-axis ) and KilA-N HMM ( y-axis ) . All scores below 1E-5 ( i . e . , marginally significant ) are blue and those below 1E-10 ( i . e . highly significant ) are red . All hits with E-values between 1E-5 and 1E-10 were further validated ( or rejected ) using an iterative search algorithm ( Jackhmmer ) against the annotated SwissProt database using the HMMER web server ( Finn et al . , 2011 ) . We then inspected these sequences manually for key conserved KilA-N residues . ( A ) Homo sapiens only has E2F/DP , ( B ) Saccharomyces cerevisiae only has KilA-N ( i . e . SBF , MBF , APSES ) . ( C ) Spizellomyces punctatus and ( D ) other basal fungi have both E2F/DP and KilA-N . ( E ) All the non-fungal eukaryote genomes that we surveyed only have E2F/DP . ( F ) Trichomonas vaginalis is one of the few eukaryotes outside of fungi that has both E2F/DP and KilA-N . The E2F/DP HMM and KilA-N HMM always have orthogonal hits ( i . e . no protein in our dataset significantly hits both HMMs ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 024 To further test the possibility that SBF was a gene duplication of E2F/DP and evolution was so rapid that sequence identity was lost , but structural and functional homology to E2F/DP was maintained , we looked for possible evidence of structural homology . The DNA-binding domains of SBF/MBF ( Taylor et al . , 1997; Xu et al . , 1997 ) and E2F/DP ( Zheng et al . , 1999 ) are structurally classified as members of the winged-helix-turn-helix ( wHTH ) family , which is found in both prokaryotes and eukaryotes ( Aravind and Koonin , 1999; Aravind et al . , 2005; Gajiwala and Burley , 2000 ) . Although the DNA-binding domains of E2F/DP and SBF/MBF are both classified as wHTH proteins , they show important differences in overall structure and mode of protein-DNA complex formation that lead us to conclude that it is highly unlikely that they are orthologs . Many wHTH transcription factors , including the E2F/DP family , have a ‘recognition helix’ that interacts with the major or minor grooves of the DNA . The E2F/DP family has an RRXYD DNA-recognition motif in its helix that is invariant within the E2F/DP family and is responsible for interacting with the conserved , core GCGC motif ( Zheng et al . , 1999 ) ( see Figure 5A: red structure ) . The RRXYD recognition motif is strikingly conserved in E2F/DP across all eukaryotes , including the E2F/DP proteins uncovered in basal fungi ( Figure 5B , left ) . The first solved SBF/MBF crystal structure , Mbp1 from S . cerevisiae in the absence of DNA , originally suggested that Mbp1 recognizes its MCB ( Mlu I cell cycle box , ACGCGT ) binding site via a recognition helix ( Taylor et al . , 1997; Xu et al . , 1997 ) . However , a recent crystal structure of PCG2 , an SBF/MBF homolog in the rice blast fungus Magnaporthe oryzae , in complex with its MCB binding site does not support this proposed mode of DNA binding ( Liu et al . , 2015 ) . In striking contrast to many wHTH structures , in which the recognition helix is the mediator of DNA binding specificity , the wing of PCG2 binds to the minor groove to recognize the MCB binding site . The two glutamines in the wing ( Q82 , Q89 ) are the key elements that recognize the core MCB binding motif CGCG ( Figure 5A , blue structure ) . Family-specific conservation in the DNA-binding domain is observed for all members of the SBF family , including basal fungal sequences ( Figure 5B , right ) . In summary , the incongruences in sequence , structure , and mode of DNA-interaction between E2F/DP and SBF/MBF families strongly suggest that SBF is not derived from E2F . 10 . 7554/eLife . 09492 . 025Figure 5 . E2F and SBF show incongruences in sequence , structure , and mode of DNA binding . ( A ) Although both proteins share a winged helix-turn-helix ( wHTH ) domain , the E2F/DP and SBF/MBF superfamilies do not exhibit significant sequence identity or structural similarity to suggest a common recent evolutionary origin according to CATH or SCOP databases . Furthermore , each wHTH has a different mechanism of interaction with DNA: the arginine and tyrosine side-chains of recognition helix-3 of E2F ( E2F4 from Homo sapiens [Zheng et al . , 1999] ) interact with specific CG nucleotides , where as the glutamine side-chains of the 'wing' of SBF/MBF ( PCG2 from Magnaporthe oryzae [Liu et al . , 2015] ) interact with specific CG nucleotides . ( B ) Sequence alignment of the DNA binding domain of representative eukaryotic E2F/DP ( left ) and fungal SBF/MBF ( right ) . The corresponding secondary structure is above the sequence alignment . Evolutionary conserved residues of sequence aligned DNA binding domains are highlighted in black . Bold sequence names correspond to E2F/DP and SBF/MBF sequences from basal fungi . Colored sequence names correspond to sequences of the structures shown in panel A . PDB IDs for the structures used are shown in parentheses . W = wing; T= turn . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 025 Since SBF is unlikely to be orthologous to the E2F family of transcription factors , we considered the straightforward alternative . Previous work has shown that the DNA-binding domain of the APSES and SBF proteins is homologous to a viral KilA-N domain ( Iyer et al . , 2002 ) . KilA-N is a member of a core set of 'viral hallmark genes' found across diverse DNA viruses that infect eubacteria , archaea , and eukaryotes ( Koonin et al . , 2006 ) . Outside the fungal SBF/APSES sub-family , little is known about the KilA-N domain structure , its DNA-binding recognition sequence , and function ( Brick et al . , 1998 ) . The wide distribution of DNA viruses and KilA-N across the three domains of life suggests that the fungal ancestor likely acquired SBF via horizontal gene transfer rather than the other way around . To broaden the scope of analysis beyond the eukaryotic genomes that we studied , we carefully surveyed all KilA-N domains present in the Pfam database . The majority of known KilA-N domains ( weighted by species , not the number of sequences ) are found in prokaryotes ( 85% ) with a smaller fraction ( 10% ) found in eukaryotes and a smaller fraction found in DNA viruses ( 5% ) . The KilA-N domains in prokaryotes appear to be either integrated by or derived from prokaryotic DNA viruses ( i . e . bacteriophage ) , and thus , we will treat them as such . Within the eukaryotes , all known KilA-N domains are found in fungal genomes with three notable exceptions . The first exception is Trichomonas vaginalis , a parasitic excavate with 1000+ KilA-N domains ( Figure 4F ) . The T . vaginalis KilA-N domains have top blast hits to prokaryotic and eukaryotic DNA viruses , e . g . Mimivirus , a large double-stranded DNA virus of the Nucleo-Cytoplasmic Large DNA Viruses ( Yutin et al . , 2009 ) . Mimiviruses are giant viruses known to infect simple eukaryotic hosts , such as Acanthamoeba and possibly other eukaryotes ( Abrahão et al . , 2014; Raoult and Forterre , 2008 ) . The second and third exceptions are found in two insects , Acyrthosiphon pisum ( 'pea aphid' ) and Rhodius prolixus ( 'triatomid bug' ) . The one KilA-N domain in A . pisum genome has a top blast hit to eukaryotic DNA viruses ( e . g . Invertebrate Iridescent Virus 6 ) . The three KilA-N domains in R . prolixus have top blast hits to prokaryotic DNA viruses ( e . g . Enterobacteria phage P1 ) . The diverse and sparse distribution of KilA-N domains throughout the eukaryotic genomes is consistent with their horizontal gene transfer into hosts from eukaryotic DNA viruses and/or via engulfed bacteria that were infected with prokaryotic DNA viruses . In fact , the horizontal transfer of genes between Mimivirus and their eukaryotic host , or the prokaryotic parasites within the host , has been shown to be a more frequent event that previously thought ( Moreira and Brochier-Armanet , 2008 ) To gain further insight into the possible evolutionary origins of the SBF subfamily via horizontal gene transfer , we aligned diverse KilA-N sequences from the Uniprot and PFAM database to the KilA-N domain of our most basal fungal SBF+APSES sequences ( Zoosporic fungi ( 'Chytrids' ) and 'Zygomycetes' ) and built a phylogenetic tree ( Figure 6 ) . There are three major phylogenetic lineages of KilA-N domains: those found in eukaryotic viruses , prokaryotic viruses , and the fungal SBF+APSES family . Our results show that the fungal SBF+APSES family is monophyletic and is strongly supported by multiple phylogenetic support metrics . This suggests a single HGT event as the most likely scenario that established the SBF+APSES family in a fungal ancestor . However , our current phylogeny is unable to distinguish whether the SBF family arrived in a fungal ancestor through a eukaryotic virus or a phage-infected bacterium . Structural and functional characterization of existing viral KilA-N domains could help distinguish between these two hypotheses . 10 . 7554/eLife . 09492 . 026Figure 6 . Viral origin of yeast cell cycle transcription factor SBF . Maximum likelihood unrooted phylogenetic tree depicting relationships of fungal SBF-family proteins , KilA-N domains in prokaryotic and eukaryotic DNA viruses . The original dataset was manually pruned to remove long-branches and problematic lineages . Our reduced KilA-N dataset has a total of 219 sequences ( Figure 6—source data 1 ) , 130 positions . Confidence at nodes was assessed with multiple support metrics using different phylogenetic programs under LG model of evolution . Colored dots in branches indicate corresponding branch supports ( red dots: PhyML aBayes ≥0 . 9; blue dots: PhyML SH-aLRT ≥0 . 80; orange: RAxML RBS ≥70% ) . Thick branches indicate significant support by at least two metrics , one parametric and one non-parametric; scale bar in substitutions per site; see Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 02610 . 7554/eLife . 09492 . 027Figure 6—source data 1 . Reduced set of KilA-N domains for phylogenetic analysis . These files contain the protein sequences used to create molecular phylogeny in Figure 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 027 Of all the members of the SBF+APSES family , the most likely candidate to be a 'founding' TF is SBF , as it is the only member present in all fungi ( Figure 3—figure supplement 1 ) . In budding yeast and other fungi , SBF functions in G1/S cell cycle regulation and binds a consensus site CGCGAA ( Gordân et al . , 2011 ) , which overlaps with the consensus site GCGSSAAA for the E2F family ( Rabinovich et al . , 2008 ) ; see Figure 7A . The APSES regulators , Xbp1 , and MBF in budding yeast bind TGCA , TCGA , ACGCGT motifs , respectively . A viral origin of the SBF+APSES family—with the founding member involved in cell cycle control—suggests the hypothesis that perhaps the founder TF functioned like a DNA tumor virus protein and hijacked cell cycle control to promote proliferation . 10 . 7554/eLife . 09492 . 028Figure 7 . Yeast cell cycle transcription factor SBF can regulate cell cycle-dependent transcription via E2F binding sites in vivo . ( A ) Phylogenetic tree of animals , chytrids , yeast labelled with E2F , SBF or both transcription factors ( TF ) if present in their genomes . The known DNA-binding motifs of animal E2F ( E2F1 ) and yeast SBF ( Swi4 ) were taken from the JASPAR database , where as Chytrid E2F and SBF motifs are unknown . ( B ) Fluorescence images of cells expressing a destabilized GFP from the SBF-regulated CLN2 promoter . ( C ) Oscillation of a transcriptional reporter in budding yeast . Characteristic time series of GFP expression from a CLN2 promoter ( SCB ) , a CLN2 promoter where the SBF binding sites were deleted ( ∆SCB ) , or a CLN2 promoter where the SBF binding sites were replaced with E2F binding sites from the human gene cluster promoters ( E2F ) . Oscillation amplitudes were quantified by scaling the mean fluorescence intensity difference from peak to trough divided by the trough intensity ( Smax - Smin ) /Smin . Circles denote time points corresponding to ( B ) . Triangles denote budding events . ( D ) Distribution of oscillation amplitudes for different genotypes and GFP reporters . swi4∆ and mbp1∆ strains have deletions of the SBF and MBF DNA-binding domain subunits respectively . t-test comparisons within and across red and blue categories yield p-values >0 . 3 or <0 . 01 respectively . Boxes contain 25th , median and 75th percentiles , while whiskers extend to 1 . 5 times this interquartile range . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 028 For the viral TF ( SBF ) to hijack cell cycle control in the fungal ancestor , it must have been able to both bind E2F regulatory regions and then activate the expression of genes under E2F in a cell cycle-regulated fashion . The overlap between the conserved E2F and SBF consensus sites suggests that ancestral SBF could bind E2F regulatory regions ( Figure 7A ) . However , a single base pair substitution in the SBF motif can reduce gene expression by up to ~95% ( Andrews and Moore , 1992 ) and flanking regions outside the core are often important for binding affinity and gene expression ( Nutiu et al . , 2011 ) . To our knowledge , no one has directly measured the extent to which animal E2F and yeast SBF bind similar sites either in vivo or in vitro . To first test whether yeast SBF can bind a canonical E2F binding site , we inserted consensus E2F binding sites in the budding yeast genome . The hijacking hypothesis would be supported in vivo if E2F binding sites could generate SBF-dependent cell cycle regulated gene expression . We used the well-studied CLN2 promoter , which has three binding sites for SBF ( SCB , Swi4 , 6-dependent cell cycle box ) in a nucleosome-depleted region ( Figure 7B-C ) . Removal of these SCB sites is known to eliminate cell cycle-dependent gene expression ( Bai et al . , 2010 ) . We replaced the complete SBF sites ( TCACGAAA ) of CLN2 ( Koch et al . , 1996 ) with a known E2F binding site ( GCGCGAAA ) from the promoters of the histone gene cluster in mammals ( Rabinovich et al . , 2008 ) . We observed significant oscillations in GFP expression , which were coordinated with the cell cycle . Importantly , the amplitude of these oscillations was dependent on the budding yeast SBF ( Swi4 ) , but not MBF ( Mbp1 ) , and disappeared when the 3 binding sites were removed ( Figure 7C–D ) . This experiment demonstrates that budding yeast SBF can bind E2F-like sites , despite the fact that Dikarya lost ancestral E2F hundreds of millions of years ago . There are , of course , other possible E2F DNA binding sites that we could have used in our experiment; we picked this one because it is a well-characterized E2F binding site . To further explore the overlap in sequence specificity , we analyzed data from high-throughput protein-binding microarray ( PBM ) assays ( Afek et al . , 2014; Badis et al . , 2008 ) of human E2F ( E2F1 ) and budding yeast SBF ( Swi4 ) . PBM assays measure , in a single experiment , the binding of recombinant proteins to tens of thousands of synthetic DNA sequences , guaranteed to cover all possible 10-bp DNA sequences in a maximally compact representation ( each 10-mer occurs once and only once ) . We used these PBM data to generate DNA motifs for E2F and SBF ( Berger et al . , 2006 ) , and to compute , for each possible 8-bp DNA sequence , an enrichment score ( or E-score ) that reflects the specificity of the protein for that 8-mer . E-scores vary between -0 . 5 and +0 . 5 , with larger values corresponding to higher affinity binding sites ( Berger et al . , 2006 ) . As shown in Figure 8 , E2F1 and Swi4 can bind a set of common motifs . For example , the E2F binding site variant that we tested in budding yeast ( GCGCGAAA , highlighted in red ) , is one of the sites commonly bound in vitro by E2F and SBF . 10 . 7554/eLife . 09492 . 029Figure 8 . High-throughput DNA binding data for yeast SBF and human E2F shows that SBF and E2F can bind shared and distinct DNA-binding sites . Plot of in vitro protein binding microarray 8-mer E-scores for Homo sapiens E2F1 ( Afek et al . , 2014 ) versus S . cerevisiae SBF protein Swi4 ( Badis et al . , 2008 ) . All 8-mer motifs colored ( E-score > 0 . 37 ) are considered significant targets with a false positive discovery rate of 0 . 001 ( Badis et al . , 2009 ) . Yellow are common 8-mer motifs bound by both E2F1 and SBF , blue are E2F-only motifs , and purple are SBF-only motifs . The E2F motif from histone cluster promoters used in Figure 7 is circled in red . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 02910 . 7554/eLife . 09492 . 030Figure 8—figure supplement 1 . Bioinformatic scan of E2F-regulated human promoters suggests possible regulation by SBF . E2F1 ( top ) and SBF ( bottom ) PBM motifs were used to scan the proximal ( 1000 bp ) promoters of E2F-regulated promoters ( CCNE1 , E2F1 , and EZH2 ) . Promoter regions with a significant hit ( 8-mer E-score > 0 . 37 ) to a E2F or SBF motif have blue or purple dot , respectively . Predicted TF-binding regions were defined as at least 2 consecutive overlapping 8-mers ( 7 nucleotide overlap ) and shaded as blue ( E2F binding region ) or purple ( SBF-binding region ) . Common or E2F & SBF binding regions were colored yellow and defined as regions that overlap in at least one full 8-mer . Note that the number of E2F binding sites predicted by in vitro PBM data is larger than the actual number of functional in vivo E2F binding sites . This is to be expected because , in the nuclear environment , E2F can be outcompeted at putative DNA binding sites by nucleosomes or other nuclear proteins . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 03010 . 7554/eLife . 09492 . 031Figure 8—figure supplement 2 . Many E2F-regulated genes in humans could be bound by SBF . Summary of E2F-only regions ( blue ) , SBF-only regions ( purple ) , and E2F and SBF co-regulated regions ( yellow ) for a set of 290 E2F-regulated promoters . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 031 Most notably , the in vitro PBM data show that there are specific motifs that can be bound only by E2F or only by SBF . To identify the key nucleotide differences between E2F-only and SBF-only binding , we created motifs of E2F-only and SBF-only sites . The consensus E2F-only ( NNSGCGSN ) and SBF-only ( NNCRCGNN ) motifs indicate that differential specificity between E2F1 and SBF is mediated by the nucleotides in the 3rd and 4th positions ( underlined ) before the invariant CG at the 5th and 6th positions . E2F has a strict preference for G in the 4th position , where as SBF has a strict preference for C in the 3rd position ( Figure 8 ) . We then scanned the promoters of known E2F target genes from the human genome ( CCNE1 , E2F1 , EZH2 ) with our empirically-defined DNA binding sites from PBM assays ( Afek et al . , 2014; Badis et al . , 2008 ) to predict putative E2F-only , SBF-only , and common sites ( Figure 8—figure supplement 1 ) . As expected , there are many predicted E2F-only and common ( E2F & SBF ) sites that could be bound by E2F in these known target genes . However , we could also find many common and SBF-only binding sites to which SBF could bind . We then extended our analysis to 290 known E2F target genes in the human genome to test the generality of SBF cross-binding to E2F sites ( Eser et al . , 2011 ) . Most E2F target promoters could be bound by SBF ( Figure 8—figure supplement 2 ) . Taken together , this set of experiments lends support to the hijacking hypothesis , where an ancestral SBF may have taken control of several E2F-regulated genes . Cell division is an essential process that has been occurring in an uninterrupted chain for billions of years . Thus , one expects strong conservation in the regulatory network controlling the eukaryotic cell division cycle . Consistent with this idea , cell cycle network structure is highly similar in budding yeast and animal cells . However , many components performing similar functions , such as the SBF and E2F transcription factors , lack sequence identity , suggesting a significant degree of evolution or independent origin . To identify axes of conservation and evolution in eukaryotic cell cycle regulation , we examined a large number of genome sequences in Archaeplastida , Amoebozoa , SAR , Haptophyta , Cryptophyta , Excavata , Metazoa and Fungi . Across eukaryotes , we found a large number of proteins homologous to metazoan rather than fungal G1/S regulators . Our analysis indicates that the last eukaryotic common ancestor likely had complex cell cycle regulation based on Cdk1 , Cyclins D , E , A and B , E2F , pRb and APC family proteins . In contrast , SBF was not present in the last common eukaryotic ancestor , and abruptly emerged , with its regulator Whi5 , in fungi likely due to the co-option of a viral KilA-N protein at the base of the fungal lineage . The origin of Whi5 is unclear because we found no homologs outside of fungi . Whi5 is a mostly unstructured protein , which , like pRb , recruits transcriptional inhibitor proteins to specific sites on DNA via transcription factor binding ( Huang et al . , 2009; Travesa et al . , 2013; Wang et al . , 2009 ) . The relatively simple structure of Whi5 suggests that it may have been subsequently co-opted as a phosphopeptide to entrain SBF activity to cell cycle regulated changes in Cdk activity ( Figure 9 ) . 10 . 7554/eLife . 09492 . 032Figure 9 . Punctuated evolution of a conserved regulatory network . Evolution can replace components in an essential pathway by proceeding through a hybrid intermediate . Once established , the hybrid network can evolve dramatically and lose previously essential regulators , while sometimes retaining the original network topology . We hypothesize that SBF may have hijacked the cell cycle of a fungal ancestor by binding cis-regulatory DNA sites of E2F and activating expression of G1/S genes , thus promoting cell cycle entry . Cell cycle hijacking in a fungal ancestor was followed by evolution of Whi5 to inhibit SBF and Whi5 was subsequently entrained to upstream cell cycle control through phospho-regulation by old or new cyclin-CDKs to create a hybrid network with parallel pathways . The hybrid network likely provided redundant control of the G1/S regulatory network , which could explain the eventual loss of E2F and its replacement by the SBF pathway in more derived fungi ( Dikarya ) . Interestingly , zoosporic fungi such as Chytrids have hybrid networks and are transitional species because they exhibit animal-like features of the opisthokont ancestor ( centrioles , flagella ) and fungal-like features ( cell wall , hyphal growth ) . We hypothesize that E2F and SBF also bind and regulate a subset of animal-specific and fungal-specific G1/S genes , which could help explain the preservation of the hybrid network in Chytrids . Ancestral SBF expanded to create an entire family of transcription factors ( APSES ) that regulate fungal-specific traits such as sporulation , differentiation , morphogenesis , and virulence . DOI: http://dx . doi . org/10 . 7554/eLife . 09492 . 032 The replacement of E2F-Rb with SBF-Whi5 at the core of the cell cycle along the fungal lineage raises the question as to how such a drastic change to fundamental regulatory network could evolve . One answer can be found in the evolution of transcription factors . When the function of an essential transcription factor does change , it often leaves behind a core part of its regulon for another factor ( Brown et al . , 2009; Gasch et al . , 2004; Lavoie et al . , 2009 ) . This process of handing off transcription factor function has been observed to proceed through an intermediate state , present in some extant genomes , in which both factors perform the function ( Tanay et al . , 2005 ) . The logic of proceeding through an intermediate state has been well-documented for the regulation of genes expressed only in yeast of mating type a ( asgs ) ( Tsong et al . , 2006 ) . In the ancestral yeast and many extant species , asgs expression is activated by a protein only present in a cells , while in other yeasts , expression is repressed by a protein only present in α cells and a/α diploids . The replacement of the ancestral positive regulation by negative regulation occurred via yeast that contained both systems illustrating how an essential function can evolve through a hybrid state ( Baker et al . , 2012 ) . Clearly , something similar happened during cell cycle evolution . It appears that the replacement of the E2F-pRb transcription regulatory complex with the SBF-Whi5 complex proceeded via a hybrid intermediate that preserved its function . In the hybrid intermediate , E2F-Rb and SBF-Whi5 may have evolved to be parallel pathways whose functions overlapped to such an extent that the previously essential E2F-Rb pathway could be lost in the transition to Dikarya . Interestingly , many basal fungi ( e . g . Chytrids ) have preserved rather than lost this hybrid intermediate , which suggests that each pathway may have specialized functions . Chytrids exhibit both animal ( e . g . centrioles , flagella , amoeboid movement ) and fungal features ( e . g . cell wall , hyphal growth ) whose synthesis needs to be coordinated with cell division . The preservation of the hybrid network in chytrids could then be explained if animal-like and fungal features are regulated by the E2F-Rb and SBF-Whi5 pathways respectively ( Figure 9 ) . Once Fungi lost many of the ancestral animal-like features during the emergence of the 'zygomycetes' or Dikarya , the ancestral E2F-pRb components could have evolved new functions or have been lost . The origin of the hybrid network at the base of Fungi is abrupt and may have been initiated by the arrival of SBF via virus . Many tumor viruses activate cell proliferation . For example , the DNA tumor viruses Adenovirus and SV40 highjack cell proliferation in part by activating the expression of E2F-dependent genes by binding pRb to disrupt inhibition of E2F ( DeCaprio , 2009 ) . While the specific mechanisms may differ , when SBF entered the fungal ancestor cell it might have activated the transcription of E2F target genes . Rather than inhibiting the inhibitor of E2F , SBF may have directly competed for E2F binding sites with transcriptionally inactive E2F-Rb complexes ( Figure 9 ) . Consistent with this model , we have shown here that SBF can directly regulate gene expression in budding yeast via a consensus E2F binding site . Thus , the cooption of a viral protein generated a hybrid network to ultimately facilitate dramatic evolution of the core cell cycle network in fungi . We used Profile-Hidden Markov Models ( profile-HMMs ) to detect homologs for each of the families studied , using the HMMER 3 package ( Eddy , 2011 ) . Profile-HMMs are sensitive tools for remote homology detection . Starting with a set of diverse yet reliable protein homologs is fundamental for detecting remote protein homology and avoiding 'model poisoning' ( Johnson et al . , 2010 ) . To this end , we used reliable training-set homologs from the cell cycle model organisms Arabidopsis thaliana , Homo sapiens , Schizosaccharomyces pombe , and Saccharomyces cerevisiae , to build the profile-HMMs used to detect homologs . Our profile-HMM search used a stringent E-value threshold of 1E-10 to detect putative homologs in the 'best' filtered protein sets ( where available ) of our 100+ eukaryotic genomes ( see Supplementary file 1A for genome details ) . All putative homologs recovered through a profile-HMM search were further validated ( or rejected ) using an iterative search algorithm ( Jackhmmer ) against the annotated SwissProt database using the HMMER web server ( Finn et al . , 2011 ) . Our profile-HMM for E2F/DP family only detects E2F or DP , where as our profile-HMM for SBF/MBF family only detects SBF/MBF ( or APSES ) . The same protein was never identified by both profile-HMMs because the sequence profiles and the structure are non-homologous . In the case of basal fungi , which have both E2F/DP and SBF/MBF , all proteins classified as an E2F/DP had clear homology to E2F or DP ( see alignment in Figure 5B ) and all proteins that we classified as SBF/MBF had clear homology to SBF/MBF ( see alignment in Figure 5B ) . A phylogenetic analysis and classification was built in four stages . In the first stage , we used MAFFT-L-INS-i ( -maxiterate 1000 ) to align the sequences of eukaryotic protein family members ( Katoh and Standley , 2013 ) . We then used probabilistic alignment masking using ZORRO ( Wu et al . , 2012 ) to create different datasets with varying score thresholds . Next , we used ProtTest 3 to determine the empirical amino-acid evolutionary model that best fit each of our protein datasets using several criteria: Akaike Information Criterion , corrected Akaike Information Criterion , Bayesian Information Criterion and Decision Theory ( Darriba et al . , 2011 ) . Last , for each dataset and its best-fitting model , we ran different phylogenetic programs that use maximum-likelihood methods with different algorithmic approximations ( RAxML and PhyML ) and Bayesian inference methods ( PhyloBayes-MPI ) to reconstruct the phylogenetic relationships between proteins . For RAxML analyses , the best likelihood tree was obtained from five independent maximum likelihood runs started from randomized parsimony trees using the empirical evolutionary model provided by ProtTest . We assessed branch support via rapid bootstrapping ( RBS ) with 100 pseudo-replicates . PhyML 3 . 0 phylogenetic trees were obtained from five independent randomized starting neighbor-joining trees ( RAND ) using the best topology from both NNI and SPR moves . Non-parametric Shimodaira-Hasegawa-like approximate likelihood ratio tests ( SH-aLRTs ) and parametric à la Bayes aLRTs ( aBayes ) were calculated to determine branch support from two independent PhyML 3 . 0 runs . For Bayesian inference we used PhyloBayes ( rather than the more frequently used MrBayes ) because it allows for site-specific amino-acid substitution frequencies , which better models the level of heterogeneity seen in real protein data ( Lartillot and Philippe , 2004; Lartillot et al . , 2009 ) . We performed Phylobayes analyses by running three independent chains under CAT and the exchange rate provided by ProtTest 3 ( e . g . CAT-LG ) , four discrete gamma categories , and with sampling every 10 cycles . Proper mixing was initially confirmed with Tracer v1 . 6 ( 2014 ) . The first 1000 samples were discarded as burn-in , and convergence was assessed using bipartition frequencies and summary statistics provided by bpcomp and tracecomp from Phylobayes . These were visually inspected with an R version of AWTY ( https://github . com/danlwarren/RWTY ) ( Nylander et al . , 2008 ) . The best phylogenies are shown in Figure 2—figure supplement 2–6 and Figure 3—figure supplement 2–4 , and were used to tentatively classify sequences into sub-families and create Figure 2—figure supplement 1 and Figure 3—figure supplement 1 . We note that the confidence of each node in the phylogenetic trees was assessed using multiple , but complementary support metrics: ( 1 ) posterior probability for the Bayesian inference , ( 2 ) rapid bootstrap support ( Stamatakis , 2006; Stamatakis et al . , 2008 ) for RAxML , and ( 3 ) non-parametric Shimodaira-Hasegawa-like approximate likelihood ratio tests ( SH-aLRTs ) and parametric à la Bayes aLRTs ( aBayes ) for PhyML . These different support metrics complement each other in their advantages and drawbacks . SH-aLRT is conservative enough to avoid high false positive rates but performs better compared to bootstrapping ( Guindon et al . , 2010; Simmons and Norton , 2014 ) . aBayes is powerful compared to non-parametric tests , but has a tendency to increase false-positive rates under serious model violations , something that can be balanced with SH-aLRTs ( Anisimova and Gascuel , 2006; Anisimova et al . , 2011 ) . Our CLN2pr-GFP-CLN2PEST constructs were all derived from pLB02-0mer ( described in Bai et al . , 2010 and obtained from Lucy Bai ) . To create pLB02-CLN2 , a synthetic DNA fragment ( IDT , Coralville , IA ) encompassing a region of the CLN2 promoter from 1 , 130 bp to 481 bp upstream of the CLN2 ORF was digested with BamHI and SphI and ligated into pLB02-0mer digested with the same enzymes . To create pLB02-E2F , which contains E2F binding sites , the same procedure was applied to a version of the promoter fragment in which the SCBs at 606bp , 581bp , and 538bp upstream of the ORF were replaced with the E2F binding site consensus sequence GCGCGAAA ( Rabinovich et al . , 2008 ) . All these plasmids were linearized at the BbsI restriction site in the CLN2 promoter and transformed . Both swi4∆ and mbp1∆ strains containing pLB02-0mer , pLB02-Cln2 , pLB02-E2F fluorescent expression reporters were produced by mating lab stocks using standard methods . JE103 was a kind gift from Dr . Jennifer Ewald . Plasmids and strains are listed in Supplementary file 1B and 1C , respectively . Imaging proceeded essentially as described in Bean et al . , 2006 . Briefly , early log-phase cells were pre-grown in SCD and gently sonicated and spotted onto a SCD agarose pad ( at 1 . 5% ) , which was inverted onto a coverslip . This was incubated on a heated stage on a Zeiss Observer Z . 1 while automated imaging occurred ( 3 min intervals , 100–300 ms fluorescence exposures ) . Single-cell time-lapse fluorescence intensity measurements were obtained using software described in Doncic and Skotheim ( 2013 ) , Doncic et al . ( 2011 ) , and oscillation amplitudes were obtained manually from the resulting traces . The single-cell fluorescence intensity traces used mean cellular intensity with the median intensity of the entire field of view subtracted , to control for any fluctuations in fluorescent background . The resulting measurements were analyzed in R . Universal PBM data for Swi4 was downloaded from the cis-BP database ( Weirauch et al . , 2014 ) . We used the PBM 8-mer E-scores reported in cis-BP for the data set M0093_1 . 02:Badis08:SWI4_4482 . 1_ArrayB . Universal PBM data for E2F1 ( Afek et al . , 2014 ) is available in the GEO database ( accession number GSE61854 , probes UnivV9_* ) . E2F1 8-mer E-scores were computed using the Universal Protein Binding Microarray ( PBM ) Analysis Suite ( Berger and Bulyk , 2009 ) . An E-score cutoff of 0 . 37 was used to call SBF and E2F binding sites . This cutoff corresponds to a false positive discovery rate of 0 . 001 ( Badis et al . , 2009 ) . To generate DNA motifs for E2F-only , SBF-only , and common sites , we used the Priority software ( Gordân et al . , 2010 ) with a uniform prior to align the 8-mers with E-score > 0 . 37 for E2F only , SBF only , or both E2F and SBF , respectively ( Figure 8 ) . Promoter sequences ( 1000 bp upstream of transcription start ) for known E2F targets ( Eser et al . , 2011 ) were retrieved from Homo sapiens genome as provided by UCSC ( hg38 ) ( BSgenome . Hsapiens . UCSC . hg38 ) and the annotation package org . Hs . eg . db from Bioconductor ( v3 . 2 ) ( Gentleman et al . , 2004; Huber et al . , 2015 ) . Only regions for which at least two consecutive sliding windows of 8-mers ( 1 nucleotide step; 7 overlapping nucleotides ) were high scoring ( E-score ≥ 0 . 37 ) were called as potential SBF or E2F binding regions . Overlapping or 'common' binding regions between E2F and SBF were defined as regions that , regardless of their difference in length ( nested or partially overlapping ) , overlapped in at least one full 8-mer ( Figure 8—figure supplement 1 ) .	Living cells grow and divide with remarkable precision to ensure that their genetic material is faithfully duplicated and distributed equally to the newly formed daughter cells . This precision is achieved through a series of steps known as the cell cycle . The cell cycle is ancient and conserved across all Eukaryotes , including plants , animals and fungi . However , some of the core proteins present in animals and fungi are unrelated . This raises the question as to how a drastic change could have occurred and been tolerated over evolution . In animals and plants , a protein called E2F controls the expression of genes that are needed to begin the cell cycle . In most fungi , an equivalent protein called SBF performs the same role as E2F , but the two proteins are very different and do not appear to share a common ancestor . This is unexpected given that fungi and animals are more closely related to one another than either is to plants . Medina et al . searched the genomes of many animals , fungi , plants , algae , and their closest relatives for genes that encoded proteins like E2F and SBF . SBF-like proteins were only found in fungi , yet some fungal groups had cell cycle regulators like those found in animals . Zoosporic fungi , which diverged early from the fungal ancestor , had both SBF- and E2F-like proteins , while many fungi later lost E2F during evolution . So how did fungi acquire SBF ? Medina et al . observed that part of the SBF protein is similar to proteins found in many viruses . The broad distribution of these viral SBF-like proteins suggests that they arose first in viruses , and a fungal ancestor acquired one such protein during a viral infection . As SBF and E2F bind similar DNA sequences , Medina et al . hypothesized that this viral SBF hijacked control of the cell cycle in the fungal ancestor by controlling expression of genes that were originally controlled only by E2F . In support of this idea , experiments showed that many E2F binding sites in modern genes are also SBF binding sites , and that E2F sites can substitute for SBF sites in SBF-controlled genes . Future experiments in zoosporic fungi , which have animal-like and fungal-like features , would provide a glimpse of how a fungal ancestor may have used both SBF and E2F . These experiments may also reveal why most fungi have retained the newer SBF but lost the ancestral and widely conserved E2F protein .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "evolutionary", "biology", "computational", "and", "systems", "biology" ]	2016	Punctuated evolution and transitional hybrid network in an ancestral cell cycle of fungi
Weight loss and anorexia are common symptoms in cancer patients that occur prior to initiation of cancer therapy . Inflammation in the brain is a driver of these symptoms , yet cellular sources of neuroinflammation during malignancy are unknown . In a mouse model of pancreatic ductal adenocarcinoma ( PDAC ) , we observed early and robust myeloid cell infiltration into the brain . Infiltrating immune cells were predominately neutrophils , which accumulated at a unique central nervous system entry portal called the velum interpositum , where they expressed CCR2 . Pharmacologic CCR2 blockade and genetic deletion of Ccr2 both resulted in significantly decreased brain-infiltrating myeloid cells as well as attenuated cachexia during PDAC . Lastly , intracerebroventricular blockade of the purinergic receptor P2RX7 during PDAC abolished immune cell recruitment to the brain and attenuated anorexia . Our data demonstrate a novel function for the CCR2/CCL2 axis in recruiting neutrophils to the brain , which drives anorexia and muscle catabolism . Cancer patients commonly present with symptoms driven by disruption of normal CNS function . Weight loss , weakness , fatigue , and cognitive decline often occur in malignancies outside the CNS , and develop prior to initiation of cancer therapy ( Meyers , 2000; Miller et al . , 2008; Olson and Marks , 2019 ) . Many of these symptoms are part of a syndrome called cachexia , a devastating state of malnutrition characterized by decreased appetite , fatigue , adipose tissue loss , and muscle catabolism ( Fearon et al . , 2011 ) . There are currently no effective treatments for cachexia or other CNS-mediated cancer symptoms . While mechanisms of CNS dysfunction during malignancy are still not well understood , inflammation in the brain is proposed as a key driver ( Burfeind et al . , 2016 ) . Inflammatory molecules ( e . g . lipopolysaccharide , cytokines ) can cause dysfunction of the appetite- , cognition- , weight- , and activity-regulating regions in the CNS , resulting in signs and symptoms nearly identical to those observed during cancer ( Braun et al . , 2011; Burfeind et al . , 2016; Grossberg et al . , 2011 ) . Moreover , cytokines and chemokines are produced in these same regions during multiple types of cancer ( Braun et al . , 2011; Michaelis et al . , 2017 ) . Our lab and others previously showed that disrupting inflammatory signaling by deleting either MyD88 or TRIF attenuates anorexia , muscle catabolism , fatigue , and neuroinflammation during malignancy ( Burfeind et al . , 2018; Ruud et al . , 2013; Zhu et al . , 2019 ) . The mechanisms by which inflammation generated in the periphery ( e . g . at the site of a malignancy ) is translated into inflammation in the brain , and how this is subsequently translated CNS dysfunction , are still not known . Circulating immune cells present an intriguing cellular candidate , as they are thought to infiltrate and interact with the brain during various states of inflammation ( Prinz and Priller , 2017 ) , yet have not been investigated as potential mediators of brain dysfunction during cancer . We utilized a syngeneic , immunocompetent , mouse model of pancreatic ductal adenocarcinoma ( PDAC ) , a deadly malignancy associated with profound anorexia , fatigue , weakness , and cognitive dysfunction ( Baekelandt et al . , 2016; Michaelis et al . , 2017 ) . We first demonstrated that the inflammatory transcripts upregulated in the CNS during PDAC consisted largely of chemokines . We then characterized the identity , properties , and function of immune cells in the brain during PDAC . We observed that circulating myeloid cells , primarily neutrophils , were recruited to the CNS early in PDAC , infiltrating throughout the brain parenchyma and accumulating in the meninges near regions important for appetite , behavior , and body composition regulation . We then demonstrated that CCR2 signaling is important for immune cell recruitment to the brain and cachexia during PDAC . Next , we blocked purinergic receptor P2RX7 signaling specifically on brain macrophages during PDAC via intracerebroventricular ( ICV ) injection of oxidized ATP ( oATP ) , which prevented circulating myeloid cell recruitment to the brain and attenuated anorexia . Taken together , these results reveal a novel mechanism by which neutrophil and other myeloid cells are recruitment to the brain , where they contribute to cachexia symptoms . Our lab previously demonstrated that inflammatory cytokine transcripts are upregulated in the hypothalamus in a mouse model of PDAC ( Burfeind et al . , 2018; Michaelis et al . , 2017; Zhu et al . , 2019 ) . To our knowledge , no studies have investigated whether inflammatory transcripts are upregulated during cachexia in other brain regions important for behavior and metabolism . Therefore , we used qRT-PCR to determine if various inflammatory cytokine or chemokine transcripts were upregulated in the hippocampus , hypothalamus , or area postrema during PDAC . We utilized a mouse model of PDAC , generated through a single intraperitoneal ( IP ) or orthotopic ( OT ) injection of C57BL/6 KrasG12D Tp53R172H Pdx1-Cre+/+ ( KPC ) cells . This well-characterized model recapitulates several key signs and symptoms of CNS dysfunction observed in humans , including anorexia , muscle catabolism , and fatigue ( Burfeind et al . , 2018; Michaelis et al . , 2019; Michaelis et al . , 2017; Zhu et al . , 2019 ) . We performed qRT-PCR at 10 days post-IP inoculation , a time when animals reliably develop anorexia , muscle catabolism , and decreased locomotor activity ( Michaelis et al . , 2017 ) . We queried transcripts previously demonstrated to be upregulated in the brain during chronic systemic inflammation ( Burfeind et al . , 2018; Grossberg et al . , 2010 ) . In addition , no studies have investigated chemokine expression in the brain during extra-CNS malignancy , so we also queried expression of chemokine transcripts . In agreement with our previous studies we observed that Il-1β was upregulated in the hypothalamus ( Figure 1 ) . It was also upregulated in the area postrema , and showed a trend toward significance in the hippocampus ( p=0 . 08 ) . However , of the other cytokine transcripts analyzed , only those coding for prostaglandin synthase D2 ( Ptgs2 – in the hypothalamus and area postrema , but not the hippocampus ) and IL-1R ( Il1r - again in the hypothalamus and area postrema , but not the hippocampus ) were upregulated . The anti-inflammatory transcript Il10 was upregulated in the area postrema only . Interestingly , the transcript coding for nitric oxide synthase 2 ( Nos2 – induced during inflammation and mainly expressed by endothelial cells ) was downregulated in all three brain regions . Several chemokine transcripts associated with myeloid cell chemotaxis were upregulated in different brain regions during PDAC . Two of the three IL-8 orthologues , Cxcl1 and Cxcl2 , were highly upregulated in all three regions investigated . Ccl2 was highly upregulated in the hippocampus , and nearly significantly upregulated in the hypothalamus ( p=0 . 06 ) . Alternatively , Cxcl9 was downregulated in both the area postrema and hypothalamus , whereas Cxcl10 was downregulated in the area postrema , yet upregulated in the hippocampus . Lastly , the third IL-8 orthologue , Cxcl5 , was downregulated in the hippocampus . Based on our observation that there is robust upregulation of several chemokine transcripts in the CNS in our mouse model of cachexia , along with our previous data showing that the transcript for the leukocyte adhesion molecule , P-selectin is upregulated in the brain during cachexia ( Michaelis et al . , 2017 ) , we hypothesized that immune cells infiltrated the brain . We utilized flow cytometry to perform an initial brain-wide analysis of infiltrating immune cells in our PDAC model . Using 10-color flow cytometry of whole brain homogenate ( Figure 2A ) , we characterized brain immune cells at three time points: 5 days post-inoculation ( d . p . i ) ( before anorexia , fatigue , and muscle mass loss onset ) , 7 d . p . i . ( initiation of wasting and anorexia ) , and 10 d . p . i . ( robust wasting and anorexia , but 4–5 days before death ) after IP injection of PDAC cells ( see Figure 6F for typical disease progression of our KPC model ) . Compared to sham-injected animals , we observed a significant increase in CD45highCD11b+ myeloid cells in the brains of animals with PDAC ( Figure 2B ) , with an increase as a percentage of total CD45+ ( all immune cells ) and CD45high ( non-microglia leukocytes ) cells occurring at 5 d . p . i . ( Figure 2D and Figure 2—figure supplement 1D ) . Both absolute and relative number of lymphocytes ( CD45highCD11b- ) were decreased in the brains of tumor animals compared to sham animals starting at 5 d . p . i . , which was driven by a decrease in B-cells and CD4+ T-cells ( Figure 2C and Figure 2—figure supplement 1B–D ) . There was no change in number of microglia ( defined as CD45midCD11b+ ) throughout the disease course ( Figure 2C ) . Further phenotypic analysis of infiltrating myeloid cells revealed that by 7 d . p . i . , there was an increase in relative number ( as a percentage of total CD45+ and CD45high ) of Ly6CmidLy6Ghigh neutrophils , Ly6Clow myeloid cells , and Ly6Chigh monocytes ( Figure 1D , E and Figure 2—figure supplement 1D ) . We observed an increase in absolute number of neutrophils , Ly6Chigh monocytes , and Ly6Clow myeloid cells starting at 7 d . p . i . , which became significant at either 7 ( Ly6Chigh monocytes ) or 10 d . p . i . ( neutrophils and Ly6Clow myeloid cells ) ( Figure 2D and E ) . Neutrophils were by far the most numerous invading myeloid cell type , constituting 34% percent of CD45highCD11b+ cells in sham animals , and increasing to nearly 54% by 10 . d . p . i . in tumor animals ( Figure 2F ) . In order to verify that the population , we defined as ‘microglia’ were actually microglia and also confirm that Ly6G+CD45high myeloid cells were neutrophils and not an artifact of nonspecific antibody binding on microglial or other brain macrophages , we performed an additional flow cytometry experiment incorporating CX3CR1 ( a marker of macrophages ) . We observed that 95% of the population we defined as ‘microglia’ expressed CX3CR1 , while only 6% of the population we defined as ‘neutrophils’ expressed this protein , confirming the identity of these cells as microglia and neutrophils , respectively ( Figure 2—figure supplement 2 ) . We considered the population within the gate labeled ‘CD45high myeloid cells’ to be mainly infiltrating immune cells . While we drew this gate based on a clearly defined population of the cells ( see right panel on Figure 2B ) , which we demonstrated consisted mainly of neutrophils ( Figure 2F ) , it is possible that the population within this gate could also contain activated microglia . Furthermore , the population of CD45highCD11b+Ly6Clow myeloid cells could also be activated microglia . To address these issues , we generated GFP+ bone marrow chimera mice through conditioning WT mice with treosulfan to ablate marrow , then transplanting marrow from pan-GFP mice ( Ly5 . 1GFP ) ( Figure 2—figure supplement 3A ) . This system is advantageous because , unlike other alkylating agents , treosulfan does not cross or disrupt the blood brain barrier ( Capotondo et al . , 2012 ) . On average , mice that underwent bone marrow transplant ( GFP BMT mice ) exhibited 75% chimerism ( Figure 2—figure supplement 3C ) . In agreement with results from WT marrow animals , we observed that at 10 d . p . i . , thousands of GFP+ myeloid cells infiltrated the brain in tumor animals ( Figure 2—figure supplement 3B and D ) . The majority of these cells were neutrophils , with a concurrent increase in Ly6Chigh monocytes ( Figure 2—figure supplement 3C-F ) . As we observed previously , this coincided with a decrease in brain lymphocytes ( CD45+GFP+CD11b- ) in tumor animals ( Figure 2—figure supplement 3D ) . We did not observe an increase in GFP+ Ly6Clow myeloid cells ( Figure 2—figure supplement 3F ) , suggesting that the increase in CD45highCD11b+Ly6Clow cells in our WT marrow PDAC mice was a result of microglia activation , rather than infiltrating monocytes . Taken together , these data show that myeloid cells infiltrate the brain during PDAC , temporally correlating with symptom onset . The majority of infiltrating immune cells were neutrophils . Since the purpose of this study was to investigate infiltrating cells , we chose to focus our subsequent analysis on myeloid cells , with an emphasis on neutrophils . Prior studies demonstrated regional vulnerability in the CNS to immune cell invasion during systemic inflammation ( D'Mello et al . , 2009 ) . Therefore , we investigated the anatomic distribution of infiltrating myeloid cells in the CNS during PDAC . We performed immunofluorescence immunohistochemistry analysis at 10 d . p . i . in IP-inoculated animals , since all tumor-inoculated animals reliably developed anorexia , fatigue , and muscle catabolism at this time point , yet were not at terminal stage ( Michaelis et al . , 2017 ) . In addition , our flow cytometry analysis demonstrated a robust immune cell infiltrate in the brain at 10 d . p . i . For initial analysis , we defined leukocytes as CD45+ globoid cells . Although we observed scattered CD45+ globoid cells within the parenchyma in the cortex and thalamus in tumor mice ( Figure 3—figure supplement 1 ) , we observed a robust increase in leukocytes in the meninges adjacent to the hippocampus and median eminence ( ME ) ( Figure 3B and C ) . We also performed quantification in the area postrema , based on our previous experiment demonstrating chemokine transcript upregulation in this region . While there was an increase in overall CD45 immunoreactivity in the area postrema , these cells appeared ramified rather than globoid ( Figure 3D ) , suggesting microglia activation rather than immune cell infiltration . We did not observe any CD45+ cells in the lateral parabrachial nucleus ( data not shown ) , which was implicated in cancer-associated anorexia ( Campos et al . , 2017 ) . This was perhaps due to its lack of proximity to a circumventricular organ or meninges . Interestingly , we observed an increase in neutrophils ( defined as myeloperoxidase [MPO] positive , CD45+ globoid cells ) only in the meninges surrounding the hippocampus ( Figure 3B ) . This layer of meninges , known as the velum interpositum ( VI ) , is a double-layered invagination of the pia matter . This potential space is closed rostrally , communicates caudally with the quadrigeminal cistern , and is highly vascularized via a number of internal cerebral arterioles and veins . Recent studies demonstrate robust immune cell recruitment into the brain via this anatomical route after mild trauma , during CNS infection , and during CNS autoimmune disease ( Alvarez and Teale , 2006; Schmitt et al . , 2012; Szmydynger-Chodobska et al . , 2016 ) . We verified the presence of meninges in the VI with ER-TR7 labeling , which showed infiltrating neutrophils in the VI meninges in tumor mice ( Figure 3—figure supplement 2A ) . Neutrophils in the VI were degranulating , with MPO ‘blebs’ present at the edge of many cells , along with extracellular MPO ( Figure 3—figure supplement 2C ) . This phenomenon was only present in brains of tumor animals and not in brains of sham animals . We were able to confirm neutrophil identity with the plasma membrane marker Ly6G and globoid morphology ( Figure 3—figure supplement 1C ) . Neutrophil extracellular traps ( NETs ) were also present in the VI , as identified by citrillunated histone H3 and MPO co-labeling ( Figure 3—figure supplement 2E ) . We were unable to perform quantification on the number of NETs present in tumor mouse brains , due to the transient nature of these events . In the CNS parenchyma , especially in the thalamus and cortex , we frequently observed neutrophils undergoing phagocytosis by microglia , with Iba-1+ cells extending processes around MPO+ neutrophils ( Figure 3—figure supplement 2B ) . This supports previous studies showing that microglia protect the CNS parenchyma from neutrophil invasion during various states of inflammation ( Neumann et al . , 2018; Neumann et al . , 2008; Otxoa-de-Amezaga et al . , 2019 ) . The peripheral origin of the CD45+ globoid cells in the brain was assessed using our GFP BMT mice . Sham BMT mice showed very few GFP+ cells in the brain , including the cortex and thalamus ( Figure 3—figure supplement 1A ) , as well as the meninges ( data not shown ) . In contrast , there was a large increase in GFP+ cells in the brains of KPC mice at 10 d . p . i . We observed a pattern of infiltrating GFP+ cells that was identical to CD45+ globoid cells in our previous experiments , with scattered GFP+ cells in the cortex and thalamus ( Figure 3—figure supplement 1A and B ) , and accumulations of GFP+ cells in the VI ( Figure 3—figure supplement 2D ) . In agreement with our previous data , GFP+ cells were MPO+ in the VI ( Figure 3—figure supplement 2D ) . Since neutrophils were the predominant cell type infiltrating the brain during PDAC , we hypothesized that these cells are key drivers of cachexia . To test this hypothesis , we attempted to deplete neutrophils in our mouse model of PDAC . Beginning at 2 d . p . i . , we treated tumor-bearing animals with either 500 μg anti-Ly6G antibody ( clone 1A8 ) or isotype control IgG daily ( Figure 3—figure supplement 3A ) . At 10 d . p . i . , there was complete abrogation of the population we defined as neutrophils ( CD45+CD11b+Ly6Cmid/highLy6Ghigh ) in the circulation of 1A8-treated animals ( Figure 3—figure supplement 3D and E ) . However , there was a suspicious population of CD45+CD11b+Ly6CmidLy6G- cells in 1A8-treated animals , which was not present in the isotype-treated animals , and had identical forward scatter and side scatter properties as neutrophils ( Figure 3—figure supplement 3C and D ) . Since we used a fluorescently labeled anti-Ly6G antibody to identify neutrophils , it is possible that the neutralizing Ly6G antibody bound to all of the Ly6G antigens on neutrophils , therefore preventing the fluorescently labeled Ly6G antibody from binding . To address this issue , we used additional markers to identify neutrophils which did not require Ly6G labeling . It was previously reported that neutrophils could be differentiated from other circulating myeloid cells with the markers Dec205 and CD115 ( Napier et al . , 2015 ) . We identified a population of cells that was CD45+CD11b+Dec205+CD115- , which was 95% Ly6Cmid/highLy6G+ neutrophils ( Figure 3—figure supplement 3F ) . Using this new definition for neutrophils , we observed that there was no decrease in this population at 10 d . p . i . after daily treatment with 500 μg anti-Ly6G antibody ( Figure 3—figure supplement 3E ) . These results suggest that during PDAC , chronic neutrophil depletion with anti-Ly6G antibody is not possible . To identify unique mechanisms of immune cell recruitment to the brain and determine if inhibiting immune cells from infiltrating the brain attenuates cachexia , we treated OT-implanted tumor-bearing mice with either a CCR2 inhibitor ( RS504393 , Tocris ) or CXCR2 inhibitor ( SB225002 , Tocris ) . We chose CCR2 and CXCR2 since Ccl2 ( coding for the ligand for CCR2 ) , Cxcl1 ( which codes for CXCL1 , a ligand for CXCR2 ) , and Cxcl2 ( which codes for CXCL2 , also a ligand for CXCR2 ) were the most upregulated chemokine genes in dissected hippocampi ( which also included the VI ) during PDAC ( Figure 1 ) . Furthermore , these are the key chemokines for monocyte and neutrophil chemotaxis , which were the predominant cell types that infiltrated the brain in our PDAC mouse model ( Figure 2 ) . RS504393 and SB225002 were previously demonstrated to be highly effective and specific small-molecule inhibitors of their respective receptors ( Nywening et al . , 2018 ) . Based on dosing regimens optimized previously ( Nywening et al . , 2018 ) , we administered 5 mg/kg RS504393 , 10 mg/kg SB225002 , or vehicle ( DMSO ) subcutaneously twice daily starting at 3 d . p . i . ( Figure 4A ) . We used immunofluorescence analysis to quantify total CD45+ globoid cells and MPO+ cells in the VI in vehicle- , RS504393- , and SB225002-treated tumor-bearing animals . We focused our initial analysis on the VI , as it was a key region for invading immune cell accumulation . We observed a decrease in CD45+ globoid cells in the VI in RS504393-treated tumor-bearing animals compared to vehicle-treated tumor-bearing animals ( Figure 4B and C ) . Alternatively , while there was a slight decrease in CD45+ cells in the VI in SB225002-treated tumor-bearing animals compared to vehicle-treated tumor-bearing animals , this difference was not significant ( Figure 4D ) . Compared with vehicle-treated tumor-bearing animals , there was a moderate decrease in MPO+ cells in the VI in both SB225002- and RS504393-treated tumor-bearing animals , but this difference was also not significant . While all groups consumed similar amounts of food over the first 7 days of the study ( pre-cachexia ) ( Figure 4—figure supplement 1A ) , RS504393-treated animals experienced decreased anorexia , as evidenced by increased food intake , compared to vehicle-treated tumor-bearing animals ( Figure 4E ) , Alternatively , SB225002-treated tumor-bearing had similar food intake compared to vehicle-treated animals . RS504393-treated animals had a nonsignificant increase in gastrocnemius mass compared to vehicle-treated animals , but did have an increase in heart mass compared to vehicle-treated animals , indicating decreased muscle catabolism ( Figure 4F and G ) . There was no difference in heart or gastrocnemius mass in SB225002-treated animals compared to vehicle-treated animals . There was also no difference in tumor mass , or circulating immune cells in SB225002- or RS504393-treated animals compared to vehicle-treated animals ( Figure 4—figure supplement 1B and C ) . These data demonstrate that pharmacologic inhibition of CCR2 is important for cachexia and immune cell recruitment to the brain during PDAC . Therefore , we next performed additional studies to further characterize the activity of the CCL2/CCR2 axis in the brain . Using in situ hybridization we localized robust CCL2 mRNA expression exclusively within the VI during PDAC . There was no observable Ccl2 mRNA in the brains of sham animals ( Figure 5A ) . We verified these results at the protein level using Ccl2mCherry mice , which showed abundant CCL2 protein expression in the VI in tumor animals at 10 d . p . i . , exclusively expressed in Iba1+CD206+ meningeal macrophages . CCL2 protein was not expressed in VI meningeal macrophages in sham mice ( Figure 5B ) . We did not observe robust CCL2 protein expression in any other locations in the brain . Ccr2RFP/WT reporter mice were used to localize CCR2+ cells in the CNS . We observed that , at 10 d . p . i . , CCR2+ immune cells infiltrated the brains of tumor mice and accumulated in the VI ( Figure 5C ) Interestingly , a large percentage of neutrophils in the VI were CCR2+ ( Figure 5D ) , which infiltrated throughout the VI and often formed large aggregates consisting of 20 cells or more ( Figure 5—figure supplement 1A ) . CCR2+ cells were sparsely scattered within the surrounding brain parenchyma in tumor mice , whereas none were ever observed in sham controls . In order to verify CCR2 expression on neutrophils in the brains of tumor-bearing animals , we performed flow cytometry for CCR2 ( using an anti-CCR2 antibody ) on Ly6G+ circulating , liver-infiltrating , and brain-infiltrating neutrophils in both sham and PDAC-bearing animals at 10 d . p . i . As expected , we observed minimal CCR2 expression on circulating neutrophils in sham animals . While there was a slight increase in circulating CCR2+ neutrophils in tumor-bearing animals , there was no increase in CCR2+ neutrophils in the liver . Alternatively , there was a large increase in CCR2+ neutrophils in the brains of tumor-bearing animals ( Figure 5—figure supplement 1B ) . Since we observed robust production of CCL2 in meningeal macrophages during PDAC , we hypothesized that tumor-derived factors could induce CCL2 or other chemokines in brain macrophages . To test this hypothesis , we utilized an in vitro microglia culture system used previously by our laboratory ( Burfeind et al . , 2020; Zhu et al . , 2016 ) . Briefly , mixed glia were isolated from 3-day-old mouse pups , then after 14–16 days of culture , microglia were removed through shaking , were replated , and treated with PDAC conditioned media or control media ( FBS+RPMI ) ( Figure 6A ) . Sixteen hours later , RNA was isolated and qRT-PCR was performed . Chemokine transcripts differentially regulated in more than one brain region in our PDAC mouse model ( as shown in Figure 1 ) were selected for analysis . We observed that , of the chemokine transcripts queried , Ccl2 was by far the most robustly upregulated ( approximately 135-fold ) . Cxcl1 and Cxcl2 were upregulated , but not nearly as robustly ( 15 and approximately 35-fold , respectively ) ( Figure 6B ) . Alternatively , Cxcl9 and Cxcl10 were not induced . 10 ng LPS was used as a positive control , which induced robust upregulation all five chemokine transcripts ( Figure 6C ) . These results demonstrate that PDAC-derived factors induce chemokine expression in brain macrophages , and , unlike LPS , there is preferential upregulation of certain chemokines , especially CCL2 . Since small molecule inhibitors can have off-target effects , we used CCR2 knockout ( CCR2KO ) mice to verify and expand upon our findings demonstrating that CCR2 inhibition is important for cachexia and immune cell infiltration into the brain during PDAC . Using flow cytometry , we observed that at 11 d . p . i . after IP inoculation , there was a 37% decrease in total CD45high myeloid cells in the brains of CCR2KO tumor mice compared to WT tumor mice ( Figure 7A and B ) . This difference was driven by a large decrease in brain-infiltrating neutrophils and Ly6Chigh monocytes . There was also decrease in neutrophils and Ly6Chigh monocytes as a percentage of CD45high cells in the brains of CCR2KO tumor mice , indicating that differences were not due to a global decrease in infiltrating immune cells ( Figure 7B ) . This was also supported by the fact that there were no differences in microglia ( data not shown ) , Ly6Clow monocytes , or T-cells in the brains of CCR2KO tumor mice compared to WT tumor mice ( Figure 7C ) . Since CCR2+ immune cells , particularly neutrophils , localized primarily to the VI , we hypothesized that there would be a decrease in immune cells in the VI in CCR2KO tumor animals . Indeed , we observed a dramatic decrease in both total CD45+ globoid and MPO+ immune cells in the VI in CCR2KO tumor mice compared to WT tumor mice ( Figure 7D and E ) . In agreement with our findings from pharmacologic inhibition of CCR2 , we observed that CCR2KO mice had decreased anorexia during PDAC compared to WT tumor mice ( Figure 7F ) . CCR2KO tumor mice also had attenuated muscle loss compared to WT tumor mice ( Figure 7G ) . To determine whether the decreased muscle mass loss in CCR2KO mice was due to decreased muscle proteolysis , we assessed levels of transcripts key for muscle proteolysis in the gastrocnemius , including Fbxo32 ( Mafbx ) , Trim63 ( Murf1 ) , and Foxo1 , which we previously showed are induced by CNS inflammation ( Braun et al . , 2011 ) . We observed that , compared to WT tumor animals , CCR2KO tumor animals had decreased induction of Murf1 and Foxo1 ( Figure 7H ) , confirming that there was decreased catabolic drive in CCR2KO tumor mice . Since CCR2 deletion was not brain specific in the CCR2KO mice , we performed an extensive analysis of infiltrating immune cells in other organs ( Figure 7—figure supplement 1 ) . We observed minimal changes in immune cell composition in the blood , liver , and tumor in CCR2KO tumor mice compared to WT tumor mice . We only observed a decrease in circulating Ly6Chigh monocytes in CCR2KO tumor mice ( Figure 7—figure supplement 1G ) . When we assessed neutrophils in CCR2KO tumor mice in different organs as a percentage of those in WT tumor mice , we found the largest decrease to be in the brain and observed a slight increase in circulating neutrophils in CCR2KO tumor mice compared to WT tumor mice ( Figure 7—figure supplement 1F and H ) , suggesting that the decrease in brain-infiltrating neutrophils was due to a homing defect rather than inability to mobilize from the bone marrow . Therefore , our data show that CCR2 is important for neutrophil recruitment specifically to the brain , and that the decrease in brain-infiltrating neutrophils was due to a homing defect , rather than inability to mobilize from the bone marrow . Based on our data showing that CCR2 is a brain-specific chemotactic receptor for neutrophils during PDAC , we hypothesized that brain-infiltrating neutrophils are unique compared to neutrophils that infiltrate other organs . In order to characterize the phenotype of brain-infiltrating neutrophils during PDAC , we performed RNA sequencing ( RNAseq ) on FACS-sorted neutrophils from the blood , liver , tumor , and brain during PDAC at 10 d . p . i , as well as circulating neutrophils from sham animals ( Figure 7—figure supplement 2A and F ) . Principal component analysis of individual samples based on the top 500 most varying transcripts revealed that brain-infiltrating neutrophils clustered tightly together , but were distinct from those in the liver , tumor , and blood ( Figure 7—figure supplement 2B ) . Furthermore , we were able to identify over 100 transcripts that were differentially expressed in the brain-infiltrating neutrophils compared to those in the liver , tumor , and circulation ( Figure 7—figure supplement 2C-E and Figure 7—figure supplement 3 ) . To evaluate the effects of CNS inflammatory responses during PDAC independent of potential systemic effects , we treated mice with intracerebroventricular ( ICV ) oxidized ATP ( oATP ) . This potently blocks purinergic receptor P2RX7 signaling on brain resident macrophages . Signaling through this receptor is key for neutrophil recruitment to the brain during neuroinflammation ( Roth et al . , 2014 ) . Animals were surgically implanted with indwelling lateral ventricle cannulas , then inoculated IP with KPC cells 1 week later . Mice received daily ICV injections of either 500 ng oATP or vehicle ( aCSF ) , starting 3 d . p . i . ( Figure 8A ) . oATP treatment completely prevented both neutrophils and total CD45high myeloid cells from infiltrating the brain ( Figure 8B ) . There was a nonsignificant decrease in Ly6Chigh monocytes in oATP-treated tumor mice ( Figure 8B ) . Ly6Clow myeloid cells and T-cells were not affected ( Figure 8—figure supplement 1C ) . Furthermore , ICV oATP treatment did not affect any circulating immune cell population ( Figure 8—figure supplement 1D ) . When we investigated infiltrating immune cells in the VI , both CD45+ globoid cells and CD45+MPO+ neutrophils in the VI were greatly decreased in oATP-treated tumor animals compared to aCSF-treated tumor animals ( Figure 8C and D ) . We also observed that oATP treatment attenuated anorexia in tumor mice ( Figure 8E ) . There was trend toward increased gastrocnemius mass ( p=0 . 09 ) in oATP-treated tumor mice compared to aCSF-treated tumor bearing mice ( Figure 8F ) , which corresponded to a trend toward decreased induction of genes associated with proteolysis in gastrocnemius muscle ( Figure 8—figure supplement 1A ) , demonstrating that muscle catabolism was moderately attenuated by oATP administration directly into the brain . Tumor size in oATP-treated tumor mice was identical to that of aCSF-treated tumor mice ( Figure 8—figure supplement 1B ) . Since ICV oATP antagonizes P2RX7 on brain macrophages , we investigated its effect on microglia . To quantify activation state , we first assessed microglia morphology in the hippocampus . We did not observe any differences in microglia size , Iba-1 staining area , and Iba-1 intensity per cell when comparing aCSF- or oATP-treated tumor animals to oATP-treated sham animals or each other ( Figure 8—figure supplement 2A and B ) . We also assessed microglia activation state by using qRT-PCR to quantify expression of transcripts associated with microglia activation in the hippocampus . We observed no differences in expression of Tnf , Cd68 , Tmem119 , P2y12 , and P2x7r in oATP-treated tumor-bearing animals compared to aCSF-treated tumor-bearing animals ( Figure 8—figure supplement 2C ) . Several lines of investigation show that production of inflammatory mediators in the brain correlates strongly with CNS-mediated symptoms during cancer ( Burfeind et al . , 2018; Michaelis et al . , 2017 ) , yet the impact of neuroinflammation during malignancy is still not well understood . Our data show that in a mouse model of PDAC , myeloid cells , consisting predominately of neutrophils , infiltrate the brain , in a CCR2-dependent manner , where they drive anorexia and muscle catabolism . We observed that infiltrating immune cells accumulated specifically in a unique layer of meninges called the velum interpositum ( VI ) , which is adjacent to the hippocampus and the habenula , the latter of which is important for appetite regulation and is associating with cachexia in humans ( Maldonado et al . , 2018 ) . We observed robust Ccl2 mRNA and protein expression , along with CCR2+ neutrophils , exclusively in this region . The VI is implicated as a key structure for initial immune infiltration during states of neuroinflammation such as EAE ( Schmitt et al . , 2012 ) and traumatic brain injury ( Szmydynger-Chodobska et al . , 2016 ) . Indeed , the VI contains the pial microvessels that are a key aspect of the ‘gateway reflex’ , a neuro-immune pathway that involves interactions between leukocytes and neurons involved in stress response ( Tanaka et al . , 2017 ) and is implicated in gastrointestinal dysfunction during EAE ( Arima et al . , 2017 ) . While we observed myeloid cell infiltration throughout the VI , we also observed sporadic accumulation of neutrophils and other leukocytes around the same pial vessels involved in the gateway reflex ( Arima et al . , 2017 ) ( data not shown , we were unable to quantify these cells due to the sporadic nature of cell infiltration ) . The role of the gateway reflex in feeding behavior is unknown . It is possible that , in our model of PDAC , brain infiltrating neutrophils were involved in generating anorexia and muscle catabolism via a neuro-immune circuit similar to the gateway reflex , involving inflammation generated in the VI , and possibly transmitted to the habenula , or other regions involved in appetite regulation . The role and presence of infiltrating leukocytes in the CNS during systemic inflammation remain poorly understood . While previous reports show that neutrophils infiltrate the brain after septic doses of LPS or sepsis induced by cecal ligation ( He et al . , 2016 ) , it is still unknown if they contribute to neurologic sequelae ( anorexia , fatigue , cognition and memory deficits , etc . ) during and after sepsis . A series of studies utilizing a mouse model of inflammatory liver disease showed that ‘sickness behaviors’ could be attenuated if myeloid cell recruitment to the brain was abrogated via any one of several different interventions , including: 1 ) administration of a P-selectin inhibitor ( Kerfoot et al . , 2006 ) , 2 ) deleting Ccr2 ( D'Mello et al . , 2009 ) , and 3 ) inhibiting microglia activation with minocycline ( D'Mello et al . , 2013 ) . However , unlike our study , these studies did not address many CNS-mediated signs and symptoms associated with chronic disease , including anorexia and muscle catabolism , instead using social interaction as their sole measure of sickness behaviors . They also did not address whether their interventions affected monocyte infiltration in other tissues . Therefore , our results , along with previous studies , implicate brain-infiltrating myeloid cells as key players in driving CNS-mediated signs and symptoms during inflammatory disease . We were unable to deplete neutrophils , despite daily IP injections of high-dose anti-Ly6G antibody . This dosing regimen was previously thought to thoroughly deplete neutrophils ( Daley et al . , 2008; Reber et al . , 2017 ) . However , most studies used Ly6G labeling to verify depletion , which can result in incorrectly assuming that neutrophils are depleted . This issue was discussed in a recent study , which demonstrated that despite lack of Ly6G labeling , neutrophils are still present in conditions of immune activation and even increased in some compartments ( Pollenus et al . , 2019 ) . In our PDAC model , this is problematic since there is massive neutrophil mobilization from the bone marrow . Moreover , previous studies demonstrated that PDAC reprograms macrophages , effectively impairing their phagocytic capabilities ( Liu et al . , 2019; Mantovani and Sica , 2010 ) , Since macrophage phagocytosis was shown to be required for Ly6G antibody-mediated neutrophil depletion ( Bruhn et al . , 2016 ) , this may explain why the Ly6G antibody bound to neutrophils , but they were not depleted . We observed a decrease in total number of lymphocytes in the brain starting at 5 d . p . i . , which persisted throughout the course of PDAC . This was driven by a decrease in B-cells and CD4+ T-cells . We chose not pursue this further as the purpose of this series of studies was to investigate infiltrating immune cells in the brain . However , with the recent discovery of the meningeal lymphatics and immune surveillance in the brain ( Louveau et al . , 2018 ) , the role of lymphocytes in brain immune regulation is beginning to be appreciated . While the vast majority of lymphocytes in the non-inflamed murine brain are intravascular , even after thorough perfusion of the vasculature ( Mrdjen et al . , 2018 ) , there are lymphocytes in the CSF and meningeal lymphatics . Indeed , a decrease in CD4+ T-cells in the brain during PDAC may reflect a loss of immune regulation in the brain , as they cells have a key cerebroprotective role during neuroinflammation ( Liesz et al . , 2009 ) . Hypothalamic microglia were previously implicated in orchestrating a multicellular hypothalamic immune response , including infiltrating myeloid cells , during high fat diet-induced obesity ( Valdearcos et al . , 2017 ) . While we did not observe neutrophil infiltration in the hypothalamus during PDAC , we did observe infiltration of non-neutrophil C45+ globoid cells in the meninges surrounding the median eminence . This population of cells should be identified in future studies , and the role of these cells and hypothalamic microglia in PDAC cachexia should be investigated . We showed that CCR2 inhibition , but not CXCR2 inhibition , attenuated immune cell infiltration into the brain and resulted in decreased anorexia during PDAC . Ccr2 deletion resulted in even greater attenuation in cachexia and immune cell infiltration into the brain . The difference between genetic and pharmacologic CCR2 blockade is likely due to incomplete blockade of CCR2 signaling with RS504393 . It should be noted that both RS504393 and SB225002 treatment resulted in a nonsignificant decrease in neutrophils in the VI , yet only RS504393 treatment decreased cachexia , suggesting that monocytes ( since CCR2 is typically important in monocyte chemotaxis ) may be important mediators of cachexia . Also , it is worth noting that our pharmacologic inhibition of CXCR2 may have been incomplete . Therefore , it remains to be determined whether CXCR2 ligands are important for recruitment of cachexia-inducing neutrophils to the CNS . Regardless , our results are in agreement with previous studies investigating sickness behaviors during inflammatory liver disease , which showed that CCR2KO mice exhibited attenuated monocyte infiltration into the brain , along with decreased sickness behaviors ( D'Mello et al . , 2009 ) . Furthermore , it was recently reported that mice lacking Ccr2 had decreased myeloid cell infiltration into the brain and attenuated cognitive impairment during a model of sepsis induced by Streptococcus pneumoniae injection into the lungs ( Andonegui et al . , 2018 ) . In an attempt to identify inflammatory biomarkers for PDAC-associated cachexia , Talbert et al . identified CCL2 as the only cytokine or chemokine ( out of a panel of 25 ) that was increased in the serum of cachectic PDAC patients but not increased in the serum of non-cachectic patients ( Talbert et al . , 2018 ) . It is possible that the differences we observed in gastrocnemius catabolism between WT and CCR2KO tumor animals were due to differences in food intake , but the fact that we observed a significant decrease in induction of the catabolic genes Mafbx , Murf1 , and Foxo1 in CCR2KO tumor animals , which are not induced by decreased food intake/starvation ( Braun et al . , 2011 ) , makes this unlikely . However , CCR2KO tumor-bearing mice still experienced anorexia and muscle catabolism compared to CCR2KO sham mice , reflecting incomplete resolution of cachexia . It remains possible that more prominent factors , yet to be identified , contribute to cachexia during PDAC . While CCR2 is usually not considered a key receptor for neutrophil recruitment , previous studies show it is important for neutrophil chemotaxis during sepsis ( Souto et al . , 2009; Souto et al . , 2011 ) . Interestingly , while we observed a robust decrease in brain-infiltrating neutrophils in CCR2KO mice , we did not observe a decrease in liver- or tumor-infiltrating neutrophils , indicating that CCR2 is important for neutrophil recruitment specifically to the brain . Circulating neutrophils in sham animals did not express CCR2 , but a small percentage of circulating neutrophils expressed CCR2 in tumor animals , suggesting that the presence of a tumor induces CCR2 expression in neutrophils . Furthermore , a significant percentage of neutrophils in the brain expressed CCR2 during PDAC , meaning a distinct population of neutrophils is recruited to the brain from the circulation . These results , suggest that the population recruited to the brain has a distinct function from those recruited to other organs . We administered oxidized ATP , a purinergic receptor antagonist , directly into the brain and observed complete abrogation of circulating myeloid cell recruitment to the brain in tumor animals , as well as anorexia attenuation . These results show that brain inflammation is an important driver of PDAC-associated anorexia . While there was no change in microglia morphology after oATP administration , consistent with previous studies ( Martin et al . , 2019; Roth et al . , 2014 ) , nor was there a change in expression of genes associated with microglia activation , we cannot rule out the possibility that the difference in anorexia we observed were due to changes in microglia phenotype . The presence of an indwelling lateral ventricle cannula may have also induced microglia activation and influenced morphology quantification . However , we did take care to acquire images from the contralateral hemisphere . It is also worth noting that , unlike our previous experiments in non-cannulated mice , we did not observe a significant increase in brain-infiltrating Ly6Chigh monocytes in aCSF-treated tumor mice compared to oATP-treated sham mice . However , this comparison was nearly significant ( p=0 . 06 ) , and the lack of significance was likely due to heterogeneous results caused by cannulation . Furthermore , we observed an increased Ly6Chigh monocyte infiltrate in our aCSF-treated tumor animals compared to non-cannulated tumor animals , suggesting the indwelling lateral ventricle cannula did affect the inflammatory response in the brain to at least a small degree . Nevertheless , oATP completely prevented myeloid cells from infiltrating the brain during PDAC , strongly implicating these cells as mediators of anorexia . We observed that the transcriptional profile of brain-infiltrating neutrophils was distinct from those in the circulation , liver , and tumor . It was previously demonstrated that the CNS induces a more ‘inflammatory’ transcriptional profile in infiltrating myeloid cells compared to those that infiltrate other organs ( Spath et al . , 2017 ) . Reasons for this are not entirely clear , but may be due to relative lack of regulatory T-cells , the blood brain barrier preventing access to soluble anti-inflammatory factors , and presence of immunogenic substances more abundant in the brain , such as myelin-associated lipids . A few limitations should be considered when interpreting results of this study . First , our data were produced in a single model of pancreatic cancer . While our model is extensively characterized and reliably recapitulates many of the CNS-mediated symptoms observed in humans , other malignancies should also be considered . Second , it is possible , even likely , that circulating immune cells infiltrate and influence function in other organs dysfunctional during cancer ( skeletal muscle , adipose tissue , etc . ) . However , the purpose of this study was to investigate and characterize interactions between circulating immune cells and the brain during PDAC . Therefore , we chose to focus specifically on the brain so as to not overcomplicate analysis . Third , since we were not able to selectively deplete neutrophils , we cannot definitely conclude that these cells are the sole cellular drivers of cachexia during PDAC . We observed a small increase in Ly6Chi monocytes in the brains of animals during PDAC , which was also attenuated by CCR2 deletion . Therefore , these cells may have contributed to anorexia and muscle catabolism . However , there were far fewer Ly6Chi monocytes ( ≈2 , 000 ) in the brain than neutrophils ( ≈9 , 000 ) during PDAC , and these cells only constituted about 15% of brain CD45high myeloid cells ( vs . approximately 50% for neutrophils ) . Fourth , it is possible that the tumor metastasized to the brain , which drove the immune response . However , our lab has conducted countless PDAC experiments in mice and analyzed thousands of brain slices . We have yet to observe a single metastasis . Lastly , despite our extensive analysis , we cannot rule out with absolute certainty that the differences we observed in RS504393-treated or CCR2KO mice were not due to differences in tumor response . However , both the CCR2/CCL2 axis and neutrophils are reported to be ‘pro-tumor’ ( Coffelt et al . , 2016; Qian et al . , 2011 ) and therefore systemic treatment targeting neutrophils or the CCR2/CCL2 axis in humans may be particularly beneficial in that they decrease tumor size and abrogate CNS dysfunction . This would be advantageous to conventional anti-tumor therapies such as chemotherapy and checkpoint inhibitors , which are both known to cause cachexia-like symptoms ( Braun et al . , 2014; Michot et al . , 2016 ) and not effective against PDAC . In summary , we demonstrated that myeloid cells infiltrate the CNS throughout the course of PDAC and that preventing myeloid cells from infiltrating the brain attenuates anorexia and muscle catabolism . We showed there are distinct mechanisms for immune cell recruitment to the brain during systemic inflammation , and demonstrated a novel role for CCR2 in neutrophil recruitment to the brain , providing key insights into mechanisms of neuroinflammation and associated symptoms . Male and female 20–25 g WT C57BL/6J ( stock no . 000664 ) , Ptprca-EGFP ( aka Ly5 . 1-eGFP , stock no . 002014 ) , Ccl2mCherry ( stock no . 016849 ) , Ccr2RFP ( stock no . 017586 ) , and CCR2KO ( stock no . 004999 ) were purchased at Jackson Laboratories . Animals were aged between 7 and 12 weeks at the time of study and maintained at 27°C on a normal 12:12 hr light/dark cycle and provided ad libitum access to water and food . Experiments were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals , and approved by the Animal Care and Use Committee of Oregon Health and Science University . RS504393 was dissolved in DMSO and SB225002 was dissolved in ethanol . 5 mg/kg RS504393 or 10 mg/kg SB225002 were injected subcutaneously twice daily starting at 3 d . p . i . in PDAC-bearing mice . Vehicle-treated animals received the same volume in DMSO injected subcutaneously twice daily . For neutrophil depletion experiments , 500 μg of anti-Ly6G antibody ( clone 1A8 , Biolegend ) or isotype control was administered IP daily in 200 μL PBS , starting at 2 d . p . i . Our lab generated a mouse model of PDAC initiated by a single IP or orthotopic injection of 1-5e6 murine-derived KPC PDAC cells ( Michaelis et al . , 2017 ) . These cells are derived from tumors in C57BL/6 mice heterozygous for oncogenic KrasG12D and point mutant Tp53R172H with expression targeted to the pancreas via the Pdx1-Cre driver ( Foley et al . , 2015 ) . Cells were maintained in RPMI supplemented with 10% heat-inactivated FBS , and 50 U/mL penicillin/streptomycin ( Gibco , Thermofisher ) , in incubators maintained at 37°C and 5% CO2 . In the week prior to tumor implantation , animals were transitioned to individual housing to acclimate to experimental conditions . Animal food intake and body weight were measured once daily . Sham-operated animals received PBS in the same volume . Bedding was sifted daily to account for food spillage not captured by cagetop food intake measurement . Animals were euthanized between 9 and 14 days post-inoculation , when food intake was consistently decreased and locomotor activity was visibly reduced , yet signs of end-stage disease ( ascites , unkempt fur , hypotheremia , etc . ) were not present ( Michaelis et al . , 2017 ) . We extensively characterized this model and demonstrated that IP and OT inoculation resulted in similar cachexia progression and inflammatory response ( both in the CNS and systemically ) ( Burfeind et al . , 2018; Michaelis et al . , 2017; Zhu et al . , 2019 ) . WT C57BL/6J male mice aged 8–10 weeks were injected IP with the alkylating agent treosulfan ( Ovastat , a generous gift from Joachim Baumgart at Medac GmbH , Germany ) at a dose of 1500 mg/kg/day for three consecutive days prior to the day of bone marrow transplant ( BMT ) . 24 hr after the third treosulfan injection , a Ly5 . 1-EGFP male or female donor mouse aged between 2 and 6 months was euthanized and femurs , tibias , humeri , and radii were dissected . After muscle and connective tissue were removed , marrow cells were harvested by flushing the marrow cavity of dissected bones using a 25-gauge needle with Iscove’s modified Dulbecco’s medium supplemented with 10% FBS . The harvested cells were treated with RBC lysis buffer , filtered with a 70 μm cell strainer , and counted . 3–4 × 106 cells in 200 μL HBSS were transplanted immediately into each recipient mouse via tail vein injection . To prevent infection during an immunocompromised period , recipient mice received amoxicillin dissolved in their drinking water ( 150 mg/L ) for 2 weeks starting on the first day of treosulfan injection . GFP BMT mice were given at least 5 weeks for marrow reconstitution and recovery . Percent chimerism in each GFP BMT mouse was determined by flow cytometry analysis of circulating leukocytes . Mice were anesthetized under isoflurane and placed on a stereotactic alignment instrument ( Kopf Instruments ) . 26-gauge lateral ventricle cannulas were placed at 1 . 0 mm X , −0 . 5 mm Y , and −2 . 25 mm Z relative to bregma . Mice were given one week for recovery after cannula placement . Injections were given in 2 µl total volume . Oxidized ATP was dissolved in aCSF and injected at a concentration of 250 ng/μL over 5 min while mice were anesthetized under isoflurane . Mice were anesthetized using a ketamine/xylazine/acetapromide cocktail and sacrificed by transcardial perfusion fixation with 15 mL ice cold 0 . 01 M PBS followed by 25 mL 4% paraformaldehyde ( PFA ) in 0 . 01 M PBS . Brains were post-fixed in 4% PFA overnight at 4°C and cryoprotected in 20% sucrose for 24 hr at 4°C before being stored at −80°C until used for immunohistochemistry . Immunofluorescence immunohistochemistry was performed as described below . Free-floating sections were cut at 30 μm from perfused brains using a Leica sliding microtome . Sections were incubated for 30 min at room temperature in blocking reagent ( 5% normal donkey serum in 0 . 01 M PBS and 0 . 3% Triton X-100 ) . After the initial blocking step , sections were incubated in primary antibody ( listed below ) in blocking reagent for 24 hr at 4°C , followed by incubation in secondary antibody ( also listed below ) for 2 hr at room temperature . Between each stage , sections were washed thoroughly with 0 . 01 M PBS . Sections were mounted onto gelatin-coated slides and coverslipped using Prolong Gold antifade media with DAPI ( Thermofisher ) . The following primary anti-mouse antibodies were used , with company , clone , host species , and concentration indicated in parentheses: CD11b ( eBioscience , rat , M1/70 , 1:1000 ) , CD45 ( BD , rat , 30-F11 , 1:1000 ) , myeloperoxidase ( R and D , goat , polyclonal , 1:1000 ) , Ly6G ( Biolegend , 1A8 , rat , 1:250 ) , Iba-1 ( Wako , Rabbit , NCNP24 , 1:1000 ) , CD206 ( Bio-rad , rat , MR5D3 , 1:1000 ) , ER-TR7 ( Abcam , rat , ER-TR7 , 1:1000 ) , and citrillunated histone H3 ( Abcam , rat , polyclonal , 1:1000 ) . We also used a chicken anti-mCherry antibody ( Novus Biologicals , polyclonal , 1:20 , 000 ) , to amplify mCherry signal in sections from CCL2fl/fl mice and a rabbit anti-RFP antibody ( Abcam , polyclonal , 1:1000 ) to amplify RFP signal in sections from CCR2RFP/WT mice . The following secondary antibodies were used , all derived from donkey and purchased from Invitrogen , with dilution in parentheses: anti-goat AF488 ( 1:500 ) , anti-rabbit AF555 ( 1:1000 ) , anti-rat AF555 ( 1:1000 ) , anti-rat AF633 ( 1:500 ) , and anti-chicken AF555 ( 1:1000 ) . All images were acquired using a Nikon confocal microscope . Cell quantification was performed on 20X images using the Fiji Cell Counter plugin by a blinded researcher . CD45+ cells were defined as CD45 bright globoid cells , and neutrophils were defined as CD45+ MPO+ cells . The velum interpositum ( VI ) was defined as the layer of meninges ( identified by appearance of staining background ) between the hippocampus and thalamus , from bregma −1 . 7 to −2 . 6 mm . At least 8 VI images were quantified from each animal . The median eminence was defined as the base of the mediobasal hypothalamus ( far ventral part of the brain ) , adjacent to the third ventricle from bregma −1 . 95 to −2 . 5 mm . Four ME images were quantified from each animal . The area postrema was defined as the region in from bregma −7 . 2 to −7 . 75 mm . Four area postrema images were quantified from each animal . Microglia activation in the hippocampus was quantified using Fiji ( ImageJ , NIH ) . Five images of the dentate gyrus were acquired from each animal . Images were 2048 x 2048 pixels , with a pixel size of 0 . 315 μm . Images were uploaded to Fiji by a blinded reviewer ( KGB ) and converted to 8-bit greyscale images . After thresholding , microglia were identified using the ‘analyze particle’ function , which measured mean Iba-1 fluorescent intensity per cell , cell area , and percent area covered by Iba-1 staining . Primary mixed-glial cultures containing microglia and astrocytes were prepared from neonatal mouse cortices . Brain cortices from 1- to 3-day-old newborn mouse pups were dissected , then digested with papain ( Worthington Biochemical Corporation ) . Cells were passed through a 70 μm cell strainer and seeded in 75 cm2 flasks in DMEM media ( low glucose with L-glutamine , 10% FBS and 1% penicillin/streptomycin ) . Microglia were isolated 14–16 days later by shaking flasks at 200 rpm at 37°C for 1 hr . Cells were re-plated into six well plates at 5 × 105/well and maintained in DMEM media for 24 hr before stimulation . More than 90% of these isolated cells were confirmed as microglia by Iba1 staining and flow cytometry ( CD45+CD11b+ cells , data not shown ) . PDAC tumor cells were cultured in a 75 cm2 flask until confluent . 24 hr prior to treatment , 13 ml fresh media ( RPMI supplemented with 10% FBS and 1% penicillin-streptomycin ) was added for generating PDAC-conditioned media . On the treatment day , 4 mL PDAC-conditioned media mixed with 1 mL fresh RPMI media ( to ensure treated microglia were not nutrient starved ) was added to each of the three wells of the six well plate containing microglia . The other three wells each received 5 mL control media ( RPMI media ) . Three additional wells received 5 mL control media containing 10 ng LPS . 16 hr after treatment , media was removed , and adherent cells were washed with PBS then lysed . RNA was then extracted from cell lysate using a Qiagen RNAEasy kit . At 10 d . p . i . , mice were euthanized with CO2 and brains were removed then frozen on dry ice . 20 µm coronal sections were cut on a cryostat and thaw-mounted onto Superfrost Plus slides ( VWR Scientific ) . Sections were collected in a 1:6 series from the diagonal band of Broca ( bregma 0 . 50 mm ) caudally through the mammillary bodies ( bregma 5 . 00 mm ) . 0 . 15 pmol/ml of an antisense 33P-labeled mouse Ccl2 riboprobe ( corresponding to bases 38–447 of mouse Ccl2; GenBank accession no . NM_011333 . 3 ) was denatured , dissolved in hybridization buffer along with 1 . 7 mg/ ml tRNA , and applied to slides . Slides were covered with glass coverslips , placed in a humid chamber , and incubated overnight at 55°C . The following day , slides were treated with RNase A and washed under conditions of increasing stringency . Slides were dipped in 100% ethanol , air dried , and then dipped in NTB-2 liquid emulsion ( Kodak ) . Slides were developed 4 d later and cover slipped . Prior to tissue extraction , mice were euthanized with a lethal dose of a ketamine/xylazine/acetapromide and sacrificed . Hippocampal blocks and gastrocnemii were dissected , snap frozen , and stored in −80°C until analysis . RNA was extracted using an RNeasy mini kit ( Qiagen ) according to the manufacturer’s instructions . cDNA was transcribed using TaqMan reverse transcription reagents and random hexamers according to the manufacturer’s instructions . PCR reactions were run on an ABI 7300 ( Applied Biosystems ) , using TaqMan universal PCR master mix with the following TaqMan mouse gene expression assays: 18 s ( Mm04277571_s1 ) , Tnf ( Mm00443258_m1 ) , Il6 ( Mm01210732_g1 ) , Il-1β ( Mm00434228_m1 ) , Il10 ( Mm01288386_m1 ) , Ccl2 ( Mm99999056_m1 ) , Ccl3 ( Mm00441259_g1 ) , Ccl5 ( Mm01302427_m1 ) , Cxcl1 ( Mm04207460_m1 ) , Cxcl2 ( Mm00436450_m1 ) , Cxcl5 ( Mm00436451_g1 ) , Cxcl9 ( Mm00434946_m1 ) , Cxcl10 ( Mm00445235_m1 ) , Cd68 ( Mm03047343_m1 ) , Tmem119 ( Mm0052305_m1 ) , P2y12 ( Mm01950543_S1 ) , P2r × 7 ( Mm00446026_m1 ) , Gapdh ( Mm99999915_g1 ) , Fbxo32 ( Mm00499518_m1 ) , Trim63 ( Mm01185221_m1 ) , and Foxo1 ( Mm00490672_m1 ) . Relative expression was calculated using the ΔΔCt method and normalized to WT vehicle treated or sham control . Statistical analysis was performed on the normally distributed ΔCt values . Whole blood was analyzed with a veterinary hematology analyser ( HemaVet , 950FS , Drew Scientific , Oxford , CT ) to assess total white blood cell count and white blood cell differential . Mice were anesthetized using a ketamine/xylazine/acetapromide cocktail and perfused with 15 mL ice cold 0 . 01 M PBS to remove circulating leukocytes . If circulating leukocytes were analyzed , blood was drawn prior to perfusion via cardiac puncture using a 25-gauge needle , then placed in an EDTA coated tube . After perfusion , organs were extracted and immune cells were isolated using the following protocols: Data are expressed as means ± SEM . Statistical analysis was performed with Prism 7 . 0 software ( Graphpad Software Corp ) . When two groups were compared , data were analyzed with either student’s t-test or Mann-Whitney U test . When more than two groups were compared , data were analyzed with either one-way ( when multiple groups were compared to a single sham group ) or two-way ( when there were multiple genotypes within tumor and sham groups being compared ) ANOVA analysis . For single time point experiments , the two factors in ANOVA analysis were genotype or treatment . In repeated measures experiments , the two factors were group and time . Main effects of genotype , treatment , group , and/or time were first analyzed , and if one effect was significant , Bonferroni post hoc analysis was then performed . For all analyses , significance was assigned at the level of p<0 . 05 .	Weight loss , decreased appetite and fatigue are symptoms of a wasting disorder known as cachexia , which is common in several serious diseases such as AIDS , chronic lung disease and heart failure . Up to 80 percent of people with advanced cancer also develop cachexia , and there are no effective treatments . It is not known how cachexia develops , but symptoms like appetite loss and fatigue are controlled by the brain . One theory is that the brain may be responding to a malfunctioning immune response that causes inflammation . While the brain was thought to be protected from this , new research has shown that it is possible for cells from the immune system to reach the brain in some conditions . To find out if this also happens in cancer , Burfeind et al . studied mice that had been implanted with pancreatic cancer cells and were showing signs of cachexia . Samples from the mice’s brains showed that immune cells known as neutrophils were present and active . A protein known as CCR2 was found in higher levels in the brains of these mice . This protein is involved in the movement of neutrophil cells through the body . To see what effect this protein had , Burfeind et al . gave the mice a drug that blocks CCR2 . This prevented the neutrophils from entering the brain and reduced the symptoms of cachexia in the mice . To further confirm the role of CCR2 , the mice were genetically modified so that they could not produce the protein . This reduced the number of neutrophils seen in the brain but not in the rest of the body . This suggests that a drug targeting CCR2 could help to reduce the symptoms of cachexia , without disrupting the normal immune response away from the brain . This approach would still need to be tested in clinical trials before it is possible to know how effective it might be in humans .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience", "immunology", "and", "inflammation" ]	2020	Circulating myeloid cells invade the central nervous system to mediate cachexia during pancreatic cancer
Heme is required for survival of all cells , and in most eukaryotes , is produced through a series of eight enzymatic reactions . Although heme production is critical for many cellular processes , how it is coupled to cellular differentiation is unknown . Here , using zebrafish , murine , and human models , we show that erythropoietin ( EPO ) signaling , together with the GATA1 transcriptional target , AKAP10 , regulates heme biosynthesis during erythropoiesis at the outer mitochondrial membrane . This integrated pathway culminates with the direct phosphorylation of the crucial heme biosynthetic enzyme , ferrochelatase ( FECH ) by protein kinase A ( PKA ) . Biochemical , pharmacological , and genetic inhibition of this signaling pathway result in a block in hemoglobin production and concomitant intracellular accumulation of protoporphyrin intermediates . Broadly , our results implicate aberrant PKA signaling in the pathogenesis of hematologic diseases . We propose a unifying model in which the erythroid transcriptional program works in concert with post-translational mechanisms to regulate heme metabolism during normal development . Heme biosynthesis is a fundamental biological process that is highly conserved and involves eight enzymatic reactions that occur both in the cytosol and mitochondria ( Severance and Hamza , 2009 ) . In vertebrates , the most recognized role of heme is to serve as the oxygen-binding moiety in hemoglobin expressed by red blood cells ( RBCs ) . During RBC maturation , heme metabolism genes are robustly upregulated ( Chung et al . , 2012; Nilsson et al . , 2009; Yien et al . , 2014 ) . Not surprisingly , mutations in these genes are most commonly associated with hematologic defects in humans , underscoring the importance for a better understanding of the factors regulating heme biosynthesis . In particular , loss-of-function mutations in FECH ( EC 4 . 99 . 1 . 1 ) , which encodes the terminal rate-limiting enzyme in heme production , is strongly associated with the disease erythropoietic protoporphyria ( EPP ) ( Balwani and Desnick , 2012; Langendonk et al . , 2015 ) . The dependence of RBC biology on heme metabolism makes erythropoiesis an excellent system to gain insight into this process . Previous genetic analyses using RBCs have identified several mechanisms regulating heme metabolism most of which are transcriptional networks controlling mRNA expression of heme metabolism genes ( Amigo et al . , 2011; Handschin et al . , 2005; Kardon et al . , 2015; Nilsson et al . , 2009; Phillips and Kushner , 2005; Shah et al . , 2012; Shaw et al . , 2006; Wingert et al . , 2005; Yien et al . , 2014 ) . Currently , however , transcription-independent signaling mechanisms regulating heme production are poorly understood ( Chen et al . , 2009; Paradkar et al . , 2009 ) . Such mechanisms may play a critical role to couple heme metabolism to changes in the extracellular milieu , homeostasis , and development . Here , we show that heme production is regulated by EPO/JAK2 signaling in concert with the GATA1 target , Akap10 ( Fujiwara et al . , 2009 ) . During red blood cell ( RBC ) development , PKA expression becomes increased at the mitochondrial outer membrane ( OM ) through AKAP10-dependent recruitment . We found that OM PKA catalytic ( PKAc ) subunits become disengaged from the autoinhibitory PKA regulatory ( PKAr ) subunits through direct interaction with phosphorylated STAT5 downstream of EPOR activation . Furthermore , we demonstrate that FECH is a kinase target of OM PKA and its phosphorylation triggers upregulation of its activity that is required to support erythropoiesis in vivo . Our work uncovers a previously unknown facet of heme metabolism with implications on human disease . To begin examining post-translational mechanisms regulating heme metabolism , we performed an unbiased comparative analysis of the changing mitochondrial proteome in maturing RBCs . Mitochondria-enriched fractions isolated from undifferentiated and differentiated Friend murine erythroleukemia ( MEL ) cells were analyzed by quantitative mass spectrometry ( Pagliarini et al . , 2008 ) ( Figure 1A and B ) . MEL cells have been reliably used to dissect the molecular mechanisms underlying hemoglobin production in erythroid cells ( Bauer et al . , 2013; Canver et al . , 2014 ) . 10 . 7554/eLife . 24767 . 003Figure 1 . PKA activity regulates heme biosynthesis . ( A ) A schema detailing the preparation of samples enriched for mitochondria from undifferentiated and differentiated MEL cells is shown . Following preparation of samples from both cohorts , the samples were trypsin digested and labeled with different tandem mass tags ( TMTs ) , followed by mass spectrometry analysis . ( B ) Enrichment of undifferentiated ( undiff ) and differentiated ( diff ) mitochondrial samples was confirmed by western analysis for mitochondrial ( PDHA1 and HSPD1 ) and cytosolic ( GAPDH and TUBA1A ) markers prior to mass spectrometry analysis . ( C ) The upregulation of PKA regulatory ( PKAr ) and catalytic ( PKAc ) subunits as well as previously identified heme metabolism proteins in mitochondria-enriched fractions of differentiated MEL cells but not several housekeeping proteins is presented in logarithmic scale . Please see Figure 1—source data 1 for precise changes . ( D and E ) Immunoblot analyses of the expression of PKA subunits in mitochondrial fractions ( D ) and whole cell lysates ( E ) . All immunoblots were performed twice . Undiff-undifferentiated; Diff-differentiated; IB-immunoblot . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 00310 . 7554/eLife . 24767 . 004Figure 1—source data 1 . Changes in the mitochondrial expression of selection erythroid and housekeeping mitochondrial proteins . Changes in the expression of proteins together with their accession numbers are shown in log2 scale . These data are depicted in the heat map in Figure 1C . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 004 As expected , erythroid differentiation was associated with the elevated mitochondrial expression of a number of mitochondrial proteins known to have a role in erythropoiesis such as FECH , ATPIF1 , and ABCB10 while the expression of housekeeping proteins such as components of the mitochondrial transport and protein translation machinery were relatively unchanged ( Figure 1C ) . Strikingly , we also found increased expression of PKA regulatory ( PKAr; PRKAR1A , PRKAR2A , PRKAR2B ) and catalytic ( PKAc; PRKACA and PRKACB ) subunits in mitochondria-enriched fractions ( Figure 1C ) . We independently confirmed these results using immunoblotting with isoform-specific antibodies where we also found increased total expression of these PKA subunits in maturing erythroid cells ( Figure 1D and E ) . We failed to detect PRKACG because it is only present in humans and not found in our murine model ( Kirschner et al . , 2009 ) . In addition , we also could not detect PRKAR1B since its expression is restricted to neurons ( Kirschner et al . , 2009 ) . Together , our results suggest that select PKA subunits become highly expressed in mitochondria of developing erythrocytes and that MEL cells are a good model that accurately recapitulates the expected PKA expression pattern . We wondered whether increased mitochondrial PKA was specific for a particular suborganellular compartment , and next , performed a series of experiments to determine their submitochondrial expression . First , intact mitochondria isolated from maturing erythroid cells were treated with proteinase K that would digest all proteins exposed on the outer mitochondrial membrane . Immunoblot analysis of untreated and treated mitochondria revealed that the majority of PKA subunits were sensitive to proteinase K activity similar to TOM20 while VDAC1 , a mitochondrial outer membrane ( OM ) marker known to be resistant to proteinase K digestion , remained largely unaffected ( Figure 2A ) ( Rapaport , 2003; Shirihai et al . , 2000 ) . Biochemical fractionation of the mitochondria OM , intermembrane space ( IMS ) , and mitoplast ( MP ) followed by immunoblotting confirmed the predominant presence of PKA subunits in the OM fraction of maturing erythroid cells ( Figure 2B ) . 10 . 7554/eLife . 24767 . 005Figure 2 . Mitochondrial PKA is localized to the outer membrane during erythropoiesis by AKAP10 . ( A ) Intact mitochondria isolated from maturing MEL cells ( day 3 ) were untreated or treated with proteinase K and subsequently analyzed by immunoblotted with antibodies specific for the indicated proteins . ( B ) Mitochondria from day 3 maturing MEL cells were fractionated into the indicated compartments and 5 µg of protein were analyzed with immunoblotting . ( C and D ) A heat map demonstrating the increased mitochondrial expression of AKAP10 similar to PRKAR2B in maturing erythroid cells ( C ) that was confirmed using immunoblotting ( D ) . Please see Figure 2—source data 1 for precise changes . ( E and F ) Proteinase K digestion assay ( E ) and submitochondrial fractionation ( F ) showed that AKAP10 is mostly localized to the OM . ( G ) A schematic depicting the wild-type ( Akap10wt ) and the two Akap10-null alleles Akap10Cas9 ( △ex1-2/184delTG ) generated using CRISPR/Cas9 genome editing . The positions of the exon 1 ( Ex1 ) and exon 3 ( Ex3 ) CRISPR oligos are denoted . The introns are shown in black with exons in orange . The Akap10Cas9 ( ex1-2 ) allele has complete removal of exon 2 and truncates exons 1 and 3 to fuse exons 1’ and 3’ , respectively . The Akap10Cas9 ( 184delTG ) allele has a two-nucleotide deletion in exon 3 leading to a frameshift and a premature stop codon ( Stop’ ) . Both alleles are expected to disrupt the N-terminal region encoding the mitochondrial-targeting motif . ( H ) The Akap10Cas9 ( △ex1-2 ) deleted allele can only be detected when genotyping was performed with primers F and R2 while the Akap10Cas9 ( 184delTG ) allele can still be detected with primers F and R1 , resembling wild-type . These results were sequence confirmed . ( I ) Immunoblot analysis showing that neither allele gave rise to any detectable AKAP10 protein . ( J and K ) Loss of AKAP10 had no effect on total PKA subunit expression but reduced the amount of PKA subunits in whole mitochondria ( J ) as well as the OM fraction ( K ) . All immunoblots were performed twice . Undiff-undifferentiated; Diff-differentiated; OM-outer membrane; IMS-intermembrane space; MP-mitoplast; WT-wild-type; KO-knockout; IB-immunoblot . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 00510 . 7554/eLife . 24767 . 006Figure 2—source data 1 . Change in the mitochondrial expression of AKAP10 during erythroid maturation . The increase in AKAP10 ( NP_064305 ) mitochondrial expression is listed in log2 scale . These data are depicted in the heat map in Figure 2C . PRKAR2B and other controls were as previously shown in Figure 1C and Figure 1—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 006 A great deal of work has demonstrated that PKA is localized to subcellular compartments through interactions with a family of anchoring proteins called AKAPs ( a kinase anchoring proteins ) ( Wong and Scott , 2004 ) . The majority of AKAPs recruit PKA-RII subunits but not RI ( Sarma et al . , 2010 ) . However , a subclass of AKAPs can bind to both RI and RII with high affinity to regulate their subcellular distribution and have been referred to as ‘dual-specificity AKAPs’ ( Huang et al . , 1997a , 1997b; Li et al . , 2001; Sarma et al . , 2010; Wang et al . , 2001 ) . In particular , PAP7 , AKAP1 , and AKAP10 are three such AKAPs capable of localizing to mitochondria ( Huang et al . , 1997a , 1997b; Li et al . , 2001; Wang et al . , 2001; Wong and Scott , 2004 ) . Interestingly , although we failed to detect PAP7 and AKAP1 in our proteomics analysis , we found a pronounced increase in the mitochondrial expression of AKAP10 ( Figure 2C and D ) . Similar to our earlier data with PKA subunits , mitochondrial AKAP10 in maturing erythrocytes is sensitive to proteinase K digestion and primarily found in the OM fraction in maturing erythroid cells ( Figure 2E and F ) . High-throughput expression analysis has previously shown that AKAP10 expression increases in maturing erythroid cells and is a downstream target of the GATA1 erythroid lineage master transcription factor ( Fujiwara et al . , 2009; Zhang et al . , 2003 ) . To date , it has no known role in erythropoiesis or heme metabolism . However , our results thus far led us to wonder if it was responsible for regulating PKA localization in maturing RBCs , and we tested this by using CRISPR/Cas9-mediated genome editing to introduce null mutations into the endogenous AKAP10 loci . Genotyping and sequencing showed that for one AKAP10 allele [AKAP10Cas9 ( △ex1-2 ) ] , parts of exons 1 ( Ex1 ) and 3 ( Ex3 ) along with all of exon 2 including the ATG start codon were deleted by our targeting strategy ( Figure 2G and H ) . The second allele [AKAP10Cas9 ( 184delTG ) ] had a 2 base-pair deletion that resulted in a premature stop codon ( Stop’ ) ( Figure 2G and H ) . Neither allele gave rise to full-length AKAP10 protein ( KO ) as shown by immunoblotting ( Figure 2H and I ) . Total expression of PKA subunits in maturing KO cells was similar to wild-type ( WT ) cells ( Figure 2J ) . However , KO cells had reduced the levels of mitochondrial PKA subunits both in intact preparations as well as in OM-specific fractions ( Figure 2J and K ) . These results strongly suggest that AKAP10 recruits PKA to the outer mitochondrial membrane during red cell development and connects the GATA1 transcriptional program to the PKA signaling pathway . Mitochondria are the site of heme production required for hemoglobin synthesis and its physiology is a crucial part of RBC maturation ( Nilsson et al . , 2009; Shah et al . , 2012 ) . Given the increase in mitochondrial expression of PKA subunits in maturing erythroid cells , we wondered whether PKA activity had an influence on heme production . We addressed this by first using pharmacologic agents to toggle PKA function . Compounds that activate PKA such as 8-Br-cAMP and forskolin both caused an increase in the proportion of hemoglobinized cells as shown by o-dianisidine staining , which can be blocked by PKA antagonists H-89 or PKI ( 14-22 ) ( Figure 3A ) . An increase in the proportion of hemoglobinized cells was also observed when we treated MEL cells with dimethyl-prostaglandin E2 ( dmPGE2 ) ( Figure 3B ) , which is a more stable analog of prostaglandin E2 ( PGE2 ) that has a physiologic role during multiple aspects of hematopoiesis ( Goessling et al . , 2009; North et al . , 2007 ) . The effects of dmPGE2 can be similarly inhibited by PKI ( 14-22 ) ( Figure 3C ) , underscoring the specificity of dmPGE2 signaling via PKA . In contrast to PKA inhibition , the PKC inhibitor , bis-indolylmaleimide II , could not block the effects of PKA activation suggesting that the observed changes in heme synthesis are specific for PKA ( Figure 3—figure supplement 1A ) . 10 . 7554/eLife . 24767 . 007Figure 3 . Mitochondrial outer membrane PKA signaling is required for erythropoiesis . ( A–C ) o-dianisidine staining for hemoglobinized MEL cells treated with several pharmacologic modulators of PKA activity . ( A ) PKA activation with 8-Br-cAMP or high-dose forskolin ( 50 µM ) triggers an increase in heme production that is blocked by H89 or PKI ( 14-22 ) treatment at day 3 of DMSO induction . ( B and C ) A similar increase in hemoglobinization was observed with dMPGE2 that was also inhibited by PKI ( 14-22 ) . ( D ) Wild-type ( WT ) or AKAP10-knockout ( KO ) erythroid cells at day 4 of differentiation were stained with o-dianisidine . ( E and F ) AKAP10 expression was knocked-down using two different shRNAs ( E ) that lead to reduced hemoglobinization ( F ) . ( G–I ) akap10-specific morpholinos ( MOs ) were used to inhibit akap10 expression in zebrafish embryos ( G ) . These morphants were anemic with reduced hemoglobinization ( H ) and red blood cell counts ( I ) . p-value<0 . 05 , Mean ± SEM , n = 3 . All immunoblots were performed twice . 8-Br-cAMP-8-bromoadenosine 3’ , 5’-cyclic monophosphate; dmPGE2-dimethyl-prostaglandin E2; WT-wild-type; KO-knockout; shRNA-short hairpin RNA; MO-morpholino; IB-immunoblot . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 00710 . 7554/eLife . 24767 . 008Figure 3—figure supplement 1 . PKA activity regulates heme biosynthesis . ( A ) Treatment of differentiating MEL cells with 30 nM of the PKC inhibitor , bis-indolylmaleimide II , had no inhibitory effect on PKA activators . ( B ) MEL cells treated with low-dose forskolin ( 10 µM ) has no significant effect on hemoglobinization as shown by o-dianisidine staining . Mean ± SEM , n = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 008 Although our results from pharmacologic experiments suggest that widespread modulation of PKA has an impact on heme production on maturing erythroid cells , they do not explicitly examine the role of mitochondrial OM PKA . The precise contributions of distinct mitochondrial pools of PKA have been a topic of controversy ( DiPilato et al . , 2004; Lefkimmiatis et al . , 2013 ) . Emerging evidence suggest that PKA agonists such as forskolin and cAMP cannot diffuse into the mitochondrial matrix ( Acin-Perez et al . , 2009; Lefkimmiatis et al . , 2013 ) . Thus far , our pharmacologic data involved the use of a high-dose of forskolin ( Figure 3A–C ) , and when the dose was titrated down to one that was previously shown to not activate matrix PKA , we also failed to detect an effect on hemoglobinization ( Figure 3—figure supplement 1B ) ( Acin-Perez et al . , 2009 ) . It is difficult to rely solely on pharmacologic data to unambiguously dissect the contributions of intracellular PKA pools since dose responses are known to vary form one cell type to another ( Humphries et al . , 2007; Lefkimmiatis et al . , 2013 ) . However , the reduction in the levels of PKA subunits in the mitochondrial OM of AKAP10-KO maturing erythroid cells allowed us to genetically and biochemically examine the functional role of this PKA signaling compartment . Compared to wild-type controls , AKAP10-KO maturing erythroid cells exhibited a deficit in hemoglobinization ( Figure 3D ) . This defect was also observed when AKAP10 expression was inhibited using two distinct shRNAs ( Figure 3E and F ) . We next examined the in vivo significance of AKAP10 and mitochondrial OM PKA signaling by using morpholinos to block akap10 expression in zebrafish ( Danio rerio ) embryos ( termed morphants ) ( Figure 3G ) . For over two decades , the zebrafish has been an invaluable model for the study of hematopoiesis and drug discovery ( Jing and Zon , 2011; Zon and Peterson , 2005 ) . Remarkably , akap10 morphants were anemic with decreased hemoglobinization ( Figure 3H , red arrowheads ) compared to control embryos . We quantified these changes in red cell parameters by performing similar experiments on a transgenic zebrafish line in which all erythroid cells are marked by eGFP expression [Tg ( globin-LCR:eGFP ) ] ( Ganis et al . , 2012 ) . Flow-cytometry analysis revealed that akap10 morphants had reduced RBC counts ( Figure 3I ) . Together , our data suggest that mitochondrial OM PKA signaling is required for proper heme production and RBC development in vivo . Next , we asked whether mitochondrial OM PKA signaling directly regulated heme biosynthesis by phosphorylating mitochondrial heme enzymes . Of the mitochondrial enzymes ALAS2 , PPOX , CPOX , and FECH , the only enzyme with a predicted high-confidence PKA site ( R/K-R/K-X-S/T-Z , where X is any amino acid and Z is an uncharged residue ) is FECH at Thr116 ( human FECH and Thr115 for murine FECH ) ( Figure 4A ) . This residue is evolutionarily conserved and is present on one of the lips of the active site pocket positioned in the middle of a long α-helix ( Figure 4B–D ) . In its unphosphorylated form , the side chain hydroxyl group of Thr116 ( colored fuchsia ) is sandwiched between His86 ( colored blue ) and Leu87 on an adjacent α-helix , forming a hydrogen bond with His86 ( Figure 4C and D ) ( Wu et al . , 2001 ) . Structural modeling suggests that the bulk of the added phosphate on the side chain of Thr116 would cause movement of the Thr116 α-helix away from the His86 α-helix , and thereby , shift the Thr116-containing α-helix closer inwards towards the active site pocket opening and the porphyrin ring ( shown in red ) ( Figure 4C and D ) . Such a modification may also destabilize the structure sufficiently to allow for more efficient movement of the active site lip during catalysis . 10 . 7554/eLife . 24767 . 009Figure 4 . FECH is directly phosphorylated by PKA . ( A ) The motif surrounding Thr116 in human FECH constitutes a PKA phosphorylation site with a canonical Lys ( K ) and Arg ( R ) at positions −3 and −2 , respectively , and an uncharged residue at the +1 position . ( B ) This PKA phosphorylation motif is highly conserved in FECH proteins across vertebrate species . ( C ) The FECH homodimer is shown with the transparent surface in green and the subunits in solid green ribbon . PPIX is shown as a red space filling model , the [2Fe-2S] clusters as solid rust and yellow balls , and the highlighted Thr116 ( site of PKA-mediated phosphorylation ) shown as solid violet spheres . The α-helix in which Thr116 resides , highlighted with lemon green , forms one lip of the opening to the active site where porphyrin is bound in this structure ( PDB 2QD1 ) . ( D ) The structure surrounding Thr116 ( shown in fuchsia ) , which is situated in the middle of a long α-helix , is enlarged . Thr116 is in close proximity and hydrogen bonded to His86 ( dark blue ) and adjacent to Val85 ( dark green ) . The Thr116-containing α-helix is highlighted with lemon green and the protoporphyrin behind in the active site is purple . Structural modeling suggests that phosphorylation of Thr116 would result in a shift of the Thr116-containing α-helix away from the His86-containing α-helix . ( E ) Purified recombinant His-tagged human FECH was phosphorylated by purified PKAc only in the presence of ATP at a 1:1 ratio . The phosphorylated form of FECH was detected by immunoblotting with an anti-phosphothreonine antibody . ( F ) An in vitro kinase assay was also performed with wild-type and variant forms of purified His-tagged human FECH . Disruption of the lysine or arginine at the −3 and −2 positions , respectively , similarly abolishes phosphorylation in vitro as a T116A mutation as shown by immunoblotting . ( G ) Differentiated MEL cells were immunoprecipitated with the indicated antibodies and immunoblotting analysis was performed . Immunoprecipitated FECH can be recognized by two different anti-phosphothreonine antibodies directed against the upstream positive residues and the proline immediately following the threonine . ( H ) Wild-type ( WT ) or AKAP10-KO ( KO ) were lysed and immunoprecipitated with the indicated antibodies . Bound proteins were analyzed by immunoblotting . All immunoblots were performed twice . ATP-adenosine triphosphate; PPIX-protoporphyrin IX; IB-immunoblot; IP-immunoprecipitate . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 00910 . 7554/eLife . 24767 . 010Figure 4—figure supplement 1 . Activation of PKA with high-dose forskolin increases FECH phosphorylation . Differentiated MEL cells treated with a high-dose of forskolin ( 50 µM ) were lysed , immunoprecipitated with the indicated antibodies , and subjected to western analysis . High-dose forskolin increased FECH phosphorylation . This is a representative blot from two independent experiments . IB-immunoblot; IP-immunoprecipitate . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 010 To test if FECH is directly targeted by PKA for phosphorylation , we performed an in vitro kinase assay by mixing together purified His-tagged human FECH and PKAc , followed by western analysis . This experiment showed that FECH is directly phosphorylated by PKA in an ATP-dependent fashion ( Figure 4E ) . Using [γ-32P]-ATP labeling , we calculated that , in vitro , 9 . 8 ± 3 . 2% of purified FECH was phosphorylated after 30 min . Substitution of Thr116 with Ala ( T116A ) abolished this phosphorylation ( Figure 4F ) . In addition , consistent with the preference of PKAc for positively charged residues at the −3 and −2 positions ( Smith et al . , 2011 ) , mutation of either Lys113 ( K113L ) or Arg114 to Leu ( R114L ) similarly reduced FECH phosphorylation ( Figure 4F ) , strongly indicating that Thr116 of human FECH constitutes a bona fide PKA target . We also examined FECH phosphorylation in erythroid cells by performing similar immunoblot analysis . Immunoprecipitated FECH from differentiated MEL cells was detected by two different phospho-threonine antibodies—one targeting the Lys-X-X-pThr motif and another recognizing the pThr-Pro sequence ( Figure 4G ) . High-dose forskolin treatment also increased phosphorylation of FECH in differentiating MEL cells ( Figure 4—figure supplement 1 ) . Conversely , inhibition of OM PKA with loss of AKAP10 resulted in diminished FECH Thr115 phosphorylation ( Figure 4H ) . In toto , our results support a model where PKA becomes localized at the mitochondria OM of maturing erythroid cells and directly phosphorylates FECH . Past work has demonstrated that FECH is phosphorylated by protein kinase C ( PKC ) ( Sakaino et al . , 2009 ) . PKC-mediated FECH phosphorylation occurs in a domain buried within an inaccessible hydrophobic fold that did not directly impact enzyme catalysis ( Sakaino et al . , 2009 ) . In contrast , the position of Thr116 ( Thr115 in mice ) that is modified by PKA suggests that it would have a direct effect on FECH activity ( Wu et al . , 2001 ) . We first examined this by using an in vitro 55Fe-based assay to measure and compare the amount of radiolabeled deuteroporphyrin-IX ( DP ) that can be produced by unmodified and modified FECH . DP , as a more soluble analog of the naturally occurring heme precursor protoporphyrin-IX ( PPIX ) , is frequently employed in such measurements and is similarly metalated by FECH to generate deuteroheme ( Najahi-Missaoui and Dailey , 2005 ) . Significantly more radioactivity can be detected when purified His-tagged human FECH was added alone to the metalation reaction ( Figure 5A ) and the addition of purified PKAc to the reaction to catalyze the phosphorylation of FECH resulted in an approximately two-fold increase in 55Fe measurements ( Figure 5A ) . This increase in activity is not attributable to PKAc per se , which has no ferrochelatase activity , and the reaction is completely DP-substrate dependent ( Figure 5A ) . More detailed analysis on enzyme kinetics revealed that phosphorylation had a pronounced effect on maximum velocity ( vmax ) but did not significantly change the Michaelis-Menten ( Km ) constant ( Figure 5B and Figure 5—figure supplement 1A ) , suggesting that it has no major influence on substrate binding . An important caveat to these kinetic measurements is that they were performed at 25°C while all other in vitro assays were performed at 37°C and , thus , may not fully reflect enzyme kinetics both in vivo as well as other single time-point experiments . We also examined FECH activity in intact mitochondria isolated from maturing erythroid cells treated with a high dose of PKA-activating forskolin . Mitochondria from differentiating MEL cells exposed to high-dose forskolin catalyzed higher DP metalation compared to mitochondria derived from untreated cells ( Figure 5C ) . In contrast , performing the assay with N-methyl mesoporphyrin-IX ( NMMP ) —a PPIX analog that is an inhibitor of FECH and not subject to metalation ( Dailey and Fleming , 1983 ) —instead of DP , resulted is very low 55Fe extraction that was refractory to forskolin treatment ( Figure 5C ) . Conversely , FECH activity was reduced in AKAP10-KO cells that had compromised FECH phosphorylation ( Figures 4H and 5D ) . These data indicate that phosphorylation of FECH at Thr116 by OM PKA increases FECH catalytic activity . 10 . 7554/eLife . 24767 . 011Figure 5 . Phosphorylation of FECH is required for its full activity . ( A ) Single-point FECH activity was determined in vitro by measuring the amount of 55Fe incorporation into the protoporphyrin IX analog , deuteroporphyrin ( DP ) . FECH has basal activity that is significantly increased by PKA-mediated phosphorylation . This increase in activity was dependent upon both DP and ATP , highlighting the substrate specificity of the assay for DP and the dependence on phosphorylation . ( B ) Kinetic analyses were subsequently performed with 0 . 1 µM FECH , 3 µM DP , and 0 . 2–100 µM 55FeCl3 at 25°C . Phosphorylation of FECH leads to a statistically significant increase in maximum velocity ( vmax ) . There was no significant difference in the Km . Please see Figure 5—source data 1 for vmax and Km values . ( C and D ) FECH activity was measured in isolated intact mitochondria . Samples from high-dose forskolin ( FSK ) -treated differentiating MEL cells have higher FECH activity ( C ) ( p-value<0 . 05 , Mean ± SEM , n = 5 ) . In contrast , AKAP10-KO mitochondria had less FECH activity ( D ) . Very little activity was detected in samples in which DP was substituted with NMMP . ( E ) A schematic showing the intron 3 and exon 4 sequences of wild-type murine Fech as well as the CRISPR oligo and the single-stranded DNA ( ssDNA ) that were introduced as a template for DNA repair . Intronic and exonic sequences are shown in lower and upper cases , respectively . Highlighted in yellow are the three PAM ( protospacer adjacent motif ) sequences closest to the T115A mutation site that facilitates the potential use of multiple CRISPR oligos . The missense mutations necessary to generate the T115A substitution are in orange . Shown in blue are synonymous substitutions designed to either disrupt the PAM sequences to prevent cleavage of the newly introduced mutant allele or to facilitate genotyping using allele-specific primers near the T115A mutation site . ( F ) Genomic DNA was isolated from the individual clones of MEL cells and used for PCR analysis with allele-specific primers . The parental MEL cells only had the wild-type allele . The intron 3 and exon 4 sequences of these cells were sequenced to confirm these agarose gel electrophoresis results . ( G ) Undifferentiated and differentiated parental and mutant cells expressing only the FECHT115A allele were lysed and subjected to western analysis to examine the induction of FECH protein during erythroid maturation . FECHThr115Ala protein had very similar up regulation with differentiation . ( H ) Differentiated MEL cells were lysed , immunoprecipitated with the indicated antibodies , and bound proteins were subjected to western analysis . Cells expressing only mutant FECH were phosphorylation defective at Thr115 . ( I–K ) Mitochondria isolated from differentiated MEL cells expressing only endogenous FECHT115A ( Mut ) , generated by genome editing , has lower FECH activity than wild-type ( WT ) control ( I ) . These cells expressing non-phosphorylated FECH also have reduced hemoglobinization by o-dianisidine staining ( J ) and increased accumulation of PPIX substrate ( K ) as demonstrated by HPLC analysis ( p-value<0 . 05 , Mean ± SEM , n = 11 ) . ( L ) Wild-type or mutant differentiated MEL cells treated with vehicle ( MOCK ) or FSK were stained with o-dianisidine . Cells expressing mutant FECH were refractory to the effects of FSK . p-value<0 . 05 , Mean ± SEM , n = 3 , unless otherwise specified . All immunoblots were performed twice . IB-immunoblot; vi-initial velocity; Km-Michaelis-Menten constant; FSK-forskolin; NMMP:N-methyl-mesoporphyrin-IX . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 01110 . 7554/eLife . 24767 . 012Figure 5—source data 1 . Maximum velocity ( vmax ) and Michaelis-Menten ( Km ) constants . The vmax and Km constants for unphosphorylated and in vitro phosphorylated FECH are shown . The data here are graphically presented in Figure 5B and Figure 5—figure supplement 1 . p-value<0 . 05 , non-significant , Mean ± SEM , n = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 01210 . 7554/eLife . 24767 . 013Figure 5—figure supplement 1 . Phosphorylation is required for full FECH activity . ( A ) A Lineweaver-Burk plot showing changes in the vmax−1 and Km−1 when FECH is phosphorylated in vitro . Phosphorylation of FECH leads to a statistically significant increase in maximum velocity ( vmax ) . There was no significant difference in the Km . Please see Figure 5—source data 1 for vmax and Km values . ( B ) HPLC analysis of differentiated wild-type or variant FECH expressing cells showed lower levels of hemin accumulation in cells expressing only phosphorylation defective FECH . p-value<0 . 05 , Mean ± SEM , n = 12 . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 013 EPP patients harboring FECH mutations retain residual FECH activity ( Balwani and Desnick , 2012 ) , suggesting that subtle changes in FECH function have important biological implications . Thus , to examine the in vivo implications of FECH phosphorylation under physiological conditions , we used CRISPR/Cas9-directed homology repair to knock-in a T115A substitution into the endogenous Fech gene in murine erythroid cells ( Figure 5E ) . Genotyping and subsequent sequencing confirmed that mutant cells possessed only the FechT115A allele ( Figure 5F ) . Compared to wild-type protein , FECHT115A mutant protein was similarly induced upon erythroid differentiation and was phosphorylation defective ( Figure 5G and H ) . Direct measurements of enzyme activity from intact mitochondria isolated from wild-type and mutant maturing erythroid cells demonstrated that FECHT115A had diminished ferrochelatase activity ( Figure 5I ) . Furthermore , o-dianisidine staining and high-performance liquid chromatography ( HPLC ) analysis revealed that erythroid cells expressing only FECHT115A protein had reduced hemoglobinization as well as lower intracellular hemin levels ( Figure 5J and Figure 5—figure supplement 1B ) . Conversely , FECHT115A-expressing cells had concomitantly elevated accumulation of the upstream , free protoporphyrin IX ( PPIX ) precursor ( Figure 5K ) . Clinically , excess erythroid PPIX accumulation is only found in EPP cases where it serves as a diagnostic marker ( Balwani and Desnick , 2012; Whatley et al . , 2004 ) . The build-up of PPIX in FECHT115A-expressing cells strongly argues that this mutation specifically affects FECH function while the upstream heme biosynthetic pathway remains unaffected . During normal and stress erythropoiesis , erythropoietin ( EPO ) signaling through its cognate receptor tyrosine kinase ( EPOR ) regulates survival and proliferation of erythroid progenitors ( Kuhrt and Wojchowski , 2015; Testa , 2004 ) . However , there is evidence to suggest that EPO/EPOR signaling regulates other key aspects of erythropoiesis . Unfortunately , the study of such mechanisms has been hampered by the requirement of EPOR signaling in the early stages of the erythropoietic hierarchy ( Beale and Chen , 1983; Chida et al . , 1999; Socolovsky et al . , 1999; Testa , 2004 ) . Nevertheless , uncovering EPOR effectors in later differentiation stages has important clinical relevance . For example , limiting erythroid iron uptake dramatically ameliorates Polycythemia Vera ( PV ) symptoms in a murine model expressing constitutively active V617F JAK2 ( Ishikawa et al . , 2015 ) . This is likely due to STAT5-mediated transcriptional up-regulation of the transferrin receptor ( Zhu et al . , 2008 ) . Despite this , neither PV murine models nor EPO overexpressing transgenic mice show any signs of iron overload ( Li et al . , 2011; Vogel et al . , 2003 ) , indicating that EPO signaling not only promotes iron uptake in RBCs but also coordinates its physiologic assimilation into heme without excessive iron accumulation . EPOR activates several pathways including the janus kinase 2 ( JAK2 ) /signal transducer and activator of transcription 5 ( STAT5 ) , mitogen activated protein kinase ( MAPK ) , and phosphatidyl 3’-inositol kinase ( PI3K ) pathways ( Kuhrt and Wojchowski , 2015 ) , leading us to question whether PKA is also activated by EPO . We tested this by treating primary murine fetal liver erythroblasts with EPO to determine if EPOR activation had any effect on PKA signaling by monitoring phosphorylation of the prototypical PKA target , CREB , on Ser133 ( Altarejos and Montminy , 2011; Taylor et al . , 2012 ) . As expected , EPOR activation triggered Tyr694 STAT5 phosphorylation , which was similarly observed with EPO treatment of human UT7 erythroid cells ( Figure 6A–C ) . CREBSer133 phosphorylation but not STAT5Tyr694 phosphorylation was blocked by PKI ( 14-22 ) ( Figure 6D ) , indicating that EPOR signaling specifically activates PKA . In contrast , co-treatment of UT7 cells with the JAK2 inhibitor , Ruxolitinib , robustly inhibited both STAT5Tyr694 and CREBSer133 phosphorylation ( Figure 6E ) , strongly suggesting that PKA lies downstream of the EPOR/JAK2 pathway . Co-immunoprecipitation experiments further demonstrated that , following EPO exposure , PKAc dissociated from PKAr ( Figure 6F ) , which is obligate for PKAc kinase activity ( Taylor et al . , 2012 ) . 10 . 7554/eLife . 24767 . 014Figure 6 . PKA links EPO signaling with heme production during erythropoiesis . ( A ) Primary murine erythroblasts were cultured from the E13 . 5 fetal liver , starved for 2 hr and treated with EPO ( 50 U/mL ) for the indicated times . Western analysis showed that EPO triggered increased phosphorylation of STAT5Tyr694 and CREBSer133 , indicating increased JAK2 and PKA activity , respectively . ( B and C ) Human UT7 erythroid cells were serum-starved overnight , treated with EPO ( 2 U/mL ) for the indicated times , and subjected to immunoblot analysis . EPO treatment increased both STAT5Tyr694 and CREBSer133 phosphorylations similar to ( A ) . A representative blot is shown in ( B ) and densitometry quantification from three independent experiments ( n = 3 ) where the phospho-CREB signal is normalized to total CREB signal is shown in ( C ) . Time points within the first 60 min were significantly different than time 0 . ( D and E ) UT7 cells were treated with the indicated compounds , and immunoblot analysis was performed . CREBSer133 phosphorylation can be blocked by both the PKA inhibitor , 14–22 , ( D ) and the JAK2 inhibitor , Ruxolitinib ( E ) . In contrast , 14–22 had no effect on STAT5 phosphorylation ( D ) . ( F ) UT7 cells untreated or treated with EPO were lysed and immunoprecipitated with the indicated antibodies . Western analysis showed that EPO stimulation resulted in dissociation of PKAc from PKAr . ( G ) Following PHZ treatment , primary erythroblasts were harvested from the adult murine spleen , starved for 2 hr , and stimulated with EPO ( 50 U/mL ) for 30 min . Lysates were immunoprecipitated and subjected to immunoblot analysis that showed increased FECH phosphorylation with EPO-mediated activation . ( H and I ) HEL cells induced to hemoglobinize by L-ALA supplementation were treated with Ruxolitinib for 2 hr . Western blot analysis demonstrated reduced CREBSer133 phosphorylation ( H ) and FECH phosphorylation ( I ) with inhibition of the constitutively active JAK2 mutant . All immunoblots were performed twice unless otherwise specified . *p-value<0 . 05 , Mean ± SEM , n = 3 . IB-immunoblot; IP-immunoprecipitate; EPO-erythropoietin; PHZ-phenylhydrazine;HEL-human erythroleukemia; L-ALA--δ-aminolevulinic acid . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 014 To determine if PKA-mediated FECH phosphorylation was regulated by EPO signaling , we performed anti-phosphothreonine immunoblot analysis on lysates from primary murine splenic erythroblasts ( splenocytes ) treated with either EPO or vehicle . Indeed , EPO stimulation resulted in an increase in FECH phosphorylation ( Figure 6G ) . Furthermore , Ruxolitinib inhibited phosphorylation of both CREBSer133 and FECHThr116 in human erythroleukemia ( HEL ) cells that express the constitutively active JAK2V617F mutant protein ( Figure 6H and I ) . Together , our data link EPO signaling to heme metabolism through PKA . The role of PKA in erythropoiesis has remained enigmatic . This is because early studies failed to detect changes in cAMP levels despite its stimulatory effect on iron incorporation during RBC maturation that is likely mediated by the synergistic effects of CREB on STAT5 transcription ( Boer et al . , 2002 , 2003; Gidari et al . , 1971; Schooley and Mahlmann , 1975 ) . However , recent evidence indicates that PKAc activation is much more complex involving direct protein-protein interactions with other cell signaling regulators and feedback mechanisms independent of cAMP ( Taylor et al . , 2012; Wong and Scott , 2004; Yang et al . , 2013 , 1995; Zakhary et al . , 2000 ) . Thus , we next asked whether PKAc interacts with proteins in the EPOR/JAK2/STAT5 signaling pathway , leading to its activation . Co-immunoprecipitation experiments performed using lysates from HEL cells that have constitutive JAK2V617F signaling revealed that STAT5 formed a complex with PKAc ( Figure 7A ) . In contrast , STAT5 could not be detected when the immunoprecipitation was performed with a control antibody ( Figure 7A ) . The STAT5-PKAc complex formation was sensitive to pharmacologic inhibition of JAK2 function with Ruxolitinib ( Figure 7B ) . We also tested whether EPO can trigger the formation of the STAT5-PKAc complex . STAT5 only co-precipitated with PKAc in lysates harvested from EPO-stimulated human UT7 erythroid cells ( Figure 7C ) . This fraction of STAT5 was phosphorylated on Tyr694 ( Figure 7C ) . In contrast , STAT5 did not co-precipitate with PKAr subunits ( Figure 7D ) , suggesting that phospho-STAT5/PKAc form a distinct complex apart from PKAr . Lastly , the phospho-STAT5/PKAc complex can only be found in cell lysates derived from the cytosol but not the mitochondria ( Figure 7E ) and indicates that at least a fraction of active PKAc can diffuse freely to phosphorylate nuclear CREB ( Hagiwara et al . , 1993; Mayr and Montminy , 2001 ) . Based on our results , we propose a model in which PKA signaling components are localized to the outer mitochondrial membrane during erythropoiesis by AKAP10 where it becomes activated by EPO signaling to regulate heme biosynthesis ( Figure 7F ) . 10 . 7554/eLife . 24767 . 015Figure 7 . Phosphorylated STAT5 forms a molecular complex with mitochondrial PKAc . ( A ) HEL cells were lysed and immunoprecipitated with the indicated antibodies and subjected to immunoblot analysis . ( B ) HEL cells treated with MOCK or Ruxolitinib for 2 hr were subjected to similar analysis as ( A ) . ( C and D ) UT7 cells starved overnight and treated with MOCK or EPO for 10 min were lysed and immunoprecipitated with the indicated antibodies . Immunoblot analysis was performed with the indicated antibodies . ( E ) Cytosolic and mitochondrial extracts were isolated from HEL cells and immunoprecipitated with the indicated antibodies . Bound proteins were resolved on SDS-PAGE and analyzed by immunoblotting with the indicated antibodies . ( F ) Model of how , during erythropoiesis , EPO signaling activates PKA at the mitochondrial OM that is localized by the GATA1-target , AKAP10 . PKA phosphorylates FECH , which is required to achieve full FECH activity necessary to accommodate the vast heme demand for hemoglobin assembly . All immunoblots were performed twice . IB-immunoblot; IP-immunoprecipitate . DOI: http://dx . doi . org/10 . 7554/eLife . 24767 . 015 Heme metabolism genes are downstream of GATA1 during RBC development and regulation of heme production has always been thought to occur at the level of gene transcription ( Fujiwara et al . , 2009; Handschin et al . , 2005; Phillips and Kushner , 2005 ) . However , GATA1 is required for very early stages of erythroid development when the demand for heme and hemoglobin is still low ( Fujiwara et al . , 1996 ) . This raises the possibility that heme metabolism during cell differentiation requires coordinated metabolic alterations dictated by extracellular signaling cues . EPO signaling is a critical regulator of erythropoiesis and elucidating downstream signaling pathways has been an active area of research . While it is dispensable for early erythroid specification , EPO is critical for the proliferation and survival of early erythroid progenitors ( Kuhrt and Wojchowski , 2015 ) and promotes their differentiation by increasing iron uptake and reducing ‘stemness’ potential ( Decker , 2016; Ishikawa et al . , 2015; Park et al . , 2016; Zhu et al . , 2008 ) . However , nothing is known regarding how EPO signaling can influence heme metabolism . Our work supports a unifying model linking the erythroid transcriptional program with a novel PKA-dependent mechanism downstream of EPO that sheds light into how heme metabolism is coupled to development . There is a growing body of evidence that , in the absence of intrinsic apoptotic signals , mitochondrial PKA signaling within the matrix is compartmentalized due to the impermeability of the inner mitochondrial membrane ( Acin-Perez et al . , 2009; Lefkimmiatis et al . , 2013 ) . Under this scenario , phosphorylation of matrix proteins can only be achieved by activating signals within the matrix ( Acin-Perez et al . , 2009; DiPilato et al . , 2004; Lefkimmiatis et al . , 2013 ) . However , our results support an alternative explanation in which proteins are modified prior to transport . The import of nuclear-encoded mitochondrial proteins requires the maintenance of these proteins in an unfolded state ( Lodish et al . , 2012 ) , which would allow greater accessibility of target motifs . In support , mitochondrial membrane embedded BAX is , first , phosphorylated by OM PKA ( Danial et al . , 2003; Harada et al . , 1999 ) and the requirement for protein unfolding during mitochondrial import is consistent with the low level of FECH phosphorylation in vitro ( approximately 10% ) that we observed . It is also very possible that binding of the FECH substrate to the PKA kinase induces conformational changes that would render the target motif more favorable to modification . This mechanism has previously been proposed as a means to prevent ‘promiscuous’ phosphorylation ( Dar et al . , 2005; Dey et al . , 2011 ) . Regardless the mechanism , given its exposure to the cytosol and access to proteins destined for mitochondrial localization , the OM is a prime location for such modifications to occur that would coordinate mitochondrial physiology with overall cellular behavior . Accordingly , studies have shown that OM PKA has unique signaling properties in that it is similarly responsive to cytosolic activation mechanisms but remains active much longer and is largely subject to cAMP-independent regulation ( Lefkimmiatis et al . , 2013 ) . Our data where loss of AKAP10 and OM PKA signaling results in defective FECH modification and activity is consistent with this idea ( Figures 2 and 3 ) . The regulation of PKA activity involves an intricate signaling network more complex than the canonical cAMP pathway ( Lefkimmiatis et al . , 2013; Manni et al . , 2008; Yang et al . , 2013 ) . Our work implicates phosphorylated STAT5 as a novel PKAc binding protein that can displace it from autoinhibitory PKAr subunits ( Figures 6 and 7 ) and is corroborated by recent work showing that phospho-STAT proteins , particularly STAT3 and STAT5 , localize to mitochondria ( Carbognin et al . , 2016; Gough et al . , 2009; Meier and Larner , 2014; Wegrzyn et al . , 2009 ) . Interestingly , in our analysis , mitochondrial expression of PKAc subunits was not as robustly increased as PRKAR2B ( Figure 1C and D ) . Studies have demonstrated that , upon activation , mitochondrial OM PKAc begins to gradually diffuse throughout the cell ( Webb et al . , 2008 ) . Thus , the non-stoichiometric increase in PKAc compared to PKAr expression is consistent with a dynamic signaling event . To date , there have been no reported EPP-associated FECH mutations at the PKA target motif . However , the low prevalence of this disease has made discerning genotype-phenotype correlations difficult . Our findings that PKA is an effector of EPO/JAK2 signaling implicate PKA activity not only in the pathogenesis of EPP but also a spectrum of hematologic diseases ( Ishikawa et al . , 2015 ) . PRKAR1A inactivating mutations are associated with metabolic syndromes in which anemia is prevalent ( Stratakis and Cho-Chung , 2002 ) . This is in agreement with murine models where PRKAR1A deletion has the most widespread effect and is the only knockout of the PKA family with embryonic lethality ( Stratakis and Cho-Chung , 2002 ) . Paradoxically , we found that it is the mitochondrial expression of PRKAR2B , and not PRKAR1A , that is most dramatically increased in maturing erythroid cells ( Figure 1C–E ) . The complex nuances of PKA signaling make it very difficult to reconcile these findings . Regulatory subunits restrict both the localization as well as the activation of PKA ( Wong and Scott , 2004 ) , making it very challenging to distinguish the relative contributions of these two mechanisms . The dual role of PKAr is highlighted by the incomplete rescue of PRKAR1A-/--associated embryonic defects with PRKACA ablation ( Amieux and McKnight , 2002 ) . Further complicating matters is the well-documented instances of compensatory responses ( Kirschner et al . , 2009 ) , raising the possibility that prominent roles for other PKA isoforms may simply be masked . Indeed , there is both genetic and biochemical evidence supporting a pivotal and specific role for PRKAR2B in blood development and disease . Global transcriptome analysis has shown that PRKAR2B mRNA is selectively high in CD71+ early erythroid cells ( Su et al . , 2004 ) and PRKAR2B binds with higher affinity than PRKAR1A to AKAP10 ( Burns et al . , 2003 ) . The latter point is particularly important given the recent correlation of AKAP10 polymorphisms with human blood traits in genome-wide association studies ( Gieger et al . , 2011 ) . It is also notable that AKAP10 encodes many isoforms that may localize to different subcellular compartments ( Eggers et al . , 2009; Huang et al . , 1997a ) . Although our CRISPR targeting strategy was designed to specifically disrupt the N-terminal mitochondrial-targeting motif ( Figure 2G ) , non-mitochondrial AKAP10 isoforms may function in a variety of contexts both in hematopoiesis and in other aspects of development including the cardiovascular system ( Kammerer et al . , 2003; Tingley et al . , 2007 ) . Our work , here , provides further evidence that perturbations in PKA signaling have significant impact on human health ( Kammerer et al . , 2003 ) including the pathogenesis of hematologic diseases that , to date , has been unappreciated and warrants further investigation . The DS19 murine erythroleukemia ( MEL ) subclone ( RRID:CVCL_2111 ) was kindly provided by Arthur Skoultchi ( Albert Einstein Medical College , Bronx , NY , USA ) . Parental human UT7 erythroid cells ( RRID:CVCL_5202 ) were kindly provided by Meredith S . Irwin ( Hospital for Sick Children , Toronto , ON , Canada ) . Human erythroleukemia ( HEL ) cells ( RRID:CVCL_2481 ) were kindly provided by Ann Mullally ( Brigham and Women’s Hospital , Boston , MA , USA ) . All cells are mycoplasma negative and the International Cell Line Authentication Committee lists none of them as a commonly misidentified cell line . The identities of all cells were confirmed by their labs of origin since none of them are commercially available and have no standard authentication reference sample . DS19 MEL and primary murine erythroid progenitors from E13 . 5 fetal liver were cultured and differentiated as previously described ( Chung et al . , 2015 ) . Differentiating DS19 MEL cells at day 3 of 2% DMSO differentiation were treated with 10 µM ( low-dose ) or 50 µM ( high-dose ) forskolin ( ThermoFisher , Waltham , MA ) , or 50 µM 8-Br-cAMP ( Sigma-Aldrich , St . Louis , MO ) for 30 min or 10 µM dmPGE2 ( Cayman Chemicals , Ann Arbor , MI ) for 60 min and stained with o-dianisidine as described below . Inhibition with 20 µM H-98 ( Tocris Bioscience , Minneapolis , MN ) , 100 nM PKI ( 14-22 ) ( ThermoFisher Scientific ) , or 30 nM bis-indolylmaleimide II ( Tocris Bioscience ) were performed by pre-treating the cells for 30 min prior to PKA pharmacologic activation . Bulk murine erythroid progenitors from adult spleen ( splenocytes ) were prepared and EPO-stimulated as previously described ( Maeda et al . , 2009; Socolovsky et al . , 2001 ) . After two hours of rhEPO ( 50 U/mL ) stimulation , cells were harvested and subjected to immunoprecipitation and western analysis . Human UT7 erythroid cells were cultured in αMEM ( Gibco , Gaithersburg , MD ) supplemented with 20% heat-inactivated fetal bovine serum ( Serum Source International , Charlotte , NC ) and 10 ng/mL GM-CSF ( Peprotech , Rocky Hill , NJ ) . For stimulation experiments , UT7 cells were starved overnight without GM-CSF . The next morning , rhEPO ( 2 U/mL ) was added for the indicated times prior to harvesting . Human erythroleukemia cells ( HEL ) were cultured in RPMI supplemented with 10% heat-inactivated fetal bovine serum and treated with 1 µM Ruxolitinib , which was a kind gift of Dr . Ann Mullally ( Brigham and Women’s Hospital ) and added to the cells for the indicated times . Akap10-targeting and NT9 control shRNAs in the pLKO . 1-puro vector were purchased from Sigma-Aldrich . MEL cells were electroporated and monoclonal populations expressing these shRNAs were isolated as previously described ( Chung et al . , 2015 ) . The sequences of the Akap10 shRNAs were: shRNA-1 , 5’-CCGGCCAAGTCATGTTGCGATCAATCTCGAGATTGATCGCAACATGACTTGGTTTTTG-3’ and shRNA-2 , 5’-CCGGGCAAGAGCACTTTAGTGAGTTCTCGAGAACTCACTAAAGTGCTCTTGCTTTTTG-3’ . 1 × 109 DS19 MEL cells of each condition or 5 × 108 HEL cells were collected , washed once in cold PBS , and resuspended in 1 mL of MSHE buffer ( 220 mM mannitol , 70 mM sucrose , 5 mM potassium HEPES pH 7 . 4 , 1 mM EGTA pH 7 . 4 , supplemented with Complete EDTA-free protease inhibitor tablets [Roche , Indianapolis , IN] ) . Samples were then dounce homogenized and pelleted by spinning at 1000 g for 10 min at 4°C . The supernatant was separated and the leftover pellet was resuspended in 200 µL of MSHE buffer . All samples were centrifuged again at 1000 g for 10 min at 4°C . All supernatants from each undifferentiated and differentiating samples were collected and combined and centrifuged again at 1000 g for 10 min at 4°C . This supernatant was then transferred to a new tube and centrifuged at 8000 g for 20 min at 4°C . The resulting supernatant containing cytosolic proteins was transferred to another tube and flash frozen . The pellet was resuspended and washed twice more in 200 µL of MSHE buffer . The final pellet , containing mitochondrially enriched membrane fractions , was flash frozen until subjected to mass spectrometry analysis . For western analyses , the pellet was lysed in NP-40 lysis buffer . For ferrochelatase activity assays , the Mitochondrial Isolation Kit for Mammalian Cells ( ThermoScientific ) was used according to manufacturer’s instructions . The mitochondrial pellet was resuspended in 200 µL of reaction buffer and immediately used . Intact mitochondria were isolated as described above without protease inhibitors and resuspended in MSHE buffer containing 5 mg/mL digitonin that was prepared fresh at 4% in water immediately before each experiment . Samples were incubated on ice for 15 min with vortexing at maximum setting every few minutes for 10 s intervals . After 15 min , samples were centrifuged at 10 , 000 g for 10 min at 4°C . The supernatant was transferred to a second tube and the leftover pellet was re-extracted twice more with 80 µL of MSHE containing 5 mg/mL digitonin , leaving a final pellet enriched for MPs . The supernatant from the second and third extractions were discarded . The supernatant from the first extraction was then centrifuged at 144 , 000 g for 1 hr at 4°C . The supernatant containing the IMS was removed and kept in a separate tube . The pellet contained the OM fraction . Proteinase K protection assays were performed as previously described ( Shirihai et al . , 2000 ) . Protein was extracted from purified mitochondria by dissolution in 8 M urea , 50 mM Tris pH 8 . 0 , followed by probe sonication . Extracted protein was reduced and alkylated with dithiothreitol and iodoacetamide , respectively . Alkylated protein samples were first digested with endoproteinase LysC for 4 hr at ambient temperature with an enzyme to protein ratio of 200:1 . Each sample was diluted with 50 mM Tris to 1 . 5 M urea and further digested with trypsin at an enzyme to protein ratio of 50:1 , overnight and at ambient temperature . Peptides from each sample were desalted over a C18 solid phase extraction cartridge and dried down . Each sample was resuspended in 0 . 2 M triethylammonium bicarbonate pH 8 . 5 , and labeled with tandem mass tag reagents ( TMT ) as previously described ( Hebert et al . , 2013 ) . Labeled samples were pooled , dried , and fractionated across a strong cation exchange column ( Polysulfoethyl A ) . Each fraction was dried , desalted , and resuspended in 0 . 2% formic acid . All nano UPLC separations were performed on a nanoAcquity system . From each fraction , approximately 2 µg of peptides was injected onto a 75 µm inner diameter , 30 cm long , nano column packed with 1 . 7 BEH C18 particles . The mobile phases were as follows: A ) 0 . 2% formic acid and B ) 100% acetonitrile with 0 . 2% formic acid . Peptide were eluted with a gradient of increasing B from 0%30% over the course of 100 min , followed by a wash with 100% B and re-equilibration at 0% B . Eluting peptides were electrospray ionized and analyzed with an Orbitrap Elite mass spectrometer . The mass spectrometry analysis cycle was as follows . First a survey scan was performed with Orbitrap analysis at 60 , 000 resolving power at 400 m/z . Peptide precursors in the survey scan were sampled for ms/ms analysis by data dependent top 15 selection with dynamic exclusion turned on . Each peptide precursor selected for sampling was isolated in the ion trap , fragmented by higher energy collisional dissociation ( HCD ) at 35 NCE , followed by mass analysis of the fragments in the Orbitrap at resolving power 15 , 000 at 400 m/z . All data analysis was performed in the COMPASS software suite ( Wenger et al . , 2011 ) . Spectra were dssearched against a tryptic target-decoy mouse Uniprot database including protein isoforms . Methionine oxidation and TMT on tyrosine were searched as variable modifications . Cysteine carbamidomethylation , TMT on lysine and TMT on the peptide N-terminus were searched as fixed modifications . The tolerance was set to 0 . 01 Da for matching fragments to the database . Matching spectra were filtered to 1% FDR at the unique peptide level based on spectral matching score ( E-value ) and peptide precursor ppm mass error , followed by reporter ion quantitation , protein grouping according to parsimony , and filtering to 1% FDR . Reporter ion quantitation normalization was performed essentially as previously described with the following changes ( Grimsrud et al . , 2012 ) . First , the dataset was annotated using the MitoCarta1 . 0 database ( Pagliarini et al . , 2008 ) . All proteins annotated as mitochondrial for undifferentiated and differentiated samples were averaged and linear regression was performed using Microsoft Excel . The conversion factor corresponding to the slope of the linear regression was applied to all proteins in the database regardless of MitoCarta1 . 0 status and the log2 fold change was calculated . CRISPR oligos were cloned into the px335 vector as previously described ( Chung et al . , 2015 ) . The sequences of the CRISPR oligos were 5’-CACCGAATTTTGGGGGTTCGGCGTT-3’ and 5’-AAACAACGCCGAACCCCCAAAATTC-3’ . The single-stranded DNA oligo used to direct homology repair was 5’-TTGGAGTTTCGAAGGTGGAATAAAATCCACTCACTTATGTGTCCAATGATTTAGTAAGCTTGCACCATTCATTGCGAAGCGACGTGCGCCCAAAATTCAAGAGCAGTATCGCAGAATCGGAGGTGGATCCCCCATCAAGATGTGGACT-3’ . Targeting of Akap10 ( NM_019921 . 3 ) was performed as previously described with modifications ( Chung et al . , 2015 ) . The CRISPR oligos were: exon-1 , 5’-CTGCACTAGTCCGAAAACAG-3’ and exon-3 , 5’-GCAAGGCATGATTTTTAGTG-3’ . DS19 MEL cells were electroporated and cultured as previously described ( Chung et al . , 2015 ) along with 5 µL of 10 mM ssDNA oligo . Single clones were grown in 96-well plates and screened for the presence of the FechT115A allele using genomic DNA PCR followed by antisense oligonucleotide hybridization with [γ-32P]-ATP-labeled ( 10 mCi/mL , specific activity = 6000 Ci/mmol , Perkin Elmer ) wild-type ( 5’-CATCGCCAAACGCCGAACC-3’ ) and mutant ( 5’-CATTGCGAAGCGACGTGCG-3’ ) oligos as described elsewhere ( Hildick-Smith et al . , 2013 ) . Clones harboring the mutant allele were expanded and characterized by allele specific PCR ( see below ) and sequenced to confirm the presence of only the mutant allele . HPLC analysis was performed as previously described and statistical significance was determined using two-way ANOVA ( Yien et al . , 2014 ) . Genomic DNA was isolated from DS19 MEL cells according to manufacturer’s instructions ( Qiagen DNeasy kit , Germantown , MD ) . For screening FechT115A knock-in clones using dot blots , the following primers were used to generate the amplicon of interest: 5’-CTGTTTGGCTCTCCTTAG-3’ and 5’-GAGTCCTACTGTAACGAG-3’ . For allele-specific PCRs , the latter primer from above was used with either the wild-type , 5’-CATCGCCAAACGCCGAACC-3’ , or mutant , 5’-CATTGCGAAGCGACGTGCG-3’ , forward primers . For Akap10 genotyping , the primers used were: forward ( F ) primer , 5’-GAAGGGCTCGCGGACTCG-3’; reverse-1 ( R1 ) primer , 5’-CCCTGACAAAACCCTTGC-3’; and reverse-2 ( R2 ) primer , 5’-CACTTGCAGTGTTTTGGGGTTT-3’ . Site-directed mutagenesis was performed using the Agilent QuikChange Lightning Multi Site-Directed Mutagensis Kit ( La Jolla , CA ) according to the manufacturer’s instructions . The following mutagenesis primers were used: T116A , 5’-CATCGCCAAACGCCGAGCCCCCAAGATTCAAG-3’ and 5’-CTTGAATCTTGGGGGCTCGGCGTTTGGCGATG-3’; K113L , 5’-GCACCATTCATCGCCTTACGCCGAACCCCCAAG-3’ and 5’-CTTGGGGGTTCGGCGTAAGGCGATGAATGGTGC-3’; and R114L , 5’-CCATTCATCGCCAAACTCCGAACCCCCAAGATTC-3’ and 5’-GAATCTTGGGGGTTCGGAGTTTGGCGATGAATGG-3’ . Recombinant His-tagged human FECH proteins were expressed and purified as previously described ( Burden et al . , 1999 ) . The 2 . 0 Å structure of human FECH ( PDB 2QD4 ) was visualized by PyMOL ( The PyMOL Molecular Graphics System , Version 1 . 8 Schrödinger , LLC ) . One microgram of His-tagged wild-type or variant human FECH was mixed with purified PKAc according to manufacturer’s instructions ( Promega , Madison , WI ) . 20% of the kinase reaction was subjected to western blot analysis . The stoichiometry of the kinase reaction was kept at 1:1 . For kinase assays using [γ-32P]-ATP ( specific activity 6000 Ci/mmol , Perkin Elmer , Boston , MA ) , the reaction was performed with an incubation time of 30 min instead of 10 min and scaled down to where 0 . 2 pmol of purified FECH and PKAc were used . Four-fold excess [γ-32P]-ATP was added . After 30 min , each reaction was immunoprecipitated with anti-FECH antibodies ( see below ) . The incubation time for [γ-32P]-ATP labelling was three-times longer than for other assays to ensure maximal phosphorylation in vitro . The amount of radioactivity was quantified in a scintillation counter ( Chung et al . , 2015 ) and normalized to immunoprecipitation efficiency , which was calculated by determining the percent of FECH protein that was recovered in the immunoprecipitation relative to input using western blotting followed by densitometry with ImageJ ( Schneider et al . , 2012 ) . The average and standard error was calculated from three independent experiments . All immunoblots were performed according to manufacturer’s instructions and as previously described except that all phosphothreonine immunoblots were performed with an HRP-conjugated protein A secondary antibody ( Chung et al . , 2015 ) . Anti-PDHA1 ( ab67592 ) and anti-AKAP10 ( ab97354 ) rabbit polyclonal antibodies were purchased from Abcam ( Cambridge , MA ) . Mouse monoclonal anti-TUBA1A ( DM1A ) , rabbit polyclonal anti-STAT5 ( C-17 ) , anti-PRKACB ( C-20 ) , anti-PRKAR2A ( M-20 ) , and goat polyclonal anti-FECH ( C-20 ) and anti-HSPD1 ( K-19 ) antibodies were purchased from Santa Cruz Biotechnology ( Santa Cruz , CA ) . Mouse monoclonal anti-pSer133-CREB ( 1B6 ) and anti-pThr-Pro ( 9391S ) , rabbit monoclonal anti-pTyr694-STAT5 ( D46E7 ) , anti-PRKACA ( D38C6 ) , anti-SMAC ( D5S4R ) , anti-VDAC1 ( D73D12 ) , anti-TOM20 ( D8T4N ) , and anti-CREB ( D76D11 ) , rabbit polyclonal anti-Arg-X-X-pThr ( 9621S ) and anti-PRKARIA ( D54D9 ) antibodies were purchased from Cell Signaling Technology ( Danvers , MA ) . Anti-GAPDH ( MAB374 ) mouse monoclonal and anti-PRKARIIB ( ABS14 ) rabbit polyclonal antibodies were purchased from Millipore ( Billerica , MA ) . Anti-TIM23 mouse monoclonal antibody was purchased from BD Biosciences ( Woburn , MA ) . Immunoprecipitations were performed as previously described ( Chung et al . , 2010 ) . All immunoblots were performed two independent times except when otherwise specified . Densitometry was performed as previously described and analyzed by one-way ANOVA ( Chung et al . , 2015 ) . Ferrochelatase activity assays using purified His-tagged wild-type human FECH was performed by using 250 ng of purified protein in assay reaction buffer ( 0 . 6 M sorbitol , 40 mM HEPES pH 7 . 4 pH with KOH , 50 mM KCl , 1 mM MgSO4 ) . Ascorbic acid and NADH were then added to final concentrations of 0 . 4 mg/mL and 2 nM , respectively . The solution was incubated at 37°C for 5 min . After the incubation period , 0 . 2 mL of 55FeCl3 ( 38 mCi/mL , specific activity = 54 . 5 mCi/mol , Perkin Elmer ) was added to each sample along with either 5 . 5 µL of 200 µM DP or NMMP . The samples were then incubated for another 10 min at 37°C and 55Fe-radiolabeled heme was extracted as previously described and counted in a liquid scintillation counter ( Chung et al . , 2015 ) . For activity assays using intact mitochondria , the protocol was modified to use 50 µg of freshly isolated mitochondria . All experiments were performed in duplicate and three independent experiments were performed followed by statistical analysis using student’s t-test . For kinetic assays , the conditions of the experiments are described elsewhere using 0 . 1 µM FECH , 3 µM DP , and 0 . 2–100 µM 55FeCl3 except the reactions were carried out at 25°C ( Hunter et al . , 2008 ) . At 30 , 60 , 120 , 180 , 300 , 600 , and 900 s , 10% of each reaction were removed and immediately mixed with cold FeCl3 at a final concentration of 1 . 25 mM to stop the reaction . All samples were extracted and counted in a scintillation counter as described above . The amount of product over time was plotted and analyzed using regression analysis with GraphPad Prism v5 . 0 software . Michaelis-Menten analyses with the initial velocities ( vi ) were subsequently performed using GraphPad Prism v5 . 0 that calculated the maximum velocity ( vmax ) and Michaelis-Menten ( Km ) constant . Experiments were performed three times and statistical analysis was performed on GraphPad Prism v5 . 0 . Injections , o-dianisidine staining , and flow cytometry analysis were performed as described elsewhere ( Chung et al . , 2015 ) . MOs were purchased from Gene Tools , LLC ( Philomath , OR ) . Zebrafish embryos at the one-cell stage were injected with MOs targeting the exon-1/intron-1 ( MO1 ) and exon-2/intron-2 ( MO2 ) junctions of D . rerio akap10 ( XM_690206 ) . The MO sequences were as follows: MO1 , 5’-TGGAGCGGCCACTTCCTTACCTTTC-3’; MO3 , 5’-TTTAGCACTAGACACTTACCTTTGC-3’ . All zebrafish ( RRID:ZIRC_ZL1 ) and mouse experiments were performed in full compliance with the approved Institutional Animal Care and Use Committee ( IACUC ) protocols at Boston Children’s Hospital ( Protocol #15-07-2974R ) and Brigham and Women’s Hospital ( Protocol #2016N000117 ) . These studies were approved by local regulatory committees in accordance with the highest ethical standards for biomedical research involving vertebrate animals .	Heme is an iron-containing compound that is important for all living things , from bacteria to humans . Our red blood cells use heme to carry oxygen and deliver it throughout the body . The amount of heme that is produced must be tightly regulated . Too little or too much heme in a person’s red blood cells can lead to blood-related diseases such as anemia and porphyria . Yet , while scientists knew the enzymes needed to make heme , they did not know how these enzymes were controlled . Now , Chung et al . show that an important signaling molecule called erythropoietin controls how much heme is produced when red blood cells are made . The experiments used a combination of red blood cells from humans and mice as well as zebrafish , which are useful model organisms because their blood develops in a similar way to humans . When Chung et al . inhibited components of erythropoietin signaling , heme production was blocked too and the red blood cells could not work properly . These new findings pave the way to look at human patients with blood-related disorders to determine if they have defects in the erythropoietin signaling cascade . In the future , this avenue of research might lead to better treatments for a variety of blood diseases in humans .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology" ]	2017	Erythropoietin signaling regulates heme biosynthesis
Nucleation promoting factors ( NPFs ) initiate branched actin network assembly by activating Arp2/3 complex , a branched actin filament nucleator . Cellular actin networks contain multiple NPFs , but how they coordinately regulate Arp2/3 complex is unclear . Cortactin is an NPF that activates Arp2/3 complex weakly on its own , but with WASP/N-WASP , another class of NPFs , potently activates . We dissect the mechanism of synergy and propose a model in which cortactin displaces N-WASP from nascent branches as a prerequisite for nucleation . Single-molecule imaging revealed that unlike WASP/N-WASP , cortactin remains bound to junctions during nucleation , and specifically targets junctions with a ∼160-fold increased on rate over filament sides . N-WASP must be dimerized for potent synergy , and targeted mutations indicate release of dimeric N-WASP from nascent branches limits nucleation . Mathematical modeling shows cortactin-mediated displacement but not N-WASP recycling or filament recruitment models can explain synergy . Our results provide a molecular basis for coordinate Arp2/3 complex regulation . Orchestration of many complex cellular processes , including cellular motility , endocytosis , and cytokinesis , requires tight control of the assembly and disassembly of actin filament networks ( Chhabra and Higgs , 2007; Pollard and Cooper , 2009 ) . Actin-related protein ( Arp ) -2/3 complex is an important actin cytoskeletal regulator that mediates the assembly of branched actin filament networks by nucleating new ( daughter ) filaments from the sides of pre-existing ( mother ) filaments ( Figure 1A ) ( Goley and Welch , 2006; Rotty et al . , 2013 ) . When isolated from most species , the complex is inactive , and activation requires binding to the side of a preformed actin filament and association with a nucleation promoting factor ( NPF ) protein ( Figure 1A ) ( Pollard , 2007; Achard et al . , 2010 ) . In addition to binding Arp2/3 complex , NPFs discovered to date bind either actin monomers ( type I NPFs ) or filaments ( type II NPFs ) ( Goley and Welch , 2006 ) ( Figure 1B , C ) . Cellular branched actin structures contain multiple NPFs , including representatives from both classes , which frequently have non-redundant roles in actin network assembly ( Galletta et al . , 2008; Yamaguchi et al . , 2005; Ayala et al . , 2008 ) . However , the mechanism by which multiple NPFs coordinately regulate Arp2/3 complex activity is poorly understood . 10 . 7554/eLife . 00884 . 003Figure 1 . Schematic overview of branching nucleation and the proteins involved . ( A ) Overview of branching nucleation depicting the required reaction components ( Arp2/3 complex , NPF , actin monomers and actin filaments ) and the resultant Y-shaped branches . The barbed and pointed ends of the actin filaments are labeled BE and PE , respectively . ( B ) Domain organization of prototypical type I ( WASP/N-WASP ) and type II ( Cortactin ) NPFs . WH1 , WASP homology 1; B-GBD , Basic region and GTPase binding domain; V , verprolin homology ( also known as WH2 , WASP homology 2 ) ; C , central; A , acidic; NtA , N-terminal acidic region; SH3 , Src homology 3 . ( C ) Schematic overview of activation of Arp2/3 complex by two classes of nucleation promoting factors . Gray barbell indicates a generic N-WASP dimerization mechanism . Small black arrows indicate either recruitment of actin monomers to Arp2/3 complex by VCA , or recruitment of Arp2/3 complex to actin filaments by cortactin . Light blue areas on Arp2/3 complex indicate the two proposed CA binding sites . The SH3 domain of cortactin is shown here but is omitted in other figures for clarity . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 003 WASP ( Wiskot-Aldrich syndrome ) and Scar ( suppressor of cAR ) family proteins , the best studied type I NPFs , have a minimal Arp2/3-activation region called VCA ( verprolin homology , central , acidic , Figure 1B ) ( Goley and Welch , 2006 ) . The V and C regions of VCA bind actin monomers ( Kelly et al . , 2006 ) , and CA binds two sites on Arp2/3 complex ( Padrick et al . , 2011; Ti et al . , 2011 ) . Chemical crosslinking assays demonstrate that one CA site is on Arp3 and the other spans Arp2 and ARPC1 ( Padrick et al . , 2011 ) . Using two CA binding sites , VCA is thought to recruit actin monomers to the Arp2 and Arp3 subunits , stimulating a conformational rearrangement to create an Arp2-Arp3-actin hetero-oligomer that mimics a stable actin filament nucleus ( Padrick et al . , 2011; Hetrick et al . , 2013 ) . Monomeric vs oligomeric VCA regions activate the complex with distinct kinetics , presumably due to differential engagement of the two sites ( Padrick et al . , 2008 ) . Oligomerization of WASP/Scar proteins is thought to tune NPF activity in vivo ( Padrick et al . , 2008; Gohl et al . , 2010; Footer et al . , 2008 ) , so dissecting biochemical differences between monomeric and oligomeric NPFs is an important challenge . Cortactin , the prototypical type II NPF , was initially discovered as a Src kinase substrate and actin binding protein ( Wu and Parsons , 1993 ) , and was later found to directly bind and activate Arp2/3 complex ( Weed et al . , 2000 ) . Cortactin contains an N-terminal acidic region ( NtA ) , which interacts with Arp2/3 complex , 6 . 5 actin filament binding repeat sequences , and a C-terminal SH3 domain ( Figure 1B ) . Mutations in the NtA that block its interaction with Arp2/3 complex prevent assembly of actin in protrusive structures in transformed cells called invadopodia , and hinder actin-dependent vesicle trafficking required for lamellipodial protrusion and cellular motility ( Bryce et al . , 2005; Ayala et al . , 2008 ) . These observations demonstrate that cortactin plays an important role in regulating Arp2/3 complex in vivo , yet the precise mechanism of Arp2/3 complex activation is unclear . Because the actin filament-binding repeats are required for activation , it has been hypothesized that cortactin recruits Arp2/3 complex to filaments to stimulate nucleation ( Uruno et al . , 2001 ) ( Figure 1C ) . However , whether filament recruitment can explain the acceleration of branching nucleation is unknown . In addition , cortactin on its own is a weak activator of Arp2/3 complex in vitro ( Weed et al . , 2000 ) , making it uncertain how cortactin can contribute to branching nucleation in vivo . Importantly , in the presence of WASP/Scar proteins , cortactin potently activates Arp2/3 complex ( Weaver et al . , 2001; Uruno et al . , 2001 ) , suggesting these NPFs synergize to assemble branched actin structures in vivo ( see Figure 1C for schematic ) . Consistent with this hypothesis , WASP/Scar proteins and cortactin co-localize in many branched networks in vivo , including at the leading edge of motile cells , podosomes and invadopodia , and at sites of endocytosis ( Martinez-Quiles et al . , 2004; DesMarais et al . , 2009; Grassart et al . , 2010 ) . Previous data showed that N-WASP and cortactin compete for binding to Arp3 ( Weaver et al . , 2002 ) , and cortactin competes more strongly when Arp2/3 complex is bound to filaments ( Uruno et al . , 2003 ) , but the precise mechanism of synergy is unknown . Previously proposed models include scenarios in which cortactin recruits Arp2/3 complex to the mother filament , where it cooperates with VCA to activate nucleation or induces release of VCA from branch-incorporated Arp2/3 complex to stabilize the nucleus ( Weaver et al . , 2002; Uruno et al . , 2003 ) . In another model , VCA becomes sequestered at branch junctions , so the concentration of VCA available to activate Arp2/3 complex limits the rate of branching nucleation ( Siton et al . , 2011 ) . In this model , called the recycling model , cortactin binding displaces VCA from branch junctions , recycling it back into solution for more activation . Here we dissect the mechanism of synergy between N-WASP and cortactin . Using single-molecule total internal reflection fluorescence ( smTIRF ) microscopy along with biochemical assays and mathematical modeling , we show that neither a filament recruitment nor a VCA recycling model can explain synergy . Our data instead support an obligatory displacement model , in which cortactin directly targets nascent branch junctions to accelerate the release of VCA . A key concept of the model is that VCA release is required for nucleation , either with or without cortactin , and the VCA release rate modulates the rate of nucleation . We dissect the biochemical requirements for synergy in cortactin and N-WASP to show that oligomerization of N-WASP VCA is required for significant synergy , but the actin filament binding repeats of cortactin are not . In addition , we provide evidence that slow release of N-WASP at nascent branch junctions limits nucleation rates , and that synergy is dependent on the ability of cortactin to accelerate this step . Our data provide important mechanistic insights into the regulation of Arp2/3 complex by cortactin , and lay the foundation for a molecular understanding of how NPFs work together to regulate branching nucleation . Previous experiments showed that cortactin activates Arp2/3 complex weakly on its own , but potently synergizes with WASP/N-WASP ( Weed et al . , 2000; Weaver et al . , 2001; Uruno et al . , 2001 ) . To quantify synergy , we added a range of concentrations of cortactin to a reaction with GST-N-WASP-VCA ( GST-VCA ) and Arp2/3 complex ( Figure 2A ) . Cortactin dramatically increased the polymerization rate , and the concentration dependence of synergy followed a hyperbolic trend ( Figure 2B ) . The concentration of cortactin required for half-maximal synergy was 72 nM and saturating cortactin increased the maximum polymerization rate 3 . 5–3 . 8-fold over GST-VCA alone . In contrast to a previous report , cortactin did not inhibit Arp2/3 complex at any concentration we tested , up to 20 μM in pyrene actin polymerization assays or 1 μM in TIRF microscopy branching assays ( Siton et al . , 2011 ) ( Figure 1B–D and Video 1 ) . 10 . 7554/eLife . 00884 . 004Figure 2 . Cortactin synergizes with GST-N-WASP-VCA . ( A ) Time course of pyrene-actin polymerization showing synergistic activation of Arp2/3 complex by cortactin and GST-VCA . Reactions contain 2 µM 15% pyrene-actin , 20 nM Arp2/3 complex and cortactin ( Cttn ) and/or 250 nM GST-VCA as indicated . ( B ) Plot of maximum polymerization rate vs cortactin concentration for reactions conditions as in panel A with 150 nM GST-VCA . Data were fit as described in ‘Materials and methods’ . ( C ) TIRF microscopy images of reactions containing 1 µM 33% Oregon-Green actin , 10 nM Arp2/3 complex , 50 nM GST-VCA and indicated concentrations of cortactin . ( D ) Branch density time vs time for TIRF data from panel C . Error bars are the standard error of the mean for at least three regions of interest from an acquisition period . Scale bar: 2 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 00410 . 7554/eLife . 00884 . 005Video 1 . Synergistic activation of Arp2/3 complex by GST-VCA and high concentrations of cortactin . Video corresponds to images in Figure 2C . Reaction contains 1 µM 33% Oregon-Green actin , 10 nM Arp2/3 complex , 50 nM GST-VCA and 1 µM cortactin . Single-wavelength ( 488 nm ) images were acquired at a final magnification of 100× with an exposure time of 100 ms and a frame rate of 1 fps ( frames per second ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 005 Several lines of evidence suggest cortactin might synergize with GST-VCA by recruiting Arp2/3 complex to actin filament sides . First , Arp2/3 complex must bind to the side of a pre-existing filament to be activated ( Achard et al . , 2010 ) , and kinetic measurements of pyrene-labeled S . pombe Arp2/3 complex indicated this binding step is slow ( Beltzner and Pollard , 2008 ) . Second , deletion of the actin filament binding domain of cortactin abolished its weak intrinsic nucleation activity ( Uruno et al . , 2001 ) . Finally , cortactin increases copelleting of Arp2/3 complex with actin filaments ( Cai et al . , 2008 ) . To determine if filament recruitment can explain synergistic activation , we first constructed a mathematical model to describe branching nucleation in the presence of GST-VCA and Arp2/3 complex without cortactin ( Figure 3A , Figure 3—figure supplement 1 and ‘Materials and methods’ ) . Our goal was to determine how the rate constants for actin filament binding by Arp2/3 complex influence polymerization time courses , and if recruitment of Arp2/3 complex by cortactin could account for the increased rates we measured in our bulk polymerization assays containing cortactin . 10 . 7554/eLife . 00884 . 006Figure 3 . Actin filament recruitment cannot explain cortactin-mediated synergy . ( A ) Cartoon pathway of steps optimized in the kinetic model of branching nucleation . ( B ) Representative pyrene-actin polymerization time courses of Arp2/3 complex activated by GST-VCA ( dashed lines ) with simulated fits ( solid lines ) . Residuals are shown below as solid lines . Reactions contained 3 µM 15% pyrene-actin , 50 nM Arp2/3 complex and indicated concentrations of GST-VCA . ( C ) Plot showing the relationship between the quality of fit ( black line ) and the optimized value of knuc ( red line ) for simulations at a range of fixed values of kfil_on . Dashed purple and blue lines show kfil_on values supported by our analysis in Figure 7 ( blue line ) or by empirically measured koff and KD values ( purple line ) ( Hetrick et al . , 2013; Smith et al . , 2013 ) . The dashed green line indicates the minimum value of kfil_on that fits the data with a quality of fit better than 1 . 3 × 10−11 . Quality of fit was calculated by a mean-weighted residual sum of squares . ( D ) Simulations showing the effect of increased actin filament side binding sites on the half time to reach equilibrium . Simulations were run using the three different kfil_on values indicated in panel C . Empirical data from actin polymerization time courses with 3 µM 15% pyrene-actin , 20 nM Arp2/3 complex , 100 nM GST-VCA and the indicated concentrations of cortactin are shown as black circles ( bottom axis ) . Initial concentrations of modeled actin filaments in the simulation are indicated on the top axis . ( E ) Plot of the fold activation over GST-VCA alone for a range of concentrations of full-length cortactin or NtA . Reactions contain 2 µM 15% pyrene-actin , 20 nM Arp2/3 complex , 250 nM GST-VCA and indicated concentrations of cortactin or NtA . Fold activation is calculated as the maximum polymerization rate for each reaction divided by the maximum polymerization rate for the reaction without cortactin . Data were fit ( solid lines ) as described in ‘Materials and methods’ . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 00610 . 7554/eLife . 00884 . 007Figure 3—figure supplement 1 . Mathematical modeling of actin polymerization in the presence or absence of GST-VCA , Arp2/3 complex and cortactin . ( A ) Cartoon pathway of mathematical models used to fit four independent sets of pyrene-actin polymerization assays . The conditions of each reaction set and its associated model are described in Table 2 . ( B and C ) Pyrene-actin polymerization time courses run at different initial actin concentrations ( B ) or at 3 µM 15% pyrene-actin and varying GST-VCA concentrations ( C ) . Dashed lines show experimental data and solid lines show simulated fits after optimization of the floating parameters indicated in Table 2 . Residuals are shown below as solid lines . All reactions used 15% pyrene-labeled actin . Indicated GST-VCA concentrations are monomeric . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 007 We fit pyrene-actin polymerization time courses of reactions containing 50 nM Arp2/3 complex with increasing concentrations of GST-VCA to a mathematical model similar to that of Beltzner et al . ( Beltzner and Pollard , 2008 ) . The final activation step ( knuc ) was optimized by globally fitting the data from time courses at a range of concentrations of GST-VCA , while fixing all other kinetic parameters ( Figure 3A; Tables 1 and 2 ) . The off rate of Arp2/3 complex bound to a mother filament ( kfil_off ) was constrained based on the measured KD , 0 . 9 µM ( Hetrick et al . , 2013 ) . The model assumes that the final activation step ( knuc ) occurs after two actin monomers have been recruited by GST-VCA ( Padrick et al . , 2011 ) . The model fit the experimental data well , showing a good visual fit to the time courses and a low residual sum of squares ( 1 . 3 × 10−11 ) ( Figure 3B ) . Optimization of the mother filament on rate showed that kfil_on approaches a minimum threshold ( 2 . 7 × 103 M−1 s−1 ) , beyond which , increases in the on rate do not improve the fit ( Figure 3C ) . We fixed the on rate at 1 . 4 × 106 M−1 s−1 based on modeling of reactions containing cortactin ( see below and ‘Materials and methods’ ) , and determined the optimized value for knuc is 0 . 0038 s−1 . This value is 320-fold smaller than the calculated off rate of the nascent branch complex from filament sides , consistent with smTIRF studies that show Arp2/3 complex binds and is released from filaments many times before nucleating a branch ( Smith et al . , 2013 ) . 10 . 7554/eLife . 00884 . 008Table 1 . Mathematical modeling parametersDOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 008Reaction #Descriptionkon ( M−1s−1 ) koff ( s−1 ) KD ( µM ) Reference1Actin dimerization1 . 98 × 1075 . 26 × 1072 . 6 × 106Mullins 1998 , This study2Actin trimerization1 . 16 × 1074 . 07 × 1053 . 5 × 104Mullins 1998 , This study3Spontaneous nucleation1110 − 1220* , †This study4Barbed end elongation1 . 16 × 1071 . 4Pollard 19865Barbed end elongation , actin monomer bound to GST-VCA1 . 16 × 1071 . 4Pollard 1986 , Higgs 19996Barbed end elongation , two actin monomers bound to GST-VCA1 . 16 × 1071 . 4Pollard 1986 , Higgs 19997Actin monomer binds GST-VCA5 × 10630 . 6Marchand 2001 , Beltzner 20088Actin monomer binds GST-VCA with bound actin5 × 10630 . 6Marchand 2001 , Beltzner 20089Actin monomer binds GST-VCA:actin24 . 2 × 10474 . 41 . 8 × 103This study10Actin monomer binds GST-VCA:actin31 . 5 × 1071 . 040 . 069This study11Actin monomer binds GST-VCA:actin42 × 1070 . 0620 . 003This study12GST-VCA nucleation6 . 1 × 10−8This study13Arp2/3 binds actin filament1 . 37 × 1061 . 230 . 9Hetrick 201314GST-VCA binds Arp2/30 . 8 × 1060 . 0720 . 009Padrick 200815GST-VCA:actin binds Arp2/30 . 8 × 1060 . 0140 . 018Padrick 2008 , Beltzner 2008 , Kelly 200616GST-VCA:actin2 binds Arp2/30 . 8 × 1060 . 0290 . 028Padrick 2008 , Beltzner 2008 , Kelly 200617Actin monomer binds GST-VCA:Arp2/32 . 5 × 10631 . 2Marchand 2001 , Beltzner 200818Actin monomer binds GST-VCA:actin:Arp2/32 . 5 × 10631 . 2Marchand 2001 , Beltzner 200819GST-VCA:actin2:Arp2/3 binds actin filament ( kfil_on ) 1 . 37 × 1061 . 230 . 9Hetrick 201320GST-VCA binds Arp2/3:F-actin0 . 8 × 1060 . 0720 . 009Padrick 200821GST-VCA:Arp2/3 binds F-actin1 . 37 × 1061 . 230 . 9Hetrick 201322GST-VCA:Arp2/3:actin binds F-actin1 . 37 × 1061 . 230 . 9Hetrick 201323GST-VCA:Arp2/3:F-actin binds actin monomer2 . 5 × 10631 . 2Marchand 2001 , Beltzner 200824GST-VCA:Arp2/3:actin:F-actin binds actin monomer2 . 5 × 10631 . 2Marchand 2001 , Beltzner 200825Arp2/3 complex nucleation ( knuc ) 0 . 004 − 0 . 006This study26Cortactin binds actin filament1 . 21 × 1040 . 0635 . 21This study27Cortactin binds nascent branch junction2 . 0 × 1060 . 0340 . 017This study28Synergy displacement activation of Arp2/3 complex ( kdis ) 0 . 036This study29Synergy recycling , cortactin dissociates sequestered GST-VCA2 . 0 × 1060 . 0340 . 017This studyUnits are s−1 . †This value was adjusted for each full set of reactions . Underlined values were allowed to float during some optimizations , see Table 2 . 10 . 7554/eLife . 00884 . 009Table 2 . Mathematical modeling reaction setsDOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 009Reaction setReactionsInitial concentrationsVariable concentrations ( µM ) Floated parametersQuality of fit11–4–2 . 0 , 3 . 0 , 4 . 0 , 5 . 0 , 6 . 0 actin monomersk1 , k−1 , k2 , k−2 , k31 . 75 × 10−1121–123 µM actin monomers0 , 0 . 02 , 0 . 04 , 0 . 08 , 0 . 1 , 0 . 2 , 0 . 6 , 0 . 8 , 1 . 0 GST-VCAk9 , k−9 , k10 , k−10 , k11 , k−11 , k122 . 32 × 10−1131–253 µM actin monomers , 50 nM Arp2/3 complex0 , 0 . 01 , 0 . 025 , 0 . 050 , 0 . 1 , 0 . 15 , 0 . 25 , 0 . 5 , 1 . 0 GST-VCAk25 ( knuc ) 1 . 28 × 10−114a1–283 µM actin monomers , 20 nM Arp2/3 complex , 100 nM GST-VCA0 , 0 . 005 , 0 . 025 , 0 . 075 , 0 . 1 , 0 . 25 , 1 . 0 cortactinkfil_on ( k13= k19=k21=k22 ) † , k25† , k28 ( kdis ) 2 . 87 × 10−114b1–25 , 293 µM actin monomers , 20 nM Arp2/3 complex , 100 nM GST-VCA0 , 0 . 005 , 0 . 025 , 0 . 075 , 0 . 1 , 0 . 25 , 1 . 0 cortactink29 , k−294 . 26 × 10−10kfil_on is a single global variable used for the indicated reaction rates . †Only optimized for the 0 µM cortactin reaction . We then used the optimized model to determine if recruitment of Arp2/3 complex to the sides of filaments by cortactin can explain synergy . We mimicked the effect of recruitment by simulating an increase in the concentration of actin filaments to saturate Arp2/3 complex side binding . If synergy occurs purely through recruitment , the magnitude of polymerization rate increases caused by adding side-binding sites will be similar to cortactin-induced rate increases . Using kfil_on determined from the experimental data with cortactin ( 1 . 4 × 106 M−1 s−1 , see below , blue line in Figure 3C ) , we found that increasing side binding sites increased the polymerization rate , but could not account for the dramatic rate increases we observed in experiments containing cortactin ( Figure 3D ) . This suggests that filament recruitment cannot account for cortactin-mediated synergy . Because of the uncertainty in kfil_on , we repeated the simulations at additional kfil_on values . First , we used a kfil_on value ( 6 . 9 × 105 M−1 s−1 , purple line in Figure 3C ) calculated from the experimentally determined filament off rate of budding yeast Arp2/3 complex ( 0 . 625 s−1 ) and the affinity of the bovine complex for filaments ( Smith et al . , 2013; Hetrick et al . , 2013 ) . This simulation also failed to account for experimentally observed synergy . However , in a simulation at the minimum threshold on rate ( 2 . 7 × 103 M−1 s−1 , green line in Figure 3C ) , filament recruitment could fully account for synergy . Therefore , while our best estimates of kfil_on suggest that actin filament recruitment cannot explain synergy , kfil_on is not determined well enough to completely eliminate the possibility that synergy occurs through recruitment . Therefore , we next asked if the actin filament binding repeats of cortactin are required for synergy . We tested a range of concentrations of full-length cortactin and a construct containing only the NtA ( residues 1–84 ) for their ability to synergize with GST-VCA . We found NtA was synergistic with GST-VCA , demonstrating that actin filament binding is not required for synergy ( Figure 3E ) . The concentration dependence of synergy for both constructs followed a hyperbolic trend , and the concentration of NtA required for half-maximal synergy was 11 µM , 110-fold higher than for full-length cortactin . However , at saturation , NtA was as potent as full-length cortactin , increasing the maximum polymerization rate 3 . 8-fold over GST-VCA-mediated activation of Arp2/3 complex . These data demonstrate that the NtA is sufficient for synergy , but that the actin filament binding repeats allow cortactin to synergize at lower concentrations . To probe further the mechanism of synergy , we next asked if biochemical features of the type I NPF influence synergy . Dimerization of VCA is known to increase its binding affinity for Arp2/3 complex , and some evidence suggests WASP/Scar proteins function as oligomers in vivo ( Padrick et al . , 2008; Gohl et al . , 2010; Footer et al . , 2008 ) . Therefore , we compared cortactin-mediated synergy with monomeric vs GST-tagged N-WASP-VCA in a pyrene actin polymerization assay . In previous experiments , GST-VCA behaved similarly to WASP dimerized through physiological SH3-polyproline interactions , so artificial dimerization by GST can mimic in vivo dimerization mechanisms ( Padrick et al . , 2008 ) . Saturating cortactin enhanced the maximum polymerization rate of a reaction containing GST-VCA 3 . 7-fold over the rate without cortactin , whereas cortactin weakly influenced a reaction containing monomeric VCA , accelerating the reaction only ∼1 . 5-fold over VCA alone ( Figure 4A , B , Figure 4—figure supplement 1 ) . Increasing the concentration of monomeric VCA did not increase synergy , suggesting the failure to observe potent synergy is not due to under-saturation of two CA binding sites on the complex ( Figure 4B , C , Figure 4—figure supplement 1 ) . VCA dimerized with a leucine zipper ( LZ-VCA ) behaved identically to GST-VCA , demonstrating that the difference in synergy is due to the oligomerization state of the VCA rather than an artifact caused by GST ( Figure 4A ) . 10 . 7554/eLife . 00884 . 010Figure 4 . The oligomerization state of VCA is an important determinant of synergy . ( A ) Plot of the fold activation vs cortactin concentration for reactions containing 2 µM 15% pyrene-actin , 20 nM Arp2/3 complex and 250 nM GST-VCA ( black ) , 750 nM VCA ( blue ) , 750 nM VVCA ( green ) , 250 nM Leucine-zipper VCA ( LZ-VCA , magenta ) or no N-WASP ( red ) . Monomer concentrations are listed . Fold activation is calculated as described in Figure 3E . ( B ) Plot of the average fold activation for reactions containing 1 µM cortactin and the indicated concentration of GST-VCA , VCA or VVCA . Dashed line indicates no synergy . p-values were calculated by two-tailed Student’s t-test . Error bars are S . E . M . n . s . = not significant , p>0 . 05 . Asterisks indicate average fold activation values are significantly different ( p<0 . 05 ) than a fold activation value of 1 ( no synergy ) . ( C ) Fold activation vs concentration of monomeric VCA for pyrene actin polymerization assays containing 0 to 4 μM N-WASP-VCA , 500 nM cortactin and Arp2/3 complex and actin as in panel A . Dashed line shows average fold activation for a reaction containing 250 nM GST-VCA and saturating cortactin . ( D ) Cartoon showing hypothetical branching intermediates with the potential to recruit two actin monomers to Arp2/3 complex with bound cortactin . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 01010 . 7554/eLife . 00884 . 011Figure 4—figure supplement 1 . The level of synergy is the same at saturating and subsaturating concentrations of the type I NPF . Plot of fold activation vs cortactin concentration for reactions containing 2 µM 15% pyrene-actin , 20 nM Arp2/3 complex and subsaturating ( open symbols and dotted fit line ) or saturating ( closed symbols and solid fit line ) concentrations of the indicated VCA constructs . Saturating data is the same as Figure 4A and fits were performed as described in ‘Materials and methods’ . The concentration of each construct required to saturate Arp2/3 complex activity is based on data in Figure 5A . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 011 The dimerization state of N-WASP could control synergy by influencing the number of actin monomers recruited to the complex . For instance , if NtA and VCA simultaneously interact with Arp2/3 complex during activation , GST-VCA may be able to recruit two actin monomers while engaging only one NPF binding site , while VCA can only recruit one actin monomer ( Figure 4D ) . Unlike WASP and Scar , native N-WASP contains tandem V regions and may be able to recruit two actin monomers to Arp2/3 complex ( Rebowski et al . , 2010 ) . Therefore , we made a monomeric N-WASP construct with both V regions ( VVCA ) and tested its ability to synergize with cortactin . We found that VVCA is not synergistic with cortactin at any concentration ( Figure 4A , B , Figure 4—figure supplement 1 ) . These data suggest that the ability to recruit two actin monomers to the complex using one NPF binding site is not sufficient for synergy . Dimerization increases the affinity of N-WASP for Arp2/3 complex 180-fold , allowing it to saturate both NPF binding sites on the complex at lower concentrations than monomeric VCA ( Padrick et al . , 2008 ) . We next explored the importance of the affinity of the type I NPF for Arp2/3 complex in synergy . Based on a number of observations , we hypothesized that type I NPF release is required for nucleation , and that tight binding of the type I NPF can slow the final activation step . First , we observed that at saturation , VCA is a better activator of Arp2/3 complex than GST-VCA ( Figure 5A ) . Second , recent single-molecule imaging experiments show that WASP dissociates from the branch junctions before elongation of the daughter filament ( Martin et al . , 2006 ) ( Smith et al . , 2013 , in press at eLife ) . Finally , crystal structures show that the V region may block the barbed end of actin monomers , preventing interactions with incoming actin monomers required for elongation ( Chereau et al . , 2005 ) . Therefore , we hypothesized that cortactin synergizes with GST-VCA by displacing it from the nascent branch complex , increasing the rate of the final activation step by accelerating the obligatory release of GST-VCA ( Figure 5B ) . 10 . 7554/eLife . 00884 . 012Figure 5 . VCA affinity for the nascent branch junction is an important determinant of synergy . ( A ) Maximum polymerization rates verses ( monomer ) concentration of N-WASP constructs for reactions containing 20 nM Arp2/3 complex and 2 µM 15% pyrene-actin . ( B ) Cartoon depicting obligatory displacement model of cortactin-mediated synergy . Approximate location of residues ( based on 2A41 . pdb and 2VCP . pdb ) ( Chereau et al . , 2005; Gaucher et al . , 2012 ) mutated in the V region of GST-VCA are indicated in one V region . ( C ) Fluorescence anisotropy binding measurements showing competition between wild type rhodamine-VCA and unlabeled VCA constructs for actin monomers . KD , WT = 0 . 56 ± 0 . 03 µM ( black ) KD , R442A = 11 . 2 ± 1 . 3 µM , ( orange ) KD , E455R = 0 . 63 ± 0 . 06 µM ( purple ) . ( D ) Plot of fold activation vs cortactin concentration for reactions containing 20 nM Arp2/3 complex , 2 µM 15% pyrene-actin and 250 nM of a GST-VCA construct , colors as in panel A . Fold activation is calculated as described in Figure 3E . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 01210 . 7554/eLife . 00884 . 013Figure 5—figure supplement 1 . The E455R mutation in N-WASP is predicted to provide additional favorable electrostatic interactions with actin monomers . ( A ) Alignment of WH2/V sequences ( sequence accession numbers ) : Tβ4 ( NP_001106702 . 1 ) , Ciboulot isoform D ( NP_001245516 . 1 ) , MIM ( O43312 . 2 ) , WIP ( O43516 . 3 ) , N-WASP ( Q95107 . 1 ) and WASP ( P42768 . 4 ) . Residue E455 in N-WASP and homologous residues in other V regions are colored based on charge conservation . ( B ) Structural alignment ( RMSD = 0 . 458 Å ) of WIP-V ( orange—2A41 . pdb ) and N-WASP-V ( green—2VCP . pdb ) bound to an actin monomer showing the fifth residue beyond the LKKT in WIP , Arg54 , interacts with an acidic pocket of actin that includes Glu93 . Electrostatic surface representation shows acidic ( red ) and basic ( blue ) regions on actin . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 013 To test this model , we prepared a VCA construct ( GST-VCA ( R442A ) ) with a point mutation in V that decreases its affinity for actin monomers 20-fold ( Co et al . , 2007 ) ( Figure 5B , C ) . We reasoned that this mutation would decrease the affinity of GST-VCA for the nascent branch junction , but still allow V to recruit actin monomers to the Arp2/3 complex ( Co et al . , 2007 ) . We predicted that in the absence of cortactin , saturating concentrations of this mutant would have a higher maximal polymerization rate , because it would release from the nascent branch junction faster than wild-type GST-VCA . Consistent with this prediction , the maximal polymerization rate at saturation was increased 2 . 5-fold in the R442A mutant compared to wild type ( Figure 5A ) . The obligatory displacement model predicts that cortactin will be less synergistic with the GST-VCA ( R442A ) mutant because of its higher intrinsic off rate from the nascent branch complex . Our data are also consistent with this prediction . In the presence of the R442A mutant , cortactin showed approximately twofold less synergy compared to the wild-type GST-VCA ( Figure 5D ) . As an additional test of the model , we made a V region mutation , E455R , which we predicted would bind more tightly to actin monomers and the nascent branch junction . Based on mutational data and a crystal structure of the V region of WIP bound to actin , an E455R mutation was predicted to interact favorably with Glu93 on the surface of actin ( Didry et al . , 2011 ) ( Figure 5—figure supplement 1 ) . Interestingly , this mutation had no influence on the affinity of N-WASP-VCA for actin monomers ( Figure 5C ) . However , it decreased the maximal polymerization rate at saturation ( Figure 5A ) and increased synergy with cortactin ( Figure 5D ) . This suggests that the E455R/E93 interaction may occur only in the context of the nascent branch junction . Together , these data support an obligatory displacement model for synergistic activation of the complex . Our data suggest that cortactin may displace GST-VCA from branch junctions to activate the complex synergistically . This model requires that cortactin bind to nascent branch junctions and avoid being nonproductively sequestered along the sides of filaments . The actin filament-binding region of cortactin is composed of 6 . 5 tandem 37-amino acid repeats , which are unstructured ( Shvetsov et al . , 2009 ) . The multivalent architecture of its actin binding domain led us to hypothesize that cortactin may find branch junctions by diffusing along actin filaments through multiple weak and dynamic interactions , similar to the actin binding protein VASP ( Hansen and Mullins , 2010 ) . To test this , we labeled cortactin ( residues 1–336 ) with Alexa-568 and actin monomers with Oregon-Green 488 and visualized their interactions using TIRF microscopy . Single-molecules of cortactin bound to and dissociated from actin filaments during the time courses of the videos ( Figure 6A , Figure 6—figure supplement 1; Video 2 ) . The cortactin molecules bound statically , eliminating the possibility that cortactin targets nascent branch junctions by diffusing along filaments ( Figure 6B ) . We measured the lifetimes of bound cortactin molecules and fit the cumulative lifetime data to a single exponential decay equation and determined an off rate ( koff ) of 0 . 050 s−1 ( Figure 6C ) . 10 . 7554/eLife . 00884 . 014Figure 6 . Cortactin binds statically to actin filaments . ( A ) smTIRF microscopy images of single cortactin molecules ( red ) bound to polymerizing actin filaments ( green ) . TIRF reactions contained 1 µM 33% Oregon-Green actin and 2 nM Alexa568-cortactin ( residues 1–336 ) . Scale bar: 1 µm . ( B ) Kymograph showing cortactin molecules bound statically to a polymerizing filament . The barbed end ( BE ) and pointed end are indicated . Vertical scale bar: 10 s , horizontal scale bar: 1 µm . ( C ) Histogram showing binned lifetimes of single molecules of cortactin on actin filament sides . Counts were transformed into 1-cumulative frequency plot ( inset ) and fit with a single-exponential decay equation to determine the off rate , 0 . 050 s−1 n = 191 . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 01410 . 7554/eLife . 00884 . 015Figure 6—figure supplement 1 . Validation of single molecule data . ( A ) Plot of fold activation vs cortactin concentration for reactions containing 20 nM Arp2/3 complex and 250 nM GST-VCA and either full length cortactin ( circle ) or Alexa568-cortactin residues 1–336 ( square ) . ( B ) Plot of fluorescence intensity vs frame number ( left ) plus still images ( right ) for two representative spots in the 561 channel . Biotinylated Alexa568-cortactin ( 1–336 ) at 1 nM was flowed into a streptavidin-PEG reaction chamber and imaged at 5 . 5 frames per second . Scale bar = 1 µm . Single-step photobleaching indicates that both signals tracked are single molecules of cortactin . ( C ) Lifetime plots of single cortactin molecules bound to the sides of polymerizing actin filaments , with data collected at two different laser intensities ( measured at laser head ) . Data were fit with a single exponential to determine the binding lifetime ( τ ) . The lifetime does not decrease at higher laser intensity , indicating that photobleaching does not significantly influence the measurements . Data for all other figures were collected ( only ) at 25 mW . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 01510 . 7554/eLife . 00884 . 016Video 2 . Single molecules of cortactin interacting with polymerizing actin filaments . Reaction contains 1 µM 33% Oregon-Green actin ( green ) and 2 nM Alexa568-cortactin ( red ) . Images from both channels were acquired using a 50 ms exposure at a ratio of 5:1 and a frame rate of 3 . 7 frames per second ( fps ) and 1 . 6 fps for the 488- and 561-channels , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 016 Because cortactin does not diffuse along filaments to find branch junctions , we hypothesized that cortactin may target junctions simply by preferentially binding junctions over filament sides . To test this , we visualized single molecules of cortactin with preformed branch junctions . Cortactin added to a TIRF chamber with preformed branches bound to both filament sides and branch junctions ( Figure 7A , Video 3 ) . The off rate of cortactin for branch junctions was 0 . 034 s−1 , almost twofold slower than the off rate for preformed filament sides measured from the same reaction ( 0 . 063 s−1 ) ( Figure 7B ) . Interestingly , 30% of the 270 tracked cortactin molecules were bound to branch junctions even though branches made up only ∼0 . 13% ( 68 branch junctions vs ∼53 , 000 filament side binding sites ) of total cortactin binding sites ( Figure 7C , D ) . This suggests that cortactin binds with a significantly higher affinity to branch junctions than filament sides . A twofold change in the off rate is unlikely to account for this difference . To determine if cortactin binds to branch junctions with a higher on rate than filament sides , we first measured the affinity of cortactin for branch junctions and filament sides using the single-molecule data . We counted the total number of bound cortactin molecules , branch junctions and side binding sites in each frame and calculated the average fraction of cortactin bound over hundreds of frames . Using the average fraction bound , we determined that the affinity of cortactin for branch junctions is ∼300-fold greater than for filament sides: 17 nM vs 5 . 2 µM , respectively ( Figure 7E ) . Using these equilibrium constants and our previously measured off rates , the calculated on rates of cortactin for branch junctions and filament sides are 2 . 0 × 106 M−1 s−1 and 1 . 2 × 104 M−1 s−1 , respectively . Our data show that cortactin targets branch junctions by binding over two orders of magnitude more tightly to junctions than filament sides . High affinity binding to branch junctions is accomplished through a ∼160-fold increase in the on rate and further amplified by a ∼twofold decrease in the off rate . These data explain how cortactin can specifically target nascent branch junctions to displace N-WASP . 10 . 7554/eLife . 00884 . 017Figure 7 . Cortactin directly targets branch junctions with a fast on rate . ( A ) smTIRF microscopy images showing interaction of cortactin with preformed branched networks . Reactions were initiated using 1 µM 33% Oregon-Green actin , 5 nM Arp2/3 complex and 30 nM VCA and allowed to proceed for ∼6 min before flushing a solution containing 1 . 5 nM Alexa568-cortactin and 0 . 1 µM actin monomers into the reaction chamber . Single cortactin molecules ( red ) bound actin filament sides ( gray arrows ) and branch junctions ( blue arrowhead ) . Large images show a single region of interest with both side and branch binding events . Time after cortactin addition is indicated . Smaller images show examples of complete filament side and branch junction binding events . Scale bars: 1 µm . ( B ) Frequency plot of tracked cortactin lifetimes for molecules bound to filament sides ( gray ) or branch junctions ( blue ) and fit with a single exponential decay function . ( C ) Plot of the total number of tracked cortactin molecules on filament sides ( FS ) or branch junctions ( BJ ) . ( D ) Plot of the average number of cortactin filament side or branch binding sites across all analyzed frames . ( E ) Summary of kinetic and thermodynamic binding constants for each class of binding event . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 01710 . 7554/eLife . 00884 . 018Video 3 . Single molecules of cortactin binding to preformed branch junctions and filament sides . Preformed branched filament networks were created by polymerizing 1 µM 33% Oregon-Green actin ( green ) , 5 nM Arp2/3 complex and 30 nM VCA for 6 . 4 min , then flowing into the reaction chamber a solution containing 1 . 5 nM Alexa568-cortactin ( red ) and 0 . 1 µM actin monomers . Out of focus frames ( at ∼1 s in video ) represents when cortactin was flowed into the chamber . Exposure time for each channel was 50 ms with a 561:488 image ratio of 6:1 . The frame rate of the 561-channel was 5 fps . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 018 The obligatory displacement model predicts that cortactin can target nascent branch junctions , displace N-WASP , and remain bound to the branch junction without blocking elongation of the new ( daughter ) filament ( Figure 5B ) . To test this prediction , we visualized single-molecules of cortactin in a reaction during active branching nucleation . We observed multiple instances ( n = 66 ) in which cortactin bound to the side of a filament where a daughter filament was later nucleated ( Figure 8A , B , Videos 4 and 5 ) . In each instance , cortactin remained bound to the branch junction after nucleation , consistent with the predictions of the obligatory displacement model . The average lifetime at the junction after nucleation was 62 s , 2 . 1-fold longer than the average lifetime of cortactin binding to a preformed branch junction ( Figure 8C ) . The average lifetime before nucleation was 6 . 5 s . While the accuracy of pre- and post-nucleation lifetime measurements is limited by our ability to resolve newly formed daughter filaments , the data clearly indicate that cortactin stays bound to the junctions long after nucleation . During active branching , cortactin bound existing branch junctions with an average lifetime of 29 . 5 s , the same lifetime found using preformed filament reactions ( 29 . 1 s ) . Interestingly , the lifetime of cortactin molecules on filament sides was 2 . 0–2 . 5-fold lower ( 8 . 0 s ) than with preformed filaments or polymerizing filaments in the absence of Arp2/3 complex . We cannot currently explain this result , but speculate that it may be due to conformational changes in the filament caused by Arp2/3 complex binding . 10 . 7554/eLife . 00884 . 019Figure 8 . Cortactin remains at the branch junction during daughter filament elongation . ( A ) smTIRF microscopy images of polymerizing branch networks containing 1 µM 33% Oregon-Green actin , 5 nM Arp2/3 complex , 50 nM VCA and 2 nM Alexa568-cortactin ( red ) . Images show filament side ( gray arrow ) , existing branch junction ( blue arrowhead ) and nascent branch junction ( yellow arrow ) cortactin binding events . ( B ) Montage from reaction described in panel A showing a single event in which cortactin binds to a filament side , a new branch is nucleated , and cortactin remains bound during elongation . ( C ) Average lifetimes for each binding class from reaction described in panel A . Error bars represent error of the fit . ( D ) Image montage showing NtA ( yellow arrow ) remains bound for ∼4 . 5 s after daughter filament nucleation before dissociating . The reaction contained 1 µM 33% Oregon-Green actin , 10 nM Arp2/3 complex , 350 nM VCA and 10 nM Alexa568-NtA ( 1-48 ) . Scale bars: 1 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 01910 . 7554/eLife . 00884 . 020Video 4 . Single molecules of cortactin binding to branching networks . Reaction contains 1 µM 33% Oregon-Green actin ( green ) , 5 nM Arp2/3 complex , 50 nM VCA and 2 nM Alexa568-cortactin ( red ) . 561- and 488-channel images were exposed for 50 ms and 30 ms , respectively , at a 561:488 image ratio of 8:1 , with a frame rate of 5 fps for the 561-channel and 2 . 6 fps for the 488 channel . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 02010 . 7554/eLife . 00884 . 021Video 5 . Single molecule of cortactin binding to a nascent branch junction . Visible in the video are nascent branch ( left-center at 3 s ) , branch junction and filament side binding cortactin molecules . Reaction conditions are the same as video 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 021 These data show that cortactin remains at the branch junction during and after nucleation , but do not allow us to eliminate the possibility that NtA disengages from Arp2/3 complex during elongation while the actin filament binding repeats hold cortactin at the junction . To test this , we labeled an NtA fragment ( residues 1–48 ) with Alexa-568 and visualized it during branching reactions . Single-molecules of NtA were observed bound to existing branch junctions and to nascent branches from which new daughter filaments nucleated ( Figure 8C and Video 6 ) . These data indicate that NtA can directly engage Arp2/3 complex after and during branch nucleation and that it does not block daughter filament elongation . This observation is consistent with pyrene actin polymerization assays showing NtA does not inhibit nucleation even at high micromolar concentrations ( Figure 3E ) ( Weaver et al . , 2002 ) . 10 . 7554/eLife . 00884 . 022Video 6 . Single molecule of NtA binding to a nascent branch junction . Reaction contains 1 µM 33% Oregon-Green actin ( green ) , 10 nM Arp2/3 complex , 350 nM VCA and 10 nM Alexa568-NtA ( 1-48 ) ( red ) . 561- and 488-channel images were exposed for 50 ms and 30 ms , respectively , at a 561:488 image ratio of 15:1 , with a frame rate of 11 fps for the 561 channel and 1 . 7 fps for the 488-channel . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 022 Given the biochemical evidence in support of the displacement mechanism , we next built a mathematical model to determine if obligatory displacement could account for the synergy we observed in our bulk polymerization assays . This model was similar to the recruitment model described above , but included additional reactions to account for cortactin interactions with filament sides and at nascent branch junctions ( Figure 9A , Figure 3—figure supplement 1 and ‘Materials and methods’ ) . We used the kinetic rate constants determined from our single-molecule experiments to describe these reactions , and assumed that cortactin binds to the nascent branch junction with the same rate constants as mature branch junctions . Following cortactin binding to the nascent branch junction , we added a cortactin-mediated displacement activation step ( kdis ) analogous to the GST-VCA-dependent activation step ( knuc ) . Full time courses of pyrene-actin polymerization at a range of cortactin concentrations were fit with the new model , allowing only kdis and kfil_on to float while kfil_off was constrained by the previously measured KD value ( Figure 9B ) . This model fit the experimental data well ( Figure 9B , C ) . A plot of the half time to reach equilibrium ( t1/2 ) vs cortactin concentration indicated that t1/2 decreased identically in both the model and the experimental data , and both showed a t1/2 of about 80 s at saturating cortactin ( Figure 9D ) . As in the simulations of reactions without cortactin , kfil_on had a minimal threshold value , but in these simulations the minimum value was ∼500-fold greater , 1 . 4 × 106 M−1 s−1 ( Figure 9C ) . Above the threshold , the optimized value for displacement activation ( kdis ) was 0 . 036 s−1 , 10-fold higher than the activation step in the absence of cortactin , knuc ( Figure 9C ) . The simplifications and assumptions made in the construction of the model limit the precision of the optimized values in representing the true microscopic rate constants . Nevertheless , the kinetic pathway of the obligatory displacement model fits the experimental data well , indicating that this model can explain synergy . 10 . 7554/eLife . 00884 . 023Figure 9 . Mathematical model of the obligatory displacement mechanism of cortactin-mediated synergy . ( A ) Cartoon pathway of key reactions describing cortactin-mediated displacement of GST-VCA ( see ‘Materials and methods’ for full model ) . ( B ) Pyrene-actin polymerization time courses of Arp2/3 complex activated by GST-VCA and cortactin with simulated fits based on an obligatory displacement mechanism of synergy . Dashed lines show experimental data and solid lines show simulated fits after optimization of the two floating parameters , kfil_on and kdis . Residuals are shown in lower panel . Reactions contained 3 µM 15% pyrene-actin , 20 nM Arp2/3 complex , 100 nM GST-VCA and indicated concentrations of cortactin . ( C ) Plot of quality of fit ( black ) and resulting optimized kdis ( green ) vs kfil_on . Blue dashed line indicates minimum threshold kfil_on value of 1 . 4 × 106 M−1 s−1 . Quality of fit was calculated by a mean-weighted residual sum of squares . ( D ) Plot of half time to equilibrium for reactions in panel B ( black circles ) vs a simulation of the obligatory displacement model ( blue line ) or the recycling model ( red line ) . ( E ) Plot showing concentration of free GST-VCA ( dashed lines , includes any species of GST-VCA not bound to a nascent or mature branch junction ) and sequestered GST-VCA ( solid lines , includes any species bound at nascent or mature branch junction ) vs time in simulations of the recycling model run at a range of cortactin concentrations . Insert shows magnification of a section of the plot , highlighting the concentration of sequestered GST-VCA as a function of cortactin concentration . DOI: http://dx . doi . org/10 . 7554/eLife . 00884 . 023 A previously proposed model for cortactin-mediated synergy hypothesized that cortactin displaces GST-VCA from the Arp2/3 complex during elongation of the daughter filament instead of before or during nucleation ( Siton et al . , 2011 ) . In this model , called the recycling model , cortactin is hypothesized to prevent GST-VCA from being sequestered at branches , which could slow polymerization by limiting the concentration of GST-VCA in solution available to activate Arp2/3 complex . A key prediction of this model is that the concentration of free GST-VCA limits branching nucleation rates . To test this mechanism , we simulated recycling by forcing GST-VCA to remain bound after nucleation and introducing a single binding reaction whereby cortactin returns the branch junction-bound GST-VCA to the pool of non-sequestered GST-VCA ( Figure 3—figure supplement 1 ) . We attempted to use this model to fit time courses of pyrene actin polymerization containing Arp2/3 complex , GST-VCA and a range of concentrations of cortactin . The recycling displacement model could not fit the data because increasing concentrations of cortactin had no influence on modeled polymerization rates . To determine why , we plotted the concentration of sequestered GST-VCA during the modeled time courses at various concentrations of cortactin . In the absence of cortactin , only 3 . 8% of the total GST-VCA was sequestered at the end of the reaction ( Figure 9E ) . Addition of cortactin reduced the concentration of sequestered GST-VCA , but did not influence the polymerization rate because the free GST-VCA concentration did not limit the rate of the reaction . Therefore , the recycling model cannot account for the synergistic activation of Arp2/3 complex we observe in our assays . Here we dissect the mechanism of synergistic activation of Arp2/3 complex by cortactin and N-WASP . Our data support an obligatory displacement model in which cortactin specifically targets nascent branch junctions to displace GST-VCA , thereby accelerating nucleation ( Figure 9 ) . In this model , GST-VCA , two actin monomers and Arp2/3 complex assemble on the side of a filament , creating a nascent branch junction . Cortactin targets the junction , making a multivalent interaction with Arp2/3 complex and the adjacent mother filament . At the nascent branch junction , NtA can compete with CA at the Arp3 binding site on the complex , as previously reported ( Weaver et al . , 2002; Padrick et al . , 2008 ) . Release of one CA speeds release of the GST-VCA , allowing nucleation and elongation to proceed . Importantly , NtA does not block elongation , as we demonstrate by visualizing labeled NtA at nascent branch junctions during nucleation/elongation . Rather , NtA stays engaged with Arp2/3 complex and the repeats stay bound to the mother filament after elongation , allowing cortactin to remain tightly bound to the mature branch junction . Our smTIRF measurements are consistent with immuno-gold-labeled electron micrographs and three-dimensional EM reconstructions that show cortactin remains at the mature branch junction ( Cai et al . , 2008; Egile et al . , 2005 ) . The obligatory displacement model is also consistent with the observation that cortactin competes weakly with GST-VCA in the absence of filaments , but strongly in their presence ( Uruno et al . , 2003 ) . A key aspect of the obligatory displacement model is that GST-VCA must be released before nucleation . Our mutational analyses support this requirement , since a mutation in the V region that decreased its affinity for actin increased the rate of nucleation at saturating GST-VCA , suggesting weakened interactions with the nascent branch junction can increase nucleation rates . Additional evidence comes from recent single-molecule TIRF experiments , which show that VCA release precedes elongation of the daughter filament ( Martin et al . , 2006 ) ( Smith et al . , 2013 , in press at eLife ) . Why release of the type I NPF is programmed into the branching nucleation mechanism , at least in the case of dimerized N-WASP-VCA , is not clear . One possibility is that because WASP/Scar proteins are attached to membranes ( Higgs and Pollard , 2000 ) , incorporation of the release step into the nucleation mechanism ensures branches are not strongly tethered to the membrane as the growing actin network pushes outward on it . In support of this hypothesis , Akin et al . showed that increasing transient connections between bead-immobilized NPFs and nascent branch junctions decreased bead motility ( Akin and Mullins , 2008 ) . Our data suggest cortactin could stimulate release of a membrane bound NPF from Arp2/3 complex to regulate network-substrate connections , providing an additional mode by which cortactin can control the dynamics of branched actin networks . Recent experiments showed that addition of cortactin to a solution of GST-VCA coated beads increased the bead motility to an extent unlikely to be accounted for simply by an increased nucleation rate ( Siton et al . , 2011 ) . Therefore , cortactin may stimulate branched network dynamics both by increasing nucleation rates and by preventing stalling caused by tight membrane-network attachments . A second key aspect of the displacement model is that cortactin must be able to target nascent branch junctions and avoid being non-productively sequestered along filament sides . Our single-molecule experiments revealed the kinetic basis for targeting . Instead of diffusing along filament sides to find branch junctions , cortactin targets branches using a ∼300-fold increased affinity over filament sides . Most of the binding preference arises from an unusually slow on rate ( 12 , 100 M−1 s−1 ) of cortactin for filament sides . This on rate is 200–2000-fold slower than for most other actin filament binding proteins , and is unlikely to be diffusion limited ( Kovács et al . , 2004; De La Cruz et al . , 1999; Wegner and Ruhnau , 1988 ) . Binding may be slow because it requires a conformational change in filaments , in agreement with an electron microscopy reconstruction showing that cortactin binding widens the gap between protofilaments ( Pant et al . , 2006 ) . Our mathematical model of obligatory displacement did not include reactions in which cortactin binds filament sides before Arp2/3 complex rather than targeting an existing nascent branch junction . While these pathways can be inserted into the model , we note that in the case of the NtA construct , synergy must occur completely through nascent branch targeting , suggesting nascent branch targeting may be the dominant displacement pathway for the full-length protein . Additional multi-color smTIRF experiments will be required to fully map out the kinetic pathways . We showed the actin filament binding repeats of cortactin are not required for synergy , eliminating an actin filament recruitment mechanism . While it is possible that recruitment operates simultaneously with displacement in full-length cortactin , we observed that saturating concentrations of NtA activated nucleation to the same extent as saturating full-length cortactin , so there is no recruitment component of synergy that cannot be mimicked by NtA . The high concentrations of NtA required for saturation likely reflect the decreased affinity of NtA for the nascent branch junction caused by removal of the actin filament binding repeats . Importantly , cortactin can weakly activate Arp2/3 complex on its own ( Weed et al . , 2000 ) , and in contrast to synergy , this intrinsic activity requires the filament binding repeats ( Uruno et al . , 2001 ) . Therefore , filament recruitment may explain the weak intrinsic activity of cortactin observed in vitro . Our data are inconsistent with a previously proposed recycling model of synergy , in which cortactin indirectly increases nucleation rates by recycling WASP sequestered at mature branch junctions , freeing it to activate Arp2/3 complex ( Siton et al . , 2011 ) . Instead , our data indicate that cortactin directly influences nucleation at nascent branch junctions . These biochemical distinctions will have important implications in understanding the influence of cortactin on the regulation of Arp2/3 complex in vivo . In addition to activating Arp2/3 complex , cortactin has been shown to stabilize branch junctions in vitro ( Weaver et al . , 2001 ) . The average lifetime of cortactin at branch junctions was 29 . 2 s , whereas the lifetime of branches has been reported to be between 8 and 27 min ( Martin et al . , 2006; Mahaffy and Pollard , 2006 ) . Given these data , cortactin dissociates from branches much more rapidly than branches disassemble . However , cortactin has a high affinity for junctions ( KD = 17 nM ) , so it likely dissociates and rebinds branches many times during the life of a branch , even when present at low ( nanomolar ) concentrations . Therefore , cortactin dynamically stabilizes branch junctions . This mechanism is consistent with FRAP experiments that show cortactin is incorporated into treadmilling networks not just at the leading edge but also throughout the entire network , and exchanges rapidly ( Lai et al . , 2008 ) . Importantly , in vivo treadmilling networks turn over on much shorter timescales than in vitro branch lifetimes ( Lai et al . , 2008; Martin et al . , 2006 ) . In cells , the competition of cortactin with branch disassembly factors like GMF , coronin1B , and cofilin may be more important than the intrinsic branch stabilizing activity of cortactin ( Gandhi et al . , 2010; Cai et al . , 2008; Blanchoin et al . , 2000 ) . The mathematical models we present show how rate constants for filament binding by the complex influence the potential of filament recruitment to contribute to activation . Measuring filament on rates has been technically challenging , and a range of values has been reported . Experiments using pyrenyl-Arp2/3 complex from fission yeast yielded a kon of 150 M−1s−1 ( Beltzner and Pollard , 2008 ) , while a kon value of 3 × 103 M−1 s−1 was calculated for budding yeast Arp2/3 complex from smTIRF experiments , ( Smith et al . , 2013 ) . Our optimized kon value for bovine Arp2/3 complex was ∼1 × 106 M−1s−1 . Species-specific differences may account for some of the differences in these values , but are unlikely to fully explain them . In single-molecule studies , complications arise from the lack of simple methods to directly measure on rates for filaments ( Van Oijen , 2011 ) . Off rates can generally be determined directly from the lifetimes of the on state , but difficulties arise from the complexity of interactions of Arp2/3 complex with filaments . For example , budding yeast Arp2/3 complex dissociated from filaments with three distinct off rates , indicating a heterogeneous population of dissociating species ( Smith et al . , 2013 ) . Filament binding rate constants are important for not only understanding how Arp2/3 complex works with type I NPFs like WASP/N-WASP , but also how other regulators control Arp2/3 complex activity by mediating its interactions with filaments . The advent of three-color smTIRF experiments will be critical in allowing us to dissect these interactions . An important finding of this work is that potent synergy occurs only when the type I NPF is dimerized . We hypothesize that this is because dimerized type I NPFs engage both Arp2/3 complex binding sites to bind tightly to nascent branch junctions , thereby slowing the nucleation step . In our assays , we artificially oligomerized N-WASP , but in vivo , N-WASP can oligomerize by association with scaffolding proteins like Nck or BAR domain proteins ( Padrick et al . , 2008; Li et al . , 2012; Suetsugu , 2013 ) . SCAR/WAVE , another widely expressed type I NPF , has also been shown to oligomerize or cluster on membranes , suggesting it can also act as a higher order oligomer ( Gohl et al . , 2010 ) . These observations suggest the potential for cortactin-mediated synergy to activate Arp2/3 complex in multiple distinct branched networks in vivo . We note that in addition to Arp2/3 complex , cortactin has dozens of other binding partners , including some that influence its ability to regulate branched networks ( Kirkbride et al . , 2011 ) . For instance , cortactin binding to WIP , an actin momomer binding protein , greatly enhances cortactin-mediated activation of Arp2/3 complex ( Kinley et al . , 2003 ) . In addition , the SH3 of cortactin binds N-WASP to relieve its autoinhibition , providing an indirect mechanism for cortactin to upregulate branching nucleation ( Kowalski et al . , 2005 ) . Biochemical dissection of these reactions will allow us to understand precisely how cortactin coordinates branched networks in vivo . GST-tagged mouse cortactin and cortactin NtA ( residues 1–84 or 1–48 ) ( gifts from John Cooper ) were overexpressed in BL21 ( DE3 ) -RIL E . coli and purified using glutathione sepharose , Resource Q ion exchange and size exclusion chromatography columns , in that order . The GST tag was cleaved using TEV protease prior to ion exchange chromatography . Cortactin ( residues 1–336 ) or NtA ( 1–48 ) used in smTIRF were prepared for dye labeling by mutating all endogenous cysteines to serine and adding a KCK ( Lys-Cys-Lys ) tag at the C-terminus . Purified fractions from size exclusion chromatography were reacted with Alexa Fluor 568 maleimide ( Molecular Probes , Eugene , OR ) at a molar ratio of 15–20:1 ( dye:cortactin ) overnight at 4°C . Free dye was removed by extensive dialysis and a HiTrap desalting column . The concentration of labeled cortactin ( 1–336 ) was determined using absorbance at 280 nm with the dye signal subtracted using the 280:575 nm absorbance ratio of free dye . Labeled NtA concentration was estimated by measuring the dye absorbance at 575 nm and assuming that all free dye was removed and 100% of the protein was labeled . LZ-VCA was constructed by inserting the leucine zipper domain of Saccharomyces cerevisiae Gcn4 ( residues 250-281 , a gift from Alan Hinnebush ) and a 21 residue Gly-Ser linker between the TEV protease site and the N-terminus of VCA . All N-WASP-VCA constructs , including N-WASP-VVCA ( residues 392–505 ) , were purified as described previously ( Hetrick et al . , 2013 ) . Bovine Arp2/3 complex , Oregon-Green actin , rabbit muscle actin and pyrene-labeled actin were prepared as previously described ( Hetrick et al . , 2013 ) . Pyrene actin polymerization assays were performed and analyzed as described previously ( Liu et al . , 2011 ) . Fold activation due to cortactin-mediated synergy was calculated by dividing the maximum polymerization rate at a given cortactin concentration by the maximum polymerization rate of the equivalent reaction containing no cortactin . Synergistic activation vs cortactin concentration data were fit to a saturating hyperbolic equation with an added background factor equal to the activity at no cortactin . The anisotropy of a solution containing 50 nM Rhodamine-VCA , 150 nM G-actin , 10 mM Hepes pH 7 . 0 , 50 mM KCl , 1 mM MgCl2 , 1 mM EGTA , 1 mM DTT , 0 . 2 mM ATP , 350 nM Latrunculin B and increasing concentrations of VCA or VCA mutants was measured using excitation and emission wavelengths of 530-nm and 574-nm , respectively . Plots of anisotropy vs VCA concentration were fit with a previously described equation ( Wang , 1995 ) . Binding assays were repeated at least three times . The final reported KDs are the mean of the individual fits . The TIRF reaction buffer contained 50 mM KCl , 1 mM MgCl2 , 1 mM EGTA , 10 mM Imidazole pH 7 . 0 , 0 . 5% methyl cellulose cP 400 , 1 mM DTT , 0 . 2 mM ATP , 25 mM glucose , 1 mM Trolox , 0 . 02 mg/mL catalase , 0 . 1 mg/mL glucose oxidase and 2 mg/mL BSA . Methyl cellulose ( 2% cP 400 ) stock was prepared by overnight mixing at 4°C and then centrifuged for 1 hr at 245 , 070×g in a table-top ultracentrifuge . A stock solution of 5 mg/ml glucose oxidase and 1 mg/ml catalase ( GODCAT ) was prepared according to manufacturer’s instructions in water and stored at −20°C . A 2× stock of TIRF buffer was prepared by mixing KMEI ( 50 mM KCl , 1 mM MgCl2 , 1 mM EGTA , 10 mM Imidazole pH 7 . 0 ) , glucose and Trolox and incubating overnight at room temperature to allow for formation of the Trolox quinone derivative ( Cordes et al . , 2009 ) . ATP , DTT and the clarified methyl cellulose were added before filtering the mixture through a 0 . 22 µm filter and storing at −20°C . Just before use , GODCAT and BSA were added to make the final 2× TIRF buffer stock . Cover slips ( no . 1 . 5 ) were sonicated for 20–25 min in acetone , then 1 M KOH with extensive water rinsing between sonication steps . Cover slips were then briefly washed in methanol before incubating for 30 min in a 1% APTES ( ( 3-Aminopropyl ) triethoxysilane ) , 5% acetic acid methanol solution . Aminosilanized cover slips were rinsed with methanol then water and allowed to completely dry . TIRF chambers were assembled by adhering two strips of double-sided tape , separated by 5 mm , along the long axis of an aminosilanized cover slip . A 24 mm wide , 1 mm thick microscope slide was rinsed briefly with ethanol and placed perpendicular across the cover slip such that the middle of the slide and cover slip were aligned . Firm , even pressure was applied to the slide to secure it to the cover slip tape . The resulting assembly contains a 5 mm × 24 mm × 0 . 1 mm ( tape height ) reaction chamber with openings on two sides for reaction solution addition and exchange . Solution exchange was performed by using Whatman paper to wick out the chamber solution from one end resulting in the uptake of the new solution into the chamber , from the opposite end . TIRF reaction chambers were then treated with biotinylated-PEG to create a low binding surface to which actin filaments could be tethered through streptavadin and biotinylated-myosin . A single-use solution of 250–300 mg/mL methoxy PEG succinimidyl succinate ( JenKem USA , Allen , TX ) ∼0 . 1% Biotin PEG NHS Ester was prepared in 0 . 22 µm filtered 0 . 1 M NaHCO3 pH 8 . 3 . TIRF chambers were prepared for PEGylation by flowing in 0 . 1 M NaHCO3 pH 8 . 3 twice . The PEG solution was then wicked into the reaction chamber and allowed to incubate for 4–5 hr at room temperature , protected from light and in a humidity chamber to prevent the reaction chamber from drying . After PEGylation , filtered water was flowed through the reaction chamber 3–5 times to remove all the unbound PEGs . PEGylated chambers were stored at 4°C in a light protected humidity chamber for up to 1 week . Immediately before imaging , individual TIRF reaction chamber surfaces were prepared by flowing in 50–100 µM streptavidin followed by 0 . 5 µM biotinylated myosin , which was previously prepared by reacting maleimide-biotin with full length rabbit myosin II ( Cytoskeleton Inc . , Denver , CO ) on ice for 2–4 hr and stored at 4°C . The chamber was then washed with high then low salt buffer solutions ( 20 mg/mL BSA , 50 mM Tris pH 7 . 5 and 600/150 mM NaCl , respectively ) , followed by two washes with 1× TIRF buffer with GODCAT and BSA ( see above ) . Each solution was allowed to incubate in the TIRF chamber for 5-10 min . TIRF actin polymerization reactions were initiated by adding a protein solution containing the protein ( s ) of interest ( Arp2/3 complex , Alexa568-cortactin , VCA ) in 1× TIRF buffer to a solution of 6 µM 33% Oregon-Green actin , pretreated for 60 s with 50 µM MgCl2 and 200 µM EGTA , to give a final reaction solution of 1 µM 33% Oregon-Green actin and the correct concentration of the proteins of interest ( see below ) . The reaction was then wicked into the prepared reaction chamber and imaged as described below . Dual wavelength TIRF images were collected on a Nikon TE2000-U microscope outfitted with a Nikon 100× NA 1 . 49 TIRF objective , 1 . 5× auxiliary lens and an EM-CCD camera ( iXon3 , Andor or Image-EM , Hamamatsu ) . Argon 488-nm ( Dynamic Laser , Salt Lake City , UT ) and solid-state 561 nm ( Coherent , Santa Clara , CA ) lasers were used to excite Oregon-Green and Alexa-568 fluorophores , respectively . Laser beam selection and intensity was controlled using an AOTF ( Gooch & Housego ) and each beam passed through dual-band ( 488/561 ) excitation and dichroic filters ( Chroma , Bellows Falls , VT ) before entering the objective . Prior to collection at the EM-CCD , emission signals passed through dual-band dichroic and emission filters ( Chroma ) . Images were acquired using the open source microscopy software , Micro-Manager ( Edelstein et al . , 2010 ) . Image processing was performed in ImageJ , where each raw image was background subtracted using the rolling ball algorithm with a rolling ball radius of 10 pixels and subsequently smoothed using a Gaussian blur filter with a sigma of 0 . 5 . Unless noted otherwise , TIRF reactions were imaged at a final magnification of 150× with 50 ms and 30–50 ms 561- and 488-channel exposures , respectively . Images were acquired at 561:488 image channel ratios of 5–15:1 and at calculated frame rates of 1–11 frames per second . Specific imaging parameters are indicated in figure or video legends . Single molecules were tracked and lifetimes measured using a custom Matlab script ( MathWorks , Natick , MA ) . Single cortactin molecules were identified in each 561-channel image using thresholding image segmentation after removal of noise using a band pass filter . Single-molecule trajectories were created by identifying and linking identical molecules between frames using a nearest neighbor algorithm . An assembled trajectory represents a single-molecule and contains a preliminary frame-based lifetime . All identified molecules were filtered based on the following criteria: Molecule cannot be present in the first or last frame of the image acquisition period , molecule cannot have a lifetime of 1 frame , molecule average intensity must be within a standard deviation of the overall molecular average intensity and the molecule must be associated with an actin filament based on filament identification statistics performed on the corresponding 488-channel images . The molecules that passed the filter were manually tracked to verify the initially determined frame lifetime and to identify the binding class ( filament side , branch junction , nascent branch ) . We identified branches manually by visually inspecting videos . Potential branches were examined in multiple frames to verify that they were not overlapping linear actin filaments . Lifetimes were converted from frames to seconds based on calculated frame rates from image time stamps , and binned into 5 s intervals . The cumulative frequency across all bins was calculated and a plot of 1- cumulative frequency vs lifetime ( bin ) was fit with a single-exponential decay equation to determine the average off rate from which the average lifetime is the reciprocal . Single-molecules of cortactin were identified ( see above ) and classified into filament side or branch junction binding from 1000 randomly chosen frames of the preformed branch network image acquisition . Start of equilibrium binding was determined from a plot of number of particles vs frame . Number of filament binding sites was calculated for each frame at equilibrium ( 829 frames ) by a custom image processing script run in Matlab , described as follows . For each frame , pixels corresponding to filament fluorescence were identified using image segmentation followed by morphological area opening to remove non-filamentous small fluorescent objects . Pixels corresponding to branch junctions ( 5 pixels per junction ) were subtracted from the total number of pixels and this new number of pixels was divided by 3 ( average filament width in pixels ) to remove pixels corresponding to the PSF of the filament fluorescence . The final pixel number value was converted to micrometers ( 1 px = 106 . 7 nm ) to yield the total length of actin filaments in the image frame , and further converted to number of subunits using 370 subunits µm−1 ( Kuhn and Pollard , 2005 ) . A 4% error in total filament length was found between using the above algorithm and manual filament tracing on a small subsection of filaments; therefore , the described length calculation algorithm works well for the extensive length measurements needed . The total number of cortactin filament side binding sites was calculated by assuming a cortactin to F-actin subunit stoichiometry of 1:6 . The total number of branch junctions was visually counted in each frame . For each frame , the fraction bound of actin for each binding group was calculated by dividing the total number of counted molecules by the total number of binding sites . The affinity for each binding group was calculated , assuming excess ligand conditions ( see below ) , using the equation: Kd=[cortactin]fraction bound−[cortactin] , where fraction bound is the average across all analyzed frames and concentration of cortactin is 1 . 5 nM . Excess ligand conditions were established using the following calculations . The average area of a reaction chamber is 120 mm2 and the area of a single image is 2 . 98 × 10−3 mm2 ( 512 × 512 pixels at 106 . 7 nm per pixel ) , indicating that , in two dimensions , a single image composes 0 . 00248% of the total chamber area . The average number of cortactin molecules bound to junctions or filaments sides per image frame was 20 . Because unbound actin filaments were washed out of the chamber , all binding events occurred on the surface , so this number allows us to account for all actin-bound cortactin molecules in the chamber . Using the chamber-to-image ratio ( 2 . 48 × 10−5 ) , this gives an average number of cortactin molecules bound per chamber of 8 . 06 × 105 . In the reaction chamber there are 1 . 08 × 1010 cortactin molecules ( 12 µl of 1 . 5 nM cortactin ) indicating that on average 0 . 0075% ( 8 . 06 × 105/1 . 08 × 1010 ) of the total cortactin molecules are bound to actin , therefore the free cortactin concentration is essentially equal to its total concentration . To limit the number of floating parameters in our kinetic models , we ran four sets of pyrene actin polymerization assays , and fit each set to an independent model with a limited number of floating variables ( Table 2 ) . For example , to determine rate constants for spontaneous actin filament nucleation , we fit time courses of a range of concentrations of actin polymerizing without additional proteins ( Figure 3—figure supplement 1A , B ) . The optimized rate constants from this model were used to fit sets of pyrene actin polymerization assays containing additional proteins ( GST-VCA , Arp2/3 complex , and cortactin , i . e . , reaction sets 1–4 ) . To determine a set of reactions and rate constants that can describe decreased nucleation from GST-VCA-bound actin monomers , we modeled a set of reactions containing a constant concentration of actin and a range of GST-VCA concentrations ( Figure 3—figure supplement 1A , C ) . Table 2 shows each of the four sets of reactions and the simulations of their optimized global fits . While many of the rate constants for interactions in the branching nucleation reaction are known ( Tables 1 and 2 ) , we made several assumptions to allow construction of the model . Rate constants of GST-VCA binding to actin monomers were assumed to be the same as monomeric VCA ( Marchand et al . , 2001 ) . Kinetic rate constants for GST-VCA binding to Arp2/3 complex have not been measured , so we used kon values measured for monomeric VCA and adjusted the koff to account for the previously measured tighter binding of GST-VCA to Arp2/3 complex ( Padrick et al . , 2008 ) . We decreased the kon of actin for GST-VCA bound to Arp2/3 complex and increased the koff of GST-VCA:actin from Arp2/3 complex to account for competition between the complex and C for actin binding ( Kelly et al . , 2006 ) . We modeled the Arp2/3 complex activating nucleation step ( knuc ) by converting the nascent branch junction of two actin monomers bound to GST-VCA bound to Arp2/3 complex at a filament side to a barbed end which subsequently elongated at 11 . 6 × 106 M−1 s−1 ( Pollard , 1986 ) . Pointed end elongation was not included in our simulations , because the actin monomer concentration is low and Arp2/3 complex-mediated nucleation does not create free pointed ends . We assumed GST-VCA dissociates from the branch junction during nucleation ( knuc ) , except in the recycling model ( see main text and Figure 3—figure supplement 1A ) . For simplicity , the kon of Arp2/3 complex for the sides of filaments was assumed to be unaffected by GST-VCA or GST-VCA and actin monomer binding . For reactions with cortactin , the stoichiometry of cortactin:F-actin subunits was set to 1:6 . Rate constants for cortactin binding to filament sides and branch junctions were determined from the single-molecules studies . Mathematical modeling of pyrene-actin polymerization time courses was performed using COPASI ( Hoops et al . , 2006 ) . Fluorescence values were converted to actin filament concentrations by assuming 0 . 1 µM actin was unpolymerized at equilibrium . Optimization of parameters was carried out by simultaneously fitting all traces from a reaction set , using the Levenberg–Marquardt algorithm method in the parameter estimation module . To simulate the influence of actin filament recruitment by cortactin ( Figure 3D ) , we increased the initial concentration of actin filament sides but not ends .	Cells constantly sense , and react to , their environments . They can monitor or alter their surroundings by taking up or secreting various substances , and may also migrate toward food supplies , or toward signaling molecules—for example , to clot blood or heal wounds . These actions depend on the cytoskeleton , a protein meshwork that gives cells their shape; allows them to transport materials into , out of , or across their cytoplasms; and enables them to move . The filaments of the cytoskeleton are constructed from several different types of proteins , one of which is called actin . In response to signals , actin can assemble into linear filaments , or can form branches with one end anchored on an existing filament . Branch formation requires the Arp2/3 complex , which initiates and anchors branches on existing filaments , and also various ‘nucleation-promoting factors’ ( NPFs ) , which turn on the branching activity of the Arp2/3 complex . Two types of NPFs have been identified: type I interact with individual actin molecules , while type II bind to actin filaments . Previous work has shown that type I NPFs—including the N-WASP protein—have a specialized domain called VCA that binds to both the Arp2/3 complex and to actin molecules . VCA brings actin molecules to the branch site , which initiates branch formation , but how N-WASP collaborates with type II NPFs to build branches is not well understood . Helgeson and Nolen now examine how a type II NPF called cortactin works with the Arp2/3 complex and N-WASP to construct new branches on actin filaments in vitro . Cortactin appears to displace the VCA domain of N-WASP to stimulate branch formation , and then to remain associated with—and stabilize—the growing branch . Helgeson and Nolen suggest that these NPFs work together to create branches using an “obligatory displacement” model . According to this scheme , N-WASP ( or another type I NPF ) , the Arp2/3 complex and two actin molecules are bound at the site of a future branch on an actin filament , poised for branch formation . However , before more actin molecules can be added , N-WASP must be released , either slowly on its own—as Smith et al . also report in findings published concurrently in eLife—or rapidly with the help of cortactin or other type II NPFs . Although the rationale for N-WASP removal is not yet understood , type I NPFs are generally attached to the plasma membrane . When N-WASP releases the mother filament , the membrane should no longer be able to block the addition of actin molecules to a growing branch .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "biochemistry", "and", "chemical", "biology", "structural", "biology", "and", "molecular", "biophysics" ]	2013	Mechanism of synergistic activation of Arp2/3 complex by cortactin and N-WASP
The TMPRSS2:ERG gene fusion is common in androgen receptor ( AR ) positive prostate cancers , yet its function remains poorly understood . From a screen for functionally relevant ERG interactors , we identify the arginine methyltransferase PRMT5 . ERG recruits PRMT5 to AR-target genes , where PRMT5 methylates AR on arginine 761 . This attenuates AR recruitment and transcription of genes expressed in differentiated prostate epithelium . The AR-inhibitory function of PRMT5 is restricted to TMPRSS2:ERG-positive prostate cancer cells . Mutation of this methylation site on AR results in a transcriptionally hyperactive AR , suggesting that the proliferative effects of ERG and PRMT5 are mediated through attenuating AR’s ability to induce genes normally involved in lineage differentiation . This provides a rationale for targeting PRMT5 in TMPRSS2:ERG positive prostate cancers . Moreover , methylation of AR at arginine 761 highlights a mechanism for how the ERG oncogene may coax AR towards inducing proliferation versus differentiation . Prostate cancer ( PC ) is highly prevalent and lethal ( Siegel et al . , 2015 ) . Drugs targeting the Androgen Receptor ( AR ) , a 'lineage driver' of PC ( Garraway and Sellers , 2006 ) , are an important therapeutic approach . AR is an androgen ( i . e . testosterone ) -activated nuclear hormone receptor that regulates normal prostate gland growth and differentiation . In PC however , AR facilitates unregulated proliferation ( Mills , 2014 ) . While it is unclear how AR and other lineage factors switch between promoting normal lineage differentiation vs . tumor growth , it is hypothesized that somatic mutations in additional genes may facilitate such changes ( Garraway and Sellers , 2006 ) . Many PCs bear chromosomal translocations resulting in aberrant expression of the ETS transcription factor ERG , most commonly through the TMPRSS2:ERG fusion ( Shah and Chinnaiyan , 2009 ) . TMPRSS2:ERG , alone or in combination with additional genetic alterations , promotes prostate tumor formation in mice ( Baena et al . , 2013; Chen et al . , 2013; King et al . , 2009; Klezovitch et al . , 2008; Mounir et al . , 2015; Tomlins et al . , 2008 ) . ERG is recruited to many AR target genes and represses AR-dependent transcription ( Yu et al . , 2010 ) , suggesting ERG functions at least in part through attenuating AR target gene expression . However , ERG also regulates the expression of AR-independent genes thought to drive oncogenic function ( Klezovitch et al . , 2008; Mounir et al . , 2015; Tomlins et al . , 2008; Wang et al . , 2008 ) . We explored whether a deeper mechanistic understanding of ERG proliferative function could yield therapeutic insights into targeting this key PC oncogene . To identify genes that selectively facilitate the growth of TMPRSS2:ERG positive PC cells , we performed a pooled short hairpin RNA ( shRNA ) screen in TMPRSS2:ERG and AR-positive VCaP prostate cancer cells , using ERG-negative 22Rv1 cells as a control ( Materials and methods ) . The shRNA pool targets 648 genes involved in transcriptional and epigenetic regulation ( Supplementary file 1 ) . While ERG shRNAs were not in the pool , AR shRNAs were preferentially depleted from VCaP cells , underscoring AR dependence in this cell line . Thirty two ( 32 ) genes showing VCaP-selective shRNA depletion ( Materials and methods ) were considered for further study ( Figure 1A; Supplementary file 1 ) . 10 . 7554/eLife . 13964 . 003Figure 1 . Identification of PRMT5 . ( A ) Log p-value plots ( RSA metric , see Materials and methods ) of shRNA depletion from VCaP cells ( y-axis ) versus 22Rv1 cells ( x-axis ) . Grey lines denote p-value cutoff for screen hits ( 10-5 ) , with bottom right quadrant enriched for VCaP-selective screen hits . Red dots indicate screen hits that are also candidate ERG interactors from Supplementary file 2 . ( B ) Western blot of PRMT5 , AR , and ERG following ERG or control IgG immunoprecipitation from untreated ( - ) or R1881-treated ( + ) VCaP cells . ( C ) Left panel: western blots of noted proteins from 22Rv1 whole cell extracts ( WCE ) , either in parental ( Par ) cells , cells expressing exogenous ERG ( 'ERG' ) , or cells expressing a DNA-binding defective ERG ( 'Dx' ) ; PRMT5 knockdown under these conditions is as noted . Right panel: Western blot of ERG immunoprecipitation ( IP ) from 22Rv1 for ERG and PRMT5 . ( D ) PRMT5 proliferation after PRMT5 knockdown ( sh1 , sh2 and sh3 ) in VCaP cells ( see Materials and methods ) . NTC: non-targeting control . Error bars represent + SEM of three biological replicates , each with three technical repeats . ( E ) 22Rv1 proliferation as in ( C ) . ( F ) LNCaP proliferation as in ( C ) . ( G ) VCaP proliferation as in ( C ) alongside expression of shRNA-resistant wild-type ( WT ) PRMT5 , catalytically inactive PRMT5 ( G365A/R368A ) , or vector control ( Vector ) . Error bars represent + SEM of three biological replicates , each with three technical repeats . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 00310 . 7554/eLife . 13964 . 004Figure 1—figure supplement 1 . PRMT5 knockdown in prostate cancer cells . ( A ) Western blot of candidate ERG interactors from Figure 1A , as presented in Figure 1B . ( B ) Left panel: western blot for FLAG-PRMT5 and GFP-ERG constructs after immunoprecipitation of FLAG-PRMT5 in 293 cells . Right panel: western blot for HA-ERG and V5-PRMT5 after immunoprecipitation of HA-ERG from PC3 cells . ( C ) Top panel: schematic representation of four ERG deletion mutants . ERG FL: full-length ERG; ERG△NTD: ERG lacking N-terminal domain; ERG△CTD: ERG lacking C-terminal domain; ERG△PNT: ERG lacking pointed domain . Bottom left panel: western blot of noted proteins from WCEs of 293 cells expressing FLAG-PRMT5 and either ERG construct . Bottom right panel: Western blot of FLAG-PRMT5 IP from 293 cells . ( D ) VCaP , LNCaP and 22Rv1 cells targeted by PRMT5 knockdown using three shRNA sequences ( sh1 , sh2 , sh3 ) or NTC shRNA . Cells were left either treated or untreated with 100ng/ml doxycycline ( Dox ) for 7 , 4 and 5 days respectively to induce shRNA expression . Western blots were analyzed for levels of PRMT5 , total histone H4 , AR and GAPDH as loading control . ( E ) Top panel: waterfall plot of Achilles cell line panel sensitivity to knockdown of PRMT5 using PRMT5 shRNA#1 ( Kryukov et al . , 2016 ) . Prostate cancer cell lines are in red and ERG status is noted . Bottom panel: replotting of the data , colored for MTAP status—red arrows indicate the locations of the prostate cancer cell lines noted in the top panel . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 004 We next narrowed the shRNA screen hit list by focusing on candidates more likely to be ERG interacting proteins . We immunoprecipitated ERG from VCaP cells , and then identified co-immunoprecipitated proteins by mass spectrometry ( Materials and methods ) . Identified proteins ( Supplementary file 2 ) included AR and DNA-PKcs , previously known ERG interactors ( Brenner et al . , 2011; Yu et al . , 2010 ) . Eight of the VCaP-selective shRNA screen hits that also co-immunoprecipitated with ERG were further validated by directed ERG co-immunoprecipitation experiments in VCaP cells . Of these , AR and PRMT5 were the only proteins that co-immunoprecipitated with ERG but not IgG control; these interactions were not overtly influenced by exposure to an androgen analog ( R1881 , Figure 1B; Figure 1—figure supplement 1A ) . We next tested whether the ERG/PRMT5 interaction is observed in other models . PRMT5 co-immunoprecipitated with ERG in 22Rv1 cells ectopically expressing ERG . This interaction was still observed upon expression of ERG bearing mutations in the DNA binding domain ( 'Dx' , Figure 1C ) , suggesting DNA binding is not required for the ERG/PRMT5 interaction . Reciprocal co-immunoprecipitation experiments using overexpressed ERG and PRMT5 in AR-negative 293 and PC3 cells suggest the ERG/PRMT5 interaction can occur in the absence of AR ( Figure 1—figure supplement 1B ) . Further work in 293 cells using truncated ERG constructs suggested that the conserved ETS DNA binding domain of ERG was necessary for the observed co-immunoprecipitation with PRMT5 ( Figure 1—figure supplement 1C ) . Given this evidence that ERG and PRMT5 co-exist in a protein complex , we focused further efforts on PRMT5 , as to our knowledge it has not been previously linked to ERG biology . To validate the growth effects of PRMT5 knockdown , we transduced ERG-positive VCaP cells , and ERG-negative 22Rv1 and LNCaP PC cells , with three independent doxycycline ( Dox ) -inducible shRNA vectors targeting PRMT5 and a non-targeting control shRNA ( NTC ) . PRMT5 knockdown was robust in all cell lines ( Figure 1—figure supplement 1D ) . Robust growth inhibition was observed in VCaP cells; in contrast PRMT5 knockdown had no growth effects in ERG-negative 22Rv1 cells , and only minor effects in ERG negative LNCaP cells ( Figure 1D–F ) . Deletion of methylthioadenosine phosphorylase ( MTAP ) , which is common across cancers , is a major determinant of sensitivity to PRMT5 inhibition ( Kryukov et al . , 2016; Mavrakis et al . , 2016 ) ; as VCaP , LNCaP , and 22Rv1 cells are all MTAP intact , the observed sensitivity of VCaP to PRMT5 shRNA is not due to MTAP deletion . The project Achilles shRNA screen dataset ( Kryukov et al . , 2016 ) contains three prostate cancer cell lines ( VCaP , 22Rv1 and TMPRSS2:ERG positive NCI-H660 ) and one PRMT5 hairpin likely to have minimal off-target effects . This shRNA shows a trend of sensitivity in ERG-positive lines , in agreement with our findings ( Figure 1—figure supplement 1E ) . PRMT5 is a protein arginine methyltransferase that regulates multiple signaling pathways through the mono- and symmetric di-methylation of arginines on its target proteins ( Yang and Bedford , 2013 ) . To determine whether the antiproliferative effects of PRMT5 knockdown in ERG positive VCaP cells were mediated through methyltransferase activity , we expressed shRNA-resistant wild-type PRMT5 , or a catalytically inactive G365A/R368A double mutant ( Materials and methods ) ( Antonysamy et al . , 2012 ) along with PRMT5 shRNA in VCaP cells . WT PRMT5 , but not the G365A/R368A mutant , rescued the effects of PRMT5 knockdown on VCaP cell proliferation ( Figure 1G ) , indicating a requirement for PRMT5 catalytic function to support VCaP proliferation . To understand pathways affected by PRMT5 , we performed transcriptional profiling of PRMT5 knockdown in VCaP cells , followed by the identification of significantly altered pathways ( Figure 2A; Supplementary file 3; see Materials and methods ) . Among these , AR activation was the second most significantly affected pathway , and is a key pathway in common with previous reports of ERG knockdown in VCaP cells ( Chen et al . , 2013; Mounir et al . , 2015; Yu et al . , 2010 ) . AR pathway upregulation was apparent using multiple published AR gene signatures ( Figure 2—figure supplement 1 ) . Using quantitative PCR of reverse transcribed RNA ( qRT-PCR ) , we confirmed that knockdown of either PRMT5 or ERG increased the expression of the AR target genes PSA , NKX3-1 and SLC45A3 ( Figure 2B ) . Expression of shRNA-resistant WT PRMT5 , but not the G365A/R368A mutant , rescued the effects of PRMT5 knockdown on AR target gene expression ( Figure 2C; Figure 2—figure supplement 2A ) , demonstrating that PRMT5 methyltransferase activity is required for repression of AR target genes . The effect of PRMT5 knockdown was restricted to genes co-regulated by both AR and ERG , as PRMT5 knockdown did not affect previously published ( Mounir et al . , 2015 ) AR-independent ERG target genes in VCaP ( Figure 2—figure supplement 2B ) . In addition , PRMT5 knockdown did not induce AR target gene expression in ERG negative 22Rv1 or LNCaP PC cells ( Figure 2—figure supplement 2C ) . In 22Rv1 cells , exogenous ERG expression is sufficient to attenuate PSA , NKX3-1 , and SLC45A3 expression ( Mounir et al . , 2015 ) . Under these conditions , PRMT5 knockdown restored the expression of these genes to baseline levels , yet had no effect on their expression in the background of the DNA-binding defective ( i . e . inactive ) ERG mutant ( 'Dx' , Figure 2—figure supplement 2D ) . These data indicate that PRMT5’s ability to repress AR function is dependent on ERG . 10 . 7554/eLife . 13964 . 005Figure 2 . PRMT5 is an ERG-dependent inhibitor of AR signaling . ( A ) Heat map showing all genes upregulated ( red ) or downregulated ( blue ) by at least 1 . 5 fold following knockdown with PRMT5 shRNA1 ( sh1 ) or shRNA2 ( sh2 ) compared to NTC shRNA . Rows represent probe sets; columns represent individual samples ( technical replicates are marked by 1 or 2 ) . Table indicates pathways significantly upregulated by PRMT5 knockdown ( see Materials and methods , and Supplementary file 3 for significantly downregulated pathways ) . ( B ) qRT-PCR of AR targets PSA , NKX3-1 , and SLC45A3 in VCaP cells expressing the noted shRNA constructs . Expression levels were normalized as described in Materials and methods; bars represent + SEM of three biological replicates , each with three technical repeats . ( C ) qRT-PCR of PSA and NKX3-1 from VCaP cells expressing the noted shRNA constructs alongside cDNAs expressing vector control ( Vector ) , wild-type ( WT ) PRMT5 , or a catalytically dead PRMT5 mutant ( G365A/R368A ) . Data and error bars represented as in ( B ) . ( D ) Top panels: cartoons of the PSA and NKX3-1 loci . ERG and AR binding sites ( and control regions ) are noted and numbered relative to the transcription start site ( TSS ) as described in Materials and methods . Bottom panels: ERG , PRMT5 , and AR ChIP qPCR for the noted regions of PSA ( left ) or NKX3-1 ( right ) in VCaP cells upon ERG or PRMT5 knockdown . Normalization to IgG control ChIP is as described in Materials and methods; error bars represent + SEM of three biological replicates , each with three technical repeats . ( E ) Heatmap visualization of AR binding from ChIP-sequencing data as determined by normalized reads across the AR Cistrome ( Materials and methods ) in replicate samples induced using AR ligands ( DHT or R881 as indicated ) and harboring inducible PRMT5 shRNA1 ( sh1 ) , shRNA2 ( sh2 ) , or shRNA3 ( sh3 ) compared to NTC shRNA . 1659 peaks show differential binding with at least 1 . 5 fold difference ( p-value of 0 . 01 , q-value 0 . 151 ) . The majority of differentially bound sites exhibit increased binding ( 6% of the total Cistrome ) under PRMT5 knockdown conditions . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 00510 . 7554/eLife . 13964 . 006Figure 2—figure supplement 1 . AR signature analysis . Correlation between three independent androgen receptor activation gene signatures ( Malik et al . , 2015; Mendiratta et al . , 2009; Nelson et al . , 2002 ) in comparison with the top-ranked upregulated genes following PRMT5 knockdown by shRNA in VCaP cells ( Figure 2A ) . Each vertical line ( green , blue , or red ) represents the highest expressed probe set for each gene in the gene signatures . Lines are elongated if the probe set was upregulated following PRMT5 knockdown by at least 1 . 5 fold with a nominal p value <0 . 05 . The p values shown are based on a two-tailed Fisher’s exact test described in Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 00610 . 7554/eLife . 13964 . 007Figure 2—figure supplement 2 . ERG and PRMT5 effects on AR target genes are specific . ( A ) Western blot of VCaP cells expressing wild-type ( WT ) PRMT5 , a catalytic dead PRMT5 mutant ( PRMT5 G365A/R368A ) or a vector control ( Vector ) in the background of two independent shRNA vectors ( sh1 and sh2 ) . Cells were maintained in culture for 10 days in presence of 100 ng/ml doxycycline ( Dox ) . Western blot analysis shows expression levels of HA affinity tag ( overexpressed PRMT5 ) , total PRMT5 , and GAPDH as a loading control . ( B ) qRT-PCR of the migration genes PLAT , PLAU and the neuroendocrine genes NTRK3 , CHGA , GDAP1 in VCaP cells expressing either ERG or PRMT5 shRNA constructs . Expression levels were normalized as described in Materials and methods; bars represent + SEM of three replicates . ( C ) qRT-PCR of PSA , NKX3-1 and SLC45A3 in LNCaP and 22Rv1 cells expressing either NTC shRNA , PRMT5 shRNA 1 or PRMT5 shRNA 2 . Data represent normalized expression of PSA , NKX3-1 and SLC45A3 mRNA relative to the B2M transcript . Error bars represent + SEM of three replicates . ( D ) qRT-PCR for PSA , NKX3-1 and SLC45A3 in parental ( PAR ) 22Rv1 cells and following expression of either ERG or ERG DNAx and expressing either NTC shRNA , PRMT5 shRNA 1 or PRMT5 shRNA 2 ( see Materials and methods for description ) normalized relative to the B2M transcript . Error bars represent + SEM of three replicates . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 00710 . 7554/eLife . 13964 . 008Figure 2—figure supplement 3 . ERG , AR and PRMT5 recruitment . ( A ) Top panel: cartoons of the PSA and NKX3-1 loci including the promoter and enhancer regions bound by ERG and AR ( -4100 and -3800 on PSA; +10800 and +62100 on NKX3-1 ) along with other control regions . All distances shown are relative to the PSA and NKX3-1 transcription start site ( TSS ) . Bottom panels: chromatin Immunoprecipitations ( ChIP ) for ERG , PRMT5 , and AR followed by quantitative PCR using primers specific to each region as shown by the horizontal bars in top panels . Shown are the recruitments to the PSA promoter and enhancer regions ( left ) and to the NKX3-1 promoter and enhancer regions ( right ) in 22Rv1 cells in the absence and presence of ERG . All recruitments are normalized to the IgG control ChIP . Normalization and error bars are described in Materials and methods . ( B ) The top 1000 ( MACS mfold rank ) AR and ERG recruitment peaks , analyzed for evolutionary conservation in mammalian species using Phastcons , showing that ERG and AR samples have internally consistent performance . ( C ) Top-scoring motifs in all samples for AR ChIPseq ( left panel ) and ERG ChIP-seq ( right panel ) . ( D ) z-scores for the motifs identified in ( C ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 008 We next used chromatin immunoprecipitation ( ChIP ) to investigate ERG , AR , and PRMT5 recruitment to the AR targets PSA and NKX3-1 following modulation of ERG or PRMT5 expression . ERG knockdown in VCaP reduced its recruitment to previously characterized ( Wei et al . , 2010 ) binding sites on both genes ( Figure 2D ) . We also observed PRMT5 recruitment to these same sites , which was dramatically reduced upon ERG knockdown . Conversely , ERG expression in 22Rv1 cells induced PRMT5 recruitment to these same sites ( Figure 2—figure supplement 3A ) . In VCaP , PRMT5 knockdown reduced its own recruitment to both genes but had virtually no effect on ERG recruitment ( Figure 2D ) . As expected ( Yu et al . , 2010 ) , ERG expression in 22Rv1 reduced AR recruitment to both genes ( Figure 2—figure supplement 3A ) , and ERG knockdown in VCaP increased AR recruitment ( Figure 2D ) . Like ERG , PRMT5 knockdown in VCaP strongly induced AR recruitment ( Figure 2D ) . To extend these findings to a genome wide scale , we performed AR and ERG ChIP-seq experiments in androgen ( DHT or R1881 ) stimulated VCaP cells upon PRMT5 knockdown ( Materials and methods; available PRMT5 antibodies did not work in our hands for ChIP-seq ) . ERG and AR recruitment were robust in these experiments , as judged by recovery of the canonical DNA binding sites of these proteins ( Figure 2—figure supplement 3B–D ) . In agreement with directed ChIP experiments , AR ChIP-seq demonstrated that PRMT5 knockdown increased the recruitment of AR at a subset of peaks ( 6% p-val 0 . 01 , q-val 0 . 151 ) , ( Figure 2E ) , but did not significantly affect ERG binding on the same set of sites . Collectively , these data support a model where ERG recruits PRMT5 to AR targets , and PRMT5 is required for ERG-dependent attenuation of AR binding to specific regulatory regions . In many cell types , PRMT5 represses gene expression through symmetric di-methylation of histone H4 at arginine 3 ( H4R3me2s ) ( Yang and Bedford , 2013 ) , suggesting this as a mechanism for PRMT5 function at AR target genes . However , ERG expression in 22Rv1 cells did not alter H4R3me2s levels at the PSA or NKX3-1 loci ( Figure 3—figure supplement 1 ) . We therefore hypothesized that PRMT5 directly methylates AR . To test this , we immunoprecipitated AR from ERG-positive VCaP cells , and blotted for AR , mono-methyl arginine ( MMA ) , or symmetric di-methyl arginine ( SDMA ) . MMA and SDMA signals were indeed detected in AR immunoprecipitates from VCaP cells , and were reduced following knockdown of either ERG or PRMT5 ( Figure 3A ) . To confirm these findings in an additional ERG-dependent model , we also immunoprecipitated AR from the prostates of wild-type and TMPRSS2:ERG transgenic mice , the latter of which show ERG-dependent hyperproliferative phenotypes ( Mounir et al . , 2015 ) . Like VCaP , immunoprecipitated AR from TMPRSS2:ERG mouse prostates showed high mono- ( MMA ) and symmetric di-methylation ( SDMA ) levels compared to wild-type controls ( Figure 3B ) . On the other hand , LNCaP cells , which bear a translocation in the ETS factor ETV1 , do not show any SDMA signal on AR , and show reduced levels of MMA versus VCaP ( Figure 3C ) , suggesting that AR symmetric dimethylation is unique to ERG versus ETV1 . We next immunoprecipitated AR from 22Rv1 cells , which express wild-type AR as well as a roughly 80 kDa truncated AR variant that lacks its ligand binding domain ( LBD ) ( Dehm et al . , 2008 ) . In these cells , ERG expression increased MMA and SDMA signals on wild-type AR but not the truncated variant , and these ERG-dependent signals were reduced upon PRMT5 knockdown ( Figure 3D ) , suggesting the AR LBD is mono- and symmetrically di-methylated in an ERG- and PRMT5-dependent manner . 10 . 7554/eLife . 13964 . 009Figure 3 . PRMT5 methylates AR on arginine 761 . ( A ) ( A ) Left panel: western blots of noted proteins from VCaP whole cell extracts ( WCE ) after ERG or PRMT5 knockdown . Right panel: Western blot of AR immunoprecipitation ( IP ) from VCaP . SDMA: symmetric di-methyl arginine; MMA: mono-methyl arginine . ( B ) Left panel: Western blot analysis of noted proteins from homozygous TMPRSS2:ERG transgenic ( Tg/Tg ) and WT mouse tissues . Right panel: AR or IgG IP from mouse tissues followed by western blot analysis of MMA , SDMA and total AR levels . ( C ) Western blot of AR immunoprecipitation ( IP ) from VCaP and LNCaP cells grown in charcoal-stripped serum ( CSS ) and stimulated with 10nM R1881 . SDMA: symmetric di-methyl arginine; MMA: mono-methyla arginine . ( D ) Left panel: western blot of noted proteins from 22Rv1 parental ( PAR ) or ERG-expressing ( ERG ) WCEs . FL: full-length; TR: truncated ( lacking ligand binding domain , LBD ) . Right panel: Western blot of AR IP from 22Rv1 . ( E ) Left panel: RWPE-1 parental ( PAR ) and AR and ERG-expressing ( AR/ERG ) cells targeted by PRMT5 knockdown ( PRMT5 shRNA ) or NTC shRNA were left either untreated ( C ) or treated with 100ng/ml doxycycline ( D ) in the absence or presence of 1nM R1881 ( R ) for 24 hr . Western blot analysis shows expression levels of PRMT5 , ERG , AR and GAPDH from input samples ( WCE ) . Right panel: Lysates were then used for AR immunoprecipitation ( AR IP ) followed by western blot analysis using antibodies against MMA , SDMA or total AR levels . ( F ) Top panel: location of all arginines ( R ) in the AR LBD . NTD: N-terminal domain; DBD: DNA binding domain . Right panel: western blot of AR IPs from RWPE-1 cells expressing ERG with wild-type AR ( AR WT ) or R761K mutant . C: control untreated; D: Dox-treated ( ERG induction ) ; R: R1881-treated . Bottom panel: western blot analysis of MMA , SDMA and total AR levels from AR IPs in RWPE-1 cells expressing ERG with either wild-type AR ( AR WT ) or R761K mutant . C: control untreated; D: Dox-treated ( ERG induction ) ; R: R1881-treated . ( G ) Representative immunofluorescence images of Dox- and R1881-treated RWPE-1 cells expressing ERG or AR as noted above each column . AR/SDMA: proximity ligation signals using antibodies detecting AR and SDMA ( see Materials and methods ) . Scale bar , 50 µm . Data shown is a representative example of three biological replicates . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 00910 . 7554/eLife . 13964 . 010Figure 3—figure supplement 1 . H4R3me2s ChIP . Top panel: schematic representation of the PSA and NKX3-1 loci including the promoter and enhancer regions bound by ERG and AR ( -4100 and -3800 on PSA; +10800 and +62100 on NKX3-1 ) along with other control regions . All distances shown are relative to the PSA and NKX3-1 transcription start site ( TSS ) . Bottom panel: enrichment analysis of symmetric di-methyl arginine 3 on histone 4 ( H4R3me2s ) was performed by Chromatin Immunoprecipitations ( ChIP ) for H4R3me2s followed by quantitative PCR amplification using primers specific to each region as shown by the horizontal bars . Enrichment analysis was performed in 22Rv1 cells in the absence or presence of ERG . All enrichments are normalized to the IgG control ChIP . Data shown from three biological replicates , each with three technical repeats . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 01010 . 7554/eLife . 13964 . 011Figure 3—figure supplement 2 . PRMT5 methylates AR in vitro . ( A ) Area ratio of SAH levels normalized to spike-in control SAH-d4 in methyltransferase assay including PRMT5 enzyme , SAM , AR LBD ( ligand binding domain ) , ETS ( ERG ETS domain ) or PNT ( ERG PNT domain ) as noted . ( B ) Area ratio of SAH levels normalized to spike-in control SAH-d4 in methyltransferase assay including PRMT1 enzyme , SAM , AR LBD ( ligand binding domain ) , ETS ( ERG ETS domain ) or H4 control peptide as noted . ( C ) Area ratio of SAH levels normalized to spike-in control SAH-d4 in methyltransferase assay including PRMT5 enzyme , SAM , AR LBD ( ligand binding domain ) and ETS ( ERG ETS domain ) as noted; in the presence or absence of pan-PRMT inhibitor AMI-1 . ( D ) Western blot analysis of MMA and GST-AR levels from samples in ( C ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 01110 . 7554/eLife . 13964 . 012Figure 3—figure supplement 3 . AR and ERG expression in RWPE-1 , and mutation of AR LBD . ( B ) RWPE-1 control cells and stably expressing AR wild type ( AR WT ) were either left untreated ( C ) or treated with 100ng/ml doxycycline ( D ) to induce ERG expression , in the absence ( D ) or presence ( R/D ) of 1nM R1881 for 24 hr . Western blot analysis shows expression levels of AR and ERG . ( B ) qRT-PCR of the luminal genes PSA ( blue ) and NKX3-1 ( red ) from RWPE-1 cells treated as in ( A ) . Data represent normalized expression of PSA and NKX3-1 mRNA relative to the B2M transcript . Error bars represent + SEM of three replicates . ( C ) Western blot analysis of AR , ERG and GAPDH expression levels from RWPE-1 parental cells or cells stably expressing either AR wild type ( WT ) , R711K , R727K , R753K , R761K , R775K , R780K , R787K , R789K , R787/789K , R832K , R841K , R847K , R855K , R856K , R855/856K or R872K AR mutant . ( D ) RWPE-1 cells stably expressing either AR R711K , R727K , R753K , R775K , R780K , R787K , R789K , R787/789K , R832K , R841K , R847K , R855K , R856K , R855/856K or R872K mutant were used for AR IP followed by western blot analysis for MMA , SDMA and total AR levels . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 012 To further understand if the observed AR methylation was directly dependent on PRMT5 , we performed biochemical assays using purified PRMT5 ( complexed with its requisite binding partner MEP50/WDR77 ) and AR LBD . PRMT5 activity , as judged by production of SAH ( the by-product of SAM-dependent substrate methylation ) , was observed in the presence of AR LBD as substrate , but not in the presence of ERG ETS DNA binding domain or pointed ( PNT ) domain . PRMT5 activity in the presence of AR LBD further increased with the addition of ERG ETS domain protein to the reaction , but not with PNT domain ( Figure 3—figure supplement 2A ) . Unlike PRMT5 , purified PRMT1 showed no activity in the presence of AR LBD ( Figure 3—figure supplement 2B ) . PRMT5 activity on AR , in the absence or presence of ERG ETS domain , was reduced with the addition of the tool PRMT inhibitor AMI-1 ( Cheng et al . , 2004 ) . PRMT5 dependent methylation of the AR-LBD was also observed in western blots , where increased signal for MMA ( but not SDMA , likely due to low overall PRMT5 activity in vitro and the distributive nature of this enzyme [Wang et al . , 2014] ) was observed at the correct size for the AR-LBD protein in the presence of PRMT5 . This activity was increased by ERG ETS domain , and inhibited by AMI-1 ( Figure 3—figure supplement 2C–D ) . Together this data indicates that AR LBD is a substrate of the PRMT5 enzyme in vitro , and that the ERG ETS domain can facilitate greater PRMT5 activity on AR . To identify the arginine methylation site ( s ) on AR , we cloned AR cDNA and mutated all arginines in the LBD to lysine ( see Materials and methods ) . We expressed each construct in AR-negative , ERG-negative RWPE-1 prostate cells . R1881 stimulates exogenous AR to induce target gene expression in these cells . ERG expression represses this effect ( Figure 3—figure supplement 3A–B ) and induces PRMT5-dependent mono- and symmetric di-methylation of AR ( Figure 3E ) . RWPE-1 cells co-expressing ERG with each of the AR constructs were assessed for SDMA and MMA modification of AR following immunoprecipitation . All mutants were expressed at equivalent levels to wild-type AR ( Figure 3—figure supplement 3C ) . Only one AR mutant , R761K , completely lacked MMA and SDMA of AR in the presence of ERG ( Figure 3F; Figure 3—figure supplement 3D ) . We confirmed these results in RWPE-1 cells using a proximity ligation assay to detect symmetric di-methylation of AR ( Materials and methods ) . We detected strong proximity signals in RWPE-1 nuclei upon expression of wild-type AR and exposure to R1881 ( which stimulates nuclear translocation of AR ) that were dependent upon ERG expression . These signals were not detected in the AR R761K mutant cell line ( Figure 3G ) . This demonstrates that arginine 761 is the likely target of ERG- and PRMT5-dependent AR methylation . Available structural information ( Chandra V et al . , 2008; Emsley et al . , 2010; Helsen et al . , 2012 ) ( see Materials and methods ) suggests R761 methylation on AR may affect LBD interactions with the DNA binding domain ( DBD , Figure 4A ) , resulting in altered DNA binding and gene activation . To test this , we evaluated the recruitment of WT versus R761K mutant AR in RWPE-1 cells treated with R1881 , using the PSA locus as a model AR-regulated gene . Relative to WT AR , the AR R761K mutant showed enhanced recruitment to PSA and increased PSA expression . Moreover , R761K mutation prevented ERG-dependent attenuation of AR recruitment to PSA and PSA expression ( Figure 4B , C ) . In contrast to these effects , the AR R761K mutation did not affect overall AR protein levels , the ability of AR to shuttle into the nucleus upon R1881 induction ( Figure 4—figure supplement 1 ) , or the levels of ERG or PRMT5 recruitment to PSA ( Figure 4—figure supplement 1C ) . These results indicate R761 methylation mediated through ERG and PRMT5 attenuates ligand-dependent AR activation , likely through modulating interactions between the LBD and DBD . 10 . 7554/eLife . 13964 . 013Figure 4 . R761 methylation regulates AR recruitment , transcription , and proliferation . ( A ) Model of AR LBD ( PDB: 2AO6; yellow ) and AR DBD ( PDB: 1R4I; green ) interactions ( see Materials and methods ) . A modeled di-methylated R761 is shown ( red ) . Grey ribbon: TIF2 coactivator peptide . Cyan spheres: R1881 . DNA is shown as orange/blue sticks . ( B ) AR ChIP qPCR for regions of the PSA gene as in Figure 2D from RWPE-1 cells expressing wild-type ( WT , left ) AR or AR R761K ( right ) . DOX: ERG expression; R1881 is 1nM . Error bars represent + SEM of three biological replicates , each with three technical repeats . ( C ) PSA qRT-PCR in RWPE-1 cells expressing WT AR ( left ) or AR R761K ( right ) . Error bars represent + SEM of three biological replicates , each with three technical repeats . ( D ) RWPE-1 parental cells and cells expressing either wild type AR ( AR WT ) or AR R761K mutant were left either untreated ( control ) or treated with 0 . 1 , 1 or 5nM R1881 for 6 days and confluence measurements ( see Materials and methods for description ) were collected . Error bars represent + SEM of three biological replicates , each with three technical repeats . ( E ) PSA qRT-PCR from VCaP cells expressing the noted AR constructs , grown in androgen-depleted media ( charcoal-stripped serum ) . ( F ) VCaP cell proliferation upon expression of the noted AR constructs . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 01310 . 7554/eLife . 13964 . 014Figure 4—figure supplement 1 . AR R761K mutation effects in RWPE-1 . ( A ) Western blot of RWPE-1 parental cells and cells stably expressing AR wild type ( AR WT ) or AR R761K mutant . Cells were either untreated ( C ) or treated with 100ng/ml doxycycline ( D ) to induce ERG expression , in the absence or presence of 1 nM R1881 for 24 hr ( R , R/D ) . Blots for AR , ERG , and GAPDH are shown . ( B ) Representative immunofluorescence images of AR from RWPE-1 cells stably expressing either AR WT or R761K mutant . Cells were either untreated ( - ) or treated with 0 . 1 or 1nM R1881 for 24 hr . Cells were also stained with DAPI to visualize nuclei . Cells were fixed and stained as described in Materials and methods . Scale bar , 50 µm . Data shown is a representative example of three biological replicates . ( C ) ChIP qPCR of ERG ( top row ) and PRMT5 ( bottom row ) at the PSA promoter and enhancer regions in RWPE-1 cells expressing either wild type AR ( AR WT , left column ) or AR R761K mutant ( right column ) in the absence and presence of ERG ( Dox ) or R1881 ( R; 1nM for 24 hrs ) . All recruitments are normalized to the IgG control ChIP . Error bars represent + SEM of three biological replicates , each with three technical repeats . DOI: http://dx . doi . org/10 . 7554/eLife . 13964 . 014 Increased AR activity drives PC proliferation ( Chen et al . , 2004 ) . However , we noted that RWPE-1 cells expressing AR R761K , despite increased AR activity , were more prone to R1881-induced growth arrest ( Figure 4D ) . This suggested that heightened AR activity via R761K mutation ( or loss of R761 methylation ) may block proliferation in certain contexts . To further explore this , we expressed R761K mutant AR in VCaP cells . Despite transcriptional hyperactivity , as judged by increased PSA expression relative to either WT or an irrelevant R789K mutant AR , VCaP cells expressing AR R761K proliferated poorly in reduced-androgen media ( Figure 4E and F ) . Detailed mechanisms of how the tumor context affects AR and other lineage oncogenes to switch their function from lineage differentiation to proliferation in cancer has remained elusive ( Garraway and Sellers , 2006 ) . Our results indicate that a primary effect of ERG in facilitating PC proliferation is modulating AR function . We propose that ERG- and PRMT5-dependent methylation of R761 on AR reduces AR recruitment to genes that would otherwise induce differentiation , yet R761 methylation allows sufficient AR function to promote proliferation . While our results raise the question of how R761-methylated AR may still facilitate proliferation , R761 methylation could prove a relevant biomarker for AR-mediated proliferation versus arrest in TMPRSS2:ERG positive cells . Finally , as PRMT5 is an enzyme required downstream of ERG in facilitating AR proliferative function , exploring therapeutic PRMT5 inhibition in TMPRSS2:ERG positive prostate cancers may be warranted . VCaP , 22Rv1 , LNCaP , PC3 , RWPE-1 , and 293T cells were obtained from ATCC and maintained in recommended media unless otherwise specified . Cell identities were verified by SNP analyses using ABI TaqMan SNP genotyping assays ( Asuragen ) and tested for mycoplasma contamination using the MycoAlert Mycoplasma Detection kit ( Lonza ) . 22Rv1 and VCaP cells were used for a screen using a custom shRNA library ( Cellecta , Inc . ) targeting transcriptional and epigenetic regulators similar to a previously reported library ( Hoffman et al . , 2014 ) . shRNA library design and construction and viral packing were performed as previously described ( Hoffman et al . , 2014 ) . To obtain an MOI of 0 . 3 , the required volume of virus was determined using a 10 point dose curve ranging from 0 to 1ml of viral supernatant in the presence of 10 ug/ml polybrene . Infection efficiency was determined by the percentage of RFP positive cells measured by FACS analysis . Screens were run in duplicate . For the 22Rv1 screen , 14 . 4 million cells were plated 24 hr prior to infection in T-225 flasks . On the day of infection , the culture media was replaced with fresh media containing 10 ug/mL polybrene and sufficient virus was added for an MOI of 0 . 3 . 24 hr after infection , the culture media was replaced with fresh media containing puromycin . 72 hr following puromycin addition , cells were trypsinized , and 14 . 4 million cells were plated into new flasks . For the VCaP screen , 23 . 7 million cells were plated 24 hr prior to infection in T-225 flasks . On the day of infection , the culture media was replaced with fresh media containing 10 ug/mL polybrene and sufficient virus was added for an MOI of 0 . 3 . 24 hr after infection , the culture media was replaced with fresh media and cells were allowed to recover for 72 hr prior to puromycin selection . 5 days following puromycin addition , cells were trypsinized , and 23 . 7 million cells were plated into new flasks . At each passage , an aliquot of cells was used to measure transduction efficiency determined by measuring the% RFP positive cells and was typically > 90% . Cells were maintained in culture and split when confluence reached 90% and at each passage , 14 . 4 million cells ( 22Rv1 screen ) and 23 . 7 million cells ( VCaP screen ) were passaged into new flasks , ensuring a representation of >1000 cells/shRNA in the library and the% RFP positive cells was measured to ensure stability of the transduced population over time . When the cells reached 5-population doublings , 40 million cells were harvested by centrifugation and stored at −20°C . Purification of genomic DNA and PCR for library production were performed as previously described ( Hoffman et al . , 2014 ) . shRNA screen data analysis was performed as previously described ( Hoffman et al . , 2014 ) . For gene based hit calling , the The Redundant siRNA Activity or RSA metric was used as described ( Hoffman et al . , 2014 ) . Briefly , the RSA down p-value is the statistical score that models the probability of a gene 'hit' based on the collective activities of multiple shRNAs per gene . The RSA down p-value reports the statistically significant genes causing a loss in viability . All hits showing an RSA score >10-5 in the 22Rv1 screen and <10-5 in the VCaP screen ( total of 32 genes ) were used for further analysis ( see Supplementary file 1 for full list ) . Nuclear extracts of VCaP cells ( ~600 million cells ) were pre-cleared using agarose beads ( Trueblot rabbit kit , eBioscience ) and used for pulldown with either an ERG antibody ( Santa Cruz#353 ) or an anti-rabbit IgG antibody . All antibodies were pre-coupled to beads ( AminoLink Plus kit , Thermo Scientific ) , washed then used for pulldown . Immunoprecipitations were eluted at pH2 . 5 followed by TCA protein precipitation , alkylated with iodoacetamide , and separated on a NuPage 4–12% Bis-Tris gradient gel ( Invitrogen ) . Complete gel lanes were excised using a LEAP 2DiD robot and in-gel digested with trypsin ( Tecan Freedom EVO 20 ) . Peptide sequencing for the resulting resulting 16 digest samples was performed by liquid chromatography-tandem mass spectrometry using an Eksigent 1D+ high-pressure liquid chromatography system coupled to a LTQ-Orbitrap XL mass spectrometer ( Thermo Scientific ) . Peptide mass and fragmentation data were searched against a combined forward-reverse IPI database ( v3 . 55 ) using Mascot 2 . 2 ( Matrix Science ) . Peptide and protein validation were done using Transproteomic pipeline v3 . 3sqall ( Institute for Systems Biology; [http://tools . proteomecenter . org/software . php] ) using a false positive threshold of <1% for protein identifications ( See Supplementary file 2 for full list; in red are the ERG interactors also identified as shRNA screen hits ) . The Dox-inducible shRNA vector ( pLKO-Tet-On ) was previously described , as were the sequences of the nontargeting control and ERG shRNA inserts and stable cell line generation ( Mounir et al . , 2015 ) . PRMT5 shRNA sequences are as follows: PRMT5 shRNA#1: AGGGACTGGAATACGCTAATTCTCGAGAATTAGCGTATTCCAGTCCCT PRMT5 shRNA#2: AGGGACTGGAATACGTTAATTGTTAATATTCATAGCAATTAGCGTATTCCAGTCCCTPRMT5 shRNA#3: GCGGATAAAGTTGTATGTTGTGTTAATATTCATAGCACAGCATACAGCTTTATCCGC All shRNA were expressed from a puromycin resistant vector . Lentiviral production and cell transduction was as previously described ( Mounir et al . , 2015 ) . The Dox-inducible ERG constructs named ERG or ERG DNAx used for stable and inducible expression in 22Rv1 cells were generated as previously described ( Mounir et al . , 2015 ) . The cDNA expression rescue constructs were cloned into a pRETRO retroviral vector under a CMV promoter and containing a neomycin-IRES-YFP selection cassette . The HA-tagged PRMT5 sequence was generated synthetically to resist knockdown by all PRMT5 shRNAs used in this study and was cloned into a Gateway compatible entry vector . In order to resist knockdown by all PRMT5 shRNA sequences , 6–7 silent mutations ( in red below ) were introduced into the HA-PRMT5 cDNA sequence to produce a shRNA resistant version ( HA-scPRMT5 ) . For shPRMT5-1: the AGGGACTGGAATACGCTAATT target sequence was converted to CGCGATTGGAACACGTTGATT ( underlines denote altered bases ) . For shPRMT5-2: the GCGGATAAAGCTGTATGCTGT target sequence was converted to CAGAATCAAGCTCTACGCCGT ( underlines denote altered bases ) . Full sequence of scPRMT5 below: scPRMT5 sequence: ATGTACCCCTATGACGTGCCAGATTACGCCATGGCGGCGATGGCGGTCGGGGGTGCTGGTGGGAGCCGCGTGTCCAGCGGGAGGGACCTGAATTGCGTCCCCGAAATAGCTGACACACTAGGGGCTGTGGCCAAGCAGGGGTTTGATTTCCTCTGCATGCCTGTCTTCCATCCCAGGTTCAAGCGCGAGTTTATTCAGGAACCTGCTAAGAATCGGCCCGGTCCCCAGACACGATCAGACCTACTGCTGTCAGGACGCGATTGGAACACGTTGATTGTGGGAAAGCTTTCTCCATGGATTCGTCCAGACTCAAAAGTGGAGAAGATTCGCAGGAACTCCGAGGCGGCCATGTTACAGGAGCTGAATTTTGGTGCATATTTGGGTCTTCCAGCTTTCCTGCTGCCCCTTAATCAGGAAGATAACACCAACCTGGCCAGAGTTTTGACCAACCACATCCACACTGGCCATCACTCTTCCATGTTCTGGATGCGGGTACCCTTGGTGGCACCAGAGGACCTGAGAGATGATATAATTGAGAATGCACCAACTACACACACAGAGGAGTACAGTGGGGAGGAGAAAACGTGGATGTGGTGGCACAACTTCCGGACTTTGTGTGACTATAGTAAGAGGATTGCAGTGGCTCTTGAAATTGGGGCTGATTTGCCCTCTAATCACGTCATTGATCGCTGGCTTGGGGAGCCCATCAAAGCAGCCATTCTCCCCACTAGCATTTTCCTGACCAATAAGAAGGGATTTCCTGTTCTTTCTAAGATGCACCAGAGGCTCATCTTCCGGCTCCTCAAGTTGGAGGTGCAGTTCATCATCACAGGCACCAACCACCACTCAGAGAAGGAGTTTTGTAGCTACCTGCAGTACCTGGAATACTTAAGCCAGAACCGTCCTCCACCTAATGCCTATGAACTCTTTGCCAAGGGCTATGAAGACTATCTGCAGTCCCCGCTTCAGCCACTGATGGACAATCTGGAATCTCAGACATATGAAGTGTTTGAAAAGGACCCCATCAAATACTCTCAGTACCAGCAGGCCATCTATAAATGTCTGCTAGACCGAGTACCAGAAGAGGAGAAGGATACCAATGTCCAGGTACTGATGGTGCTGGGAGCAGGACGGGGACCCCTGGTGAACGCTTCCCTGCGGGCAGCCAAGCAGGCCGACCGCAGAATCAAGCTCTACGCCGTGGAGAAAAACCCAAATGCCGTGGTGACGCTAGAGAACTGGCAGTTTGAAGAATGGGGATCCCAGGTCACGGTAGTCAGCTCAGACATGAGGGAATGGGTGGCTCCAGAGAAAGCAGACATCATTGTCAGTGAGCTTCTGGGCTCATTTGCTGACAATGAATTGTCGCCTGAGTGCCTGGATGGAGCCCAGCACTTCCTAAAAGATGATGGTGTGAGCATCCCCGGGGAGTACACTTCCTTTCTGGCTCCCATCTCTTCCTCCAAGCTGTACAATGAGGTCCGAGCCTGTAGGGAGAAGGACCGTGACCCTGAGGCCCAGTTTGAGATGCCTTATGTGGTACGGCTGCACAACTTCCACCAGCTCTCTGCACCCCAGCCCTGTTTCACCTTCAGTCACCCTAATCGCGACCCCATGATTGACAACAACCGCTATTGCACCTTGGAATTTCCTGTGGAGGTGAACACAGTACTACATGGCTTTGCCGGCTACTTTGAGACTGTGCTTTATCAGGACATCACTCTGAGTATCCGTCCAGAGACTCACTCTCCTGGGATGTTCTCATGGTTTCCTATTCTGTTTCCCATCAAGCAGCCCATAACGGTACGTGAAGGCCAAACCATCTGTGTGCGTTTCTGGCGATGCAGCAATTCCAAGAAGGTGTGGTATGAGTGGGCTGTGACAGCACCAGTCTGTTCTGCTATTCATAACCCCACAGGCCGCTCATATACCATTGGCCTCTGA . Generation of the PRMT5 catalytic dead mutant was performed by site-directed mutagenesis ( QuickChange II , Agilent ) through mutation of G365 to A and R368 to A ( Antonysamy et al . , 2012 ) . VCaP , 22Rv1 , LNCaP and RWPE-1 cell lines ( ATCC ) were grown in vendor-recommended media and maintained in a humidified 5% CO2 incubator at 37°C . Doxycycline ( Dox , Sigma ) was used at 100 ng/ml . Cell proliferation was measured in 6-well plates ( Corning ) using automated confluence readings ( IncuCyte EX , Essen Bioscience ) . R1881 ( Sigma ) and charcoal-stripped serum ( Omega Scientific ) were used where indicated . 2–5 ug of AR , ERG or IgG control antibody was coupled to 1mg of magnetic beads according to manufacturer’s protocol ( Invitrogen Dynabeads Antibody Coupling kit#143 . 11D ) . After coupling , 1 mg of the antibody/bead mixture was incubated with 1–5 mg of protein lysate overnight under rotation at 4°C . IP samples were then washed with RIPA buffer containing protease/phosphatase inhibitor cocktail for 3–4 washes and resuspended in non-reducing loading buffer , boiled and loaded on a gel for western blot analysis . Immunoprecipitations were performed using the following antibodies: anti-ERG antibody ( Epitomics# 2805–1 ) , anti-HA antibody ( Roche#11815016001 ) , anti-AR antibody ( Thermo Scientific# MA5-13426 ) or anti-IgG antibody ( Rockland Immunochemicals# RL011-0102 ) . Procedures were previously described ( Mounir et al . , 2015 ) and the following antibodies were used at 1:1000 dilutions and incubated overnight at 4°C: ERG ( Epitomics# 2805–1 ) , PRMT5 ( CST# 2252; SIGMA#P0493 ) , GAPDH ( Millipore#MAB374 ) , H4 ( CST#2592 ) , AR ( Santa Cruz#sc-7305 ) , HA ( Roche#11815016001 ) , Symmetric Di-methyl arginine ( SDMA , CST#13222 ) , Mono-methyl arginine ( MMA , CST#8711 ) , TRIP12 ( Abcam#ab86220 ) , EIF4E ( CST#9742 ) , CDC42 ( BD Transduction Laboratories#610929 ) , HDAC1 ( CST#2062 ) , SMARCB1/SNF5 ( Bethyl Laboratories# A301-087A ) , SMARCE1/BAF57 ( Bethyl Laboratories# A300–810A ) . Generation of labeled cDNA , hybridization to Affymetrix U133plus2 human arrays , and data normalization were performed as described ( Mounir et al . , 2015 ) . For the candidate signatures in Figure supplement 2A ( Malik et al . , 2015; Mendiratta et al . , 2009; Nelson et al . , 2002 ) , a two-tailed fisher’s exact test was used to determine if probesets representing genes in those signatures were under- or over-represented in the set of probesets that were up- or down-regulated at least 1 . 5-fold compared to expressed but non-differentially-expressed probesets , with a nominal p-value of 0 . 05 or less . For an unbiased approach ( Figure 2A ) , pathways derived from GO terms and transcription-factor networks were analyzed for overrepresentation via a one-tailed interpolated fisher’s exact test , using genes that varied 1 . 5-fold or more with a nominal p-value of 0 . 05 or less compared to all genes represented on the array; Benjamini-Hochberg ( BH ) correction was then applied to these p-values ( Wiederschain et al . , 2007 ) . The VCaP microarray dataset ( Figure 2A ) is available at the NCBI Gene Expression Omnibus ( accession number GSE65965 ) . Black line ( Figure supplement 2A ) represents expressed probe set position and is ranked by average fold-change . Blue , green , and red lines indicate where the probe sets mapping to genes in the androgen receptor activation signatures appear in our data set and show the cumulative sum of the probe sets in the androgen receptor activation signatures that overlap with our gene list ( only the highest expressing probe set was used per gene ) . The dashed line represents the hypothetical cumulative sum for a random list of genes that are unenriched . RNA isolation was performed as previously described ( Mounir et al . , 2015 ) . Taqman reactions ( Applied Biosystems ) were performed using Gene Expression master mix , FAM-labeled probes for PSA , NKX3-1 and SLC45A3 and VIC-labeled probe for Beta-2-macroglobulin ( B2M ) as a normalization control . Samples were run on a 7900HT Real-Time PCR machine ( Applied Biosystems ) and data was analyzed and normalized according to manufacturer’s instructions ( 2-ΔCt method ) . VCaP , 22Rv1 and RWPE-1 cells were treated as specified followed by cross-linking with 1% formaldehyde for 10 min . Cells were next lysed in 1% SDS and sonicated until DNA ladder is below 1 kb ( Diagenode ) . Sheared chromatin was then used for IP with specific primary antibodies ( 2-4 ug; previously tested ) pre-complexed with Protein A/G Dynabeads and incubated overnight under rotation at 4°C . The next day , ChIP samples were washed with RIPA buffer and TE followed by reverse crosslinking using 1%SDS and 30 ug/ml proteinase K ( Invitrogen ) at 65°C for 6 hr with beads . The eluates were then purified using the QIAquick PCR purification kit ( Qiagen ) and used for qPCR with the following primer sets: PSA -4100: acctgctcagcctttgtctc AND ttgtttactgtcaaggacaatcg PSA -3800: agaattgcctcccaacactg AND cagtcgatcgggacctagaa PSA -100: cttccacagctctgggtgt AND aaaccttcattccccaggac PSA +700: agccccagactcttcattca AND atgcagatttggggaatcag NKX3-1 -2800: gagagcagctgttcctccac AND acgagccttttccacctttc NKX3-1 -200: agggaggagagctggagaag AND tcctccctaggggattcct NKX3-1 +2150: accaggatgaggatgtcacc AND cagggacagagagagccttg NKX3-1 -+10800: tctctcgttggctcctgatt AND ccagcttttgttccttcctg NKX3-1 +62100: cggtttattgcccatgaaga AND aacagggctcacagtgcttt VCaP cells harboring Dox inducible shRNAs targeting PRMT5 where grown to 80% confluency and re-seeded ( day 0 ) into full media containing 100 ng/ml Doxycycline . On day 3 media was replaced with ‘hormone reduced’ media , containing 100 ng/ml Doxycycline . Cells were stimulated with adding indicated ligands ( DHT ( Sigma ) , R1881 [Sigma] ) or vehicle on day 4 and harvesting of cells was performed on day 5 . Cells were harvested by fixation using 1% methanol free formaldedyde ( Polysciences , Cat#18814 ) in PBS at room temperature . Fixation was stopped after 8 min by replacing fixation buffer with ice cold PBS containing 125 mM glycine and 5 mg/ml BSA . Cells were further washed once using ice-cold PBS and re-suspended into 500 ul of PBS containing Complete Protease Inhibitors ( Roche ) . Cells where then pelleted and supernatant removed , and the resulting pellet either snap frozen in liquid Nitrogen or immediately re-suspended in lysis buffer for further processing . For ChIP the resulting cell lysate was sonicated using a Covaris E210 instrument according to manufacturers recommendations . Each ChIP reaction was performed using soluble fraction chromatin corresponding to 7 . 5 ug purifed DNA and 4 ug of antibodies . Antibodies were allowed to bind overnight before capture on protein A magnetic beads ( Invitrogen , Dynal ) . Bound beads where washed 4 times in RIPA buffer containing 500 mM LiCl , and 2 times with TE buffer before being re-suspended in containing 100 mM NaHCO3 and 1% ( w/v ) SDS . Crosslink reversal was done at 65C°C for 6 hr and ChIP DNA were isolated using DNA purification beads ( MagBio ) . ChIP-seq libraries were generated using the KAPA HTP library preparation kit ( Kapa biosciences ) . All handling of samples after sonication was done on using a Sciclone NGS Workstation ( P/N SG3-31020-0300 , PerkinElmer ) . Sequencing was performed on an Illumina NextSeq500 instrument . Reads passing Illumina standard QC were mapped to genome version Hg19 using BWA , and binding sites ( ‘peaks’ ) were identified using MACS2 , evolutionary conservation scores at peak locations was calculated using Phastcons and enriched DNA motifs using MDscan and Seqpos . These were performed using the ChiLin QC pipeline ( liulab . dfci . harvard . edu/WEBSITE/software ) . Peaks from AR ChIP-sequencing samples with a MACS2 enrichment score higher than 10 were extended to a uniform 400 bp across all samples and overlapping peaks where collapsed to generate a union of all peaks . This resulted in a Cistrome of 25 , 593 peaks that were used in all genome wide ChIP analyses . Using the features of this Cistrome as GTF , read counts from BWA mapped Bam files were processed using the Qlucore 3 . 1 . 19 software . Heatmaps and statistical test ( two-sided t-test using correction for multiple hypothesis testing ) of differential binding scores on the 25 , 593 features were performed in Qlucore v3 . 1 . 19 . The AR and ERG ChIPseq datasets are available at the NCBI Gene Expression Omnibus ( Accession number GSE79128 ) . RWPE-1 cells were treated as specified , fixed with 4% paraformaldehyde for 45 min at room temperature ( Electron Microscopy Sciences ) , blocked with 5% goat serum , 0 . 5% Triton X-100 in PBS for 2 hr and incubated with 1:50 dilutions of AR ( LSBIO #LS-C87494 ) and symmetric di-methyl arginine antibodies ( CST#13222 ) in 5% goat serum and 0 . 05% Triton X-100 . Fixed samples were incubated overnight at 4°C in primary antibody before incubations with proximity ligation assay ( PLA ) secondary antibodies ( Duolink , Sigma ) . The secondary antibody incubation , ligation , amplification and final wash steps were performed according to the manufacturer’s specifications . Confocal microscopy was performed using an LSM 510 META ( Carl Zeiss , Inc . ) with a 40x C-Apochromat objective , NA 1 . 2 . Images were collected and processed using Zen software ( Carl Zeiss , Inc . ) . Full length Homo sapiens androgen receptor ( AR ) sequence ( transcript variant 1; NM_000044 . 3 ) was synthesized to include a 5’ NotI and 3’ BamHI sites and used as a template for the mutagenesis of each arginine in the ligand binding domain of AR into lysine ( QuikChange XL site-directed mutagenesis kit , Agilent ) . Following sequence verification to ensure mutation incorporation , each AR mutant sequence was cloned into the pLVX vector via the 5’ NotI and 3’ BamHI as previously described ( Mounir et al . , 2015 ) . Immunofluorescence of RWPE-1 cells was performed by fixing cells for 45 min at room temperature by adding 4% paraformaldehyde ( Electron Microscopy Sciences ) and incubated with 1:50 dilution of AR antibody ( LSBIO #LS-C87494 ) . Confocal microscopy was performed using an LSM 510 META ( Carl Zeiss , Inc . ) with a 40x C-Apochromat objective , NA 1 . 2 . Images were collected and processed using Zen software ( Carl Zeiss , Inc . ) . The heterodimeric structure of PPARγ-RXRα ( PDB Code: 3DZY ) was used as a template to overlay individual domain structures of AR including the DBD in complex with DNA ( PDB Code: 1R4I ) and the LBD in complex with coactivator peptide TIF2 ( iii ) and ligand R1881 ( PDB Code: 2AO6 ) . A superposition was achieved using secondary structure matching in COOT ( Emsley et al . , 2010 ) . The AR DBD was superposed onto chain A of RXRα and the AR-LBD was superposed onto chain B of PPARγ , resulting in the final overlay shown in Figure 4A . A dimethylated Arg was superposed onto R761 ( NCBI Reference Sequence: NP_000035 . 2; some publications may refer to it as R760 when using the previous Reference Sequence number ) within the AR LBD using least squares fit and matching only mainchain atoms , after which the non-methylated Arg was removed from the resulting model . While structure determination of AR containing its intact DBD and LBD has remained elusive , structural data of each individual domain , as well as intact structures within the nuclear receptor family , lead to a valuable understanding of interdomain communication . A previous study utilized the heterodimeric structure of PPARγ and RXRα to apply in silico three-dimensional alignment and docking analysis , followed by mutational analysis , to propose a DBD-LBD interface within AR , including R761 ( Figure 4A ) ( Chandra V et al . , 2008; Helsen et al . , 2012 ) . We hypothesize that R761 is involved in key interactions at this interface and that its methylation would add hydrophobicity , eliminating any polar interactions , as well as steric bulk . This disruption at the DBD-LBD interface could result in destabilization of the quaternary structure , resulting in an inhibitory effect on AR activation . While AR R761K closely mimics the properties of wt AR , the substitution eliminates the possibility of methylation by PRMT5 and , therefore , eliminates the possibility of this type of disruption , accounting for the observed increase in activation . The gene encoding human AR LBD ( residues 663–919 ) , was inserted into a pGEX-6P-1 vector and expressed as a GST-tagged fusion protein in BL21 Star ( DE3 ) cells . Cells were grown in TB2 medium containing 10 µM dihydrotestosterone ( DHT ) and induced with 1 mM IPTG for 14 –16 hr at 16°C . Cells were resuspended in buffer A containing 50 mM Tris-HCl ( pH 7 . 3 ) , 150 mM NaCl , 10% glycerol , 0 . 25 mM TCEP , and 10 µM DHT , to which 50 µg/ml DNase I and protease inhibitor cocktail ( Roche ) were added . Cells were lysed using an M-110L Microfluidizer at 18 , 000 psi , followed by the addition of 0 . 5% CHAPS to the lysate prior to high speed centrifugation . For one-step batch purification , the soluble extract was incubated with 2 ml of glutathione sepharose 4 fast flow medium ( GE Healthcare ) for 1 hr at 4°C with rotational mixing . The sepharose medium was washed in buffer A with the addition of 0 . 5% CHAPS . Elution was accomplished by resuspending and incubating the media for 10 min in the wash buffer plus 10–20 mM reduced glutathione . The eluted fractions were then combined and concentrated to 0 . 3 mg/ml . PRMT5 enzymatic activity was assessed by monitoring S-adenosyl-L-homocysteine ( SAH ) product formation utilizing liquid chromatography-tandem mass spectrometry ( LC-MS/MS ) . 0 . 5–1 uM of PRMT5/MEP50 recombinant enzyme ( BPS , cat#51045 ) was incubated with 50 uM SAM , 2 uM GST-AR LBD and/or 5 uM ETS or PNT ERG protein for 2 hr at 37°C . Reactions were quenched to 0 . 1% HCOOH followed by addition of [β , β , γ , γ-2H4]-SAH ( SAH-D4 ) in 20% DMSO as an internal standard for MS quantification . Samples were sonicated with a Hendrix SM-100 sonicator ( Microsonics Systems ) and centrifuged . SAH was separated from the reaction mixture by reversed phase chromatography using polar endcapped C18 reversed phase columns ( Synergi Hydro-RP , 2 . 5 μm , 100 Å , 20 x 2 mm , Phenomenex ) and detected using a 4000 QTRAP Hybrid Triple Quadrupole/Linear Ion Trap LC-MS/MS system ( AB Sciex ) .	Prostate cancers are among the most common types of cancer in men , which , like other cancers , are driven by genetic mutations . Roughly half of all prostate cancers contain a genetic change that incorrectly fuses two genes together , causing the cells to produce abnormally high levels of a protein called ERG . ERG is a transcription factor , a protein that binds to specific sequences of DNA to influence the activity of nearby genes . ERG represses genes that help to prevent prostate cancers from growing , and so promotes prostate cancer development . Like most other transcription factors , ERG is difficult to target with drugs and no therapies that directly prevent the activity of ERG currently exist . Mounir et al . wanted to find out whether ERG cooperates with other proteins to cause prostate cancer cells to grow , with the hope that these proteins could be more easily targeted with a drug . By using various biochemical techniques in human prostate cancer cell lines , Mounir et al . found that ERG interacts with an enzyme called PRMT5 . This interaction enables PRMT5 to chemically modify other proteins to change their activity . In the case of prostate cancer cells , PRMT5 inappropriately modifies the androgen receptor , a protein that regulates the growth of normal prostate cells . This abnormal modification contributes to the excessive growth of the cancer cells . Although PRMT5 will be easier to target with drugs than ERG , it also has many other roles besides those described by Mounir et al . Much more work is therefore needed to investigate whether PRMT5 could be safely targeted to treat patients with prostate cancer .	[ "Abstract", "Introduction", "Results", "and", "discussion", "Materials", "and", "methods" ]	[ "short", "report", "cell", "biology", "cancer", "biology" ]	2016	ERG signaling in prostate cancer is driven through PRMT5-dependent methylation of the Androgen Receptor
Cell-to-cell viral infection , in which viruses spread through contact of infected cell with surrounding uninfected cells , has been considered as a critical mode of virus infection . However , since it is technically difficult to experimentally discriminate the two modes of viral infection , namely cell-free infection and cell-to-cell infection , the quantitative information that underlies cell-to-cell infection has yet to be elucidated , and its impact on virus spread remains unclear . To address this fundamental question in virology , we quantitatively analyzed the dynamics of cell-to-cell and cell-free human immunodeficiency virus type 1 ( HIV-1 ) infections through experimental-mathematical investigation . Our analyses demonstrated that the cell-to-cell infection mode accounts for approximately 60% of viral infection , and this infection mode shortens the generation time of viruses by 0 . 9 times and increases the viral fitness by 3 . 9 times . Our results suggest that even a complete block of the cell-free infection would provide only a limited impact on HIV-1 spread . In in vitro cell cultures and in infected individuals , viruses may display two types of replication strategies: cell-free infection and cell-to-cell infection ( Sattentau , 2008; Martin and Sattentau , 2009; Talbert-Slagle et al . , 2014 ) . Both transmission means require the assembly of infectious virus particles ( Monel et al . , 2012 ) , which are released in the extracellular medium for cell-free transmission , or concentrated in the confined space of cell-to-cell contacts between an infected cell and bystander target cells in the case of cell-to-cell transmission . It has been shown that most enveloped virus species , including human immunodeficiency virus type 1 ( HIV-1 ) , a causative agent of AIDS , spread via cell-to-cell infection , and it is considered that the replication efficacy of cell-to-cell infection is much higher than that of cell-free infection ( Sattentau , 2008; Martin and Sattentau , 2009; Talbert-Slagle et al . , 2014 ) . However , it is technically impossible to let viruses execute only cell-to-cell infection . In addition , since these two infection processes occur in a synergistic ( i . e . , nonlinear ) manner , the additive ( i . e . , linear ) idea that ‘total infection’ minus ‘cell-free infection’ is equal to ‘cell-to-cell infection’ does not hold true universally . Hence , it was difficult to estimate and compare the efficacies of cell-free and cell-to-cell infection , and different reports provided different estimates ( Dimitrov et al . , 1993; Carr et al . , 1999; Chen et al . , 2007; Sourisseau et al . , 2007; Zhong et al . , 2013 ) . Thus , the quantitative information that underlies cell-to-cell infection has yet to be elucidated and its impact on virus spread remains unclear . In this study , through coupled experimental and mathematical investigation , we demonstrate that the efficacy of cell-to-cell HIV-1 infection is 1 . 4-fold higher than that of cell-free infection ( i . e . , cell-to-cell infection accounts for approximately 60% of total infection ) . We also show that the cell-to-cell infection shortens the generation time of viruses by 0 . 9 times , and increases the viral fitness by 3 . 9 times . These findings strongly suggest that the cell-to-cell infection plays a critical role in efficient and rapid spread of viral infection . Furthermore , we discuss the role of the cell-to-cell infection in HIV-1 infected individuals , based on in silico simulation with our estimated parameters . A static cell culture system ( i . e . , a conventional cell culture system ) allows viruses to perform both cell-free and cell-to-cell infection . On the other hand , Sourisseau et al . have reported that the cell-to-cell infection can be prevented by mildly shaking the cell culture infected with viruses ( Sourisseau et al . , 2007 ) . Consistent with the previous report ( Sourisseau et al . , 2007 ) , we verified that shaking did not induce nonspecific consequences on HIV-1 infection ( Figure 2—figure supplement 1 ) . To quantitatively estimate the efficacy of the cell-free infection and that of the cell-to-cell infection respectively , we adopted this experimental method ( see ‘Materials and methods’ ) . Static cultures of Jurkat cells , an HIV-1-susceptible human CD4+ T-cell line , allow HIV-1 to propagate both by the cell-free and cell-to-cell infection , while under shaking conditions , Jurkat cells allows HIV-1 to replicate only by the cell-free infection ( Figure 1A ) . 10 . 7554/eLife . 08150 . 003Figure 1 . Cell culture systems and the basic reproduction number under cell-to-cell and cell-free infection . ( A ) Static and shaking cultures of Jurkat cells . The static and shaking cell cultures allow human immunodeficiency virus type 1 ( HIV-1 ) to perform both cell-free and cell-to-cell infection , and only cell-free infection , respectively . ( B ) The basic reproduction number , R0 , is defined as the number of the secondly infected cells produced from a typical infected cell during its infectious period . In the presence of the cell-to-cell and cell-free infection , the basic reproduction number consists of two sub-reproduction numbers through the cell-free infection , Rcf , and through the cell-to-cell infection , Rcc , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 08150 . 003 Previous mathematical models , which have been widely used for data analyses , essentially describe only the cell-free infection ( Nowak and May , 2000; Perelson , 2002; Iwami et al . , 2012a , 2012b ) or implicitly both infection modes ( Komarova and Wodarz , 2013; Komarova et al . , 2013a , 2013b ) . Here we used the following revised model including both infection modes explicitly: ( 1 ) dT ( t ) dt=gT ( t ) ( 1−T ( t ) +I ( t ) Tmax ) −βT ( t ) V ( t ) −ωT ( t ) I ( t ) , ( 2 ) dI ( t ) dt=βT ( t ) V ( t ) +ωT ( t ) I ( t ) −δI ( t ) , ( 3 ) dV ( t ) dt=pI ( t ) −cV ( t ) , where T ( t ) and I ( t ) are the numbers of uninfected and infected cells per ml of a culture , respectively , and V ( t ) is the viral load measured by the amount of HIV-1 p24 per ml of culture supernatant . The target cells ( we used Jurkat cells ) grow at a rate g with the carrying capacity of Tmax ( the maximum number of cells in the cell culture flask ) . The parameters β , δ , p and c represent the cell-free infection rate , the death rate of infected cells , the virus production rate , and the clearance rate of virions , respectively . Note that c , g , and δ include the removal of virus , and of the uninfected and infected cells , due to the experimental samplings . In our earlier works ( Iwami et al . , 2012a , 2012b; Fukuhara et al . , 2013; Kakizoe et al . , 2015 ) , we have shown that the approximating punctual removal as a continuous exponential decay has minimal impact on the model parameters and provides an appropriate fit to the experimental data . In addition , we introduce the parameter ω , describing the infection rate via cell-to-cell contacts ( Sourisseau et al . , 2007; Sattentau , 2008; Sigal et al . , 2011 ) . In the shaking cell culture system , we fixed ω = 0 because the shaking inhibits the formation of cell-to-cell contacts completely ( Sourisseau et al . , 2007 ) . In previous reports , Komarova et al . used a quasi-equilibrium approximation for the number of free virus , and incorporated the dynamics of V ( t ) into that of I ( t ) in Komarova and Wodarz ( 2013 ) , Komarova et al . ( 2013a ) , and Komarova et al . ( 2013b ) . However , in cell culture system , the clearance of virions usually is not much larger than the death rate of infected cells , like in vivo ( see below ) . This fact does not validate the quasi-equilibrium approximation , and it may affect the quantification of the dynamics of the cell-to-cell and cell-free infection . We introduced the above full model , relying on a carefully designed experiment , to accurately extract the quantitative information that underlies HIV-1 infection . Furthermore , our experimental datasets include all time-series of the number of uninfected , infected cell , and virions . Thus , our coupled experimental and mathematical investigations with a sufficient datasets allowed us to estimate all parameters in Equations 1–3 , and to compute the basic reproduction number , generation time , and Malthus coefficient ( see below ) . Correctly estimated parameter sets with possible variation are required to reproduce model prediction for pure cell-to-cell infection in silico . However , point estimation of the model parameter set by a conventional ordinary least square method does not capture possible variations of kinetic parameters and model prediction . To assess the variability of kinetic parameters and model prediction , we perform Bayesian estimation for the whole dataset using Markov Chain Monte Carlo ( MCMC ) sampling ( see ‘Materials and methods’ and Supplementary file 1 ) , and simultaneously fit Equations 1–3 with ω > 0 and ω = 0 to the concentration of p24-negative and -positive Jurkat cells and the amount of p24 viral protein in the static and shaking cell cultures , respectively . Here we note that g and Tmax were separately estimated and fixed to be 0 . 47 ± 0 . 10 for the static culture and 0 . 54 ± 0 . 09 for the shaking culture per day , and ( 1 . 51 ± 0 . 02 ) × 106 and ( 1 . 22 ± 0 . 02 ) × 106 cells per flask of medium from the cell growth experiments , respectively ( see ‘Materials and methods’ , Figure 2—figure supplement 2 and Supplementary file 2 ) . In addition , we used c value of 2 . 3 per day , which is estimated from daily harvesting of viruses ( i . e . , the amount of p24 have to be reduced by around 90% per day by the daily medium-replacement procedure ) . The remaining four common parameters β , ω , δ and p , along with the six initial values for T ( 0 ) , I ( 0 ) and V ( 0 ) in the static and the shaking cell cultures , were determined by fitting the model to the data . Experimental measurements , which were below the detection limit , were excluded in the fitting . The estimated parameters of the model and derived quantities are given in Table 1 , and the estimated initial values are summarized in Supplementary file 3 . The typical behavior of the model using these best-fit parameter estimates is shown together with the data in Figure 2 , which reveals that Equations 1–3 describe these in vitro data very well . The shadowed regions correspond to 95% posterior predictive intervals , the dashed lines give the best-fit solution ( mean ) for Equations 1–3 , and the dots show the experimental datasets . This suggests that the parameters that were estimated are representative for the various processes underlying the HIV-1 kinetics including the cell-to-cell and cell-free infection . 10 . 7554/eLife . 08150 . 004Table 1 . Parameters estimated by mathematical-experimental analysisDOI: http://dx . doi . org/10 . 7554/eLife . 08150 . 004Parameter nameSymbolUnitExp . 1Exp . 2Exp . 3Ave . ± S . D . Parameters obtained from simultaneous fit to time-course experimental dataset Rate constant for cell-free infectionβ10−6 × ( p24 day ) −15 . 59* ( 3 . 54–8 . 41 ) †3 . 27 ( 2 . 05–5 . 01 ) ‡3 . 70 ( 2 . 28–5 . 77 ) 4 . 18 ± 1 . 41 Rate constant for cell-to-cell infectionω10−6 × ( cell day ) −10 . 88 ( 0 . 45–1 . 39 ) 1 . 25 ( 0 . 70–1 . 97 ) 1 . 13 ( 0 . 64–1 . 79 ) 1 . 09 ± 0 . 33 Production rate of total viral proteinpday−10 . 37 ( 0 . 22–0 . 59 ) 0 . 59 ( 0 . 34–0 . 92 ) 0 . 54 ( 0 . 31–0 . 86 ) 0 . 50 ± 0 . 16 Death rate of infected cellsδday−10 . 45 ( 0 . 32–0 . 64 ) 0 . 54 ( 0 . 38–0 . 75 ) 0 . 50 ( 0 . 36–0 . 68 ) 0 . 50 ± 0 . 10Quantities derived from fitted values Basic reproduction number through cell-free infectionRcf–2 . 88 ( 2 . 34–3 . 53 ) 2 . 27 ( 1 . 98–2 . 66 ) 2 . 43 ( 2 . 04–2 . 95 ) 2 . 44 ± 0 . 23 Basic reproduction number through cell-to-cell infectionRcc–2 . 95 ( 1 . 48–4 . 70 ) 3 . 65 ( 1 . 77–6 . 05 ) 3 . 39 ( 1 . 82–5 . 38 ) 3 . 39 ± 0 . 91 Basic reproduction numberR0–5 . 83 ( 4 . 20–7 . 75 ) 5 . 92 ( 3 . 99–8 . 46 ) 5 . 83 ( 4 . 21–7 . 89 ) 5 . 83 ± 0 . 94 Contribution of cell-to-cell infectionRccRcf+Rcc–0 . 50 ( 0 . 34–0 . 63 ) 0 . 60 ( 0 . 44–0 . 72 ) 0 . 57 ( 0 . 43–0 . 70 ) 0 . 57 ± 0 . 07Mean value . †95% confidence interval . ‡Average and standard deviation of merged values in experiment 1 , 2 , and 3 . 10 . 7554/eLife . 08150 . 005Figure 2 . Dynamics of HIV-1 infection in Jurkat cells through cell-free and cell-to-cell infection . Jurkat cells were inoculated with HIV-1 ( at multiplicity of infection 0 . 1 ) in the static and shaking cell cultures . Panels A and B show the time-course of experimental data for the numbers of uninfected cells ( top ) and infected cells ( middle ) , and the amount of viral protein p24 ( bottom ) in the static and shaking cell culture systems , respectively . The shadow regions correspond to 95% posterior predictive intervals , the dashed curves give the best-fit solution ( mean ) for Equations 1–3 to the time-course dataset . All data in each experiment were fitted simultaneously . In panels A and B , the results of three independent experiments are respectively shown . DOI: http://dx . doi . org/10 . 7554/eLife . 08150 . 00510 . 7554/eLife . 08150 . 006Figure 2—figure supplement 1 . No effect of the shaking procedure on HIV-1 cell-free infection . Jurkat cells were infected with HIV-1 ( at multiplicity of infection 1 ) as described in ‘Materials and methods’ , and the infected cells were cultured in the static and the shaking condition . By harvesting the cells at 24 and 48 hr postinfection , the cells were analyzed by flow cytometry as described in ‘Materials and methods’ . The percentage of the average of p24-positive cells are shown with SD . The assay was performed in triplicate , and the representative result is shown . Note that the ratio of input virus to target cells ( multiplicity of infection ) of this experiment is 10-fold higher than that of the experiment shown in Figure 2 . This is for the clear detection of the infected cells ( p24-positive cells ) during early time points . DOI: http://dx . doi . org/10 . 7554/eLife . 08150 . 00610 . 7554/eLife . 08150 . 007Figure 2—figure supplement 2 . Dynamics of Jurkat cell growth . Dynamics of Jurkat cell growth in the static and shaking cell cultures . By harvesting the cells for 37 days ( A ) in the static and ( B ) in the shaking cell cultures , the growth kinetics of Jurkat cells in these conditions was estimated as described in Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 08150 . 00710 . 7554/eLife . 08150 . 008Figure 2—figure supplement 3 . Dot plots of infected cells by flow cytometry . Representative results of flow cytometry ( experiment 1 ) . Time-course results of flow cytometry analyses on experiment 1 of static ( left ) and shaking ( right ) cultures are respectively shown . The cells positive for p24 antigen is gated in pink , and the number in the bottom right of the gate indicates the percentage of p24-positive cells . The data is available upon request . DOI: http://dx . doi . org/10 . 7554/eLife . 08150 . 008 Our model ( i . e . , Equations 1–3 ) applied to time-course experimental data under static and shaking conditions ( i . e . , Figure 2A and Figure 2B , respectively ) allowed to extract the kinetic parameters in the model ( see Table 1 ) , in particular the rate constant for the cell-free infection ( β ) and the rate constant for the cell-to-cell infection ( ω ) . However , from the estimated values of β and ω , we could not directly compare the efficiency of the two infection modes , because of the different units of measure of these parameters ( p24/day for β , and cells/day for ω ) . To quantify each infection mode and overcome the above difficulty , we derived the basic reproduction number R0 ( Perelson and Nelson , 1999; Nowak and May , 2000; Iwami et al . , 2012b ) , an index reflecting the average number of newly infected cells produced from any one infected cell ( see mathematical appendix in ‘Materials and methods’ ) . Note that secondly infected cells are produced from both the cell-free and cell-to-cell infection . Interestingly , in spite of nonlinear interaction between the two modes of virus transmission , our derivation of R0 revealed that the secondly infected cells were the sum of the basic reproduction number through the cell-free infection Rcf = βpTmax/δc and the basic reproduction number through the cell-to-cell infection Rcc = ωTmax/δ , ( i . e . , R0 = Rcf + Rcc ) ( see Figure 1B ) . Using all accepted MCMC parameter estimates from the time-course experimental datasets , we calculated that on average the mean of the total basic reproductive number is R0 = 5 . 83 ± 0 . 94 ( average ± standard deviation ) , and the mean number of secondly infected cells through the cell-free infection and the cell-to-cell infection are Rcf = 2 . 44 ± 0 . 23 and Rcc = 3 . 39 ± 0 . 91 , respectively ( see Table 1 ) . The distributions of calculated R0 , Rcf , and Rcc , are shown in Figure 3A–C , respectively . These estimates indicate that the contribution of the cell-to-cell infection is almost 60% on average ( i . e . , Rcc/ ( Rcc + Rcf ) = 0 . 57 ± 0 . 07: Table 1 ) and this mode of infection is predominant during the HIV-1 spread in Jurkat cells . In Figure 3D , the distributions of calculated ratio are shown . Interestingly , this estimation is consistent with that by Komarova and Wodarz ( 2013 ) , Komarova et al . ( 2013a ) , and Komarova et al . ( 2013b ) , although they did not take into account the difference of the death rate in the shaking and static conditions . 10 . 7554/eLife . 08150 . 009Figure 3 . Distribution of the basic reproduction numbers , generation time , and Malthus coefficient . The distribution of the basic reproduction number , R0 , the number of secondary infected cells through the cell-free infection , Rcf , and the cell-to-cell infection , Rcc , calculated from all accepted Markov Chain Monte Carlo ( MCMC ) parameter estimates are shown in A , B , and C , respectively . The contribution of the cell-to-cell infection ( i . e . , Rcc/ ( Rcf + Rcc ) ) is distributed as in D . For each plot , the last 15 , 000 MCMC samples among the total 50 , 000 samples are used . a . u . , arbitrary unit . DOI: http://dx . doi . org/10 . 7554/eLife . 08150 . 009 We also derived the viral generation time , defined as the time it takes for a population of virions to infect cells and reproduce ( Perelson and Nelson , 1999 ) , from Equations 1–3 in the static and shaking cell cultures ( see mathematical appendix in ‘Materials and methods’ ) . In the presence and absence of the cell-to-cell infection ( i . e . , for the static and shaking cell cultures , respectively ) , the mean generation time is calculated as 1/δ + Rcf/cR0 = 2 . 22 ± 0 . 32 days and 1/δ + 1/c = 2 . 47 ± 0 . 32 days , respectively ( see Table 2 ) . Thus , cell-to-cell infection shortens the generation time by on average 0 . 90 times , and enables HIV-1 to efficiently infect target cells ( Sato et al . , 1992; Carr et al . , 1999 ) . Furthermore , we calculated the Malthus coefficient , defined as the fitness of virus ( Nowak and May , 2000; Nowak , 2006 ) ( or the speed of virus infection ) ( see mathematical appendix in ‘Materials and methods’ ) . In the presence and absence of the cell-to-cell infection , the Malthus coefficient is calculated as 1 . 86 ± 0 . 37 and 0 . 49 ± 0 . 05 per day , respectively ( see Table 2 ) . Thus , cell-to-cell infection increases the HIV-1 fitness by 3 . 80-fold ( corresponding to 944-fold higher viral load 5 days after the infection ) and plays an important role in the rapid spread of HIV-1 . Thus , the efficient viral spread via the cell-to-cell infection is relevant , especially at the beginning of virus infection . 10 . 7554/eLife . 08150 . 010Table 2 . Generation time and Malthus coefficient of virus infectionDOI: http://dx . doi . org/10 . 7554/eLife . 08150 . 010Cell culture systemExp . 1Exp . 2Exp . 3Ave . ± S . D . Generation time of HIV-1 Static cell culture2 . 51 days2 . 08 days2 . 22 days ( 2 . 22 ± 0 . 32 ) ‡ days ( 1 . 78–3 . 38 ) days ( 1 . 54–2 . 78 ) days ( 1 . 69–2 . 93 ) days– Shaking cell culture2 . 73† days2 . 34 days2 . 47 days ( 2 . 47 ± 0 . 32 ) days ( 1 . 99–3 . 59 ) days ( 1 . 77–3 . 06 ) days ( 1 . 91–3 . 18 ) days–Malthus coefficient of HIV-1 Static cell culture1 . 61 day−12 . 03 day−11 . 86 day−1 ( 1 . 86 ± 0 . 37 ) day−1 ( 1 . 10–2 . 27 ) day−1 ( 1 . 32–3 . 01 ) day−1 ( 1 . 26–2 . 72 ) day−1– Shaking cell culture0 . 57 day−10 . 46 day−110 . 49 day−1 ( 0 . 49 ± 0 . 05 ) day−1 ( 0 . 47–0 . 67 ) day−1 ( 0 . 38–0 . 56 ) day−1 ( 0 . 39–0 . 61 ) day−1–*Mean value . †95% confidence interval . ‡Average and standard deviation of merged values in experiment 1 , 2 , and 3 . HIV-1 , human immunodeficiency virus type 1 . While the shaking culture prevents the cell-to-cell infection , it is technically difficult to completely block the cell-free infection . Here , using our estimated kinetic parameters ( Table 1 and Supplementary file 3 ) , we carried out a ‘virtual experiment’ eliminating the contribution of the cell-free infection using all accepted MCMC estimated parameter values , allowing to estimate only the cell-to-cell infection , in silico ( see Figure 4 ) . Our simulated mean values ( represented by solid lines ) of the cell-to-cell infection of HIV-1 are consistently located between the time course of experimental data under the static conditions ( closed circles , including both the cell-free and cell to cell infections ) and those under the shaking conditions ( open circles , reflecting only the cell-free infection ) . The shadowed regions correspond to 95% posterior predictive intervals . In terms of the dynamics of infected cells and virus production , the simulated values corresponding to cell-to-cell virus propagation , are closer to experimental data from the coupled cell-free and cell-to-cell infection , than to data from the cell-free infection only . This shows that the cell-free infection , which contributes approximately 40% to the whole HIV-1 infection process , plays a limited role on the virus spread . In other words , even if we could completely block the cell-free infection , the cell-to-cell infection would still effectively spread viruses ( Sigal et al . , 2011 ) . We address this point in ‘Discussion’ . 10 . 7554/eLife . 08150 . 011Figure 4 . Simulating cell-to-cell infection of HIV-1 . Using our estimated parameters , the pure cell-to-cell infection is simulated in silico ( solid curves ) . The simulated values are located between the time course of experimental data under the static conditions ( closed circles ) and those under the shaking conditions ( open circles ) . The shadowed regions correspond to 95% posterior predictive intervals . DOI: http://dx . doi . org/10 . 7554/eLife . 08150 . 011 Through experimental-mathematical investigation , here we quantitatively elucidated the dynamics of the cell-to-cell and cell-free HIV-1 infection modes ( Figure 2 and Table 1 ) . We derived the basic reproduction number , R0 , and divided it into the numbers of secondly infected cells through the cell-free infection , Rcf , and the cell-to-cell infection , Rcc , respectively ( Figure 1B and mathematical appendix in ‘Materials and methods’ ) . Based on our calculated values of these three indexes , we found that about 60% of the viral infection is attributed to the cell-to-cell infection in the in vitro cell culture system ( Table 1 ) , which is consistent with previous estimation by Komarova and Wodarz ( 2013 ) , Komarova et al . ( 2013a ) , and Komarova et al . ( 2013b ) . In addition , we revealed that the cell-to-cell infection effectively promotes the virus infection by reducing the generation time ( ×0 . 9 times ) , and by increasing the Malthus coefficient ( ×3 . 80 times ) ( Table 2 ) . When we consider the significance of the cell-to-cell infection in patients infected with HIV-1 , it should be noted that the environment of immune cells including CD4+ T-cells in vivo is radically different from the conditions of in vitro cell cultures . For instance , lymphocytes are closely packed in lymphoid tissues such as lymph nodes , and thereby , the frequency for the infected cell to contact with adjacent uninfected cells in vivo would be much higher than that in in vitro cell cultures . In addition , Murooka et al . have directly demonstrated that HIV-1-infected cells converge to lymph nodes and can be vehicles for viral dissemination in vivo ( Murooka et al . , 2012 ) . Moreover , certain studies have suggested that cell-to-cell viral spread is resistant to anti-viral immunity such as neutralizing antibodies and cytotoxic T lymphocytes ( Martin and Sattentau , 2009 ) . Therefore , these notions strongly suggest that the contribution of the cell-to-cell infection for viral propagation in vivo may be much higher than that estimated from the in vitro cell culture system . As another significance of cell-to-cell viral spread , Sigal et al . have suggested that the cell-to-cell infection permits viral replication even under the anti-retroviral therapy ( Sigal et al . , 2011 ) . This is attributed to the fact that the multiplicity of infection per cell is tremendously higher than that reached by an infectious viral particle . However , in the previous report ( Sigal et al . , 2011 ) , the contribution of the cell-to-cell infection remained unclear . To further understand the role of the cell-to-cell infection , we quantified the contributions of the cell-to-cell and cell-free infection modes ( Table 1 ) . Interestingly , we found that the cell-to-cell infection mode is predominant during the infection . Furthermore , our virtual experiments showed that a complete block of the cell-free infection , which is highly susceptible to current antiviral drugs , provides only a limited impact on the whole HIV-1 infection ( Figure 3 ) . Taken together , our findings further support that the cell-to-cell infection can be a barrier to prevent the cure of HIV-1 infection , which is discussed in Sigal et al . ( 2011 ) . However , it should be noted that some papers have shown that cell-to-cell spread cannot overcome the action of most anti-HIV-1 drugs ( Titanji et al . , 2013; Agosto et al . , 2014 ) . To fully elucidate this issue , further investigations will be needed . In addition to HIV-1 , other viruses such as herpes simplex virus , measles virus , and human hepatitis C virus drive their dissemination via cell-to-cell infection ( Sattentau , 2008; Talbert-Slagle et al . , 2014 ) . Although the impact of cell-to-cell viral spread is a topic of broad interest in virology , it was difficult to explore this issue by conventional virological experiments , because an infected cell is simultaneously capable of achieving cell-to-cell infection along with producing infectious viral particles . By applying mathematical modeling to the experimental data , here we estimated the sole dynamics of cell-free infection in the cell culture system . The synergistic strategy of experiments with mathematical modeling is a powerful approach to quantitatively elucidate the dynamics of virus infection in a way that is inaccessible through conventional experimental approaches . Jurkat cell line ( Watanabe et al . , 2012 ) was cultured in the culture medium: RPMI 1640 ( Sigma , St . Louis , MO ) containing 2% fetal calf serum and antibiotics . The virus solution was prepared as previously described ( Sato et al . , 2010 , 2013 , 2014; Iwami et al . , 2012a ) . Briefly , 30 μg of pNL4-3 plasmid ( Adachi et al . , 1986 ) ( GenBank accession no . M19921 . 2 ) was transfected into 293T cells by the calcium-phosphate method . At 48 hr post-transfection , the culture supernatant was harvested , centrifuged , and then filtered through a 0 . 45-μm-pore-size filter to produce virus solution . The infectivity of virus solution was titrated as previously described ( Iwami et al . , 2012a ) . Briefly , the virus solution obtained was serially diluted and then inoculated onto phytohemagglutinin-stimulated human peripheral blood mononuclear cells in a 96-well plate in triplicate . At 14 days postinfection , the endpoint was determined by using an HIV-1 p24 antigen enzyme-linked immunosorbent assay ( ELISA ) kit ( ZetptoMetrix , Buffalo , NY ) according to the manufacture's procedure , and virus infectivity was calculated as the 50% tissue culture infectious doses ( TCID50 ) according to the Reed-Muench method . For HIV-1 infection , 3 × 105 of Jurkat cells were infected with HIV-1 ( multiplicity of infection 0 . 1 ) at 37°C for 2 hr . The infected cells were washed three times with the culture medium , and then suspended with 3 ml of culture medium and seeded into a 25-cm2 flask ( Nunc , Rochester , NY ) . For the static infection , the infected cell culture was kept in a 37°C/5% CO2 incubator as usual . For the shaking infection , the infected cell culture was handled as previously described ( Sourisseau et al . , 2007 ) . Briefly , the cell culture was kept on a Petit rocker Model-2230 ( Wakenyaku , Japan ) placed in 37°C/5% CO2 incubator , and was gently shaken at 40 movements per min . The amount of virus particles in the culture supernatant and the number of infected cells were routinely measured as follows: a portion ( 300 μl ) of the infected cell culture was routinely harvested , and the amount of released virions in the culture supernatant was quantified by using an HIV-1 p24 antigen ELISA kit ( ZetptoMetrix ) according to the manufacture's procedure . The cell number was counted by using a Scepter handled automated cell counter ( Millipore , Germany ) according to the manufacture's protocol . The percentage of infected cells was measured by flow cytometry . Flow cytometry was performed with a FACSCalibur ( BD Biosciences , San Jose , CA ) as previously described ( Sato et al . , 2010; Sato et al . , 2011 , 2013 , 2014; Iwami et al . , 2012a ) , and the obtained data were analyzed with CellQuest software ( BD Biosciences ) . For flow cytometry analysis , a fluorescein isothiocyanate-labeled anti-HIV-1 p24 antibody ( KC57; Beckman Coulter , Pasadena , CA ) was used . The representative dot plots are shown in Figure 2—figure supplement 3 . The data is available upon request . The remaining cell culture was centrifuged and then resuspended with 3 ml of fresh culture medium . It should be noted that the procedure for HIV-1 infection was performed at time t = −2 day in the figures . Because there is no viral protein production in the first day after infection , each in vitro experimental quantity was measured daily from t = 0 day ( i . e . , 2 days after HIV-1 inoculation ) . The detection threshold of each value are the followings: cell number ( cell counting ) , 3000 cells/ml; % p24-positive cells ( flow cytometry ) , 0 . 3%; and p24 antigen in culture supernatant ( p24 antigen ELISA ) , 80 pg/ml . A statistical model adopted in the Bayesian inference assumes measurement error to follow normal distribution with mean zero and unknown variance ( error variance ) . A distribution of error variance is also inferred with the Gamma distribution as its prior distribution . Posterior predictive parameter distribution as an output of MCMC computation represents parameter variability . Distributions of model parameters and initial values were inferred directly by MCMC computations . On the other hand , distributions of the basic reproduction numbers and the other quantities were calculated from the inferred parameter sets ( Figure 3 for graphical representation ) . A set of computations for Equations 1–3 with estimated parameter sets gives a distribution of outputs ( virus load and cell density ) as model prediction . To investigate variation of model prediction , global sensitivity analyses were performed . The range of possible variation is drawn in Figure 2 as 95% confidence interval . Technical details of MCMC computations are summarized in Supplementary file 1 . We here estimate the growth kinetics of Jurkat cells , which have been commonly used for HIV-1 studies , under the normal ( i . e . , mock-infected ) condition with the following mathematical model: ( 4 ) dT ( t ) dt=gT ( t ) ( 1−T ( t ) Tmax ) , where the variable T ( t ) is the number of Jurkat cells at time t and the parameters g and Tmax are the growth rate of the cells ( i . e . , Log2/g is the doubling time ) and the carrying capacity of the cell culture flask , respectively . Nonlinear least-squares regression ( FindMinimum package of Mathematica9 . 0 ) was performed to fit Equation 4 to the time-course numbers of Jurkat cells in the normal condition . The fitted parameter values are listed in Supplementary file 2 and the model behavior using these best-fit parameter estimates is presented together with the data in Figure 2—figure supplement 2 . The linearized equation of Equations 1–3 at the virus-free steady state , ( Tmax , 0 , 0 ) , is given as follows: ( 5 ) dI ( t ) dt=βTmaxV ( t ) +ωTmaxI ( t ) −δI ( t ) , ( 6 ) dV ( t ) dt=pI ( t ) −cV ( t ) . Let b ( t ) be the number of newly produced infected cells in the linear phase: ( 7 ) b ( t ) :=βTmaxV ( t ) +ωTmaxI ( t ) . Applying the variation of constants formula to Equations 5 , 6 , we have ( 8 ) V ( t ) =V ( 0 ) e−ct+∫0te−c ( t−s ) pI ( s ) ds , ( 9 ) I ( t ) =I ( 0 ) e−δt+∫0te−δ ( t−z ) b ( z ) dz . Inserting Equation 9 into Equation 8 to exchange the order of integrals , we have ( 10 ) V ( t ) =g ( t ) +p∫0t∫0xe−c ( x−θ ) −δθdθb ( t−x ) dx , whereg ( t ) ∶=V ( 0 ) e−ct+∫0te−c ( t−s ) pI ( 0 ) e−δtds . From Equation 7 and Equations 9 , 10 , we arrive at the following renewal equation:b ( t ) =h ( t ) +∫0tΨ ( x ) b ( t−x ) dx , where h ( t ) is given byh ( t ) ∶=ωTmaxI ( 0 ) e−δt+βTmaxg ( t ) , and the kernel Ψ ( x ) is given byΨ ( x ) ∶=βTmaxp∫0xe−δθ−c ( x−θ ) dθ+ωTmaxe−δx , =βTmaxpδc ( ϕ1∗ϕ2 ) ( x ) +ωTmaxδϕ1 ( x ) . In the above expression , ϕj ( x ) denotes the probability density function given byϕ1 ( x ) =δe−δx , ϕ2 ( x ) =ce−cx , and , ∗ denotes the convolution of functions . From the general theory of the basic reproduction number ( Inaba , 2012 ) , R0 for the reproduction of infected cells is given byR0=∫0∞Ψ ( x ) dx=βTmaxpδc+ωTmaxδ=Rcf+Rcc , where Rcf and Rcc denote the reproduction numbers for infected cells mediated by the cell-free and cell-to-cell infection , respectively . Next we consider the reproduction process of viruses . Let ρ ( t ) := pI ( t ) be the number of newly produced viruses at time t . From Equations 8 , 9 , we obtain ( 11 ) ρ ( t ) =pI ( 0 ) e−δt+∫0te−δ ( t−z ) ( βTmaxpV ( z ) +ωTmaxρ ( z ) ) dz , where ( 12 ) V ( t ) =V ( 0 ) e−ct+∫0te−c ( t−s ) ρ ( s ) ds . Inserting Equation 11 into Equation 12 , we again arrive at the following renewal equation:ρ ( t ) =q ( t ) +∫0tΨ ( x ) ρ ( t−x ) dx , where q ( t ) is given byq ( t ) ∶=pI ( 0 ) e−δt+∫0te−δ ( t−z ) pβTmaxV ( 0 ) e−czdz . Note that the reproduction kernel Ψ ( x ) for the virus reproduction is the same as the kernel for the cell reproduction . Thus the probability density function of the virus reproduction is given byψ ( x ) ∶=Ψ ( x ) R0=RcfR0 ( ϕ1∗ϕ2 ) ( x ) +RccR0ϕ1 ( x ) . Then the generation time for the virus reproduction , denoted by G , is calculated as follows:G∶=∫0∞tψ ( t ) dt=RcfR0Gcf+RccR0Gcc≤Gcf , where Gcf : = 1/δ + 1/c and Gcc : = 1/δ are the generation times for virus reproduction mediated by the cell-free and cell-to-cell infection , respectively . The Malthusian coefficient for the virus reproduction must be given as the dominant real root of the Euler-Lotka equation as∫0∞e−λxΨ ( x ) dx=βTmaxpδcϕ1^ ( λ ) ϕ2^ ( λ ) +ωTmaxδϕ1^ ( λ ) =1 , where ϕj^ denotes the Laplace transformation of a function ϕj . That is , ϕ1^ ( λ ) =∫0∞e−λxϕ1 ( x ) ds=δδ+λ , ϕ2^ ( λ ) =∫0∞e−λxϕ2 ( x ) ds=cc+λ . Therefore the Euler-Lotka equation can be calculated explicitly as follows:βTmaxpδcδc ( δ+λ ) ( c+λ ) +ωTmaxδδδ+λ=1 , which is reduced to a quadratic equation , ( 13 ) λ2+δc ( Gcc+ ( 1−Rcc ) ( Gcf−Gcc ) ) λ+δc ( 1−R0 ) =0 . If R0 > 1 , Equation 13 has a unique positive root , which is no other than the Malthusian coefficient for the virus reproduction , so it is calculated as , λ=−δc ( Gcc+ ( 1−Rcc ) ( Gcf−Gcc ) ) +δ2c2 ( Gcc+ ( 1−Rcc ) ( Gcf−Gcc ) ) 2−4δc ( 1−R0 ) 2 .	Viruses such as HIV-1 replicate by invading and hijacking cells , forcing the cells to make new copies of the virus . These copies then leave the cell and continue the infection by invading and hijacking new cells . There are two ways that viruses may move between cells , which are known as ‘cell-free’ and ‘cell-to-cell’ infection . In cell-free infection , the virus is released into the fluid that surrounds cells and moves from there into the next cell . In cell-to-cell infection the virus instead moves directly between cells across regions where the two cells make contact . Previous research has suggested that cell-to-cell infection is important for the spread of HIV-1 . However , it is not known how much the virus relies on this process , as it is technically challenging to perform experiments that prevent cell-free infection without also stopping cell-to-cell infection . Iwami , Takeuchi et al . have overcome this problem by combining experiments on laboratory-grown cells with a mathematical model that describes how the different infection methods affect the spread of HIV-1 . This revealed that the viruses spread using cell-to-cell infection about 60% of the time , which agrees with results previously found by another group of researchers . Iwami , Takeuchi et al . also found that cell-to-cell infection increases how quickly viruses can infect new cells and replicate inside them , and improves the fitness of the viruses . The environment around cells in humans and other animals is different to that found around laboratory-grown cells , and so more research will be needed to check whether this difference affects which method of infection the virus uses . If the virus does spread in a similar way in the body , then blocking the cell-free method of infection would not greatly affect how well HIV-1 is able to infect new cells . It may instead be more effective to develop HIV treatments that prevent cell-to-cell infection by the virus .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "computational", "and", "systems", "biology", "microbiology", "and", "infectious", "disease" ]	2015	Cell-to-cell infection by HIV contributes over half of virus infection
Cross-synaptic synchrony—correlations in transmitter release across output synapses of a single neuron—is a key determinant of how signal and noise traverse neural circuits . The anatomical connectivity between rod bipolar and A17 amacrine cells in the mammalian retina , specifically that neighboring A17s often receive input from many of the same rod bipolar cells , provides a rare technical opportunity to measure cross-synaptic synchrony under physiological conditions . This approach reveals that synchronization of rod bipolar cell synapses is near perfect in the dark and decreases with increasing light level . Strong synaptic synchronization in the dark minimizes intrinsic synaptic noise and allows rod bipolar cells to faithfully transmit upstream signal and noise to downstream neurons . Desynchronization in steady light lowers the sensitivity of the rod bipolar output to upstream voltage fluctuations . This work reveals how cross-synaptic synchrony shapes retinal responses to physiological light inputs and , more generally , signaling in complex neural networks . Everyday activities depend on the reliability of neural computation . Yet the mechanistic building blocks of the underlying circuits can exhibit highly stochastic behavior . Understanding neural computation requires bringing these two perspectives together , in particular , it requires identifying , under physiological conditions , the sources of noise in neural signals and how such noise is controlled ( Deneve et al . , 2001; Averbeck et al . , 2006 ) . Sensory systems , for example , must encode and transmit physiological stimuli accurately and quickly based on inherently stochastic processes such as transduction and synaptic transmission . Neural circuits share a number of common architectural features . Cross-synaptic synchrony ( CSS ) —synchronization of transmitter release at different output synapses of a single neuron—is an important factor in how these common architectural features will impact the coding and transmission of physiological signals ( Salinas and Sejnowski , 2000; Rosenbaum et al . , 2013 , 2014 ) . For example , the strength of network correlations generated by divergent synaptic output from a single presynaptic neuron depends directly on CSS . Similarly , if CSS is high , multiple parallel synapses between a pre- and postsynaptic neuron can enhance transmission of upstream noise and mitigate the impact of intrinsic synaptic noise . Circuits using graded signals or action potentials share these issues because the small number of vesicles associated with transmission of physiological signals cause synaptic release to vary even for nominally fixed presynaptic signals such as action potentials ( Del Castillo and Katz , 1954; Allen and Stevens , 1994 ) . Two issues have hindered progress in understanding the importance of cross-synaptic synchrony for neural signaling . First , quantitative anatomical information about convergent and divergent wiring patterns is essential for understanding the influence of CSS , but such information is lacking for most brain regions ( Denk et al . , 2012 ) . Second , the experimental conditions for studying neurotransmitter release biophysically often preclude studying physiological signaling in the same circuit . Retinal signaling at low light levels provides an opportunity to tackle these issues directly because of the wealth of available anatomical information ( Kolb , 1970; Kolb and Famiglietti , 1974; Tsukamoto et al . , 2001; Tsukamoto and Omi , 2013 ) and the ability to study synaptic mechanisms in the context of physiological light stimuli ( Sampath and Rieke , 2004; Dunn and Rieke , 2008; Oesch and Diamond , 2011 ) . In starlight , signals from rod photoreceptors traverse the retina through the specialized rod bipolar circuit ( for review see Bloomfield and Dacheux , 2001 ) . Behavioral and physiological studies indicate that the sensitivity of this circuit approaches limits set by noise in the rod photoreceptors themselves , indicating that little noise is generated at downstream synapses ( for review see Field et al . , 2005 ) . Rod bipolar cells ( RBCs ) provide a key component of this circuit; they receive dendritic input exclusively from rods , and provide output to several types of amacrine cells via anatomically distinct and stereotypic connections ( Nelson and Kolb , 1984; Tsukamoto and Omi , 2013 ) . Here , we exploit the distinct anatomical features of these connections to quantitatively characterize the role of cross-synaptic synchrony in retinal signaling at low light levels . We find that RBC output synapses can exhibit near-perfect synchronization for small physiological changes in presynaptic voltage such as those encountered near visual threshold . This reveals a surprisingly low level of stochastic behavior at individual synapses under dark-adapted conditions . AII and A17 amacrine cells provide the two main postsynaptic targets of RBCs . As described below , the connectivity between RBCs and these postsynaptic neurons provides an opportunity ( 1 ) to measure cross-synaptic synchrony ( CSS ) using paired A17 recordings , and ( 2 ) to examine its role in neural transmission by recording feedforward signaling in the AII amacrine cell and other downstream circuit elements . Each rod bipolar cell releases glutamate from ∼50 ribbon synapses ( Sterling and Lampson , 1986; Tsukamoto et al . , 2001; Tsukamoto and Omi , 2013; Mehta et al . , 2014 ) ( Figure 1A ) . These specialized synapses often exist as dyads , in which each presynaptic ribbon is shared by two postsynaptic targets , most commonly one AII ( Figure 1A , purple ) and one A17 ( Figure 1A , green ) amacrine cell . AII and A17 amacrine cells differ in morphology , wiring configuration and functional contributions to retinal processing . AII amacrine cells convey rod-mediated signals to ganglion cells via gap junctions made with the axon terminals of On cone bipolar cells and via glycinergic synapses made with both the axon terminals of Off cone bipolar cells and the dendrites of Off ganglion cells . They have a compact dendritic field ( ∼40 μm ) and receive direct synaptic input from ∼10 RBCs . A17 amacrine cells provide GABAergic feedback inhibition to RBC axon terminals . They have a wide dendritic field ( ∼300 μm ) and collect input from more than 100 RBCs , which provide their only known source of excitation . Central to our work here , each RBC can make tens of synaptic contacts onto a single AII amacrine cell ( Tsukamoto and Omi , 2013 ) , but typically only one synaptic contact onto a single A17 ( Ellias and Stevens , 1980; Nelson and Kolb , 1985; Zhang et al . , 2002; Grimes et al . , 2010 ) . 10 . 7554/eLife . 03892 . 003Figure 1 . Same presynaptic neuron , very different postsynaptic noise properties . ( A ) Three-dimensional EM reconstruction of a rod bipolar cell axon terminal ( pink ) , presynaptic ribbons are represented with black markers ( 46 in total ) . AII ( purple ) and A17 ( green ) amacrine cells are complimentary postsynaptic partners at individual RBC ribbon synapses , but unlike the A17 , AIIs receive synaptic input from multiple presynaptic ribbons . ( B and C ) Voltage-clamp recordings from AII ( B ) and A17 ( C ) amacrine cells ( Vhold ∼−60 mV ) in retinas from wild-type mice demonstrate that tonic excitatory synaptic input ( from RBCs ) to these two cell types can be very different under physiological recording conditions . Under dark-adapted conditions large noise events are observed at RBC→AII connections ( B ) but not at RBC→A17 connections ( C ) . ( D ) AII recordings from retinal slices lacking Cx36-containing gap junctions ( i . e . , Gjd2 knockout mouse , where electrical synapses between AII amacrine cell dendrites and On cone bipolar axon terminals have been eliminated ) exhibited similar behavior to recordings from wild-type retina . Under these conditions synaptic events were analyzed . Inset: fast synaptic events ( with 10–90% rise time ≤1 ms , i . e . , miniature excitatory postsynaptic current or mEPSC ) exhibited amplitudes that were less than a tenth of that of the largest events . ( E ) Population statistics for the synaptic noise recorded from AII and A17 amacrine cells in wild type and Gjd2−/− recordings under dark-adapted conditions ( control or drugs ) . On average , noise recorded from AII amacrine cells ( WT ) was >10 times larger than noise recorded from A17 amacrine cells ( unpaired t test p = 3 × 10−6 ) . Neither an inhibitory cocktail ( 2 µM Strychnine , 20 µM SR95531 and 50 µM TPMPA ) or mibefridil ( 10 µM , T-type Cav channel antagonist ) caused a significant change in the noise recorded from AII amacrine cells in darkness . DOI: http://dx . doi . org/10 . 7554/eLife . 03892 . 003 Spontaneous excitatory synaptic inputs to voltage-clamped AII ( Figure 1B ) and A17 ( Figure 1C ) amacrine cells differed dramatically ( identical recording conditions , see ‘Materials and methods’ ) . Both cell types received substantial excitatory input in complete darkness as evinced by the suppression of holding current and noise by 10 µM NBQX , an AMPA receptor antagonist ( data not shown ) ; however , spontaneous current fluctuations observed in AII amacrines were much larger in amplitude ( σ2 = 1000 ± 154 pA2 , n = 20 ) than those observed in A17 amacrines ( σ2 = 72 ± 22 pA2 , n = 17; Figure 1E ) . AII amacrine cells receive conventional excitatory ( i . e . , glutamatergic ) synaptic input from RBCs and direct electrical input from other AIIs and cone bipolar cells via gap junctions . Deletion of connexin-36 disrupts the gap junctional input ( Gjd2 knockout mouse , note: this mouse is also commonly referred to as the Cx36 knockout mouse; Deans et al . , 2001 , 2002 ) ; under these conditions , AII input currents continued to exhibit large variability in darkness ( Figure 1D , E ) . Variability in the AII inputs was insensitive to pharmacological block of the receptors mediating feedforward ( to the AII ) and feedback ( to the RBC axon terminal ) inhibition ( Figure 1E ) . Together , these results indicate that the large spontaneous fluctuations in the AII inputs arise from excitatory RBC inputs and do not require synaptic inhibition . How can synaptic inputs from RBCs to AII and A17 amacrine cells differ so markedly ? Multiple factors , such as differences in the cells' electrical properties , could contribute; we hypothesized that a key factor was differences in the connectivity of AII and A17 amacrine cells with RBCs and synchronization of output synapses within individual RBC axon terminals . Since A17 amacrine cells receive input , on average , from one ribbon-type synapse per RBC , their synaptic input should not be affected by synchronization across ribbons . AII amacrine cells , however , receive inputs from multiple synapses per RBC and hence their inputs should be shaped by RBC CSS . In support of this synchronization hypothesis , closer examination of the noisy AII input currents in Gjd2 knockout mice revealed that the largest spontaneous current fluctuations were many times larger in amplitude and total charge than the average miniature excitatory postsynaptic current ( mEPSC; see ‘Materials and methods’; Figure 1D ) . The differences in RBC connectivity with AII and A17 amacrines and in excitatory inputs to the two postsynaptic amacrine cells ( in darkness ) suggest that CSS substantially shapes RBC synaptic output . The RBC's CSS cannot be measured under dark-adapted conditions using imaging approaches because even two-photon ( i . e . , infrared ) imaging produces too much rod activation to maintain the retina in a dark-adapted state ( Euler et al . , 2009 ) . Instead , as described below , we took advantage of the sparse , stereotyped connectivity between RBCs and A17s to characterize the CSS of RBCs under physiological conditions . Each A17 amacrine cell is contacted by a large fraction of the RBCs within its dendritic field ( ∼50% in rabbit , Zhang et al . , 2002 ) ; therefore , pairs of A17s with highly overlapping dendrites receive synaptic contact from many of the same RBCs ( i . e . , RBCs that are common to both A17s; Figure 2A , B ) . Because single ribbons typically provide input to an AII–A17 dyad and a single RBC typically contacts an A17 only once ( Figure 1A ) , highly overlapping A17s receive input from different ribbon-type synapses made by many of the same ( i . e . , common ) RBCs ( Figure 2B ) . Thus synchronized output from synapses within individual RBCs should cause the synaptic input to nearby A17 amacrine cells to covary . 10 . 7554/eLife . 03892 . 004Figure 2 . Strong covariation in overlapping A17 amacrine cells reflects highly synchronized cross-synaptic release from individual RBCs under dark-adapted conditions . ( A ) Paired recordings from neighboring A17 amacrine cells in the wild type retinal slice preparation were used to measure the CSS of RBC output under near-physiological conditions . ( B ) Pairs of highly overlapping A17 amacrine cells contact many of the same RBCs but at different synaptic locations ( arrows ) . Same RBC serial EM reconstruction as in Figure 1 but with an additional reconstructed A17 amacrine cell dendrite from a different A17 ( AII is removed ) . ( C–E ) Paired recordings from neighboring A17 amacrine cells revealed strong covariation in excitatory synaptic input from RBCs under dark-adapted conditions . Dim backgrounds increased presynaptic release ( C ) but decreased correlated activity in neighboring A17 amacrine cells ( D and E; p = 0 . 0053 for change relative to dark , n = 8 pairs ) . Upon returning to darkness for ∼2 min the strong covariation of presynaptic input recovers to that observed before the background was presented . ( E ) Population data for cross-correlation functions in darkness ( left ) , 0 . 5 R/rod/s ( middle ) and after returning to darkness ( i . e . recover , right ) . Thick lines represent the mean , shaded regions represent ±SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 03892 . 004 Paired recordings from neighboring A17 amacrine cells ( distance between somas < 80 μm ) revealed strong correlations in excitatory synaptic input in the dark ( The peak of the cross-correlation function in darkness , that is , the Dark CCpeak , was 0 . 51 ± 0 . 03 , n = 8 pairs; Figure 2C–E ) . Eliminating excitatory synaptic transmission between RBCs and A17s with the AMPA-receptor antagonist NBQX eliminated the correlations ( data not shown ) and dim backgrounds reversibly reduced correlation strength ( dim CCpeak = 0 . 34 ± 0 . 02 , p = 0 . 0053 , n = 8 pairs; Figure 2C–E ) . These results are consistent with strong synchronization of RBC synapses , but they could also reflect electrical coupling between A17 amacrines and/or divergence of upstream rod noise to two or more RBCs . Direct measurements of electrical coupling between highly overlapping A17s revealed an electrical resistance of 9 . 8 ± 1 . 3 GΩ ( n = 6 pairs; Figure 3A , B ) , more than 30 times the average A17 input resistance ( ∼300 MΩ ) . Substantial dark correlations were present in pairs with the highest resistance ( >15 GΩ ) , suggesting at most a small contribution from electrical coupling . We did not attempt to eliminate electrical coupling using genetic manipulations because the connexins forming gap junctions between A17s have not been identified . However , pharmacological experiments described below ( see section ‘Redundant connections and CSS scale dark noise transmission’ ) provide additional evidence for little contribution of gap junctions to correlations in A17 signals . Contributions from upstream divergence were also minimal , as revealed by direct recordings from pairs of RBCs with touching somas ( Figure 3C , D ) . Spontaneous excitatory synaptic input to neighboring RBCs was at most weakly correlated in darkness ( 0 . 03 ± 0 . 02 , n = 6; Figure 3E ) , indicating minimal correlations in the signals of neighboring RBCs due to rod divergence . Together these experiments indicate that synchronized release from different synapses made by the same RBC dominates the measured correlations in inputs to nearby A17 amacrine cells . 10 . 7554/eLife . 03892 . 005Figure 3 . Network divergence and electrical coupling only weakly contribute to correlations observed in highly overlapping A17 amacrine cells . ( A and B ) Overlapping A17s exhibit weak electrical coupling . ( A ) Example recording: strong correlations are observed in overlapping A17 amacrine cells ( left ) , even in the absence of significant electrical coupling ( right ) . ( B ) The gap junctional resistance was estimated by determining the slope of the ΔV-I relationship . ( C–E ) Dendritic input to neighboring RBCs is only weakly correlated in darkness . ( C ) Confocal reconstruction of a paired recording from RBCs with touching somas . ( D ) Example traces from touching RBC pair . Each recording trial consisted of 2 s of complete darkness followed by a 10 ms flash ( to monitor sensitivity ) . ( E ) Cross correlations were derived for each recording pair before averaging across cells ( mean ± SEM ) . These experiments were conducted using wild-type retinal slices . DOI: http://dx . doi . org/10 . 7554/eLife . 03892 . 005 The strength of correlations in spontaneous inputs to neighboring A17 amacrine cells in darkness indicated a surprising level of synaptic synchronization considering the lack of visual stimuli and the fact that the presynaptic RBCs are non-spiking neurons . Thus even if the two recorded A17s receive input from an identical set of RBCs , correlation strengths near 0 . 5 require that two synapses made by the same RBC must be coactive at least half the time . Thus strong synaptic synchronization requires large , coordinated increases in the probability of release ( a notion supported by the electrically compact nature of the RBC , Protti and Llano , 1998 ) and low intrinsic variability at individual synaptic connections ( see ‘Discussion’ ) . The remaining experiments investigate the origin and functional impact of such coordinated release . The strength of correlations in the spontaneous inputs to neighboring A17s will be controlled by the extent to which the cells receive input from common RBCs and by the strength of CSS in the RBC output . As described below , quantitative anatomical measurements of RBC-A17 connectivity ( Figure 4 ) allowed us to relate the measured input correlations to nearby A17 amacrines to the strength of CSS in RBC output . This analysis indicates near-perfect synchronization in the output of RBC synapses in the dark . 10 . 7554/eLife . 03892 . 009Figure 4 . Interpreting A17-A17 correlations in terms of RBC cross-synaptic synchrony . A17 amacrine cell-RBC connectivity was assessed using immunohistochemistry and single-cell injections . ( A ) A17 amacrine cells have long , thin neurites that are studded by synaptic varicosities . Connectivity between RBCs and Lucifer-yellow ( LY ) injected A17s were determined within 20 μm concentric rings centered on the soma of the injected A17 cell . The outermost ring from panels A and D was removed from the image for better viewing of the proximal dendrites but were included in all analyses . ( B ) Inset from A , RBCs were labeled using antibodies against PKCα ( red ) . Synapses between the RBC and A17 cells were identified by sites of appositions between A17 varicosities and the RBC axon terminal ( see ‘Materials and methods’ ) . Axonal boutons of four RBCs are colorized separately . ( C ) Orthogonal rotation of the image stack showing a side view of the four RBC axon terminals connected to the A17 amacrine cell in A and B . ( D ) Connectivity map for the A17 cell example in A . Red dots represent the axons of connected RBCs , and gray dots represent the axons of RBCs that did not contact the A17 . The percentage ( E ) and number ( F ) of RBC connections was determined as a function of radial distance for four injected A17s from four different animals . A17 dendrites traverse ∼40 μm of the inner plexiform before reaching sublaminas 4 and 5 ( where they make the majority of their synaptic contacts with RBCs ) , therefore , the most central concentric ring actually corresponds to dendritic distances between 40 and 60 μm , the second ring corresponds to dendritic distances between 60 and 80 μm and so on . ( G ) Changes in the peak amplitude of the cross correlation function reflect luminance-dependent changes in cross-synaptic synchronization as determined by the connectivity and Equation 1 ( purple line ) . Data are presented as mean ± SEM; SEMs are represented by error bars ( E and F ) or shaded regions ( G ) . These anatomical experiments were conducted on whole mount retinas taken from Igfbp2-GFP mice . Also see Figure 4—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 03892 . 00910 . 7554/eLife . 03892 . 010Figure 4—figure supplement 1 . Images from Igfbp2-GFP C57BL6 retina . ( left ) Image of a single A17 ( anti-lucifer yellow , red ) injected with lucifer yellow . White arrow points to the soma , scale bar is 30 μm . ( middle ) GFP-positive cell bodies ( anti-GFP , green ) are present in both the inner nuclear layer and ganglion cell layer in this line . ( left ) Merged image of the GFP positive A17 cell . DOI: http://dx . doi . org/10 . 7554/eLife . 03892 . 010 Assuming that the measured correlations illustrated in Figure 2 reflect entirely synchronization of RBC synapses , the measured correlation strength ( i . e . , peak of the cross-correlation function ) can be expressed in terms of the RBC's CSS ( βsync ) , the fraction of RBCs that are common to both A17s ( i . e . , Pshared; see ‘Materials and methods’ ) , the density of RBC connections , and an electrotonic scaling ( γ ) accounting for the attenuation of distal inputs when measured at the soma . Assuming complete dendritic overlap of two neighboring A17 amacrine cells , this relationship can be approximated by summing across circular rings centered on the soma: ( 1 ) CCt=0=βsync∑rPshared ( r ) nrexp ( −2 ( r+r0 ) /γ ) ∑rnrexp ( −2 ( r+r0 ) /γ ) where r is the mean radial distance from the soma for a given ring ( Figure 4A , D ) , r0 is the length of the initial descending dendrites ( see ‘Materials and methods’ ) , and nr is the number of RBCs contacted within each concentric ring ( Figure 4F ) . The numerator in Equation 1 represents the fraction of inputs to one A17 shared with the other , weighted by the electrotonic attenuation and the degree of synchrony . The denominator represents the total input to the cells . The assumptions in the model—for example , complete dendritic overlap of the two cells—cause Equation 1 to underestimate the synchrony required to explain a given correlation strength . We estimated the anatomical parameters of Equation 1 by filling individual A17 amacrine cells with Lucifer yellow ( Figure 4A ) and labeling RBCs using antibodies against PKCα ( Haverkamp and Wassle , 2000 ) ( see ‘Materials and methods’ ) . A17 synaptic inputs were located by the small , bead-like varicosities along their dendrites ( Figure 4A ) . Synaptic contacts between RBCs and A17s were identified by assessing volume overlap in 3-D between the postsynaptic A17 varicosities and the presynaptic PKCα-labeled RBCs ( Figure 4B , C; see ‘Materials and methods’ ) . Connectivity was assessed in concentric rings ( Δr = 20 μm ) centered on the cell body of each A17 and plotted as a function of radial distance ( Figure 4E ) . Pshared in Equation 1 represents the fraction of the RBC inputs to a given A17 that are shared by an overlapping A17; assuming that the two A17s are independently wired , Pshared is equivalent to the A17→RBC connection probability ( Figure 4E ) . Figure 4F plots the number of synaptic contacts made onto a single A17 per ring ( nr ) . The electronic distance ( γ = 32 . 3 μm ) for the A17 dendrites was taken from previous work ( Grimes et al . , 2010 ) . Given the parameters measured above , Equation 1 provides a correspondence between the measured strength of correlations in the inputs to neighboring A17s and the strength of CSS across RBC synapses ( purple line in Figure 4G ) . In other words , Equation 1 allows us to map correlation strength ( x-axis in Figure 4G ) to CSS strength ( y-axis ) . The slice preparations used for the paired A17 recordings will disrupt the distal dendrites . To correct for this , we reduced nr for rings with a radius exceeding 20 μm ( see ‘Materials and methods’ ) ; this correction affected the relation between CSS and correlation strength less than 5% . Because our focus was on using A17s to monitor RBC output , our results are otherwise relatively insensitive to a loss of dendrites in slicing . Applying Equation 1 to the A17 paired recordings , we estimate that the measured peak cross-correlation of 0 . 51 ± 0 . 03 corresponds to a RBC CSS value of 0 . 80 ± 0 . 08 in darkness . Dim backgrounds reversibly reduced the synchrony of RBC output ( CSS: 0 . 52 ± 0 . 05 , n = 8 pairs; Figure 4G ) . This brings us to two key results . ( 1 ) In the dark , release from RBC ribbons is nearly perfectly synchronized . This high CSS , together with differences in connectivity , can at least partially account for the dramatic differences in the properties of RBC synaptic inputs to AII and A17 amacrine cells in darkness ( Figure 1 ) . ( 2 ) This synchronization decreases with steady light . What produces a high degree of CSS in the RBC's output in the absence of visual stimuli ? First , synaptic failures and other sources of variability at individual synapses must be minimized; a full complement of releasable vesicles and multi-vesicular release likely contribute to minimizing intrinsic synaptic variability at individual RBC synapses ( see ‘Discussion’ ) . Second , different RBC synapses must experience common fluctuations in release probability so that they become coactive . Fluctuations in the dendritic synaptic input to RBCs from rod photoreceptors and consequent fluctuations in RBC voltage could cause release probability to covary . The pharmacological experiments described below support this proposal . Rod photoreceptors provide input to RBC dendrites at sign-inverting glutamatergic synapses containing mGluR6 postsynaptic receptors . To reveal the role of upstream rod noise in controlling RBC output , we used agonists and antagonists of these receptors while recording from AII and A17 amacrine cells ( Figure 5 ) . After collecting dark records from an AII or A17 amacrine cell , RBC dendritic input was suppressed by the mGluR6 agonist APB ( Slaughter and Miller , 1981 ) . APB increases mGluR6 activity and produces a clear reduction in the mean RBC synaptic input ( Sampath and Rieke , 2004 ) . APB substantially decreased both the holding current and noise observed in the inputs to AII and A17 amacrine cells , indicating that release from RBCs had been strongly suppressed ( Figure 5A–C ) . 10 . 7554/eLife . 03892 . 006Figure 5 . Highly synchronized synaptic noise at RBC→AII connections under dark-adapted conditions is driven by upstream rod-dependent noise . ( A–B ) mGluR6 agonists and antagonists can be used to override rod→RBC synaptic connections and probe cross-synaptic release properties at RBC→AII connections . Application of the mGluR6 agonist APB ( 5 µM , blue-middle ) hyperpolarizes the RBCs and shuts down synaptic transmission ( i . e . , output ) to the postsynaptic AII ( A ) and A17 ( B ) amacrine cells . Addition of the mGluR6 antagonist LY ( 0 . 5–2 µM , yellow-right ) restores tonic release from RBC output synapses ( i . e . , similar holding current ) , however , RBC output synapses are now insensitive to fluctuations in transmitter release between rods and RBCs . ( C ) Summary graph comparing network noise properties observed by AII and A17 amacrine cells ( n = 8 for all bars ) . The AII amacrine cell inherits ( from RBCs ) an order of magnitude more network noise than the A17 amacrine cell ( blue: σ2Con − σ2APB ) but recovers only a small fraction of this noise when tonic release from RBC synapses is restored ( yellow: σ2LY+APB − σ2APB ) . ( D ) Bath application of ‘APB’ and ‘APB + LY’ strongly suppress correlated activity in overlapping A17s ( APB: p = 0 . 0088 for change relative to dark; APB + LY: p = 0 . 0034 for change relative to dark; n = 4 pairs ) . ( E ) Although the majority of tonic presynaptic glutamate release can be recovered in the presence of ‘APB + LY’ , CSS measurements indicate that RBC output synapses are highly desynchronized under these conditions , thus partially explaining the differences in recovered variance in the AII and A17 amacrine cells . Also see Figure 5—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 03892 . 00610 . 7554/eLife . 03892 . 007Figure 5—figure supplement 1 . Bath application of a solution containing 1 µM LY341495 and 5 µM APB suppresses dendritic input and voltage fluctuations in RBCs . 2 s of dark record were collected in current clamp before delivering a dim 10 ms flash to monitor sensitivity ( vertical dashed line ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03892 . 007 Next , we added the mGluR6 antagonist LY341495 . A mixture of receptor agonist and antagonist should suppress RBC voltage fluctuations while permitting control of the mean RBC voltage and release rate via changes in the agonist/antagonist ratio ( Ala-Laurila et al . , 2011 ) . Indeed , antagonist concentrations could be identified that produced postsynaptic holding currents , and hence RBC synaptic release rates , near those in darkness ( dashed lines in Figure 5A , B ) . Similar agonist/antagonist mixtures maintained the mean RBC voltage while suppressing both light responses and dark noise ( Figure 5—figure supplement 1 ) . This manipulation , however , did not restore noise in the inputs to AII and A17 amacrine cells to its dark level; the suppression of noise was particularly clear in the AII inputs ( Figure 5C ) . Thus similar release rates ( dark vs appropriate agonist/antagonist mixture ) can produce very different levels of synaptic noise . The larger change in variance of the AII inputs compared to the A17 inputs is consistent with the differences in connectivity and a role of CSS in causing small changes in RBC voltage to produce large changes in AII input ( Figure 1 ) . The sensitivity of synaptic noise to suppressing fluctuations in RBC dendritic input suggests that RBC voltage fluctuations synchronize synaptic release in the dark . If this is the case , then the strength of correlated synaptic input to neighboring A17s ( i . e . , CSS ) should be sensitive to suppressing RBC voltage fluctuations . Thus , we repeated the pharmacological manipulations of RBC dendritic inputs while recording from A17 pairs ( Figure 5D ) . In control conditions , the peak cross-correlation for these pairs was 0 . 43 ± 0 . 05 ( CSSdark = 0 . 71 ± 0 . 08 , n = 4 pairs ) . Suppressing RBC synaptic release with APB eliminated correlations ( 4 ± 2% of that in darkness ) . Restoring the mean release rate with the mGluR6 antagonist modestly increased correlation strength ( compared to APB ) , but it remained significantly less than that in the dark ( peak correlation 0 . 11 ± 0 . 03 , corresponding to an estimated CSS of 0 . 18 ± 0 . 05 , p = 0 . 0034 , Figure 5D , E ) . The agonist/antagonist experiments also provide additional evidence against a primary role for gap junctions between A17s in correlating responses of nearby cells . The cross-correlation measures the fraction of the total noise in one cell that is correlated with noise in another cell . Non-rectified electrical connections between two A17s should cause a fraction of the electrical signal of one A17 to be shared with the other . Correlations in the A17 signals produced by gap junctions should hence be insensitive to decreasing the total noise in the A17 signals by suppressing fluctuations in RBC input as the same fraction of noise should remain correlated . The sensitivity of correlation strength to decreasing RBC voltage fluctuations pharmacologically indicates that gap junctions play at most a modest role in producing correlations , confirming the importance of synchrony across RBC synapses . These results indicate that CSS strength depends on whether noise in the RBC output is produced by upstream sources or is intrinsic to the synapse ( Figure 5 ) . Specifically , high CSS in the dark is generated by fluctuations in RBC dendritic input and the resulting fluctuations in RBC voltage . Gain is high within the rod bipolar pathway in the dark ( Dunn et al . , 2006 ) , helping ensure that signal and noise inherited from the rod photoreceptors rather than noise introduced later in retinal processing dominates activity in downstream cells ( e . g . , AII amacrine cells ) . Redundant connections between RBCs and AII amacrine cells and high CSS at these connections provide key elements of such amplification . With increasing light level , the rod bipolar pathway ceases to be the sole route for signals to traverse the retina , as rod and cone signals are conveyed to ganglion cells through the cone bipolar circuits ( Xin and Bloomfield , 1999; Deans et al . , 2002; Trexler et al . , 2005; Manookin et al . , 2008 ) . Under these conditions , the need for a high gain pathway is supplanted by a need to suppress noise so as not to contaminate signals in the cone bipolar circuits . Indeed , as described below , we find that noise transmission and CSS decrease with increasing light level . Transmission of noise from RBCs to AII amacrine cells was assessed over a 1000-fold range of light levels ( ∼0 . 5-500 R/rod/s; Figure 6 ) . RBCs depolarize by ∼10 mV over this light range , while the mean excitatory synaptic input to AII amacrines decreases ( Jarsky et al . , 2011; Grimes et al . , 2014 ) . The variance of the RBC signals changed by less than a factor of two across this luminance range ( Figure 6A , D ) , while the variance of the AII amacrine signals decreased 10-fold ( Ke et al . , 2014 ) ( Figure 6B , D ) . AII noise also decreased in retinas lacking gap junctions between AII dendrites and On cone bipolar axons ( i . e . , Gjd2 knockout mouse; Figure 6D ) , indicating it was a property of the RBC input to the AII . 10 . 7554/eLife . 03892 . 011Figure 6 . Rod bipolar cell output synapses continue to desynchronize with increasing luminance , reducing the transmission of rod-dependent noise at higher backgrounds . ( A–C ) Individual recordings from a RBC ( A and C red ) and AII amacrine cell ( B and C black ) in the presence of a dim background ( 0 . 5 R/rod/s , left ) and 1000-fold brighter background ( right ) illustrate the noise reduction across the RBC . ( D ) Population data for voltage-clamp recordings of excitatory synaptic input and current clamp recordings of membrane signaling in RBCs ( red ) and AII amacrine cells ( black ) . Noise recorded from RBCs remained relatively constant across this range of backgrounds ( comparison of noise at 500 relative to 0 . 5 R/rod/s , V-clamp: p = 0 . 92 , n = 7; I-clamp: p = 0 . 18 , n = 4 ) while noise recorded from AII amacrines was reduced ∼10-fold ( comparison of noise at 500 relative to 0 . 5 R/rod/s , V-clamp: p = 0 . 0039 , n = 5; I-clamp: p = 0 . 019 , n = 5 ) . AII amacrine cell recordings from the retinas of mice lacking connexin36-containing gap junctions ( black squares ) indicate that neither gap junctions , nor the secondary rod pathway , are required for this transition ( comparison of noise at 500 relative to 0 . 5 R/rod/s , V-clamp: p = 0 . 017 , n = 5 ) . Error bars represent ± SEM across cells . ( E ) Average autocorrelation functions for a population of AII amacrine cells recordings under steady-state illumination at 0 . 5 and 500 R/rod/s . Slower temporal correlations in the input currents are strongly reduced in the AII amacrine cell across this range of luminance . Inset: the reduction in temporal correlations of the RBC voltage response is much less than that observed in the AII . ( F ) The ‘LY+APB’ manipulation greatly reduces temporal correlations in RBC output , similarly to adaptation to 500 R/rod/s . ( G–H ) Paired-recordings from A17 amacrine cells reveal that RBC output synapses become increasingly desynchronized/independent as the retina is adapted to higher luminance ( CCpeak = 0 . 23 ± 0 . 05 at 500 R/rod/s vs 0 . 54 ± 0 . 06 in darkness , n = 4 pairs , p = 0 . 029 ) . Taken together these data indicate that RBC synapses desynchronize and reduce rod-dependent noise transmission when the retina is adapted to brighter conditions , when the cone-driven circuits are beginning to convey more of the visual information . Thick lines represent means , shaded regions represent ±SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 03892 . 011 Steady light changed the kinetics of the noise in the AII inputs more than the kinetics of noise in the RBC voltage , as determined from autocorrelograms ( Figure 6E ) . Specifically , the broad temporal correlations characteristic of the AII input currents at 0 . 5 R/rod/s or in the dark were largely absent at 500 R/rod/s , while the kinetics of the voltage fluctuations in presynaptic RBCs changed relatively little ( Figure 6E ) . The rapid kinetics of noise in the AII inputs at 500 R/rod/s matched the kinetics of noise in the presence of the mGluR6 agonist/antagonist mixture introduced in Figure 5 ( FWHM of the autocorrelation function was 3 . 7 ± 0 . 3 ms in steady light vs 4 . 3 ± 0 . 3 ms in drugs , light n = 5 , drugs n = 4; Figure 6F ) . The broad temporal correlations in the dark are consistent with synchronized release occurring with temporal correlations dictated by the kinetics of RBC voltage fluctuations . The narrow correlations are consistent with release occurring independently of RBC voltage fluctuations . This suggests that the decrease in correlation width produced by the agonist/antagonist mixture or by steady light reflects a relative increase in asynchronous release . We recorded from A17 pairs to test for a change in CSS with increasing light . Steady light producing 500 R/rod/s reduced the peak correlations by more than ∼50% ( Figure 6G ) and reduced the estimated CSS to 0 . 36 ± 0 . 08 ( Figure 6H ) . Rod input to the RBC dendrites continued to produce some correlated output even in bright steady light , as indicated by comparison to the lower correlations ( and CSS ) observed when using the mGluR6 agonist/antagonist mixture ( Figure 6H ) . The experiments in this section indicate that CSS of the RBC output synapses decreases with increasing light level , such that the synapses becomes less sensitive to fluctuations in RBC voltage . This result is consistent with recent studies showing that the increase in mean RBC voltage with increasing light level produces presynaptic depression by reducing the pool of available vesicles at the RBC→AII synapse ( Dunn and Rieke , 2008; Jarsky et al . , 2011; Oesch and Diamond , 2011 ) . Differences in vesicle availability across synapses will increase their variability and thus reduce CSS . Lowered CSS in turn causes less effective transmission of noise or small signals produced upstream in the rod photoreceptors . What impact does the RBC's CSS have on downstream signaling in the retinal output neurons ( i . e . , retinal ganglion cells , or RGCs ) ? To answer this question , we recorded synaptic input to pairs of On alpha and Off sustained RGCs ( Figure 7 ) . At low light levels , excitatory inputs to On alpha RGCs originate from modulation of the On cone bipolar synaptic output via electrical coupling with AII amacrine cells , while inhibitory input to Off sustained RGCs originates directly from glycinergic output of the AII ( Murphy and Rieke , 2008 ) . Thus the primary source of correlations in these signals comes from fluctuations in AII voltage , which , as we show above , is sensitive to CSS . 10 . 7554/eLife . 03892 . 012Figure 7 . Dark noise and CSS drive strongly-correlated synaptic activity in highly-overlapping On alpha - OFF sustained ganglion cell pairs . ( A ) Confocal reconstruction of a paired recording from an On alpha ( yellow ) and OFF sustained ( blue ) ganglion cell . ( B ) Example traces of simultaneous recordings of excitatory input to an On alpha ( black ) and inhibitory input to an Off sustained ( red ) ganglion cells . At the end of each recording epoch a brief flash was delivered to monitor sensitivity over time . ( C ) Peak cross correlations measurements from five pairs indicate that synaptic activity is slightly more correlated in the darkness than in the presence of a dim constant background ( p = 0 . 037 for a change in CCpeak relative to dark , n = 5 pairs ) . ( D ) Suppression of outer retinal activity transmission with APB ( 5–10 μM ) eliminates ganglion cell correlations ( p = 0 . 0048 for change in CCpeak relative to dark , n = 4 pairs ) . Additional application of LY ( at similar concentrations to Figure 5 ) produces weak correlations in the pairs ( p = 0 . 0089 for a change in CCpeak relative to dark , n = 4 pairs ) . Thick lines represent means , shaded regions represent ±SEMs . These experiments were conducted using wild type whole mount retinal preparations . DOI: http://dx . doi . org/10 . 7554/eLife . 03892 . 012 Excitatory input to the On alpha RGC and inhibitory input to the Off sustained RGC were highly correlated in the dark ( CCpeak = 0 . 43 ± 0 . 04 , n = 5 pairs; Figure 7B , C ) . Dim steady light ( 0 . 5 R/rod/s ) increased tonic synaptic input to the two cells but , if anything , decreased correlations in synaptic inputs ( CCpeak = 0 . 38 ± 0 . 04 , n = 5 pairs; Figure 7C ) . Bath application of the mGluR6 agonist APB suppressed synaptic input and eliminated correlated activity in On alpha-Off sustained RGC pairs ( Figure 7D ) . Adding the mGluR6 antagonist LY341495 to restore RBC release to near-dark levels ( but with low CSS , see Figure 5 ) produced weak correlations in synaptic input ( CCpeak = 0 . 06 ± 0 . 05 , n = 4 pairs ) . Thus , correlations in the RGC synaptic inputs and CSS observed in the RBC synaptic output share a similar dependence on normal dendritic input to RBCs from rod photoreceptors , suggesting that high CSS at the RBC→AII synapse plays an important role in creating ganglion cell correlations in darkness . A combination of CSS and redundant connections between RBCs and AII amacrine cells help to amplify and transmit small modulations in RBC voltage , whether noise or signals , and by doing so mitigate the impact of noise intrinsic to downstream synapses . The ability of the dark-adapted visual system to detect a small number of absorbed photons ( Hecht et al . , 1942 ) places considerable constraints on the underlying mechanisms . For example , behavioral sensitivity requires that rod photoreceptors detect single photons , and we now have a good understanding of how that is achieved mechanistically ( reviewed by Rieke and Baylor , 1998 ) . Behavioral sensitivity also requires that retinal synapses maintain low noise so as not to obscure the single photon responses of the rods . Similar requirements on the fidelity of synaptic transmission arise in other neural circuits that sense subtle changes in input . Synaptic noise first threatens the fidelity of visual signaling at the synapse between rods and RBCs . This is an unusual sign-inverting synapse in which ongoing release of glutamate from the rod photoreceptors acts via metabotropic glutamate receptors to close ion channels in the RBC dendrites ( Slaughter and Miller , 1981 ) . Mean release rate at these synapses is highest in the dark , with light exposure leading to a decrease in release rate . In complete darkness , saturation of the postsynaptic metabotropic cascade suppresses the transmission of rod noise; this synaptic saturation enhances the sensitivity of retinal signals more than 10-fold ( van Rossum and Smith , 1998; Field and Rieke , 2002; Berntson et al . , 2004; Sampath and Rieke , 2004 ) . The potential pitfalls associated with synaptic noise recur as signals traverse the retina , mainly via conventional chemical synapses . Our work here details how the operation of synapses between RBCs and AII amacrine cells maximizes sensitivity to upstream activity while minimizing added noise in darkness . Specifically , highly synchronized vesicle release across parallel RBC output synapses amplifies RBC voltage fluctuations produced by rod signals and noise while minimizing added synaptic noise . Quite surprisingly , achieving the estimated dark CSS of 0 . 8 requires that the dark fluctuations in RBC voltage produce near-deterministic changes in vesicle release at individual synapses ( Figure 8A ) . Multiple release sites at each ribbon synapse ( each RBC ribbon has ∼10 active zones; Figure 8B ) , high vesicle availability and postsynaptic receptor saturation ( Tong and Jahr , 1994 ) ( Figure 8C ) will facilitate low synaptic variability . The low rate of ongoing release in darkness compared to that in the presence of steady light ensures that the readily releasable vesicle pool in the RBC terminal remains full ( Singer and Diamond , 2006 ) , an important component of strong synaptic synchronization . These results highlight that the RBC output synapses operate far from a regime in which vesicle release follows Poisson statistics , and this is important for their ability to reliably transmit the small signals forming the basis of night vision . 10 . 7554/eLife . 03892 . 008Figure 8 . Multiple active zones ( AZ ) per synapse and low synaptic variability enhance CSS in darkness . ( A ) CSS measurements constrain synaptic variability at individual synaptic connections . Assuming a homogeneous release probability , a CSS measurement of 0 . 8 in darkness indicates that the coefficient of variation ( CV ) at individual synapses must be ≤0 . 5 . ( B ) Multiple release sites/active zones improve reliability at individual synapses . Previous work indicates that each RBC ribbon synapse has ∼10 active zones , thus facilitating multi-vesicular release . ( C ) Postsynaptic receptor saturation can further improve reliability at individual synapses . If the synaptic receptors are saturated by the release of five or more vesicles ( dark gray ) then the response to the release of >5 vesicles will be identical to the response to five vesicles . This reflects a tradeoff between dynamic range and reliability . DOI: http://dx . doi . org/10 . 7554/eLife . 03892 . 008 The control of synaptic output via fluctuations in RBC voltage requires fine control of the synaptic operating point . As described above , if the RBC is too depolarized in the dark the spontaneous release rate will go up ( Jarsky et al . , 2011 ) , which will both increase stochastic fluctuations in release and deplete the pool of releasable vesicles . If the RBC is too strongly hyperpolarized , the synapse will be ineffective in transmitting single photon responses generated in the rods . Thus effective transmission of single photon responses requires a balance , maintained through the ongoing level of dendritic input to the RBC from the rods and inhibitory feedback onto the RBC synaptic terminal from A17 amacrine cells . Functional and anatomical work shows that most neural circuits contain multiple cell types organized to process circuit inputs in parallel . This architectural similarity highlights several common motifs in neural computation: ( 1 ) divergence via multiple output synapses can produce correlated activity in downstream circuit elements ( Kazama and Wilson , 2009 ) ; ( 2 ) integration and processing of functionally dissimilar inputs from different parallel circuits controls computation in many neurons ( Olsen et al . , 2007; Fischer et al . , 2008; Schnell et al . , 2010 ) ; and ( 3 ) neurons and synapses often participate in multiple functional circuits ( Munch et al . , 2009; Grimes et al . , 2014 ) , a possible outcome of the evolutionary pressures to maximize computational capacity while economizing the necessary biological hardware . Cross-synaptic synchrony impacts each of these issues . Retinal ganglion cells , like output neurons in many neural circuits , exhibit strong noise correlations ( Arnett and Spraker , 1981; Mastronarde , 1983; Murphy and Rieke , 2008; Cafaro and Rieke , 2010; Volgyi et al . , 2013 ) , and these correlations can be dominated by divergent noise from common upstream circuit elements ( Brivanlou et al . , 1998; Trong and Rieke , 2008; Ala-Laurila et al . , 2011 ) . Cross-synaptic synchrony is a key determinant of whether anatomical divergence will produce correlated signals in downstream neurons . When cross-synaptic synchrony is high , postsynaptic targets of a given neuron will receive highly correlated input . Changes in CSS , such as with mean light level as observed here , will then control the strength of noise correlations produced by divergence , without morphological changes in the circuit connections . Changes in CSS could also play an important role in the integration of signals from distinct parallel circuits . For example , as light levels increase from darkness , the need for high gain imposed by detecting sparse photons is reduced and visual perception depends less critically on signals traversing the rod bipolar pathway . Over a substantial range of light levels , visual signals elicit simultaneous activity in rod and cone photoreceptors and their associated circuitry ( Naarendorp et al . , 2010 ) . These parallel signals are combined through several shared circuit elements ( e . g . , AII amacrine cells ) before they are transmitted to targets in the central nervous system . Efficient transmission of noisy rod signals through the rod bipolar pathway could jeopardize cone signals under these conditions . As luminance increases the RBC depolarizes , eventually evoking synaptic depression by way of vesicle depletion and Cav inactivation ( Singer and Diamond , 2006; Jarsky et al . , 2011; Oesch and Diamond , 2011 ) , mechanisms that likely underlie the decrease in cross-synaptic synchrony we observe here . The observed decrease in synchrony within the rod bipolar pathway serves to decrease transmission of rod noise to shared downstream circuit elements . Other highly interconnected brain regions might use similar mechanisms for dynamically regulating signal transmission in parallel circuits prior to signal integration . Experiments were conducted on whole mount and slice ( 200 μm thick ) preparations taken from dark-adapted Gjd2 knockout ( Deans et al . , 2001 , 2002 ) or wild-type C57/BL6 mice . Retinas were isolated under infrared visualization and stored in oxygenated ( 95% O2/5% CO2 ) Ames medium ( Sigma , St . Louis , MO ) at ∼32°C to 34°C . Once under the microscope , tissue preparations were perfused by the same Ames solution at a rate of ∼8 ml/min . Isolated retinas were either flattened onto polyL-lysine slides ( whole mount ) or embedded in agarose and sliced as previously described ( Dunn et al . , 2006; Murphy and Rieke , 2006 ) . Retinal neurons were visualized and targeted for whole-cell recordings using video DIC with an infrared light source ( >950 nm ) . Data in Figures 1–3 , 5 , 6 and Figure 5—figure supplement 1 were collected from retinal slices , whereas data in Figures 4 , 7 and Figure 4—figure supplement 1 were collected from whole mount preparations . To ensure that retinal recordings consistently reflected a dark-adapted state , only one recording ( single cell or paired ) came from each dark-adapted retina preparation ( i . e . , slice or whole-mount ) . Voltage clamp recordings were obtained using pipettes ( RGCs: 2–3 MΩ , AII and A17 amacrine cells: 5–6 MΩ , bipolar cells: 10–14 MΩ ) filled with an intracellular solution containing ( in mM ) : 105 Cs methanesulfonate , 10 TEA-Cl , 20 HEPES , 10 EGTA , 2 QX-314 , 5 Mg-ATP , 0 . 5 Tris-GTP , and 0 . 1 Alexa ( 488 , 555 , or 750 ) hydrazide ( ∼280 mOsm; pH ∼7 . 3 with CsOH ) . Current clamp recordings used an intracellular solution containing ( in mM ) : 123 K-aspartate , 10 KCl , 10 HEPES , 1 MgCl2 , 1 CaCl2 , 2 EGTA , 4 Mg-ATP , 0 . 5 Tris-GTP , and 0 . 1 Alexa ( 488 , 555 or 750 ) hydrazide ( ∼280 mOsm; pH ∼7 . 2 with KOH ) . NBQX ( 10 μM; Tocris , Briston , United Kingdom ) , APB ( 5–10 μM; Tocris ) , LY341495 ( ∼0 . 5–2 μM; Tocris ) , Mibefridil ( 10 μM ) , or an inhibitory cocktail ( 20 μM SR95531 , 50 μM TPMPA and 2 μM strychnine; Tocris ) was added to the perfusion solution as indicated in Figures 1 , 5–7 and Figure 5—figure supplement 1 . To isolate excitatory or inhibitory synaptic input , cells were held at the estimated reversal potential for inhibitory or excitatory input of ∼−60 mV and ∼+10 mV . Absolute voltage values were not corrected for liquid junction potentials ( K+-based = −10 . 8 mV; Cs+-based = −8 . 5 mV ) . For all experiments , full field illumination ( diameter: 560 μm ) was delivered to the preparation through a customized condenser from blue ( peak power at 470 nm ) or green ( peak power at 510 nm ) LEDs . We estimated the strength of CSS in the RBC output based on paired recordings from highly overlapping A17 amacrine cells and the calculation outlined below . The calculation sums over circular disks centered on the soma . The variance in the response of a single cell is then ( 2 ) σtotal2=∑rnrexp ( −2 ( r+r0 ) /γ ) where nr is the number of synaptic contacts and γ is the electrotonic scaling factor for synaptic inputs at a particular radial distance ( the electrotonic length factor for A17 dendrites comes from Grimes et al . , 2010 ) . A17 dendrites traverse ∼40 μm of the inner plexiform before reaching sublaminas 4 and 5 ( where they make the majority of their synaptic contacts with RBCs ) ; r0 accounts for the length of these initial descending dendrites . The common variance can then be defined as ( 3 ) σshared2=βsync∑rPshared ( r ) nrexp ( −2 ( r+r0 ) /γ ) where βsync is the strength of CSS of RBC synapses ( between 0 and 1 ) , and Pshared is the percentage of contacted RBCs that are common to the two cells within a particular ring . The cross correlation function at zero time lag is mathematically defined as the shared variance over the geometric mean of the independent variances; in terms of CSS and the A17 paired connectivity the cross correlation function can be defined as in Equation 1 . TaroTools event detection plug-ins ( for Igor Pro ) were used to examine postsynaptic currents in darkness from AII amacrine cells from Gjd2 knockout mice . Miniature excitatory postsynaptic currents were identified by setting the event detection threshold to −2 pA and requiring a 10–90% rise time of 1 ms or less . Fluctuating baselines ( likely due to gap junction input ) prohibited effective event detection in WT AIIs . All data are presented as mean ± SEM and two-tailed paired student's t tests were used to test significance unless otherwise noted . With the assumption that synapses in the RBC's axon exhibit homogeneous release probability , the cross synaptic synchrony can be related to the variability at individual synapses as ( 4 ) βsync=11+CVsingle2where CVsingle is the coefficient of variation for transmission at a single synapse . Variability at individual synapses was modeled explicitly in Figure 8 by examining probabilistic signaling for a varying number of independent release sites/active zones at a given ribbon synapse ( with fixed vesicle availability and quantal response ) . Variability in transmission was calculated assuming no variability in the postsynaptic response to a quantal release event as ( 5 ) CVsingle=1−PrN*Prwhere N is the number of release sites/active zones at a single ribbon synapse . In cases when two or more vesicles are synchronously released at a single synapse , postsynaptic receptors could experience saturation ( Tong and Jahr , 1994 ) . This possibility was modeled by equating postsynaptic responses to 2 or more released vesicles . 200 trials were run for each value of Pr with 10 active zones at a given ribbon . For each trial , a release event was initiated when a randomly generated number was less than Pr . The coefficient of variation was then calculated across trials ( SD/mean ) . On alpha-like RGCs , Off sustained RGCs , RBCs , A17 amacrine cells , and AII amacrine cells were identified by soma morphology and electrophysiological characteristics . Cell identity was often further confirmed post-recording by imaging the dye-filled arbors ( Alexa 488 , 555 or 750 ) using confocal microscopy or epifluorescence . For experiments probing different levels of luminance ( Figures 2 , 6 and 7 ) , we allowed 30–120 s of adaptation at each luminance level before steady state was reached , and data were analyzed . Recordings from slice preparations were performed within ∼4 hr of retinal dissection , and we specifically targeted neurons that were ≥20 μm below the surface of the slice . RBCs were selected for when saturating flashes from darkness produced reliable and robust events both before and after light adaptation . RBC recordings were kept short ( typically 2–5 min ) to minimize washout effects . AII amacrine cells could be targeted particularly deep in the slice ( ∼40–50 μm ) and provided stable long lasting recordings ( ∼30 min ) . To maximize the overlap in RBC sampling , we targeted pairs of A17 amacrine cells whose cell bodies were separated by less than 80 μm . To target A17 cells in whole mount mouse retina , the BAC Igfbp2 Gensat transgenic line ( www . gensat . org ) was reconstituted from cryo-frozen sperm ( FVB background , stock# 030560-UCD , www . mmrrc . org ) using in vitro fertilization of Cd1/C57 hybrid eggs; mice were then bred into a C57/BL6 background . We confirmed morphologically that amacrine cells labeled in this line were A17 amacrines ( Siegert et al . , 2009 ) ( Figure 4—figure supplement 1 ) . GFP-positive A17 amacrine cells were targeted in 3- to 6-week-old mice . A17 cells were injected using sharp electrodes ( tip resistance ∼150 MΩ ) with 2% Lucifer yellow ( in 200 mM KCl ) prior to fixation with 4% paraformaldehyde in 0 . 1 M phosphate buffered saline ( PBS ) for 20 min . The retinas were rinsed with PBS and incubated for 72 hr with rabbit polyclonal lucifer yellow ( 1:500 , Invitrogen , Carlsbad , CA ) and mouse monoclonal PKC ( 1:500 , Sigma ) antibodies in PBS with 0 . 5% Triton and 5% donkey serum . Retinas were incubated with secondary antibodies ( anti-rabbit Alexa Fluor conjugate , Invitrogen and anti-mouse DyLight conjugate , Jackson ImmunoResearch , West Grove , PA ) for 12 hr in PBS . Retinas were then mounted in Vectashield ( Vector labs , Burlington , CA ) . Images were acquired with an Olympus FV1000 microscope using a 1 . 35 NA 60× oil objective , at a voxel size of 0 . 102 × 0 . 102 × 0 . 3 μm or 0 . 204 × 0 . 204 × 0 . 3 μm . Raw image stacks were processed with MetaMorph ( Universal Imaging ) and Amira ( Mercury Computer Systems ) . To identify sites of apposition between PKC positive RBCs and A17 amacrine cell varicosities , pixel overlap was assessed upon rotation of the image volumes in 3D using Amira . Synaptic contact was defined when the fluorescent signals overlapped by >1 pixel at all angles of the 3D rotation . To determine the percentage of RBCs in the field of view that contacted the A17 cell , individual RBCs were digitally isolated and reconstructed using the ‘label-field’ function of Amira . To generate connectivity maps of the A17s , RBCs axonal locations were assessed in concentric rings spaced 20 μm apart , centered on the soma of the injected A17 . A17 dendrites traverse ∼40 μm of the inner plexiform before reaching sublaminas 4 and 5 ( where they make the majority of their synaptic contacts with RBCs ) ; therefore , the most central concentric ring corresponds to dendritic distances between 40 and 60 μm , the second ring corresponds to dendritic distances between 60 and 80 μm and so on . The A17→RBC connectivity patterns derived from whole mount preparations were used to derive the slope factor that relates the RB's CSS to correlations measured in highly overlapping A17s ( Equation 1 and Figure 4G ) . To account for the effects of slicing in our calculations , we divided the number of contacted RBCs within a given ring by two ( except for the first ring ) . Using these values and Equation 1 , we estimate the slope ( purple line in Figure 4G ) to be 0 . 64 ± 0 . 03 for highly overlapping A17s recorded in the slice preparation ( 0 . 67 ± 0 . 03 for whole mount ) . A previously published data set was analyzed ( Briggman et al . , 2011 ) ( retina k0563 ) . Voxel dimensions were 12 × 12 × 25 nm3 . Segmentation of identified RBCs , AIIs , and A17s were performed using ITK-SNAP ( Yushkevich et al . , 2006 ) ( www . itksnap . org ) and rendered in Matlab .	The human eye is capable of detecting a single photon of starlight . This level of sensitivity is made possible by the high sensitivity of photoreceptors called rods . There are around 120 million rods in the retina , and they support vision in levels of light that are too low to activate the photoreceptors called cones that allow us to see in color . This is why we cannot see colors in the dark . Signals are relayed through the retina via a circuit made up of multiple types of neurons . The activation of rods leads to activation of cells known as ‘rod bipolar cells’ which , in turn , activate amacrine cells and ganglion cells , with the latter sending signals via the optic nerve to the brain . All of these neurons communicate with one another at junctions called synapses . Activation of a rod bipolar cell , for example , triggers the release of molecules called neurotransmitters: these molecules bind to and activate receptors on the amacrine cells , enabling the signal to be transmitted . For the brain to detect that a single photon has struck a rod , the eye must transmit information along this chain of neurons in a way that is highly reliable while adding very little noise to the signal . Grimes et al . have now revealed a key step in how this is achieved . Electrical recordings from the mouse retina revealed that , in the dark , small fluctuations in the activity of rod bipolar cells lead to the near-deterministic release of neurotransmitters . This reduces the impact of random fluctuations in neurotransmitter release produced at individual synapses and ensures that the signals from rod bipolar cells ( and thus from rods ) are transmitted faithfully through the circuit with minimal added noise . As light levels increase , this tight synchrony of transmitter release breaks down , reducing the sensitivity to individual photons . Given that many other brain regions share the features that enable retinal cells to coordinate the release of neurotransmitters , this mechanism might be used throughout the brain to increase the signal-to-noise ratio for the transmission of information through neural circuits .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2014	Cross-synaptic synchrony and transmission of signal and noise across the mouse retina
Gamma rhythms are known to contribute to the process of memory encoding . However , little is known about the underlying mechanisms at the molecular , cellular and network levels . Using local field potential recording in awake behaving mice and concomitant field potential and whole-cell recordings in slice preparations we found that gamma rhythms lead to activity-dependent modification of hippocampal networks , including alterations in sharp wave-ripple complexes . Network plasticity , expressed as long-lasting increases in sharp wave-associated synaptic currents , exhibits enhanced excitatory synaptic strength in pyramidal cells that is induced postsynaptically and depends on metabotropic glutamate receptor-5 activation . In sharp contrast , alteration of inhibitory synaptic strength is independent of postsynaptic activation and less pronounced . Further , we found a cell type-specific , directionally biased synaptic plasticity of two major types of GABAergic cells , parvalbumin- and cholecystokinin-expressing interneurons . Thus , we propose that gamma frequency oscillations represent a network state that introduces long-lasting synaptic plasticity in a cell-specific manner . Neural oscillations are thought to play an important role in learning and memory processing ( Axmacher et al . , 2006; Düzel et al . , 2010; Nyhus and Curran , 2010 ) . Learning is based on activity-dependent modification of synaptic strength in order to incorporate transient experiences into persistent memory traces ( Citri and Malenka , 2008 ) . Gamma-band oscillations and sharp wave-ripple activity ( SWR ) , involved in memory encoding ( Jutras and Buffalo , 2010 ) and consolidation ( Buzsáki , 1989; Girardeau et al . , 2009; Jadhav et al . , 2012 ) , respectively , appear to be interlinked in the course of memory processing . However , to date , it had been unclear whether gamma rhythms itself represent a network state that can directly promote the formation of long-lasting synaptic plasticity within the cortical network . The hippocampus is an important structure for memory acquisition , consolidation and spatial orientation ( Buzsáki and Moser , 2013; Eichenbaum and Cohen , 2014 ) . Within the hippocampus , the CA3 area constitutes an autoassociative neural network , in which three pathways converge: mossy fibers and associational-commissural ( A/C ) and perforant path ( PP ) projections . Each of these pathways display plasticity ( Berzhanskaya et al . , 1998; Malenka and Bear , 2004; Nicoll and Schmitz , 2005 ) , which has seen the CA3 area harnessed as a well-suited and popular model for studying activity-dependent modification of synaptic transmission . However , hippocampal neural plasticity phenomena have traditionally been studied using tetanus- , pairing- or chemically induced plasticity-protocols , while the applicability of network oscillations , such as used in our study , as investigative tool had been unproven to date . GABAergic interneurons ( INs ) have been shown to play a major role in controlling oscillatory activity , as well as synaptic transmission and plasticity in cortical networks ( Ben-Ari , 2006; Klausberger and Somogyi , 2008 ) . Major inhibitory cell types , parvalbumin- ( PV ) and cholecystokinin ( CCK ) -expressing INs provide distinct forms of inhibition and have complementary roles in cortical circuits . In fact , PV-expressing INs are assumed to act as “fast signaling devices” ( Jonas et al . , 2004 ) and provide precisely-timed phase-modulated inhibition to control the timing of discharge in individual neurons as well as the synchronization and emergence of oscillations at the level of the network ( Cobb et al . , 1995; Gloveli et al . , 2005a; Bartos et al . , 2007; Sohal et al . , 2009 ) . In contrast , CCK-expressing INs show a slower but variable discharge pattern and asynchronous GABA release ( Hefft and Jonas , 2005; Daw et al . , 2009 ) . High expression of various receptors for neuromodulators ( Freund , 2003; Armstrong and Soltesz , 2012 ) post- and presynaptically , suggests that these INs may regulate excitability in the network by mediating inhibition in a behavioral state-dependent manner . We investigated the interaction and interdependence of two classical hippocampal network patterns , gamma frequency oscillations and SWRs , important in memory encoding and consolidation , respectively . We found that in vivo theta-nested gamma oscillations have an enhancing effect on subsequent SWRs in awake behaving mice . Analysis of the underlying molecular , cellular and synaptic mechanisms in vitro slice preparations showed changes in SWR-associated excitatory synaptic strength between pyramidal cells ( PC ) that are mediated postsynaptically and depend on metabotropic glutamate receptor-5 ( mGluR5 ) activation . In stark contrast to excitation , alteration of inhibitory synaptic strength was independent of postsynaptic activation and less pronounced , reflecting an IN-specific , directionally biased synaptic plasticity , as demonstrated in our study for two major GABAergic inhibitory cell types , PV- and CCK-expressing INs . Our results suggest that gamma frequency oscillations represent a network state that promotes the formation of long-lasting synaptic plasticity in the hippocampal area CA3 , leading to modification of synaptic strengths in a cell-specific manner . We investigated two major context-dependent activity patterns , SWRs and gamma frequency oscillations , in awake behaving mice . Using local field potential ( LFP ) recordings from dorsal hippocampus we found spontaneous SWRs in resting states of quietly sitting mice , while running behavior was accompanied by theta-nested gamma oscillations ( Figure 1A ) . As both rhythms have been proposed as closely linked to memory processing ( Axmacher et al . , 2006; Girardeau et al . , 2009; Jutras and Buffalo , 2010; Jadhav et al . , 2012 ) , we studied the general interdependence of these two network patterns in a behavioral paradigm . 10 . 7554/eLife . 14912 . 003Figure 1 . Theta-nested gamma rhythms enhance hippocampal SWRs in vivo . ( A ) Representative LFP recording in an awake mouse illustrates the occurrence of different network states in a behavioral-dependent manner . The initial spontaneous SWRs during a quiet state ( left , SWR ) are replaced by a running-associated theta-nested gamma rhythm ( middle ) , followed by another rapid reversal to SWRs ( right , p-SWR ) . Note the higher amplitude of the p-SWRs . Prolonged running period is marked by the black bar above . ( B ) ( i , ii , iii ) Left , three example excerpts from the trace in ( A ) at higher temporal resolution . ( i ) Left , the initial quiet state example band-pass filtered at 2–300 Hz and 100–300 Hz to illustrate the SWRs and the corresponding ripple component , respectively . Two SWRs are accentuated in red . Right , a single SWR together with its wavelet transform ( color-coded power spectral density with superimposed corresponding ripple trace in white ) . ( ii ) Left , the theta-nested gamma example with a small excerpt shown above . Right , the corresponding spectral analysis demonstrates the predominant theta ( 7 . 3 Hz ) and gamma ( 46 . 3 Hz ) peak . ( iii ) , the p-SWR with the same type of illustration as for the SWR shown in ( i ) . ( C ) Left , a direct comparison of the mean values of SWR areas before and after the intervening gamma episode ( n = 11 , 4 mice ) highlight a significant increase in SWR areas ( p=0 . 0006 , Wilcoxon signed rank test ) . The respective grand means are indicated by the horizontal bold bars . Right , the corresponding percentage increase of mean SWR areas . DOI: http://dx . doi . org/10 . 7554/eLife . 14912 . 003 As the two rhythms are each correlated with separate behavioral states ( resting or running ) , control of behavioral expression is a means of targeting the corresponding network pattern ( Figure 1A and B ) . Thus , we used prolonged running episodes , at an average of 3 min , ( mean 3:06 ± 0:32 min , n = 11 , 4 mice; range from 2:28 to 4:30 min depending on the actual running performance ) , to investigate the interaction between the two network patterns . The impact of running-associated theta-nested gamma frequency oscillations on subsequent SWRs was evaluated by comparing the areas of SWRs in the two-minute time windows directly preceding and following a theta-nested gamma episode . The post-gamma SWR ( p-SWR ) areas exhibited significantly enlargement at an average of 25 . 7% ( n = 11 , p=0 . 0006 , Figure 1C ) and no significant change in frequency ( SWR: 0 . 18 ± 0 . 10 Hz; p-SWR: 0 . 16 ± 0 . 10 Hz , n = 11 , p=0 . 23 ) . This result indicates an enhanced network activity and suggests a surprisingly direct effect of the running associated theta-nested gamma oscillations . But the fact that the internal state might not be fixed for an extended time period hampered conclusive interpretation of this result . In particular , the altered p-SWR could also reflect a change of the animals internal state including the contribution of a different set of cell assembles ( Buzsáki , 2015 ) , which is difficult to control in vivo . Consequently , in order to carry out the experiments under well-controlled conditions with the option to investigate the underlying mechanism in detail , we switched to a well-established in vitro model . We subsequently investigated the synaptic , cellular and molecular mechanisms of gamma oscillation-induced effects on SWR activity in in vitro acute hippocampal slices , a model that permits the reproduction of both oscillatory network patterns ( Gloveli et al . , 2005a , 2005b; Maier et al . , 2009; Dugladze et al . , 2012 ) . Prompted by our in vivo results , we investigated the interaction and interdependence of gamma frequency oscillations and SWRs by monitoring LFPs in the stratum pyramidale of the hippocampal area CA3 . In good agreement with our in vivo results , SWRs and gamma frequency oscillation patterns represented two ‘competing’ , mutually exclusive network states in vitro: spontaneously occurring SWRs ( mean frequency: 1 . 33 ± 0 . 10 Hz , n = 30 ) disappeared shortly ( 31 . 0 ± 2 . 8 s ) after bath application of kainic acid ( KA , 400 nM ) and reappeared within a few minutes ( 14 . 6 ± 0 . 7 min ) after KA washout ( Figure 2A ) . However , also in line with our in vivo data , the two network patterns were not fully independent – plastic changes initiated in the network by means of persistent gamma activity altered the subsequent SWR pattern ( Figure 2A and B ) . The p-SWRs exhibited a significantly increased area ( by 69 . 7 ± 15 . 1% , n = 30 , p<0 . 0001 , Figure 2C ) and a small decrease in incidence ( to 0 . 90 ± 0 . 10 Hz , n = 30 , p<0 . 0001 ) . In addition , we found a slight but significant increase in average ripple number ( by 8 . 5 ± 2 . 5% , n = 12 , p=0 . 0065 ) and an elevated oscillatory ripple frequency ( by 4 . 1 ± 1 . 8% , n = 12 , p=0 . 041 ) . These changes were accompanied by a significant increase of post-gamma sharp wave amplitude ( by 44 . 6 ± 19 . 3% , n = 12 , p=0 . 042 ) and a non-significantly altered sharp wave duration ( increased by 2 . 5 ± 2 . 9% , n = 12 , p=0 . 41 ) . In good agreement with these data , gamma oscillations induced by bath application of carbachol ( 20 µM ) , an alternative drug to trigger persistent gamma oscillations based on a different mechanisms ( Fisahn et al . , 1998; 2002; Hajos et al . , 2004 ) , also resulted in a significant increase in SWR area ( by 21 . 0 ± 4 . 2% , n = 16 , p=0 . 0002 , see also Zylla et al . , 2013 ) , indicating that gamma activity itself , and not the pharmacological agent , is responsible for the network alterations . Moreover , we found a highly significant positive correlation of the network gamma oscillations ( power×duration ) with the SWR-area increase ( R = 0 . 58 , n = 30 , p=0 . 0007 ) and no correlation with the SWR incidence ( R = −0 . 28 , n = 30 , p=0 . 14 ) . In line with this , in a few cases where KA application failed to introduce gamma frequency oscillations , no changes in SWR-area were observed ( reduction by 6 . 0 ± 19 . 4% , n = 8 , p=0 . 35 ) . Notably , in comparison to KA , carbachol-triggered gamma oscillations exhibited less spectral power ( p=0 . 007 ) and accordingly induced smaller SWR area changes ( p=0 . 02 ) , suggesting an activity-dependent mechanism of oscillation-induced neuronal network plasticity . Furthermore , we also found a reinforcing effect of gamma rhythms on subsequent gamma episodes ( Figure 3 ) . Together , these results emphasize the general potential of gamma oscillations to modify network activities . 10 . 7554/eLife . 14912 . 004Figure 2 . Gamma rhythms promote long-lasting alterations in the network activity . ( A ) SWRs recorded in the stratum pyramidale of the CA3 region occurred spontaneously ( left ) , disappeared shortly after bath application of KA ( middle ) and reappeared with a significantly higher amplitude after KA washout ( right ) . ( B ) Example of the wavelet transform ( color-coded power spectral density ) for three consecutive highlighted SWRs ( white trace ) before ( SWR ) and after ( p-SWR ) intermediate gamma oscillations . ( C ) p-SWR areas ( red-filled squares ) increased significantly compared to the SWR areas ( gray-filled squares ) . MPEP ( 50 µM ) administration largely prevented p-SWR area increase ( black , open triangles ) . The time courses of drug applications are depicted schematically . The horizontal lines mark the p-SWR data points used for statistical analyses . The significance stars compare the pre-gamma data with the marked post-gamma data . The number of asterisks indicates the significance level ( Student's t-test ) . Insert , examples of SWR ( gray ) and p-SWR ( red and black ) without ( left ) and with MPEP ( right ) administration during gamma rhythms . ( D ) Effects of AP5 ( 50 µM , green open squares ) and MPEP+AP5 ( black filled triangles ) on SWR area increase . Insert , examples of corresponding SWR ( gray ) and p-SWR ( green and black ) . ( E ) Gamma oscillation-induced SWR area increase ( LTP ) is reduced significantly by administration of AP5 ( green open bar ) , MPEP ( black open bar ) and MPEP+AP5 ( black filled bar ) . DOI: http://dx . doi . org/10 . 7554/eLife . 14912 . 00410 . 7554/eLife . 14912 . 005Figure 3 . Gamma frequency oscillations promote changes in network activity . ( A ) Brief ‘weak’ field gamma episodes were induced by bath application of 50 nM KA . After this test period , ‘conventional’ gamma frequency oscillations were induced by 400 nM KA application , followed by KA washout achieving a complete cessation of oscillatory gamma activity . In a third step , the network behavior was again tested with low KA concentration as applied in the first step . Left , a ‘weak’ gamma episode ( top , gray: 1st ‘weak’ gamma ) becomes significantly stronger ( bottom , black: 2nd ‘weak’ gamma ) after ‘conventional’ ( ‘KA 400 nM’ ) gamma oscillations . Right , corresponding spectral analysis of 1st and 2nd ‘weak’ gamma . ( B ) Summary bar charts of peak power and frequency obtained before ( 1st ‘weak’ gamma ) and after ‘conventional’ gamma ( 2nd ‘weak’ gamma ) . Spectral power of gamma rhythms increased from 0 . 72 x 10-4 ± 0 . 39 x 10-4 mV2/Hz to 1 . 34 x 10-4 ± 0 . 50 x 10-4 mV2/Hz ( n = 10 , p=0 . 003 ) while frequency remained unchanged , illustrating that the intervening gamma episode has a reinforcing effect . DOI: http://dx . doi . org/10 . 7554/eLife . 14912 . 005 To reveal the molecular mechanism underlying the network plasticity we examined the effects of activation of metabotropic glutamate ( mGluR ) 5- and/or N-methyl-D-aspartate receptors ( NMDAR ) that have been proposed to play an important role in hippocampal synaptic plasticity and memory ( Zalutsky and Nicoll , 1990; Nakazawa et al . , 2002; Naie and Manahan-Vaughan , 2004 ) . Administration of mGluR5 antagonist , 2-methyl-6- ( phenylethynyl ) pyridine ( MPEP ) largely reduced the SWR area increase ( by 83 . 4 ± 6 . 2% , n = 16 , p<0 . 0001 , Figure 2C and E ) . A similar , albeit less pronounced effect was observed for the NMDAR antagonist , DL-2-Amino-5-phosphonopentanoic acid ( AP5 ) ( reduction by 51 . 7 ± 8 . 9% , n = 13 , p<0 . 0001 , Figure 2D and E ) . Finally , this form of plasticity was abolished by joint application of the mGluR5 and NMDAR antagonists ( reduction by 91 . 8 ± 12 . 2% , n = 14 , p<0 . 0001 , Figure 2D and E ) . Taken together , these results suggest that in the hippocampal area CA3 , gamma frequency oscillations influence the subsequent network activity through mGluR5- and NMDAR-dependent mechanisms . We next studied activity-dependent alteration in synaptic transmission , defined here by synaptic strength , in CA3 PC by examining long-lasting changes in p-SWR-associated excitatory and inhibitory synaptic currents ( p-EPSCs and p-IPSCs , Figure 4 ) . Our data demonstrate that parallel to the field p-SWR ( Figure 2 ) , cells held in current-clamp mode during gamma oscillations ( see Materials and Methods ) exhibit long-lasting increase in the area of p-EPSCs ( by 104 . 2 ± 21 . 2% , n = 14 , p=0 . 0003 , Figure 4B ) . The change in the EPSC strength correlates positively with the magnitude of SWR-area increase ( R = 0 . 56 , n = 14 , p=0 . 040 ) . Conversely , p-EPSCs recorded in PCs held in voltage-clamp mode at −70 mV during gamma oscillations decreased rather than increased ( by 40 . 6 ± 7 . 7% , n = 13 , p=0 . 0002 , Figure 4B ) , suggesting that the increase of EPSC area depend on PC postsynaptic depolarization . The p-EPSC in voltage-clamp mode might also be affected by altered inhibition ( see below ) . 10 . 7554/eLife . 14912 . 006Figure 4 . Gamma frequency oscillations support long-lasting synaptic plasticity . ( A ) Light micrograph of an example CA3 PC . Insert: The regular firing pattern of this PC in response to a depolarizing current injection . ( B ) The area of SWR-associated EPSC increases significantly after gamma frequency oscillations ( gray and red filled squares for EPSCs and p-EPSC , respectively ) . Administration of MPEP ( 50 µM ) prevents the increase of p-EPSC ( gray and black filled triangles illustrate EPSC and p-EPSC , respectively ) . Holding PCs in voltage clamp configuration at −70 mV during gamma frequency oscillations leads to a significant decrease in EPSC area ( gray and red open squares for EPSC and p-EPSC , respectively ) . The significance stars compare the pre-gamma data with the marked post-gamma data . Insert: Representative examples of EPSC ( gray ) and p-EPSC recorded without ( left , red ) and with MPEP ( middle , black ) , as well as using voltage clamping of cells during gamma rhythms ( right , red , gamma-VC ) . ( C ) SWR-associated IPSCs exhibit a moderate increase in area in PCs held in both current- ( filled gray and blue triangles ) and voltage-clamp mode ( open gray and black triangles for IPSC and p-IPSC , respectively ) during gamma rhythms . Inserts: Representative examples of corresponding IPSC ( gray ) and p-IPSC ( gamma-CC , blue and gamma-VC , black ) . ( D ) Contra-directional change in p-EPSC to p-IPSC ratio for PCs held in current- ( gamma-CC ) vs . voltage-clamp mode ( gamma-VC ) during gamma rhythms ( normalized to pre-gamma values ) . DOI: http://dx . doi . org/10 . 7554/eLife . 14912 . 006 To clarify the underlying molecular mechanism , we compared the properties of these currents in the presence and absence of mGluR5 antagonist MPEP . Similar to the effects on the LFP , bath application of MPEP strongly reduced the increase in p-EPSC area of CA3 PCs ( reduction by 90 . 3 ± 7 . 1% , n = 6 , p=0 . 0001 , Figure 4B ) . These data provide direct evidence for an mGluR5-dependent increase of the excitatory synaptic strength onto PCs as an effect of an intermediate gamma episode . Close temporal correlation existed between these changes and the network alterations described above . We further investigated whether oscillatory gamma activity-dependent modification of the hippocampal network also includes alteration in inhibitory synaptic strength . In contrast to the strongly potentiated p-EPSC , p-IPSCs showed a less pronounced but still significant increase ( 32 . 8 ± 12 . 9% , n = 8 , p=0 . 039 , Figure 4C ) . Altogether , p-EPSC and p-IPSC alterations resulted in a significant increase in the PC EPSC-to-IPSC ratio ( by 44 . 4 ± 14 . 6% , p=0 . 019 , Figure 4D ) . In stark contrast to the EPSC potentiation , changes in IPSCs were independent of postsynaptic activation . PCs held in voltage-clamp mode during gamma oscillations exhibited a similar IPSC increase of 30 . 3 ± 7 . 3% ( n = 6 , p=0 . 0089 , Figure 4C ) , indicating activity-dependent changes in presynaptic inhibitory INs . To further elucidate the cellular mechanisms underlying the differential alterations in PC excitatory and inhibitory synaptic strength we examined the gamma frequency-dependent changes in two major inhibitory cell types: fast spiking PV-expressing INs targeting perisomatic or proximal dendritic domains of PCs and regular spiking CCK-containing perisomatic targeting cells . Fast-spiking PV-positive cells ( Figure 5A ) showed strong potentiation of EPSCs ( increased by 89 . 8 ± 20 . 2% , n = 12 , p=0 . 0010 ) , whereas IPCSs decreased slightly but significantly ( n = 5 , p=0 . 038 , Figure 5B ) . Combined , the latter translated into a significant rise in the EPSC-to-IPSC ratio ( from 3 . 78 ± 0 . 83 to 7 . 71 ± 1 . 39 , n = 5 , p=0 . 012 , Figure 5B ) . Similarly to the effect on PCs , application of mGluR5 antagonist MPEP strongly reduced the increase in p-EPSC area of PV-expressing INs ( reduction by 84 . 6 ± 4 . 2% , n = 5 , p=0 . 0001 , Figure 5B ) . Markedly different , even inverse alterations were observed in the regular firing CCK-containing perisomatic targeting INs ( Figure 6A ) : IPSCs were increased ( by 61 . 01 ± 16 . 0% , n = 5 , p=0 . 019 ) , whereas EPSCs showed no change ( reduction by 0 . 1 ± 10 . 6% , n = 8 , p=0 . 99 ) . As such , the EPSC-to-IPSC ratio was significantly reduced in these INs ( from 2 . 14 ± 0 . 39 to 1 . 21 ± 0 . 26 , n = 5 , p=0 . 024 , Figure 6B ) . These data , quantified by alterations in the EPSC-to-IPSC ratio , demonstrate that gamma frequency oscillations induce cell type-specific synaptic plasticity in the CA3 inhibitory network , with enhanced net excitation of PV-expressing INs , but reduced activation of CCK-expressing INs . 10 . 7554/eLife . 14912 . 007Figure 5 . Increased excitability of fast spiking PV-expressing interneurons . ( A ) Top: A confocal image of a typical PV-expressing IN filled with biocytin . Insert: Fast firing pattern in response to depolarizing current injection . Bottom: Immuno-reactivity of the biocytin filled cell for PV . ( B ) Normalized EPSC ( gray squares ) , IPSC ( gray triangles ) , p-EPSC ( red squares ) and p-IPSC ( blue triangles ) recorded from PV-positive INs held in current-clamp mode during gamma rhythms . Administration of MPEP ( 50 µM ) prevents the increase of p-EPSC ( gray and black open triangles illustrate EPSC and p-EPSC , respectively ) . The significance stars compare the pre-gamma data with the marked post-gamma data . Inserts: left , representative examples of EPSC and IPSC ( gray ) with corresponding p-EPSC ( red ) and p-IPSC ( blue ) ; right , the EPSC-to-IPSC ratio before ( pre ) and after ( post ) gamma rhythms demonstrates that the excitability significantly increases in PV-positive INs . DOI: http://dx . doi . org/10 . 7554/eLife . 14912 . 00710 . 7554/eLife . 14912 . 008Figure 6 . Reduced excitability of regular spiking CCK-expressing interneurons . ( A ) Top: A confocal image of a typical CCK-expressing IN filled with biocytin . Insert: Regular firing patterns in response to depolarizing current injection . Bottom: Immuno-reactivity of the biocytin filled cell for CCK . ( B ) Normalized EPSC ( gray squares ) , IPSC ( gray triangles ) , p-EPSC ( red squares ) and p-IPSC ( blue triangles ) recorded from CCK-expressing INs held in current-clamp mode during gamma frequency oscillations . The significance stars compare the pre-gamma data with the marked post-gamma data . Inserts: Left , representative examples of EPSC and IPSC ( gray ) with corresponding p-EPSC ( red ) and p-IPSC ( blue ) ; right , the EPSC-to-IPSC ratio before ( pre ) and after ( post ) gamma rhythms demonstrates that the excitability significantly decreases in CCK-positive INs . DOI: http://dx . doi . org/10 . 7554/eLife . 14912 . 008 We have demonstrated that gamma frequency oscillations induce activity-dependent and cell type-specific synaptic plasticity in hippocampal area CA3 . Moreover , our results illustrate the impact of oscillatory gamma activity on SWRs , a network state associated with the process of memory consolidation ( Girardeau et al . , 2009; Jadhav et al . , 2012 ) . The plastic changes require mGluR5 mediated activation , indicating that this receptor might be critically involved in memory processing . We found that SWRs in vivo displayed a significantly enlarged area after a running episode , indicating a reinforcing effect of the running associated theta-nested gamma oscillations . Our data are well in line with a recent publication ( Bittner et al . , 2015 ) demonstrating that the induction of new place-fields initiated during active running results in altered neuronal activity during subsequent SWRs . While the oscillatory theta component does not seem to be essential for the induction of plastic changes in the hippocampus in vivo ( Brandon et al . , 2014 ) , gamma rhythms are thought to constitute time windows of synchronized neural activity that promote spike-time-dependent synaptic plasticity ( Axmacher et al . , 2006 ) and enhance signal transmission ( Sohal et al . , 2009 ) . In line with this , our in vitro results clearly demonstrate that a gamma frequency episode significantly affects subsequent network activities including gamma oscillations and SWRs . The gamma activity-induced effect ( SWR area increase ) was independent of the pharmacologic agent ( KA vs . carbachol ) used for their induction , but did correlate with the presence and power of gamma frequency oscillations . Indeed , activity dependent increase of SWR amplitude was shown by high-frequency electrical stimulation ( Behrens et al . , 2005 ) . Thus , our results provide comprehensive data that the gamma oscillations and not the pharmacologic agents themselves ( Zylla et al . , 2013 ) are responsible for the observed network plasticity . Different forms of plasticity have been described for the three excitatory input systems converging on CA3 PCs: mossy fibers and associational-commissural ( A/C ) and perforant path ( PP ) projections ( Urban and Barrionuevo , 1996; McMahon and Barrionuevo , 2002; Nakazawa et al . , 2002; Kobayashi and Poo , 2004; Nicoll and Schmitz , 2005; Rebola et al . , 2011 ) . However , crucially , the latter publications and similar studies on hippocampal neuronal plasticity have certain methodological limitations . First , they were usually elicited by high-frequency electrical stimulation of neurons providing afferent input , whereas the predominant firing rate of CA3 PCs and their afferent neurons in vivo is far less frequent ( Hahn et al . , 2007; Jung and McNaughton , 1993 ) . Second , electrical stimulation was usually limited to one of these inputs , whereas , in the intact hippocampal network , individual inputs do not act in isolation , but converge onto postsynaptic cells . Thus , during physiological activity patterns , such as gamma frequency oscillations , different inputs to CA3 PCs may act synergistically , with their joint activity resulting in a specific alteration of synaptic strength . Consequently , the here investigated oscillatory pattern might constitute a more physiological paradigm that can elucidate network-dependent mechanisms of synaptic plasticity . With our approach , we reveal a unique role of gamma frequency oscillations in activity-dependent modification of hippocampal network . Our results highlight this oscillatory network rhythm as a fundamental mechanism to induce synaptic plasticity and a potential primary driving force for memory processing . Nevertheless , the specific role of an individual input for gamma-dependent plasticity in hippocampal network remains to be clarified . Our data lend further support to the hypothesis that , overall , the two major memory relevant oscillatory patterns , gamma frequency oscillations and SWRs that are generated during different behavioral states in freely moving animals ( Chrobak et al . , 2000 ) , can be considered two ‘competing’ , mutually exclusive network states: spontaneous occurring SWRs disappeared shortly after onset of gamma rhythms and reappeared after their termination , both in vivo and in vitro . However , these two network patterns are not fully independent: plastic changes initiated in the network during persistent gamma activity were reflected in a subsequent altered SWR activity ( Figure 1 and Figure 2 ) . Consistent with the here observed tight link of gamma oscillations and SWR activity , sleep-dependent memory consolidation is associated with increased gamma activity ( Ognjanovski et al . , 2014 ) and cells active during exploratory behavior exhibit enhanced SWR-associated EPSCs in subsequent slice preparations ( Mizunuma et al . , 2014 ) . Our data suggests that mGluR5 is a key component of the process underlying the observed plastic changes in the hippocampal CA3 network . In line with our findings , impairment of both LTP and spatial learning as well as place field encoding of novel environments induced by mGluR5 antagonists have been reported ( Naie and Manahan-Vaughan , 2004; Zhang and Manahan-Vaughan , 2014 ) . Group I mGluRs , comprising of mGluR5 and mGluR1 , are preferentially expressed postsynaptically in CA3 PC dendrites ( Shigemoto et al . , 1997 ) . Even though NMDAR might be involved , our results show that mGluR5 is more central in gamma network oscillation-induced synaptic plasticity . The effect can only partially be explained by NMDAR-modulation , with mGluR5 obviously exerting a more complex impact on the neuronal network dynamics , affecting both PC and IN activity . Interestingly , dysregulation of mGluR5 has been reported in several profound neurological disorders , such as schizophrenia ( Conn et al . , 2009; Nickols and Conn , 2014 ) , autistic spectrum disorders ( Williams , 2012 ) and fragile X syndrome ( Michalon et al . , 2012 ) , altogether pointing towards a pivotal regulatory function for this receptor . Our results highlight mGluR5 now in the general context of memory processing and neuronal plasticity . In contrast to the postsynaptically mediated potentiation of PCs excitatory currents , inhibitory currents only underwent minimal changes that were independent of postsynaptic activation . These differences could be explained by a cell-specific , directionally biased synaptic plasticity at PC-IN and IN-IN synapses as demonstrated for two major types of GABAergic inhibitory cells , PV- and CCK-expressing INs . Gamma network oscillations alter synaptic strength within PV-expressing INs in favor of excitation ( Alle et al . , 2001 ) , while CCK-expressing INs are subject to stronger inhibition , as demonstrated by the EPSC-to-IPSC ratio analysis . Importantly , inhibition provided by these two types of GABAergic cells is not uniform . Fast-spiking PV-expressing INs mediate a rapid , phasic-form of inhibition , which contributes to the precise timing of neuronal synchronization and emergence of network oscillations ( Gloveli et al . , 2005a; Sohal et al . , 2009; Schlingloff et al . , 2014 ) . In contrast , regular firing CCK-expressing INs mediate slower inhibition ( Hefft and Jonas , 2005; Daw et al . , 2009 ) and modulate excitability in cortical networks in a behavioral state-dependent manner . Thus , the two IN types mediate distinct forms of inhibition and could contribute differentially to cortical network activity . Our data now suggest that divergent forms of synaptic plasticity observed in these two IN types could result in a reduced tonic but increased phasic inhibition onto PC . These changes in turn might lead to enhanced network excitability and promote synaptic plasticity within the cortical circuits . In summary , we conclude that gamma frequency oscillations represent a network state responsible for activity-dependent and cell type-specific synaptic plasticity , interlinking two memory-relevant network patterns , namely , gamma rhythms and SWRs . Experiments were performed on P27-P33 ( in vivo ) and P18-P23 ( in vitro ) C57/Bl6 mice . All animal procedures were approved by the Regional Berlin Animal Ethics Committee ( Permits: G0151/12 and T 0124/05 ) and were in full compliance with national regulations . We recorded LFP from head-fixed mice , a well-established approach that allowed us to conduct prolonged running episodes . Mice were first implanted with a head-holder and a recording chamber ( 1 . 5% isoflurane anesthesia ) and then habituated to a spherical treadmill for around 12 days . Afterwards , a small craniotomy ( approx . 2 . 3 mm rostro-caudal and 2 . 5 mm lateral from bregma; 1 . 5% isoflurane anesthesia ) was performed inside the recording chamber and the exposed area covered with a layer of silicone elastomer ( Kwik-Sil , World Precision Instruments ) . The mouse was allowed to recover for at least 2 hr before the recording session started ( one recording session per mouse ) . LFP from the left hippocampus were recorded with glass pipettes , while we were using the control of behavioral expression without a task-specific reward to target certain network patterns . To investigate the impact of a theta-nested gamma episode on SWRs we first waited for a prolonged resting period with a quietly sitting mouse on the spherical treadmill allowing us to record spontaneous SWR activity . Then , once the mouse had begun to move independently , a pressurized air stream was applied to the bottom of the Styrofoam ball , resulting in a smooth ball rotation that encouraged a running behavior accompanied by theta-nested gamma oscillations . If the mouse stopped running and began to balance the air-supported ball instead , we accelerated the ball slightly until running behavior was restored , maintaining an activity phase around 3 min in total depending on the actual running performance . Turning off the air pressure usually terminated the running behavior and initiated another resting period , once again accompanied by spontaneous occurring SWRs . In order to reduce the stress level while maintaining the attentional component , some dummy runs were performed prior to the final recording session . All in vivo LFP data were analyzed in Matlab ( MathWorks Inc . , Natick , Massachusetts ) by means of custom-made routines . We compared the SWR areas of two time periods ( 120 s each ) ending 10 s before and starting 30 s after the prolonged running episode . LFP recordings were divided into a sharp wave ( filtered 2–50 Hz ) and a ripple ( filtered 100-300 Hz ) trace . We used the ripple trace to automatically preselect SWRs based on a voltage and a spectral threshold criterion . In detail , we first used a voltage threshold ( mean plus six standard deviations of event-free recording ) for a primary selection of individual ripples and grouped adjacent single ripples to a ripple event . We then took 70 ms cutouts of the event-free recording preceding those ripple events and calculated each maximum absolute wavelet coefficients of the complex Morlet wavelet transform ( 27 wavelet scales , 20 kHz sampling rate ) . We used the mean plus one standard deviation of this distribution as a spectral threshold criterion and discarded all ripple events , in which the maximum absolute wavelet coefficient did not exceed the threshold value . The preceding and subsequent local minima in the sharp wave trace were used to automatically identify SWR start and end points . However , the bandpass ( 2–50 Hz ) filtered in vivo sharp wave trace still exhibited a remarkable variation , leading to incorrect boundaries in some cases . Consequently , the sharp wave , ripple and original recording traces were scrutinized by eye ( Forro et al . , 2015 ) . We rejected erroneously detected SWRs and manually adjusted the automatically identified start and end points if required . Finally , the SWR area was defined in the sharp wave trace as the area beneath the curve enclosed by those start and end points . However , comparing the uncorrected automatically identified SWRs we also obtained a statistically significant difference . Spectral power densitiy of gamma frequency oscillations were determined with an Welch algorithms ( pwelch ) and the complex Morlet wavelet transform ( cmor2-1 ) was used to display SWR ( bandpass filter 100–300 Hz , 134 wavelet scales , 20 kHz sampling rate ) . The animals were anesthetized with inhaled isoflurane , decapitated and the brains removed . Tissue blocks containing the hippocampal formation were mounted on a Vibratome ( Leica VT1200 ) in a chamber filled with ice-cold artificial cerebrospinal fluid ( ACSF ) . Transverse hippocampal slices were cut at 400 µm thickness and incubated for at least 1 hr in a holding ‘interface’ chamber ( continuously oxygenized with carbogen and perfused with ACSF at ∼2 mL/min ) and then transferred to the recording ‘submerged’ chamber ( perfused at a rate of 6 mL/min ) , both at 33 ± 1°C . The solution used during cutting , incubation and recording contained ( in mM ) : NaCl , 129; KCl , 3; NaH2PO4 , 1 . 25; CaCl2 , 1 . 6; MgSO4 , 1 . 8; NaHCO3 , 21; glucose , 10; saturated with 95% O2 and 5% CO2 , pH 7 . 4; 290–310 mOsm . LFP were obtained from the stratum pyramidale of the hippocampal CA3 area . KA ( 400 nM , unless indicated otherwise ) or carbachol ( 20 µM ) were applied in the bath to induce network gamma frequency oscillations . The SWR oscillations occurred spontaneously , disappeared shortly after bath application of KA or carbachol and reappeared within a few minutes after their washout . mGluR5 and/or NMDAR activation was blocked by MPEP ( 50 µM , Tocris Bioscience ) and/or AP5 ( 50 µM , Tocris Bioscience ) . MPEP and/or AP5 were launched simultaneously to KA , but continued throughout the entire oscillatory gamma network episode . Field oscillations were low pass filtered at 5 kHz , digitized at 10 kHz ( Digidata 1440A , Axon Instruments ) and analyzed with the pClamp software package ( notch filter 50 Hz; Axon Instruments ) . Oscillatory peak power and frequency was determined by averaging several consecutive fast Fourier transforms ( FFT ) . SWRs were identified and the area under curve calculated ( pClamp software , Axon Instruments ) . A Student’s t-test was used for statistical comparisons unless stated otherwise; differences were considered significant if p<0 . 05 . Average values are expressed as mean ± SEM . Spearman’s rho was used to assess statistical dependence . Pre- and post-gamma data values were normalized to the mean of all prior gamma data . The EPSC-to-IPSC ratio was used to assess the net changes in cellular excitability . We further analyzed the spectral components of the LFPs with custom routines written in Matlab . Signals were zero-phase digital filtered from 2–300 Hz using a Butterworth filter , 50 Hz components including their harmonics were removed using a second-order infinite impulse response notch filter . A complex Morlet wavelet transform ( cmor2-1 ) was used to display SWRs ( bandpass filter 100–300 Hz , 134 wavelet scales , 20 kHz sampling rate ) . The patch-clamp recordings were obtained from PCs and INs of hippocampal CA3 area visualized by infrared differential interference contrast video microscopy . The intrinsic and firing properties of cells were measured in whole-cell current-clamp mode as described previously ( Gloveli et al . , 2005a ) . In order to follow the Hebbian plasticity rules , during gamma frequency oscillations , cells were recorded in current-clamp mode enabling them to generate action potentials . In an additional set of experiments , the PCs were held in voltage-clamp mode at −70 mV during gamma activity to prevent their depolarization . Whole-cell recording pipettes ( 3–5 MΩ ) were filled with a solution containing ( in mM ) : K-gluconate , 135; KCl , 5; ATP-Mg , 2; GTP-Na , 0 . 3; HEPES , 10; plus biocytin , 0 . 5% ( pH 7 . 4 and 290 mOsm ) . A Multiclamp 700B amplifier and pClamp software ( Axon Instruments ) were used for current- and voltage-clamp recordings . The holding potential in voltage-clamp mode was either −70 mV or 0 mV to record the EPSCs and IPSCs , respectively . The areas under curve were calculated for EPSCs and IPSCs and the EPSC-to-IPSC ratios were determined . The seal resistance before establishing whole-cell mode was ≥2 GΩ . The series resistance ( range 12–18 MΩ ) was not compensated , but was repeatedly monitored during the experiment by measuring the amplitude of the capacitive current in response to a −10 mV pulse . Experiments , in which the series resistance increased by >20% were discarded . Signals were low-pass filtered at 5 kHz , digitized at 10 kHz ( Digidata 1440A ) and analyzed using pClamp software . The firing properties of IN [fast ( >100 Hz ) , non-accommodating vs . regular] were studied using intrasomatic current injection ( 0 . 5 nA ) . Electrophysiological identification was confirmed by post hoc immunostaining and biocytin staining . For immunolabeling of interneurons , slices were immersed overnight in a fixative solution containing 4% paraformaldehyde ( PFA ) in 0 . 1 M phosphate buffer ( PB ) , washed three times in 0 . 1 M PB and subsequently in 0 . 025 phosphate-buffered saline ( PBS; pH 7 . 3 ) . Slices were then incubated in PBS containing 1% Triton X-100 , 10% goat serum and Mouse on Mouse ( M . O . M ) blocking reagent ( 2 drops per 2 . 5 ml solution ) for 1 hr at room temperature ( RT ) . To visualize PV- and CCK-containing cells , we used antibodies against PV ( mouse , Swant , Marly , CH ) and CCK ( mouseCURE , Los Angeles , CA ) diluted 1:5000 in PBS containing 5% goat serum and 1% Triton X-100 . Slices were incubated with primary antibodies for 48 hr at RT . After rinsing three times in PBS , sections were incubated in the PBS solution containing 0 . 5% Triton X-100 , 5% goat serum , goat anti-mouse conjugated with ( for PV ) Alexa fluor 546 ( Invitrogen Corporation , Carlsbad , CA ) or ( for CCK ) Alexa fluor 568 ( Invitrogen Corporation , Carlsbad , CA ) diluted 1:500 or ( for biocytin-filled neurons ) Alexa fluor 647 ( in some experiments 350 ) conjugated avidin diluted 1:500 ( Invitrogen Corporation , Carlsbad , CA ) . Slices were mounted on glass slides in the glycerol-based , aqueous mountant Vectashield ( Vector Laboratories ) under coverslips at 48 hr after incubation with the secondary antibodies . Labeled cells were visualized using 20x and/or 60x objectives on a confocal microscope system ( Leica ) . To examine the full extent of somato-dendritic compartments and axonal arborization , the intensity of Z-stack projections was optimized and the images were overlaid . Slices were processed as described previously in principle ( Dugladze et al . , 2012 ) . For biocytin staining , slices with biocytin-filled cells were removed from the chamber and immersed overnight in a fixative solution containing 4% paraformaldehyde ( PFA ) in 0 . 1 M phosphate buffer ( PB ) . Slices were washed three times in 0 . 1 M PB . The avidin–biocytin complex reaction ( Vectastain ABC kit , Camon laboratory service ) took place overnight at 4°C in the presence of 0 . 3% Triton X-100 ( Sigma-Aldrich ) . Afterwards the sections were rinsed several times before development with 0 . 02% diaminobenzidine in 0 . 1 M PB . The reaction product was intensified with 0 . 5% OsO4 and sections were mounted and coverslipped . Stained cells were reconstructed with the aid of a Neurolucida 3D system ( MicroBrightField , Inc ) . Matlab source code files for the calculation of FFT , Welch’s spectrogram and the wavelet transformation are available on our homepage ( https://glovelilab . wordpress . com ) .	Changes in the strength of synapses – the connections between neurons – form the basis of learning and memory . This process , which is known as synaptic plasticity , incorporates transient experiences into persistent memory traces . However , a single synapse should not be viewed in isolation . Neurons typically belong to extensive networks made up of large numbers of cells , which show coordinated patterns of activity . The synchronized firing of the neurons in such a network is referred to as a network oscillation . The frequency of an oscillation – that is , the number of times per second that its component cells are active at the same time – reflects distinct physiological functions . For example , high frequency oscillations called gamma waves help new memories to form , but it is not clear exactly how they do this . By studying gamma oscillations in a brain region called the hippocampus , Zarnadze , Bäuerle et al . provide insights into the underlying mechanisms . Signals from “excitatory” neurons make the neuron on the other side of the synapse more likely to fire in response , and signals for “inhibitory” neurons make it less likely to fire . By recording the activity of excitatory neurons in mouse brain slices , Zarnadze , Bäuerle et al . show that gamma oscillations increase the strength of excitatory synapses in the hippocampus , allowing neurons to signal more easily across these connections . Blocking the activity of a protein called metabotropic glutamate receptor 5 prevents this increase in excitatory synaptic strength , suggesting that these receptors play an important role in memory processing . In contrast to excitatory neurons , gamma oscillations have different effects on two types of inhibitory neurons within the hippocampus . The oscillations increase the excitability of gamma-supporting inhibitory neurons , but at the same time reduce that of gamma-disturbing inhibitory neurons . These opposing changes in turn support synaptic plasticity . By showing that gamma oscillations contribute to changes in synaptic strength within the hippocampus , Zarnadze , Bäuerle et al . help to explain the importance of these rhythms for memory processing . Further research is now needed to fully decipher the roles of different cell types , and the synaptic connections between them , in the formation of new memories .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2016	Cell-specific synaptic plasticity induced by network oscillations
Adolescent synaptic pruning is thought to enable optimal cognition because it is disrupted in certain neuropathologies , yet the initiator of this process is unknown . One factor not yet considered is the α4βδ GABAA receptor ( GABAR ) , an extrasynaptic inhibitory receptor which first emerges on dendritic spines at puberty in female mice . Here we show that α4βδ GABARs trigger adolescent pruning . Spine density of CA1 hippocampal pyramidal cells decreased by half post-pubertally in female wild-type but not α4 KO mice . This effect was associated with decreased expression of kalirin-7 ( Kal7 ) , a spine protein which controls actin cytoskeleton remodeling . Kal7 decreased at puberty as a result of reduced NMDAR activation due to α4βδ-mediated inhibition . In the absence of this inhibition , Kal7 expression was unchanged at puberty . In the unpruned condition , spatial re-learning was impaired . These data suggest that pubertal pruning requires α4βδ GABARs . In their absence , pruning is prevented and cognition is not optimal . During the pubertal period , the density of dendritic spines decreases by half in widespread areas of the CNS ( Huttenlocher , 1979; Zehr et al . , 2006; Petanjek et al . , 2011; Koss et al . , 2014 ) , including the CA1 hippocampus and temporal lobe of both rodents ( Yildirim et al . , 2008 ) and humans ( Tang et al . , 2014 ) , sites essential for learning and memory ( Pastalkova , 2006 ) . Dendritic spines express NMDA receptors ( NMDARs ) at excitatory synapses ( Matsuzaki et al . , 2004 ) which can be activated to form memory traces ( Bannerman et al . , 2008 ) . A modelling study ( Ruppin , 1999 ) suggests that an optimal spine density , produced by developmental pruning of unnecessary synapses , may be necessary not only for the ability to form memories but also the ability to re-learn or 'update' previously learned information . Despite the implied importance of synaptic pruning during adolescence , the mechanisms underlying spine elimination in CA1 hippocampus during puberty are not yet known nor are the behavioral outcomes of altered spine density . At certain times in development , scavenging by immune system components such as the microglia plays a role in spine pruning ( Schafer et al . , 2012; Sekar et al . , 2016 ) . This system is likely the final step in synapse elimination in several CNS areas including the CA3 hippocampus , but does not have a role in synapse loss of CA1 hippocampal pyramidal cells during puberty ( Shi et al . , 2015 ) . One factor not yet considered in adolescent pruning is the role of inhibition in the brain mediated by GABAA receptors ( GABARs ) . GABARs mediate most inhibition in the brain and are pentameric membrane proteins , of diverse sub-type , which conduct a Cl- current . In the hippocampus , GABARs are either expressed sub-synaptically , where they generate a phasic current , or extrasynaptically , where they underlie a tonic inhibitory conductance ( Stell and Mody , 2002 ) . During the pubertal period ( PND 35–44 ) of female mice , we have shown that α4βδ GABARs transiently emerge on dendritic spines of CA1 pyramidal cells , adjacent to excitatory synapses ( Shen et al . , 2010; Aoki et al . , 2012 ) . These extrasynaptic receptors , which are sensitive to low levels of ambient GABA ( <1 μM ) ( Brown et al . , 2002 ) , generate a shunting inhibition which reduces NMDAR-generated current ( Shen et al . , 2010 ) . However , NMDA current is robust in pubertal mice lacking expression of α4βδ GABARs ( Shen et al . , 2010 ) , suggesting that the inhibition mediated by α4βδ GABARs produces the impairment rather than a lack of functional NMDARs at puberty . We have also shown that the pubertal increase in hippocampal α4βδ GABAR expression prevents induction of long-term potentiation , an in vitro model of learning , and impairs spatial learning of female mice ( Shen et al . , 2010; Shen et al . , 2016 ) . These deficits were not observed at puberty in α4 KO ( Shen et al . , 2016 ) or δ KO ( Shen et al . , 2010 ) mice , implicating pubertal α4βδ GABARs as the mediating factor . We have extended these studies to show here that expression of α4βδ GABARs at the onset of puberty initiates synaptic pruning in the female mouse hippocampus , which ultimately reduces spine density post-pubertally ( comparing spine density at PND 35 versus PND 56 ) . In the α4 KO mouse , pruning does not take place and the cognitive ability of the post-pubertal mice is impaired . We also show α4βδ and NMDAR involvement in the pruning process by the administration of selective drugs during the pubertal period ( PND 35–44 ) to determine effects on spine density post-pubertally ( PND 56 ) . We further suggest that α4βδ-triggered pruning is due to impairment of NMDAR activation which regulates expression of kalirin-7 ( Kal7 ) , a Rho guanine nucleotide exchange factor ( GEF ) important for stabilizing the actin cytoskeleton ( Penzes et al . , 2001 ) . Spine density of both proximal and distal dendrites of CA1 pyramidal cells decreased ~50% from PND 35 ( puberty onset ) to PND 56 ( post-pubertal , p<0 . 05 ) in female mice ( Figure 1a , b , Figure 1—figure supplement 1 ) . To test the role of α4βδ GABARs in spine pruning , we examined spine density across the same age range in α4 KO mice . ( Mice with both alleles of the α4 chain of the GABAR gene ( Gabra4 ) inactivated are referred to here as α4 KO . ) In contrast to the wild-type ( WT ) mice , there was no significant change in spine density during adolescence in α4 KO mice , for which spine density was 100–150% greater than in WT mice post-pubertally ( p<0 . 05 ) , implicating α4βδ GABARs in spine pruning ( Figure 1a , b , Figure 1—figure supplement 1 ) . Adolescent synaptic pruning was also observed in the male ( Figure 1—figure supplement 2 ) , where α4βδ GABAR expression is also increased at puberty ( unpublished data ) : Spine density of CA1 hippocampal pyramidal cells decreased by ~42% from puberty to post-puberty ( p<0 . 05 ) , an effect not observed in the male α4 KO mouse . 10 . 7554/eLife . 15106 . 003Figure 1 . Synaptic pruning of CA1 hippocampus of adolescent female mice is prevented in the α4 knock-out . Pub , pubertal; post-Pub , post-pubertal . ( a ) CA1 hippocampal pyramidal cells , Pub and post-Pub ( 8-week old ) WT and α4 KO female mouse hippocampus . Upper panel , neurolucida images , scale , 50 µm; lower panel , z-stack ( 100x ) images; scale , 20 µm . Additional images and data from male mice provided in Figure 1—figure supplement 1 and Figure 1—figure supplement 2 , respectively . Source data for all figures are available as separate files . ( b ) Averaged data for spine density , Proximal ( left ) , WT , t-test , t ( 41 ) =7 . 15 , p<0 . 0001* , power=1; n= 21–22 neurons ( 5–6 mice ) /group; α4 KO , t-test , t ( 47 ) =0 . 43 , P=0 . 67; n= 24–25 neurons ( 6 mice ) /group; post-Pub , WT vs . α4 KO , t-test , t ( 45 ) =5 . 8 , p<0 . 0001; Distal ( right ) , WT , t-test , t ( 28 ) =5 . 73 , p<0 . 0001 , power=1; n= 15 neurons ( 5–6 mice ) /group; α4 KO , t-test , t ( 39 ) =2 . 11 , P=0 . 04; n= 20–21 neurons ( 6 mice ) /group; post-Pub , WT vs . α4 KO , t-test , t ( 33 ) =8 . 1 , p<0 . 0001* . p<0 . 05 vs . Pub; p<0 . 05 vs . WT . ( Figure 1—source data 1 ) ( c ) Quantification of spines according to type , p<0 . 05 vs . other pubertal/genotype groups . Mushroom , ANOVA , F ( 2 , 54 ) =110 . 65 , p<0 . 0001* , power=1; Stubby , ANOVA , F ( 2 , 54 ) =23 . 1 , p<0 . 0001 , power=1; Thin , ANOVA , F ( 2 , 54 ) =9 . 29 , p=0 . 0003* , power=0 . 94; Bifurcated , ANOVA , F ( 2 , 54 ) =39 , p<0 . 0001* , power=1; ( n=19 neurons , 5 mice/group ) . p<0 . 05 vs . other groups . ( Figure 1—source data 2 ) ( d ) Representative high-contrast z-stack images; scale , 10 µm . ( e ) Representative mEPSCs , post-Pub WT and α4 KO . Scale , 50pA , 10 s . ( f ) Averaged data , mEPSC frequency; t-test , t ( 16 ) =11 . 4 , p<0 . 0001* , power=1; n= 8–10 cells ( mice ) /group . ( Figure 1—source data 2 ) DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 00310 . 7554/eLife . 15106 . 004Figure 1—source data 1 . Spine counts/20 μm on dendrites of CA1 hippocampal pyramidal cells for Figure 1b for wild-type ( WT ) and α4 knock-out ( KO ) female mice assessed at puberty ( Pub , PND 35 , identified by vaginal opening ) and post-puberty ( Post-pub , PND 56 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 00410 . 7554/eLife . 15106 . 005Figure 1—source data 2 . Spine counts/10 μm for different spine-types on dendrites of CA1 hippocampal pyramidal cells for Figure 1c for Pub and Post-pub WT and Post-pub α4 KO . Spines were identified as: mushroom , stubby , thin or bifurcated ( Bif ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 00510 . 7554/eLife . 15106 . 006Figure 1—source data 3 . Figure 1f . mEPSC frequency , # mEPSCs/s recorded from CA1 hippocampal pyramidal cells using whole cell patch clamp techniques for post-pubertal WT ( left ) and α4 KO mice . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 00610 . 7554/eLife . 15106 . 007Figure 1—figure supplement 1 . Neurolucida images of spine density across pubertal stage and α4 genotype . Representative Neurolucida drawings . Scale , 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 00710 . 7554/eLife . 15106 . 008Figure 1—figure supplement 2 . Synaptic pruning of CA1 hippocampus of adolescent male mice is prevented in the α4 knock-out . Pub , pubertal; post-Pub , post-pubertal . ( a ) CA1 hippocampal pyramidal cells , Pub and post-Pub ( 8-week old ) WT and α4 KO male mouse hippocampus . ( a ) Representative z-stack ( 100x ) images; scale , 5 µm . ( b ) Averaged data for spine density , WT , pub vs . post-pub , t-test , t ( 22 ) =5 . 84 , p<0 . 0001* , power=1; n= 12 neurons ( 3 mice ) /group; α4 KO , pub vs . post-pub , t-test , t ( 22 ) =2 , P=0 . 97; n= 12 neurons ( 3 mice ) /group; post-pub , WT vs . α4 KO , t-test , t ( 22 ) =12 . 5 , p<0 . 0001* , power=1 . p<0 . 05 vs . Pub; p<0 . 05 vs . WT . ( Source data 1 ) DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 00810 . 7554/eLife . 15106 . 009Figure 1—figure supplement 2—source data 1 . Spine counts/20 μm on dendrites of CA1 hippocampal pyramidal cells for Figure 1b for wild-type ( WT ) and α4 knock-out ( KO ) male mice assessed at puberty ( Pub , PND 35 ) and post-puberty ( Post-pub , PND 56 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 009 Spine morphology was also characterized in the female mouse across pubertal state . Mushroom spines , thought to be 'learning spines' ( Bourne and Harris , 2007 ) , decreased by ~85% , while stubby spines decreased by 50% post-pubertally in WT mice ( p<0 . 05 ) ; α4 knock-out prevented these changes , resulting in a 500–600% increase in mushroom spines and a 40% decrease in thin spines ( p<0 . 05; Figure 1c , d ) compared to the WT post-pubertal hippocampus . Dendritic length was unaltered ( Table 1 ) . Decreased spine density in WT post-pubertal hippocampus was accompanied by decreased frequency of miniature excitatory post-synaptic currents ( mEPSCs ) , reflecting fewer synapses , compared with the α4 KO hippocampus ( Figure 1e , f ) . 10 . 7554/eLife . 15106 . 010Table 1 . Dendrite length is not altered during adolescence or after α4 knock-out . Mean ± SEM , n=4 neurons ( mice ) /group . Dendrite length , ANOVA , F ( 3 , 15 ) =0 . 35 , p=0 . 80 . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 01010 . 7554/eLife . 15106 . 011Table 1—source data 1 . Dendrite length for pubertal ( Pub ) and post-pubertal ( Post-pub ) WT and α4 KO female mice . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 011Dendrite length ( Mean ± SEM ) PubertalPost-pubertalWT190 ± 5 . 8205 ± 27 . 5α4 KO183 . 7 ± 5 . 5195 ± 9 . 6 NMDA-generated current was reduced at puberty ( Figure 2a , b ) , even after blockade of GABARs at inhibitory synapses with 200 nM SR95531 ( Stell and Mody , 2002 ) . In contrast , NMDAR current was robust in the α4 KO hippocampus ( Figure 2a , b ) , as we have previously shown for the δ KO hippocampus ( Shen et al . , 2010 ) , and after total GABAR blockade with 120 μM SR95531 ( Figure 2a , b ) . Thus , we tested the hypothesis that α4βδ receptors reduce spine number via impairment of NMDAR activation by examining whether increasing expression of NMDARs during puberty could reduce pruning by overwhelming the α4βδ-generated inhibition . To this end , MK-801 was administered , at a dose shown to increase hippocampal NMDAR expression compensatorily ( Gao and Tamminga , 1995 ) , during the pubertal period ( 0 . 25 mg/kg , i . p . , once daily for 10 days ) . Spine density was evaluated post-pubertally . Pubertal MK-801 administration increased spine density by 100% in both proximal and distal dendrites post-pubertally ( p<0 . 001 ) . This reflected a significant increase in the density of mushroom and stubby spines ( Figure 2c–e ) . Conversely , blockade of NMDARs with memantine , which does not alter NMDAR expression ( Cole et al . , 2013 ) , administered to pubertal α4 KO mice reduced spine density post-pubertally by 50% ( p<0 . 01 ) . Memantine decreased mushroom and stubby spines and increased thin spines ( p<0 . 05 , Figure 2f–h ) . These data suggest that reduced NMDAR activity mediates synaptic pruning at puberty and that α4βδ GABARs are novel regulators of NMDARs which trigger this process . 10 . 7554/eLife . 15106 . 012Figure 2 . NMDA receptors maintain spines during puberty . ( a ) Representative EPSCs ( black ) and NMDA EPSCs ( red ) recorded during puberty in WT or α4 KO hippocampus , in some cases during α5 ( 50 nM L655 ) or total ( 120 μM SR95531 ) GABAR blockade . In all other cases , 200 nM SR95531 was bath applied block synaptic GABARs ( Stell and Mody , 2002 ) . Scale , 150 pA , 15 ms . ( b ) Averaged NMDA/AMPA ratios; ANOVA , F ( 3 , 31 ) =20 . 21 , p=0 . 0001 , power=1; n=8–10 cells ( mice ) /group . ( Figure 2—source data 1 ) p<0 . 05 vs . WT . ( c ) Inset , Drug treatment during puberty ( PND 35–44 ) was tested for its effect on post-pubertal spine density ( PND 56 ) . Z-stack images , pub and post-pub hippocampus , showing the effects of pubertal vehicle or MK-801 treatment , at a dose shown to increase NMDAR expression ( Gao and Tamminga , 1995 ) . Scale , 6 μm . ( d ) Averaged spine density . Proximal ( left ) : ANOVA , F ( 2 , 32 ) =54 . 16 , p<0 . 0001 , power=1 , n= 11–12 neurons ( 5 mice ) /group; Distal ( right ) l: ANOVA , F ( 2 , 32 ) =460 . 1 , p<0 . 0001* , power=1; n=11–12 neurons ( 5 mice ) /group . ( Figure 2—source data 2 ) p<0 . 05 vs . other groups . ( e ) Quantification of spine types . Mushroom , ANOVA , F ( 2 , 33 ) =24 . 7 , p<0 . 0001; Stubby , ANOVA , F ( 2 , 33 ) =25 . 4 , p<0 . 0001; Thin , ANOVA , F ( 2 , 33 ) =7 . 66 , P=0 . 002; power=0 . 9–1; n=12 neurons ( 6 mice ) /group . p<0 . 05 vs . other groups . ( Figure 2—source data 3 ) ( f ) Z-stack images , pub and post-pub hippocampus , showing the effects of pubertal vehicle or memantine ( MEM ) treatment , a NMDAR blocker which does not alter NMDAR expression ( Cole et al . , 2013 ) . Scale , 6 μm . ( g ) Averaged spine density . Proximal: ANOVA , F ( 2 , 54 ) =64 . 12 , p<0 . 0001* , power=1 , n=17–20 neurons ( 4–5 mice ) /group; Distal: ANOVA , F ( 2 , 56 ) =33 . 2 , p<0 . 0001* , power=1 , n=19–20 neurons ( 4–5 mice ) /group . ( Figure 2—source data 4 ) p<0 . 05 vs . other groups . ( h ) Quantification of spine types . Mushroom , ANOVA , F ( 2 , 45 ) =89 . 9 , p<0 . 0001; Stubby , ANOVA , F ( 2 , 45 ) =9 . 4 , P=0 . 0004; Thin , ANOVA , F ( 2 , 45 ) =13 . 7 , P=0 . 0001; Bifurcated , ANOVA , F ( 2 , 45 ) =17 . 7 , p<0 . 0001; power=1 , n=16 neurons ( 4–5 mice ) /group . ( Figure 2—source data 5 ) p<0 . 05 vs . other groups . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 01210 . 7554/eLife . 15106 . 013Figure 2— source data 1 . Figure 2b: NMDA EPSC/ AMPA EPSC ratios recorded from CA1 hippocampal pyramidal cells using whole cell patch clamp techniques for post-pubertal WT ( a ) , α4 KO mice ( b ) , WT hippocampus with SR95531 ( c ) and WT hippocampus with L-655 , 708 ( L655 ) ( d ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 01310 . 7554/eLife . 15106 . 014Figure 2—source data 2 . Figure 2d: Spine counts/20 μm on dendrites of CA1 hippocampal pyramidal cells – proximal ( left ) and distal ( right ) for pubertal ( Pub ) , Post-pubertal ( Post-pub ) – vehicle ( VEH ) , and Post-pub MK-801 ( treated with MK-801 during the pubertal period ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 01410 . 7554/eLife . 15106 . 015Figure 2—source data 3 . Figure 2e: Spine counts/10 μm for different spine-types on dendrites of CA1 hippocampal pyramidal cells for Figure 1c for Pub , Post-pub vehicle ( VEH ) and Post-pub MK-801 ( treated with MK-801during the pubertal period ) . Spines were identified as: mushroom , stubby , or thin . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 01510 . 7554/eLife . 15106 . 016Figure 2—source data 4 . Figure 2g: Spine counts/20 μm on dendrites of CA1 hippocampal pyramidal cells – proximal ( left ) and distal ( right ) for α4 KO: pubertal ( Pub ) , Post-pubertal ( Post-pub ) – vehicle ( VEH ) , and Post-pub memantine ( treated with memantine during the pubertal period ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 01610 . 7554/eLife . 15106 . 017Figure 2—source data 5 . Figure 2h: Spine counts/10 μm for different spine-types on dendrites of CA1 hippocampal pyramidal cells for Figure 1c for α4 KO: Pub , Post-pub vehicle ( VEH ) and Post-pub memantine ( treated with memantine during the pubertal period ) . Spines were identified as: mushroom , stubby , thin or bifurcated ( Bif ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 017 In contrast to α4βδ GABARs , α5βγ2 , the primary extrasynaptic GABAR in CA1 hippocampus ( Caraiscos et al . , 2004 ) , did not impair NMDAR activation during puberty . Reduction of current through α5βγ2 GABARs was accomplished with the partial inverse agonist L-655 , 708 ( L655 , 50 nM ) ( Ramerstorfer et al . , 2010 ) , which had no effect on evoked NMDA current ( Figure 2a , b ) recorded from pubertal slices . In order to test the effect of GABAR sub-types on synaptic pruning , we administered L655 during the pubertal period ( PND 35–44 ) and assessed spine density post-pubertally ( PND 56 ) . As predicted , pubertal administration of L655 produced no change in spine density post-pubertally ( Figure 3 ) , nor did the benzodiazepine lorazepam ( Figure 3—figure supplement 1 ) which targets the primarily synaptic γ2-containing GABARs ( Sigel , 2002 ) . These findings suggest that α4βδ GABARs selectively reduce spine density at puberty . As expected , total GABAR blockade during puberty ( picrotoxin , 3 mg/kg , i . p . ) prevented pruning post-pubertally ( Figure 3 ) , increasing spine density by ~200% . Both mushroom ( >900% ) and stubby ( 100% ) spines were increased while thin spines were decreased ( 75% , p<0 . 05 ) . 10 . 7554/eLife . 15106 . 018Figure 3 . Effect of GABAR blockade on spine density in the post-pubertal hippocampus . Inset , Drug treatment during puberty ( PND 35–44 ) was tested for its effect on post-pubertal spine density ( PND 56 ) . Drugs: PTX , picrotoxin , a GABAR antagonist; L655 , L-655 , 708 , an inverse agonist at α5-GABAR; VEH , vehicle ( oil ) . ( a ) Neurolucida images , post-Pub CA1 pyramidal cells , following pubertal drug treatment; scale , 50 µm . ( b ) z-stack ( 100x ) images; scale , 10 µm . ( c ) Spine density , Proximal ( left ) : ANOVA , F ( 2 , 30 ) =45 . 5 , p<0 . 0001* , power=1; Distal ( right ) : ANOVA , F ( 2 , 30 ) =60 . 8 , p<0 . 0001* , power=1; n=11 neurons ( 6 mice ) /group . ( Figure 3—source data 1 ) p<0 . 05 vs . other groups . ( d ) Spine morphology changes . Mushroom , ANOVA , F ( 2 , 45 ) =104 . 2 , p<0 . 0001; Stubby , ANOVA , F ( 2 , 45 ) =4 . 78 , p=0 . 013; Thin , ANOVA , F ( 2 , 45 ) =1 . 37 , P=0 . 27; power=0 . 8–1 , n=16 neurons ( 6 mice ) /group . ( Figure 3—source data 1 ) p<0 . 05 vs . other groups . Lorazepam effects on spine density are depicted in Figure 3—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 01810 . 7554/eLife . 15106 . 019Figure 3—source data 1 . Figure 3c: Spine counts/20 μm on dendrites of CA1 hippocampal pyramidal cells – proximal ( left ) and distal ( right ) for Post-pubertal ( Post-pub ) mice treated with L-655 , 708 ( L655 , left ) , vehicle ( VEH , middle ) or picrotoxin ( Picro , right ) , during the pubertal period . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 01910 . 7554/eLife . 15106 . 020Figure 3—source data 2 . Figure 3d: Spine counts/10 μm for different spine-types on dendrites of CA1 hippocampal pyramidal cells for Post-pubertal ( Post-pub ) mice treated with L-655 , 708 ( L655 , left ) , vehicle ( VEH , middle ) or picrotoxin ( Picro , right ) , during the pubertal period . Spines were identified as: mushroom , stubby , or thin . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 02010 . 7554/eLife . 15106 . 021Figure 3—figure supplement 1 . Pubertal lorazepam treatment does not alter spine density in post-pubertal mice . ( a ) Representative z-stack images; ( b ) Averaged spine densities for proximal ( left ) and distal ( right ) dendrites of post-pubertal female mice treated during the pubertal period with the positive GABA modulator lorazepam , which targets non-α4 GABARs of the α[1–3 , 5]βγs sub-type . Proximal: t ( 23 ) =1 . 02 , p=0 . 32; Distal: t ( 20 ) =0 . 28 , p=0 . 78; Scale , 20 μm; n=11–13 neurons ( 5 mice ) /group . ( Figure 3—figure supplement 1—source data 1 ) DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 02110 . 7554/eLife . 15106 . 022Figure 3—figure supplement 1—source data 1 . Spine counts/20 μm on dendrites of CA1 hippocampal pyramidal cells – proximal ( left ) and distal ( right ) for post-pubertal mice treated with MK-801 during the pubertal period . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 022 Kal7 is a spine protein involved in dynamic spine changes ( Ma et al . , 2008 ) . Kal7 expression in hippocampal dendrites decreased by almost 50% at puberty ( p<0 . 05 ) compared to pre-puberty ( PND 28–30 ) , an effect prevented by knock-out of α4 , which increased its expression by ~120% ( p<0 . 001 ) ( Figure 4a–d , Figure 4—figure supplement 1 ) . We tested whether NMDARs played a role in Kal7 expression , which is activity-dependent ( Ma et al . , 2011 ) . Increasing NMDAR expression at puberty increased Kal7 expression by ~100% ( p<0 . 001; Figure 4e , f ) , while NMDAR blockade with memantine ( 10 mg/kg , i . p . ) reduced Kal7 expression by ~50% in the adult CA1 hippocampus ( Figure 4g , h ) . These findings suggest that activity-dependent expression of Kal7 requires NMDAR activation that is regulated by α4βδ-mediated inhibition . 10 . 7554/eLife . 15106 . 023Figure 4 . NMDA receptor-dependent Kalirin-7 expression decreases at puberty . ( a , c , e , g ) Representative images , scale , 2 . 5 μm . Arrows , spines . ( a ) Phalloidin ( Phal ) , Kalirin-7 ( Kal7 ) and merged images from pre-pub and pub CA1 hippocampus . ( b ) Mean pixel intensity , t-test , t ( 26 ) =29 . 2 , p<0 . 0001 , power=1; n=14 neurons ( 6 mice ) /group . ( c ) Pfn2 , Kal7 and merged images from pub WT and α4 KO CA1 . ( d ) Mean pixel intensity , t-test , t ( 26 ) =12 . 0 , p<0 . 0001 , power=1; n=14 neurons ( 4 mice ) /group . ( e ) Phal , Kal7 and merged images from pub CA1 hippocampus following in vivo treatment with vehicle or MK801 to increase NMDAR expression ( Gao and Tamminga , 1995 ) . ( f ) Mean pixel intensity , t-test , t ( 26 ) =6 . 25 , p<0 . 0001 , power=1; n=14 neurons ( 5 mice ) /group . ( g ) Phal , Kal7 and merged images from post-pub CA1 hippocampus following in vivo treatment with vehicle or memantine ( MEM ) , an NMDAR blocker . ( h ) Mean pixel intensity , t-test , t ( 26 ) =6 . 5 , p<0 . 0001 , power=1; n=14 neurons ( 5 mice ) /group . Original uncropped images of Kal7 immunohistochemistry are shown in Figure 4—figure supplement 1 . ( Figure 4—source data 1 ) ( i ) Representative z-stack images , Pub , post-Pub Kal7 KO . Scale , 10 μm . ( j ) Averaged data , spine density . Proximal: t ( 32 ) =0 . 06 , p=0 . 95 , n=17 neurons ( 6 mice ) /group; Distal: t ( 32 ) =0 , p=1 , n=17 neurons ( 6 mice ) /group . ( Figure 4—source data 2 ) DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 02310 . 7554/eLife . 15106 . 024Figure 4—source data 1 . Figure 4b , d , f , h: Measurements of Kalirin-7 ( Kal7 ) luminescence taken from CA1 hippocampal pyramidal cells for Pre-pub and Pub WT ( 4b ) , Pub , WT and α4 KO ( 4d ) , Pub WT-treated with MK-801 or vehicle ( VEH ) ( 4f ) and Post-pub WT-treated with memantine or VEH ( 4h ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 02410 . 7554/eLife . 15106 . 025Figure 4—source data 2 . Figure 4j: Spine counts/20 μm on dendrites of CA1 hippocampal pyramidal cells – proximal ( left ) and distal ( right ) for pubertal ( Pub ) and post-pubertal ( Post-pub ) Kal7 KO mice . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 02510 . 7554/eLife . 15106 . 026Figure 4—figure supplement 1 . Kalirin-7 expression varies across pubertal stage , α4 genotype and level of pubertal NMDAR expression . Representative images of Kalirin-7 ( Kal7 ) expression in CA1 hippocampus across pubertal stage ( a ) , in pubertal WT and α4 KO hippocampus ( b ) , following pubertal treatment with vehicle or MK-801 to increase NR1 expression ( Aoki et al . , 2012 ) ( c ) or following post-pubertal treatment with vehicle or memantine to block NMDARs ( d ) . Scale , 10 μm . ( Averaged data of pixel intensity included in Figure 3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 026 Because Kal7 is necessary for maintenance of spine number ( Ma et al . , 2003 ) , we tested the hypothesis that synaptic pruning may be mediated by the decrease in Kal7 expression at the onset of puberty . To this end , we examined spine density in pubertal and post-pubertal hippocampus from the Kal7 KO mouse , where such a pubertal decrease could not occur . ( Mice with both alleles of the Kal7 gene ( Kalrn7 ) inactivated are referred to here as Kal7 KO . ) In fact , synaptic pruning was prevented in the Kal7 KO , for which spine density was reduced by 40% for both age groups ( Figure 4i , j ) . These data suggest that synaptic pruning may require the decrease in Kal7 expression at puberty in the WT mouse . Theoretical analysis has suggested that high densities of mature mushroom spines impair changes in synaptic strength ( Ruppin , 1999 ) , thus predicting that long-term depression ( LTD ) would be impaired by the high density of mature spines in post-pubertal α4 KO hippocampus . This was the case , where an 8 ± 2 . 1% depression was observed 1 hr after low frequency stimulation ( LFS ) compared to the 32 ± 8 . 4% depression observed in post-pubertal WT hippocampus ( p<0 . 05 , Figure 5a ) . In contrast , theta burst-induction of NMDAR-dependent long-term potentiation ( LTP ) , an in vitro model of learning , was not altered in α4 KO post-pubertal hippocampus ( Figure 5b ) . 10 . 7554/eLife . 15106 . 027Figure 5 . Induction of long-term depression and re-learning are impaired under conditions of high spine density in the α4 KO mouse . ( a ) Induction of long-term depression ( LTD ) using low frequency stimulation ( arrow ) . WT , black , α4 KO , red . t-test , t ( 6 ) =3 . 56 , p=0 . 01 , power=0 . 84; n=4/group . ( Figure 5—source data 1 ) Inset , representative field EPSPs . Scale , 0 . 2 mV , 20 ms . ( b ) Induction of long-term potentiation ( LTP ) using theta burst stimulation ( arrow ) . WT , black , α4 KO , red . t-test , t ( 7 ) =0 . 28 , p=0 . 78; n=4–5/group . ( Figure 5—source data 2 ) Inset , representative field EPSPs . Scale , 0 . 2 mV , 25 ms . ( c ) [Inset , The active place avoidance task ( APA ) . The animal is trained to avoid a shock zone ( red ) on a rotating arena . Day 1 , training for zone 1; day 2 , training for zone 2 . ] Average latency to enter shock zone 1 ( Z1 ) and 2 ( Z2 ) , Acquisition . t-test , Zone 1 , t ( 9 ) =0 . 02 , p=0 . 99; Zone 2 , t ( 10 ) =3 . 37 , p=0 . 007* , power=0 . 86; n=5–7 mice . ( d ) Average latency to enter shock zone 1 ( Z1 ) and 2 ( Z2 ) , Retention . * t-test , Zone 1 , t ( 9 ) =1 . 17 , p=0 . 27; Zone 2 , t ( 10 ) =3 . 08 , p=0 . 012* , power=0 . 80; n=5–7 mice . ( Figure 5—source data 3 ) ( e ) Locomotor activity ( left , t test , t ( 10 ) =0 . 67 , p=0 . 52 ) and # shocks/entry , a measure of escape behavior ( right , t test , t ( 10 ) =0 . 08 , p=0 . 93 ) . n=5–7 mice/group . ( Figure 5—source data 4 ) ( f ) Inset , the multiple placement object recognition task ( MPORT ) . Sequence of positions ( 1–3 ) of object 2 across 3 training trials . Novel position preference for positions 2 and 3 . Position 2 , t-test , t ( 23 ) =0 . 85 , p=0 . 40; Position 3 , t ( 23 ) =4 . 61 , p<0 . 0001 , power=1; WT , n=15 mice; α4 KO , n=10 mice . ( Figure 5—source data 5 ) ( g ) Locomotor activity ( left , t-test , t ( 23 ) =0 . 34 , p=0 . 74; WT , n=15 mice; α4 KO , n=10 mice ) and # approaches , a measure of object interest ( right , t t-test , t ( 23 ) =0 . 97 , p=0 . 339; WT , n=15 mice; α4 KO , n=10 mice ) ( Figure 5—source data 6 ) . Effects on MK-801 and memantine on learning and re-learning are depicted in Figure 5—figure supplement 1 . Picrotoxin effects on learning and re-learning are depicted in Figure 5—figure supplement 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 02710 . 7554/eLife . 15106 . 028Figure 5—source data 1 . Figure 5a: Percent baseline slope of field EPSPs recorded after low frequency ( 1 Hz ) stimulation to induce LTD for post-pubertal WT and α4 KO CA1 hippocampus ( 120 min , 30 s intervals ) . Each column is a separate slice recording . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 02810 . 7554/eLife . 15106 . 029Figure 5—source data 2 . Figure 5b: Left , Percent baseline slope of field EPSPs recorded after theta burst stimulation to induce LTP for post-pubertal WT and α4 KO CA1 hippocampus ( final 20 min 100 min after LTP induction , 30 s intervals ) . Each column is a separate slice recording . Right , Averaged values for the final 20 min . segment . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 02910 . 7554/eLife . 15106 . 030Figure 5—source data 3 . Figure 5c: Learning acquisition ( left ) and retention ( right ) for zone 1 of the active place avoidance task ( APA ) . Latency to enter shock zone ( s ) for post-pub WT and α4 KO mice . Figure 5d , Re-learning acquisition ( left ) and retention ( right ) for zone 2 of the APA . Latency to enter shock zone ( s ) for post-pub WT and α4 KO mice . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 03010 . 7554/eLife . 15106 . 031Figure 5—source data 4 . Figure 5e: #shocks/entry ( left ) and locomotor activity ( right ) for post-pub WT and α4 KO mice assessed for the active place avoidance task ( APA ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 03110 . 7554/eLife . 15106 . 032Figure 5—source data 5 . Figure 5f: Learning acquisition for zones 1–3 for post-pub WT ( left ) and α4 KO ( right ) of the multiple placement object relocation task ( MPORT ) . Duration of time spent , M , moved object; unm , unmoved object; m/unm , ratio of time spent moved versus unmoved object . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 03210 . 7554/eLife . 15106 . 033Figure 5—source data 6 . Figure 5g: Locomotor activity ( left ) and # approached , a measure of interest ( right ) for for post-pub WT ( left ) and α4 KO ( right ) mice using MPORT . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 03310 . 7554/eLife . 15106 . 034Figure 5—figure supplement 1 . NMDAR antagonist treatment alters behavioral flexibility . Treatment with MK-801 or memantine during puberty , to increase NR1-NMDAR expression or decrease NMDAR activity ( Gao and Tamminga , 1995; Cole et al . , 2013 ) , respectively , produces opposite effects on behavioral flexibility tested using MPORT post-pubertally . ( a ) Post-pubertal increases in spine density produced by pubertal treatment with MK-801 resulted in a decrease in position preference for trial 3 of MPORT , reflecting a reduced ability to re-learn object position . Position 2: student’s unpaired t-test , t ( 12 ) =0 . 07 , p=0 . 95; Position 3 , t ( 12 ) =2 . 47 , p=0 . 03* , power=0 . 92 ( n=7/group ) p<0 . 05 vs . Veh . ( Figure 5—figure supplement 1—source data 1 ) ( b ) Post-pubertal decreases in spine density produced by pubertal treatment of α4 KO mice with the NMDAR antagonist memantine resulted in an increase in position preference for trial 3 of MPORT , reflecting an increased ability to re-learn object position . Position 2: student’s unpaired t-test , t ( 12 ) =0 . 54 , p=0 . 60; Position 3 , t ( 12 ) =4 . 32 , p=0 . 0005 , power=0 . 99 ( n=8/group ) p<0 . 05 vs . Veh . ( Figure 5—figure supplement 1—source data 2 ) DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 03410 . 7554/eLife . 15106 . 035Figure 5—figure supplement 1—source data 1 . Learning acquisition for positions ( Pos ) 2 and 3 for the multiple placement object relocation task ( MPORT ) . Ratio of time spent for the moved versus unmoved object for post-pubertal mice treated with MK-801 during the pubertal period . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 03510 . 7554/eLife . 15106 . 036Figure 5—figure supplement 1—source data 2 . Learning acquisition for positions ( Pos ) 2 and 3 for the multiple placement object relocation task ( MPORT ) . Ratio of time spent for the moved versus unmoved object for post-pubertal α4 KO mice treated with memantine during the pubertal period . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 03610 . 7554/eLife . 15106 . 037Figure 5—figure supplement 2 . Pubertal GABAR antagonist treatment impairs behavioral flexibility post-pubertally . Mice were treated with picrotoxin ( Picro , 3 mg/kg , i . p . ) , to block GABARs , or vehicle during puberty ( PND 35–44 ) and tested on PND 56 using MPORT . Post-pubertal increases in spine density produced by pubertal treatment with Picro resulted in a decrease in position preference for trial 3 of MPORT , reflecting a reduced ability to re-learn object position . However , initial learning ( position 2 ) was not impaired compared to vehicle-treated animals . Position 2: student’s unpaired t-test , t ( 10 ) =1 . 24 , p=0 . 24; Position 3 , t ( 10 ) =3 . 6 , p=0 . 0024 , power=0 . 96; n=6/group p<0 . 05 vs . Veh . ( Figure 5—figure supplement 2—source data 1Source data 1 ) DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 03710 . 7554/eLife . 15106 . 038Figure 5—figure supplement 2—source data 1 . Learning acquisition for positions ( Pos ) 1–3 for the multiple placement object relocation task ( MPORT ) . Ratio of time spent for the moved versus unmoved object for post-pubertal mice treated with picrotoxin during the pubertal period . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 038 The behavioral outcome of altered spine density was tested by examining learning and re-learning using the hippocampus-dependent active place avoidance ( APA ) ( Pastalkova , 2006 ) task and the multiple placement object relocation task ( MPORT ) ( Barker and Warburton , 2011 ) . In both tasks , post-pubertal α4 KO mice showed impaired acquisition and retention of the second location , despite initial learning scores similar to WT mice ( Figure 5c-d , f ) . This was a cognitive deficit because locomotor activity , shock tolerance and object interest did not differ from WT ( Figure 5e , g ) . As predicted , reducing synaptic pruning in the WT mouse with MK-801 impaired re-learning performance on MPORT , while restoring synaptic pruning in the α4 KO mice by blocking NMDARs with memantine , improved performance on this task ( Figure 5—figure supplement 1 ) . These data suggest that optimal cognition in adulthood is dependent upon adequate synaptic pruning in adolescence . Our data suggest that when adolescent synaptic pruning is prevented , resulting in abnormally high spine density in the adult ( α4 knock-out , MK-801 administration ) , re-learning is impaired . In order to explain this outcome , we examined the effect of initial learning and re-learning of MPORT on the distribution of spine types in CA1 hippocampal pyramidal cells . To this end , mouse brains were processed with the Golgi method by 1–2 hr after each learning or re-learning paradigm . Initial learning resulted in a ~150% increase in mushroom spine-types ( p<0 . 05 ) , with similar increases in stubby spine types ( ~200% , p<0 . 05 ) ( Figure 6 ) . After the second learning trial ( re-learning ) , mushroom spine types were additionally increased by ~50–100% ( p<0 . 05 ) , while thin spine types were decreased by ~70% ( Figure 6 ) ; the density of stubby spines did not change significantly . Thus , increases in mushroom spine density accompanied both learning and re-learning trials . Pubertal administration of picrotoxin ( 3 mg/kg , i . p . ) to block GABARs also prevented synaptic pruning ( Figure 3 ) and impaired re-learning ( Figure 5—figure supplement 2 ) in the post-pubertal mouse . Under these conditions , mushroom spine density was almost 300% greater in the naïve condition compared to the untreated mouse ( Figure 3 , 6 ) , and additional increases in mushroom spine density were only observed after learning ( ~50% increase , p<0 . 05 ) , but not after relearning ( Figure 6 ) . Stubby spines were additionally increased to a lesser extent ( ~20% ) in the distal dendrites after learning . Because re-learning was impaired in this unpruned condition , these findings suggest that only successful learning/re-learning increases the mushroom spine density of CA1 hippocampal pyramidal neurons . 10 . 7554/eLife . 15106 . 039Figure 6 . Learning and re-learning increase mushroom-type dendritic spines in CA1 hippocampus following adolescent synaptic pruning . ( a ) Representative z-stack images from CA1 hippocampal pyramidal cells illustrating changes in spine type and number after hippocampal-dependent learning and re-learning , compared to naïve conditions . Scale , 5 μm . ( b , c ) Means ± S . E . M . for proximal and distal dendrites . Proximal , Mushroom , ANOVA , F ( 2 , 39 ) =44 . 9 , p<0 . 0001 , power=1; Stubby , ANOVA , F ( 2 , 39 ) =6 . 0 , p=0 . 005* . power=0 . 86; Thin , ANOVA , F ( 2 , 39 ) =7 . 24 , p=0 . 004; power=0 . 97 , n=14 neurons ( 5 mice ) /group . p<0 . 05 vs . other groups . Distal , Mushroom , ANOVA , F ( 2 , 39 ) =84 . 1 , p<0 . 0001* , power=1; Stubby , ANOVA , F ( 2 , 39 ) =13 . 7 , p<0 . 0001* , power=1; Thin , ANOVA , F ( 2 , 39 ) =13 , p<0 . 0001; power=1 , n=14 neurons ( 4–6 mice ) /group . p<0 . 05 vs . other groups . *p<0 . 05 vs . naïve . ( Figure 6—source data 1 ) ( d ) Representative z-stack images from hippocampus of adult mice treated during the pubertal period with 3 mg/kg picrotoxin ( Figure 3 ) to prevent synaptic pruning . Changes in spine type and number are evident after hippocampal-dependent learning and re-learning , compared to naïve conditions . Scale , 5 μm . ( e , f ) Means ± S . E . M . for proximal and distal dendrites . Proximal , Mushroom , ANOVA , F ( 2 , 39 ) =12 . 6 , p<0 . 0001 , power=0 . 99; Stubby , ANOVA , F ( 2 , 39 ) =3 . 78 , p=0 . 03* . power=0 . 86; Thin , ANOVA , F ( 2 , 39 ) =0 . 87 , p=0 . 43 , n=14 neurons ( 5 mice ) /group . p<0 . 05 vs . other groups . p<0 . 05 vs . naïve . Distal , Mushroom , ANOVA , F ( 2 , 39 ) =33 . 1 , p<0 . 0001 , power=1; Stubby , ANOVA , F ( 2 , 39 ) =3 . 87 , p<0 . 029* , power=1; Thin , ANOVA , F ( 2 , 39 ) =0 . 42 , p=0 . 66 , n=14 neurons ( 5 mice ) /group . p<0 . 05 vs . other groups . *p<0 . 05 vs . naïve . ( Figure 6—source data 2 ) DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 03910 . 7554/eLife . 15106 . 040Figure 6—source data 1 . Figure 6b: ( proximal ) , 6c ( distal ) , Spine counts/10 μm for different spine-types on dendrites of CA1 hippocampal pyramidal cells assessed 1–2 hr after learning , re-learning or naïve conditions . Spines were identified as: mushroom , stubby , or thin . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 04010 . 7554/eLife . 15106 . 041Figure 6—source data 2 . Figure 6e: ( proximal ) , 6f ( distal ) , Spine counts/10 μm for different spine-types on dendrites of CA1 hippocampal pyramidal cells assessed 1–2 hr after learning , re-learning or naïve conditions in mice with high spine densities ( treated with picrotoxin during the pubertal period ) . Spines were identified as: mushroom , stubby , or thin . DOI: http://dx . doi . org/10 . 7554/eLife . 15106 . 041 Pubertal synaptic pruning is seen in many CNS areas ( Huttenlocher , 1979; Zehr et al . , 2006; Petanjek et al . , 2011 ) , and is correlated with EEG changes in humans ( Campbell et al . , 2012 ) , where the steepest reduction in slow wave ( delta , 1–4 Hz ) activity occurs at the onset of puberty . Our findings suggest that α4βδ GABARs play a critical role in this process . α4βδ GABARs are uniquely localized to spines during puberty where they reduce NMDAR current ( Shen et al . , 2010 ) , necessary for spine stability ( Alvarez et al . , 2007 ) . In contrast , blockade of the predominant extrasynaptic GABAR in CA1 hippocampus , α5β3γ2 , did not facilitate NMDAR activation or alter synaptic pruning during the pubertal period , nor did modulation of synaptic γ2-containing GABARs with 200 nM SR95531 or lorazepam . Thus , these findings suggest that α4βδ GABARs selectively trigger synaptic pruning at puberty via impairment of NMDAR activation . This is in contrast to visual cortex , where synaptic α1β2γ2 GABARs target dendritic spines ( Kawaguchi and Kubota , 1997 ) , and play a role in spine maturation ( Heinen et al . , 2003 ) . Pubertal synaptic pruning is not dependent upon the ovarian hormone estradiol ( Yildirim et al . , 2008 ) , which in fact increases spine density ( Woolley et al . , 1997 ) . In the present study , MK-801 administration during the pubertal period of female mice increased the density of dendritic spines post-pubertally , resulting in increases in both mushroom and stubby spine-types . A recent study ( Han et al . , 2013 ) reports that similar MK-801 treatment across the peri-pubertal period in male rats also increases the density of stubby spines in hippocampus , but decreases the mushroom spine-types . The reason for the disparity in the two outcomes may be due to gender or developmental differences , as the latter study used rats across an age range that likely includes both pre-pubertal and pubertal ages ( Marty et al . , 2001 ) . Although MK-801 is an NMDA antagonist , it has been shown to increase NMDAR expression , as a compensatory effect 24 hr after administration ( 0 . 1–1 . 0 mg/kg ) , selectively in the CA1 region of the hippocampus ( Gao and Tamminga , 1995 ) . This is the most likely mechanism by which MK-801 increased spine density in the present study where injections were administered once a day during the pubertal period . In the pre-frontal cortex , only a single low dose of MK-801 increases NMDAR expression on pyramidal cells ( Xi et al . , 2009 ) , while higher doses have no effect , but decrease NMDAR expression and AMPA receptor-mediated currents of fast-spiking interneurons ( Wang and Gao , 2012 ) suggesting that effects of this drug are cell-type and region specific . Other studies have suggested that peri-adolescent administration of MK-801 alters GABAergic circuits in adulthood ( Thomases et al . , 2013 ) . We cannot rule out the additional possibility that the MK-801 treatment also produced changes in the GABAergic circuitry of the hippocampus which may have contributed to the observed changes in spine density post-pubertally . α4βδ GABAR-induced impairment of NMDAR activation at puberty reduces expression of Kal7 , a spine protein involved in spine restructuring which binds to the post-synaptic density ( PSD ) ( Penzes et al . , 2001 ) . Kal7 activates the small GTPase Rac1 which regulates the actin cytoskeleton via P21-activated kinases within the spine ( Ma et al . , 2014 ) . Although many proteins localize to the spine , Kal7 is one of the few shown to be necessary for maintenance of existing spines ( Penzes et al . , 2001; Ma et al . , 2003 ) . That may explain results from the present study where a 50% reduction in Kal7 expression at puberty resulted in a 50% decrease in spine number . The fact that synaptic pruning was prevented in the absence of Kal7 expression suggests that dynamic regulation of Kal7 expression during adolescence may be one factor underlying synaptic pruning . However , many other spine proteins have been identified which alter spine density , including shank3 , IQGAP1 , valosin , axin and NEDD9 ( Roussignol et al . , 2005; Gao et al . , 2011; Wiesmann et al . , 1975; Knutson et al . , 2016; Chen et al . , 2015 ) . Thus , synaptic remodeling during adolescence may incorporate a more complex array of spine protein changes . Several studies have suggested that scavenging by microglia ( Schafer et al . , 2012 ) or direct autophagy ( Tang et al . , 2014 ) prunes spines . In particular , the C4 complement system is abnormal in schizophrenia; C4 activates C3 , which binds to target spines and promotes their engulfment by phagocytic cells ( Sekar et al . , 2016 ) . This system may be target and developmentally specific – as it does not underlie adolescent pruning in CA1 hippocampus ( Shi et al . , 2015 ) . In addition , the involvement of the complement cascade in synaptic pruning would likely be the final step in spine removal and does not preclude involvement of other systems , such as NMDAR inhibition by α4βδ receptors which would be the initial trigger for the pruning process . The mushroom spines were selectively removed during pubertal synaptic pruning in the wild-type mouse , leaving an abundance of thin spines . In order to better understand the role of the various spine types in cognition , we directly examined the changes in spine morphology produced by learning and re-learning a spatial task in the wild-type mouse . Our results suggest that learning increased mushroom spines , consistent with earlier reports ( Beltrán-Campos et al . , 2011; González-Ramírez et al . , 2014 ) , which we also observed by 2 hr after re-learning , when the density of thin spines also decreased . These two spine types are well-characterized and sub-serve different functions: Thin spines express abundant numbers of NMDARs , are highly motile and plastic ( Kasai et al . , 2003; Holtmaat et al . , 2006 ) , while the larger mushroom spines are relatively more stable and express AMPA receptors predominantly ( Dumitriu et al . , 2010; Matsuzaki et al . , 2001 ) . Recent studies using two photon technology to assess changes in spines produced by NMDAR-dependent LTP , an in vitro model of learning , have shown that smaller 'thin' spines develop enlarged heads and resemble 'mushroom' spines following LTP induction , accompanied by increased expression of AMPA receptors ( Hill and Zito , 2013; Kopec et al . , 2006; Harvey and Svoboda , 2007 ) . These larger spine heads have larger post-synaptic densities and active zones , consistent with stronger synaptic connections ( Bell et al . , 2014 ) . Typically , these changes are first observed within 15 min . with peak effects 40 min to 2 hr after LTP induction , similar to the time-course of our effects . Our findings suggest that a high spine density produces cognitive impairment . Although modelling studies have predicted the cognitive outcome of increases in spine density ( Ruppin , 1999 ) , most experimental studies to date have only examined the effect of spine loss on cognition ( Oaks et al . , 2016 ) . The high spine density in the unpruned condition in the present study selectively impaired re-learning , while learning a spatial task was similar to controls . The impairments in re-learning in the unpruned conditions may be due to the higher prevalence of mushroom-type spines and lower density of thin spine-types . Mushroom spine density was only increased above its already elevated level in the unpruned mouse following the learning trial . No additional increases in mushroom spines were observed for the unsuccessful re-learning trial . There may be a maximum number of mushroom spines that can be supported by a dendritic segment due to the energy requirement necessary for spine maintenance or due to spatial constraints . Alternatively , the lower density of thin spines may have been insufficient to permit successful re-learning . The changes in spine density observed after learning and re-learning protocols were of the same magnitude as those observed during adolescence , reflecting the high plasticity of spines , although changes occurred in the opposite direction . Changes in spine types were distinct for the two events as well: during learning and re-learning , mushroom spine-types increased , while these spine-types decreased during adolescence . In addition , the spine density of the control , post-pubertal mice may reflect the lower limit of synapse number because these mice were housed in non-enriched cages with limited sensory stimuli . Numerous studies have shown that exposure to more complex environments , even for 1 hr per day , increases spine density in many areas of the CNS , including CA1 hipppocampus ( Turner and Greenough , 1985; Jung and Herms , 2014; Kitanishi et al . , 2009 ) . Recent studies report that environmental enrichment increases large spines and enhances spine turnover ( Jung and Herms , 2014; Kitanishi et al . , 2009 ) , suggesting that spine dynamics are environmentally specific . LTD induction was also impaired in the unpruned condition ( α4 KO ) , consistent with other reports which describe impairment in LTD under conditions where thin spine number is reduced ( Spiga et al . , 2014 ) . Two photon studies have indeed shown that this protocol reduces thin spine number ( Nägerl et al . , 2004 ) , thus suggesting that a critical number of thin spines may be necessary for LTD induction . LTD has been suggested as a cellular mechanism underlying synaptic changes necessary for re-learning ( Nicholls et al . , 2008 ) , where weaker synaptic connections mediated by thin spines are reduced . In contrast to the present results , studies reporting more massive decreases in thin spine number , as a result of aging or chronic alcohol exposure , also observed impairments in learning and LTP ( Dumitriu et al . , 2010; Spiga et al . , 2014 ) , suggesting that the critical number of thin spines necessary for learning is below that required for relearning . The results from the present study may have relevance for the cognitive impairment in autism and schizophrenia where synaptic pruning is abnormal ( Hutsler and Zhang , 2010; van Spronsen and Hoogenraad , 2010 ) . Adolescent synaptic pruning of the temporal lobe does not occur in autism ( Tang et al . , 2014 ) , leaving an abundance of dendritic spines ( Hutsler and Zhang , 2010 ) which are associated with impairments in reversal learning ( D'Cruz et al . , 2013 ) , similar to our results in the α4 KO mouse . In fact , reduced α4 expression has been reported in autism ( Fatemi et al . , 2010 ) , which is correlated with increased risk of developing this disorder ( Collins et al . , 2006 ) , although identified genetic abnormalities in α4 and/or δ genes in autism and schizophrenia are relatively rare ( Ma et al . , 2005; Bullock et al . , 2008 ) . Both disorders are more likely to occur in males ( Croen et al . , 2002; Iacono and Beiser , 1992 ) , for which we also show α4βδ involvement in adolescent pruning . Initial deficits of autism appear in early childhood , but loss of cognitive gains are frequently reported in adolescence following improvement earlier in development ( Sigman and McGovern , 2005; Gillberg and Steffenburg , 1987 ) . Thus , the lack of synaptic pruning in adolescence may contribute to this developmental slow-down . The results from the present study may suggest novel therapeutic strategies to normalize disrupted synaptic pruning in these disorders . Female and male C57/BL6 mice were housed in a reverse light:dark cycle ( 12: 12 ) . Mice were tested for spine density at puberty onset ( ~PND 35 ) to compare with a post-pubertal age ( PND 56 ) . In some cases , pubertal mice ( ~PND 35–44 ) were injected with drugs or vehicle ( oil ) to target certain populations of GABARs or NMDARs and tested for spine density and learning/re-learning at 8-weeks of age . This pubertal time period was selected because it has been established that α4βδ GABARs increase expression on dendritic spines of CA1 hippocampal pyramidal cells beginning at puberty onset ( vaginal opening or preputial separation , ~PND 35 ) and are maintained for the following 9 days ( Shen et al . , 2010; Aoki et al . , 2012 ) . In one study pre-pubertal mice ( PND 28–31 ) were also tested . In some studies , mice with deletions of the GABAR α4 subunit or kalirin-7 ( Kal7 ) were used . α4 KO mice have mutations in exon 3 of Gabra4 and were developed on a mixed C57BL/6J and SJL genetic background ( Chandra et al . , 2006 ) and back-crossed with C57BL/6J mice . Both sets of WT and α4 KO mice were bred on site from α4+/- mice originally supplied by G . Homanics ( Univ . of Pittsburgh ) , with additional C57BL/6J mice from Jackson Laboratories ( Bar Harbor , Maine ) because results were similar to WT mice bred in-house . Genotyping of the tails was used to identify mice that were homozygous α4 KO . α4 KO mice are functional δ knock-outs ( Sabaliauskas et al . , 2012 ) ; they were used rather than δ KO to spare the α1βδ present on interneurons ( Glykys et al . , 2007 ) . Kal7 KO mice were supplied by R . E . Mains ( U . Conn . Health Center ) ( Ma et al . , 2008 ) . These mice lack the terminal exon unique to the Kal7 gene ( Kalrn7 ) and were developed on a C57BL/6J background . Female mice were used because the onset of puberty is a physical sign ( vaginal opening ) that is directly correlated with the hormonal changes that trigger α4βδ GABAR expression , which has been well-characterized ( Shen et al . , 2007 ) . Drugs administered during puberty ( once a day for 10 d – PND 35-PND 44 ) : picrotoxin at a dose sub-threshold for seizure ( Verleye et al . , 2008; Zolkowska et al . , 2012 ) ( 3 mg kg-1 , i . p . ) to block all GABARs; L-655 , 708 ( 0 . 35 mg kg-1 , i . p . ) , an inverse agonist of α5-containing GABARs ( Ramerstorfer et al . , 2010; Zurek et al . , 2012 ) ; MK-801 ( 0 . 25 mg kg-1 , i . p . ) , which at this dose , increases NMDAR expression ( Gao and Tamminga , 1995 ) ; memantine ( 10 mg kg-1 , i . p . ) , an NMDAR antagonist which does not alter NMDAR expression ( Cole et al . , 2013 ) , and lorazepam ( 0 . 25 mg kg-1 , i . p . in oil ) , which targets γ2-containing GABARs ( Sigel , 2002 ) . Unless otherwise indicated , saline was used as vehicle . Estrous cycle stage was determined by the vaginal cytology in 8-week old animals with established regular cycles , and these mice were not used in the stage of proestrus . Procedures were in accordance with the SUNY Downstate Institutional Animal Care and Use Committee . Whole brains from euthanized animals were processed for Golgi impregnation using the FD Neurotechnologies FD Rapid Golgi Stain kit . Coronal sections were prepared using a vibratome ( Leica VT1200s ) set to a thickness of 250 µm . Pyramidal cells from the CA1 hippocampus were reconstructed using Neurolucida software ( MicroBrightField ) . The neurons were viewed with a 100× oil objective on an Olympus BX51 upright light microscope . The Neurolucida program projects the microscope image onto a computer drawing tablet . The neuron's processes are traced manually while the program records the coordinates of the tracing to create a digital , three-dimensional reconstruction . Z-stack projection photomicrographs ( 0 . 1 μm steps ) were taken with a Nikon DS-U3 camera mounted on a Nikon Eclipse Ci-L microscope using a 100x oil objective and analyzed with NIS-Elements D 4 . 40 . 00 software . Camera Lucida drawings of dendrites were completed using a Nikon 710 microscope at 100x oil with a drawing tube attached . Reconstructed neurons were analyzed using Neurolucida Explorer built-in Sholl analysis software for spine density . Proximal dendrites were one-third of the distance or less from the cell soma while distal dendrites were one-third of the distance or less from the ends of dendritic branches . Spine density was similar in stratum oriens and stratum radiatum; therefore , these data were pooled . Spine types were determined using the semi-automated Spine Classifier of NeuronStudio ( http://research . mssm . edu/cnic/tools-ns . html ) , a program that allows for the reconstruction of neurons and classification of spines from z-stacks . Briefly , stubby spines had a length to width ratio of ~1 , mushroom spines were identified by a ≥ . 35 μm head width , with a head dia:neck dia >2 , while thin spines were classified if the head dia: neck dia < 1 . 2 and a length:width >3 ( Arellano et al . , 2007 ) . All spine density and morphology assessments were made with the investigator blinded to the condition of the animals tested . Mice were anesthetized with urethane ( 0 . 1 ml 40% ) and transcardially perfused using a peristaltic pump with a flow-rate of 12–15 mls/min , first with saline , followed by 4% paraformaldehyde ( PFA ) buffered to pH 7 . 4 with 0 . 1 M phosphate buffer ( PB ) . Brains were dissected and post-fixed 48 hr in 4% PFA at 4°C . Coronal sections of the dorsal hippocampus were cut on a vibratome ( Leica VT1200s ) at a thickness of 35 μm . Sections were blocked in 0 . 01 M PBS supplemented with 1% bovine serum albumin , 0 . 25% Triton and 0 . 05% sodium azide for 2 hr . Then , sections were incubated with anti-Kal7 ( ab52012 , Abcam , 1:200 ) and , in some cases , anti-Pfn2 ( 60094-2-Ig , Proteintech , 1:50 ) , to detect actin , diluted in the blocking solution overnight at 4°C . After washing , sections were incubated with fluorescent secondary antibody , or in some cases , fluorescent phalloidin , to detect actin: For staining using Kal7 and Pfn2 , rabbit anti-goat Alexafluor 568 and donkey anti-mouse Alexafluor 488 ( both at 1:500 ) , respectively , were used . For staining using Kal7 and phalloidin , rabbit anti-goat Alexafluor 488 ( 1:500 ) and phalloidin-conjugated to Alexafluor 568 ( 1:20 ) , respectively , were used . Following a 2 hr incubation at room temperature , sections were mounted on slides with ProLong Gold Antifade Reagent . Images were taken with a Olympus FluoView TM FV1000 confocal inverted microscope with objective UPLSAPO 60x NA:1:35 ( Olympus , Tokyo , Japan ) to show Kal7 , Pfn2 or phalloidin and merged images . Images were analyzed for luminosity ( Kal7 staining ) using the region of interest ( ROI ) program of Image J software ( NIH ) . In all experiments , actin is displayed as red and Kal7 as green . In order to enhance visualization of dendritic spines for Figure 4 , the brightness is increased by 12 and the contrast by 40 in all images . However , the original non-enhanced images are presented in Figure 4—figure supplement 1 . Mice were rapidly decapitated; the brains were removed and cooled using an ice cold solution of artificial cerebrospinal fluid ( aCSF ) containing ( in mM ) : NaCl 124 , KCl 2 . 5 , CaCl2 2 , NaH2PO4 1 . 25 , MgSO4 2 , NaHCO3 26 , and glucose 10 , saturated with 95% O2 , 5% CO2 and buffered to a pH of 7 . 4 . Following sectioning at 400 μm on a Leica VT1000S vibratome , slices were incubated for 1 hr in oxygenated aCSF . Pyramidal cells in the CA1 hippocampal slice were visualized using a differential interference contrast ( DIC ) -infrared upright microscope , and recorded using whole cell patch clamp procedures in voltage clamp mode at 2630° C , as described ( Shen et al . , 2010 ) . Patch pipets were fabricated from borosilicate glass using a Flaming-Brown puller to yield open tip resistances of 2–4 MΩ . For whole cell recordings of miniature excitatory post-synaptic currents ( mEPSCs ) , the aCSF contained 120 μM SR95531 ( 6-imino-3- ( 4-methyoxyphenyl ) -1 ( 6H ) -pyridazinebutanoic acid hydrobromide ) to block GABARs . ( Pipet solution ( in mM ) : 140 K-gluconate , 2 MgCl2 , 10 HEPES , 10 BAPTA , 2 Mg-ATP , 0 . 5 CaCl2-H2O , 0 . 5 Li-GTP , pH 7 . 2 , 290 mOsm . ) Recordings were carried out at -60 mV . 1 μM tetrodotoxin ( TTX ) was added to block voltage-gated Na+ channels . Recordings were conducted with a 2 kHz 4-pole Bessel filter at a 10 kHz sampling frequency using an Axopatch 200B amplifier and pClamp 9 . 2 software . Electrode capacitance and series resistance were monitored and compensated; access resistance was monitored throughout the experiment , and cells discarded if the access resistance increased more than 10% during the experiment . In most cases , the data represent one recording/animal . Whole cell patch clamp recordings were carried out , as above , except that the aCSF contained 1 mM MgCl2 ( instead of 2 mM ) , 10 μM strychnine , 10 μM D-serine , and 50 μM CGP 35348 , as previously described ( Shen et al . , 2010 ) . Excitatory currents were evoked in the presence of 200 nM SR95531 , in order to block the synaptic GABARs ( Stell and Mody , 2002 ) , with low frequency stimulation ( 0 . 05 Hz ) at intensities close to threshold ( 100–400 μA ) using a tungsten bipolar electrode placed ~500 μm away in the stratum radiatum . Stimulation intensity was adjusted to achieve an EPSC amplitude of ~400–500 pA ( typically 75–150 μA ) . After baseline recordings of the glutamatergic EPSC , 5 μM NBQX was applied to unmask the NMDA component . In some cases , the NMDAR antagonist APV ( 50 μM ) was applied to verify the nature of the NMDA current . The NMDA:AMPA ratio was calculated as ( amp . EPSPNMDA ) / ( amp . EPSPNMDA+AMPA ) – ( amp . EPSPNMDA ) . Currents were recorded from pubertal hippocampus , WT or α4 KO , to assess the role of α4βδ GABARs in reducing NMDAR current . In some cases L-655 , 708 ( 50 nM ) or SR95531 ( 120 μM ) was bath applied to block α5βγ2 GABARs or all GABARs , respectively . Evoked EPSCs and mEPSCs were detected using a threshold delimited event detection subroutine in pClamp10 . 3 . Only data with a stable baseline and rapid rise time were included in the analysis . Event frequency was assessed and averaged . The aCSF was similar to above except that the MgSO4 concentration was 1 mM . Hippocampal slices were placed between nylon nets in a submerged chamber of an upright microscope . Field EPSPs ( fEPSPs ) were recorded extracellularly from the stratum radiatum of CA1 hippocampus using an aCSF-filled glass micropipet ( 1–5 mΩ ) in response to stimulation of the Schaffer collateral-commissural pathway using a pair of insulated tungsten bipolar electrodes . The intensity of the stimulation was adjusted to produce 50% of the maximal response . LTD was induced using LFS ( 1 Hz ) for 900 pulses ( 15 min ) ( Dunwiddie and Lynch , 1978 ) . fEPSP slope was assessed every 30 sec with an Axoprobe-1A amplifier and pClamp 10 . 3 for 20 min . before and 1 hr after LTD induction . LTP was induced using theta burst stimulation ( Larson et al . , 1986 ) ( TBS , 8–10 trains of 4 pulses at 100 Hz , delivered at 200 ms intervals , repeated 3x at 30s intervals ) which is a physiological stimulation pattern ( Larson et al . , 1986 ) . EPSP responses were recorded at 30s intervals for 20 min . before and 120 min . after TBS ( producing 1–4 mV EPSPs ) . For both paradigms , the strength of synaptic excitatory responses was assessed by measuring the slope ( initial 20–80% ) of the EPSP rising phase . Data are expressed as a% of the average response from the 20 min . control period for each slice , and are averaged for all slices ( mean ± SEM ) across the time-course of the experiment , as we have described ( Shen et al . , 2010 ) . All drugs were from Sigma Chemical Co . ( St . Louis , MO ) . This is a hippocampal-dependent spatial memory task ( Koistinaho et al . , 2001 ) , which requires LTP in the CA1 hippocampus ( Pastalkova , 2006 ) . After an initial 10 min habituation to a rotating platform ( 40 cm dia , 1 rpm ) , mice were trained for 3 10-min trials/hour to avoid a mild foot shock ( <0 . 2 mA , sub-threshold for stress hormone release [Friedman et al . , 1967] ) in a 60o sector of the disk ( Inset , Figure 5 ) . The time to first enter the avoidance zone for each trial was assessed as an indicator of learning acquisition , and 120 s was set as the learning criterion ( Shen et al . , 2010 ) . Additional trials were administered if the animals did not reach the learning criterion of a 120 s latency to first enter the avoidance zone . On day 2 , animals were initially tested with the shock zone position from the previous day ( first trial , zone 1 ) to reactivate their memory of the previous day . Then , the shock zone was changed to a different location ( zone 2 ) and animals trained until learning criteria was achieved . On day 3 , animals were tested for retention of zone 2 . All trials and inter-trial intervals were 10 min long . The number of trials to reach learning criterion and the average latency to enter the shock zone ( trial #3 ) were assessed as measures of learning acquisition for the initial location ( zone 1 ) and re-learning of the second location ( zone 2 ) . In addition retention of this spatial memory was also assessed for both zone 1 and zone 2 and expressed as the latency to enter the shock zone for the first trial on the day after learning . The position of the avoidance zone was stationary with respect to the room spatial frame of reference , which required active avoidance behavior because the disk was rotating . The position of the mouse on the disk was tracked by PC-based software that analyzed images from an overhead camera at 60 Hz . The time to first enter the electrified sector was assessed offline as a measure of spatial learning acquisition across the training trials . In addition , the number of shocks/entry was also tabulated as a measure of escape behavior to validate that there were no differences in pain threshold or sensorimotor behavior which would alter escape behavior across groups . Animals were tested for learning and re-learning of spatial relationships using the hippocampal-dependent ( Barker and Warburton , 2011 ) MPORT ( multiple placement object recognition task , Figure 5 ) which assesses spatial memory based on the fact that mice naturally prefer novel object locations . This protocol is an variant of the human multiple placement task used to test re-learning in patients with neuropathologies , including those with autism ( D'Cruz et al . , 2013 ) . Following an initial habituation to an empty arena for 1 hr and re-visit to the home cage ( 20 min ) , mice were allowed to examine 2 identical objects at opposite ends of the arena for 10 min ( position 1 ) . Following a 20 min re-visit to the home cage , mice were tested for two additional 10 min trials after one of the objects was re-located to two new positions ( positions 2 and 3 ) . All test trials were separated by a 20 min re-visit to the home cage . The duration of examination ( T ) of the moved ( M ) and unmoved ( U ) objects were quantified . The discrimination ratio for detecting the moved object was quantified as: ( T-M ) / ( T-U ) . Both locomotor activity and total # approaches , a measure of interest in the objects , were also quantified across groups . Multiple trials were used where the location for one object was varied in order to test the ability of the animal to remember new locations . In experiments where spine type and number were quantified , animals were sacrificed by 1–2 hr after acclimation ( naïve ) , learning trial 1 ( learning ) or learning trial 2 ( re-learning ) . Behavioral data from animals used for spine typing was analyzed . All data are presented as the mean ± the standard error of the mean ( SEM ) using Origin 8 . 5 . 1 . A power analysis to determine the minimum sample size needed to achieve statistical significance was performed for all experiments achieving statistical significance ( algorithms: http://www . originlab . com/doc/Origin-Help/PSS-ANOVA-Algorithm; http://www . originlab . com/doc/Origin-Help/PSS-tTest2-Algorithm ) . Data were shown to fit a normal distribution using the Kolmogorov-Smirnov test for normality , and Levene’s test was used to confirm equal variance between groups being compared . All data were included in the analysis unless statistically defined as an outlier ( >2 standard deviations from the mean ) . Golgi , IHC , and behavioral experiments were performed in duplicate ( exact n’s are indicated in the figure legends ) . For Golgi and IHC experiments , 2–4 neurons were evaluated/mouse with 4–6 mice used per group . A statistically significant difference between groups for the LTD and LTP studies was determined by averaging EPSP slope in the final 20 min . for each recording; these numbers were averaged across groups and compared using the student’s unpaired t-test . Comparisons of the degree of change across groups for all other experimental procedures were analyzed with a student’s unpaired t-test ( 2 groups ) or one-way analysis of variance ( ANOVA , 3+ groups ) . Post-hoc comparisons for the ANOVA were made with a post-hoc Tukey’s test . For all tests , the level of significance was determined to be p<0 . 05 . A complete description of the statistical analyses for all experiments ( including n’s , p values and power for significant findings ) is detailed in the figure legends .	Memories are formed at structures in the brain known as dendritic spines . These structures receive connections from other brain cells through regions called synapses . In humans , the number of these brain connections increases dramatically from birth to childhood , reflecting a period of rapid learning . However , the number of these brain connections halves after puberty , a dramatic reduction shown in many brain areas and for many species , including humans and rodents . This process is referred to as adolescent synaptic pruning and is thought to be important for optimal learning in adulthood because it is disrupted in autism and schizophrenia . Synaptic pruning is believed to remove unnecessary brain connections to make room for new relevant memories . However , the process that triggers synaptic pruning is not known . Within the brain , proteins called inhibitory GABA receptors are targets for chemicals that reduce the activity of nerve cells . As brain connections must be kept active to survive , inhibitory receptors could help to trigger synaptic pruning . Afroz , Parato et al . now show that , at puberty , the number of a particular type of GABAA receptor increases in the brain of female mice . This triggers synaptic pruning in the hippocampus , a key brain area necessary for learning and memory . By reducing brain activity , these inhibitory receptors also reduce the levels of a protein in the dendritic spine that stabilizes the scaffolding of the spine to maintain its structure . Mice that do not have these GABAA receptors maintain a constant high level of brain connections throughout adolescence , and synaptic pruning does not occur in their brains . These mice were initially able to learn to avoid a specific location that provided a mild shock to their foot . However , when this location changed the mice were unable to re-learn where to avoid , suggesting that too many brain connections limits learning potential . Brain connections are regulated by many factors , including the environment and stress . Future studies will test how these additional factors alter synaptic pruning in adolescence , and will test drugs that target these inhibitory receptors to manipulate adolescent pruning . These findings may suggest new treatments for “normalizing” synaptic pruning in conditions where this process occurs abnormally , such as autism and schizophrenia .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology", "neuroscience" ]	2016	Synaptic pruning in the female hippocampus is triggered at puberty by extrasynaptic GABAA receptors on dendritic spines
To understand complex regulatory processes in multicellular organisms , it is critical to be able to quantitatively analyze protein movement and protein-protein interactions in time and space . During Arabidopsis development , the intercellular movement of SHORTROOT ( SHR ) and subsequent interaction with its downstream target SCARECROW ( SCR ) control root patterning and cell fate specification . However , quantitative information about the spatio-temporal dynamics of SHR movement and SHR-SCR interaction is currently unavailable . Here , we quantify parameters including SHR mobility , oligomeric state , and association with SCR using a combination of Fluorescent Correlation Spectroscopy ( FCS ) techniques . We then incorporate these parameters into a mathematical model of SHR and SCR , which shows that SHR reaches a steady state in minutes , while SCR and the SHR-SCR complex reach a steady-state between 18 and 24 hr . Our model reveals the timing of SHR and SCR dynamics and allows us to understand how protein movement and protein-protein stoichiometry contribute to development . During development , multicellular organisms must coordinate patterning , maintenance , and growth of different cell types . This dynamic coordination is achieved through complex spatio-temporal signaling mechanisms ( Voas and Rebay , 2004; Scheres , 2007; Han et al . , 2014; Sozzani and Iyer-Pascuzzi , 2014; Sablowski , 2015; Tuazon and Mullins , 2015; Fisher and Sozzani , 2016 ) . Many of the signaling mechanisms regulating development utilize mobile signals that cross cell boundaries ( Koizumi et al . , 2011; Xu et al . , 2011; Gallagher et al . , 2004 ) . In plants , the intercellular movement of transcription factors and the spatio-temporal control of protein complex formation regulate many processes including cell fate specification . The key to a systems-level understanding of development in multicellular organisms is the ability to obtain quantitative information about various signaling factors . In the Arabidopsis root , the SHORTROOT-SCARECROW ( SHR-SCR ) mediated gene regulatory network ( GRN ) is a well-characterized developmental pathway that controls ground tissue patterning and endodermal cell fate specification ( Levesque et al . , 2006; Cui et al . , 2007; Sozzani et al . , 2010; Cruz-Ramırez et al . , 2012; Long et al . , 2015; Moreno-Risueno et al . , 2015 ) . SHR is transcribed in the vasculature ( Helariutta et al . , 2000; Nakajima et al . , 2001 ) , and then the protein moves to the adjacent cell layer where it is retained in the nuclei of the Quiescent Center ( QC ) , Cortex/Endodermis Initials ( CEI ) and endodermis ( Nakajima et al . , 2001; Gallagher et al . , 2004; Gallagher and Benfey , 2009 ) . In these cells , SHR activates the expression of the downstream transcription factor SCR ( Helariutta et al . , 2000; Levesque et al . , 2006; Cui et al . , 2007; Sozzani et al . , 2010 ) , which , as shown by yeast two hybrid experiments , interacts with SHR and prevents further SHR movement ( Heidstra et al . , 2004; Cui et al . , 2007; Long et al . , 2015 ) . Although there have been important advances in identifying the essential features that govern the SHR-SCR GRN ( Gallagher et al . , 2004; Sena et al . , 2004; Cui et al . , 2007; Gallagher and Benfey , 2009; Sozzani et al . , 2010; Cruz-Ramırez et al . , 2012 ) , the ability to measure key network parameters that may contribute to patterning and cell fate specification remains a fundamental bottleneck . New imaging tools that enable parameter quantification and acquisition of in vivo kinetic parameters could provide quantitative information that describes temporal and spatial dynamics of proteins in multicellular organisms . Thus , we explored the possibility of combining scanning Fluorescence Correlation Spectroscopy ( scanning FCS ) techniques . Unlike more common time correlation FCS techniques , which only use temporal information , scanning FCS techniques utilizes both the spatial and temporal information present in a confocal raster scan to measure protein movement , protein-protein interactions , and the stoichiometry of protein complexes . ( Petrasek and Schwille , 2008; Digman and Gratton , 2011 ) . Previously , these techniques have only been used to quantify protein mobility and the dynamics of protein association in cell cultures ( Digman et al . , 2005a; 2005b; Digman and Gratton , 2009a; Digman et al . , 2009a; Jameson et al . , 2009; Rossow et al . , 2010; Hinde et al . , 2010; 2011; Vetri et al . , 2011 ) . We combined the techniques of Raster Image Correlation Spectroscopy ( RICS ) , Pair Correlation Function ( pCF ) and Number and Brightness ( N&B ) to analyze SHR and SCR mobility and interaction at high spatio-temporal resolution . By using RICS and pCF ( Digman et al . , 2005a; 2005b; Brown et al . , 2008; Digman and Gratton , 2009a; 2009b; Jameson et al . , 2009; Rossow et al . , 2010; Hinde et al . , 2010; 2011; Vetri et al . , 2011; Digman and Gratton , 2011 ) , we quantified the rate and directionality of SHR movement . Specifically , we used RICS and a 3D Gaussian diffusion model ( Digman et al . , 2005a; 2005b ) to calculate the diffusion coefficient of SHR in different root cell types . We also acquired line scans and performed pCF analyses ( Hinde et al . , 2010 ) to assess the directionality of SHR movement in these different cell types . Moreover , we used N&B and cross-correlation analyses ( Digman et al . , 2008; 2009a; 2009b ) to characterize the oligomeric state of SHR and the stoichiometry of the SHR-SCR complex , respectively . Finally , we incorporated the diffusion coefficient of SHR and the stoichiometry of the SHR-SCR complex into a mathematical model of SHR and SCR dynamics . Our results demonstrate that these parameters can be used in predictive mathematical models , allowing us to understand how protein movement and stoichiometry of protein complexes contribute to developmental processes . Further , our study highlights how these non-invasive scanning FCS techniques can be used to experimentally measure protein movement and protein-protein interactions within multicellular organisms . A key parameter in biological models is molecular diffusion , which is frequently estimated based on published measurements from single cell organisms ( Spiller et al . , 2010 ) . In order to measure protein movement in a multicellular organism such as Arabidopsis , we used Raster Image Correlation Spectroscopy ( RICS ) , which returns an autocorrelation function ( ACF ) by correlating fluorescence intensity fluctuations in pixels in an image over time and space . The diffusion coefficient ( DC ) is then calculated by fitting the ACF with a diffusion model ( Figure 1 ) . Since the fit of the diffusion model depends on the choice of imaging parameters , such as pixel size and pixel dwell time , we first set these parameters by performing RICS analysis on Green Fluorescent Protein ( GFP ) driven by the CaMV 35S constitutive promoter ( 35S:GFP ) ( Table 1 , see Materials and methods ) . In the Arabidopsis root , the resulting DC for free GFP was 6 . 33 ± 0 . 37 μm2/s ( n = 34 for different cells , including vascular , endodermal , and QC cells ) ( Figure 1—figure supplement 1 ) . We obtained similar diffusion coefficients using two different confocal microscopes ( Zeiss 780 and Zeiss 710 ) . 10 . 7554/eLife . 14770 . 003Figure 1 . Diffusion coefficients obtained by performing RICS on SHR:SHR-GFP in shr2 . ( a ) Schematic showing image acquisition and RICS analysis . ( Left ) A time series of 100 frames ( time points ) acquired using predetermined imaging parameters ( Table 1 ) . ( Middle ) Autocorrelation function ( ACF ) calculated from the time series . Red represents a high ACF value , blue represents a low ACF value . ( Right ) Fit of the ACF to a Gaussian diffusion model to calculate the diffusion coefficient . ( b–d ) Representative images of SHR:SHR-GFP in shr2 taken in regions containing the vasculature and endodermis ( b ) , endodermis only ( c ) , vasculature and QC ( d ) . Cell walls are marked in red using propidium iodide ( PI ) . Below each image is its ACF fit using the Gaussian model and the calculated diffusion coefficient for that representative image . ( d ) 128x128 pixel region of interest ( ROI ) used for RICS ( white frame ) . ( e ) Bar graph showing average diffusion coefficients of 35S:GFP ( n = 34 ) , SHR:SHR-GFP in shr2 ( n = 40 ) for vasculature and endodermis , n = 19 for endodermis , n = 20 for vasculature and QC ) and SHR:SHR-GFP in SCRi ( vasculature and endodermis , n = 14 ) . Groups that have different symbols are significantly different from each other and from the 35S:GFP line ( Wilcoxon with Steel-Dwass , p<0 . 05 ) . Error bars are s . e . m . Source data is provided in Figure 1—source data 1–4 . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 00310 . 7554/eLife . 14770 . 004Figure 1—source data 1 . Diffusion coefficient of 35S:GFP line obtained using RICS with the Zeiss 780 and Zeiss 710 instruments . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 00410 . 7554/eLife . 14770 . 005Figure 1—source data 2 . Diffusion coefficient of SHR:SHR--GFP in shr2 line obtained using RICS with the Zeiss 780 and Zeiss 710 instruments . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 00510 . 7554/eLife . 14770 . 006Figure 1—source data 3 . Diffusion coefficient of SHR:SHR--GFP in SCRi line obtained using RICS with the Zeiss 780 instrument . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 00610 . 7554/eLife . 14770 . 007Figure 1—source data 4 . Statistical analysis of diffusion coefficients obtained by RICS . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 00710 . 7554/eLife . 14770 . 008Figure 1—figure supplement 1 . RICS analysis on the 35S:GFP line . ( a ) Region of interest of 35S:GFP in vasculature cells . ( b ) Autocorrelation function ( ACF ) calculated using RICS . Red represents a high ACF value , blue represents a low ACF value . ( c ) Fit of diffusion model and calculation of diffusion coefficient from the ACF . Residuals of fit are shown at top of graph . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 00810 . 7554/eLife . 14770 . 009Table 1 . Recommended imaging conditions for RICS and N&B . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 009MethodPixel size ( μm ) Pixel dwell time ( μs ) Line scan time ( ms ) Number of framesImage sizeLaser intensityGainRICS0 . 05 to 0 . 112 . 61 or 25 . 217 . 56 or 15 . 1350 to 100256x2561 . 0% to 4 . 0%800 to 1000N&B0 . 112 . 61 or 25 . 217 . 56 or 15 . 1350 to 100256x2561 . 0% to 12%800 to 1000 We next used RICS to determine if the DC of SHR differs between the vasculature , where it is produced , and the endodermis and QC , where it moves and interacts with SCR ( Heidstra et al . , 2004; Cui et al . , 2007; Long et al . , 2015 ) . To this end , we used a SHR:SHR-GFP translational fusion , which complements the shr2 mutant phenotype ( SHR:SHR-GFP in shr2 ) ( Nakajima et al . , 2001 ) . Our RICS analysis showed that SHR moves at a rate of 2 . 45 ± 0 . 26 μm2/s ( n = 40 ) and 1 . 73 ± 0 . 15 µm2/s ( n = 16 ) within a population of vascular and endodermal cells and vascular and QC cells , respectively ( Figure 1 ) . Notably , in a population of only endodermal cells , SHR moves at a significantly slower rate ( 1 . 29 ± 0 . 14 μm2/s n = 19 , Wilcoxon with Steel-Dwass , p = 0 . 0303 ) ( Figure 1 ) . To investigate whether the reduction in SHR movement in the endodermis may be due to its interaction SCR ( Cui et al . , 2007; Levesque et al . , 2006; Sozzani et al . , 2010; Moreno-Risueno et al . , 2015 ) , we measured the DC of SHR in a SCR RNAi ( SCRi ) line ( SHR:SHR-GFP in SCRi ) ( Cui et al . , 2007 ) . In this line , in which the levels of SCR are reduced , SHR diffusion in populations of only endodermal cells was similar to that in populations of vascular and endodermal cells ( 2 . 40 ± 0 . 29 μm2/s , n = 14 , Wilcoxon with Steel-Dwass , p = 0 . 9337 ) ( Figure 1 ) . Overall , our RICS data are in agreement with genetic and molecular data that show SHR movement is restricted by SCR ( Heidstra et al . , 2004; Cui et al . , 2007; Long et al . , 2015 ) . Most importantly , our RICS data provide a precise quantification of how SHR diffusion is affected by the presence of SCR . An open question is whether SHR moves unidirectionally , only from the inner to the outer cell layers , or in a bidirectional fashion . The former would be consistent with an active transport mechanism from the vasculature into the endodermis . To detect the route of intercellular movement of SHR between the vasculature and endodermis as well as between the endodermis and cortex , we used pair correlation function ( pCF ) analysis on line scans ( Figure 2 ) . We used pCF to measure the directionality of movement by correlating pixels that are separated by a specific pixel distance ( Hinde and Cardarelli , 2011 ) . To account for differences in cell size , as well as cell wall orientation within our images , we used three different distances: 5 pixels , 7 pixels , and 9 pixels . Generally , the pCF analysis returns a carpet , or heatmap , that shows the fluorescence correlation over time ( y-axis ) and space ( x-axis ) . If proteins move across the cell wall , there is an arch that represents the delay in movement . If proteins are unable to cross the barrier , the arch is absent ( Hinde et al . , 2010 ) ( Figure 2 ) ( see Materials and methods ) . Thus , we performed a binary analysis on each carpet to determine the movement of SHR between cells by looking at the presence , or absence , of these arches . Specifically , we recorded a 1 if the carpet showed an arch and a 0 if no arch was present ( Figure 2 ) . We took the average of these values from the different pixel distances ( 5 , 7 , and 9 ) to represent one biological replicate . We then calculated the protein Movement Index ( MI ) , which is the average of all biological replicates ( Figure 2 ) . As a positive control , we acquired pCF data for 35SGFP , which had a MI = 0 . 71 ± 0 . 07 ( n = 15 ) . As a negative control we used 3xGFP , which was shown to restrict free GFP movement in roots ( Kim et al . , 2005 ) . To this end , we drove the 3xGFP using a root vasculature promoter ( TMO5:3xGFP , Schlereth et al . , 2010 ) which had a MI = 0 . 26 ± 0 . 05 ( n = 19 ) ( Figure 2—figure supplement 1 ) . 10 . 7554/eLife . 14770 . 010Figure 2 . Pair correlation function ( pCF ) analysis showing direction of SHR movement . ( a ) Schematic of image acquisition and pCF analysis . ( Left ) Line scans acquired using predetermined imaging conditions ( Table 1 ) . Carpets of the forward ( middle ) and reverse ( right ) pCF analysis . The orange arch indicates delayed movement , while the absence of an arch ( green lines ) indicates no movement . ( b ) pCF analysis of SHR:SHR-GFP in shr2 . Cell walls are marked with PI . Lines indicate the laser path going across the vasculature , endodermis , and cortex . pCF carpets for each direction are shown . Orange arches indicate movement . ( c ) pCF analysis of SHR:SHR-GFP in SCRi . Cell walls are marked with PI . Lines indicate the laser path across the vasculature , endodermis , the extra layer , and the cortex . pCF carpets for each direction are shown . Orange arches indicate movement . ( d ) Bar graph showing average movement index of 35S:GFP ( n = 15 ) , TMO5:3xGFP ( n = 19 ) , SCR:SCR-GFP ( n = 14 ) , SHR:SHR-GFP in shr2 ( n = 20 ) between vasculature and endodermis , n = 22 between endodermis and cortex ) , and SHR:SHR-GFP in SCRi ( n = 14 between vasculature and endodermis , n = 17 between endodermis and cortex ) . Stars denote groups that are different from TMO5:3xGFP , crosses indicate groups that are different from 35S:GFP ( Wilcoxon with Steel-Dwass , p<0 . 05 ) . Error bars are s . e . m . Source data is provided in Figure 2—source data 1 and 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 01010 . 7554/eLife . 14770 . 011Figure 2—source data 1 . pCF of 35S:GFP , TMO5:3xGFP , SCR:SCR-GFP , SHR:SHR-GFP in shr2 , and SHR:SHR-GFP in SCRi lines . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 01110 . 7554/eLife . 14770 . 012Figure 2—source data 2 . Statistical analysis of movement index obtained by pCF . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 01210 . 7554/eLife . 14770 . 013Figure 2—figure supplement 1 . Pair correlation function analysis of 35S:GFP , SCR:SCR-GFP , and TMO5:3xGFP . ( a ) pCF analysis of 35S:GFP in vasculature cells . Cell walls are marked with PI . Orange arches indicate movement . ( b ) pCF analysis of TMO5:3xGFP in vasculature cells . Cell walls are marked with PI . Green lines indicate no movement . ( c ) pCF analysis of SCR:SCR-GFP in endodermal and cortical cells . Cell walls are marked with PI . Orange arches indicate movement . Green lines indicate no movement . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 013 We then used pCF to measure SHR movement across the vasculature , endodermis , and cortex . We found that SHR moves from the vasculature to the endodermis ( MI = 0 . 58 ± 0 . 03 , n = 20 , Wilcoxon with Steel-Dwass , p = 0 . 6356 ) , consistent with previous experimental data ( Helariutta et al . , 2000; Nakajima et al . , 2001 ) ( Figure 2 ) . However , from the endodermis back to the vasculature , the MI of SHR was significantly lower than the MI of free GFP ( MI = 0 . 36 ± 0 . 07 , n = 20 , Wilcoxon with Steel-Dwass , p = 0 . 0368 ) and not significantly different from the MI of the 3xGFP ( Wilcoxon rank-sum test , p = 0 . 5974 ) , suggesting that SHR is unable to move back to the vasculature ( Figure 2 ) . In addition , we found that SHR moves bidirectionally between the endodermis and cortex , as the MI is not significantly different from that of free GFP ( MI = 0 . 61 ± 0 . 06 from endodermis to cortex , MI = 0 . 53 ± 0 . 06 from cortex to endodermis , n = 20 , Wilcoxon with Steel-Dwass , p = 0 . 9524 and p = 0 . 3909 respectively ) . Given that our RICS analysis showed that the presence of SCR reduces SHR mobility in the endodermis , we measured SHR intercellular movement with pCF in the absence of SCR . Accordingly , we performed a pCF analysis using SHR:SHR-GFP in the SCRi line . Line scan measurements were taken across vascular , endodermal , and cortical cells as well as across the extra layer between the endodermis and cortex which is a direct consequence of the RNAi reduction of SCR ( Cui et al . , 2007 ) . We not only observed SHR movement from the vasculature to the endodermis with a MI = 0 . 66 ± 0 . 07 ( n = 14 ) but also from the endodermis back to the vasculature with a MI = 0 . 78 ± 0 . 09 ( n = 14 ) ( Figure 2 ) . Since the MI for SHR:SHR-GFP in SCRi in both directions is not significantly different from the MI for 35S:GFP ( Wilcoxon rank-sum test , p = 0 . 9553 for endo to vasc , p = 0 . 9999 for vasc to endo ) it suggests that movement is unrestricted in both directions ( Figure 2 ) . Similarly , we found that SHR is still able to move bidirectionally between the endodermis and cortex ( MI = 0 . 61 ± 0 . 05 from endodermis to cortex , MI = 0 . 59 ± 0 . 07 from cortex to endodermis , n = 17 , Wilcoxon with Steel-Dwass , p = 0 . 9997 and p = 0 . 5510 respectively ) . These results suggest that the presence of SCR prevents SHR movement specifically from the endodermis back to the vasculature while it does not affect the movement between the endodermis and cortex . To determine if SCR can move with SHR from the endodermis to the cortex , we used pCF on the SCR:SCR-GFP . Our results show that SCR does not move between the endodermis and the cortex , as the MI in either direction is significantly lower than that of 35S:GFP ( MI = 0 . 31 ± 0 . 08 from endodermis to cortex , MI = 0 . 31 ± 0 . 07 from cortex to endodermis , n = 14 , Wilcoxon with Steel-Dwass , p = 0 . 0236 and p = 0 . 0054 respectively ) . Taken together , our pCF results provide information about the directionality of SHR movement and indicate that SCR restricts SHR movement from the endodermis to the vasculature . Stoichiometry is an important feature of protein complexes , as some transcription factors must form higher order complexes in order to function ( Nakashima et al . , 2012; Sornaraj et al . , 2016 ) . We used the Number and Brightness technique ( N and B ) , which relies on the RICS image acquisition , to investigate the oligomeric state of the SHR protein in different root cell types . We used the average fluorescence intensity , and the variance in fluorescence , to determine the brightness of particles and their number in an image ( Digman et al . , 2008; 2009a ) ( Figure 3 , see Materials and methods ) . In order to use N&B to measure SHR oligomeric state , we first obtained the brightness of the autofluorescence ( immobile fraction ) and of monomeric GFP protein . We used roots expressing 35SGFP to calculate the S-factor , an imaging parameter that shifts the brightness of the image , such that the immobile fraction has a brightness value of 1 ( Digman et al . , 2008 ) . In addition , we used the 35S:GFP line to measure the brightness of monomeric GFP protein ( Figure 3 and Table 2 ) ( see Materials and methods ) . 10 . 7554/eLife . 14770 . 014Figure 3 . N&B analysis of the SHR oligomeric state . ( a ) Schematic of image acquisition and N&B analysis . ( Left ) Image acquisition for N&B is the same as for RICS analysis . ( Middle ) The mean and variance of intensity used to calculate the brightness and number of particles . ( Right ) The background brightness ( red ) set to 1 by adjusting the S-factor ( Table 2 ) . The monomer ( blue ) positioned at the predetermined brightness of monomeric GFP ( Table 2 ) . Homodimer ( green ) particles shown to be twice as bright as the monomer . ( b , c , d ) 35S:GFP used to determine the molecular brightness of monomeric GFP ( b , e , h ) Region of interest selected for N&B analysis of 35S:GFP , SHR:SHR-GFP in shr2 , and SHR:SHR-GFP in SCRi . Cell walls are marked with PI . Note that the extra layer in ( h ) is a result of the SCRi background . ( c , f , i ) Brightness vs intensity for 35S:GFP , SHR:SHR-GFP in shr2 , and SHR:SHR-GFP in SCRi . The red , blue , green boxes indicate the autofluorescence ( B = 1 ) , monomer ( B = ε = 0 . 28 ± 0 . 01 ) and homodimer ( B = 2ε ) , respectively . ( d , g , j ) Color-coding of the brightness for 35S:GFP , SHR:SHR-GFP in shr2 , and SHR:SHR-GFP in SCRi . Red , blue , and green represent background ( autofluorescence ) , monomer , and homodimer , respectively . ( k ) Bar graph showing average percent of SHR homodimer for SHR:SHR-GFP in vascular cells ( n = 40 ) , SHR:SHR-GFP in endodermal cells ( n = 19 ) , and SHR:SHR-GFP in SCRi ( n = 14 ) . Error bars are s . e . m . Star denotes sample that is significantly different from the other two ( Wilcoxon with Steel-Dwass , p<0 . 05 ) . Source data is provided in Figure 3—source data 1–3 . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 01410 . 7554/eLife . 14770 . 015Figure 3—source data 1 . Oligomeric state of SHR:SHR--GFP in shr2 line obtained using N&B with the Zeiss 780 and Zeiss 710 instruments . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 01510 . 7554/eLife . 14770 . 016Figure 3—source data 2 . Oligomeric state of SHR:SHR--GFP in SCRi line obtained using N&B with the Zeiss 780 instrument . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 01610 . 7554/eLife . 14770 . 017Figure 3—source data 3 . Statistical analysis of the oligomeric state of SHR collected using N&B . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 01710 . 7554/eLife . 14770 . 018Table 2 . N and B parameters for SimFCS software analysis . SEM is given . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 01810 . 7554/eLife . 14770 . 019Table 2—source data 1 . Monomeric brightness of 35S:GFP line obtained using N&B with the Zeiss 780 and Zeiss 710 instruments . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 01910 . 7554/eLife . 14770 . 020Table 2—source data 2 . S-factor of the 35S:GFP background line obtained using N&B with the Zeiss 780 and Zeiss 710 instruments . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 02010 . 7554/eLife . 14770 . 021Table 2—source data 3 . S-factor of the UBQ10:mCherry background line and monomeric brightness of UBQ10:mCherry line obtained using N&B with the Zeiss 780 instrument . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 021Confocal model and objectiveS-factor ( green channel ) S-factor ( red channel ) Monomer brightness ( green channel ) ( counts/pixel dwell/molecule ) Monomer brightness ( red channel ) ( counts/pixel dwell/molecule ) Cursor sizeLSM 780 , 40 x 1 . 2 NA water1 . 34 ± 0 . 02 ( n = 17 ) 1 . 00 ± 0 . 01 ( n = 24 ) 0 . 28 ± 0 . 01 ( n = 13 ) 0 . 34 ± 0 . 02 ( n = 7 ) 42 ± 0 . 69 ( n = 13 ) LSM 710 , 40 x 1 . 2 NA and 63 x 1 . 2 NA water0 . 92 ± 0 . 004 ( n = 20 ) N/A0 . 24 ± 0 . 01 ( n = 20 ) N/A50 ( n = 20 ) Source data is provided in Figure 2—source data 1 ( Monomer brightness for green channel and cursor size ) ; Table 2—source data 2 ( S-factor , green channel ) ; and Table 2—source data 3 ( Monomer brightness and S-factor , red channel ) Red channel data was not collected on the LSM 710 After calibration , we used N&B on SHR:SHR-GFP to determine the oligomeric state of SHR in different cell types . Our results indicated that SHR exists primarily as a monomer in the vasculature , given that SHR homodimer formation was only 2 . 6% ± 0 . 3% in these cells ( Figure 3 ) . Notably , SHR monomeric proteins are observed in both the nuclei and in the cytoplasm of vasculature cells . By contrast , in the nuclei of the endodermis , there are significantly more SHR homodimers present ( 7 . 5% ± 0 . 9% homodimer , Wilcoxon with Steel-Dwass , p<0 . 0001 ) ( Figure 3 ) . Moreover , in the endodermis SHR is present at very low levels outside of the nuclei ( Figure 3 ) . To further understand the differences in SHR oligomeric state , we determined if the homodimer of SHR present in the endodermis could be affected by the presence of SCR . Therefore , we performed N&B on SHR:SHR-GFP in the SCRi line and observed that SHR is mainly found as a monomer in the endodermis ( 1 . 9% ± 0 . 4% homodimer , Wilcoxon with Steel-Dwass , p = 0 . 2546 ) , indicating that SCR influences the oligomeric composition of SHR ( Figure 3 ) . These N&B results provide a quantitative assessment of SHR’s oligomeric states and their distribution in the vasculature and endodermis . The N&B analysis revealed that SHR exists both as a monomer and as a homodimer in the endodermis . We next asked if both the monomer and homodimer are able to form a complex with SCR . To test this hypothesis , we performed cross-N&B , which can determine the stoichiometry of the SHR-SCR complex . Cross N&B requires that each protein be tagged with a different fluorophore . The analysis then determines which proteins are in a complex by calculating the cross-correlation between the two channels at each pixel ( Figure 4 , see Materials and methods ) . Accordingly , we generated a transgenic line containing both SHR and SCR tagged with different fluorophores ( SHR:SHR-GFP & SCR:SCR-mCherry ) ( Figure 4—figure supplement 1 ) . We reasoned that SCR may also exist in higher oligomeric states , which would increase the possible binding ratio of the SHR-SCR complex; therefore we first used N&B on SCR:SCR-mCherry to determine the oligomeric state of SCR . Initially , we determined the S-factor and brightness of monomeric mCherry protein in the root ( UBQ10:mCherry ) as we did for the monomeric 35SGFP protein ( Figure 4—figure supplement 2 and Table 2 ) . In the SCR:SCR-mCherry line , we detected mostly monomers with 4 . 7% ± 0 . 5% of homodimers ( Figure 4 ) . We tested the oligomeric state of SCR using a SCR:SCR-GFP fusion protein ( Figure 4—figure supplement 2 ) , as it was shown that the type of fluorescent tag can change the behavior of a protein and its aggregation ( Brown et al . , 2008 ) . We found that the SCR:SCR-GFP line had 5 . 4% ± 0 . 7% of homodimers , which is similar to the SCR:SCR-mCherry line ( Figure 4—source data 1 ) . Therefore , when performing the cross-N&B analysis , we considered the possibility that the homodimer of SCR could be part of the SHR-SCR complex . 10 . 7554/eLife . 14770 . 022Figure 4 . Cross-N&B analysis of a SHR/SCR double-tagged line . ( a ) Schematic of cross N&B analysis . ( Left ) A double-tagged line used for imaging . The B1 ( GFP brightness ) vs B2 ( mCherry brightness ) graph is used to select the region for cross-correlation . ( Middle ) The brightness cross-correlation ( Bcc ) used to determine GFP pixels that cross-correlate with mCherry pixels . ( Right ) Stoichiometry plot that displays the protein complexes detected in the image . ( b ) Expression of SHR:SHR-GFP/SCR:SCR-mCherry marker line in root endodermis . ( c ) Bcc vs B1 graph for SHR . The blue and green boxes represent the SHR monomer and homodimer , respectively , that form a complex with SCR . ( d ) Color-coding of the cross brightness of the SHR:SHR-GFP/SCR:SCR-mCherry line . Blue represents SHR monomer binding SCR monomer , while green represents SHR homodimer binding SCR monomer . ( e ) Stoichiometry histogram from cross N&B analysis . The orange line at ( 1 , 1 ) represents a high proportion of monomeric SHR bound to monomeric SCR ( 84 . 77% ± 1 . 58% ) , while the green line at ( 2 , 1 ) represents a lower proportion of homodimeric SHR bound to monomeric SCR ( 15 . 23% ± 1 . 58% ) . ( f ) Bar graph showing average percentages of the 1:1 and 2:1 SHR-SCR complex ( n = 17 ) . Error bars are s . e . m . Source data is provided in Figure 4—source data 1 and 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 02210 . 7554/eLife . 14770 . 023Figure 4—source data 1 . Oligomeric state of SCR:SCR-GFP and SCR:SCR-mCherry lines obtained using N&B with the Zeiss 780 instrument . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 02310 . 7554/eLife . 14770 . 024Figure 4—source data 2 . Stoichiometry of the SHR:SHR-GFP/SCR:SCR-mCherry complex obtained using cross N&B with the Zeiss 780 instrument . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 02410 . 7554/eLife . 14770 . 025Figure 4—figure supplement 1 . Longitudinal confocal root sections of SHR:SHR-GFP/SCR:SCR-mCherry line . Inset: Red ( SCR:SCR-mCherry ) , green ( SHR:SHR-GFP ) , BF , and merged channels . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 02510 . 7554/eLife . 14770 . 026Figure 4—figure supplement 2 . N&B analysis of UBQ10 and SCR oligomeric state . ( a , d , g ) Region of interest of UBQ10:mCherry , SCR:SCR-mCherry , and SCR:SCR-GFP in the root . Both SCR:SCR-mCherry ( d ) and SCR:SCR-GFP ( g ) are shown in the endodermis . ( b , e , h ) ( b , e , h ) Brightness ( B ) vs intensity graphs for UBQ10:mCherry , SCR:SCR-mCherry , and SCR:SCR-GFP . The red , blue , and green boxes indicate the autofluorescence ( B=1 ) , monomer ( B1 = ε1 = 0 . 28 ± 0 . 01 for GFP; B2 = ε2 = 0 . 34 ± 0 . 02 for mCherry ) and homodimer ( B1 = 2monomeric B1 for GFP; B2 = 2monomeric B2 for mCherry ) ( Table 2 ) . ( c , f , i ) Color-coding of the distribution of the brightness of UBQ10:mCherry , SCR:SCR-mCherry , and SCR:SCR-GFP . Red , blue , and green represent autofluorescence , monomer , and homodimer respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 026 After analyzing the SHR:SHR-GFP and SCR:SCR-mCherry separately , we performed cross-N&B on the SHR:SHR-GFP/SCR:SCR-mCherry line to determine the stoichiometry of the complex . The cross-N&B analysis returns a stoichiometry diagram that represents the proportion of different complexes ( Figure 4 ) . We found that both the monomer and the homodimer of SHR bind the monomer of SCR , suggesting that SHR and SCR bind with a 1:1 and 2:1 stoichiometry ( Figure 4 ) . We were not able to detect any complexes that contain the homodimer of SCR . Additionally , we determined that 84 . 8% ± 1 . 6% of the SHR-SCR complexes have 1:1 stoichiometry while 15 . 2% ± 1 . 6% have 2:1 stoichiometry ( Figure 4 ) . Our cross N&B results reveal that both the monomer and homodimer of SHR are able to bind the monomer of SCR , while the homodimer of SCR does not seem to be part of this complex . SHR and SCR dynamics have been previously modeled in the endodermis , but this model did not take into account either the intercellular movement of SHR or the stoichiometry of the SHR-SCR complex ( Cruz-Ramırez et al . , 2012 ) . After experimentally determining the rate of SHR movement , SHR oligomeric state , and the binding ratio of the SHR-SCR complex , we sought to incorporate this information into a mathematical model of SHR and SCR , with the goal of determining how protein movement and stoichiometry affect SHR and SCR dynamics . Therefore , we constructed a compartmental model that only measures SHR , SCR , and the SHR-SCR complex in both the vasculature and the endodermis ( see Materials and methods ) . After constructing the model , we performed a sensitivity analysis to determine the most influential parameters . We chose to use Sobol indices to measure how sensitive the model is to each parameter ( Sobol , 2001; see Materials and methods ) . Briefly , the total Sobol effects index measures how much the model outcome varies as the parameters are changed . If small changes in the parameter values cause large changes in the model outcome , then that parameter is more influential . Using this measure , we found that the rate of movement of SHR from the vasculature to the endodermis ( a1 ) and from the endodermis to the vasculature ( a2 ) are both highly influential parameters ( Figure 5—figure supplement 1 ) . Thus , this suggests that SHR movement is a key component of the model that greatly influences the dynamics of SHR in the endodermis . Next , we sought to use the model , in conjunction with our experimentally determined diffusion coefficients , to ( i ) simulate SHR and SCR dynamics in the endodermis , and ( ii ) estimate values for the other parameters . Most of the parameter values were chosen based on the previous mathematical model ( Cruz-Ramírez et al . , 2012 ) . However , since this model did not account for different oligomeric states of SHR we estimated some of the parameters using the N&B data ( Supplementary file 3; see Materials and methods ) . Our mathematical model predicts that SHR reaches a steady state in the vasculature and endodermis in a matter of minutes . The levels of SCR increase greatly in the first 3 hr , which is supported by data that show that SCR expression in a SHR inducible system is significant after 3 hr ( Sozzani et al , 2010 ) . While the 1:1 SHR-SCR complex increases greatly in the first 3 hr , the 2:1 complex does not form until after 9 hr . This is because the SHR homodimer does not form until about 9 hr into the simulation ( Figure 5 ) . We reasoned that this is a plausible scenario because SCR should exceed 60% of steady-state levels to trigger homodimer formation ( see Materials and methods ) . Finally , the entire system reaches a steady-state between 18 and 24 hr ( Figure 5 ) . This suggests that cell division occurs once SCR and the SHR-SCR complexes reach their steady state values . 10 . 7554/eLife . 14770 . 027Figure 5 . Mathematical model simulations of SHR and SCR illustrate how reduction of SCR affects the formation of SHR homodimer and SHR-SCR complex . ( a , b , c ) Model simulations of wild type showing how ( a ) SCR and the 1:1 SHR-SCR complex greatly increase in the first 3 hr , ( b ) SHR homodimer and the 2:1 SHR-SCR complex do not form until around 9 hr , ( c ) the entire system reaches a steady state between 18–24 hr . ( d ) Model simulations of SCR RNAi showing a reduction in SHR homodimer , SCR , 1:1 SHR-SCR complex , and 2:1 SHR-SCR complex levels after 24 hr . The model outcomes show SHR in the vasculature ( black ) , SHR monomer in the endodermis ( solid blue ) , SHR homodimer ( dashed blue ) , SCR ( red ) , 1:1 SHR-SCR complex ( solid green ) , and 2:1 SHR-SCR complex ( dashed green ) . Parameter values and initial conditions are given in Supplementary file 3 . Source data is provided in Figure 5—source data 1 and 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 02710 . 7554/eLife . 14770 . 028Figure 5—source data 1 . Sobol total effects indices computed for SHR-SCR mathematical model . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 02810 . 7554/eLife . 14770 . 029Figure 5—source data 2 . Area measurements of vascular and endodermal cells . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 02910 . 7554/eLife . 14770 . 030Figure 5—figure supplement 1 . Sensitivity analysis of mathematical model of SHR and SCR . Bar graphs showing average Sobol total indices ( n = 10 ) for SHR in vasculature ( a ) , SHR monomer in endodermis ( b ) , SHR homodimer ( c ) , SCR ( d ) , 1:1 SHR-SCR complex ( e ) , and 2:1 SHR-SCR complex ( f ) . Indices were normalized to mean 0 , variance 1 before averaging . Bars represent s . e . m . Stars denote parameters that have significantly higher total effects indices ( Wilcoxon with Steel-Dwass , p<0 . 10 ) . Source data is provided in Figure 5—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 03010 . 7554/eLife . 14770 . 031Figure 5—figure supplement 2 . Functional form of k2 parameter in mathematical model . k2 is the rate of SHR homodimer formation and depends on the concentration of SCR . Once SCR passes a critical value ( C0 = 360 ) , SHR homodimer formation switches on . The homodimer formation rate has a maximum value of L = 0 . 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 14770 . 031 In addition to simulating what happens in wild type , we also wanted to observe how decreasing SCR affects the model dynamics and how it reflects our pCF and N&B data . Accordingly , in the SCRi model , SHR movement is bidirectional and no SHR homodimer forms ( see Materials and methods ) . In this model , we assume that SCR levels are maintained at below 60% of wild type levels as in agreement with previous experimental data ( Cui et al , 2007 ) . At these lower levels of SCR , we observe that the steady-state value of the 1:1 SHR-SCR complex is reduced by half ( Figure 5 ) . In addition , we observe that SHR homodimers do not form as seen in the N&B analysis ( Figure 3 ) . Since SHR homodimers do not form , the 2:1 SHR-SCR complex does not exist ( Figure 5 ) . These results suggest that the extra layer in the SCRi line could form due to a reduction in the levels of the SHR-SCR complex . However , there are still other unknown factors that could contribute to the formation of the extra layer . The methodologies we describe are rapid , direct , and convenient approaches for characterizing the quantitative and qualitative behavior of proteins in vivo . Combining RICS and pCF provided detailed understanding of SHR movement both within and between cells of the Arabidopsis root . Trafficking along the radial axis is normally unidirectional - SHR moves from the vasculature to the endodermis , and not in the opposite direction ( Figure 2 ) . In the absence of SCR , SHR trafficking is bidirectional . In agreement with the pCF results , our RICS data showed that the DC of SHR in the endodermis increases significantly in the absence of SCR as compared to wild type , indicating that removal of SCR affects the diffusive behavior of SHR . Taken together , these results provide further evidence that SCR , which is present only in the endodermis , spatially restricts SHR movement . The mechanisms underlying this restricted movement remain unclear , and future experiments should focus on uncovering other factors , in addition to SCR , that can bind SHR ( Long et al . , 2015; Moreno-Risueno et al . , 2015 ) . In this study , combining RICS and pCF provided quantitative information about the speed and direction of protein movement . Using RICS and pCF , which allow both intra- and inter-cellular analysis of protein movement , we were able to determine the in vivo flow of molecules in a multicellular organism . The average value of the diffusion coefficient obtained with RICS could be obtained with other techniques such as single-point FCS or Fluorescence Recovery After Photobleaching ( FRAP ) , but these techniques have limitations , such as returning only temporal information ( Miyawaki , 2011 ) . On the other hand , both RICS and pCF are applied to frames or lines , respectively , which increase the spatial resolution and the statistical power of the analysis by simultaneously measuring the movement of many individual molecules in time and space . Moreover , RICS and pCF introduce a spatial component that can determine whether slow movement is due to a binding interaction or slow diffusion , which FRAP and single-point FCS cannot do . Thus , these scanning FCS methods are robust against cell movement and other artifacts that could bias measurements taken with other single-point methods . The DC measurements of SHR in both the shr2 complemented line and SCRi are significantly lower than those of freely-moving 35S:GFP ( Wilcoxon rank-sum test , p<0 . 0001 ) ( Figure 1 ) . The slower movement of SHR could be due to its size alone . However , since the DC of SHR is three-fold smaller than the DC of 35S:GFP , and the DC is inversely proportional to the cube root of the molecular weight of the protein ( Young et al . , 1980 ) , the change in DC is not attributable to the molecular weight of SHR ( ~60 kDa ) . If it were , then the molecular weight of SHR would have to be 27-fold higher than that of GFP , or approximately 729 kDa ( Prendergast and Manni , 1978 ) . Further , the decrease in the DC of SHR relative to 35S:GFP , in addition to the pCF analysis which shows unidirectional movement , suggests an active regulation of SHR movement , which is in agreement with previous data ( Gallagher et al . , 2004; Sena et al . , 2004; Gallagher and Benfey , 2009 ) . Future work could expand our understanding of SHR movement by examining it in callose synthase gain-of-function mutants , which block transport via the plasmodesmata ( Vaten et al . , 2011 ) , or in Shortroot interacting embryonic lethal ( SIEL ) mutant alleles , which have reduced SHR movement ( Koizumi et al , 2011 ) . The N&B results provided a quantitative assessment of SHR’s oligomeric states and their distribution across cell types . SHR exists in the vasculature primarily as a monomer and in the endodermis as a monomer and a homodimer ( Figure 3 ) . The presence of the SHR homodimer in the nucleus and loss of the homodimer in the SCRi line indicate that SCR is required to maintain SHR in its higher oligomeric forms . Taken together , these results provide insights into the molecular mechanisms by which SCR regulates SHR movement . They also raise new questions , such as how SCR maintains SHR in multimeric forms , and how formation of higher oligomeric complexes helps restrict SHR movement . By using our experimentally determined parameters in a mathematical model , we were able to determine that SHR reaches a steady state in a matter of minutes , while SCR and the SHR-SCR complex stabilize within 24 hr . This suggests that future experiments that aim to understand the details of the SHR-SCR GRN should focus on a time scale of hours to measure its dynamics . In addition , the sensitivity analysis revealed that diffusion is one of the most important parameters in our model , motivating the need to experimentally measure the diffusion coefficient . In the simulation of SCRi , our model showed that the 2:1 complex does not form and the 1:1 complex is reduced to half of normal levels . This suggests that the mutant layer in the SCRi line is likely due to a reduction in the SHR-SCR complex . These results only scratch the surface of what is likely to be a complex network regulating the spatial localization of SHR as a mobile transcription factor and developmental regulator . Furthermore , they highlight the importance of physical interactions between transcription factors as a regulatory component of transcription factor intercellular trafficking networks . The three scanning FCS methodologies utilized here ( pCF , RICS , and N&B ) gave consistent results providing strong evidence for their reliability . Our data suggest that the application of in vivo molecule tracking techniques is virtually limitless , which opens exciting new opportunities in all fields of biology . Prior to plating , Arabidopsis seeds were dry sterilized using 50% bleach and 1 . 5 ml of HCl for at least 1 hr , imbibed with 500–700 μL of sterile water , and vernalized for 2 days at 4°C in complete dark . After vernalization , seeds were plated on 1x MS ( Murashige and Skooge ) media supplemented with 1% sucrose and grown vertically at 22°C in long day conditions ( 16 hr light/8 hr dark ) . Seedlings were 5 days old when imaged . The 35SGFP , SHR:SHR-GFP in shr2 , SCR:SCR-GFP in scr4 , SHR:SHR-GFP in SCRi , and TMO5:3xGFP lines are described in ( Ruiz et al . , 1998; Nakajima et al . , 2001; Cui et al . , 2007; Sabatini et al . , 2003; Schlereth et al , 2010 ) . For the SCR:SCR-mCherry construct , the coding sequence ( CDS ) of the mCherry fluorescent protein ( Goedhart et al . , 2007 ) was amplified using primers with flanking attB sites: mCherry-R2R3 F: 5’-GGGGACAGCTTTCTTGTACAAAGTGGCTATGGTGAGCAAGGGCGAGGAG-3’ and mCherry -R2R3 R: 5’-GGGGACAACTTTGTATAATAAAGTTGCTTACTCACTTGTACAGCTCGTCCATGCC-3 and recombined into pGEMTeasyR2R3 vector by Gateway BP reaction . SCR coding sequence in pDONR221-derived entry clones was previously described ( Welch et al . , 2007 ) . The root expression vector was created using endogenous SCR promoter in pH7m34GW binary vectors by multiple Gateway LR reactions as described ( Long et al . , 2015 ) . We performed all statistical analyses using the Wilcoxon rank-sum test with Steel-Dwass for multiple comparisons at significance level α = 0 . 05 . We chose the Wilcoxon test as not all of our data are normally distributed ( Shapiro-Wilk Goodness of Fit test , p<0 . 0001 , Supplementary file 1 ) . In addition , the Wilcoxon test adjusts for our sample sizes , which were unequal between groups . The Steel-Dwass test is used after the Wilcoxon test to perform pairwise comparisons . All statistical analyses were performed using JMP software ( http://www . jmp . com ) . The Point Spread Function ( PSF ) measures the radius of the laser beam and is experimentally measured in order to perform Raster Image Correlation Spectroscopy ( RICS ) analysis ( Rigler et al . , 1993 ) . The PSF was measured for each objective and each confocal microscope used for image acquisition . To calibrate the PSF for our objective lens , we performed RICS on free EGFP in an aqueous solution . While we determined the PSF using free EGFP in solution , it is possible that the PSF varies depending on the cell or tissue we are imaging . However , the exact value of the PSF is not as important for these scanning FCS techniques as it is for single-point FCS since the scanning techniques measure the time a molecule takes to go from one pixel to the next ( Digman et al . , 2005b; Petrasek and Schwille , 2008 ) . Thus we made the assumption that the PSF determined using the EGFP solution is an accurate estimation of the true PSF . Accordingly , raster images of a solution of 0 . 6 µM EGFP were acquired using commercial CLSMs , including the Zeiss 780 and 710 ( Zeiss Inc , Germany ) . We determined the PSF by fitting the autocorrelation function ( ACF ) to the intensity fluctuations of free EGFP in aqueous solution , obtained from the raster images , while fixing the known EGFP diffusion coefficient of 78 μm2/s ( Chen et al . , 2002 ) . The ACF then returned the experimental PSF beam waist ( Supplementary file 2 ) . We found that the PSF measurement was different for different objectives ( Supplementary file 2 ) . RICS is a technique that has been developed to determine the rate of movement ( either passive diffusion or regulated movement ) of fluorescently-labeled particles in a small volume ( i . e femtoliter volume of the PSF ) . In particular , RICS can be used to determine the rate of movement of a GFP-labeled protein , such as SHR:SHR-GFP . During a RICS measurement , the laser performs a raster scan across a selected region of interest . The raster scan involves scanning from left to right across a set number of pixels and then retracing , without backtracking across the already imaged pixels , to perform the next line below . The scanning is repeated for multiple consecutive lines until a 256x256 pixel frame is created , and then starts over again to obtain between 50 to 100 time points . Because each pixel is collected at a different time , and we know where each pixel is located , there is spatial and temporal information included in each individual image ( Figure 1 ) . This spatio-temporal information can be used to extract the diffusion coefficient of a population of molecules using the RICS algorithm , which has two steps: ( 1 ) background subtraction and ( 2 ) image correlation . The background subtraction removes stationary and slow-moving objects so that the image autocorrelation only detects the dynamics of diffusing species ( Digman et al . , 2005a ) while the image correlation compares each pixel to its adjacent pixel in both the x and y direction . For the RICS analysis the pixel size , pixel dwell time , line scan time , number of frames , and image size were set prior to acquisition . The parameters were set so that each pixel is sampled at a slightly faster rate than the particles move in the solution ( Digman et al . , 2005a ) . The laser intensity was set so that the signal to noise ratio is high , but within a range that does not cause photobleaching ( Table 2 ) . Since all of these parameters affect the ACF fit and , therefore , the estimation of the diffusion coefficient , we determined the optimal parameters for the A . thaliana root system by testing different values while imaging the 35S:GFP genotype . We chose parameter values that resulted in an ACF with a good fit to the data and low residuals ( Table 1 ) . We found that these parameter values remained the same between different confocal microscopes . Moreover , since sample movement can create artifacts in the ACF , which results in an erroneous diffusion coefficient ( Digman et al . , 2005a ) , no data were analyzed that had significant sample movement ( i . e . sample movement that shifts the region of interest outside of the imaging frame ) . The RICS-ACF is decomposed into two correlation functions that depend on ξ ( the spatial lag in x ) and ψ ( the spatial lag in y ) . The first correlation function , S ( ξ , ψ ) , calculates the spatio-temporal correlation due to the scanning of the microscope . The second correlation function , G ( ξ , ψ ) , calculates the spatio-temporal correlation due to particles diffusing in the medium . The ACF , GS ( ξ , ψ ) , takes both of these correlations into account by multiplying them: GS ( ξ , ψ ) = S ( ξ , ψ ) * G ( ξ , ψ ) . The functions are constructed assuming that the distribution of fluorescence intensities follows a 3D Gaussian distribution . The decomposition of the ACF into two parts allows RICS to distinguish random , Brownian motion from diffusing particles in the medium ( Digman et al . , 2005a ) . It is possible for artifacts to occur in the ACF due to slow , mobile structures within the volume . To eliminate these artifacts from our analysis , a moving average with a 10 frame time window was applied to the time series images to subtract the immobile fraction ( Digman et al . , 2005a ) . The first and last 5 frames of the time series were ignored since in this time window there is not enough information to calculate the moving average . For the remainder of the frames , the average intensity of the 5 preceding and 5 following frames was subtracted . For example , the average of frames 10–20 was subtracted from frame 15 . The ACF was calculated using the PSF of the beam ( Supplementary file 2 ) and the other parameters that were set before imaging ( Table 1 ) . The only unknown parameters in the ACF function were DC , the diffusion coefficient of the particles , and G ( 0 ) , which is inversely proportional to the number of molecules present . The diffusion coefficient returned by the software was the value that best fits the data ( see SimFCS Software Analysis ) . Pair correlation function ( pCF ) is a technique that allows us to measure the movement of a protein along a line . To do this , we scanned a 32 pixel line through a region of interest in the root . The region of interest was chosen such that 2–3 cells are contained within the frame . To visualize cell walls , we used propidium iodide ( PI ) , which causes the walls to fluoresce red when excited with the 488 nm emission line of an argon laser . In our images , the line scans across the middle of the frame , and the sample was positioned such that the line does not scan directly over a cell wall . Additionally , the line was placed towards the middle of the cell so that movement in both the nucleus and cytoplasm can be measured . The imaging area was cropped such that the edges of the line overlap exactly with the outer cell walls . Once the imaging area was cropped , a reference image was taken to check for image movement . The selected 32 pixel line was then rapidly scanned 2 × 105 times at a pixel dwell time of 12 . 61 µs . Note that the pixel size was not set as a constant , but rather changed for each image depending on the position of the line scan and the size of the cells analyzed . As soon as the line scan finished , another reference image was taken to check if the root had moved during imaging . If the reference images suggested that the root had moved during imaging , then that line scan was not used for analysis . The pair correlation function ( pCF ) for two points at a distance δr as a function of the delay time τ is calculated using Equation ( 1 ) : ( 1 ) G ( τ , δr ) =⟨F ( t , 0 ) . F ( t+τ , δr ) ⟩⟨F ( t , 0 ) ⟩⟨F ( t , δr ) ⟩−1 , where F ( t , 0 ) is the fluctuation in fluorescence intensity at pixel 0 and F ( t+τ , δr ) is the fluctuation in fluorescence intensity at some other pixel position ( δr ) at different time delays ( τ ) ( Hinde et al . , 2010; Hinde et al . , 2011; Digman and Gratton , 2009b ) . The result of the pCF analysis is a carpet , or heatmap , that displays the correlation in fluorescence over time ( y-axis ) and space ( x-axis ) . Molecules that move across a barrier display a characteristic ‘arch’ pattern in the pCF carpet output , whereas molecules that do not move across the barrier do not ( Hinde et al . , 2010 ) ( Figure 2 ) . Since the delay time recovered by the pair correlation function analysis is variable , we performed a binary analysis on the pCF carpets to look for movement ( presence of an arch pattern ) or no movement ( no arch ) ( see SimFCS Software Analysis ) . N&B is used to determine the number ( N ) and brightness ( B ) of particles in a volume , which allows us to determine the amount of aggregation of particles . This is useful in determining the oligomeric state of GFP- and mCherry-labeled proteins such as SHR:SHR-GFP , SCR:SCR-GFP , and SCR:SCR-mCherry . A time course of raster-scanned images was obtained using the confocal microscope ( see RICS ) . Certain imaging parameters had to be determined so that the pixels were not under or over sampled ( see RICS ) . In addition , lines expressing monomeric forms of GFP ( 35S:GFP ) and mCherry ( UBQ10:mCherry ) were used to set the background fluorescence and to measure monomer brightness ( see SimFCS Software Analysis ) . We obtained both the mean and the variance of the intensity distribution at each pixel in order to determine the number ( N ) and brightness ( B ) of the particles . The mean , <k> , and the variance , σ2 , of the intensity distribution are given by Equations 2 and 3: ( 2 ) <k> =∑i=1KkiK ( 3 ) σ2=∑i=1K ( ki−<k> ) 2K where K is the number of time points and ki is the fluorescence counts for time point i . The number and brightness of the particles can be determined from the mean and variance of the intensity distribution alone due to the assumption that the occupation of particles follows a Poisson distribution ( Digman et al . , 2008 ) . Using moment analysis ( Qian and Elson , 2000 ) , the apparent number ( N ) and apparent brightness ( B ) of the particles are defined in Equations 4 and 5 . ( 4 ) N= <k>2σ2 ( 5 ) B= σ2<k> Note that if the average intensity is fixed and the variance increases , B increases but N decreases . This is because fewer , larger particles cause greater intensity fluctuations as the laser scans than many small particles . The true number of particles , n , and the true brightness , ε , can be calculated from N and B respectively . However , the apparent brightness B is used for the software analysis ( see SimFCS Software Analysis ) . Cross N&B follows the same theory as N&B but involves two particles that are marked with different fluorescent proteins ( Digman et al . , 2009b ) . Thus , Cross N&B is used to determine the binding ratio of a protein-protein interaction . We specifically use Cross N&B to look at the stoichiometry of the SHR-SCR complex using the SHR:SHR-GFP/SCR:SCR-mCherry line . The apparent number of particles , N , and the apparent brightness , B , were calculated for the green and the red channels separately ( see N&B ) . The cross-variance , σcc2 , is defined in Equation 6: ( 6 ) σcc2= ∑i=1K ( Gi−<G> ) ( Ri− <R> ) K where Gi and Ri are the pixel intensities in the green and red channels , respectively , at time i , and <G> and <R> are the mean intensities of the green and red channels . When the cross-variance is zero , the fluctuations in the two channels are independent . When the cross-variance is positive or negative , the two channels are correlated or anti-correlated , respectively . The cross-brightness , BCC , and the cross-number , NCC , are defined in Equations 7 and 8 . ( 7 ) Bcc= σcc2<G><R> ( 8 ) Ncc= <G><R>σcc2 To determine the stoichiometry of a protein-protein complex , the brightness of each protein was compared to the cross-brightness at each pixel ( Digman et al . , 2009b ) . A large , positive cross-brightness indicates that the two proteins bind at that pixel in the image . The brightness of each protein in the complex determines the stoichiometry ( see SimFCS Software Analysis ) . The SimFCS Software ( Digman et al . , 2005a ) , developed at the Laboratory for Fluorescence Dynamics ( www . lfd . uci . edu ) , is used to perform RICS , pCF , N&B , and Cross N&B analysis on raster or line scans obtained using a confocal microscope . For the RICS analysis , the software can reduce the region of interest ( ROI ) from 256x256 pixels to 128 × 128 pixels in order to obtain a more enriched cell population . For example , we used this feature to obtain QC-enriched populations ( Figure 1 ) . After selecting the ROI to use , the software uses the moving average ( see RICS ) to eliminate any artifacts from immobile fractions . Then , the software fits the RICS-ACF using the imaging parameters provided ( Table 1 ) and returns the diffusion coefficient of the protein . The diffusion coefficient returned results in the ACF curve that best fits the data ( Figure 1 ) . Goodness of fit was determined by comparing the residuals to the amplitude of the ACF . We only kept images where the maximum value of the ACF was three fold larger than the greatest residual in order for the RICS analysis to be reliable ( Figure 1 ) . Images that had residuals larger than this threshold generally had low laser intensity or sample movement were not used for analysis . For pCF , the line scan file was loaded as a 32 pixel by 2 × 105 pixel image where the x axis represents position along the line and the y axis represents time . When there was photobleaching in the sample , we eliminated some of the frames acquired at later time points . A period average of 800 frames was then applied so that trends in the fluorescence carpet are easier to see . The carpet is displayed as a gradient , with red corresponding to high correlation and blue corresponding to low correlation . Next , the autocorrelation of each of the 32 pixels in the line scan was calculated using a moving average of 200 frames . The resulting image was a 32 column image where each of the columns represented the autocorrelation value of that pixel . At this step , the 32 columns were aligned with the reference image to determine the pixel location of the cell wall . The location of the cell wall was then used as the column distance ( δr ) in order to calculate the pCF . The software calculated the pCF by correlating pixels that are δr apart , moving from left to right . The pCF in the opposite direction was then calculated by correlating pixels from right to left , instead of left to right . To account for the fact that the cell wall is not straight , the pCF was calculated using pixel distances of 5 , 7 , and 9 . Given that there is heterogeneity in the cell size and cell wall orientation in the root , we would not capture differences by using only one pixel distance . Finally , the color scale of the pCF was adjusted such that high correlation is represented as red , low correlation is represented as blue , and no correlation is black . N&B analysis was applied to the same data set as RICS . First , we calibrated the software using a monomeric form of the fluorescent proteins , namely , 35S:GFP and UBQ10:mCherry . Images were taken of A . thaliana roots expressing 35S:GFP and UBQ10:mCherry that contained and did not contain the background ( background refers to a region of the image that does not contain the root ) using the same experimental settings as the RICS analysis ( Figure 3 ) . RICS analysis was run on the images to ensure a good ACF fit . Then , N&B analysis was first run on the background images . The software plots the brightness versus the intensity of each pixel . Since the intensity is not used in the N&B calculation , the exact values of the intensity for each fluorophore do not matter . Background images have two distinct populations representing the monomer and the background . The background brightness was standardized by setting the S-factor parameter such that the background population is centered at B = 1 ( Figure 3 ) . This ensured that the brightness of the monomer was calibrated for the detector output . Since background brightness can vary between microscopes and laser or detector settings , the S-factor was calculated for each microscope and experimental set-up ( Table 2 ) . Once the S-factor was set , N&B analysis was run on the 35S:GFP and UBQ10:mCherry images with no background . All particles with B > 1 were bounded by a rectangle , or cursor , on the brightness graph , and the position and size of the cursor on the B axis were recorded ( Table 2 , Figure 3 ) . The size of the cursor measures the distribution of the monomer in the image . The position on the B axis represents the brightness of the GFP or mCherry monomer . The x axis represents the intensity and is not used in the N&B analysis: thus , the intensity axis can vary between images . Since these parameters can vary by microscope , the cursor size and monomer brightness were calculated for each microscope ( Table 2 ) . The quality of acquired data for N&B analysis of GFP- and mCherry-labeled proteins was first determined by RICS analysis of the acquired images to ensure a good ACF fit . The S-factor was set to the value determined by the background image analysis ( Table 2 ) . A cursor size was selected that took into account the entire distribution of monomer detected in the brightness histogram . Another cursor of the same size was then positioned at the brightness value that corresponds to twice the brightness of the monomer , as any pixels inside the higher rectangle represent a homodimer of the protein ( Figure 3 ) . The percentage of monomer and homodimer were then calculated by dividing the number of pixels inside the monomer , or homodimer , cursor by the total number of fluorescent pixels ( monomer plus homodimer ) . For Cross N&B using images that contain both GFP- and mCherry-labeled protein , the B1-B2 channel was used to determine the pixels in the green channel that were positively correlated with the red channel . The cursor was positioned in the area of the B1-B2 channel that corresponded to where the GFP and mCherry monomers are located . The cursor was then expanded to include any higher oligomeric states that are present , and that area was set as the correlation area ( Figure 4 ) . Once the correlation area was set , the Brightness cross correlation ( Bcc ) channel was used to determine the brightness of the green and red channels at each of the correlated pixels ( Digman et al , 2009b ) . The software highlighted the mCherry pixels that correlated with the GFP pixels using a green curve . The percentage of each complex stoichiometry ( 1:1 , 2:1 , etc ) was then calculated by overlaying cursors on GFP monomer , mCherry monomer , GFP homodimer , and mCherry homodimer . As in N&B analysis , the percentage was computed by dividing the pixels in the 1:1 complex by the total number of fluorescent pixels ( 1:1 plus 2:1 ) . Finally , the software returned a stoichiometry plot that displayed the most likely stoichiometry of the protein-protein complex ( Figure 4 ) . We constructed a mathematical model that incorporated our experimentally determined parameters and simulated SHR and SCR dynamics in the root . Our model assumed that transcription and translation happens quickly . Because of this , we modeled transcription and protein movement terms in the same equation . Additionally , we assumed that all proteins have linear degradation terms . First , we developed a model reflecting wild type conditions ( Model 1 ) . We modeled six different variables: SHR in the vasculature ( Sv ) , SHR monomer in the endodermis ( Se ) , SHR homodimer in the endodermis ( S2e ) , SCR in the endodermis ( C ) , 1:1 SHR-SCR complex in the endodermis ( SC ) , and 2:1 SHR-SCR complex in the endodermis ( S2C ) . The model consisted of six ordinary differential equations ( ODEs ) that measure how each of the variables changes over time . We assumed that SHR is constantly produced at rate k1 as there is no information on upstream regulators of SHR . Since our pCF analysis showed that SHR only moves from the vasculature to the endodermis , possibly through an active transport mechanism ( Gallagher et al . , 2004; Sena et al . , 2004; Gallagher and Benfey , 2009 ) , we modeled the movement of SHR using an active transport term , where a1is the active transport rate . We defined a1 as the experimentally determined diffusion coefficient ( D1 ) divided by the area of a vasculature cell ( A1 ) . We measured the area of vascular cells ( n = 19 ) using ImageJ , and averaged them to determine A1 ( Supplementary file 3 ) . Although the diffusion coefficient returned by RICS is from a population of vascular and endodermal cells , we assumed that it is a good approximation of SHR movement between one vascular and one endodermal cell . We included a second active transport term for movement in the reverse direction , from the endodermis to the vasculature , where a2is the active transport rate . However , based on the pCF analysis , a2 = 0 since normally there is no bidirectional movement . Adding linear degradation gave us the equation for the change in SHR in the vasculature over time . dSvdt=k1−a1Sv+a2Se−d1Sva1= D1A1 , a2=0 Given that SHR is not produced in the endodermis , there is no production term in the equation . Thus , the only SHR present in the endodermis is the SHR that moves from the vasculature . This leads to the equation for the change in SHR monomer in the endodermis over time . dSedt=a1Sv−a2Se−d2Sea2=0 The SHR homodimer forms from two SHR monomers . Our N&B analysis revealed that SHR homodimer does not form in a SCRi line . This suggested that the homodimer formation rate , k2 , should depend on the concentration of SCR . To account for this , we modeled k2 as a logistic function of the concentration of SCR . Once the SCR concentration reaches a critical value C0 , k2 will increase at rate k until it reaches a maximum value of L ( Figure 5—figure supplement 2 ) . We chose values for C0 , k , and L based on the N&B data ( see Parameter Estimation ) . Thus , the equation for the change in SHR homodimer over time isdS2edt=k2 ( C ) Se2−d3S2ek2 ( C ) = L1+e−k ( C−C0 ) . Unlike the other variables , SCR production is not a linear term but rather a Hill equation . This structure was chosen because it has been shown that the SHR-SCR complex activates SCR expression ( Cui et al , 2007 ) . We assumed that both the 1:1 and 2:1 SHR-SCR complexes can activate SCR . In addition , SCR has been shown to autoregulate itself ( Cui et al , 2007; Moreno-Risueno et al , 2015 ) . Therefore , the change in SCR over time is expressed as:dCdt=k3 ( K1D2C+K1DSC+S2CK1D2K2D+K1DK2DSv+K1D2C+K1DSC+S2C ) −d4C . Finally , our N&B analysis revealed that both the monomer and homodimer of SHR can bind SCR and form a complex . The final two equations in our model measure the change in these complexes over time . dSCdt=k4SeC−d5SCdS2Cdt=k5S2eC−d6S2C In addition to the wild type model , we constructed a model that simulates SCRi ( Model 2 ) . Our pCF analysis revealed that SHR movement in the SCRi line is bidirectional , so now a2 is defined as the experimentally determined diffusion coefficient ( D2 ) divided by the area of an endodermal cell ( A2 ) . We determined the average area of endodermal cells by averaging the area of representative cells ( n = 19 ) as we did for vascular cells ( Supplementary file 3 ) . Since it had been shown that a 60% reduction of SCR is required to produce the mutant layer , we assumed that SCR concentrations are maintained below 60% and that the change in SCR over time is zero ( Cui et al . , 2007 ) . dSvdt=k1−a1Sv+a2Se−d1SvdSedt=a1Sv−a2Se−d2SedS2edt=k2 ( C ) Se2−d3S2edCdt=0dSCdt=k4SeC−d5SCdS2Cdt=k5S2eC−d6S2Ca1= D1A1 , a2= D2A2 , k2 ( C ) = L1+e−k ( C−C0 ) The sensitivity analysis was performed to determine the most influential parameters in our model . Notably , we reasoned that small changes in highly influential parameters could result in large changes in the model outcome . In addition , it has been shown that parameter estimation can become more computationally complex and produce more uncertainty in the parameter values as the number of estimated parameters increases ( Smith , 2014 ) . Therefore , we focused on estimating only the most influential parameters . We chose to use Sobol decomposition to measure how sensitive the model is to a particular parameter ( Sobol , 2001 ) . Sobol decomposition is a variance-based method , meaning that the sensitivity of the model to a parameter is quantified by calculating the variance in the model outcome . In addition , Sobol decomposition is a global sensitivity method , so we are exploring the entire parameter space in order to determine the most influential parameters ( Smith , 2014 ) . Since the Sobol decomposition allows us to calculate numerous Sobol indices , we chose to use the total effects index to measure sensitivity in our model . Accordingly , the total effects index takes into account how sensitive the model is to a single parameter as well as combinations of more than one parameter . Thus , by using the total-effects index , we take into account any parameter interactions in our model . To calculate the Sobol total effects index , we rewrote model 1 in the form Y=f ( X1 , X2 , … , X15 ) where the Xi represent the 15 parameters . Y is the model outcome , which in this case must be a scalar . The ODE solution is a set of values over time , so the solution must be numerically integrated to obtain a single , constant value ( Smith , 2014 ) . Consequently , the total effects index for parameter i , STi , is defined asSTi=EX~i ( VXi ( Y\|X~i ) ) V ( Y ) ( 1 ) where E ( ) denotes the expected value , V ( ) denotes the variance , and X~i is the vector of parameters without the ith parameter value . The numerator represents the expected variance in the model if all factors except Xi are fixed . If this term is very large , then that means that Xi contributes greatly to variance in the model: in other words , Xi is an influential parameter . We divided by the variance in the model outcome so that the value of the total index is normalized across different outcomes ( Saltelli et al , 2010; Sobol , 2001 ) . We calculated the index for each parameter using Monte Carlo evaluations and built two matrices A and B , which are 1000 rows by 15 columns . Each row represented a random draw of all 15 parameters from the parameter space ( Supplementary file 3 ) . For each parameter i = 1 , 2 , … , 15 , we constructed a third matrix C that was identical to A except that the ith column of A was replaced with the ith column of B . Model 1 was then evaluated for all 1000 rows . When we evaluated the ODE , we obtained 6 different outcomes: one for each variable we measured . To understand how each parameter affects all of the different variables we calculated the total effects index for each variable separately . After evaluating the model , we numerically integrated the solution to obtain a constant value . The result is a 1000 by 6 matrix , where each row represents a model evaluation and each column represents one of the variables ( SHR in the vasculature , SHR monomer , etc ) . We then used each column to evaluate ( 1 ) , giving us the total effects index for parameter i with respect to each of the variables . Since the numerator of ( 1 ) cannot be computed exactly , we estimated it using one of the most accurate estimators , EX~i ( VXi ( Y\|X~i ) ) ≈12N∑j=1N ( f ( A ) j−f ( C ) j ) 2 , where N is the number of Monte Carlo iterations ( in our case , N = 1000 ) ( Saltelli et al . , 2010 ) . Finally , we repeated the entire process 10 times to obtain technical replicates ( Supplementary file 4 ) . We used the Wilcoxon test with the Steel-Dwass test for multiple comparisons at significance level α = 0 . 1 to determine parameters that are significantly more influential . For the initial model simulations , we derived most of the model parameters from the previous model of SHR and SCR in the CEI ( Supplementary file 3 ) . It has been shown that the mutant layer in the SCRi line occurs when the concentration of SCR drops to 60% of WT levels ( Cui et al . , 2007 ) . Therefore , for the homodimer formation term k2 , we chose C0 to be 60% of the steady-state value of SCR . By choosing this value , the homodimer will not form until the concentration of SCR is above 60% . Additionally , we chose k = 0 . 1 so that homodimer formation occurs rapidly after SCR passes the threshold ( Supplementary file 3 ) . We expected that our model would have high levels of SHR monomer and 1:1 complex relative to the levels of SHR homodimer and 2:1 complex as shown by our N&B data . However , using these parameters , we were unable to replicate these results . We reasoned that estimating better values for some of the parameters could improve our model simulation . Thus , we used the results from our sensitivity analysis to identify the most influential parameters to estimate . We chose to estimate d2 , k3 , K2D , and d4 based on their high sensitivity indices ( Figure 5 and Figure 5—figure supplement 1 ) . Although k1 and d1 are influential parameters , we chose not to estimate them as varying their values only changes the overall level of SHR and not the oligomeric dynamics . We also chose to estimate L , which is the maximum value of k2 . Although k2 is not as influential as some of the other parameters , its functional structure comes from the N&B analysis , so its maximum value should be derived from the N&B data . For the parameter estimation , we started parameters at their default values ( Supplementary file 3 ) and varied them one at a time until we were able to replicate the N&B data . To replicate the N&B data , we required that the SHR homodimer accounted for 7 . 5% of the total SHR proteins , while the 2:1 complex accounted for 15 . 2% of the total SHR-SCR complexes ( Figure 3 and Figure 4 ) . We found that increasing d2 , decreasing K2D , and decreasing L achieved this relationship . In addition , decreasing K2D resulted in SCR increasing over time ( Figure 5D ) . We did not change the values of k3 and d4 . Using N&B to experimentally determine the oligomeric state and protein-protein stoichiometry of SHR and SCR allowed us to estimate better values for some parameters and , further , improve the reliability of the conclusions we derived from the model .	Stem cells are a specific type of cell found in both plants and animals . These cells can divide to produce daughter cells that can take on the role of any of the different tissues and organs within the plant or animal . A plant known as Arabidopsis is often used as a model in scientific research . In Arabidopsis , two proteins called SHORTROOT and SCARECROW are known to control the ability of stem cells in the roots to divide . SHORTROOT is made in cells at the center of the root known as the vasculature . From there , it moves to the next cell layer ( called the endodermis ) where it interacts with SCARECROW to form a protein complex . Here , Clark et al . investigated how quickly SHORTROOT moves between cells , the direction it moves in , and how it interacts with SCARECROW . The experiments used a new imaging technique called scanning fluorescence correlation spectroscopy to track the movements of SHORTROOT molecules in the root . This technique relies on the protein of interest ( in this case , SHORTROOT ) being attached to a fluorescent protein so that it is visible when the cells are examined . In plants that had lower levels of SCARECROW , SHORTROOT moves between cells more quickly and in an unrestricted manner . This suggests that SCARECROW forms a complex with SHORTROOT to restrict its movement in the endodermis . The experiments also show that SHORTROOT is only able to leave the endodermis to return to the vasculature when SCARECROW levels are lower than normal . Clark et al . developed a model to map the behavior of SHORTROOT and SCARECROW in the root and predict how the levels of these proteins change over time . One of the next steps following on from this work would be to test whether other proteins restrict the movement of SHORTROOT , perhaps by studying mutant plants in which SHORTROOT is less able to move .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "plant", "biology", "developmental", "biology" ]	2016	Tracking transcription factor mobility and interaction in Arabidopsis roots with fluorescence correlation spectroscopy
In higher eukaryotes , the endoplasmic reticulum ( ER ) contains a network of membrane tubules , which transitions into sheets during mitosis . Network formation involves curvature-stabilizing proteins , including the reticulons ( Rtns ) , as well as the membrane-fusing GTPase atlastin ( ATL ) and the lunapark protein ( Lnp ) . Here , we have analyzed how these proteins cooperate . ATL is needed to not only form , but also maintain , the ER network . Maintenance requires a balance between ATL and Rtn , as too little ATL activity or too high Rtn4a concentrations cause ER fragmentation . Lnp only affects the abundance of three-way junctions and tubules . We suggest a model in which ATL-mediated fusion counteracts the instability of free tubule ends . ATL tethers and fuses tubules stabilized by the Rtns , and transiently sits in newly formed three-way junctions . Lnp subsequently moves into the junctional sheets and forms oligomers . Lnp is inactivated by mitotic phosphorylation , which contributes to the tubule-to-sheet conversion of the ER . The mechanisms by which organelles are shaped and remodeled are largely unknown . The endoplasmic reticulum ( ER ) is a particularly intriguing organelle , as it consists of morphologically distinct domains that change during differentiation and cell cycle . In interphase , the ER consists of the nuclear envelope and a connected peripheral network of tubules and interspersed sheets ( Shibata et al . , 2009; Chen et al . , 2013; English and Voeltz , 2013a; Goyal and Blackstone , 2013 ) . The network is dynamic , with tubules continuously forming , retracting , and sliding along one another . During mitosis in metazoans , the nuclear envelope disassembles and peripheral ER tubules are transformed into sheets ( Lu et al . , 2009; Wang et al . , 2013 ) . How the network is generated and maintained , and how its morphology changes during the cell cycle , is poorly understood . Previous work has suggested that the tubules themselves are shaped by two evolutionarily conserved protein families , the reticulons ( Rtns ) and DP1/Yop1p ( Voeltz et al . , 2006 ) . These are abundant membrane proteins that are both necessary and sufficient to generate tubules . Members of these families are found in all eukaryotic cells . The Rtns and DP1/Yop1p seem to stabilize the high membrane curvature seen in cross-sections of tubules and sheet edges ( Hu et al . , 2008; Shibata et al . , 2009 ) . How these proteins generate and stabilize membrane curvature is uncertain , but they all contain pairs of closely spaced trans-membrane segments and have an amphipathic helix that is required to generate tubules with reconstituted proteoliposomes ( Brady et al . , 2015 ) . It has been proposed that the Rtns and DP1/Yop1p form wedges in the lipid bilayer and arc-shaped oligomers around the tubules ( Hu et al . , 2008; Shibata et al . , 2009 ) . Connecting tubules into a network requires membrane fusion , which is mediated by membrane-anchored GTPases , the atlastins ( ATLs ) in metazoans and Sey1p and related proteins in yeast and plants ( Hu et al . , 2009; Orso et al . , 2009 ) . These proteins contain a cytoplasmic GTPase domain , followed by a helical bundle , two closely spaced trans-membrane segments , and a cytoplasmic tail ( Bian et al . , 2011; Byrnes and Sondermann , 2011 ) . Mammals have three isoforms of ATL , with ATL-1 being prominently expressed in neuronal cells . Mutations in ATL-1 can cause hereditary spastic paraplegia , a neurodegenerative disease that is characterized by the shortening of the axons in corticospinal motor neurons ( Salinas et al . , 2008 ) . This leads to progressive spasticity and weakness of the lower limbs . A role for ATL in membrane fusion is supported by the fact that proteoliposomes containing purified Drosophila ATL undergo GTP-dependent fusion in vitro ( Bian et al . , 2011; Orso et al . , 2009 ) . Furthermore , the fusion of ER vesicles in Xenopus laevis egg extracts is prevented by the addition of ATL antibodies or a cytosolic fragment of ATL ( Hu et al . , 2009; Wang et al . , 2013 ) . Finally , ATL-depleted Drosophila larvae have fragmented ER , and the depletion of ATL or expression of dominant-negative ATL mutants in tissue culture cells leads to long , unbranched tubules ( Hu et al . , 2009; Orso et al . , 2009 ) . Crystal structures and biochemical experiments have led to a model in which ATL molecules sitting in different membranes dimerize through their GTPase domains ( trans-interaction ) , and undergo a conformational change during the GTPase cycle , thereby pulling the two membranes together and fusing them . Interestingly , ATL molecules sitting in the same membrane can also form GTPase-dependent dimers ( cis-interaction ) ( Liu et al . , 2015 ) . The fusion of membrane tubules generates three-way junctions , which are small , triangular sheets with negatively curved edge lines ( Shemesh et al . , 2014 ) . How junctions are maintained is unknown , but it has been suggested that they are stabilized by the lunapark protein ( Lnp ) , a conserved membrane protein ( Chen et al . , 2012; Shemesh et al . , 2014; Chen et al . , 2015 ) . Lnp contains two closely spaced transmembrane segments and a Zn2+-finger domain ( Chen et al . , 2012 ) . Lnp localizes preferentially to three-way junctions , but there is disagreement as to whether it stabilizes newly formed three-way junctions ( in mammalian cells ) , or functions in ring closure , i . e . the disappearance of junctions ( in yeast ) ( Chen et al . , 2012; Shemesh et al . , 2014; Chen et al . , 2015 ) . Furthermore , Sey1p and some isoforms of ATL also localize to three-way junctions and could therefore play a role in their stabilization ( Hu et al . , 2009; English and Voeltz , 2013b; Yan et al . , 2015 ) . Thus , the exact role of Lnp is unknown . Although the available evidence suggests that Rtns/DP1 , ATLs , and Lnp are major proteins responsible for determining ER morphology , it is unknown how they cooperate . For example , it is unclear whether one of these components acts upstream of another , whether they are all equally important to generate an ER network , and whether they have distinct roles in tubule or junction formation . It is also unknown whether any of these proteins are involved in the characteristic tubule-to-sheet conversion during mitosis . Mammalian tissue culture cells offer the opportunity to study ER morphology by removing or overexpressing ER-shaping membrane proteins . So far , however , essentially all experiments employed fixed tissue culture cells that transiently express proteins or RNAi constructs , which results in poor viability , heterogeneity among cells , and fixation artifacts . Consequently , ER morphology changes are difficult to interpret . ER morphology can also be studied with extracts from Xenopus laevis eggs . Both crude extracts and isolated membranes can be used to form a tubular network in vitro ( Dreier and Rapoport , 2000; Wang et al . , 2013 ) . With interphase extracts , the network consists exclusively of tubules connected by three-way junctions , whereas a tubule-to-sheet conversion and a loss of three-way junctions are observed with mitotic ( meiotic ) extracts ( Wang et al . , 2013 ) . Because all known components involved in generating ER morphology are integral membrane proteins , their function can be tested with dominant-negative constructs , but not by depletion experiments . Here , we have used live-cell imaging of stable mammalian tissue culture cells and Xenopus egg extracts to address how the known ER shaping proteins cooperate to generate and maintain a tubular ER network . We demonstrate that ATL function is not only required to form an ER network but , surprisingly , is also needed to maintain it . The integrity of tubules is maintained by a balance of ATL and Rtn function . Lnp is not essential for network formation , but its inactivation or absence causes the loss of three-way junctions and tubules in favor of sheets . Our results suggest mechanisms by which a tubular ER network is formed and maintained , as well as changed during mitosis . Previous experiments have shown that the transient expression of a dominant-negative ATL mutant in mammalian cells converts the ER network into long , unbranched tubules ( Hu et al . , 2009; Rismanchi et al . , 2008 ) . However , these experiments were performed with fixed overexpressing cells , in which it is difficult to exclude artifacts . We therefore revisited the effect of ATL and its mutant forms , using stable cell lines and live-cell imaging . We first used CRISPR technology to generate U2OS cells that express at endogenous levels GFP-calreticulin , an established luminal ER marker . Live-cell imaging by spinning-disk confocal microscopy revealed both a tubular network and interdispersed sheet-like structures ( Figure 1A; first column; Figure 1—figure supplement 1A ) . The GFP-calreticulin expressing cells were then infected with lentivirus constructs to generate stable cell lines that also express fluorescently-tagged ATL . The expression of wild type ATL isoforms had only a moderate effect on ER morphology ( Figure 1A; second and third columns; Figure 1—figure supplement1B , C ) . Fluorescently tagged wild type ATL-1 localized throughout tubules ( Figure 1A; second column ) , whereas wild type ATL-3 and ATL-2 localized in punctae at three-way junctions ( Figure 1A; third column; Figure 1—figure supplement 1D , respectively ) . Dimerization-defective ATL-1 or ATL-3 mutants , carrying the R217Q or R213Q mutations ( Byrnes and Sondermann , 2011 ) , respectively , distributed throughout the ER ( Figure 1A , fourth and fifth column ) . Thus , the punctate localization of ATL-3 to three-way junctions is due to its dimerization with endogenous ATL . The different localization patterns of ATL-1 and ATL-3 have been attributed to the fact that ATL-1 hydrolyzes GTP faster than ATL-3; faster GTP hydrolysis would lead to less concentration in junctions ( Yan et al . , 2015 ) . 10 . 7554/eLife . 18605 . 003Figure 1 . ATL is required to maintain tubules and junctions in mammalian cells . ( A ) Peripheral ER network of U2OS cells expressing GFP-calreticulin from the endogenous promoter in wild type cells . Where indicated , the cells also stably expressed mCherry-tagged wild type ATL-1 or ATL-3 or the corresponding dimerization-defective mutants ( ATL-1 R217Q or ATL-3 R213Q ) . Scale bar = 10 µm . ( B ) As in ( A ) , but with cells stably expressing mCherry-tagged ATL-1 or ATL-3 mutants defective in GTP hydrolysis ( ATL-1 K80A or ATL-3 K73A ) . The first and second columns show ATL-1 K80A-expressing cells , in which the ER is either fragmented or converted into long , unbranched tubules . ATL-1 K80A R217Q is both GTPase- and dimerization- defective . The bottom row shows three time points for calreticulin staining . Stationary pixels appear white , while those moving appear in colors . Scale bar = 10 µm . ( C ) As in ( A ) , but with cells stably expressing mCherry-tagged cytoplasmic fragments of wild type ATL-1 ( cytATL-1 WT ) , a mutant defective in GTP hydrolysis ( cytATL-1 K80A ) at low or high level , or a mutant defective in dimerization ( cytATL-1 R217Q ) . CytATL-1 K80A R217Q contains both mutations . The bottom two rows show a close-up image of the peripheral network of the cells depicted in the first two rows . The bottom row shows three time points for calreticulin staining . Stationary pixels appear white , while those moving appear in colors . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 00310 . 7554/eLife . 18605 . 004Figure 1—figure supplement 1 . Effect of ATL overexpression on ER morphology in U2OS cells . ( A ) Image of a whole U2OS cell expressing GFP-calreticulin . ( B ) As in ( A ) , but a cell expressing GFP-ATL-1 . ( C ) As in ( A ) , but a cell expressing GFP-ATL-3 . Scale bar = 10 µm . ( D ) Peripheral ER network in a U2OS cell stably expressing GFP-tagged ATL-2 . Scale bar = 10 µm . ( E ) Image of a U2OS cell expressing GFP-ATL-1 K80A , untreated ( left panel ) or treated with 1 µM nocodazole for 90 min ( right panel ) . Scale bar = 10 µm . ( F ) Peripheral ER network in a U2OS cell stably expressing a GTPase-defective mutant of ATL-2 ( GFP-ATL-2 K107A ) . The right panel shows three time points . Stationary pixels appear white , while those moving appear in colors . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 004 Next we expressed dominant-negative , GTPase-defective mutants of fluorescently-tagged ATL . Expression of ATL-1 K80A caused the conversion of most of the ER into small vesicles , which rapidly moved around the cell , as shown by time-lapse imaging ( Figure 1B; first column; the lowest row shows three different time points with different colors; see also Video 1 ) . In many cells , long , unbranched tubules were also seen ( Figure 1B; second column; Video 2 ) . These membrane tubules were aligned with microtubules and seemed to be pulled out from a membrane reservoir near the nucleus after cell division ( not shown ) . Indeed , treatment of the cells with the microtubule-disassembling drug nocodazole led to the disappearance of the long membrane tubules ( Figure 1—figure supplement 1E ) . Expression of a double mutant of ATL-1 , which is both GTPase- and dimerization- defective ( ATL-1 K80A R217Q ) left the reticular ER intact ( Figure 1B; third column ) , demonstrating that the ATL-1 K80A mutant causes ER morphology changes by forming inactive dimers with endogenous ATL . In agreement with previous experiments ( Rismanchi et al . , 2008 ) , we find that ATL-1 R217Q causes ER morphology defects in cells that express high amounts of this protein , but this is not caused by interference with endogenous ATL , but rather by non-specific membrane crowding . Vesiculation of the ER was also observed with dominant-negative , GTPase-defective ATL-2 or ATL-3 mutants ( ATL-3 K73A or ATL-2 K107A; Figure 1B; fourth column; Figure 1—figure supplement 1F , respectively ) . 10 . 7554/eLife . 18605 . 005Video 1 . ER in U2OS cells expressing dominant-negative ATL-1 . U2OS cell stably expressing GFP-ATL-1 K80A ( GTPase-defective mutant ) with most of the peripheral ER fragmented . Images were acquired with a spinning disk confocal microscope at 0 . 05 sec intervals for 5 sec . The video is displayed at the same rate it was acquired ( 20 frames per sec ) . Image scale: 87 × 66 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 00510 . 7554/eLife . 18605 . 006Video 2 . ER in U2OS cells expressing dominant-negative ATL-1 . U2OS cell stably expressing GFP-ATL-1 K80A ( GTPase-defective mutant ) with many unbranched tubules and a fragmented peripheral ER . Images were acquired with a spinning disk confocal microscope at 0 . 05 sec intervals for 5 sec . The video is displayed at the same rate it was acquired ( 20 frames per sec ) . This cell is also depicted in Figure 1— figure supplement 1A , left panel . Image scale: 87 × 66 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 006 Expression of a cytoplasmic fragment of the GTPase-defective , dominant-negative ATL-1 K80A mutant ( cytATL-1-K80A-mCherry ) converted the ER into long , unbranched tubules at low expression levels ( Figure 1C; second column ) , and into small vesicles at high levels ( third column ) . A cytoplasmic fragment of wild type ATL-1 ( cytATL-1-mCherry ) only generated long , unbranched tubules , even at high expression levels ( fourth column ) , likely because it interacts with endogenous ATL only transiently during the GTPase cycle and is therefore a weaker inhibitor than cytATL-1 K80A . Consistent with this assumption , cytATL-1 K80A localized more prominently to the ER than wild type cytATL-1 ( not shown ) , and non-dimerizing versions of cytATL-1 K80A or cytATL-1 , which carry the R217Q mutation ( Byrnes and Sondermann , 2011 ) , did not affect the morphology of the ER ( columns five and six ) . With the exception of a report in which cytATL was overexpressed ( Moss et al . , 2011 ) , ATL inhibition was thought to generate only long , unbranched tubules , rather than cause ER-vesiculation . This is probably due to the use of fixed cells , in which small vesicles are lost or difficult to image . On the other hand , the phenotype is similar to that described for ATL-depleted Drosophila larvae ( Orso et al . , 2009 ) . ER fragmentation has not been observed in S . cerevisiae cells lacking the ATL orthologue Sey1p ( Hu et al . , 2009 ) , perhaps because ER SNAREs provide an alternative pathway of ER fusion in this organism ( Anwar et al . , 2012 ) . To test whether acute inhibition of ATL function affects the integrity of the ER network , we employed the Xenopus egg extract system . As reported previously , an ER network can be generated from membrane fragment in a crude interphase extract ( Figure 2A ) ; network formation is prevented when membrane fusion is inhibited by the addition of a dominant-negative cytoplasmic fragment of Xenopus ATL ( cytATL ) at the beginning of the reaction ( Wang et al . , 2013 ) . Xenopus ATL is homologous to mammalian ATL-2 and is the only ATL annotated in the Xenopus genome . Surprisingly , we found that cytATL also disassembled a preformed network . At low concentrations , a fragmented tubular network remained ( Figure 2B ) . At higher concentrations , only small membrane structures were observed ( Figure 2C ) . A non-dimerizing mutant of cytATL ( cytATL R232Q ) had no effect on the integrity of the network ( Figure 2D ) , likely because it did not bind to the endogenous ATL . Inhibition of endogenous ATL by addition of GTPγS also disassembled the network ( Figure 2E ) . Similar results were obtained with a fractionated system , consisting either of Xenopus membranes and membrane-depleted cytosol ( Figure 2F ) or of membranes only ( Figure 2—figure supplement 1A ) . These results indicate that ATL function is not only required to form an ER network , but also to maintain it . 10 . 7554/eLife . 18605 . 007Figure 2 . ATL is required to maintain an ER network in Xenopus egg extracts . ( A ) An ER network was generated with a crude interphase Xenopus egg extract and stained with the lipophilic fluorescent dye DiIC18 . The sample was imaged with a spinning disk confocal microscope . Scale bar = 10 µm . ( B ) As in ( A ) , but in the presence of 1 µM cytoplasmic fragment of Xenopus ATL ( cytATL ) . Scale bar = 10 µm . ( C ) As in ( A ) , but with 2 µM cytATL . Scale bar = 10 µm . ( D ) As in ( A ) , but with 2 µM of the dimerization-defective mutant fragment cytATL R232Q . Scale bar = 10 µm . ( E ) As in ( A ) , but with 1 . 5 mM GTPγS . Scale bar = 10 µm . ( F ) An ER network was generated with interphase cytosol , light membranes , and an energy-regenerating system . After 30 min , buffer , 2 µM cytATL , 2 µM cytATL R232Q , or 2 mM GTPγS were added . The membranes were stained with octadecyl rhodamine . Scale bar = 20 µm . ( G ) A mitotic ER network was generated with a crude Xenopus extract containing DiIC18 and 2 µM cytATL R232Q . Scale bar = 10 µm . ( H ) As in ( G ) , but with 2 µM wild type cytATL . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 00710 . 7554/eLife . 18605 . 008Figure 2—figure supplement 1 . ER network formed with Xenopus extract fractions is disassembled by ATL inactivation . ( A ) An ER network was generated with interphase light membranes . Buffer , 2 µM cytATL , 2 µM cytATL R232Q , or 2 mM GTPγS were added subsequently and the membranes were stained with octadecyl rhodamine . ( B ) A mitotic ER network was formed from the interphase cytosol and light membranes by the addition of non-degradable cyclin B∆90 . After addition of 2 µM cytATL or 2 µM cytATL R232Q , the membranes were stained with octadecyl rhodamine . Scale bars = 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 008 ATL was also required to maintain an ER network in mitotic ( meiotic ) extracts . A dimerization-defective cytATL-R232Q mutant did not affect the mitotic ER network; as in untreated mitotic extracts ( Wang et al . , 2013 ) , many small sheets connected by short tubules were seen ( Figure 2G ) . In contrast , blocking the function of endogenous ATL by addition of cytATL resulted in the disassembly of the network ( Figure 2H ) . Similar results were obtained with a mitotic network that was generated by incubating isolated membranes with cytosol that was moved into mitosis by the addition of non-degradable cyclin ( Figure 2—figure supplement 1B ) . To follow the disassembly of the ER network in real time and minimize its mechanical disruption , we used a microfluidics device . A network was first generated in a flow-chamber from a crude extract , and then cytosol , containing cytATL fused to GFP ( cytATL-GFP ) , was slowly introduced . When cytATL-GFP arrived in the chamber , the network disassembled , starting at the site where cytATL-GFP arrived ( Figure 3; Video 3 ) . Close observation of the disassembly reaction showed that tubules frequently first detach from one another ( arrows ) before they crumble and convert into smaller structures ( Figure 3 ) . The initial detachment of the tubules suggests that some three-way junctions contained tethered , rather than fused tubules , and that ATL inactivation caused their untethering . 10 . 7554/eLife . 18605 . 009Figure 3 . Real-time disassembly of an ER network in the presence of cytATL . A network was formed from a crude Xenopus egg extract in the presence of the dye DiIC18 in a computer-controlled microfluidics device . CytATL-GFP ( 5 µM ) in cytosol containing an energy regenerating system was then slowly perfused into the chamber at a total laminar flow rate of 0 . 5 µL/min from multiple ports ( one is indicated by a blue arrow ) . The arrival of cytATL- GFP arrival in the chamber was monitored by GFP fluorescence ( not shown ) . Images represent snapshots of a real-time video ( see Video 3 ) . Yellow arrows point to the detachment or breakage of tubules . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 00910 . 7554/eLife . 18605 . 010Video 3 . Disassembly of an in vitro generated ER network by cytATL . An ER network was formed in a microfluidics chamber from Xenopus crude extract containing DiIC18 . Xenopus egg cytosol containing 5 µM cytATL-GFP was slowly perfused into the chamber using a computer-controlled pneumatic pump ( blue arrow indicates perfusion port ) while images were acquired with a spinning disk confocal microscope at 10-sec intervals for 9 min . The video is shown at 3 frames per sec . Yellow arrowheads indicate ER disassembly events . Still images of this video were used for Figure 3 . Scale bar = 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 010 The final structures of network disassembly were larger than the small vesicles that serve as starting material for network formation . Occasionally , dynamic sheets were seen from which short tubules emerge and retract ( not shown ) . These structures are possibly explained by residual ATL activity . They were much more abundant during early time points of network formation ( Figure 4A ) , or if network formation was slowed down by partial inhibition of ATL , for example , by adding a low concentration of GTP to a membrane-only reaction ( Figure 4B ) , or by adding GDP and AlF4- to a reaction consisting of membranes and cytosol ( Figure 4C ) . These intermediates in network formation are dynamic , small structures from which tubules continuously emanate and retract , although the disappearance of some tubules may simply be due to their bending or movement out of the focal plane ( Videos 4 , 5 ) . These results indicate that during network formation , ATL first mediates the fusion of small vesicles into small , dynamic structures , which subsequently give rise to longer tubules . Inactivation of ATL disassembles the tubules into small membrane structures . Our experiments also indicate that , at endogenous levels , the reticulons and DP1 are insufficient to maintain the integrity of tubules in the absence of ATL function . 10 . 7554/eLife . 18605 . 011Figure 4 . Intermediates during in vitro ER network formation . ( A ) DiIC18-prelabeled light membranes were mixed with buffer and an energy regenerating system . The sample was imaged immediately by confocal microscopy . Scale bar = 2 µm . ( B ) Xenopus egg light membranes were incubated in the presence of 150 µM GTP and octadecyl rhodamine . Scale bar = 1 µm . ( C ) Interphase Xenopus egg cytosol , light membranes , 0 . 67 mM GDP-AlF4- , and an energy-regenerating system were incubated for 30 min . The membranes were stained with octadecyl rhodamine . Scale bars = 10 µm . Insets and images in C’ and C’’ show magnified views of small sheets from which short tubules emanate . Scale bars = 1 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 01110 . 7554/eLife . 18605 . 012Video 4 . Intermediates of an in vitro generated ER network at early time points . An ER network assembly reaction was prepared by mixing DiIC18-prelabeled Xenopus egg light membranes with buffer and an energy regenerating system . The sample was imaged immediately using a spinning-disk confocal microscope at 0 . 5-sec time intervals for 39 sec . The video is shown at 10 frames per sec . Scale bar = 2 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 01210 . 7554/eLife . 18605 . 013Video 5 . Intermediates of an in vitro generated ER network at early time points . As in Video 4 , but the image was acquired at 0 . 2-sec time intervals for 5 sec . Scale bar = 5 µm . The video is shown at 5 frames per sec . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 013 Functional ATL localized mostly to three-way junctions in an ER network assembled from crude Xenopus extracts , as demonstrated by the addition of fluorescently labeled , affinity-purified antibodies raised against Xenopus ATL ( Figure 5A ) . In contrast , when cytATL labeled with the fluorescent dye Alexa 488 was added at low concentrations , the label was seen throughout the tubules ( Figure 5B ) . These molecules likely represent inactive dimers formed with endogenous ATL; the fraction of inactivated endogenous ATL is apparently small enough to maintain the integrity of the network . Similar results were obtained with a fractionated system , consisting of membranes and cytosol . Again , antibodies to Xenopus ATL stained three-way junctions ( Figure 5—figure supplement 1A ) , whereas cytATL-GFP stained the entire network ( Figure 5—figure supplement 1B; left panel ) . As expected , cytATL R232Q-GFP , which fails to bind endogenous ATL , localized to the cytosol ( Figure 5—figure supplement 1B; rightmost panel ) . These results indicate that functional ATL localizes to three-way junctions , whereas inactivated ATL distributes throughout the tubules . 10 . 7554/eLife . 18605 . 014Figure 5 . Localization of ATL in an in vitro generated ER network . ( A ) An interphase ER network was generated with a crude Xenopus egg extract containing the dye DiIC18 . Endogenous ATL was visualized by including 16 nM Alexa488-labeled , affinity-purified antibodies raised against Xenopus ATL ( ATL AbAlexa488 ) . Note that ATL localizes preferentially to three-way junctions . Scale bar = 10 µm . ( B ) An interphase ER network was assembled as in ( A ) and labeled with DiIC18 and 0 . 6 µM Alexa488-labeled cytATL . Note that cytATL localizes throughout the tubules , marking the position of inactivated endogenous ATL . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 01410 . 7554/eLife . 18605 . 015Figure 5—figure supplement 1 . Localization of endogenous and inactivated ATL in a Xenopus ER network . ( A ) An interphase ER network was generated with cytosol , DiIC18-prelabeled light membranes , and an energy regenerating system . Endogenous ATL was visualized by including 10 . 5 nM Alexa488-labeled , affinity-purified antibodies against Xenopus ATL . The sample also contained sperm chromatin to stabilize the network by reducing thermal convection . Scale bars = 1 µm . ( B ) A mixture of interphase cytosol , light membranes , and an energy regenerating system was incubated in the presence of 0 . 5 µM cytATL-GFP , but absence of membrane stain , for 30 min ( left panel ) . Parallel reactions were performed with 2 µM cytATL ( R232Q ) -GFP and the membrane stain octadecyl rhodamine ( middle and right panels ) . Scale bars = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 015 We noticed that inhibition of ATL function affected network formation more than network maintenance . The addition of low concentrations of GDP plus BeF3- , a mixture that locks endogenous ATL into an inactive , dimeric state , prevented the de novo formation of an ER network , but left a pre-formed network intact ( Figure 6 ) . At higher concentrations , both network formation and maintenance were affected . Similar observations were made with GTPγS ( Figure 6—figure supplement 1 ) . These experiments show that the de novo formation of an ER network is more sensitive to ATL inhibition than the maintenance of a preformed network . 10 . 7554/eLife . 18605 . 016Figure 6 . Effect of GTP analogs on ER network formation and maintenance . GDP-BeF3- was added at 0 . 67 mM or 2 mM either before ( upper panels ) or after ( lower panels ) formation of an ER network from Xenopus egg cytosol ( C ) , light membranes ( M ) , and an energy regenerating system . The membranes were stained with octadecyl rhodamine . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 01610 . 7554/eLife . 18605 . 017Figure 6—figure supplement 1 . ER network maintenance is less sensitive to ATL inhibition than ER network formation . GTPγS was added at 1 mM or 2 mM either before ( upper panels ) or after ( lower panels ) formation of an ER network from Xenopus egg cytosol ( C ) , light membranes ( M ) , and an energy regenerating system . The membranes were stained with octadecyl rhodamine . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 017 Next we tested the effect of the reticulons on ER morphology , using live-cell imaging of stable mammalian cell lines . We first expressed in U2OS cells a mCherry-fusion of Rtn4a , which has a long cytoplasmic N-terminus preceding the conserved membrane-embedded , reticulon-homology ( RHD ) domain ( Voeltz et al . , 2006 ) . At low expression levels , mCherry-Rtn4a distributed together with GFP-calreticulin throughout the tubules of the ER ( Figure 7A ) . At higher levels , unbranched tubules were seen ( Figure 7B ) . Often these tubules were depleted of GFP-calreticulin , suggesting that these were too narrow to accommodate luminal proteins , as observed previously in transient overexpression experiments ( Hu et al . , 2008 ) . In addition , some of the ER was fragmented; the membrane fragments contained both GFP-calreticulin and mCherry-Rtn4 and moved rapidly ( Figure 7C; Video 6 ) , but they seemed to be larger than in cells expressing dominant-negative ATL mutants ( Figure 1B ) . Perhaps , they correspond to fractured tubules . Fragmentation of the ER by overexpression of Rtn4a had not been observed in fixed cells , again highlighting the need for live-cell imaging . 10 . 7554/eLife . 18605 . 018Figure 7 . Interplay between ATL and the reticulons . ( A ) Peripheral ER network in a U2OS cell expressing GFP-calreticulin under the endogenous promoter in wild type cells or in cells stably expressing mCherry-tagged Rtn4a . The bottom row shows three time points . Stationary pixels appear white , while those moving appear in colors . Scale bar = 10 µm . ( B ) As in ( A ) , but with a higher expression level of mCherry-Rtn4a , resulting in unbranched tubules . ( C ) As in ( B ) , but showing a cell with fragmented ER . ( D ) As in ( B ) , but with cells that also stably express GFP-ATL-1 or GFP-ATL-3 . ( E ) As in ( B ) , but with cells that also express a GTPase defective ATL-1 mutant ( GFP-ATL-1 K80A ) . The second column shows three time points for both the ATL and Rtn channels . Stationary pixels appear white , while those moving appear in colors . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 01810 . 7554/eLife . 18605 . 019Figure 7—figure supplement 1 . Co-overexpression of ATL mutants and Rtn4a in U2OS cells . ( A ) Peripheral ER network in a U2OS cell expressing a high level of mCherry-tagged Rtn4a and GFP-ATL-1 R217Q , a dimerization defective mutant . Scale bar = 10 µm . ( B ) Peripheral ER network in a U2OS cell expressing a high level of mCherry-tagged Rtn4a and GFP-ATL-1 K80A , a GTPase defective mutant . The second column shows three time points for both the ATL and Rtn channels . Stationary pixels appear white , while those moving appear in colors . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 01910 . 7554/eLife . 18605 . 020Video 6 . ER morphology in U2OS cells after overexpression of Rtn4a . U2OS cells expressing GFP-calreticulin under the endogenous promoter as well as stably expressing mCherry- Rtn4a . Left panel: calreticulin signal , right panel: mCherry-Rtn4a signal . Images were acquired with a spinning disk confocal microscope at 0 . 4 sec intervals for 4 sec . The video is displayed at the same rate it was acquired ( 2 . 5 frames per sec ) . Image scale: 87 × 66 µm for each panel . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 020 The co-overexpression of Rtn4a and wild type ATL-1 or ATL-3 restored the integrity of the network , making the membrane fragments disappear and returning tubular junctions ( Figure 7D ) . This effect is due to the fusion activity of ATL because the co-expression of the dimerization-defective ATL-1 R217Q mutant did not restore the network ( Figure 7—figure supplement 1A ) . As expected , the co-overexpression of the dominant-negative ATL1- K80A mutant and Rtn4a resulted in both long , unbranched tubules and small vesicles ( Figure 7E and Figure 7—figure supplement 1B ) . Interestingly , Rtn4a and ATL seemed to segregate at least partially , perhaps because Rtn displaces ATL molecules from long tubules . Taken together , our results suggest that an appropriate balance between ATL and Rtn4a is required for network formation; too little ATL function , or too much Rtn4a , results in similar morphology defects , i . e . long , unbranched tubules and fragmented ER . Next we tested the role of lunapark ( Lnp ) , arguably the least understood player in ER morphology . We generated a CRISPR knock-out of lunapark in U2OS cells that express GFP-calreticulin ( Figure 8—figure supplement 1A ) . Cells lacking Lnp exhibited a proliferation of peripheral sheets and a reduction of tubules and junctions ( Figure 8A; quantification in Figure 8B ) . However , the tubular network did not completely disappear in all cells . Immunostaining with reticulon antibodies showed that the residual tubular ER was often pushed to the periphery of the cells ( Figure 8—figure supplement 1B ) . Thus , Lnp is not essential to form tubules or three-way junctions , but it affects their abundance . These results are consistent with the ER morphology reported for a Lnp knock-out in S . cerevisiae ( Chen et al . , 2012 ) , although the small cell size of this organism makes the distinction between tubules and sheets difficult . 10 . 7554/eLife . 18605 . 021Figure 8 . Lnp determines the abundance of three-way tubular junctions in mammalian cells . ( A ) Views of wild type U2OS and Lnp-deleted ( LnpΔ ) cells expressing GFP-calreticulin from the endogenous promoter . LnpΔ cells were generated by CRISPR targeting the start codon of the LNP gene . The bottom row shows magnifications of the boxed areas of the peripheral ER . Scale bars = 10 µm . ( B ) Quantification of the LNP deletion phenotype depicted in A . Wild type and LnpΔ cells were scored blindly for the appearance of peripheral ER . ( C ) Peripheral ER in a LnpΔ cell expressing GFP-calreticulin , as well as stably expressing a low level of Lnp-mCherry . Scale bars = 10 µm . ( D ) As in ( C ) , but with cells expressing increasing levels of Lnp-mCherry ( left to right ) . Scale bars = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 02110 . 7554/eLife . 18605 . 022Figure 8—figure supplement 1 . ER morphology in U2OS cells lacking or overexpressing Lnp . ( A ) Extracts of wild type U2OS cells and a Lnp-deleted U2OS clonal cell line were analyzed by immunoblotting with Lnp and Rtn4 antibodies . Equal amounts of total protein were loaded , and GSK3β was used as a loading control . ( B ) Peripheral ER in a U2OS cell lacking Lnp expressing GFP-calreticulin from the endogenous promoter . The cells were fixed and analyzed for GFP fluorescence and for endogenous Rtn4a , using specific antibodies and fluorescently labeled secondary antibodies . Scale bar = 10 µm . ( C ) Peripheral ER in wild type U2OS cells and cells highly expressing Lnp-mCherry . The ER is visualized by GFP-calreticulin expressed under the endogenous promoter . Scale bar = 2 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 022 When a fusion of wild type Lnp with mCherry ( Lnp-mCherry ) was stably expressed at low levels in CRISPR knock-out cells , the reticular network was restored; peripheral sheets were reduced in favor of tubules and junctions ( Figure 8C; second column ) . Lnp-mCherry localized preferentially to punctae at three-way junctions ( Figure 8C ) . At higher expression levels , Lnp-containing sheets became more prominent ( Figure 8D ) , and at still higher levels , tubular structures with extremely large diameters were formed ( >500 nm; Figure 8D; Figure 8—figure supplement 1C ) . To better understand how Lnp is targeted to three-way junctions , we stably expressed various Lnp mutants at low levels in CRISPR knock-out cells . Lnp consists of an N-terminal myristoylated glycine ( Moriya et al . , 2013 ) , a coiled-coil domain ( CC1 ) , two closely spaced trans-membrane segments ( TM1 and TM2 ) , another coiled-coil domain ( CC2 ) , a predicted unstructured segment , a Zn2+-finger domain implicated Lnp dimerization ( Casey et al . , 2015 ) , and a C-terminal unstructured segment ( Figure 9A ) . A Lnp mutant , in which the N-terminal myristoylation site was abolished ( G2A mutation ) , failed to localize to junctions ( Figure 9B ) . Likewise , when a short , unrelated linker sequence was inserted after CC1 ( Figure 9B ) or before CC2 ( not shown ) , Lnp no longer concentrated in punctae ( Figure 9B ) , suggesting that the two coiled-coil domains need to be in register . Finally , truncation of Lnp before the Zn2+-finger domain also abolished Lnp localization to three-way junctions , whereas a truncation at a more C-terminal site had no effect ( Figure 9B ) . In all cases , localization of Lnp to three-way junctions correlated with the conversion of peripheral sheets into a tubular network , indicating that Lnp exerts its effect on ER morphology at junctions . At high levels , all Lnp mutants generated sheets and aberrant structures ( not shown ) . Taken together , these results show that localization to three-way junctions requires several Lnp features , including the Zn2+-finger implicated in dimerization . Indeed , pull-down experiments with extracts of mammalian cells expressing different Lnp constructs show that the region containing the Zn2+-finger is required for Lnp-Lnp interaction ( Figure 9—figure supplement 1A , B ) . 10 . 7554/eLife . 18605 . 023Figure 9 . Lnp domains important for junction localization . ( A ) Schematic representation of wild type ( WT ) Lnp and mutants tested for proper localization in the peripheral ER . CC1 , CC2 , coiled-coil domains 1 and 2 , respectively; TM1 , TM2 , trans-membrane segments 1 and 2 , respectively; LNPRK , lunapark motif ( single letter code ) ; the segment indicated in red was inserted ( extra linker ) . ( B ) Peripheral ER in U2OS cells expressing GFP-calreticulin under the endogenous promoter in wild type or Lnp-lacking cells ( LnpΔ ) . Where indicated , the cells also stably expressed wild type or mutant Lnp at low levels . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 02310 . 7554/eLife . 18605 . 024Figure 9—figure supplement 1 . Lnp-Lnp interaction is mediated by the Zn2+-finger domain-containing region . ( A ) Schematic representation of Lnp truncation mutants tested for interaction . Hemagglutinin ( HA ) -tagged Lnp 99–428 was used as bait for pull-downs of mCherry-tagged truncations . ( B ) Immunoblots of co-immunoprecipitation experiments . Each of the mCherry-tagged fragments and the HA-tagged bait were expressed separately in 293T cells . Cells were lysed , and the levels of mCherry-tagged truncations were normalized according to fluorescence at 590 nm , except Lnp 235–303 which was poorly expressed . After mixing with lysate containing HA-Lnp 99–428 , pull-downs were performed with HA-antibodies and the samples were analyzed by immunoblotting with HA- and mCherry- antibodies . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 024 To test whether Lnp function is dependent on ATL , we generated stable cell lines that overexpress both proteins as fluorescently tagged constructs . The large sheets observed when Lnp-mCherry was expressed at high level in wild type U2OS cells disappeared when wild type GFP-ATL-3 or GFP-ATL-1 were co-expressed ( Figure 10A; compare second and third with first column ) . The ER network was restored , and the ATL proteins localized as in wild type cells . GFP-ATL-3 was always close to Lnp-mCherry , and was often found at the edges of the Lnp-containing structures , but the two proteins did not co-localize ( Figure 10A; second column ) . Expression of GFP-ATL-1 also caused Lnp-mCherry to redistribute from large to small sheet-like structures ( Figure 10A; third column ) , even though ATL-1 itself localized mostly throughout the tubules . Taken together , these results indicate that ATL affects the localization of Lnp , although the bulk of ATL and Lnp do not seem to interact physically . In agreement with this conclusion , no interaction was seen in pull-down experiments ( not shown ) . Our experiments also suggest that ATL functions upstream of Lnp , a conclusion that is supported by the observation that Lnp overexpression did not significantly affect the localization of the ATLs ( Figure 10A ) , and did not reverse ER fragmentation in cells expressing dominant-negative ATL mutants ( Figure 10—figure supplement 1A ) . 10 . 7554/eLife . 18605 . 025Figure 10 . Interplay of ATL , Lnp , and Rtn . ( A ) Peripheral ER in U2OS cells stably expressing Lnp-mCherry alone or together with GFP-ATL-3 or GFP-ATL-1 . Scale bar = 10 micron . ( B ) Peripheral ER in Lnp-lacking U2OS cells expressing GFP-calreticulin . Where indicated , the cells also stably expressed mCherry-tagged ATLs or Rtn4a . Scale bar = 10 micron . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 02510 . 7554/eLife . 18605 . 026Figure 10—figure supplement 1 . ATL acts upstream of Lnp in U2OS cells . ( A ) Peripheral ER in U2OS cells stably expressing Lnp-mCherry and GFP-ATL-1 K80A ( GTPase defective mutant ) . The second and fourth rows show three time points . Stationary pixels appear white , while those moving appear in colors . The top five panels depict two entire cells , while the bottom five panels show magnifications of the peripheral ER of these cells . Scale bars = 10 µm . ( B ) Peripheral ER in Lnp-lacking U2OS cells expressing GFP-calreticulin . The cells also stably express mCherry-tagged ATL mutants defective in GTPase activity . The bottom row shows three time points . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 026 The stable expression of wild type ATL in Lnp-lacking cells converted peripheral ER sheets into tubules and junctions ( Figure 10B; second and third column ) , whereas dominant-negative ATL mutants fragmented the ER or led to long , unbranched tubules ( Figure 10—figure supplement 1B ) . These are the same phenotypes observed when these constructs were expressed in wild type cells ( Figure 1 ) . Similarly , as in wild type cells , low-level expression of Rtn4a in Lnp knock-out cells resulted in restoration of the tubular network ( Figure 10B; fourth column ) , whereas at higher levels it caused the appearance of long tubules and membrane fragments ( not shown ) . These results show that , at elevated levels , ATL and Rtn do not depend on Lnp function . To further investigate the role of Lnp in shaping the ER , we used the Xenopus extract system . We reasoned that a cytoplasmic fragment of Xenopus Lnp ( cytLnp ) , containing the sequence following TM2 ( Figure 11A ) , might interact with the endogenous protein and serve as a dominant-negative reagent . Indeed , when purified cytLnp was added to a crude extract at the beginning of a network formation reaction , small sheets appeared over time , which replaced three-way tubular junctions ( Figure 11B; the reduction of three-way junctions is quantitated in Figure 11C ) . In addition , the tubules of the network became shorter . The number of three-way junctions was also reduced when cytLnp was added to a network formation reaction performed with membranes and cytosol ( Figure 11D ) or with membranes alone ( Figure 11—figure supplement 1A ) . In these cases , the junctions were converted into bright membrane structures , which look different from those observed in crude extracts ( Figure 11B ) . Whether these structures are sheets therefore remains uncertain . Three-way junctions were also abolished when cytLnp was added after network formation ( Figure 11—figure supplement 1B ) . Affinity-purified antibodies raised against Xenopus Lnp had the same effect ( Figure 11E and Figure 11—figure supplement 1C ) , confirming that Lnp inactivation causes the change in ER morphology . A C-terminal fragment of cytLnp ( cytLnp-C ) had the same effect on the integrity of three-way junctions as cytLnp , whereas two different N-terminal fragments ( cytLnp-N1 and –N2 ) were inactive ( Figure 11F ) . Pull-down experiments confirmed that cytLnp and cytLnp-C , but not cytLnp-N1 or –N2 , interact with endogenous Lnp ( Figure 11—figure supplement 1D , E ) . The Zn2+-finger-containing constructs cytLnp and cytLnp-C likely form inactive oligomers with the endogenous full-length protein . Consistent with this assumption , gel filtration experiments indicated that cytLnp and cytLnp-C form oligomers , in contrast to cytLnp-N1 and cytLnp-N2 , which elute as monomers ( Figure 11—figure supplement 1F ) . Taken together , these results are consistent with those obtained in mammalian cells , and show that Lnp stabilizes three-way tubular junctions and tubules . Lnp-Lnp interactions involving the Zn2+-finger containing domain seem to be required for the function of Lnp . 10 . 7554/eLife . 18605 . 027Figure 11 . Effect of Lnp inactivation on an in vitro generated ER network . ( A ) Schematic representation of wild type and mutant Xenopus Lnp . Phos indicates the domain phosphorylated during mitosis . ( B ) An ER network was generated for 20 min with crude Xenopus egg extract in the presence of the dye DiIC18 and either buffer ( left panel ) or 2 µM of a cytoplasmic fragment of Lnp ( cytLnp ) . Scale bar = 10 µm . ( C ) The number of three-way junctions at different conditions was quantitated . Error bars indicate the mean ± SD of three independent experiments . ( D ) An interphase ER network was formed with Xenopus egg cytosol ( C ) , light membranes ( M ) , an energy regenerating system , in the presence or absence of 5 µM cytLnp . The network was stained with octadecyl rhodamine . Scale bar = 10 µm . ( E ) Buffer or 200 nM of affinity-purified Lnp antibodies were added to a preformed interphase network generated with cytosol , membranes , and an energy regenerating system . Scale bar = 10 µm . ( F ) As in ( D ) , except that 5 µM cytLnp-N1 , cytLnp-N2 or cytLnp-C were added at the beginning of the network formation reaction . Scale bars = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 02710 . 7554/eLife . 18605 . 028Figure 11—figure supplement 1 . Cytoplasmic fragments of Lnp interfere with the function of endogenous Xenopus Lnp . ( A ) Light membranes ( M ) were incubated with an energy regenerating mix and either buffer or 5 µM cytLnp for 15 min . The membranes were stained with octadecyl rhodamine . Scale bar = 5 µm . ( B ) Buffer or 5 µM of cytLnp was added to a preformed network formed from cytosol ( C ) and membranes ( M ) . Scale bar = 5 µm . ( C ) Buffer or 200 nM of affinity-purified Lnp antibody was added to a preformed network formed from membranes only . Scale bar = 5 µm . ( D ) Interaction of endogenous Xenopus egg Lnp with various Lnp fragments . A detergent extract of light membranes was incubated with empty resin or hemagglutinin ( HA ) -tagged Lnp fragments ( see Figure 11A ) . After pull-down with HA antibodies , the bound fractions were analyzed by immunoblotting with antibodies to Xenopus Lnp . ( E ) Interaction of purified Xenopus cytLnp with various cytLnp fragments . HA-tagged Lnp fragments ( see Figure 11A ) was incubated with cytLnp , and subsequently HA antibodies . Bound fractions were analyzed by SDS-PAGE and stained with Coomassie blue . ( F ) Purified cytLnp or fragments of it ( see Figure 11A ) were analyzed by gel filtration on a Superdex 200 column . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 028 The network formed with crude Xenopus extracts after addition of cytLnp looks indistinguishable from that in mitosis , consisting of small sheets connected by multiple , short tubules ( compare Figures 11B and 2G ) . In both cases , the number of three-way junctions was reduced to about the same extent compared with an interphase extract ( Figure 11C ) . Similar results were obtained with a fractionated system consisting of membranes and cytosol; here too , a mitotic network ( Wang et al . , 2013 ) was indistinguishable from an interphase network formed in the presence of cytLnp ( Figure 11D ) . The similarity suggests that Lnp is inactivated during mitosis . We tested whether Lnp is phosphorylated during mitosis , as this may provide a mechanism of inactivation . Lnp is indeed phosphorylated in mitotic ( meiotic ) Xenopus extracts . Endogenous Lnp , which ran as a single band in interphase extracts , was converted into two higher molecular weight species ( Figure 12A ) . The same size shifts were seen with His-tagged cytLnp added to either interphase or mitotic extracts ( Figure 12B ) . Both higher molecular weight species disappeared when cytLnp was pulled down with cobalt beads , indicating that Lnp modification is reversible . Addition of phosphatase inhibitors during pull-down resulted in the disappearance of the higher molecular weight species , but preserved the lower one ( Figure 12C ) . Addition of N-ethylmaleimide preserved both bands . These results indicate that at least the smaller of the two modified species corresponds to mitotically phosphorylated Lnp . It is possible that the larger species carries an additional modification . Mass spectrometry revealed that Xenopus Lnp is mitotically phosphorylated at multiple sites , most of which are located in the unstructured domain , located between CC2 and the Zn2+-finger domain ( Figure 12D ) . It is unclear whether all sites are equally modified in a given Lnp molecule . 10 . 7554/eLife . 18605 . 029Figure 12 . Mitotic phosphorylation of Lnp . ( A ) Xenopus egg interphase cytosol , membranes , and an energy regenerating system were incubated with buffer ( Ci ) or non-degradable cyclin B∆90 ( Cm ) for 40 min . The samples were analyzed by SDS-PAGE and immunoblotting with Xenopus Lnp antibodies ( XlLnp Ab ) . The lower panel shows an immunoblot with MPM2 antibodies that recognize mitotically phosphorylated proteins . ( B ) As in ( A ) , but cytLNP was incubated with cytosol in the absence of membranes . ( C ) Purified HA- and His-tagged cytLnp ( HA-cytLnp ) was incubated with interphase cytosol with or without cyclin B∆90 . An aliquot was analyzed directly by SDS-PAGE ( input ) , while the remainder was incubated with cobalt resin in the presence of various inhibitors ( P-inhib is a phosphatase inhibitor cocktail ( Sigma ) ; OA , okadaic acid; NEM , N-ethylmaleimide ) . Material eluted from the beads with imidazole ( eluate ) was analyzed by SDS-PAGE and immunoblotting with Xenopus Lnp antibodies . Band 1 , unmodified HA-cytLnp; bands 2 and 3 , mitotically modified Lnp . ( D ) Alignment of the cytosolic domains of human and Xenopus LNP sequences . Residues in blue are phosphorylated in interphase , as determined by mass spectrometry . Residues in red are additionally phosphorylated during mitosis . ( E ) U2OS cells were grown in complete medium and left untreated or treated with 100 nM nocodazole overnight . Interphase cells were scraped off , while mitotically arrested cells were collected as non-adherent cells . Equal amounts of total protein were analyzed by SDS-PAGE and immunoblotting with antibodies to human Lnp ( HsLnp ) . Lnpi and Lnpm , interphase and mitotic Lnp , respectively . ( F ) Lnp-mCherry or Lnp-mCherry with Ser and Thr phosphorylation sites mutated to Ala ( S/T ->A ) were stably expressed in U2OS cells . Cells were arrested in mitosis by incubation with 100 nM nocodazole overnight . Where indicated , 100 nM bortezomib was present overnight . Equal amounts of total protein from interphase ( I ) or mitotic ( M ) cells were analyzed by SDS-PAGE and immunoblotting with mCherry antibodies . The arrowhead indicates the position of mitotically phosphorylated Lnp . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 02910 . 7554/eLife . 18605 . 030Figure 12—figure supplement 1 . Mitotic phosphorylation weakens Lnp-Lnp interaction . ( A ) Lnp-Lnp interaction is weakened during mitosis . Purified cytLnp and SBP-tagged cytLnp ( cytLnp-SBP ) were incubated with interphase cytosol in the absence or presence of cyclin B∆90 . The samples were incubated with streptavidin resin , bound material was eluted with biotin , and analyzed by SDS-PAGE and staining with Coomassie blue . The left panels show the input material . ( B ) Purified cytLnp-HA or a phosphomimetic version of it ( cytLnp-E-HA ) were incubated with Sumo-tagged cytLnp ( Sumo-cytLnp ) or a phosphomimetic version of it ( Sumo-cytLnp-E ) . Pull-downs were performed with anti-HA resin and the bound material was analyzed by SDS-PAGE and staining with Coomassie blue . ( C ) Lnp residues that were mutated to aspartate or alanine to generate phosphomimetic ( S/T → D ) or unphosphorylatable ( S/T → A ) mutants are shown in red . ( D ) Peripheral ER in Lnp-lacking U2OS cells ( Lnp-/- ) expressing GFP-calreticulin . Where indicated , the cells also stably express at low level mCherry-tagged wild type Lnp , or phosphomimetic or unphosphorylatable mutants . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18605 . 030 Lnp is also mitotically phosphorylated in mammalian U2OS cells . Immunoblotting showed that endogenous Lnp increases in size and decreases in abundance in cells arrested in mitosis with the microtubule-depolymerizing drug nocodazole ( Figure 12E ) . Like other proteins regulated during the cell cycle in mammalian cells , Lnp seems to be degraded following phosphorylation . Overexpressed Lnp-mCherry also underwent a size shift during mitosis and slightly increased in abundance when its degradation was prevented by addition of the proteasome inhibitor bortezomib ( Figure 12F ) . A Lnp-mCherry mutant in which phosphorylation sites were mutated to Ala did not undergo a size shift in mitosis nor a change in abundance with bortezomib treatment . The mitotic phosphorylation sites in mammalian Lnp were in the same domain as in Xenopus Lnp ( Figure 12D ) . Pull-down experiments showed that the oligomerization of cytLnp is weakened in mitotic Xenopus extracts ( Figure 12—figure supplement 1A ) . A phosphomimetic cytLnp mutant containing several Ser/Thr to Glu mutations ( cytLnp-E ) also had a weakened tendency to oligomerize ( Figure 12—figure supplement 1B ) . These results suggest that mitotic phosphorylation reduces the interaction of Lnp molecules , which in turn leads to the destabilization of three-way junctions and tubules . Consistent with this model , a phosphomimetic mutant of full-length Lnp did not localize to three-way junctions when expressed at low levels in CRISPR knock-out cells , in contrast to a mutant in which the same residues were changed to Ala ( Figure 12—figure supplement 1C , D ) . Thus , mitotic phosphorylation may cause the disappearance of Lnp from three-way junctions; the junctions are then converted into larger sheets . Our results provide insight into how the ER-shaping proteins ATL , Rtn , and Lnp cooperate to generate and maintain a tubular ER network . We show that ATL is not only required to form a network , but also has a hitherto unrecognized role in maintaining it . When ATL is inhibited , three-way tubular junctions disappear and much of the network disassembles into small membrane structures , indicating that physiological concentrations of the curvature-stabilizing proteins Rtn and DP1 are insufficient to maintain the integrity of tubules in the absence of ATL function . Overexpression of Rtn4a leads to long , unbranched tubules and fragmented ER . Both phenotypes are restored when ATL is co-overexpressed . Taken together , our data indicate that a balance between ATL and Rtn function is required . Lnp is not essential for network formation or maintenance; its inactivation or deletion only reduces the abundance of three-way junctions and tubules in favor of larger sheets . Lnp is phosphorylated during mitosis , which weakens its oligomerization and may contribute to the characteristic tubule-to-sheet conversion of the ER during mitosis . Previous experiments have established that ATL mediates the fusion of ER membranes ( Orso et al . , 2009; Bian et al . , 2011 ) . A GTP-hydrolysis- dependent conformational change in ATL trans-dimers pulls the apposing membranes together so that they can fuse . ATL-mediated fusion requires multiple ATL molecules in each membrane ( Liu et al . , 2015 ) , and multiple cycles of GTP hydrolysis ( Liu et al . , 2015; Saini et al . , 2014 ) . It is therefore likely that ATL molecules are concentrated at the site of fusion between two ER tubules . Cis-dimerization in the same membrane ( Liu et al . , 2015 ) and nucleotide-independent interactions through the TM segments ( Liu et al . , 2012 ) may also increase ATL localization at these sites before and after fusion . Eventually , however , ATL would diffuse away from three way junctions and distribute throughout the tubules . This model may explain why mammalian ATL-2 and ATL-3 , as well as Xenopus ATL , preferentially localize to three-way junctions . Mammalian ATL-1 is only marginally concentrated , perhaps because fewer molecules are required for a successful fusion event or because it diffuses away more rapidly from the fused junction . ATL-mediated fusion of tubules is clearly required to form and maintain an ER network . When ATL function is moderately inhibited , long , unbranched tubules are formed , likely because these cannot be linked into a network . When ATL is more strongly inhibited , even tubules are disrupted . A possible explanation for the essential role of ATL in maintaining the integrity of tubules is based on the observation that free ends of tubules are rarely seen in mammalian cells or in networks generated in vitro with Xenopus egg extracts; essentially all tubule ends are anchored either in three-way junctions or are associated with molecular motors or microtubule tips . We therefore propose that tubules are unstable when they have free ends . Free ends have a high membrane curvature at the tip , and the elastic energy would be reduced if they retracted in favor of other tubules that are anchored , or if they shed small vesicles . Shedding of vesicles from a free tubule end would be favored by Rtn , which itself imposes high curvature on the membrane . At physiological concentrations , Rtn would stabilize the high membrane curvature of tubules in cross-section , but it may prefer to sit in a vesicle of the same radius , particularly if it formed a hydrophobic insertion , rather than a scaffold around the bilayer . ATL would be essential for tubule integrity , as it would counteract the vesicle shedding at the tips of tubules by capturing shedding vesicles via its tethering function and then fusing them back onto the tubule . This model is consistent with our observations of early intermediates during network formation in Xenopus extracts . Initially , vesicles seem to fuse into dynamic small membrane structures that continuously emanate and retract short tubules . These tubules have free ends and are unstable , and their dynamic nature may correspond to a situation in which their shortening is reversed by their ATL-mediated fusion with small vesicles . Our interpretation implies that the shedding and fusion of vesicles at tubule ends occur at comparable rates , whereas the breakage of tubules or three-way junctions may be kinetically disfavored . Indeed , live-cell imaging of the ER in mammalian cells or Xenopus egg extracts shows that tubules rarely undergo fission in the middle of the tubule . Instead , retraction of tubules is a frequent event and might involve the shedding of vesicles at tubule tips . In addition to ensuring the integrity of the free ends of tubules , ATL might stabilize the ER network at three way junctions . Because ATL needs multiple cycles of trans-dimerization to achieve fusion , it may tether tubules together at three way junctions that have not yet fused . ATL inactivation would cause the disassembly of a preformed ER network by the untethering of these tubules , leading to the appearance of free , unstable tubule ends . It is unclear whether this model can explain the disassembly of the entire network in the Xenopus system , as it would imply that a high percentage of three-way junctions is actually only tethered , rather than fused . It is also possible that ATL is required to maintain the integrity of fused three-way junctions , for example , by stabilizing the negatively curved edge line bordering the triangular junctional sheet . We also found that a significantly stronger inhibition of ATL is required to disassemble a preformed network than to prevent its de novo formation . Perhaps , trans-dimerization and fusion are more sensitive to ATL inhibition than ATL concentration at three-way junctions . It has been proposed that the yeast ortholog of ATL , Sey1p , is merely a tethering molecule akin to Rab GTPases , and that the actual fusion is mediated by ER SNAREs ( Lee et al . , 2015 ) . However , mutations in Sey1p and ER SNAREs have synthetic effects in S . cerevisiae ( Anwar et al . , 2012 ) , indicating that they function in parallel pathways . Our present results , and those demonstrating that ATL and Sey1p mediate fusion on their own ( Orso et al . , 2009; Bian et al . , 2011; Anwar et al . , 2012 ) , argue that tethering and fusion are just different stages of a reaction carried out by the same protein . The proposed balance between ATL and Rtn function may explain our overexpression results . The overexpression of Rtn generates very narrow tubules , from which the luminal protein calreticulin is displaced . Assuming that ATL is also displaced , the appearance of long , unbranched tubules may simply be explained by their inability to form new three-way junctions or to fuse vesicles to the end of tubules . In addition , Rtn overexpression could lead to its oligomerization , which could contribute to the formation and stabilization of long tubules . Rtn4a overexpression also generates membrane fragments , which appear to be larger than those observed upon ATL inactivation . Perhaps , these fragments originate from the breakage of the narrow tubules . Co-overexpression of ATL with Rtn would rescue both phenotypes , unbranched tubules and fragmentation , as it allows ATL to localize into the tubules to restore three-way junction formation and to fuse membrane fragments together . Our studies were restricted to Rtn4a , and it remains to be seen whether other Rtn isoforms and DP1 behave in a similar manner . Our results also provide some insight into the function of Lnp . This protein is clearly not essential for the generation or maintenance of an ER network . In mammalian cells , some reticular network is retained in its absence , and even in Xenopus extracts , where Lnp inactivation converts most three-way junctions into larger sheets , there is still a network . Many of these sheets are bordered by negatively curved edge lines , suggesting that Lnp is not a major stabilizer of such edges ( Shemesh et al . , 2014 ) . Based on the observation that Lnp forms sheet-like structures when overexpressed in mammalian cells , it seems possible that Lnp localizes to the sheets of tubular junctions . This is consistent with the fact that ATL-3 sits at the edges of expanded junctional sheets in Lnp-overexpressing cells . Because ATL is absolutely required to generate tubular junctions , one may assume that ATL first forms a native junction and sits at its edges , and then Lnp moves to the interior sheet . Perhaps , a Lnp scaffold prevents the reticulons and DP1 from moving into the three-way junction and thus counteracts junction expansion; when Lnp is absent or inactivated , the three-way junction area could increase into a larger sheet . Alternatively , Lnp may somehow promote the curvature-stabilizing function of the reticulons and DP1 , thus helping in tubule formation . The localization of Lnp to three-way junctions requires its oligomerization . One domain that is needed for its localization and mediates dimerization is the Zn2+-finger ( our results and [Casey et al . , 2015] ) . However , the coiled-coil domains are probably also important . These regions immediately flank the TMs in all species and our experiments suggest that their correct registry is required for Lnp’s localization to three-way junctions . The N-terminal myristic acid is also important , as previously reported ( Moriya et al . , 2013 ) , perhaps because it anchors the N-terminus in the membrane . How exactly the different features of Lnp cooperate to localize Lnp to three-way junctions remains to be clarified . Our results confirm that the ER in mammalian cells and Xenopus extracts undergoes a tubule-to-sheet transition during mitosis ( Lu et al . , 2009; Wang et al . , 2013 ) . The ER in mitotic Xenopus extracts looks strikingly similar to that generated by Lnp inactivation , and the absence of Lnp in mammalian cells also causes a tubule-to-sheet conversion . These results suggest that Lnp inactivation may be responsible for , or at least contribute to , the ER morphology changes during mitosis . Consistent with this idea , Lnp is mitotically phosphorylated in Xenopus extracts and mammalian tissue culture cells . In mammalian cells , Lnp is also degraded , as seen with other mitotically phosphorylated proteins . Phosphorylation of Lnp interferes with its oligomerization , and phosphomimetic Lnp mutants no longer localizes to three-way junctions and have a lower propensity to form oligomers . The effect on oligomerization can be explained by the fact that most phosphorylation sites are located in a region between the second coiled-coil and Zn2+ finger domains , both of which may be involved in Lnp-Lnp interactions . Taken together , these results suggest that mitotic phosphorylation reduces Lnp’s oligomerization , which in turn results in its departure from three-way junctions . The absorption of membrane lipids into the junctions would result in their expansion into larger sheets . It is possible that other factors , such as the disruption of the ER – cytoskeleton interaction , or the release of translating ribosomes from the ER , contribute to the transition from tubules to sheets during mitosis . Sheet formation during mitosis might be required to accommodate membrane proteins that prefer low membrane curvature areas and sit during interphase in the relatively flat nuclear envelope . Taken together , our results suggest a simple model for the generation and maintenance of a tubular ER network . ATL is required to both form and maintain a network . It mediates the fusion of tubules by forming GTP hydrolysis-dependent trans-dimers and transiently sits in the newly formed three-way junctions . Lnp subsequently moves into the junctional sheets , a process that may require its oligomerization . The reticulons stabilize high membrane curvature in tubules and may cause vesicle shedding at free tubule ends , a process counteracted by ATL through its tethering and fusion activity . During mitosis , Lnp is phosphorylated , causing it to dissociate into monomers and leave the three-way junctions . This would result in the expansion of three-way junctions into larger sheets . Experiments with reconstituted , purified ER shaping proteins are required to test this simple model and to understand the exact molecular mechanism of network formation . Maltose-binding protein-cyclin B∆90 ( MBP-cyclin B∆90 ) lacking the N-terminal destruction box of cyclin B was a gift from Dr Randy King ( Harvard Medical School , MA ) . Codon-optimized cytATL ( residues 1–462 ) of Xenopus laevis ATL was cloned into pGEX-4T-3 ( GE Healthcare ) as described previously ( Wang et al , 2013 ) . A fusion of cytATL and superfolder GFP was cloned in the pPROEX-HTb vector ( Lifetechnologies ) . A cytosolic fragment of Xenopus lunapark ( cytLnp ) ( 99–441 ) and truncations thereof ( cytLnp-N1 ( 99–238 ) , cytLnp-N2 ( 124–238 ) and cytLnp-C ( 239–441 ) ) were expressed in the pET28b vector as N-terminal His6 fusions with or without a C-terminal HA tag . CytLnp was also cloned into a pET28b vector with or without a C-terminal SBP tag and an N-terminal 3C protease site after the His-tag ( cytLnp-SBP ) , as well as with an N-terminal HA tag after the His-tag and a 3C protease site ( HA-cytLnp ) or with an N-terminal His14 tag and a SUMO protease cleavage site ( Sumo-cytLnp ) . A phosphomimetic cytLnp was generated , in which ten Ser/Thr sites modified only during mitosis ( as determined by tandem mass spectrometry ) were mutated to Glu ( Figure 12D ) . This sequence was cloned into a pET28b vector expressing either a N-terminal His6 and a C-terminal HA tag ( cytLnp-E-HA ) , or an N-terminal His14 tag and a SUMO protease cleavage site ( Sumo-cytLnp-E ) . Point mutations were generated using the QuickChange Site Directed Mutagenesis Kit ( Stratagene ) . For mammalian gene constructs , Lnp , ATL , and Rtn genes were subcloned from plasmids described in previous papers ( Zhu et al . , 2003; Voeltz et al . , 2006; Shemesh et al . , 2014; Rismanchi et al . , 2008 ) . eGFP and mCherry tags were fused to proteins via Gibson or restriction cloning and inserts were ligated into pHAGE2 lentiviral construct using PacI and NotI sites . Point mutations in the respective constructs were generated using the QuickChange Site Directed Mutagenesis Kit ( Stratagene ) or PCR . More extensive mutations ( phosphomimetic , phosphoalanine variants of Lnp ) were created by PCR-splicing of synthesized DNA fragments ( Thermo Fisher ) . CRISPR/Cas9-mediated genome editing was performed using the pX330 vector expressing wild type S . pyogenes Cas9 ( Addgene ) ( Cong et al . , 2013 ) . The guide RNA ( gRNA ) used for Lnp knockout was GTGGATTATTTTCTCGATGG , targeting the start codon . gRNA for insertion of eGFP into the calreticulin gene was AGGAGCAGTTTCTGGACGG , targeting the end of the signal sequence . Endogenous calreticulin was tagged with GFP using a donor plasmid that inserted an eGFP and a tetra-alanine linker immediately after the signal peptide cleavage site of the CALR gene . Homology arms were 650 base pairs long on both sides . All plasmid inserts were confirmed by sequencing . All proteins were expressed in BL21-CodonPlus ( DE3 ) -RIPL ( Agilent ) or BL21 E . coli strains ( NEB ) . CytATL , cytATL ( R232Q ) , and MBP-cyclin B∆90 were purified , as described previously ( Wang et al . , 2013 ) . CytATL-GFP , cytATL ( R232Q ) -GFP and all cytLnp proteins except cytLnp-SBP were isolated using a Ni-NTA resin ( Thermo Scientific ) , followed by anion-exchange chromatograph ( HiTrap Q HP , GE Healthcare ) and gel filtration ( Superdex 200 , GE Healthcare ) . CytLnp-SBP was purified using a Ni-NTA resin , followed by purification on a streptavidin resin ( Thermo Scientific ) and gel filtration ( Superdex 200 , GE Healthcare ) . The proteins were snap-frozen in 20 mM HEPES pH 7 . 5 , 150 mM KCl , 250 mM sucrose , and 1 mM dithiothreitol ( DTT ) . For the gel filtration analysis in Figure 11—figure supplement 1F , 400 µg of purified His6-cytLnp/-N1/-N2/-C in 200 µL were injected onto a Superdex 200 column in buffer containing 20 mM HEPES pH 7 . 5 , 150 mM KCl , and 1 mM DTT . Antibodies to full-length Xenopus ATL and His6-cytLnp were raised in rabbits . For affinity purification of antibodies , cytATL and cytLnp ( 5 mg ) were desalted by gel filtration using phosphate saline buffer ( PBS ) pH 7 . 2 . The protein was crosslinked to 2 mL Affigel-15 resin ( Biorad ) overnight at 4°C . Unbound protein was washed with elution buffer ( 100 mM glycine-HCl pH 2 . 5 , 150 mM NaCl ) , and the column re-equilibrated with PBS buffer pH 7 . 2 . To affinity-purify the antibodies , the resin was incubated with clarified crude serum for 4 hr at 4°C . The antibodies were eluted with 100 mM glycine HCl pH 2 . 5 , 150 mM NaCl and immediately neutralized with 1 M Tris/HCl pH 8 . The buffer was exchanged to 20 mM HEPES pH 7 . 5 , 150 mM KCl , 250 mM sucrose by dialysis , and the sample was snap frozen . ATL antibodies were labeled with Alexa 488-NHS ester ( Invitrogen ) as follows . CytATL-conjugated resin ( 0 . 1 mL ) was incubated with 1 mL of crude clarified rabbit serum for 4 hr at 4°C . The resin was washed with labeling buffer ( 30 mM Hepes/KOH pH 8 . 4 , 150 mM NaCl ) , and then incubated for 1 hr at room temperature with 1 mL labeling buffer containing 100 µg Alexa488-NHS ester . Unreacted dye was removed by extensive washing with labeling buffer , and labeled antibodies were eluted using acidic elution buffer , followed by immediate neutralization with 1 M Tris buffer pH 8 . 0 . The buffer was then exchanged to 20 mM HEPES pH 7 . 5 , 150 mM KCl , 250 mM sucrose using PD-10 columns ( GE Healthcare ) and snap frozen . Metaphase-arrested crude Xenopus laevis egg extracts ( CSF extract ) , interphase cytosol , light membranes , pre-labeled membranes , and de-membranated sperm were prepared as described ( Wang et al . , 2013 ) . ER network formation using crude extracts was performed as described previously ( Wang et al . , 2013 ) . Briefly , a CSF extract was incubated with 100 µg/mL DiIC18 for 45 min at 18°C . An interphase ER network was generated by adding 0 . 5 mM CaCl2 to a fresh extract together with 1/10 of total volume of DiIC18 pre-labeled CSF extract . The samples were incubated for 7 min at 18°C . A mitotic ( meiotic ) ER network was generated by omitting CaCl2 . The samples ( 8 µL ) were placed between two PEG-passivated glass coverslips ( 22 × 22 mm ) ( passivation as previously described [Wang et al . , 2013] ) , mounted on top of a metal slide with a 20 mm diameter hole , and sealed with VALAP ( 1:1:1 mix of vaseline , lanolin and paraffin ) . The mounted sample was incubated for 10 min at 16°C , and then allowed to equilibrate to room temperature for 5 min before imaging on a spinning-disk confocal microscope . ER network formation with cytosol and membranes or membranes alone was performed as described previously ( Wang et al . , 2013 ) . Briefly , cytosol and membranes were mixed at a 20:1 volume ratio , or membranes were mixed with ELB200 ( 50 mM HEPES/KOH , pH 7 . 5 , 200 mM KCl , 2 . 5 mM MgCl2 , and 250 mM sucrose , 1 mM DTT ) at a 1:20 ratio . All reactions contained an energy regenerating system , except for the experiment in Figure 4B , which was performed in the presence of 150 µM GTP . The samples were incubated for 15–30 min at room temperature . Proteins or various GTP analogues were either added at the beginning of the network assembly or to a pre-formed network . An aliquot of the reaction was mixed with octadecyl rhodamine ( Invitrogen ) at 10 µg/mL and applied to a passivated No . 1 . 5 coverslip sandwich sealed with VALAP . When DiIC18-pre-labeled interphase membranes were used , octadecyl rhodamine was omitted . In Figure 4C , the GDP/AlF4– sample was generated by adding 3 . 35 mM MgCl2 , 6 . 7 mM NaF , 0 . 335 mM AlCl3 , and 0 . 67 mM GDP ( final concentrations ) . In Figure 6 , the GDP/BeF3- sample was generated by adding 3 . 35 mM MgCl2 , 5 . 36 mM NaF , 1 . 34 mM BeSO4 , and 0 . 67 mM GDP , or 10 mM MgCl2 , 16 mM NaF , 4 mM BeSO4 , and 2 mM GDP . The reactions were performed in 2 mm diameter open-top chambers of a commercial microfluidics plate ( Cellasic Onix model M04L ) . Sample perfusion was software-controlled using pneumatic pumps ( Cellasic Onix platform , EMD Millipore ) . The wells and conductive micro channels used in the experiment were previously drained of preserving buffer and flushed with Xenopus cytosol supplemented with an energy regeneration system to prevent dilution of the crude extract . The glass-bottom microfluidics plate was mounted on the stage of an inverted spinning-disk confocal microscope and imaged in real-time during the experiment . The 2 mm round chamber was rinsed twice with crude Xenopus egg extract . To assemble an interphase ER network in the chamber , 2 µL of extract pre-labeled with DiIC18 was added , layered with 2 µL of silicone oil to prevent evaporation , and incubated for 15 min at room temperature . Xenopus cytosol supplemented with an energy regeneration mix and 5 µM GFP-cytATL was perfused from well #5 in the plate at 4 psi pump pressure ( calculated flow of 0 . 5 µL/min according to manufacturer specifications ) . Arrival of GFP-cytATL at the ER network-containing chamber was followed by GFP fluorescence and took approximately 7 min . U2OS cells were not authenticated by STR profiling . The cells were treated with plasmocin , but they may not be completely free of mycoplasma contamination . The cells were cultured in DMEM supplemented with 10% fetal bovine serum , 25 mM HEPES and penicillin/streptomycin . All stable cell lines were generated by lentiviral transduction using pHAGE2 vectors using standard methods . Briefly , lentivirus was packaged in 293T cells using Trans-IT ( Mirus ) to co-transfect packaging and lentiviral vectors . Six to twelve hours post-transfection , medium was refreshed , and virus-containing supernatants were collected for 24 hr , spun down , and added to U2OS cells at a 1:5 dilution . No blasticidin selection was used , in order to obtain populations with varying levels of gene expression . For fluorescent-protein-tagged viral constructs , cells were sometimes sorted by flow cytometry to obtain cells with certain levels of expression . For GFP insertions into the genomic CALR locus , cells were transfected with guide RNA encoding plasmid , donor DNA , and wild-type Cas9 . GFP positive cells were selected several days post-transfection using flow cytometry . For LNP deletion , isolated clones were first screened by immunoblotting for absence of Lnp protein and then verified by PCR and sequencing of genomic DNA around the start codon . Primary antibodies used for mammalian cell experiments were: anti-calreticulin from rabbit ( Abcam ) , anti-reticulon 4 from goat ( Santa Cruz ) , anti-Lnp from rabbit ( Sigma ) , HA ( Roche ) , mCherry ( polyclonal made in rabbits ) . Secondary antibodies for immunofluorescence were Alexa-conjugated donkey-antibodies directed against mouse , rabbit , or goat immunoglobulin ( Invitrogen ) . Secondary antibodies for immunoblotting were HRP conjugated anti-goat ( Thermo Fisher ) , aanti-rabbit and anti-mouse immunoglobulin ( GE Healthcare ) . Anti-HA affinity matrix ( Sigma ) was used for pull-down experiments . Nocodazole was purchased from Sigma . Live imaging of mammalian cells was performed inside an Okolab stage top incubator warmed to 37°C . Cells were grown in #1 . 5 glass bottom Mat-Tek dishes and switched to phenol red- free Opti-MEM just prior to imaging . For immunostaining of fixed cells , U2OS were grown on acid-washed glass coverslips and fixed using 2% formaldehyde and 0 . 2% glutaraldehyde in PBS for 15 min , then washed and quenched with 1 mg/mL sodium borohydride in PBS for 5 min . Cells were permeabilized by incubation in PBS with 0 . 1% Triton X-100 for 5 min . Primary and secondary antibodies were incubated with coverslips for 45 min each in blocking solution ( 1% low IgG-containing fetal bovine serum ( FBS ) in PBS ) . Coverslips were mounted with mounting medium ( Vectashield ) on slides . All samples were visualized using a spinning disk confocal head ( CSU-X1; Yokogawa Corporation of America ) with Borealis modification ( Spectral Applied Research ) and a quad bandpass 405/491/561/642 dichroic mirror ( Semrock ) . The confocal was mounted on a Ti inverted microscope ( Nikon ) equipped with a 60× Plan Apo NA 1 . 4 oil immersion objective or a 100x Plan Apo 100x NA 1 . 4 oil immersion objective and the Perfect Focus System for continuous maintenance of focus ( Nikon ) . Green fluorescence images were collected using a 491-nm solid-state laser controlled with an AOTF ( Spectral Applied Research ) and ET525/50 emission filter ( Chroma Technology Corp . ) . Red fluorescence images were collected using a 561-nm solid-state laser controlled with an AOTF ( Spectral Applied Research ) and ET620/60 emission filter ( Chroma Technology Corp . ) . All images were acquired with a cooled CCD camera ( ORCA AG; Hamamatsu Photonics ) controlled with MetaMorph software ( version 7 . 0; Molecular Devices ) and archived using ImageJ ( National Institutes of Health ) and Photoshop CS5 ( Adobe ) . In some cases , linear adjustments were applied to enhance the contrast of images using levels in the image adjustment function of ImageJ and Photoshop . Three-way junctions in interphase ER networks were quantified automatically using a custom ImageJ script described previously ( Shemesh et al . , 2014 ) . Three way junctions in mitotic and cytLnp-treated ER networks were counted manually . Interphase cytosol containing 5 µM of His6-cytLnp and an energy regenerating system was incubated with buffer or 0 . 1 mg/mL MBP-cyclin B∆90 for a 1 hr at room temperature . The reactions were stopped by addition of 1% SDS . After dilution in binding buffer ( 50 mM Tris pH 7 . 5 , 150 mM NaCl , 20 mM imidazole , 250 mM β-glycerophosphate , 30 mM NEM and 0 . 63% TX-100 ) , cobalt resin ( Clontech ) was added to purify His6-cytLnp . After a 30-min incubation at 4°C , the resin was briefly washed with binding buffer three times . His6-cytLnp was eluted in binding buffer with 250 mM imidazole , and subjected to SDS-PAGE . His6-cytLnp bands were excised and phosphorylation sites were identified by mass spectrometry ( MS/MS ) . For the experiment in Figure 12C , similar pull-down experiments were performed as described above , except HA-cytLnp was used and various inhibitors ( 250 mM β-glycerophosphate , 1% phosphatase inhibitor cocktail 3 ( Sigma , P-inhib mix ) , 1 μM okadaic acid ( OA ) or 30 mM NEM ) were included in the pull-down experiment as indicated in the figure . The input material and proteins eluted from the beads were subjected to immunoblot analysis using Xenopus Lnp antibodies . Human lunapark phosphorylation sites were mapped by stably expressing Lnp-SBP in U2OS cells . For an interphase sample , cells were placed in 0 . 2% FBS in DMEM overnight . For a mitotic sample , cells were incubated in 10% FBS in DMEM with 100 nM nocodazole overnight; mitotically arrested cells were collected by blowing off the cells from the plate . Cells were lysed in lysis buffer ( 150 mM NaCl , 20 mM Tris pH 7 . 5 , and protease inhibitors leupeptin , pepstatin and chymostatin ) containing 1% TX-100 . Streptavidin resin ( Thermo Fisher ) was used to pull down Lnp . The resin was washed three times with lysis buffer containing 0 . 1% TX-100 , followed by elution with 2 mM biotin . Eluates were TCA precipitated and run on an SDS gel . Silver stained bands were excised and analyzed by MS/MS . For the experiment in Figure 9—figure supplement 1 , 293T cells were infected with lentivirus to express HA- and mCherry-tagged Lnp truncations . The cells were lysed in lysis buffer ( 150 mM NaCl 20 mM Tris pH 7 . 5 , and protease inhibitors leupeptin , pepstatin and chymostatin ) containing 1% TX-100 . Cell lysates containing mCherry tagged constructs were normalized to each other using a plate reader that measured fluorescence at a wavelength of 590 nm ( Molecular Devices ) . All incubations also contained the same total protein concentration , adjusted by adding lysate from uninfected cells , as well as the same amount of HA-tagged bait protein . The samples were incubated with anti-HA affinity matrix ( Sigma ) for 4 hr at 4°C , and the resin was washed three times in lysis buffer and 0 . 1% TX-100 before elution with SDS sample buffer . Eluates were analyzed by SDS-PAGE followed by immunoblotting . The same membrane was blotted with anti-mCherry antibodies , stripped of signal using azide solution and reprobed with anti-HA antibody . For the experiment in Figure 11—figure supplement 1D , 10 µL of Xenopus membranes and an energy regenerating system were incubated with buffer or 5 µM His6-cytLnp/N1/N2/C-HA for 15 min at room temperature . Binding buffer ( 20 mM HEPES pH 7 . 5 , 50 mM KCl ) was added to a total 200 µL-volume . The membranes were solubilized with 1% Triton X-100 for 30 min and insoluble material was removed by centrifugation at 100 , 000 g for 30 min at 4°C . Ten µL of the supernatant were saved as input material and the rest was incubated with pre-washed HA agarose resin ( Sigma ) for 2 hrs at 4°C . After washing of the resin , bound material was eluted with SDS sample buffer by incubation at 37°C for 10 min . The samples were analyzed by SDS-PAGE and immunoblotting with Xenopus Lnp antibodies . For the pull-down experiment shown in Figure 11—figure supplement 1E , His6-cytLnp was mixed with buffer or His6-cytLnp-N1/N2/C-HA at a 5:1 ratio for 1 hr at 4°C in binding buffer ( 20 mM HEPES pH 7 . 5 , 100 mM KCl , 0 . 05% TX-100 ) . One percent of the reaction was saved as input material and the rest was incubated with pre-washed HA agarose resin for 1 hr at 4°C . After washing of the resin , the bound material was eluted with 200 mM glycine/HCl pH 2 . 5 , 150 mM NaCl , 0 . 05% TX-100 for 10 min at room temperature . The samples were analyzed by SDS-PAGE and Coomassie blue staining . The pull-down experiments in Figure 12—figure supplement 1B were performed in a similar way . The bait was either His6-cytLnp-HA or His6-cytLnp-E-HA and the prey was either Sumo-cytLnp or Sumo-cytLnp-E . For the experiment in Figure 12—figure supplement 1A , interphase cytosol , an energy regenerating system , and His6-cytLnp were incubated with buffer , with buffer and His6-cytLnp-SBP , or with cyclin B∆90 and His6-cytLnp-SBP for 1 hr at room temperature . The ratio of His6-cytLnp to His6-cytLnp-SBP was 3:1 . The volume was adjusted to 200 µL with binding buffer ( 20 mM HEPES pH 7 . 5 , 100 mM KCl , 0 . 05% TX-100 , 1 µM okadaic acid ( Sigma ) , 1% phosphatase inhibitor cocktail 3 ( Sigma ) , 1 mM DTT ) . One percent of the input was directly subjected to immunoblotting with the Xenopus Lnp antibodies . The rest was incubated with 15 µL of pre-washed magnetic streptavidin resin ( Thermo scientific ) for 1 hr at 4°C and washed briefly with binding buffer three times . Bound material was eluted with binding buffer containing 2 mM biotin and analyzed by SDS-PAGE and Coomassie blue staining .	The endoplasmic reticulum is a compartment within the cells of plants , animals and other eukaryotes . This compartment plays a number of roles within cells , for example , serving as the site where many proteins and fat molecules are built . Most often the endoplasmic reticulum exists as a network of thin tubules . However , this shape changes during the lifetime of a single cell , and the endoplasmic reticulum converts into flattened structures known as sheets when the cell divides . Three classes of proteins are known to affect the shape of the endoplasmic reticulum . Proteins called reticulons ( called Rtns for short ) stabilize the highly curved membranes that make up the thin tubules , while proteins called atlastins ( ATLs ) fuse these tubules together to form the interconnected network . However , the exact role of the third protein – called lunapark ( Lnp ) – is unknown . Moreover , it is not clear how these three proteins work together to coordinate their individual activity to shape the endoplasmic reticulum . Now , Wang , Tukachinsky , Romano et al . have used mammalian cells grown in the laboratory and extracts from the eggs of the frog Xenopus laevis to study these three proteins in more details . Unexpectedly , the experiments showed that ATL’s activity was not only required to form a tubular network but also to maintain it . When ATL was inactivated , the network disassembled into small spheres called vesicles . Increasing the amount of Rtn within the endoplasmic reticulum also caused it to disassemble , but increasing the amount of ATL could reverse this fragmentation . Thus , maintaining the tubular network requires a balance between the activities of the ATL and Rtn proteins , with ATL appearing to tether and fuse tubules that are stabilized by the Rtns . Wang et al . also found that the tubular network of the endoplasmic reticulum can form without Lnp , but fewer tubules and junctions are formed . These findings suggest that Lnp might act to stabilize the junctions between tubules . Further experiments showed that Lnp is modified by the addition of phosphate groups before the cell begins to divide . Wang et al . propose that this modification switches Lnp off and helps the endoplasmic reticulum to convert into sheets . Further work is now needed to investigate exactly how Rtn , ATL , and Lnp shape the endoplasmic reticulum . These future experiments will likely have to use simpler systems , in which the purified proteins are incorporated into artificial membranes .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology" ]	2016	Cooperation of the ER-shaping proteins atlastin, lunapark, and reticulons to generate a tubular membrane network
The influence of biomechanics on the tempo and mode of morphological evolution is unresolved , yet is fundamental to organismal diversification . Across multiple four-bar linkage systems in animals , we discovered that rapid morphological evolution ( tempo ) is associated with mechanical sensitivity ( strong correlation between a mechanical system’s output and one or more of its components ) . Mechanical sensitivity is explained by size: the smallest link ( s ) are disproportionately affected by length changes and most strongly influence mechanical output . Rate of evolutionary change ( tempo ) is greatest in the smallest links and trait shifts across phylogeny ( mode ) occur exclusively via the influential , small links . Our findings illuminate the paradigms of many-to-one mapping , mechanical sensitivity , and constraints: tempo and mode are dominated by strong correlations that exemplify mechanical sensitivity , even in linkage systems known for exhibiting many-to-one mapping . Amidst myriad influences , mechanical sensitivity imparts distinct , predictable footprints on morphological diversity . The uneven tempo of phenotypic evolution is a universal feature of biological systems , from proteins to whole-organism traits ( Simpson , 1944; Gingerich , 2009; Zhang and Yang , 2015 ) . Intrinsic and extrinsic mechanisms affecting rates of evolution have been probed extensively ( Wake et al . , 1983; Gillooly et al . , 2005; Eberhard , 2010; Zhang and Yang , 2015 ) . However , biomechanics – the intersection of mechanics and biology – is a key axis influencing phenotypic evolution ( Arnold , 1992 ) that has been less often examined , and infrequently through the use of quantitative and comparative datasets ( Holzman et al . , 2012; Wainwright et al . , 2012; Collar et al . , 2014; Muñoz et al . , 2017 ) . Because rates of morphological divergence and speciation are often coupled ( Rabosky and Adams , 2012 ) , connecting biomechanics to morphological evolution enriches our understanding of the processes shaping diversification . An enduring paradox in evolutionary biomechanics and functional morphology is whether strong correlations among traits ( sometimes termed constraints ) enhance or limit evolutionary diversification ( Gould , 1989; Antonovics and van Tienderen , 1991; Schwenk , 1994 ) . For example , strong morphological correlations could be predicted to reduce the rate or amount of morphological evolution , because even slight changes could compromise the system’s proper functioning ( Raup and Gould , 1974 ) . Conversely , a strong correlation could enhance evolutionary change by providing a morphological pathway for adaptation ( Holzman et al . , 2012; Muñoz et al . , 2017 ) . A weak association could enhance the freedom to vary ( Collar et al . , 2014; Schaefer and Lauder , 1996 ) , or , conversely , weaken , and thereby reduce , the pathways to morphological change ( Alfaro et al . , 2005; Collar and Wainwright , 2006 ) . Therefore , depending on the context , strong and weak correlations have been construed to enhance or restrict evolutionary diversification . A comparative approach can empirically resolve this conundrum , for example , by comparing rates and phylogenetic patterns of morphological evolution in similar , independently-evolved systems . Here , we leverage the multiple independent evolutionary origins of four-bar linkage systems ( Figure 1 ) to test how morphological and mechanical correlations impact two key aspects of morphological evolution: tempo ( rate at which morphological disparity accumulates ) and mode ( evolutionary pattern of trait shifts across phylogeny ) . Four-bar linkages are closed-chain systems comprised of four rigid links that rotate to transmit motion and force ( Anker , 1974; Muller , 1996; Westneat , 1990; Martins , 1994 ) ( Figure 1A ) . Mechanical output of four-bar linkages is often measured in terms of kinematic transmission ( KT ) , a simple metric that can quantify a tradeoff between displacement and force across different linkage configurations ( see Materials and methods section for further information about KT ) . Four-bar linkages are widespread in nature , and enable a rich diversity of behaviors in vertebrates and invertebrates ( Westneat , 1990; Wainwright et al . , 2005; Patek et al . , 2007; Olsen and Westneat , 2016 ) . For example , four-bar linkages actuate mouth opening for suction feeding in fishes ( Westneat , 1990; Martins , 1994; Muller , 1987 ) , flexion and extension of the vertebrate knee ( Hobson and Torfason , 1974 ) , rapid strikes of the stomatopod raptorial appendage ( Patek et al . , 2004; Patek et al . , 2007; McHenry et al . , 2012; McHenry et al . , 2016 ) , and skull kinesis in birds ( Hoese and Westneat , 1996 ) . Four-bar linkages , like any system built of three or more parts , exhibit many-to-one mapping , meaning that similar mechanical outputs ( e . g . KT ) can be produced through different combinations of morphology ( e . g . link lengths; Wainwright et al . , 2005; Wainwright , 2007 ) . Four-bar linkages also exhibit mechanical sensitivity , which occurs when KT is disproportionately sensitive to variation in some links and relatively insensitive to variation in others ( Anderson and Patek , 2015; Muñoz et al . , 2017 ) . Therefore , four-bar linkages exhibit ( 1 ) both weak and strong correlations among parts and outputs and ( 2 ) multiple evolutionary origins across the Metazoa . As such , four-bar linkages are a fertile testing ground for comparative analyses of evolutionary biomechanics and morphology . To our knowledge , the connection between mechanical sensitivity and morphological evolution has only been studied in the four-bar linkage system of the mantis shrimp ( Stomatopoda ) raptorial appendage ( Muñoz et al . , 2017; Anderson and Patek , 2015 ) ( Figure 1 ) . The mantis shrimp four-bar system is used for feeding , fighting , and substrate manipulation via extremely rapid strikes of their raptorial appendages ( Patek et al . , 2004;Patek et al . , 2007; Patek et al . , 2013; Patek and Caldwell , 2005; McHenry et al . , 2016; deVries et al . , 2012; Green and Patek , 2015; Green and Patek , 2018; Crane et al . , 2018 ) . Spearing stomatopods that harpoon mobile , soft-bodied prey have higher KT ( greater displacement ) than smashing mantis shrimp that bludgeon hard-shelled prey using linkages with lower KT ( greater force ) ( Anderson et al . , 2014; McHenry et al . , 2016 ) . In mantis shrimp , mechanical sensitivity is associated with accelerated evolution: the link most tightly correlated with KT exhibits the fastest rate of evolution ( Muñoz et al . , 2017 ) . Tantalizing patterns often occur in individual clades or mechanical systems , yet few are robust to tests in multiple lineages . Therefore , previous findings in mantis shrimp leave uncertain whether the enhanced evolutionary rate associated with mechanical sensitivity is a system-specific finding or instead occurs across an array of taxa and reflects a more general pattern . Furthermore , the initial studies in mantis shrimp were analyses of evolutionary tempo , but the phylogenetic pattern of these rate changes ( mode ) was not analyzed . Analysis of mode can resolve whether phylogenetic shifts to a higher or lower KT are exclusively accompanied by shifts in link ( s ) to which KT is most sensitive , or occur through different morphological pathways that are not necessarily tied to mechanical sensitivity . Here we examine rates of morphological evolution ( tempo ) in four particularly well-studied systems ( Figure 1 ) : ( 1 ) oral four-bar linkages of 101 species of wrasses ( Family: Labridae ) and ( 2 ) oral four-bar linkages of 30 species of cichlids ( Family: Cichlidae ) , ( 3 ) opercular four-bar systems in 19 species of sunfish ( Family: Centrarchidae ) and ( 4 ) the previously-published dataset in mantis shrimp that was analyzed using the same methods as in this study . In fish , the oral four-bar system actuates the upper jaw and the opercular four-bar actuates the lower jaw ( Figure 1 ) ; together they open a large space in the mouth that creates negative pressure to suction prey ( Westneat , 1990; Martins , 1994 ) . Similarly to mantis shrimp ( Anderson et al . , 2014; Anderson and Patek , 2015 ) , the mechanical tradeoffs between displacement and force represented by KT appear to generally track fish trophic ecology ( Wainwright et al . , 2004; Hulsey and Garcia De Leon , 2005 ) . For example , fish that pursue elusive prey tend to have oral four-bar linkages with higher KT ( resulting in greater displacement for snagging rapid prey ) , whereas those that scrape algae tend to have lower KT ( resulting in greater force , such as for dislodging sessile , encrusted food items from hard surfaces ) ( Hulsey and Garcia De Leon , 2005 ) . Finally , we examine the phylogenetic pattern ( mode ) of shifts in KT and links across the especially well-sampled oral four-bar system in wrasses , to test how KT and link lengths change across the phylogeny and whether these changes occur concordantly as predicted by mechanical sensitivity . We estimated the Brownian motion evolutionary rate parameter , σ2 ( bounded by its 95% confidence interval ) , which represented the net rate of phenotypic change over time ( Felsenstein , 1985; Martins , 1994; O'Meara et al . , 2006 ) for the three mobile links – input , output , and coupler – of each four-bar system ( See Materials and ethods ) . A single consistent result emerged from our analysis of evolutionary rates: stronger correlations between link lengths and kinematic transmission ( KT ) were associated with faster rates of morphological evolution . In each system , we found that mechanical sensitivity was always associated with a faster rate of link evolution ( Figure 2; Supplementary file 1 ) . Evolutionary rate can be artificially inflated by greater trait variance ( O'Meara et al . , 2006; Adams , 2013 ) ; we incorporated intraspecific measurement error into our rate estimates and confirmed that a higher evolutionary rate was not driven by greater variance ( Supplementary file 2 ) . Even though rates of morphological evolution consistently tracked mechanical sensitivity , the particular links associated with mechanical sensitivity differed across the four-bar systems ( Supplementary files 3–6 [rotatable 3D phylomorphospace plots]; Table 1 ) . For example , in the cichlid oral four-bar system , mechanical output was positively correlated with input link length ( PGLS r2=0 . 62 , p<10−6 ) , inversely correlated with the coupler link length ( r2=0 . 27 , p=0 . 002 ) , and exhibited no relationship with the output link length ( Supplementary file 3; Table 1 ) . By contrast , in three systems - the wrasse oral four-bar , the sunfish opercular four-bar , and the stomatopod raptorial four-bar - the output link length was a strong predictor of mechanical output ( PGLS r2>0 . 66 , p<10−11 ) , whereas the coupler link only weakly predicted mechanical output ( PGLS r2<0 . 14 ) ( Supplementary files 4–6 ) . The oral four-bars of cichlids and wrasses share a common evolutionary origin ( Alfaro et al . , 2004 ) ; nonetheless , rate differences were predicted by mechanical sensitivity rather than shared ancestry . Hence , analogous four-bar systems do not result in common patterns of mechanical sensitivity , whereas mechanical sensitivity is consistently a strong predictor of evolutionary rate differences . We next examined the phylogenetic pattern of trait shifts in KT and each mobile link in the wrasse oral four-bar system . We applied a Bayesian framework in the program bayou ( Uyeda and Harmon , 2014 ) to reconstruct phylogenetic shifts in the Ornstein-Uhlenbeck ( OU ) optimal trait parameter ( θ ) for morphological components and mechanical output . The OU model of evolution is characterized by the presence of an adaptive peak , with the peak representing the optimal value for a given trait . Thus , θ reflects the evolutionary optimal trait value as inferred from an OU-process on the phylogeny . To be clear for interdisciplinary readers , θ is not a metric for calculating a biomechanically optimal trait for a certain mechanical function . By estimating θ across the wrasse phylogeny , we pinpointed the nodes associated with strongly supported shifts to higher or lower values in KT and link size . We performed this analysis only on the largest and most species-rich dataset ( wrasses: >100 species sampled ) , because evolutionary inferences are unstable with fewer than 50 taxa ( Uyeda and Harmon , 2014 ) . We detected three well-supported evolutionary shifts in KT ( posterior probability [pp] range 0 . 65–0 . 99; Supplementary file 7 ) ( Figure 3 ) . For each of these shifts in mechanical output , we also detected strongly supported shifts in the output and input links , but never the coupler link ( Figure 3; Figure 3—figure supplement 1 ) . Therefore , the three shifts in KT occur through three different morphological pathways , but only via the changes in the links to which KT is most mechanically sensitive . The evolutionary shifts to higher mechanical output ( increased KT ) occur twice – once in razorfishes ( pp = 0 . 67 ) and once in the branch leading to the Creole wrasse , Clepticus parrae ( pp = 0 . 99 ) . In razorfishes , the transition to higher KT is accompanied by a shift to a smaller output link ( pp = 0 . 88 ) , and in Creole wrasse , by both an increase in input link length ( pp = 0 . 97 ) and a reduction in output link length ( pp = 0 . 77 ) . A transition to lower KT in the Anampses clade is accompanied by a reduction in input link length ( pp = 0 . 95 ) , with no concomitant shifts in output link length . Amidst the morphological , behavioral and ecological diversity of four-bar linkages , in every system we tested , greater mechanical sensitivity is associated with faster morphological evolution . The connection between mechanical sensitivity and evolutionary rate is therefore robust to independent origins and distinct behavioral functions , suggesting a generalizable phenomenon in four-bar linkage systems . These findings address a longstanding conundrum of constraints in evolution ( discussed in Gould , 1989; Antonovics and van Tienderen , 1991; Schwenk , 1994 ) – specifically , whether strong correlations among traits should enhance or reduce evolutionary change – by demonstrating that strong correlations between components and mechanical output accelerate evolutionary change ( Figure 4 ) . Our results further reveal how many-to-one mapping and mechanical sensitivity enable multiple configurations while simultaneously biasing those configurations to a subset of traits . Both absolute and relative link sizes influence evolutionary rates , and the structural geometry of four-bar linkages is central to understanding these findings ( Muller , 1996 ) . In terms of absolute size , the same length change applied to small and large links is proportionally larger for the small link , such that KT is most influenced by the change to the small link . Therefore , changes to the smallest links induce disproportionately large changes in the system’s geometry and , therefore , the transmission of force and motion ( Anderson and Patek , 2015; Hu et al . , 2017 ) . In terms of relative size , greater disparity among link sizes in a four-bar system results in greater evolutionary rate disparity . These effects of size are especially apparent in the stomatopod raptorial four-bar linkage and sunfish opercular four-bar linkage . In both of these systems , the output link is approximately an order of magnitude smaller than the input and coupler links , and it also exhibits an order of magnitude faster evolution ( Supplementary file 1 ) . In contrast , relative link lengths vary less dramatically in the oral four-bar system of cichlids and wrasses , and the corresponding evolutionary rate shifts are statistically weaker , although still exhibiting two- to four-fold rate differences ( Figure 2; Supplementary file 1 ) . Biological sizes , whether of genomes , cells , or the organisms themselves , help sculpt macroevolutionary dynamics by influencing patterns of trait evolution or shaping deeper-scale patterns of lineage diversification ( Hanken and Wake , 1993; Uyeda et al . , 2017 ) . Here , link size plays a central role in determining the physical basis for mechanical sensitivity and evolutionary rate disparity , indicating that size-scaling relationships in biomechanics can mediate evolutionary dynamics . Although we connect mechanical sensitivity to a relatively faster rate of evolution , an interesting next step is to disentangle whether such traits evolve more quickly because of strong directional selection on small links or whether the other , relatively larger traits of the system evolve more slowly due to stabilizing selection ( Arnold , 1983; Arnold , 1992 ) . Statistically comparing these two possibilities requires especially broad sampling: in the growing age of big data in digital morphology and phylogenetics , this task is rapidly becoming feasible ( Davies et al . , 2017 ) . Our analysis of the phylogenetic pattern ( mode ) of trait evolution exemplifies the integration of mechanical sensitivity and many-to-one mapping . An implicit assumption of many-to-one mapping is freedom of evolution: theoretically , any alternative configurations yielding a similar mechanical output should be equally likely to evolve ( Wainwright et al . , 2005; Wainwright , 2007 ) . Our findings in wrasses , however , demonstrate that mechanical sensitivity biases evolutionary transitions to traits with the greatest influence on mechanical output ( input and output links ) . For each of the three major KT shifts in wrasses , we detected three distinct morphological pathways involving either the input link , output link , or both ( but never the coupler link ) . Correspondingly , both the output link and input link are strong predictors of KT in the wrasse oral four-bar , and the coupler is a weak predictor of KT . Among these links , there is some redundancy , as evidenced by the various morphological pathways accompanying transitions in KT . Therefore , mechanical sensitivity restricts the freedom of evolution central to many-to-one mapping by biasing evolutionary transitions to traits with the strongest effect on mechanical output . When considered in the broader context of ecology and behavioral function , our findings raise as many questions as they answer . For example , if the shortest link is the output link , an animal achieves greater output rotation of the system than they input . Mantis shrimp use their shortest link ( output link ) to dramatically amplify an approximately nine-degree rotation of the input link; this enables the notoriously fast angular velocity of their predatory appendages ( Patek et al . , 2004 , Patek et al . , 2007; Cox et al . , 2014; McHenry et al . , 2016 ) . In the centrarchids , the input rotation is controlled by a muscle too small to generate a large gape: instead they use linkage geometry to amplify gape ( Durie and Turingan , 2004; Camp and Brainerd , 2015; Camp et al . , 2015 ) . In both cases , the location of the smallest link as the output link is directly related to the behavioral and ecological function of the linkage mechanism . Do small links participate in fewer or more mechanical functions than the larger links ? Does selection favor the evolution of small linkages as pathways for strong mechanical sensitivity ? Are macroevolutionary shifts in linkage geometry concordant with changes in diversification rate ? In the case of the oral four-bar of teleosts , the maxilla ( output link ) is correlated with jaw protrusion , a key ecological aspect of fish diversification ( Hulsey and Garcia De Leon , 2005 ) . As such , the most mechanically influential links may play a crucial role in shaping diversification during speciation and adaptive radiation . Perhaps the most imperative message of these findings is the necessity for multiple levels of analysis ( Jablonski , 2017a;Jablonski , 2017b ) . Simply demonstrating many-to-one mapping in biomechanical systems is likely to miss a rich suite of evolutionary and mechanical dynamics that shape diversification ( Figure 4 ) . By considering the tempo and mode of evolution , generalizable interactions between mechanics and diversification are likely to emerge ( Scales and Butler , 2016; Blanke et al . , 2017 ) . Evolutionary studies of many-to-one mapping have flourished in recent years , with particular focus on how natural selection and developmental constraints impact the evolution of complex mechanical systems in the wild ( e . g . Martinez and Sparks , 2017; Moody et al . , 2017; Thompson et al . , 2017 ) . Explicit consideration of mechanical sensitivity can enhance these studies by providing a general blueprint of the mechanical and evolutionary expectations for phenotypic diversification . Deciphering the abiotic and biotic factors impacting the tempo and mode of phenotypic diversification is a topic of perennial interest and debate ( Simpson , 1944; Mayr , 1963; Gould , 1977; Gould , 2002; Schluter , 2000; West-Eberhard , 2003; Arnold , 2014 and references therein ) . Our findings reveal that mechanical sensitivity impacts macroevolutionary dynamics and point toward general rules connecting biomechanics and morphological diversification ( Figure 4 ) . Mechanical sensitivity is consistently associated with increased rates of evolutionary change , and the biomechanical impacts of link length provide a proximate explanation for the association between four-bar mechanics and the tempo of evolutionary change . Whereas many-to-one mapping in four-bar linkages theoretically supplies multiple morphological pathways for mechanical variation ( Alfaro et al . , 2004; Alfaro et al . , 2005; Wainwright , 2007 ) , mechanical sensitivity influences the tempo and mode of how these pathways evolve . This conclusion is relevant beyond mechanical systems . Many-to-one mapping is widespread in biomechanics ( Wainwright et al . , 2005 ) and can be applied even more broadly to assess various hierarchical phenomena in biology , such as molecular and cellular processes , including epistatic genotype-phenotype interactions ( Phillips , 2008 ) , physiological adaptations ( Scott , 2011; Cheviron et al . , 2014 ) , and organism-level phenomena , including neurophysiological processes governing behavior ( Katahira et al . , 2013; York and Fernald , 2017 ) . Extending this framework within and beyond biomechanics may yield a predictive understanding of the tempo and mode of evolutionary diversification . In the teleost oral four-bar system , the lower jaw ( coronoid process - mandible-quadrate joint ) functions as the input link and its rotation relative to the fixed link ( suspensorium - neurocranium ) causes outward rotation in the maxilla ( output link ) , resulting in premaxillary protrusion ( Westneat , 1990; Martins , 1994 ) . The teleost opercular four-bar linkage system facilitates feeding through lower jaw movement . In this case , the operculum ( input link ) rotates relative to the suspensorium ( fixed link ) . This rotation transfers through the interopercle bone and the interopercle-mandible ligament ( coupler link ) , and then to the retroarticular process of the mandible ( output link ) . Rotation of the output link actuates jaw depression , which also contributes to the motion of the oral four-bar system that facilitates feeding via protrusion of the premaxilla ( Westneat , 1990; Martins , 1994 ) . During a mantis shrimp strike , the input link ( meral-V ) rotates distally , relative to the rest of the merus ( fixed link ) ( Patek et al . , 2007 ) . This distal rotation releases the stored elastic energy , and pushes the carpus segment ( output link ) , causing it to rotate and actuate the swinging arm . The coupler of the system is an extensor muscle that remains contracted during the strike . Multispecies data from four-bar linkage systems were gathered from previous studies of the raptorial four-bar of mantis shrimp ( Order: Stomatopoda ) , the oral four-bar of wrasses ( Family: Labridae ) and cichlids ( Family: Cichlidae ) , and the opercular four-bar of sunfish ( Family: Centrarchidae ) . The wrasse morphological and biomechanical dataset ( Alfaro et al . , 2004 ) is comprised of 101 species from 32 genera that we pruned down from 122 species using a recent time-calibrated phylogeny ( Baliga and Law , 2016 ) . Topology , branch lengths , and divergence times were estimated in a Bayesian framework using a relaxed clock model approach , with both mitochondrial and nuclear genes , and six fossils providing calibration points . The cichlid morphological and biomechanical dataset ( Hulsey and Garcia De Leon , 2005 ) is comprised of 30 species from 13 genera , which we pruned down from the 97 species represented in a recent time-calibrated mitochondrial phylogeny ( Hulsey et al . , 2010 ) . The sunfish dataset ( Hu et al . , 2017 ) consists of morphological and biomechanical data for 19 species from eight genera . The time-calibrated centrarchid phylogeny was estimated using both mitochondrial and nuclear DNA and six fossil calibration points ( Near et al . , 2005 ) . The stomatopod morphological and biomechanical dataset ( Anderson and Patek , 2015 ) consisted of 36 species from six superfamilies . We used a time-calibrated phylogeny ( Porter et al . , 2010 ) , which we pruned from 49 species to the 36 analyzed in this study . The tree was constructed using both mitochondrial and nuclear genes , and fossil data defined the calibration points . Phylogenies and tables with raw data are provided in Supplementary files 8–15 . Kinematic transmission ( KT ) is the most widely used and readily available metric for characterizing the mechanical output of four-bar linkages ( Olsen and Westneat , 2016 ) . KT is calculated as the ratio of angular output motion relative to angular input motion ( Hulsey and Wainwright , 2002 ) . All else being equal , KT reflects a tradeoff between displacement and force: a higher KT yields greater displacement through more output rotation relative to input rotation , whereas lower KT yields greater force that occurs at the expense of displacement ( Westneat , 1994 ) . A strength of dimensionless metrics like KT is that they allow comparison across diverse groups of organisms . Furthermore , KT is calculated from the angles of rotation during motion and not from the linkages themselves ( unlike , for example , mechanical advantage ) , such that examining the relationship between mechanical output and morphology is not autocorrelative . Though widely applied and useful in various contexts , there are limitations to the robustness of KT as a mechanical metric . Recent work demonstrates that non-planar motion occurs in some biological linkage systems and that those systems should be studied as three-dimensional mechanisms ( Olsen and Westneat , 2016; Olsen et al . , 2017 ) . The effect of non-planar movement on calculations of KT varies among systems and is unlikely to be a major source of error in the systems examined here ( Patek et al . , 2007; McHenry et al . , 2012 , McHenry et al . , 2016; Anderson et al . , 2014 ) . Another issue is that KT is dynamic , meaning that its magnitude changes during the rotation of the input link ( Patek et al . , 2007 ) . In two of the fish systems examined ( oral four-bar in wrasses and cichlids ) , KT was measured statically , specifically with the input link fixed at a starting angle of 30° in the lower-jaw rotation . This angle was chosen because it is biologically relevant for fish feeding ( Westneat , 1990 ) , and because starting angle was less important than link length for determining KT ( Wainwright et al . , 2005 ) . In the sunfish and the mantis shrimp , KT was calculated in a dynamic fashion , by measuring the minimum value of KT over the course of its full rotation ( Anderson and Patek , 2015; Hu et al . , 2017 ) . To assess the effect of static versus dynamic methods for measuring KT , we re-analyzed the previously-collected four-bar linkage data for sunfish and mantis shrimp and calculated static KT measurements . Minimum KT measured previously on sunfish and mantis shrimp was determined by calculating instantaneous KT at every 0 . 1° of input rotation and using the minimum value found over the entire course of four-bar rotation ( Anderson and Patek , 2015; Hu et al . , 2017 ) . To convert this dynamic measure to a static KT comparable to those in the wrasse and cichlid datasets , we averaged instantaneous KT over a specified overall input link rotation . For mantis shrimp , we chose an overall input rotation of 9° , which was reported as an average rotation of the meral-V ( Patek et al . , 2007 ) and we used minimum KT over this range for subsequent analyses . In sunfish , the overall rotation was set at 5° . We chose this angle because it included the majority of the four-bar rotation , including the point at which minimum KT was found in all but one species ( Micropterus coosae ) . We found that patterns of mechanical sensitivity were not impacted by the use of dynamic or static measures of KT ( Supplementary file 16 ) . To estimate mechanical sensitivity in each four-bar linkage system , we measured the relationship between KT and morphology ( link length ) . To account for differences in scale , we log-transformed all traits prior to analyses ( Gingerich , 2009; O'Meara et al . , 2006; Ackerly , 2009; Adams , 2013 ) . Relatedness is assumed to result in similar residuals from least-squares regressions , indicating non-independence of data points ( Felsenstein , 1985 ) . However , the extent to which phylogeny impacts the covariance structure of the residuals can vary substantially ( Revell , 2010 ) . To account for this , we employed a phylogenetic generalized least squares ( PGLS ) analysis , in which the maximum likelihood estimate of phylogenetic signal ( λ ) in the residual error is simultaneously estimated with the regression parameters . This method outperforms other approaches ( including non-phylogenetic approaches ) under a wide range of conditions ( Revell , 2010 ) . Regressions were performed using the pgls function in the R package caper with KT as the response variable and size-corrected linkages as the predictor variables ( Orme et al . , 2012; R Core Development Team , 2014 ) . Following established methods in fish ( Westneat , 1990; Westneat , 1994 ) and mantis shrimp ( Anderson et al . , 2014; Anderson and Patek , 2015; Muñoz et al . , 2017 ) , size-independent linkage measurements were calculated by dividing the output , input , and coupler links by the length of the fixed link . Nonetheless , estimates of mechanical sensitivity were robust to alternative size corrections ( Supplementary file 17 ) . To visualize variation in mechanical sensitivity among links , we created rotatable 3D phylomorphospace plots for each system ( phylomorphospace3d function , phytools package , Revell , 2010; interactive HTML plots: rglwidget function in the rgl package ( https://r-forge . r-project . org/projects/rgl/ ) . We estimated and compared the Brownian Motion ( BM ) rate parameter ( σ2 ) for the output , input , and coupler links using a likelihood ratio test . Specifically , we compared the likelihood of a model in which σ2 varied among traits to one in which the rates were constrained to be equal ( Adams , 2013 ) . Because all traits are linear and were log-transformed , differences in evolutionary rates represent the amount of relative change in proportion to the mean and can be statistically compared ( O'Meara et al . , 2006; Ackerly , 2009; Adams , 2013 ) . We bounded our estimates of σ2 using a 95% confidence interval , which we derived from the standard errors of evolutionary rate . We obtained standard errors from the square root diagonals of the inverse Hessian matrix , using code provided by D . Adams . We then fitted a model in which rates of link evolution were constrained to be equal ( null hypothesis: σ2input = σ2output = σ2coupler ) and a model in which evolutionary rates were free to vary among links ( σ2input ≠ σ2output ≠ σ2coupler ) , and compared the models using likelihood ratio tests ( Adams , 2013 ) . We performed the same rate comparisons on every pairwise combination of mobile links . To connect evolutionary rate differences to mechanical sensitivity , we calculated the correlation between KT and link length for each system using phylogenetic generalized least squares regression ( PGLS ) . Higher variance in a trait can artificially inflate its estimated evolutionary rate ( Ives et al . , 2007; Adams , 2013 ) . In both mantis shrimp and sunfish , the output link had more variance than the input and coupler links , which could have impacted its accelerated evolutionary rate relative to the other traits . Thus , we repeated the evolutionary rate comparisons while explicitly incorporating within-species measurement error using the ms . err option with the compare . rates function ( Adams , 2013 ) ( Supplementary file 2 ) . To test whether phylogenetic transitions in mechanical output are associated with the mechanical sensitivity of the system , we statistically assessed the number and location of strongly supported ( i . e . high posterior probability ) evolutionary shifts in all traits . We employed a reversible-jump Bayesian approach to test the fit of an Ornstein-Uhlenbeck ( OU ) model with one or more shifts in the trait optimum parameter ( θ ) using the bayou . mcmc function in the R package bayou v . 1 . 0 . 1 ( Hansen , 1997; Butler and King , 2004; Uyeda and Harmon , 2014 ) . The inference of evolutionary trait shifts is problematic when fewer than 50 species are included ( Uyeda and Harmon , 2014 ) ; hence we only performed this analysis on wrasses ( 101 species ) . The method employs Markov chain Monte Carlo ( MCMC ) to sample the number , magnitudes , and locations of evolutionary shifts in the trait optimum on the time-calibrated phylogeny . Priors were defined such that they allowed a maximum of one shift per branch and equal probability among branches . For each trait , we performed two replicate analyses of two million MCMC generations each; for every analysis , we discarded the first 30% as burn-in . We assessed run convergence using Gelman’s R-statistic ( Gelman and Rubin , 1992 ) and visual comparison of likelihood traces . We estimated the location ( branch number and position along branch ) and the posterior probabilities of shifts for each trait .	Imagine going for a swim on a shallow reef . You might see mantis shrimp striking at fish or snails , and reef fish gulping down smaller fish and plankton . Despite how different these movements are , rapid mantis shrimp strikes and fish suction are guided by the same mechanics: four-bar linkages . These shared mechanical systems evolved independently , much like the wings of birds and butterflies . Certain researchers study how organisms evolve based on biomechanics , the field of science that applies principles from mechanics to study biological systems . Four-bar linkages , which are widespread in nature , consist of a loop made of four bars ( or links ) connected by four joints . The system allows a wide range of motions , and it is found anywhere from oil pumpjacks to the inside of the human knee . Researchers are interested in how similar mechanical systems like four-bar linkages influence the diversification of distantly related organisms , such as fish and crustaceans . Changes in an element of a four-bar linkage can have widely different consequences because of a phenomenon known as mechanical sensitivity . Modifications of highly mechanically sensitive parts will have a dramatic effect on the system , while alterations in other areas have little or no effect . Whether the most mechanically sensitive parts evolve faster or slower than the less sensitive elements is still up for debate . Changes in the sensitive elements could be severely constrained because these modifications may compromise the survival of the organisms . However , they could also help species adapt quickly to new environments . So far , researchers have found that in the four-bars linkage of the mantis shrimp , the most mechanically sensitive parts evolve the fastest . Yet , it was unclear whether this would also apply to other species . Here , Muñoz et al . compared four-bar linkages in three families of fish and in mantis shrimp , and discovered that the most mechanically sensitive elements are the smallest links . These can undergo changes in length that have a strong impact on how the linkage works . In addition , evolutionary analyses showed that the most mechanically sensitive parts do indeed evolve the fastest in both mantis shrimp and fish . More work is now required to see if this pattern holds across various organisms , and if it can be considered as a general principle that drives evolution .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "ecology" ]	2018	Strong biomechanical relationships bias the tempo and mode of morphological evolution
Growth factor signaling is essential for pattern formation , growth , differentiation , and maintenance of stem cell pluripotency . Nodal-related signaling factors are required for axis formation and germ layer specification from sea urchins to mammals . Maternal transcripts of the zebrafish Nodal factor , Squint ( Sqt ) , are localized to future embryonic dorsal . The mechanisms by which maternal sqt/nodal RNA is localized and regulated have been unclear . Here , we show that maternal control of Nodal signaling via the conserved Y box-binding protein 1 ( Ybx1 ) is essential . We identified Ybx1 via a proteomic screen . Ybx1 recognizes the 3’ untranslated region ( UTR ) of sqt RNA and prevents premature translation and Sqt/Nodal signaling . Maternal-effect mutations in zebrafish ybx1 lead to deregulated Nodal signaling , gastrulation failure , and embryonic lethality . Implanted Nodal-coated beads phenocopy ybx1 mutant defects . Thus , Ybx1 prevents ectopic Nodal activity , revealing a new paradigm in the regulation of Nodal signaling , which is likely to be conserved . Nodal factors are secreted signaling proteins of the transforming growth factor-β family , with essential functions in axis formation and germ layer specification during embryonic development in sea urchins , snails , ascidians , frogs , fish , and mammals ( Jones et al . , 1995; Collignon et al . , 1996; Erter et al . , 1998; Feldman et al . , 1998; Rebagliati et al . , 1998; Sampath et al . , 1998; Hudson and Yasuo , 2005; Shen , 2007; Constam , 2009; Grande and Patel , 2009; Duboc et al . , 2010 ) . Nodal signaling has also been shown to be important for maintaining human ES cell pluripotency ( James et al . , 2005; Vallier et al . , 2005 ) . Misregulated Nodal signaling has been found associated with tumor metastases ( Topczewska et al . , 2006 ) . Therefore , understanding the mechanisms that regulate Nodal signaling is crucial . Nodal signaling is regulated by transcription factors such as DRAP1 , FoxH1 and Oct4 ( Sirotkin et al . , 2000; Cao et al . , 2008 ) . Signal transduction occurs by binding of Nodal ligands to the receptor complex , and activation of downstream Smad effectors ( Shen , 2007; Schier , 2009 ) . Feedback regulation of Nodal signaling is mediated by the Lefty antagonists ( Cheng et al . , 2000; Meno et al . , 2001; Branford and Yost , 2002; Feldman et al . , 2002 ) . Work in Xenopus , zebrafish , and humans showed that Nodal signaling is regulated by miRNAs , but the precise mechanism is unknown ( Schier , 2009; Luo et al . , 2012 ) . Nodal signaling is also influenced by secretion , endocytosis , lysosomal degradation , post-translational modifications , and processing of the ligands ( Zhang et al . , 2004; Shen , 2007; Tian et al . , 2008; Constam , 2009 ) . Spatially restricted translation of exogenous xCR1 reporters in frogs has been suggested ( Zhang et al . , 2009 ) . But so far , a direct role for translational control in regulation of Nodal signaling has not been uncovered . We showed previously that maternal RNA encoding the zebrafish Nodal factor , Squint ( Sqt ) , is localized to two cells by the 4-cell stage , and predicts embryonic dorsal ( Gore et al . , 2005 ) . RNA localization is an important mechanism that generates asymmetry in cells and organisms . For example , bicoid RNA localization in Drosophila oocytes and embryos is required for specification of anterior cell fates , and localization of maternal pem-1 and macho-1 RNAs determines the posterior end of ascidian embryos ( Nishida and Sawada , 2001; Sardet et al . , 2003; St Johnston and Nüsslein-Volhard , 1992 ) . Mechanisms to ensure correct transport of the RNA and inhibition of translation until the RNA reaches its destination are essential for this process ( Johnstone and Lasko , 2001; Martin and Ephrussi , 2009 ) . In addition , translational control is an important step for regulation of some RNAs . For instance , a proportion of maternal nanos RNA is uniformly distributed in the cytoplasm of Drosophila embryos but is not translated , and Nanos protein is only synthesized from localized nanos RNA at the posterior pole ( Gavis and Lehmann , 1994; Smibert et al . , 1996; Bergsten and Gavis , 1999; Crucs et al . , 2000 ) . In zebrafish embryos , transport of maternal sqt/nodal RNA to future dorsal is dependent on the microtubule cytoskeleton ( Gore et al . , 2005 ) . However , how maternal sqt RNA is regulated until it reaches future dorsal was not known . To understand global regulation of sqt/nodal we carried out a screen for sqt 3′UTR-binding proteins , and show here , that the conserved Y box-binding protein 1 ( Ybx1 ) binds the 3′ untranslated region ( UTR ) in sqt RNA . Genetic analysis of ybx1 mutants shows that maternal Ybx1 function is essential for embryonic development . Loss of Ybx1 function causes mis-localization of sqt RNA and precocious Sqt protein translation , leading to premature and uncontrolled Nodal signaling , and embryonic lethality . Thus , maternal Ybx1 is required for translational control of Nodal signaling . Since the 3′UTR of mammalian nodal RNAs can localize in fish embryos , it is likely that this control mechanism of translational repression is conserved . Our results identify a new mode of regulation of Nodal signaling , and highlight the role of maternal factors in regulation of growth factor signaling and cell-type specification in vertebrates . The dorsal localization element ( DLE ) of sqt RNA resides in the first 50 nucleotides of the sqt 3′UTR , and encompasses sequence and structural elements ( Gilligan et al . , 2011 ) . To identify the proteins that specifically recognize the DLE , 100-nucleotide long radioactive probes spanning the sqt 3′UTR were used for RNA gel-shift assays with zebrafish whole embryo extracts ( Figure 1A , B ) . We observed a number of binding activities in gel-shift assays with sqt probes ( Figure 1B ) . The DLE-containing sqt1 probe was bound by an activity , which we named sqt-RNA Binding Factor 1 ( SRBF1; arrow in Figure 1B ) . Competition gel-shift assays with control gfp , vg1 and cyclops RNA show that SRBF1 preferentially binds the sqt DLE ( Figure 1C , D ) . RNA-cross-linking assays show that SRBF1 is approximately 48–50 kDa ( Figure 1—figure supplement 1 ) . To precisely map the SRBF1 binding site , a 10-nucleotide sqt1 deletion series was tested for binding . Whereas deletions in the coding sequence did not affect SRBF1 binding , deletions 1–4 ( Δ1–Δ4 , Figure 1C , E ) abolish , or significantly reduce binding to the sqt1 probe . The SRBF1 binding site overlaps with sequences required for dorsal localization of sqt RNA ( i . e . , Δ1 and Δ2; [Gilligan et al . , 2011] , and Figure 1C , E ) . Thus , SRBF1 is the activity that binds to the sqt DLE . 10 . 7554/eLife . 00683 . 003Figure 1 . SRBF1 binds the sqt Dorsal Localization Element ( DLE ) . ( A ) Schematic of overlapping 100 nucleotide radioactive RNA gel-shift probes spanning the sqt 3′UTR . Position of DLE is highlighted in magenta . ( B ) Autoradiogram showing sqt 3′UTR probes incubated with embryo extract . Several binding activities were observed on the various probes . The ‘sqt RNA Binding Factor 1’ ( SRBF1; black arrow ) shift , is detected on the DLE-containing sqt1 probe , and not on other probes . ( C ) Schematic showing the SRBF1 binding site . sqt DLE is highlighted in magenta and the red octagon indicates the stop codon . ( D ) Competition gel-shift assay shows that SRBF1 binds specifically to sqt RNA . The sqt 3′UTR with 50 nucleotides of coding sequence competes more strongly than control gfp , vg1 or cyclops ( cyc ) RNA for binding to sqt1 probe . Triangles represent 4-fold increases ( from 5 ng to 80 ng ) of cold competitor RNA . Thus , SRBF1 preferentially binds DLE-sequences . ( E ) The SRBF1 binding site overlaps the DLE . RNA gel-shifts were performed with the sqt1 10 nt deletion series . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 00310 . 7554/eLife . 00683 . 004Figure 1—figure supplement 1 . SRBF1 is an ∼50 kDa protein . ( A ) RNA cross-linking autoradiogram showing that an ∼50 kDa UV cross-linking activity present in fraction#63 is similar to total embryo extracts . The ∼50 kDa peptide was excised from a native PAGE gel , and identified by mass spectrometry . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 004 To identify SRBF1 , we purified it by column chromatography , and screened individual fractions by gel mobility-shift and RNA cross-linking assays ( Figure 2A ) . A 48 kDa factor that co-fractionated with SRBF1 activity ( Figure 2B , C ) was identified by mass spectrometry to be the conserved nucleic acid binding protein , Y box-binding protein 1 ( Ybx1 ) . Ybx1 contains a ‘cold shock’ domain ( CSD; Figure 2—figure supplement 1A , B ) , similar to bacterial cold shock protein CspA ( Eliseeva et al . , 2012 ) . Ybx1 homologs are associated with localized RNAs in Drosophila , Ciona , and Xenopus , and mutations in mouse Ybx1 cause lethality ( Bouvet et al . , 1995; Wilhelm et al . , 2000; Tanaka et al . , 2004; Eliseeva et al . , 2012 ) . Ybx1 contains a conserved actin binding domain ( ABD ) , also found in Drosophila Ypsilon schachtel ( Yps ) ( Figure 2—figure supplement 1A ) . The dimerization domain ( DD ) and non-canonical nuclear localization signal ( NLS ) are conserved amongst the vertebrate Ybx1 proteins ( Figure 2—figure supplement 1A ) ( Eliseeva et al . , 2012 ) . 10 . 7554/eLife . 00683 . 005Figure 2 . SRBF1 contains the nucleic acid binding protein Ybx1 . ( A ) Extracts from 5000 embryos collected at 20 mpf were sequentially fractionated on multiple chromatography columns , until SRBF1 was partially pure . At each stage , fractions containing SRBF1 activity were pooled , and loaded onto the next column for further purification . ( B ) A representative native PAGE gel showing SRBF1 purification . Gel-shift analysis of fractions from the heparin and phenyl sepharose columns show SRBF1 activity in fractions 32–37 from heparin and fractions 62–63 from the phenyl sepharose columns . Fractions 33–35 were pooled and added to the hydrophobic columns . Fraction 63 from the phenyl sepharose column contains partially purified SRBF1 . ( C ) A Coomassie-blue stained SDS-PAGE gel of the fractions in B show a ∼48 kDa band that co-fractionates with SRBF1 ( black arrowhead in fraction#63 ) . The 48 kDa band from fraction#63 was excised , sequenced by mass spectrometry , and found to contain Ybx1 peptides . ( D ) Gel-shift analysis shows recombinant Ybx1 ( rYbx1 ) , similar to embryonic SRBF1 , binds to sqt1 , but not to control gapdh or antisense sqt1 probes . ( E ) Ybx1 binds sqt RNA in vivo . RT-PCR shows sqt RNA but not control gapdh or wnt8a RNA in RNA-IP with αYbx1 antibodies . Control IgG antibodies do not show any RT-PCR product . RT-PCR from whole embryo lysates is the positive control . PCR product sizes are indicated on the left . ( F ) Schematic diagram showing domain structure of wild-type and mutant Ybx1 proteins . The position of amino acid substitutions is indicated by arrows ( V83 in red and all other residues in black ) . Deletions are indicated by dashed lines . The actin binding domain ( ABD ) , single stranded DNA-binding domain ( ssDBD; magenta ) , RNA-binding domains 1 and 2 ( RNP1 , 2; hashed black lines ) , Cold shock domain ( CSD , blue ) , dimerization domain ( DD; orange ) , and nuclear localization sequence ( NLS ) are shown; numbers indicate the amino acid residue . ( G ) Domain analysis of Ybx1 . The nucleic acid binding domain ( ssDBD , magenta bar in F; CSD , blue bar in F; RNP1 , 2 , hashed lines in F ) is required for binding to sqt1 , as is the dimerization domain ( DD , orange bar in F ) . In contrast , the C-terminus of Ybx1 ( Δ143–310 and Δ233–310 ) is dispensable for binding to sqt1 . A western blot with α-His tag antibodies shows expression of the mutant Ybx1 proteins . ( H ) Point mutations in Ybx1 identify key amino acid residues that confer sqt RNA binding . K44 , F54 , and H67 are expected to contact RNA based upon NMR structure prediction . F54A abolishes binding , whereas H67Q does not affect binding at the protein concentrations shown . V83F abolishes sqt1 binding , whereas V94I binds sqt1 . Western blot with α-His tag antibodies shows expression of mutant Ybx1 proteins . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 00510 . 7554/eLife . 00683 . 006Figure 2—figure supplement 1 . Alignment of Ybx1 sequences shows conservation across species . ( A ) Alignment of Ybx1 sequences indicating the actin binding domain ( ABD ) , single stranded DNA-binding domain ( ssDBD ) , cold shock domain ( CSD ) , dimerization domain ( DD ) , and nuclear localization sequence ( NLS ) . Species names and Genbank Accession numbers are as follows: Homo sapiens , AAI06046 . 1; Mus musculus , AAH61634 . 1; Gallus gallus , NM_204414 . 1; Danio rerio , AAI68507 . 1; Xenopus laevis , AAH41191 . 1; Drosophila melanogaster , NM_079309 . 3 . ( B ) Alignment of cold shock proteins from bacterial species with eukaryotic CSD-containing proteins . The K44 , F54 , and H67 highlighted residues were identified by NMR to contact RNA . The V83 and V94 residues that were mutated by ENU ( identified by TILLING ) are also highlighted . Species name and GenBank Accession numbers for bacterial proteins are Pseudomonas putida , ADR61621 . 1; Mycobacterium tuberculosis , CCE39069 . 1; Salmonella enterica , CAA72682 . 1; Bacillus licheniformis , AAU39879 . 1DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 00610 . 7554/eLife . 00683 . 007Figure 2—figure supplement 2 . Ybx1 specifically binds to the sqt 3′UTR . ( A ) Gel-shift assay showing that recombinant Ybx1 binds to sqt1 probe similar to embryo extracts , whereas recombinant Lin28A does not bind to sqt1 . The rLin28A lane is from a different part of the same gel ( demarcated by a dotted line ) . ( B ) Specificity of Ybx1 binding to sqt1 . Gel-shift , showing that recombinant Ybx1 ( black arrowhead ) competes with native Ybx1 ( arrow ) for binding to the sqt1 probe . Triangles indicate fivefold increments of E . coli lysate or rYbx1 . ( C ) Ybx1 binds sqt1 but not wnt8a , vg1 or gapdh probes . RNA gel-shift assay shows that only sqt1 probe shows a mobility shift with rYbx1 ( black arrow ) whereas probes spanning the wnt8 3′UTR ( wnt8a 1-4 ) or vg1 3′UTR ( vg1 1-3 ) do not show mobility shift with rYbx1 . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 007 Ybx1 is an abundant RNA-binding protein , with many functions . Therefore , to confirm if Ybx1 is SRBF1 , ybx1 cDNA sequences were cloned , and recombinant Ybx1 ( rYbx1 ) was tested for sqt DLE-binding in gel-shift assays . Embryonic SRBF1 and rYbx1 bind to sense sqt1 RNA , but not to control gapdh , or to antisense sqt1 probes ( Figure 2D ) . Control Escherichia coli lysate did not bind to sqt1 or gapdh probes , and recombinant zebrafish Lin28A , that also contains a cold shock domain ( Moss et al . , 1997 ) , did not bind the sqt DLE ( Figure 2D and Figure 2—figure supplement 2A ) . Semi-quantitative competition gel-shift assays show that embryonic SRBF1 and rYbx1 bind to sqt DLE sequences with the same specificity ( Figure 2—figure supplement 2B ) . However , rYbx1 does not bind to the UTRs of other localized RNAs , such as vg1 and wnt8a ( Figure 2—figure supplement 2C ) . To determine if Ybx1 forms protein–RNA complexes in vivo with sqt RNA , we performed RNA-immunoprecipitation ( RNA-IP ) with embryo lysates , and RT-PCR to detect sqt . RNA-IP with anti-Ybx1 antibodies shows a sqt product , whereas control ( RT- ) and RNA-IP using IgG antibodies do not show any product ( Figure 2E ) . Under the same conditions , gapdh and wnt8a RNA are not detected . Therefore , Ybx1 specifically binds to sqt RNA in early embryos , and has all the characteristics of SRBF1 . To identify the regions of Ybx1 that bind the sqt DLE sequence and confer specificity to the interactions , we made a series of deletions that removed each of the various domains ( single-stranded DNA-binding domain , ssDBD; RNA-binding domain 1 , 2 , RNP1 , 2; cold shock domain , CSD; dimerization domain , DD; actin binding domain , ABD; nuclear localization signal , NLS ) individually , and one that removes the entire C-terminal half of the protein ( Figure 2F ) . We also made point mutations affecting three amino acid positions ( K44Q , F54A and H67Q ) that are ( 1 ) conserved between bacterial cold shock proteins and Ybx1 , ( 2 ) shown to be required for RNA binding in bacterial cold shock proteins ( Schröder et al . , 1995; Manival et al . , 2001 ) , and ( 3 ) suggested by NMR of human Ybx1 to be in contact with RNA ( Kloks et al . , 2002 ) ( Figure 2—figure supplement 1B ) . We find that the C-terminal half of the protein , the ABD , and the NLS are dispensable for sqt RNA binding ( Figure 2F , G , Figure 2—figure supplement 1A and data not shown ) . By contrast , mutations within or overlapping the CSD and RNP domains abolish RNA binding ( Figure 2F–H ) . Mutations in the DD also affect binding to RNA . The K44Q , H67Q and V94I mutant proteins were still able to bind the DLE-containing probe at the concentrations shown , whereas the F54A and V83F mutations completely abolished sqt RNA binding ( Figure 2F , H ) . These results show that while Ybx1 binds the sqt DLE mainly via its cold-shock domain , other regions such as the DD , RNP and ssDBD are also required for binding to sqt RNA . Expression of ybx1 RNA is ubiquitous , at all embryonic stages , and western blots show maternal and zygotic Ybx1 protein expression ( Figure 3—figure supplement 1 ) . To obtain mutants affecting ybx1 , we generated zinc finger nuclease ( ZFN ) deletions and screened ENU-induced mutations by TILLING . The ybx1sg8 ZFN allele results in truncated Ybx1 protein lacking the C-terminus ( Ybx1Δ197–310; Figure 3A ) . By TILLING , ybx1V83F ( referred henceforth as ybx1sa42 ) and ybx1V94I were identified ( Figures 2F and 3A ) . RNA gel-shift assays show that recombinant Ybx1sa42 ( rYbx1V83F ) has no detectable DLE-binding , whereas recombinant Ybx1sg8 protein ( rYbx1Δ197–310 ) , which has the RNA-binding CSD , binds the sqt DLE ( Figures 2H and 3B ) . Therefore , ybx1sa42 affects the RNA-binding CSD of Ybx1 , whereas ybx1sg8 likely encodes a truncated Ybx1 peptide . 10 . 7554/eLife . 00683 . 008Figure 3 . Maternal Ybx1 is essential for early development . ( A ) Schematic of Ybx1 showing the various domains , the V83F mutation in the CSD in ybx1sa42 , and deletion of residues 197–310 in ybx1sg8 mutants . Black box in Ybx1sg8 indicates frameshift after residue 197 and premature stop after residue 205 . ( B ) rYbx1V83F lacks detectable DLE-binding activity similar to vector control , whereas rYbx1 and rYbx1Δ197–310 peptides , and embryo lysates show binding to sqt1 probes . Western blots to detect 6xHis epitope tags show expression of recombinant Ybx1 proteins . ( C ) Mybx1sa42 embryo extracts show no detectable binding to sqt1 probe compared to control extracts . ( D ) Schematic representation of the ybx1 genomic locus ( blue ) with positions of viral 2a peptide ( magenta bar ) and gfp ( green box ) indicated . Red triangles indicate Ds transposon terminal repeats . ( E ) DIC photomicrographs showing 16-cell , 64-cell , 1000-cell and 50% epiboly stage embryos . Mybx1sa42 embryos are viable at 28 . 5°C . Mybx1sg8 embryos cleave aberrantly after 16-cells ( open arrowhead ) . Mybx1sa42 embryos at 23°C , and Mybx1sg8 embryos fail to initiate gastrulation , form syncytia ( black arrowheads ) , and arrest . Zygotic Ybx1-GFP expression from PTg does not rescue gastrula arrest in Mybx1 , whereas maternal Ybx1-GFP expression from MTg leads to normal gastrulation . Scale bar , 100 μm . ( F ) Histogram showing rescue of gastrulation and survival till prim5 stage of Mybx1 mutants at 23°C by two independent MTg lines ( MTg #4 and MTg #6 ) . Some embryos with zygotic expression of ybx1 ( PTg ) from both lines can initiate gastrulation , but none survive to prim5 . Error bars show standard deviation from three experiments . Number of embryos is shown on top of the histogram . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 00810 . 7554/eLife . 00683 . 009Figure 3—figure supplement 1 . Expression of ybx1 RNA and Ybx1 protein in wild-type embryos . ( A ) RT-PCR shows that ybx1 RNA is expressed at all stages examined . RT- ( − ) and actin PCRs were used as controls . ( B ) In situ hybridizations show that ybx1 RNA is not spatially restricted in embryos . Control sense probes show no expression . Scale bar , 100 μm . ( C ) Western blots to detect Ybx1 in embryo lysates show expression at all stages examined . Tubulin protein was detected as a control . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 009 Homozygous ybx1sa42 and ybx1sg8 mutant embryos are viable and fertile at 28 . 5°C , the normal ambient temperature for zebrafish . Extracts of embryos from homozygous ybx1sa42 females ( Mybx1sa42 ) lack detectable gel-shift activity with sqt1 probes ( Figure 3C ) . Mybx1sa42 mutant embryos develop normally at 28 . 5°C . However , at 23°C , by early blastula stages , marginal cells in Mybx1sa42 mutants lose cell membranes and a large syncytial layer forms over the yolk ( black arrowheads in Figure 3E ) . Mybx1sa42 embryos fail to initiate gastrulation , arrest and do not survive , whereas control embryos from homozygous males ( Pybx1 ) survive and develop normally . Mybx1sg8 mutant embryos divide normally till 16-cells , but subsequent cleavages are aberrant , the embryos fail to develop normally , and arrest ( Figure 3E ) . Thus , maternal Ybx1 is required for embryonic development . Injection of ybx1 mRNA into embryos did not rescue Mybx1 mutants ( data not shown ) . Therefore , we generated stable ybx1-2a-gfp transgenic lines harboring genomic ybx1 sequences fused with viral 2a peptide and GFP sequences ( Figure 3D , E ) . Zygotic expression of Ybx1-2a-GFP from paternally inherited Tg ( ybx1-2a-gfp ) transgenes ( PTg ) did not rescue Mybx1 mutant embryos ( n = 345 ) , although a few embryos initiate gastrulation ( Figure 3E , F ) . By contrast , maternal expression of Ybx1-2a-GFP ( MTg ) from two independent transgenic insertions rescued Mybx1 mutants ( Figure 3E , F ) . MTg expression allowed mutant embryos to undergo gastrulation and survive ( Figure 3F; n > 200 embryos for each line ) . Thus , maternal expression of Ybx1 is essential for gastrulation and normal development . Since Ybx1 was identified as a sqt DLE-binding protein , we examined sqt RNA localization in mutant embryos . In Mybx1sa42 embryos at 28 . 5°C , there is a lag in sqt RNA transport at the 1-cell stage , but sqt localization is comparable to wild-type and control embryos by 4-cells ( Figure 4A ) ( Gore and Sampath , 2002; Gore et al . , 2005 ) . At 23°C , sqt RNA localization in Mybx1sa42 embryos is aberrant at 1-cell and 4-cell stages , forming aggregates in the yolk , that fail to localize to future dorsal cells ( Figure 4A ) . Transport of sqt RNA is also disrupted in Mybx1sg8 embryos ( Figure 4—figure supplement 1A ) . To determine if Ybx1 functions generally in localization of maternal RNAs , we examined if other maternal transcripts were localized correctly in Mybx1 mutants . Localization of cortical ( vasa , eomesa ) , axial streamer ( snail1a , cyclin B1 ) , and vegetal ( wnt8a , grip2 ) RNAs is unaffected in Mybx1sa42 mutants ( Figure 4A and Figure 4—figure supplement 1A , B ) . Therefore , Ybx1 does not generally affect all RNA distribution , and amongst the maternal RNAs examined , only sqt localization is disrupted in early Mybx1 embryos . Maternal Ybx1-2a-GFP expression rescues sqt localization in mutant embryos in contrast to PTg expression ( Figure 4A ) . Thus , consistent with Ybx1 binding to the sqt DLE , maternal Ybx1 function is required for sqt RNA localization . 10 . 7554/eLife . 00683 . 010Figure 4 . Ybx1 is required for localization and regulated processing of sqt RNA . ( A ) Control embryos at 23°C show sqt RNA localization at the 1-cell and 4-cell stage . sqt RNA transport is delayed in 1-cell Mybx1sa42 embryos at 28 . 5°C , but localizes correctly by the 4-cell stage . At 23°C , sqt RNA largely remains in the yolk even at 4-cell stage and sqt RNA that reaches the blastoderm is mis-localized . Localization of sqt RNA is restored in Mybx1 by ybx1-2a-gfp MTg , but not by PTg . Localization of wnt8a RNA is normal in Mybx1sa42 mutants at 28 . 5°C and 23°C . Scale bar , 100 μm . ( B ) Q-RT-PCRs to detect total sqt RNA levels show a mild reduction in Mybx1 compared to controls . Error bars show standard deviation from three experiments . ( C ) RT-PCR to detect sqt in control and Mybx1 mutants at 1-cell , 4-cell and 16-cell stages . Products are indicated on the right , and sizes on the left . Polyadenylated sqt RNA is detected from 16-cells in controls , and at 1-cell in Mybx1 mutants . Splicing of sqt intron 1 and sqt intron 2 occurs earlier in Mybx1 embryos compared to controls . PCR to detect actin is shown as control . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 01010 . 7554/eLife . 00683 . 011Figure 4—figure supplement 1 . Localization of sqt RNA is affected in Mybx1 embryos , whereas vasa , eomesa , snail1a , grip2 and cyclin B1 are unaffected . ( A ) Localization of sqt RNA is disrupted in Mybx1sg8 embryos at the 1-cell and 4-cell stage . Localization of wnt8a and vasa is generally unaffected in Mybx1sg8 ( black arrowheads and black arrow ) . In a very small number of Mybx1sg8 embryos at the 4-cell stage , some wnt8a RNA is observed in the yolk ( open arrowhead ) , reflecting a slight lag in transport . Scale bar , 100 μm . ( B ) Localization of maternal vasa , eomesa , snail1a , grip2 and cycin B1 transcripts is unaffected in Mybx1sa42 embryos at 28 . 5°C and 23°C . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 011 Ybx1 can function as a transcriptional , post-transcriptional , or translational regulator ( Eliseeva et al . , 2012 ) . To determine whether these processes are affected in mutant embryos , we examined sqt RNA expression ( Figure 4B , C ) . Quantitative Real-Time PCR shows that sqt RNA levels are marginally reduced in Mybx1 mutants compared to controls ( Figure 4B ) . In wild-type embryos maternal sqt RNA is non-polyadenylated until the 16-cell stage ( Lim et al . , 2012 ) , but in Mybx1 embryos , polyA-sqt is detected even at the 1-cell stage , indicating precocious poly-adenylation ( Figure 4C ) . We observed un-spliced sqt RNA in control eggs and embryos ( Gore et al . , 2007; Lim et al . , 2012 and Figure 4C ) , in contrast to a previous report ( Bennett et al . , 2007 ) . RT-PCRs to detect sqt intron 1 and intron 2 show that splicing of both introns is accelerated in Mybx1 embryos compared to controls ( Figure 4C ) . These results show that regulated processing of sqt pre-mRNA requires maternal Ybx1 function . To determine if Sqt protein translation is affected in mutant embryos , RNA encoding Sqt-GFP fusion protein was injected , and GFP expression was examined at various stages . Remarkably , Sqt-GFP is observed in 16-cell-stage Mybx1 embryos , whereas in controls , Sqt-GFP is only detected in blastulae ( Figure 5A ) , consistent with the requirement for the Nodal receptors and co-receptor , Oep , from late blastula stages for signaling ( Gritsman et al . , 2000; Hagos and Dougan , 2007 ) . Furthermore , Sqt-GFP levels are elevated in Mybx1 embryos compared to controls ( Figure 5B ) . Control GFP and Wnt8a-GFP expression is similar in mutant and control embryos , indicating that translation of other proteins is not affected ( Figure 5—figure supplement 1A , B ) . Sqt protein expression is premature in Mybx1 embryos . Therefore , maternal Ybx1 is required to repress Sqt translation in early embryos . 10 . 7554/eLife . 00683 . 012Figure 5 . Ybx1 interacts with the translation initiation machinery and represses translation of sqt RNA . ( A ) Western blot to detect GFP shows injected sqt-gfp is translated by 16-cells in Mybx1 , whereas in controls , Sqt-GFP is detected at blastula stages , and lacZ control injection shows no Sqt-GFP . ( B ) Sqt-GFP protein expression is precocious and elevated in Mybx1 embryos . Error bars in B show standard deviation from three experiments . ( C ) Co-immunoprecipitation in embryo lysates followed by western blot analysis shows that Ybx1 interacts with eIF4E . eIF4G binds poorly with Ybx1 . Faint smear in control IgG lane for eIF4G is spillover from input lane ( see Figure 5—figure supplement 1C for complete blot for eIF4G ) . ( D ) Antibodies towards Ybx1 , eIF4G and eIF4E pull down sqt RNA in embryos lysates . RT-PCR on the embryos lysates in panel C shows sqt RNA but not control gapdh or wnt8a RNA in RNA-IP with αYbx1 antibodies . Control IgG antibodies do not show any RT-PCR product , whereas antibodies to the translation initiation factor eIF4E can pull down sqt RNA , wnt8a and gapdh RNA , and antibodies to eIF4G detects weak bands for sqt and gapdh in the RNA-IPs . RT-PCR from whole embryo lysates is the positive control . PCR product sizes are indicated on the right . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 01210 . 7554/eLife . 00683 . 013Figure 5—figure supplement 1 . Translation of control RNAs is not affected in Mybx1 mutant embryos . ( A ) Control GFP is not translated differentially in Mybx1 and control Pybx1 embryos . GFP RNA injected at the 1-cell stage is expressed similarly in 64-cell control and Mybx1 embryos . Scale bar , 100 μm . ( B ) Expression of Wnt8a-GFP protein ( 70 kDa ) is similar in control and Mybx1 embryos at cleavage and early blastula stages . Tubulin expression was detected as control . ( C ) Western blot to detect eIF4G in co-immunoprecipitations from embryo lysates using antibodies to Ybx1 , eIF4E and eIF4G shows a 220 kDa band only with eIF4G pull-down but not with Ybx1 or eIF4E . IgG antibodies were used as negative control and input embryo lysate is the positive control . A faint smear is observed in part of the IgG lane , due to spillover from the input . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 013 To determine how Ybx1 regulates translation of sqt RNA , we examined if Ybx1 forms complexes in vivo with translation initiation factors and sqt RNA . Extracts from wild-type embryos were immunoprecipitated with antibodies to Ybx1 , eIF4G or eIF4E , followed by western blot , and RT-PCR to detect sqt RNA . Co-immunoprecipitation assays show that Ybx1 interacts with eIF4E but not with eIF4G , and RNA-IP experiments show that sqt RNA is detected in pull-downs with antibodies towards Ybx1 , eIF4G and eIF4E . In contrast , gapdh and wnt8a RNA can be detected in RNA-IP with the eIF4G and eIF4E proteins , but not with Ybx1 . Hence , Ybx1 binds sqt RNA and the 5′ 7-methyl-guanosine cap binding protein eIF4E , but is not found in translation initiation complexes with gapdh and wnt8a RNA ( Figure 5C , D ) . Ybx1 has been shown to interact with the 5′ cap complex and inhibit translation by displacing eIF4G ( Nekrasov , 2003 ) . Taken together , these results provide evidence for Ybx1 in regulation of sqt translation by binding to the translation initiation machinery and the 3′UTR of sqt RNA . Since Sqt protein is translated prematurely in Mybx1 mutants , we then determined when Nodal signaling is activated in mutant embryos by examining phosphorylation of the downstream effector , Smad2 ( ten Dijke and Hill , 2004 ) . Consistent with premature Sqt-GFP translation , endogenous Smad2 is phosphorylated in 64-cell stage Mybx1 embryos , whereas in control embryos , phospho-Smad2 expression is detected only at late blastula/early gastrula stages ( Figure 6A ) . Quantification of phospho-Smad2 levels shows premature and elevated levels of Nodal signaling in Mybx1 ( Figure 6B ) . Thus , Nodal signaling is precociously activated at cleavage stages in mutant embryos . 10 . 7554/eLife . 00683 . 014Figure 6 . Nodal signaling is deregulated in Mybx1 embryos . ( A ) Phosphorylated-Smad2 is detected at the 64-cell stage in Mybx1 embryos . ( B ) Phospho-Smad2 levels are elevated in Mybx1 embryos at cleavage stages , compared to controls . ( C ) Quantitative real-time RT-PCR shows that Nodal target ( sqt , gsc , ntl , bon ) and YSL gene expression ( mxtx2 ) is elevated in Mybx1 compared to controls , whereas expression of lefty2 , the Wnt target , boz , ventral mesoderm gene vox , FGF target spry4 , and the enveloping layer ( EVL ) marker cldE , is either not significantly altered or marginally reduced . ( D ) Whole mount in situ hybridization shows expanded YSL domains of sqt , gsc , and mxtx2 in Mybx1 embryos; the cldE expression domain is not significantly different from controls , and vox is not detectable . Scale bar , 100 μm . ( E ) Mybx1 mutants have expanded YSL . DAPI staining to detect nuclei and E-cadherin immunostaining to detect membranes shows 1 tier of YSL nuclei ( undergoing division ) in control embryos . In Mybx1sa42 and Mybx1sg8 embryos , multiple tiers of YSL are observed ( yellow boxed area ) . Bottom panels show higher magnification of yellow , boxed area . Cell membranes are clearly demarcated in control embryos , but appear fragmented in Mybx1 mutants . Scale bars , 100 μm . ( F ) Histogram showing YSL nuclei numbers in Mybx1 and control embryos , with or without ybx1 transgenes at 23°C . ∼75% of control embryos have no YSL nuclei and only 25% show 1–6 YSL nuclei , whereas ∼80% of Mybx1 embryos and Mybx1 embryos with PTg , show 7 or more YSL nuclei , and ∼25% show >20 YSL nuclei . Mybx1 embryos with ybx1 MTg show reduced numbers of YSL nuclei . Number of embryos scored is indicated above the histogram . Error bars in B and C show standard deviation from three experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 014 Analysis of target genes of various signaling pathways shows that expression of Nodal targets ( gsc , ntl , bon and sqt ) is increased in Mybx1 embryos by the 512-cell stage ( Figure 6C ) . Expression of the extra-embryonic Yolk Syncytial Layer ( YSL ) genes , mxtx2 and hhex , is also significantly elevated . By contrast , expression of the Wnt targets boz and vox , the FGF target spry4 , and the enveloping layer ( EVL ) gene cldE , is either unchanged or marginally reduced in Mybx1 mutants compared to controls ( Figure 6C , D ) . The YSL expression domain of the Nodal target genes sqt and gsc is expanded in Mybx1 , as is YSL expression of mxtx2 , whereas in control Pybx1 embryos sqt expression is restricted to a few cells , and gsc and mxtx2 are typically not detected ( Figure 6D ) . We found no difference in lft2 , boz , vox or vent expression between Mybx1 and control embryos ( Figure 6C , D and data not shown ) . Thus , early Wnt and FGF signaling targets are not affected in Mybx1 mutants , whereas expression of many Nodal target genes is precocious and their levels increased . YSL expression of mxtx2 is increased in Mybx1 embryos ( Figure 6C , D ) . Accordingly , marginal cells lose cell membranes by early blastula stages , and syncytial nuclei accumulate over the yolk ( arrowheads in Figure 3E and yellow boxed area in Figure 6E ) . The margin between the blastoderm and YSL , evident by E-cadherin immunolocalization in control embryos , is not clearly demarcated in Mybx1 embryos . Increased numbers of YSL nuclei ( YSN ) were observed in Mybx1sa42 and Mybx1sg8 embryos ( white arrowheads in Figure 6E ) , and sometimes , nearly 50 YSN were observed at the 512–1000 cell stage . ∼50% of Mybx1 embryos have more than 13 nuclei , whereas control embryos show few or no YSN ( Figure 6F ) . The premature formation and increased numbers of YSN leads to substantially fewer cells in the blastoderm , failure to initiate epiboly , and embryonic lethality . These phenotypes are rescued by maternal ybx1-2a-gfp transgenes ( Figure 6F ) . Thus , the extra-embryonic YSL forms precociously and is expanded in Mybx1 . Ybx1 is a multi-functional regulator of many target genes . This raises the question of whether the phenotypes observed in Mybx1 mutants are a direct consequence of Sqt/Nodal translation and diffusion from the yolk in the absence of Ybx1 function , or due to other potential effects of Ybx1 . To directly determine the effects of excess Nodal protein from the yolk , we implanted Affi-gel beads that were pre-soaked in either control BSA protein or purified mouse Nodal protein , into the yolk of wild-type embryos , and examined YSN ( schematic in Figure 7A ) . Bead implantation itself did not disrupt morphogenesis or development ( Figure 7A ) . Control BSA bead-implanted embryos showed 1 tier of YSL ( with 4–5 nuclei; n = 17 , Figure 7B , C ) , similar to wild-type embryos ( Kimmel and Law , 1985 ) . By contrast , most Nodal bead-implanted embryos showed more YSN ( 75% , n = 32 embryos; arrowhead in Figure 7B , C ) . Therefore , Nodal diffusion from the yolk is sufficient for YSL expansion . Nodal bead implantation in the yolk of MZoep mutant embryos , which cannot respond to Nodal signals ( Schier , 2009 ) , did not affect the YSL ( 0% , n = 13; Figure 7B , C ) . Thus , excess Nodal signaling from the yolk directly induces premature and expanded extra-embryonic YSL . Similar YSL expansion and gastrulation defects were reported in lefty-1;lefty-2 double-morphants , where Nodal signaling is deregulated in the absence of the Lefty inhibitors ( Branford and Yost , 2002; Feldman et al . , 2002 ) . Taken together , these results suggest that the Mybx1 phenotypes are likely due to precocious , unregulated and elevated Nodal signaling by de-repression of Sqt translation . 10 . 7554/eLife . 00683 . 015Figure 7 . Excess Nodal protein from the yolk directly expands the extra-embryonic YSL . ( A ) Affi-gel beads presoaked in either BSA or mNodal were implanted at the 32-cell stage and fixed at 1000-cell stage . BSA-bead implanted embryos develop normally and have no morphological defects at 24 hpf , similar to control embryos . ( B ) DAPI staining shows one tier of YSL nuclei in BSA bead-implanted wild-type embryos whereas mNodal bead-implanted embryos show many nuclei ( arrowhead ) . MZoep embryos with Nodal beads are similar to control BSA bead-implanted embryos . Bead position indicated by blue dot . Scale bar , 100 μm . ( C ) Histogram showing percent wild-type or MZoep embryos with more YSN after bead implants . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 015 However , expanded YSL could also arise from defects in late cytokinesis during early embryogenesis , leading to aberrant syncytial layer formation ( Yabe et al . , 2009 ) . To distinguish between these possibilities , we blocked Nodal signaling in Mybx1 mutants by two strategies . Firstly , we injected capped mRNA encoding inhibitor Lefty1 into Mybx1 embryos to block Nodal signaling . Injections in control Pybx1 embryos were used to assess the efficacy of the RNA . Immunostaining with antibodies to detect E-cadherin in cell membranes and DAPI staining to detect nuclei show that that the expanded YSL phenotype in Mybx1 embryos is suppressed by injection of lefty1 RNA , but not with lacZ RNA ( Figure 8A ) . The margin between the blastoderm and YSL , which is not clearly demarcated in Mybx1 embryos , is restored upon injection of lefty1 RNA ( Figure 8A ) . The majority of Mybx1 mutant embryos injected with lacZ RNA fail to initiate gastrulation and none complete gastrulation . In contrast , more than 50% of mutant embryos injected with lefty1 RNA initiate epiboly movements and of these , ∼80% complete gastrulation , and survive till prim5 stages ( Figure 8B ) . Lefty1 RNA-injected Mybx1sa42 mutant embryos do not manifest cytokinesis failure or YSL defects even at the restrictive temperature . Therefore , the YSL defects and the failure to initiate gastrulation in Mybx1 mutant embryos are due to excessive Nodal signaling and not a result of cytokinesis defects . 10 . 7554/eLife . 00683 . 016Figure 8 . YSL and gastrulation defects in Mybx1 mutants can be rescued by blocking Nodal signaling . ( A ) DAPI-stained nuclei and E-cadherin immunostained membranes are clearly demarcated in lacZ and lefty1 RNA-injected control embryos . E-cadherin staining appears fragmented and number of YSL nuclei is increased in Mybx1 mutant embryos injected with control lacZ RNA ( white arrowhead ) . In Mybx1 embryos injected with lefty1 RNA , the number of YSL nuclei is restored to normal levels and membrane staining does not appear fragmented . Bottom panels show higher magnification of area boxed in yellow; scale bar , 100 μm . ( B ) Histogram showing rescue of gastrulation and survival till prim5 stage by injection of lefty1 RNA but not lacZ RNA in Mybx1 mutants that were subjected to temperature shift at 23°C . Error bars show standard deviation from three experiments . Number of embryos is shown on top of the histogram . ( C ) Histogram showing gastrulation and % survival in embryos from ybx1sa42/sa42;sqtcz35/+ crosses and Mybx1 mutants subjected to temperature shift at 23°C . Most embryos from ybx1sa42/sa42;sqtcz35/+ crosses initiate and complete gastrulation in comparison to Mybx1 sa42 mutants . ( D ) Histogram showing survival at 23°C and genotypes of embryos from matings of ybx1sa42/sa42;sqtcz35/+ , in comparison to Mybx1sa42 embryos which do not survive at 23°C . The expected % for each genotype is 25% for Mybx1sa42;sqtcz35/cz35 and Mybx1sa42;sqt+/+ , and 50% for Mybx1sa42;sqtcz35/+ . All Mybx1sa42;sqtcz35/cz35 embryos ( which have no Sqt signaling ) survive , whereas many Mybx1sa42;sqtcz35/+ and Mybx1sa42;sqt+/+ do not survive at 23°C . Number of embryos scored is indicated above the histogram , and % observed for each genotype is indicated to the right of the colored bars . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 01610 . 7554/eLife . 00683 . 017Figure 8—figure supplement 1 . Mybx1;Zsqtcz35/cz35 mutant embryos show phenotypes typical of reduced Nodal activity . DIC images of prim5 stage embryos from ybx1sa42/sa42;sqtcz35/+ intercrosses show a range of classic nodal phenotypes . Phenotypes scored ( wild type- , squint- , MZmidway- , cyc;sqt- , antivin/lefty overexpression class VI- , and ventralized ichabod 1b-like ) are indicated above the images , and numbers are given at the bottom of each image . Data compiled from two independent crosses . DOI: http://dx . doi . org/10 . 7554/eLife . 00683 . 017 Secondly , we generated ybx1;sqt compound mutants . Embryos mutant for the sqtcz35 allele express maternal sqt RNA that is localized , but the mutant Sqt protein is truncated and non-functional ( Feldman et al . , 1998; Bennett et al . , 2007; Lim et al . , 2012 ) . The sqtcz35 mutation selectively abolishes Sqt signaling without affecting the early functions of maternal sqt RNA or activity of other zebrafish Nodals . We did not recover any ybx1;sqt double homozygous adults ( N > 120 fish ) , but interestingly , most embryos from ybx1sa42/ybx1sa42;sqtcz35/+ intercrosses , which are essentially Mybx1 but where some have reduced Sqt or no Sqt , undergo gastrulation at 23°C unlike Mybx1 single mutants ( Figure 8C , D ) . Mybx1;Zsqt compound mutants show phenotypes typical of reduced Nodal activity such as those observed in MZmidway mutant embryos , or upon complete loss of Nodal activity ( Figure 8—figure supplement 1 ) ( Thisse et al . , 2000; Schier , 2009; Slagle et al . , 2011 ) . Similar to Mybx1 embryos injected with lefty RNA , YSL expansion is not observed in Mybx1;Zsqt mutants ( data not shown ) and these embryos go through gastrulation . These findings demonstrate that the Mybx1 mutant phenotypes are a direct consequence of precocious and deregulated maternal Sqt/Nodal signaling . In this study , we have provided the first direct evidence of translational control of Nodal signaling by a key maternal factor , Ybx1 , and demonstrated that it is essential for embryonic development . Our use of a temperature-sensitive ybx1 allele , that allows selective and conditional disruption of maternal Ybx1 function at early stages , shows that Nodal signaling is the only pathway affected at these stages in the mutants . This allele can potentially be a used as a tool to identify other genes and processes regulated by Ybx1 . Ybx1 is an abundant molecule and neither the RNA nor protein is spatially restricted . How , then , is Ybx1 binding specificity achieved ? It is possible that other regions of Ybx1 than the CSD , and other factors in the Ybx1–RNP complex confer specificity to the interactions . Such context-dependent specificity has been observed for many transcription factor complexes and other RNA-binding proteins as well . For example , a bipartite RNA recognition module in Lin28 ( which contains a CSD ) binds to two distinct regions of let-7 RNA to regulate its biogenesis , and two distinct RNA-binding domains in fragile X mental retardation protein ( FMRP ) , recognize distinct RNA elements ( Tong et al . , 2000; Nam et al . , 2011 ) . In support of this possibility , we find that in addition to the residues in the CSD , the DD , RNP and ssDBD domains of Ybx1 are also required for binding to sqt RNA , and likely confer specificity to the interactions . By contrast , the CSD-containing Lin28 protein does not bind to the sqt DLE . Furthermore , the ybx1sg8 mutation , which deletes the Ybx1 C-terminus , which is thought to be a protein interaction domain ( Wolffe , 1994 ) , results in more severe phenotypes and earlier lethality than ybx1sa42 . It is likely that the residues lacking in the truncated Ybx1sg8 peptide are also important for its functions . Our findings show that a major function of maternal Ybx1 is to regulate Nodal signaling via its effects on sqt RNA localization , processing , and translation . Maternal sqt RNA is largely non-polyadenylated at early cleavage stages , and polyA-containing sqt RNA is normally detected from the 16-cell stage ( Lim et al . , 2012 ) . In Mybx1 embryos , polyA-sqt RNA is detected even at the 1-cell stage , indicating premature polyadenylation . Regulating the length of the polyA tail is known to mediate translational control of the RNA; for example , cyclin A RNA is stored in Drosophila oocytes with short polyA tails ( Morris et al . , 2005 ) . Ybx1 interacts with the translation initiation factor eIF4E and the sqt 3′UTR . Interactions of 3′UTR binding proteins with translation initiation factors , such as the CPEB-Maskin-eIF4E complex , has been shown in translational control of maternal RNAs in Xenopus oocytes . Maskin binds the cap-binding factor eIF4E , and prevents interactions between eIF4G and eIF4E , which is required for recruitment of the 40S ribosome subunit to the 5′end of mRNAs , and thereby represses translation ( Cao and Richter , 2002 ) . Similarly , in Drosophila oocytes , cup binds eIF4E and Bruno to regulate oskar RNA translation ( Nakamura et al . , 2004 ) . Mammalian YB1 prevents eIF4G from binding to eIF4E , and blocks initiation of translation ( Nekrasov et al . , 2003 ) . Binding of Ybx1 to the sqt 3′UTR and eIF4E likely prevents eIF4G and eIF4E complex formation , and in Mybx1 mutants , in the absence of Ybx1 function , Sqt translation occurs precociously . Thus , Ybx1 binding to the translation initiation factors and the sqt 3′UTR can lead to translational repression of sqt RNA . Consistent with Ybx1 being a DLE-binder , sqt RNA localization is disrupted in Mybx1 , and many Sqt/Nodal target genes ( including sqt ) show precocious and elevated expression . Surprisingly , the Nodal target lefty2 is not detected in mutant embryos . Therefore , lefty2 expression requires factors or inputs that are missing in Mybx1 . The lack of feedback inhibition in the absence of Lefty2 together with elevated Sqt/Nodal levels likely exacerbates deregulated Nodal signaling in Mybx1 . The YSL expansion and gastrulation defects in Mybx1 mutants can be rescued by blocking Nodal signaling via lefty overexpression or by using the sqtcz35 genetic background that lacks the signaling functions of Sqt , indicating that these phenotypes arise from excess Nodal signaling . Interestingly , Mybx1;Zsqt compound mutant embryos are similar to cyc;sqt double mutants ( Feldman et al . , 1998 ) , suggesting that maternal Ybx1 may have additional functions in regulation of Nodal signaling . We also found that Wnt signaling targets are not induced in Mybx1 mutants , where wnt8a RNA localization and Wnt8a-GFP protein expression are normal , but maternal sqt RNA is mis-localized . Therefore , the response to the maternal Wnt signal requires dorsal localization of maternal sqt RNA . This supports our previous findings , where over-expression of localized non-coding sqt RNA increased dorsal β-Catenin nuclei numbers and elevated Wnt target gene expression , and mis-localization of sqt RNA by morpholinos that also block Sqt translation resulted in loss of dorsal β-Catenin accumulation ( Lim et al . , 2012 ) . We had previously shown that human NODAL 3′UTR fused with lacZ localizes dorsally in zebrafish ( Gore et al . , 2005 ) . This was surprising since mammalian embryos are thought to undergo regulative development . Moreover , Nodal RNA is not localized in early mouse embryos ( Robertson et al . , 2003; Cheong and Sampath , unpublished observations ) . Ybx1 binds the DLE , and regulates localization and translation of sqt . The sqt DLE , therefore , encompasses a localization and translational control element . This suggests that the mammalian NODAL 3′UTR may also harbor a translational control module . Our finding that maternal sqt/nodal must be translationally repressed , and that deregulated maternal Nodal signaling is catastrophic , shows that this is an essential control mechanism . Translational control is a new paradigm in regulation of this pathway . It will be interesting to see if this mechanism regulates Nodal signaling in other organisms or biological processes . Since human NODAL and ALK7 receptors are expressed in the ovary and placenta , and elevated NODAL is associated with pre-eclamptic placentas ( Nadeem et al . , 2011 ) , precise regulation of maternal Nodal signaling is likely to be important for human placentation . Moreover , uncontrolled and deregulated Nodal signaling has been associated with metastasizing tumors , underscoring the importance of precise and timely regulation of Nodal signaling . Finally , Nodal signaling is essential for maintaining stem cell pluripotency , and current methods to generate and maintain embryonic stem cell ( ESC ) and induced pluripotent stem cells ( iPSC ) rely upon transcription factors . Our finding that Nodal signaling is maternally regulated by translational repression could allow modulation of these important therapeutic cells by this new mechanism . Embryos were homogenized in 1/10 vol lysis buffer ( 20 mM Tris pH 8 . 0 , 100 mM NaCl , 0 . 1 mM EDTA , 1 mM 6-aminohexanoic acid , 1 mM PMSF , 25% glycerol ) to make extracts . Debris was pelleted by centrifugation ( 20 , 000×g , 4°C , 1 min ) , and supernatants flash frozen in 50 µl aliquots in liquid N2 . 100 nucleotide long probes spanning the 3′UTR of sqt , wnt8a ( Lu et al . , 2011 ) and vg1 ( Bally-Cuif et al . , 1998 ) were synthesized and used in RNA gel-shift assays . Templates for the probes were generated by PCR with an extended phage T3 RNA polymerase promoter ( AATTAACCCTCACTAAAGGGAGAA ) appended to the 5′end of the 5′primer , and gel-purified . Primers are listed in Supplementary file 1 . Radioactively labeled probes were transcribed with T3 RNA polymerase ( Promega , Madison , WI ) , mixed with extracts , and used in electrophoretic mobility-shift assays . For the competition gel-shift assays ∼0 . 1 ng of radioactive probe was competed with 5–80 ng of unlabeled RNA . RNA cross-linking reactions were essentially the same as RNA gel-shifts , except that the reactions were UV-cross-linked for 5 min in a Stratalinker ( Stratagene , La Jolla , CA ) , digested with RNase A ( 0 . 5 µg ) for 1 hr at 37°C , and separated on an SDS-PAGE gradient gel ( 7% , 29:1 acrylamide:bisacrylamide to 12% , 19:1 acrylamide:bisacrylamide ) at ∼5 mA/1 mm gel overnight , dried , and auto-radiographed . Extracts were made as above , and flash frozen in 2 ml aliquots . Chromatography was performed on an Akta purifier ( GE Healthcare , Little Chalfont , UK ) . 200–500 mg of protein extract was injected through a 0 . 2 µm syringe filter ( Minisart; Sartorious , Göttingen , Germany ) to a pre-equilibrated heparin HiTrap column ( GE Healthcare ) and eluted with a ( NH4 ) 2SO4 gradient . Fractions were collected and assayed by gel-mobility shift with sqt1 probes . Positive fractions were pooled and loaded onto coupled octyl sepharose and phenyl sepharose columns . In the conditions used , SRBF1 passes through octyl sepharose and binds to the phenyl sepharose column . The columns were uncoupled , and SRBF1 was eluted from the phenyl sepharose column with a ( NH4 ) 2SO4 gradient . Positive fractions were pooled , dialyzed , and loaded onto a 1 ml heparin HiTrap column ( GE Healthcare ) , eluted with a NaCl gradient , collecting 1 ml fractions . We used 1–5 µl of each fraction for gel-shifts or RNA cross-linking assays . Fractions were concentrated and loaded on an SDS-PAGE gradient gel . The gel was stained with colloidal Coomassie blue ( Kang et al . , 2002 ) and the 48 kDa band was excised and analyzed by mass spectrometry . The coding sequence of ybx1 was amplified by PCR ( with primers including restriction sites , for NcoI and BamHI or BglII ) from zebrafish ovary or embryo cDNA , restriction digested , and cloned into pTrcHISa . Mutations were made by site-directed mutagenesis ( Zheng et al . , 2004 ) . Template plasmid was amplified by PCR with partially overlapping forward and reverse primers ( Supplementary file 1 ) using Vent Polymerase ( NEB , Ipswich , MA ) , digested with DpnI , and transformed into XL1blue cells . Libraries of ENU-mutagenized zebrafish were screened for point mutations in the coding region of ybx1 ( Winkler et al . , 2011 ) . A region encompassing exons two to four of zebrafish ybx1 ( chromosome 8: 49299968 to 49308225; Ensembl Zv9 ) was amplified by nested PCR using primers listed in Supplementary file 1 . Sanger sequencing of PCR fragments was performed with universal M13 forward sequencing primer . Primary hits were amplified and re-sequenced independently and verified . Mutant ybx1sa42 zebrafish ( which harbor a V83F amino acid substitution ) were propagated further and bred to homozygosity . For generating deletions in ybx1 we used a pair of zinc finger nucleases recognizing exon 5 of ybx1 ( Toolgen Inc . , Seoul , South Korea ) ( Doyon et al . , 2008; Meng et al . , 2008 ) . Capped mRNA was synthesized from linearized plasmids , and 25 pg RNA of each zinc finger nuclease pair was injected in 1-cell-stage AB wild-type embryos . Injected embryos were raised to adulthood and progeny screened for mutations in the ybx1 locus by PCR and sequencing . We identified several small deletions at the target site . The ybx1sg8 allele used in this study has a 5-nucleotide deletion in exon 5 of ybx1 , which leads to a frame-shift after amino acid 197 and premature termination after amino acid 205 . Wild-type , ybx1sa42 , ybx1sg8 , sqtcz35 and oeptz57 fish were maintained at 28 . 5°C , and embryos obtained by natural mating using standard procedures , in accordance with institutional animal care regulations ( Westerfield , 2007 ) . Embryos from homozygous ybx1sa42 females were collected , incubated at 28 . 5°C until the first cell division , and then shifted to 23°C for observing the temperature-sensitive phenotype . A few homozygous ybx1sa42 females yield embryos that manifest a range of phenotypes , some of which survive at 23°C . In this study , homozygous ybx1sa42 females that yielded 100% embryos arrested at gastrula stages were used in all experiments . Embryos from homozygous ybx1 males and wild-type females ( Pybx1 ) , are indistinguishable from wild-type embryos , and were used as controls . For examining ybx1;sqt double mutant phenotypes , embryos from matings of ybx1sa42/sa42;sqtcz35/+ fish were incubated at 28 . 5°C until the 4-cell stage to allow sqt RNA localization , shifted to 23°C until the 128-cell stage , and subsequently returned to 28 . 5°C until observation at gastrula and prim5 stages . The genotypes of mutants were determined by PCR using primers listed in Supplementary file 1 . Capped synthetic lefty1 RNA was synthesized from linearized plasmid using the mMessage mMachine SP6 kit ( Invitrogen , Carlsbad , CA ) . 2 pg aliquots of lefty1 RNA were injected into Mybx1sa42 mutant or Pybx1sa42 control embryos at the 1-cell stage . Capped lacZ RNA was injected as a control . The embryos were incubated at 28 . 5°C until the 4-cell stage to allow sqt RNA localization , shifted to 23°C until the 256-cell stage , and subsequently returned to 28 . 5°C until observations at gastrula and prim5 stages . An 8 . 26 kb ybx1 genomic fragment was amplified by PCR , fused with the viral peptide 2a and gfp sequences , cloned into pMDs6 plasmid and co-injected with Ac II transposase mRNA into ybx1sa42 embryos at the 1-cell stage ( Emelyanov et al . , 2006 ) . Injected embryos were raised to adulthood , and progeny were screened for GFP expression . Two independent Tg ( ybx1-2a-gfp ) transgenic lines were used in this study . RNA-IP was carried out using embryos lysates ( Niranjanakumari et al . , 2002 ) . 20 mpf embryos were cross-linked in formaldehyde , and lysed . Anti-Ybx1 ( 4F12 , Sigma , St . Louis , MO ) , anti-eIF4G ( #2469 , Cell Signaling Technology , Danvers , MA ) and anti-eIF4E ( #2067 , Cell Signaling Technology ) antibodies were bound to protein A/G beads ( Calbiochem , EMD Millipore , Billerica , MA ) , incubated with wild-type embryo lysate at 4°C , washed , and eluted . Half of the eluate was used to detect proteins by western blot and the remainder was used for RNA extraction using TRIzol reagent ( Invitrogen ) , followed by RT-PCR to detect sqt , wnt8a , and gapdh ( primer details in Supplementary file 1 ) . E . coli BL21 cells were transformed with plasmids encoding wild-type and mutant Ybx1 . Expression of recombinant protein in lysates was detected by Western blots with an anti-6xHis antibody ( 1:2500; sc50973 , Santa Cruz Biotechnology Inc . , Dallas , TX ) , and equal amounts of E . coli lysates were used in gel-shift assays . To detect Sqt translation , Pybx1sa42 and Mybx1sa42 embryos were injected with 20 pg sqt-GFP RNA . Whole embryo lysates ( 50 µg ) were separated on an 8% SDS-PAGE gel , transferred to High bond-C Extra Membrane ( GE Healthcare ) , and immunoblotting was performed using anti-GFP primary antibodies ( 1:2500; ab290 , Abcam , Cambridge , UK ) and HRP-conjugated anti-rabbit IgG secondary antibodies ( 1:10 , 000; DAKO , Glostrup , Denmark ) . Endogenous phospho-Smad2 was detected using anti-pSmad2 primary antibodies ( 1:1000; #3101 , Cell Signaling Technology ) , and HRP-conjugated anti-rabbit IgG secondary antibodies ( 1:5000; DAKO ) . Endogenous Ybx1 expression in embryos was detected using a mouse anti-Ybx1 antibody ( 1:1000; 4F12 , Sigma ) , and HRP-conjugated anti-mouse IgG secondary antibody ( 1:10 , 000; DAKO ) . Anti-eIF4E ( 1:2000; #2067 , Cell Signaling Technology ) and anti-eIF4G ( 1:2000; #2469 , Cell Signaling Technology ) antibodies were used in co-immunoprecipitation assays and western blots to detect interactions with Ybx1 . Total RNA was extracted from embryos using TRIzol reagent ( Invitrogen ) , and 500 ng RNA from WT , Pybx1sa42 or Mybx1sa42 embryos was used for first-strand cDNA synthesis . PCR reactions were performed as described ( Lim et al . , 2012 ) using primers listed in Supplementary file 1 . 1-cell and 4-cell stage embryos were fixed in buffer containing 4% paraformaldehyde , 4% sucrose , and 120 µM calcium chloride in 0 . 1M phosphate buffer ( pH 7 . 2 ) . Blastula stage embryos were fixed in 4% paraformaldehyde/PBS . Fixed embryos were processed for whole mount in situ hybridization ( Tian , 2003 ) to detect claudinE , cyclinb1 , eomesodermin , goosecoid , mxtx2 , squint , vasa , vox , wnt8a , and ybx1 expression ( Stachel et al . , 1993; Yoon et al . , 1997; Howley and Ho , 2000; Melby et al . , 2000; Gore et al . , 2005; Siddiqui et al . , 2010; Hong et al . , 2011; Lu et al . , 2011; Du et al . , 2012; Lim et al . , 2012 ) . We used anti-E-cadherin antibodies to detect cell membrane adhesions . Control or mutant embryos at the 1000-cell stage were fixed in 4% paraformaldehyde/PBS , and processed for fluorescence immunohistochemistry using rabbit polyclonal anti-E-cadherin antibodies ( gift from CP Heisenberg ) and Alexa-488-conjugated goat anti-rabbit secondary antibodies ( Molecular Probes , Eugene , OR ) . For detecting nuclei , embryos were fixed with 4% parafomaldehyde/PBS , washed in PBS containing Tween-20 ( PBST ) , incubated with 500 pg/ml DAPI , and washed with PBST . To label yolk syncytial nuclei in live embryos , 4 nl of 0 . 5 mM SYTOX orange ( Invitrogen ) was injected into the yolk of 64-cell stage embryos . Labeled nuclei were scored at 512-1K cell stages . Affi-Gel blue beads ( 50–100 mesh; Bio-Rad Laboratories Inc . , Hercules , CA ) were pre-soaked in Bovine Serum Albumin ( BSA; 100 µg/ml; NEB , Ipswich , MA ) or mouse Nodal protein ( 125-250 µg/ml; R&D systems , Minneapolis , MN ) for 30 min . Single Affigel beads were implanted into the yolk of dechorionated 32-cell stage embryos by making a small incision in the yolk with a tungsten needle , and nudging the bead into the yolk with pair of fine forceps . For DAPI or SYTOX staining , implanted embryos were cultured in 30% Danieau’s buffer , and processed as described above .	In many organisms , embryonic development is controlled in part by RNAs that are deposited into the egg as it forms inside the mother . These ‘maternal RNAs’ may localize to particular regions of the egg or embryo , where they are then exclusively translated into protein and carry out their specific function . This helps to establish asymmetry in the developing organism—that is , to produce tissues that will eventually become the top or bottom , front or back , and left or right of the organism . One such maternal RNA encodes Nodal , a key signaling molecule that is conserved across vertebrate and some invertebrate organisms . In zebrafish , the equivalent RNA is called squint , and plays an important role in embryonic development . The squint RNA deposited by the mother localizes to the dorsal region—the embryo’s back—and signals that region to make dorsal tissues , but how squint is regulated is not well understood . Now , Kumari et al . identify a protein that controls the positioning of squint RNA , and find that it can also prevent this RNA from being translated into protein . The squint RNA contains a ‘dorsal localization element’ that recruits it to the dorsal cells of the embryo by the 4-cell stage ( i . e . , within two cell divisions after the egg is fertilized ) . Kumari et al . identified a protein called Ybx1 that could bind to this element: this protein may help to correctly position RNAs in many other organisms , including fruit flies and mammals . Strikingly , embryos formed abnormally when their maternally derived Ybx1 protein was mutant , and these mutations also prevented the squint RNA from localizing properly . This suggests that maternally derived Ybx1 protein directly regulates the squint RNA . As well as positioning the squint RNA correctly , the embryo must translate this RNA into protein at the right time . In embryos with mutant maternal Ybx1 protein , the Squint protein could be detected at the 16-cell stage , whereas in wild-type embryos this protein is not translated until the 256-cell stage; this indicates that Ybx1 protein might normally repress the translation of the squint RNA . Indeed , Kumari et al . found that Ybx1 binds to another protein—eIF4E—that recruits mRNAs to the ribosome ( the cell’s translational machinery ) . Ybx1 might therefore prevent eIF4E from associating with other components of the ribosomal complex , and initiating the translation of the squint RNA , until additional signals have been received . It will be interesting to determine how widespread this regulatory mechanism is in other organisms .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology" ]	2013	An essential role for maternal control of Nodal signaling
When epithelia become too crowded , some cells are extruded that later die . To extrude , a cell produces the lipid , Sphingosine 1-Phosphate ( S1P ) , which activates S1P2 receptors in neighboring cells that seamlessly squeeze the cell out of the epithelium . Here , we find that extrusion defects can contribute to carcinogenesis and tumor progression . Tumors or epithelia lacking S1P2 cannot extrude cells apically and instead form apoptotic-resistant masses , possess poor barrier function , and shift extrusion basally beneath the epithelium , providing a potential mechanism for cell invasion . Exogenous S1P2 expression is sufficient to rescue apical extrusion , cell death , and reduce orthotopic pancreatic tumors and their metastases . Focal Adhesion Kinase ( FAK ) inhibitor can bypass extrusion defects and could , therefore , target pancreatic , lung , and colon tumors that lack S1P2 without affecting wild-type tissue . Epithelial cells must act collectively to provide a protective barrier for the organs they encase even though they continuously turn over through cell death and division . The link between cell division and death is critical: if the relative death rate is too high , barrier function diseases may result whereas if division outpaces cell death , epithelia could become neoplastic . We previously identified a process critical for promoting cell death when cells within epithelia become overcrowded termed epithelial extrusion ( Rosenblatt et al . , 2001; Eisenhoffer et al . , 2012 ) . The stretch-activated channel Piezo-1 senses cell crowding and enables some cells to produce the bioactive sphingolipid , Sphingosine 1-phosphate ( S1P ) , which binds G-protein coupled receptors ( S1P2 ) in neighboring cells to activate Rho-mediated assembly and contraction of an intercellular actomyosin ring ( Gu et al . , 2011 ) . This contraction squeezes live cells apically out of the epithelial sheet while simultaneously closing the gap that might have resulted from the cell's exit , thus preserving epithelial barrier function . Because live extruded cells become stripped from the underlying matrix and its associated survival signaling , they later die by anoikis ( Frisch and Francis , 1994 ) . Advanced tumors typically have increased survival signaling that overrides anoikis , suggesting that cells could survive following extrusion . In this case , the direction a cell extrudes can impact its later fate . Typically , epithelia extrude cells apically into the lumen of the tissue ( Slattum et al . , 2009 ) , which would act to essentially eliminate tumor cells with upregulated survival signaling . However , some cells are extruded basally into the tissue encased by the epithelium ( Slattum et al . , 2009 ) . If basally extruded cells survive following extrusion , they might be able to invade into the underlying tissue ( Slattum and Rosenblatt , 2014 ) . Interestingly , we have found that oncogenic mutations in either adenomatous polyposis coli or K-Ras misregulate apical extrusion and drive extrusion basally ( Marshall et al . , 2011; Slattum et al . , 2014 ) . Here , we examined the long-term effects of disrupting the S1P-S1P2 epithelia extrusion-signaling pathway . We found that inhibition of S1P2 leads to large epithelial masses in both zebrafish epidermis and cultured epithelia and increased rates of basal extrusion . Moreover , disrupting extrusion by a variety of methods leads to chemotherapy resistance . Inhibition of Focal Adhesion Kinase ( FAK ) , a key survival signal generated from cell-matrix adhesion , selectively promotes apoptosis in cells where extrusion is defective and eliminates epidermal cell masses formed in zebrafish S1P2 mutants without affecting epithelial morphology and function . Pancreatic Ductal Adenocarcinomas ( PDACs ) have little to no S1P2 , which could explain why these tumors are typically more invasive and chemo-resistant . HPAF II human pancreatic cancer cells cannot extrude apically and instead extrude basally , survive , and proliferate following extrusion . Ectopically expressing S1P2 in HPAF II cells rescues apical extrusion and apoptosis and reduces orthotopic mouse tumors and their metastases . Together , our results suggest that defective extrusion may be a new mechanism for how PDACs and other carcinomas lacking S1P2 initiate and invade . Furthermore , FAK inhibitors , which are currently in clinical trials for other tumors , may provide an effective therapeutic opportunity to treat pancreatic cancer without destroying nearby normal tissue . To test if the S1P-S1P2-Rho signaling pathway that controls extrusion ( Gu et al . , 2011 ) was critical for preventing neoplastic growth over time , we knocked down S1P2 in Human Bronchial Epithelial ( HBE ) cells ( Figure 1A ) and grew them for up to 3 weeks after they formed an intact monolayer . S1P2-depleted HBE epithelia , which are extrusion-deficient ( Gu et al . , 2011 ) , accumulated into masses over three layers thick whereas control-knockdown monolayers retained a single layer ( Figure 1B ) . Because cell extrusion typically promotes epithelial cell death ( Eisenhoffer et al . , 2012; Marinari et al . , 2012 ) and because S1P2 depletion did not affect the proliferation rate in a yellow tetrazolium MTT ( 3- ( 4 , 5-dimethylthiazolyl-2 ) -2 , 5-diphenyltetrazolium bromide ) assay ( Figure 1C ) , masses were likely to arise due to reduced apoptosis . Additionally , zebrafish larvae carrying a loss-of-function mutation in S1P2 ( Miles apart [Mil] ) cannot extrude apoptotic epidermal cells ( Gu et al . , 2011 ) , and similarly accumulated numerous epidermal cell aggregates throughout the body ( 18 ± 3 . 5 aggregates/fish , n = 22 ) by only 5 days post fertilization ( dpf ) ( Figure 1D , E ) . By contrast , masses were undetectable in heterozygote Mil or WT siblings of the same age ( Figure 1D , E ) . 10 . 7554/eLife . 04069 . 003Figure 1 . Loss of S1P2 and extrusion leads to accumulation of epithelial cell masses . ( A ) S1P2 immunoblot of HBE cells expressing control ( left ) or S1P2-specific shRNA ( right ) with α-tubulin as loading control . ( B ) Representative images of HBE cells ( DNA only ) expressing control ( left ) or S1P2-specific shRNA ( right ) grown for 3 weeks . Scale bar , 10 µm . ( C ) Proliferation assay indicates that S1P2-knockdown cells proliferate at the same rate as wild type controls cells . ( D ) Representative DIC micrographs of 5-dpf WT ( top ) and Mil ( S1P2 mutant ) ( bottom ) zebrafish larvae , where cartoon shows region where fish was imaged . Scale bars , 100 µm where red box indicates region imaged . ( E ) Quantification of epidermal clumps of 22 zebrafish larvae . DOI: http://dx . doi . org/10 . 7554/eLife . 04069 . 003 We next wondered if extrusion-deficient cells were also more resistant to cell death in response to apoptotic stimuli . While extrusion promotes apoptosis during normal homeostasis by extruding live cells that later die from loss of contact to matrix-derived survival signaling ( Eisenhoffer et al . , 2012 ) , treating epithelia with apoptotic stimuli causes cells to simultaneously die and extrude ( Rosenblatt et al . , 2001; Andrade and Rosenblatt , 2011 ) . Because extrusion normally drives cell death , could it also help promote apoptosis in response to apoptotic stimuli by eliminating competing survival signaling associated with the underlying matrix ? We find that disrupting extrusion signaling also disrupted apoptosis in response to a variety of apoptotic stimuli . HBE monolayers lacking S1P2 ( Figure 2A ) or treated with a selective S1P2 receptor antagonist , JTE-013 ( Figure 2B ) had greatly reduced rates of apoptosis in response to a strong apoptotic stimulus , UV-C , compared to controls . Madin–Darby Canine Kidney ( MDCK ) monolayers treated with S1P2 antagonist were similarly resistant to several common chemotherapy drugs that cause apoptosis ( Figure 2B , C ) . 10 . 7554/eLife . 04069 . 004Figure 2 . Disruption of S1P2-extrusion signaling reduces apoptotic response . ( A ) Quantification of UV-induced apoptotic cells in HBE monolayers expressing control or S1P2-specific shRNA . ( B ) Quantification of UV-induced apoptosis of HBE monolayers in the presence or absence of the S1P2 antagonist JTE-013 . ( C ) Quantification of indicated chemotherapy-induced apoptotic MDCK cells in the presence or absence of JTE-013 , where all error bars are STD ( p < 0 . 01 , and p < 0 . 001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04069 . 004 The reduced cell death rates in epithelia lacking S1P2 were due to disruption of extrusion rather than altered S1P signaling , since other inhibitors of extrusion , Rho kinase inhibitor ( Y-27632 ) , myosin II inhibitor ( Blebbistatin ) , or Rac inhibitor ( EHT1864 ) all decreased cell death rates to the extent that they inhibit extrusion ( Figure 3A ) . In each case , the ratio of cell death to extrusion inhibition is ∼1:1 ( Figure 3C ) . Inhibition of apoptosis was not due to increasing levels of S1P , which can act as a pro-survival signal , as S1P levels in apoptotic cells varied independently of extrusion inhibition ( Figure 3B ) . Since freshly plated single MDCK cells are resistant to apoptotic stimuli , we tested if these same compounds reduced apoptosis in similarly aged single MDCKs by treating with EGTA to disrupt cadherin-dependent cell–cell contacts . Inhibitors that blocked apoptosis by blocking extrusion in an intact monolayer do not impact the apoptosis rates of single cells that are incapable of extrusion ( Figure 3D ) . Similarly , UV-induced apoptosis was unaltered in single HBE cells lacking S1P2 when HBE monolayers where treated with EGTA ( Figure 3D ) . Additionally , inhibiting S1P2 with JTE-013 in a cell line that cannot extrude but expresses this receptor ( Clair et al . , 2003; Pham et al . , 2013 ) , NIH 3T3 fibroblasts , does not affect the cell death rate in response to UV-C ( Figure 3E ) . These data together suggest that increased cell survival is linked with the inability to extrude rather than to any intrinsic block of the apoptosis pathway . 10 . 7554/eLife . 04069 . 005Figure 3 . Decreased apoptosis is due to blocked extrusion rather than S1P signaling . ( A ) Rates of MDCK cell death ( left Y-axis , blue ) correspond with cell extrusion rates ( right Y-axis , yellow ) in response to UV-C when treated with extrusion inhibitors . ( B ) Representative images of apoptotic cells with and without compounds that block extrusion . When extrusion occurs , the dying cell DNA lies above ( out of plane from ) the neighboring cells with a contracted actin ring but when it fails , it lies in the same plane as surrounding cells with an uncontracted actin ring . Only the S1P2 antagonist JTE013 causes significant S1P accumulation in the dying cell ( second column ) , whereas blocking extrusion with the other compounds does not impact S1P levels , where p values of each drug treatment compared to control are listed on each S1P panel as asterisks ( n = 4 ) . Bar = 10 µm . ( C ) Ratio of reduction of extrusion to reduction of apoptosis shows nearly a 1:1 correlation throughout , where p-values compared to S1P2 are not significant . ( D ) Compounds used to block extrusion do not affect apoptosis rates in single MDCK cells treated with EGTA in response to UV . ( E ) Quantification of UV-induced apoptotic NIH 3T3 cells in the presence of vehicle or JTE-013; All results are expressed as mean values ± STD of three separate experiments ( p < 0 . 01 , p < 0 . 005 , p < 0 . 005 , and **p < 0 . 0001 ) , and NS in graphs B , D , and E indicate that p values of a unpaired T-test are not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 04069 . 005 Since disruption of S1P2 in epithelia results in reduced apoptosis and cellular masses both in vitro and in vivo , we wondered if this receptor might be deficient in carcinomas . Our analysis of published tumor microarray data found S1P2 mRNA to be significantly reduced in PDAC ( Buchholz et al . , 2005; Segara et al . , 2005; Badea et al . , 2008 ) , and some lung and colon tumors ( Bhattacharjee et al . , 2001 ) , compared to their corresponding normal tissues . To investigate if cancer cells lacking S1P2 also have extrusion and apoptosis defects , we analyzed a pancreatic adenocarcinoma cell line , HPAF II , that has reduced S1P2 levels ( Figure 4A ) and forms epithelial monolayers necessary for assaying extrusion . We used MDCK and HBE cells as controls , which are well characterized in several extrusion studies ( Rosenblatt et al . , 2001; Slattum et al . , 2009; Gu et al . , 2011 ) , as the only immortalized normal pancreatic cells , HPDEs , cannot form a confluent monolayer ( data not shown ) . 10 . 7554/eLife . 04069 . 006Figure 4 . Pancreatic cancer cell line HPAF II accumulates into masses and extrudes basally . ( A ) S1P2 immunoblot of HBE ( left ) , HPAF II ( middle ) , and MDCK ( left ) cells with α-tubulin as loading control . ( B ) Cell death rates in response to UV-C . ( C ) Quantification of cell extrusion events from three independent experiments; n = 300 apoptotic cells per cell line , error bars are STD where <0 . 01 and *<0 . 0001 . ( D ) Representative confocal projection and XZ cross-section ( from region in dashed line above ) of HPAF II cells that grew into masses rather than monolayers . ( E ) Representative confocal projections of HPAF II ( upper panel ) and HBE ( lower panel ) cells undergoing apical ( left ) or basal ( middle ) extrusion , with XZ sections below . Basal extrusion was scored when an actin ring contracted above the dying cell ( marked by DNA and caspase-3 staining ) and the DNA of the dying cell lies in the same plane as the neighboring cells . Scale bars , 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 04069 . 006 Our experiments show that the reduced S1P2 levels in HPAF II cells disrupted apical extrusion , leading to reduced apoptosis rates and enhanced basal extrusion . Similar to HBE monolayers lacking S1P2 , HPAF II cells formed masses within a week of culture and displayed extrusion defects and reduced rates of UV-C-induce apoptosis ( Figure 4A–D ) . While ∼50% of cells did not extrude , most of the remaining cells extruded in the opposite direction—basally , underneath the layer ( Figure 4D , E ) at rates similar to when MDCK monolayers are treated with S1P2 antagonist ( Slattum et al . , 2014 ) . Basal extrusion of cells with upregulated survival signaling could potentially enable their invasion beneath the epithelium ( Slattum and Rosenblatt , 2014 ) . To investigate if basally extruded cells can survive following extrusion ( Slattum and Rosenblatt , 2014 ) , we analyzed extrusion from three-dimensional cysts , where the fate of basally extruded cells can be followed outside the cyst , rather than beneath a monolayer . MDCKs were used as controls , which , like HPAF II cells , form cysts with hollow apical lumens of 34 µm ± 5 µm in diameter when grown in Matrigel ( Figure 5B ) . Approximately 30% of HPAF II cysts extrude cells basally that survive , compared to only ∼3% of MDCK cysts ( Figure 5C , D ) . Live imaging confirmed that 28 . 6% of basally extruded cells remained alive throughout a 12-hr video , whereas those from MDCK cysts died during this time ( Video 1 , n = 5 videos of each ) . Importantly , S1P2-GFP expression is sufficient to rescue apical extrusion ( Figure 5C ) , decrease the frequency of live cells that basally extrude ( Figure 5D ) , and increase the percentage of cysts with dead cells in their lumens ( Figure 5E ) . These data suggest that the S1P-S1P2 signaling required for extrusion is critical not only for promoting cell death but also for preventing basal extrusion , which could enable cells to invade . 10 . 7554/eLife . 04069 . 007Figure 5 . Exogenous expression of S1P2 rescues apical extrusion and cell death . ( A ) GFP immunoblot of HPAF II cells expressing S1P2 GFP . ( B ) Representative confocal projections of MDCK , HPAF II , and HPAF II-S1P2 cysts , where scale bar = 10 µm . ( C ) Percentages of MDCK , HPAF II GFP , and HPAF II S1P2 cysts with basal extrusion; n = 300 cysts per cell line . ( D ) Quantification of MDCK , HPAF II GFP , and HPAF II S1P2 cysts extruding live cells basally; n = 300 cysts per cell line . ( E ) Frequency of HPAF II GFP and HPAF II S1P2 cysts with dying cells inside the lumen; n = 300 cysts per cell line . All results are expressed as mean values ± STD of three separate experiments ( p < 0 . 01 , and **p < 0 . 001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04069 . 00710 . 7554/eLife . 04069 . 008Video 1 . An HPAF II cyst growing without apoptotic stimuli extrudes live cells basally . Note that some cells extrude and die while others survive and/or migrate away . DOI: http://dx . doi . org/10 . 7554/eLife . 04069 . 008 Because extrusion promotes cell death in response to apoptotic stimuli ( Rosenblatt et al . , 2001; Andrade and Rosenblatt , 2011 ) and during normal homeostasis ( Eisenhoffer et al . , 2012; Marinari et al . , 2012 ) , we hypothesized that it does so by eliminating the competing survival signaling associated with cell-matrix attachment . If so , increased cell survival when extrusion is blocked would derive from prolonged cell attachment to the underlying matrix . Since Focal Adhesion Kinase ( FAK ) is critical for matrix-dependent survival ( Frisch et al . , 1996 ) , we investigated if FAK were increased in cells targeted for death when extrusion was blocked . Surprisingly , we found that control MDCK cells in early stages of extrusion have far higher levels of active FAK , by immunostaining with a phospho-FAK antibody , than surrounding live cells but that these levels decrease during later stages of extrusion ( Figure 6A ) . This increase in pro-survival phospho-FAK in cells targeted to die mimics the increased levels of S1P , another pro-survival signal , in cells triggered to extrude and die that also decrease once cells extrude ( Figure 3C and [Gu et al . , 2011] ) . However , late-staged apoptotic cells ( detected by piknotic DNA ) still have high levels of active FAK when extrusion is blocked with S1P2 antagonist ( Figure 6A ) . This paradoxical survival signaling increase in cells targeted to die may reflect cell-intrinsic compensatory signals to apoptotic signaling that eventually decrease as cells commit to apoptosis . The fact that these survival signals stay high when cell extrusion is blocked suggests that this increased survival signaling derives from inability to detach from matrix . 10 . 7554/eLife . 04069 . 009Figure 6 . Inhibition of FAK activity specifically increases cell death in epithelial cells lacking S1P2 . ( A ) Immunostaining of active phospho-FAK in early and late control extrusions and in a JTE-013 ( S1P2 antagonist ) -inhibited extrusion with late apoptotic cell or one with the FAK inhibitor PF573228 , with averaged arbitrary fluorescence units and their p-values compared to early extruding cells in graph on right ( n = 10 measurements each over three separate experiments ) . ( B ) Quantification of UV-induced MDCK apoptosis in the presence of control , JTE-013 , or EHT1864 with or without treatment of the FAK inhibitor PF573228 , where n = 3000 . ( C ) Quantification of UV-induced apoptosis of MDCK cells and those expressing FRNK , where n = 3000 . ( D ) Quantification of PF573228-induced apoptosis of HPAF II cells , where n = 3000 . ( E ) Representative confocal projections of HPAF II cysts treated with control or PF573228 . Scale bars = 10 µm . ( F ) Frequencies of HPAF II cysts with dying cells , live extruding cells , or neither , where n = 300 . All quantification results are expressed as mean values ± STD of three separate experiments ( p < 0 . 05 , p < 0 . 01 , p < 0 . 001 , and *p < 0 . 0001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04069 . 009 Because FAK activity remains high in cells where extrusion is blocked , we wondered if chemically blocking FAK activation would rescue the apoptosis rates of monolayers lacking functional S1P2 signaling . The specific FAK inhibitor PF 573228 had no effect on untreated or UV-treated wild type monolayers ( Figure 6B and [Slack-Davis et al . , 2007] ) , likely because cells not targeted to extrude have quite low levels of active FAK ( Figure 6A ) . However , this same FAK inhibitor rescued the cell death rates of monolayers where extrusion is blocked with S1P2 antagonist JTE013 or Rac inhibitor to those seen in wild type monolayers ( Figure 6B ) . FAK inhibitor dramatically decreased phospho-FAK staining in cells where JTE013 blocks extrusion ( Figure 6A ) . Over-expression of a dominant negative FAK isoform ( FRNK ) ( Park et al . , 2004 ) acted similarly to FAK inhibitor ( Figure 6C ) . Importantly , FAK inhibitor alone induced apoptosis of HPAF II cells in monolayers ( Figure 6D ) and even those basally extruding from HPAF II cysts ( Figure 6E , F ) . Additionally , treatment with FAK inhibitor to cysts that had already accumulated live basally extruded cells was sufficient to nearly double apoptosis rates of basally extruded cells ( from 15 ± 2% to 28 ± 3% ) when added for only 2 hr . Because inhibition of FAK appears to promote cell death of only extrusion-defective epithelial cells without affecting normal epithelia , we wondered if FAK inhibition could eliminate the epidermal masses of Mil zebrafish embryos as they formed without adversely impacting the animal . While FAK inhibitor had no visible effect on wild-type zebrafish at 5 dpf , it greatly decreased the number and size of epidermal cell masses in S1P2-mutant embryos of the same age ( Figure 7A–C ) . While FAK inhibitor treatment sloughed off many of the epidermal cells , which could be found at the bottom of the dish , it also increased the apoptosis rate within the masses ( Figure 7D ) . To test if FAK inhibitor could eliminate epidermal masses after they form , we needed to inducibly knockdown S1P2 later in development , since Mil zebrafish mutants die due to heart defects around the time cell masses form ( ∼5 dpf; [Kupperman et al . , 2000] ) . To do so , we used we photo-activated S1P2 morpholino at 24 hpf ( see [Eisenhoffer et al . , 2012] for characterization of this method ) to knockdown S1P2 after heart development occurred . S1P2 morphants had 0 . 18-fold lower S1P2 protein levels ( Figure 7E ) and phenocopied the epidermal masses seen in Mil mutants ( Figure 7F ) . Addition of FAK inhibitor to an S1P2 morphant at 5 dpf with epidermal masses caused the masses to slough off within 19 hr ( Figure 7F , where n = 6 videos total ) . Remarkably , we found that while Mil embryos had poor epidermal barrier function , as assayed by Texas Red-DextranMW70 permeability , epidermal permeability was significantly reduced with FAK inhibitor ( Figure 7A ) . These results suggest that FAK inhibitor alone may selectively target masses resulting from cells defective in extrusion and improve overall epithelial integrity without affecting the normal surrounding tissue . 10 . 7554/eLife . 04069 . 010Figure 7 . FAK inhibitors eliminate epidermal cell masses in S1P2 zebrafish mutants and improve epidermal barrier function without affecting wild type zebrafish . ( A ) Representative confocal projections of 5-dpf WT ( left ) and Mil ( S1P2 mutant ) ( right ) zebrafish larvae treated with DMSO or FAK inhibitor PF573228 , where high Texas-Red Dextran indicates poor permeability in Mil but is greatly reduced when barrier function is improved with FAK inhibitor treatment . Scale bar = 10 µm and red box indicates region of fish imaged . ( B ) 5 dpf Mil and wild type zebrafish treated with and without FAK inhibitor . Note that while FAK inhibitor-treated Mil have other developmental defects ( heart and circulation ) , there are no obvious clumps as seen in the untreated fish . Scale bar = 100 µm and red box indicates region of fish imaged . Note FAK inhibitor does not affect WT zebrafish . ( C ) Quantification of epidermal masses in 5 dpf Mil zebrafish larvae with and without PF573228 . ( D ) Quantification of apoptotic cells within epidermal masses with and without PF573228 . For both , error bars are SD and p values are *<0 . 0001 . ( E ) Immunoblot showing knockdown of S1P2 by photo-activatable morpholinos . ( F ) Stills from a video where PF573228 was added to S1P2 morphant at 5 dpf , where red arrows show the edge of the epidermis over time , scale bar = 50 µm and red box indicates region of fish imaged . Time is hours:minutes following FAK inhibitor addition . Note: epidermal cells that are sloughed off become embedded in the agarose where fish is mounted . DOI: http://dx . doi . org/10 . 7554/eLife . 04069 . 010 We have shown that disrupting S1P/S1P2 signaling inhibits epithelial cell death , causes masses , and promotes a potential mechanism for invasion—basal extrusion , which together could promote tumor formation and progression . Yet , it was not clear if this signaling pathway plays a role in malignancy . To test the role of S1P2 in tumorigenesis , we orthotopically transplanted HPAF II tumor cells expressing either GFP or S1P2-GFP into nude mice and found that S1P2 expression was sufficient to markedly reduce both tumor size and metastatic frequency ( Figure 8A , B ) . 10 . 7554/eLife . 04069 . 011Figure 8 . Exogenous S1P2 expression reduces orthotopic pancreatic tumors and rates of metastasis in mice . ( A ) Representative images of HPAF II GFP and HPAF II S1P2 orthotopic xenograft tumors in nude mice . ( B ) Summary of tumor weights and metastatic frequency . DOI: http://dx . doi . org/10 . 7554/eLife . 04069 . 011 Further , we found that human pancreatic carcinomas have strikingly down-regulated S1P2 protein levels . Because tumors typically have different stromal to epithelial ratios compared to uninvolved pancreatic tissue that can confound microarray data , we immunostained fixed tissue slices for both S1P2 and cytokeratin to highlight epithelial cells so that we could compare S1P2 protein levels in the epithelia alone ( Figure 9A ) . We found that S1P2 was significantly lower in pancreatic cancer ( PDAC ) cells compared to epithelial acini from uninvolved neck margins , from which PDACs may arise , or to pancreatic intraepithelial neoplasia ( PanIN ) precursor lesions ( Figure 9A–C ) . Importantly , lower S1P2 expression correlates with later tumor stages in both averaged ( Figure 9B ) and five patient-matched samples ( Figure 9C ) . Loss of S1P2 expression in pancreatic cancers suggests that defective extrusion may contribute to human PDAC development . 10 . 7554/eLife . 04069 . 012Figure 9 . Human pancreatic tumors have reduced S1P2 expression . ( A ) H&E ( top panel ) and confocal fluorescence images ( middle and bottom panel ) of normal acinar cells from uninvolved neck margin , PanIN , and invasive cancer cells . Scale bars , 100 and 10 µm , respectively . ( B ) Quantification of S1P2 fluorescence intensity in acinar cells , PanIN , and invasive cancer cells from five individual patients . p values were calculated with a paired t test . ( C ) Changes of S1P2 fluorescence intensity from normal acinar cells to invasive cancer cells in each individual patient . DOI: http://dx . doi . org/10 . 7554/eLife . 04069 . 012 Our work presents a new paradigm for how an aggressive class of carcinomas may form and progress: failed extrusion ( Figure 10 ) . Normal epithelial cells produce S1P to trigger their extrusion and death once they become too crowded , simultaneously maintaining correct cell density and barrier function . Epithelia with defective S1P/S1P2 signaling cannot extrude apically . Extrusion-defective epithelia retain cells , which can result in resistance to homeostatic and chemotherapy-induced cell death and neoplastic masses . Further , a small number of cells also die without getting extruded , which can disrupt barrier function . Aside from allowing access of inappropriate signals , poor epithelia barrier function could cause chronic inflammation—an important factor for tumor progression ( Coussens and Werb , 2002 ) . Additionally , defective apical extrusion signaling shifts extrusion basally , which could allow transformed cells to invade the underlying tissue ( Slattum and Rosenblatt , 2014 ) . 10 . 7554/eLife . 04069 . 013Figure 10 . Model for how extrusion can promote cell death and suppress tumor formation . Apical extrusion promotes death of grey-blue cell ( top panel ) . Here , pro-survival signals phospho-FAK and S1P ( which also promotes extrusion ) increase in an early extruding cell but decrease once a cell is extruded and targeted to die ( right , cell with piknotic nucleus ) . However , when apical extrusion is blocked due to lack of S1P2 receptor ( bottom panel ) , epithelial cells do not die and can accumulate ( left cell and those accumulating above ) from increased matrix-derived survival signaling ( arrows from matrix neighboring cells signaling to P-FAK ) . Additionally , cells can still basally extrude , which could potentially enable their invasion beneath the layer ( right cell ) . Basally extruded cells may also have high P-FAK , since they are sensitive to FAK inhibitor when extruded into matrix in vitro , yet this point will be critical to test in vivo in disseminating tumors . Other cells may still die but not extrude ( grey-blue cell with piknotic nucleus ) , leading to poor barrier function and inflammation , which could also promote tumor progression . DOI: http://dx . doi . org/10 . 7554/eLife . 04069 . 013 Basal extrusion may be a common hallmark of invasive tumor types . We have recently discovered that oncogenic KRasV12 expression degrades S1P through autophagy and causes cell masses and basal extrusion , similar to the extrusion defects observed when S1P2 is absent ( Slattum et al . , 2014 ) . KRasV12 is an important driver for the same cancers that lack S1P2—pancreatic , lung , and colon carcinomas—and its expression alone reduces S1P2 ( Slattum et al . , 2014 ) , which may explain why PanIN precursors have reduced S1P2 expression . Further , we have found that another oncogenic mutation , truncation of the adenomatous polyposis coli gene , also results in increased basal extrusion . While it is not clear what mechanisms drive tumor cell invasion , our work showing that exogenous expression of S1P2 can dramatically reduce basal extrusion rates and orthotopic tumor metastasis rates in tumor cells that lack this receptor suggests that S1P2-mediated extrusion may play an important role in metastatic cell invasion . Because cancer cells lacking S1P2 have increased survival signaling due to an inability to detach from the matrix and its associated survival signaling through increased active FAK , we found that FAK inhibitor on its own could rescue cell death rates to those seen in wild type cells ( Figure 7D ) . Surprisingly , FAK inhibitor could also reverse other extrusion defects , such as poor barrier function and survival of basally extruded cells , factors that together could contribute to tumor progression . The fact that adding FAK inhibitor can rescue these defects when added after they form further supports the notion that anoikis results from extrusion and also suggests that FAK inhibitors may be particularly good at treating pancreatic and other carcinomas defective in extrusion . Moreover , we expect a specific FAK inhibitor to not cause the common toxicities associated with standard chemotherapies , as it does not affect normal epithelial tissue . Another FAK inhibitor , PF-00562271 , reduces tumor growth , metastases , and ameliorates tumor microenvironment when used in an orthotopic mouse model for pancreatic cancer ( Stokes et al . , 2011 ) , suggesting this drug could be promising for pancreatic cancer patients . However , it is important to note that PF-00562271 also inhibits a FAK-related kinase Pyk-2 , which promotes non-specific cell death ( Schultze and Fiedler , 2011 ) . The phase I clinical trial for PF-00562271 showed that while this drug was tolerated fairly well , it did cause nausea , vomiting , and diarrhea ( Infante et al . , 2012 ) , symptoms indicative of poor gut barrier function , likely due to excessive non-specific apoptosis . Our results suggest that a newer more specific FAK inhibitor , such as VS-4718 ( Shapiro et al . , 2014 ) , may provide a more targeted therapy for patients with pancreatic and lung carcinomas that have aberrant extrusion signaling without the common toxicities associated with older chemotherapies . Based on our previous findings that extrusion drives normal epithelial cell turnover , we have found that disruption of extrusion may contribute to a class of cancers with poor prognosis . We find that cancer cells that lack the extrusion-signaling axis not only have reduced cell death rates but also have poor barrier function and a propensity to extrude cells basally , properties that could lead to higher invasion and metastatic rates . Therefore , aberrant extrusion signaling in pancreatic and lung carcinomas could not only contribute to tumor initiation but also progression . Importantly , specific inhibition of FAK , which does not disrupt normal epithelial tissue , is sufficient to reverse all of the effects of disrupted extrusion and could provide a better , less toxic therapy for this aggressive class of tumors . MDCK II cells were cultured in Dulbecco's minimum essential medium ( DMEM ) high glucose with 5% FBS ( all from HyClone , Logan , UT ) and 100 μg/ml penicillin/streptomycin ( Invitrogen , Grand Island , NY ) at 5% CO2 , 37°C . HBE cells were cultured in MEM supplemented with 10% FBS and l-glutamine in a flask coated with human fibronectin type I ( BD , Franklin Lakes , New Jersey ) , bovine collagen I ( Advanced BioMatrix , San Diego , CA ) , and BSA ( Invitrogen ) . HPAF II cells were cultured in MEM ( HyClone ) supplemented with 10% FBS . Culturing HPAF II and MDCK cells in Matrigel generated HPAF II and MDCK cysts , respectively . Briefly , a single cell suspension of HPAF II cells was resuspended in Growth Factor Reduced Matrigel ( BD Biosciences ) final concentration 4% and placed in eight well coverglass chambers ( Nalge Nunc , Rochester , NY ) coated with a thin polymerized layer of Matrigel . For live imaging , cells were placed on 24 well glass-bottom culture dishes ( MatTeK Corporation , Ashland , MA ) . After 20 min incubation at 37°C , cell-growth medium was added on top . Cysts were allowed to grow for the indicated duration and analyzed by time-lapse imaging or fixed with 4% paraformaldehyde for immunostaining . We first transfected MDCK II cells with neomycin-resistant pTet-ON regulator plasmid , encoding rtTA protein ( reverse tTA , tetracycline-controlled transactivator ) . The stable transfected MDCK II cells were selected by cultivation in media containing 500 μg/ml G418 . We then transfected Tet-ON MDCK II cells with FAK-CD-TRE-2-hyg plasmids ( Golubovskaya et al . , 2009 ) and selected for stably transfected cells with 0 . 1 mg/ml hygromycin . Expression of FAK-CD was induced with 2 µg/ml doxycycline for 4 days . MDCK II , HBE , or HPAF II cells grown to confluence on glass coverslips were exposed to 1200 μJ/cm2 UV254 using a Spectrolinker ( Spectroline , Westbury , NY ) to induce apoptotic extrusion and incubated for 2 hr before fixation . Cells were treated with 10 μM JTE-013 ( Tocris Bioscience , United Kingdom ) , 10 μM Y-27632 , 10 μM Blebbistatin , 10 μM EHT 1864 ( all from Sigma–Aldrich , Saint Louis , MO ) , 10 μM PF 573228 ( Tocris Bioscience ) or 1% DMSO as a control for 10 min before UV treatment . To induce apoptosis with chemotherapy drugs , cells were treated with 20 μg/ml Cisplatin , 1 μM Gemcitabine , or 20 μg/ml 5-Fluorouracil ( all from Sigma–Aldrich ) for 24 hr . Cells were fixed with 4% formaldehyde in PBS at 37°C for 20 min , permeabilized for 10 min with 0 . 5% Triton in PBS , blocked with AbDil ( PBS with 0 . 1% Triton X-100 and 2% BSA ) for 10 min , and incubated with primary antibody for 1 hr . Antibody concentrations used for immunostaining were: 1:200 rabbit anti-active caspase-3 ( BD ) , 1:100 rabbit anti-phospho FAK ( Tyr 397 ) ( Cell Signaling , Danvers , Massachusetts ) , and 50 μg/ml anti-S1P mAb ( LPath Inc . , San Diego , CA ) . Alexa Fluor 488 goat anti–rabbit IgG and Alexa Fluor 488 goat anti–mouse IgG were used as secondary antibodies to detect active caspase-3 and S1P , respectively . Actin was detected with Alexa Fluor 568–phalloidin ( Invitrogen ) . DNA was detected with 1 μg/ml Hoechst 33 , 342 ( Sigma–Aldrich ) . Pancreatic tissue sections ( 3 µm ) were generated from formalin-fixed , paraffin-embedded tissue collected from PDAC patient resections at the University of Utah Hospital . The sections were deparaffinised and rehydrated by incubating in citrus clearing solvent ( CCS; Richard Allen Scientific , Kalamazoo , MI ) , 100% , 95% , 80% , 70% ethanol , and PBS . For immunofluorescence , antigens were retrieved by heating the slides in boiling 10 mM sodium citrate for 20 min , then rinsed three times with PBS , blocked with 5% BSA/0 . 5% Tween-20 in PBS for 4 hr , and incubated overnight with anti-S1P2 ( Imgenex , San Diego , CA ) and anti-pan cytokeratin ( Sigma–Aldrich ) at 4°C , rinsed five times with PBS , incubated in Alexa-488 anti-mouse antibody , 1 µg ml−1 Hoechst , and Alexa-568 anti-rabbit antibody for 2 hr , rinsed three times in PBS , and mounted in Prolong Gold ( Invitrogen ) . Fluorescence micrographs of stained slides were obtained using a Leica DM 6000B microscope and captured using a Micromax charge-coupled device camera ( Roper Scientific , Sarasota , Florida ) . IPlab Software was used to control the camera and to process images . Pixel intensity interested area was measured with ImageJ . The University of Utah Institutional Review Board approved the use of human tissue in this study . Tissue sections were obtained from excess clinical pathology tissue from patients , deidentified , resected for pancreatic adenocarcinoma at the University of Utah Huntsman Cancer Institute with appropriate informed consent for use of samples for research purposes ( IRB_00010924 ) . We sorted Mil zebrafish embryos from their WT homozygous and heterozygous siblings at 2 days post-fertilization ( dpf ) by the presence or absence of tail blisters . We then treated half of Mil mutants and half of the wild type siblings with FAK inhibitor ( 10 μM PF 573228 ) for 3 days and added Texas Red-DextranMW70 30 min before fixation . Embryos were then fixed in PBS with 4% formaldehyde and 0 . 1% Triton X-100 overnight , blocked with 2 mg/ml BSA for 2 hr , and stained for anti-E Cadherin ( Gentex , Zeeland: MI ) for 4 hr followed by incubation with Alexa Fluor 488 anti–rabbit IgG Ab . DNA was visualized using 1 μg/ml DAPI . The antisense morpholino oligonucleotides and photo-morpholino oligonucleotides were acquired from Gene Tools , LLC ( Philomath , OR ) . For the photo-morpholino experiments , the translation blocking antisense morpholino ( 4 ng/embryo of each ) was mixed at a 1:1 molar ratio with a 25 bp sense photo-morpholino and injected into 1–2-cell-stage wild-type AB zebrafish embryos . At 28–32 hpf , embryos were exposed to 350 nm light for 20 s to release the caging sense morpholino , then treated with 10 μM PF 573228 ( FAK inhibitor ) , and filmed by timelapse video microscopy on a spinning disc confocal . Confocal Imaging was performed in a Nikon Eclipse TE300 inverted microscope converted for spinning disc confocal microscopy ( Andor Technologies , United Kingdom ) using a 40× Nikon Apo LWD lenses . Images were acquired with an electron-multiplied cooled charge-coupled device camera ( DV887 1004X1002; Andor Technologies ) driven by Andor IQ2 imaging software . All images were processed further using Photoshop and Illustrator ( Adobe , San Jose , CA ) , and QuickTime Pro ( Apple , San Jose , CA ) software . Live imaging of HPAF II cysts was taken with an OLYMPUS 1X71 inverted microscope using a 20× lens . The images were taken every 5 min for 12 hr . Temperature was controlled by a Weather station connected to the microscope . For live imaging , FAK inhibitor treated S1P2 morpholino fish were anesthetized with 0 . 02% Tricaine in E3 , mounted in 1% low melt agarose and imaged on a spinning disc confocal at 20× , capturing a z-series every 2 min ( Eisenhoffer and Rosenblatt , in press ) for 3–6 hr . To quantify the frequency of apoptosis within a monolayer , we counted the number of active caspase-3 positive cells still in contact with the monolayer per 1000 cells . We excluded round cells with strong caspase-3 staining that were not associated with monolayers , which were likely extruded well before experimental treatments could have impacted them . To quantify extrusion , extruding cells were manually scored based on the presence of an actin ring compared to where apoptotic cell localized with respect to its neighboring cells . Apoptotic cells above the plane of the monolayer with strong actin staining around and below them were defined as apically extruded cells . Apoptotic cells remaining in the monolayer and underneath an apical actin ring were considered basally extruded cells . Active caspase-3-positive cells that were not surrounded by a distinguishable actin ring were defined as non-extruded apoptotic cells . Whole-cell extracts were prepared by resuspending cells in NP40 Cell Lysis Buffer ( Invitrogen ) plus protease inhibitor cocktail and PMSF ( Roche , Switzerland ) . Proteins were resolved by SDS-PAGE using NuPage gels ( Invitrogen ) , and transferred to polyvinylidene difluoride membrane ( Thermo , Waltham , MA ) . Membranes were blocked with 5% dry milk and probed with anti-tubulin 1:1000 ( Sigma–Aldrich ) and anti-GFP 1:10 , 000 ( Clontech , Mountain View , CA ) or anti S1P2 1:500 ( Santa Cruz , Santa Cruz , CA ) and identified using horseradish peroxidase conjugated secondary antibodies and enhanced chemiluminescense . pLL5 . 0 is a lentiviral expression plasmid containing a U6 promoter to drive expression of the shRNA sequence and a 5′-long terminal repeat to drive the expression of GFP . Designing and cloning of S1P2-specific shRNA were performed as previously described ( Gu et al . , 2011 ) . The full length of human S1P2 was PCR amplified and ligated into the EcoRI/BamHI sites of pLL5 . 0 . Retroviral production and infections were as described ( Gu et al . , 2011 ) . Infected HPAF II or HBE cells were sorted for GFP by using a BD FACSAria Cell Sorter . Animals were handled according to protocols approved by the University of Utah Institutional Animal Care and Use Committee . Mice were anesthetized under isoflurane gas; the abdominal skin and muscle were incised just off the midline and directly above the pancreas to allow visualization of the pancreatic lobes; the pancreas was gently retracted and positioned to allow for direct injection of a 100 µl bolus of 1 × 106 HPAF II cells expressing GFP or S1P2 GFP using a 1 cc syringe with a 30 gauge needle; the pancreas was placed back within the abdominal cavity; and both the muscle and skin layers were closed . 8 weeks later , mice were sacrificed and xenograft tumors were resected and weighed . Metastatic tumors within abdominal wall , liver , and mesentery were also examined and resected . P-FAK was quantified using Nikon Elements as the ‘ROI Sum Intensity’ on ROI statistics using the same ROI size for each projection micrograph measured , subtracting background average fluorescence . Lumen sizes were also measured using Nikon Elements using ‘Radius size’ using the largest diameter in the ‘Annotations and Measurements’ analysis package . The statistical analysis was performed using an unpaired or paired t test . Values of p < 0 . 05 were considered significant .	Epithelial cells cover the surface of our bodies , line our lungs , stomach and intestines and serve as a protective layer around other organs . If too many epithelial cells die and are not replaced , this protective layer may erode and lead to organ damage . However , if too many new cells grow , tumors can form . One process that helps to maintain the right number of epithelial cells is called extrusion . When too many epithelial cells are present , the resulting overcrowding triggers this process to squeeze excess cells out of the layer and away from the organ . Usually , these cells quickly die . However , if the pathway that regulates this process—which involves a receptor protein called S1P2—is disturbed , the cells may instead be pushed into the space between the epithelial layer and the organ . When this happens , the cells are more likely to survive and may then form a tumor that invades the organ . Gu et al . interfered with extrusion by reducing the levels of the S1P2 receptor in layers of human epithelial cells grown in the laboratory . Fewer epithelial cells were squeezed out of these cell layers , making the layers up to three times as thick in places . Moreover , mutant zebrafish lacking the S1P2 receptor also accumulated epithelial masses throughout their bodies . Gu et al . found that disrupting the extrusion process made the cells resistant to chemotherapy , and that certain hard-to-treat human pancreatic , lung , and colon cancers had lower levels of the S1P2 receptors . Boosting the activity of S1P2 receptors helped to restore normal extrusion and reduced the size of pancreatic tumors in mice . Gu et al . then focused on an enzyme called Focal Adhesion Kinase that helps cells to survive . Treating zebrafish with a drug to block the activity of this enzyme left normal fish unharmed . However , in mutant fish with malfunctioning extrusion pathways , the drug rescued the number of cells that died , reduced the size and number of masses , and cured their leaky skin barrier . If further studies confirm the results , the drug may offer a new , less toxic , treatment for certain cancers that do not respond to currently available treatments .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology", "cancer", "biology" ]	2015	Defective apical extrusion signaling contributes to aggressive tumor hallmarks
Mechanical force and Wnt signaling activate β-catenin-mediated transcription to promote proliferation and tissue expansion . However , it is unknown whether mechanical force and Wnt signaling act independently or synergize to activate β-catenin signaling and cell division . We show that mechanical strain induced Src-dependent phosphorylation of Y654 β-catenin and increased β-catenin-mediated transcription in mammalian MDCK epithelial cells . Under these conditions , cells accumulated in S/G2 ( independent of DNA damage ) but did not divide . Activating β-catenin through Casein Kinase I inhibition or Wnt3A addition increased β-catenin-mediated transcription and strain-induced accumulation of cells in S/G2 . Significantly , only the combination of mechanical strain and Wnt/β-catenin activation triggered cells in S/G2 to divide . These results indicate that strain-induced Src phosphorylation of β-catenin and Wnt-dependent β-catenin stabilization synergize to increase β-catenin-mediated transcription to levels required for mitosis . Thus , local Wnt signaling may fine-tune the effects of global mechanical strain to restrict cell divisions during tissue development and homeostasis . Mechanical cues are critical for regulating cellular growth , morphology , and behavior in developing and adult tissues . Recently , cadherin-mediated cell-cell adhesions have been identified as core components that regulate the response of multicellular tissues to externally applied strain ( Whitehead et al . , 2008; Desprat et al . , 2008; Kim et al . , 2011 ) . Significantly , the cadherin-associated transcriptional co-activator β-catenin accumulates in the nucleus following mechanical strain ( Farge , 2003; Benham-Pyle et al . , 2015 ) . β-Catenin transcriptional activity drives cellular proliferation and is a downstream effector of the Wnt signaling pathway ( Nelson and Nusse , 2004; Clevers and Nusse , 2012 ) . β-Catenin protein levels in cells are tightly controlled by sequestering β-catenin at cell-cell contacts and active degradation of cytosolic β-catenin ( Whitehead et al . , 2008; Desprat et al . , 2008; Kim et al . , 2011; Aberle et al . , 1997; Ikeda et al . , 1998; Su et al . , 2008 ) . The E-cadherin-bound pool of β-catenin is regulated by a balance of tyrosine kinase and tyrosine phosphatase activities ( Farge , 2003; Benham-Pyle et al . , 2015; Muller et al . , 1999; Piedra et al . , 2001; Lilien and Balsamo , 2005; Tan et al . , 2016 ) . The affinity between E-cadherin and β-catenin at junctions is decreased by the activity of cytoplasmic and receptor tyrosine kinases ( EGFR , Src , Abl , Fyn/Fer ) ( Nelson and Nusse , 2004; Clevers and Nusse , 2012; Matsuyoshi et al . , 1992; Takeda et al . , 1995; Coluccia et al . , 2007; Krejci et al . , 2012 ) and increased by the activity of tyrosine phosphatases ( PTB1B , SHP-3 , PTP , LAR ) ( Lilien and Balsamo , 2005; Tan et al . , 2016; Sap et al . , 1994; Hellberg et al . , 2002; Xu et al . , 2002 ) . In normal epithelia , cytoplasmic levels of β-catenin are very low due to targeting of β-catenin for degradation through β-catenin phosphorylation on Serine 45 by Casein Kinase I ( CKI ) , which primes β-catenin for the further phosphorylation of Serine 33 , Serine 37 , and Threonine 41 by Glycogen Synthase Kinase 3β ( GSK3β ) ( Aberle et al . , 1997; Amit et al . , 2002; Liu et al . , 2002 ) . CKI/GSK3β phosphorylated β-catenin is ubiquitinated within the Axin/Adenomatous Polyposis coli complex , and then degraded in the proteasome ( Winston et al . , 1999 ) . Accumulation of cytosolic β-catenin can be triggered by Wnt , the mammalian homolog of Drosophila Wingless . When Wnt ligand binds to Frizzled/LRP receptors , Axin is recruited to the plasma membrane and CKI/GSK3β are inactivated , resulting in inhibition of β-catenin ubiquitination and degradation ( Li et al . , 2012; Vinyoles et al . , 2014 ) . As a result , β-catenin accumulates in the cytoplasm , translocates to the nucleus , and activates target gene transcription in a complex with Tcf/Lef ( Clevers and Nusse , 2012 ) . Wnt/β-catenin activation broadly promotes cell proliferation and tissue expansion during developmental patterning . Consequently , mis-regulation of the Wnt signaling pathway , particularly β-catenin degradation , and downstream β-catenin transcriptional activity disrupt contact-mediated cell cycle inhibition , drive epithelial to mesenchymal transition , and promote cellular transformation and cancer progression ( Orford et al . , 1999; Korinek et al . , 1997; Clevers , 2006; Chen et al . , 2012 ) . In addition to its known roles in cadherin-mediated cell-cell adhesion and as a downstream effector of Wnt-mediated proliferation , β-catenin is a mechanotransducer . Mechanical strain induces nuclear accumulation and increased transcriptional activity of β-catenin ( Benham-Pyle et al . , 2015; Sen et al . , 2008 ) , and mechanical perturbation of living tissues is associated with increased β-catenin signaling and induction of downstream transcriptional targets ( Farge , 2003; Farge and development , 2011 ) . β-Catenin transcriptional activation downstream of mechanical force or Wnt stimulation has been studied separately . It remains unknown whether β-catenin’s role as a target of mechanical force is independent , redundant or synergistic to Wnt signaling in tissue homeostasis . Previously , we showed that mechanical strain drives the re-entry of a quiescent epithelial monolayer into the cell cycle by sequential , but independent induction of Yap1 ( G0 to G1 ) and then β-catenin transcriptional activities ( G1 to S ) : inhibition of Yap1 and β-catenin transcriptional activities was sufficient to block cell cycle entry and G1 to S transition , respectively ( Benham-Pyle et al . , 2015 ) . However , cells that exit quiescence following mechanical strain accumulate in S/G2 and do not enter mitosis , suggesting that a further activation event is required to complete cell cycle progression . Here we show that mechanical strain-induced Src phosphorylation of β-catenin and Wnt3A pathway-dependent stabilization of cytoplasmic β-catenin synergize to increase β-catenin transcriptional activity to levels that drive cell cycle progression through S/G2 and mitosis . Physical constraints and mechanical cues regulate cell proliferation in multicellular tissues . In cell culture models of quiescent or growing epithelial cell monolayers , mechanical strain induces cell cycle re-entry and increases the number of actively cycling cells ( Benham-Pyle et al . , 2015; Streichan et al . , 2014 ) . To test directly if mechanical strain is sufficient to drive cells into mitosis , super-confluent monolayers of quiescent normal kidney epithelial ( MDCK ) cells ( Benham-Pyle et al . , 2015 ) were formed on flexible silicone substrates either in a single-well biaxial cell stretching device compatible with live imaging ( Figure 1—figure supplement 1 ) , or in an integrated strain array ( ISA ) ( 34 , see also Materials and methods ) . The design and fabrication of the biaxial cell stretching device compatible with live imaging allowed for direct visualization of strained monolayers with an inverted fluorescence microscope . Briefly , quiescent monolayers were formed on compliant silicone substrates in a PDMS well , surrounded by a pneumatic chamber separated by a thin silicone wall . Vacuum pressure applied to the pneumatic chamber deflected the silicone wall outwards , resulting in biaxial stretch accompanied by equi-biaxial in-plane strain ( for details , see Figure 1—figure supplement 1 , and Materials and methods ) . The live cell stretcher and ISA were able to apply maximum strains of 8 . 5 and 15% , respectively . The maximum level of static biaxial stretch was applied and held for up to 24 hr , and cells were either imaged live or fixed ( ISA ) , and then processed for quantitative image analysis using MATLAB scripts which enabled unbiased image quantitation of large numbers of cells ( see Materials and methods ) . A fluorescence ubiquitination-based cell cycle indicator ( Fucci MDCK-2 , see [Streichan et al . , 2014] ) was used to monitor cell cycle dynamics following mechanical strain . Fucci MDCK cells stably express mKO2-Cdt1 ( red fluorescence ) during G0 and G1 phases , and mAG-Geminin ( green fluorescence ) beginning at S and ending at mitosis when Geminin is degraded . Thus , the level of mAG-Geminin fluorescence indicates time from entering into S , and loss of mAG-Geminin fluorescence marks entry into mitosis; the transition in cell fluorescence over time from red to green to red marks the transition of cells from G1 into S , then S/G2 into mitosis , and the re-entry of daughter cells into G1 , respectively . In the absence of mechanical strain , quiescent epithelial monolayers maintained a steady turnover rate over 24 hr that was characterized by a low , but constant number of cells in S/G2 ( ~10% Geminin-positive , Figure 1A , C , Video 1 ) and mitosis ( ~1 division/hour/0 . 1 mm2 , Figure 1B , D , Video 1 ) . Upon application of mechanical strain , there was an immediate , small , but statistically significant increase in the number of Geminin-positive cells ( Figure 1A , C , see also [Streichan et al . , 2014] , Video 2 ) that did not increase further until 8 hr following strain when there was a constant , linear increase through 24 hr; however , there was not a significant increase in the number of cells entering mitosis ( Figure 1C , Video 2 ) . A previous study reported that the fraction of mitotic cells in a suspended MDCK cell monolayer was also very low ( ~0 . 5% ) and increased slightly ( ~2 . 5% ) upon significantly higher levels of strain ( ~30% ) than used here ( Wyatt et al . , 2015 ) . Since mechanical strain-induced cell cycle re-entry results in cells entering S phase 6–8 hr following application of strain ( Benham-Pyle et al . , 2015; Aragona et al . , 2013 ) , an increase in Geminin-positive cells at 8 hr is consistent with an increase in the number of cells that had exited quiescence ( G0 ) , proceeded through G1 , and then entered S . 10 . 7554/eLife . 19799 . 003Figure 1 . Mechanical strain is sufficient to drive cell cycle re-entry , but not entry into mitosis . ( A ) Distribution of mAG-Geminin in Fucci MDCK monolayers 0 hr or 24 hr after the application of No Strain or High Strain ( ~8 . 5% ) using the biaxial live cell stretcher . Scale bar: 150 μm . ( B ) Distribution of mitotic events ( white arrow heads ) in Fucci MDCK monolayers 12 hr or 24 hr after the application of No Strain or High Strain ( ~8 . 5% Strain ) using the biaxial live cell stretcher . Scale bar: 100 μm . ( C ) Quantification of percent Geminin positive cells in Fucci-MDCK monolayers; high strain is statistically significant ( p<0 . 05 ) relative to no strain from 1–24 hr . ( D ) Number of mitotic events per hour in Fucci-MDCK monolayers; there is no statistically significant difference at any time point . ( E ) Single cell tracking quantification of the number of cell objects accumulated in S/G2 ( red to green fluorescence , left ) or passed through S/G2 and divided ( red to green to red fluorescence , right ) during 24 hr . All quantifications were from at least 3 independent experiments and included analysis of at least 9500 cells . Quantifications were mean +/- SEM; unpaired t-test p values<0 . 001 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 00310 . 7554/eLife . 19799 . 004Figure 1—source data 1 . Data used to construct graphs in Figure 1 and Figure 1—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 00410 . 7554/eLife . 19799 . 005Figure 1—figure supplement 1 . Design and calibration of bi-axial live cell stretcher . Schematic representation of the side view ( A ) and top view ( B ) of the biaxial live cell stretcher . Representative image ( C ) and quantification ( D ) of displacement of immobilized fluorescent microspheres on the live cell stretcher imaging membrane with applied pressure of 0–65 kPa . Representative image of distance to nearest neighbor calculation ( E ) and quantification of average distance to nearest neighbor in Fucci MDCK monolayer at 0 ( Vacuum Off ) and 65 kPa ( Vacuum On ) of applied pressure ( F ) . Quantifications were from at least 3 independent experiments and distance to nearest neighbor calculations included analysis of 6943 cells ( 65 kPa ) or 8142 cells ( 0 kPa ) . Quantifications are mean ± SEM; Kolmogorov-Smirnoff test p values<0 . 001 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 00510 . 7554/eLife . 19799 . 006Figure 1—figure supplement 2 . Mitotic events and distance to nearest neighbor in fixed MDCK monolayers after mechanical strain . ( A ) Nuclear stain ( Hoescht ) of fixed MDCK monolayers 2–24 hr after application of mechanical strain using the ISA . ( B ) Quantification of average distance to nearest neighbor ( B ) or number of mitotic figures observed ( C ) 2–24 hr after application of mechanical strain . Quantifications were from at least 3 independent experiments and distance to nearest neighbor calculations included analysis of 3350–5215 cells . Quantifications are mean ± SEM; Kolmogorov-Smirnoff test p values 0 . 05 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 00610 . 7554/eLife . 19799 . 007Video 1 . Representative movie of a Fucci-MDCK monolayer treated with DMSO after no strain . Same data as in Figures 1 , 5 , and 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 00710 . 7554/eLife . 19799 . 008Video 2 . Representative movie of a Fucci-MDCK monolayer treated with DMSO after mechanical strain . Same data as in Figures 1 , 5 , and 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 008 Despite an increase in cells progressing through G1 and S upon mechanical strain , the number of observed mitotic events remained similar to unstrained monolayers ( Figure 1B , D , Video 1 and Video 2 ) . This was confirmed by single cell tracking of Geminin-positive cells: the number of cells that transitioned from G1 to S ( red to green fluorescence ) increased with the application of mechanical strain ( Figure 1E , left ) , but the number of cells that transitioned from G1 to S and then divided ( red to green to red fluorescence , Figure 1B , white arrows ) did not change ( Figure 1E , right ) . Imaging of fixed MDCK monolayers using the ISA also revealed no difference in the number of mitotic events observed between monolayers that were mechanically strained or not ( Figure 1—figure supplement 2 ) . Together , these results indicate that mechanical strain of quiescent epithelial monolayers resulted in cell cycle re-entry and the accumulation of cells in S/G2 but , significantly , these cells did not enter mitosis . The prolonged S/G2 phase in cells following mechanical strain could be due to the accumulation of DNA damage and activation of the DNA damage checkpoint . This cell cycle checkpoint regulates cell-cycle arrest by activating DNA repair pathways , inducing cell death by apoptosis ( Zhou and Elledge , 2000 ) and inhibiting mitotic entry ( Furnari et al . , 1997; Liu et al . , 2000 ) . The small surface area ( 0 . 81 cm2 ) of the ISA does not provide sufficient cell numbers for biochemical characterization ( see Materials and methods ) . Therefore , to test whether mechanical strain induced an accumulation of cells in S/G2 due to strain-induced DNA damage , monolayers were stained for the DNA damage associated histone variant phospho-γH2A . X ( Rogakou et al . , 1998; McManus and Hendzel , 2005 ) , and the DNA repair proteins p53 and p53 binding protein 1 ( p53BP1 ) ( Zhang et al . , 2009; Wagstaff et al . , 2016 ) . Unstrained , quiescent MDCK monolayers had very low levels of phospho-γH2A . X , p53 , and p53BP1 ( Figure 2A , C E ) . Upon mechanical strain , the level of DNA damage – indicated by increased γH2A . X staining – increased marginally ( Figure 2A , C ) , but levels of p53 and p53BP1 did not ( Figure 2C–F ) . The level of DNA damage ( γH2A . X positive cells ) in mechanically strained monolayers was considerably less than monolayers treated with the DNA damage inducing agent MMS ( Figure 2A , B ) or in actively cycling cells ( Figure 2A , B ) . Similarly , p53 and p53BP1 levels were significantly higher in MMS treated monolayers ( Figure 2C–F ) and in actively cycling cells , compared to strained and un-strained quiescent monolayers . Thus , the increase in DNA damage observed with mechanical strain was likely the result of increased DNA synthesis upon cell cycle re-entry , and not due to the abnormal accumulation of DNA damage and activation of the DNA damage checkpoint . 10 . 7554/eLife . 19799 . 009Figure 2 . Mechanical strain does not induce significant DNA damage or activation of DNA repair . Distribution of phospho-γΗ2A . X ( A ) , p53 ( C ) , and p53BP1 ( E ) in MDCK monolayers 24 hr after No Strain or High Strain ( 15% ) using the ISA , or in actively cycling cells treated with DMSO or MMS . Quantification of percent cells phospho-γH2A . X- ( B ) , p53- ( D ) , or p53BP1-positive ( F ) in each condition . Quantifications were from 3 independent experiments and included analysis of 397–3135 cells per experiment . Quantifications were mean +/- SEM; unpaired t-test p values<0 . 05 ( * ) , <0 . 01 ( ) , and <0 . 001 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 00910 . 7554/eLife . 19799 . 010Figure 2—source data 1 . Data used to construct graphs in Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 010 Since the accumulation of cells in S/G2 following mechanical strain is unlikely to be explained by a DNA damage mitotic checkpoint , we explored the hypothesis that a mitotic driver might be insufficiently activated . β-Catenin , a transcription factor downstream of the Wnt signaling pathway ( Nelson and Nusse , 2004; Aberle et al . , 1997; Morkel et al . , 2003 ) , has a well-characterized role in cellular proliferation and cell cycle progression , and we showed previously that β-catenin transcriptional activity is induced 6–8 hr following mechanical strain and remains at a constant level thereafter ( Benham-Pyle et al . , 2015; Tan et al . , 2016 ) . In normal epithelial cells , β-catenin protein levels are tightly regulated through a combination of sequestration in the cadherin complex at cell-cell contacts and active degradation of cytosolic β-catenin by the proteasome ( Aberle et al . , 1997; Ikeda et al . , 1998; Su et al . , 2008 ) . Both processes depend on the phosphorylation state of β-catenin , and we tested whether mechanical strain induced β-catenin transcriptional activity through changes in β-catenin phosphorylation . Although the activities of several tyrosine kinases have been reported to affect β-catenin binding to the cadherin adhesion complex ( Lilien and Balsamo , 2005 ) , we focused on Src phosphorylation of Y654 β-catenin which has been correlated directly with increased Wnt/β-catenin activity ( van Veelen et al . , 2011; Kajiguchi et al . , 2012 ) and activation by tissue compression in vivo ( Whitehead et al . , 2008; Fernández-Sánchez et al . , 2015; Brunet et al . , 2013 ) ; to block β-catenin degradation in the cytoplasm , we focused on β-catenin phosphorylation by CKI/GSK3β by inhibiting the priming phosphorylation by CKI ( Aberle et al . , 1997; Amit et al . , 2002; Liu et al . , 2002 ) . Specifically , monolayers were treated with either the Src inhibitor SU6656 ( Blake et al . , 2000 ) or the CKI inhibitor D4476 ( Rena et al . , 2004 ) . Then , monolayers were fixed and processed for image analysis 8 hr after application of mechanical strain , when β-catenin transcriptional activity increased ( Benham-Pyle et al . , 2015 ) . An MDCK cell line stably expressing the TOPdGFP reporter ( Maher et al . , 2009 ) was used to measure β-catenin transcriptional activity . Mechanical strain of monolayers in the absence of SU6656 induced nuclear accumulation of β-catenin and increased β-catenin transcriptional activity as measured by the appearance of nuclear TOPdGFP ( Figure 3A , B; Figure 3—figure supplement 1 , see also [Benham-Pyle et al . , 2015] ) . Significantly , SU6656 blocked both the nuclear accumulation and transcriptional activity of β-catenin following mechanical strain ( Figure 3A , B; Figure 3—figure supplement 1 ) . Since β-catenin transcriptional activity is required for G1 to S transition following mechanical strain ( Benham-Pyle et al . , 2015 ) , we also measured DNA replication by EdU incorporation in SU6656-treated monolayers after 24 hr ( Figure 3C , D ) . In the absence of SU6656 , mechanical strain resulted in a significant increase in EdU-positive cells , as reported previously ( Benham-Pyle et al . , 2015 ) . However , treatment with SU6656 blocked EdU incorporation , indicating that Src activity was required for cell cycle re-entry and progression into S following mechanical strain . 10 . 7554/eLife . 19799 . 011Figure 3 . Mechanical strain induces a Src-dependent increase in Y654 phosphorylated β-catenin and β-catenin transcriptional activity . Distribution of TOPdGFP at 8 hr ( A ) , β-catenin at 8 hr ( A , insets ) , EdU at 24 hr ( C ) , and pY654 β-catenin at 8 hr ( E ) in MDCK monolayers after No Strain or High Strain ( 15% ) applied by the ISA , treated with either DMSO or the Src inhibitor SU6656 ( 10 μM ) . Scale bars: 25 μm . Quantification of percent cells TOPdGFP- ( B ) or EdU- ( D ) positive and quantification of average pY654 β-catenin intensity per pixel ( F ) ; note that the small surface area ( 0 . 81 cm2 ) of the ISA does not provide sufficient cell numbers for biochemical characterization . Quantifications were from at least 3 independent experiments and for the TOPdGFP and EdU quantifications included analysis of 677–1168 cells per experiment . Quantifications were mean +/- SEM; unpaired t-test ( B , D ) or Kolmogorov-Smirnoff ( F ) test p values<0 . 05 ( * ) , <0 . 01 ( ) , and <0 . 001 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 01110 . 7554/eLife . 19799 . 012Figure 3—source data 1 . Data used to construct graphs in Figure 3 and Figure 3—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 01210 . 7554/eLife . 19799 . 013Figure 3—figure supplement 1 . Distribution of β-catenin in monolayers treated with either DMSO or the Src Inhibitor SU6656 8 hr after the application of No Strain or High Strain ( 15% ) using the ISA . Scale bars: 25 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 01310 . 7554/eLife . 19799 . 014Figure 3—figure supplement 2 . The Src Inhibitor PP2 blocks strain-induced increases in Y654 phosphorylated β-catenin and β-catenin transcriptional activity . Distribution of TOPdGFP at 8 hr ( A ) , β-catenin at 8 hr ( A , insets ) , EdU at 24 hr ( C ) , and pY654 β-catenin at 8 hr ( E ) in MDCK Monolayers after No Strain or High Strain ( 15% ) applied by the ISA , treated with either DMSO or the Src inhibitor PP2 . Quantification of percent cells TOPdGFP- ( B ) or EdU- ( D ) positive and quantification of average pY654 β-catenin intensity per pixel ( F ) . Quantifications were from at least 3 independent experiments and for the TOPdGFP and EdU quantifications included analysis of 707–1478 cells per experiment . Quantifications were mean +/- SEM; unpaired t-test ( B , D ) or Kolmogorov-Smirnoff ( F ) test p values<0 . 05 ( * ) , <0 . 01 ( ) , and <0 . 001 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 01410 . 7554/eLife . 19799 . 015Figure 3—figure supplement 3 . Distribution of β-catenin in monolayers treated with either DMSO or the Src Inhibitor PP2 8 hr after the application of No Strain or High Strain ( 15% ) using the ISA . Scale bars: 25 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 01510 . 7554/eLife . 19799 . 016Figure 3—figure supplement 4 . EGFR inhibition reduces an increase in pY654 β-catenin following mechanical strain , but does not affect β-catenin transcriptional activity or cell cycle re-entry . Distribution of TOPdGFP at 8 hr ( A ) , β-catenin at 8 hr ( A , insets ) , EdU at 24 hr ( C ) , and pY654 β-catenin at 8 hr ( E ) in MDCK monolayers after No Strain or High Strain ( 15% ) applied by the ISA and treated with either DMSO or the EGFR inhibitor PD153035 . Scale bars: 25 μm . Quantification of percent cells TOPdGFP- ( B ) or EdU- ( D ) positive and quantification of average pY654 β-catenin intensity per pixel ( F ) . Quantifications were from at least 3 independent experiments and for the TOPdGFP and EdU quantifications included analysis of 677–1226 cells per experiment . Quantifications were mean +/- SEM; unpaired t-test ( B , D ) or Kolmogorov-Smirnoff ( F ) test p values<0 . 05 ( * ) , <0 . 01 ( ) , and <0 . 001 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 01610 . 7554/eLife . 19799 . 017Figure 3—figure supplement 5 . Distribution of β-catenin in monolayers treated with either DMSO or the EGFR Inhibitor PD153035 8 hr after the application of No Strain or High Strain ( 15% ) using the ISA . Scale bars: 25 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 017 Tyrosine 654 on β-catenin is a known Src phosphorylation site , and levels of pY654 β-catenin were examined by immunofluorescence with a pY654 β-catenin-specific antibody ( Rhee et al . , 2007 ) ; the small surface area ( 0 . 81 cm2 ) of the ISA does not provide sufficient cell numbers for biochemical characterization ( see Materials and methods ) . Eight hours after mechanical strain , levels of pY654 β-catenin in the cytoplasm and nucleus had increased significantly in mechanically strained cells ( DMSO , control ) ; note that levels of pY654 β-catenin fluorescence at cell-cell contacts appeared to be low , as expected since pY654 β-catenin has a weaker affinity for binding E-cadherin ( Coluccia et al . , 2007; Huber and Weis , 2001; Zeng et al . , 2006 ) . However , treatment with SU6656 resulted in a complete block of this strain-induced increase in Y654 β-catenin , which remained at levels similar to the unstrained control ( Figure 3E , F ) . Similar results to those with SU6656 treatment were obtained upon treatment with the tyrosine kinase inhibitor PP2 ( Hanke et al . , 1996 ) , which blocked increases in pY654 β-catenin , nuclear localization and transcriptional activity of β-catenin , and EdU incorporation following mechanical strain ( Figure 3—figure supplement 2 and 3 ) . Increased pY654 β-catenin levels have been correlated with activation of Epidermal Growth Factor Receptor ( EGFR ) signaling ( Lilien and Balsamo , 2005; Shibata et al . , 1996; Hazan and Norton , 1998 ) . To test whether EGFR activation was involved in strain-induced accumulation of pY654 β-catenin and β-catenin signaling , monolayers were treated with the EGFR inhibitor PD153035 ( Fry et al . , 1994; Bos et al . , 1997 ) . EGFR inhibition did not significantly affect strain-induced increases in either β-catenin transcriptional activity or cell cycle re-entry ( Figure 3—figure supplement 4A–D and 5 ) . Inhibition of EGFR by PD153035 reduced the increase in pY654 β-catenin following mechanical strain by ~40% ( Figure 3—figure supplement 4E , F ) , unlike the complete inhibition of strain-induced pY654 β-catenin accumulation by the Src inhibitor SU6656 ( Figure 3E , F ) . These results indicate that EGFR activation may contribute to the increase in pY654 β-catenin following mechanical strain ( Muhamed et al . , 2016 ) but it is not required for strain-induced pY654 β-catenin-mediated activation of cell cycle progression . To test whether inhibition of CKI phosphorylation affected β-catenin transcriptional activity in response to mechanical strain , monolayers were treated with the CKI inhibitor D4476 ( Rena et al . , 2004 ) . D4476 resulted in an increase in cytoplasmic and nuclear β-catenin in the absence of mechanical strain , but a significantly greater increase in cytoplasmic and nuclear β-catenin after the application of mechanical strain ( Figure 4A; Figure 4—figure supplement 1 ) . Similarly , D4476 caused an increase in levels of β-catenin transcriptional activity indicated by the TOPdGFP reporter system in unstrained monolayers , but a significantly greater increase in mechanically strained monolayers ( Figure 4B ) . D4476 treatment also increased EdU incorporation in combination with mechanical strain , indicating that CKI inhibition increased the probability of cells progressing from G1 into S . D4476 treatment did not increase EdU incorporation in un-strained monolayers ( Figure 4C , D ) , indicating that CKI inhibition alone did not increase cell cycle re-entry from quiescence , which requires mechanical strain and Yap1 transcriptional activity ( Benham-Pyle et al . , 2015 ) . D4476 treated monolayers were also stained for pY654 β-catenin to test whether CKI inhibition effected the accumulation of pY654 β-catenin following mechanical strain . Significantly , CKI inhibition did not appear to change the level of pY654-β-catenin before or after mechanical strain compared to the control ( DMSO ) ( Figure 4E , F ) . These results indicate that mechanical strain-induced changes in the phosphorylation state of β-catenin by Src and modulation of β-catenin degradation by CKI regulate strain-induced increases in cytoplasmic β-catenin and β-catenin trancriptional activity . 10 . 7554/eLife . 19799 . 018Figure 4 . Inhibition of Casein Kinase I ( CKI ) increases β-catenin transcriptional activity in MDCK quiescent monolayers , independent of mechanical strain . Distribution of TOPdGFP at 8 hr ( A ) , β-catenin at 8 hr ( A , insets ) , EdU at 24 hr ( C ) , and pY654 β-catenin at 8 hr ( E ) in MDCK Monolayers after No Strain or High Strain ( 15% ) applied by the ISA and treated with either DMSO or the CKI inhibitor D4476 . Quantification of percent cells TOPdGFP- ( B ) or EdU- ( D ) positive and average pY654 β-catenin intensity per pixel ( F ) . Quantifications were from at least 3 independent experiments and , for the TOPdGFP and EdU quantifications , included analysis of 832–1364 cells per experiment . Quantifications were mean +/- SEM; unpaired t-test ( B , D ) or Kolmogorov-Smirnoff ( F ) test p values<0 . 05 ( * ) , <0 . 01 ( ) , and <0 . 001 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 01810 . 7554/eLife . 19799 . 019Figure 4—source data 1 . Data used to construct graphs in Figure 4 and Figure 4—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 01910 . 7554/eLife . 19799 . 020Figure 4—figure supplement 1 . Distribution of β-catenin in monolayers treated with either DMSO or the CKI Inhibitor D4476 8 hr after the application of No Strain or High Strain ( 15% ) using the ISA . Scale bars: 25 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 020 Mechanical strain induced cell cycle re-entry and accumulation of cells in S/G2 but was insufficient to drive cells into mitosis , suggesting an additional level of regulation for cell transition from S/G2 to mitosis in the presence of mechanical strain . Since β-catenin transcriptional activity is required for progression through S phase , we considered whether further increasing the level of β-catenin activity , through inhibition of β-catenin degradation , might be sufficient to drive cells into , and through mitosis in the presence of mechanical strain . D4476-treated Fucci-MDCK monolayers were imaged on the biaxial live imaging cell stretcher for 24 hr following application of mechanical strain . In the absence of mechanical strain , the number of Geminin-positive cells in S/G2 in both control ( DMSO ) and D4476 treated monolayers was very low and did not change over 24 hr ( Figure 5A , B , Video 3 ) . In contrast to control monolayers , in which the number of Geminin-positive cells increased gradually after 6–8 hr ( Figure 5A , C , Video 1 ) , D4476 treatment resulted in a faster accumulation of Geminin-positive cells , resulting in a significant increase in the number of cells in S/G2 after 24 hr compared to the DMSO control ( Figure 5A , C , Video 4 ) . Single-cell tracking of Geminin-positive cells showed an increase in cells transitioning from G1 to S ( red to green fluorescence , Figure 5D ) in mechanically strained monolayers treated with D4476 compared to monolayers treated with either D4476 or mechanical strain alone . 10 . 7554/eLife . 19799 . 021Figure 5 . Inhibition of β-catenin degradation promotes progression from G1 into S/G2 following mechanical strain . ( A ) Distribution of mAG-Geminin in Fucci MDCK monolayers treated with DMSO , D4476 , or D4476 and iCRT3 0 hr or 24 hr after the application of No Strain or High Strain ( ~8 . 5% ) using the biaxial live cell stretcher . Scale bar: 150 μm . Quantification of percent cells Geminin positive in Fucci-MDCK monolayers after No Strain ( B ) or High Strain ( 15% ) ( C ) ; percent cells Geminin positive in D4476 treated monolayers are statistically significant ( p<0 . 05 ) relative to DMSO monolayers following mechanical strain at 16–24 hr . ( D ) Single cell tracking quantification of number of cell objects accumulated in S/G2 ( red to green fluorescence , left ) during 24 hr . All quantifications were from 2–4 independent experiments and included analysis of at least 15 , 000 cells . Quantifications were mean +/- SEM; unpaired t-test ( B , C ) or Kolmogorov-Smirnoff ( D ) test p values<0 . 01 ( ) , and <0 . 001 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 02110 . 7554/eLife . 19799 . 022Figure 5—source data 1 . Data used to construct graphs in Figure 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 02210 . 7554/eLife . 19799 . 023Video 3 . Representative movie of a Fucci-MDCK monolayer treated with D4476 after no strain . Same data as in Figures 5 and 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 02310 . 7554/eLife . 19799 . 024Video 4 . Representative movie of an FUCCI-MDCK monolayer treated with D4476 after mechanical strain . Same data as in Figures 5 and 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 024 To assess cell entry into mitosis , Fucci MDCK cells were tracked and the number of mitotic events per hour was measured as a function of the degradation of Geminin and cells switching from green to red fluorescence ( Figure 6A , Videos 1–6 ) . In unstrained monolayers , D4467 treatment increased the level of mitotic events beginning 6 hr after treatment and this level then gradually decreased to approximately the control level over 24 hr ( Figure 6B , Video 3 ) . Mechanically strained monolayers treated with D4476 had increased numbers of mitotic events after 5 hr , which continued to increase and remained significantly elevated over 24 hr ( Figure 6C , Video 4 ) . Single-cell tracking of Geminin-positive cells confirmed that the number of cells that transitioned from G1 into S and then divided ( red to green to red fluorescence , Figure 6A , white arrows ) increased in mechanically strained monolayers treated with D4476 , but not in the presence of D4476 or mechanical strain alone ( Figure 6D ) . 10 . 7554/eLife . 19799 . 025Figure 6 . Inhibition of β-catenin degradation promotes mitotic entry following mechanical strain . ( A ) Distribution of mitotic events ( white arrow heads ) in Fucci MDCK monolayers 12 hr or 24 hr after the application of No Strain or High Strain ( ~8 . 5% ) using the biaxial live cell stretcher and treatment with DMSO , D4476 , or D4476 and iCRT3 . Scale bar: 100 μm . ( B ) Number of mitotic events per hour in treated Fucci-MDCK monolayers following No Strain; mitotic events in D4476 treated monolayers were statistically significant ( p<0 . 05 ) compared to DMSO treated monolayers from 6–16 hr , and at 22 and 24 hr ( C ) Number of mitotic events per hour in treated Fucci-MDCK monolayers following High Strain; mitotic events in D4476 treated monolayers were statistically significant ( p<0 . 05 ) when compared to DMSO treated monolayers from 5–24 hr . ( D ) Single cell tracking quantification of number of cell objects that passed through S/G2 and divided ( red to green to red fluorescence , right ) during 24 hr . All quantifications were from 2–4 independent experiments and included analysis of at least 15 , 000 cells . Quantifications were mean +/- SEM; Kolmogorov-Smirnoff test p values<0 . 05 ( * ) , <0 . 01 ( ) , and <0 . 001 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 02510 . 7554/eLife . 19799 . 026Figure 6—source data 1 . Data used to construct graphs in Figure 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 02610 . 7554/eLife . 19799 . 027Video 5 . Representative movie of a Fucci-MDCK monolayer treated with D4476 and iCRT3 after no strain . Same data as in Figures 5 and 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 02710 . 7554/eLife . 19799 . 028Video 6 . Representative movie of a Fucci-MDCK monolayer treated with D4476 and iCRT3 after mechanical strain . Same data as in Figures 5 and 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 028 CKI contributes in multiple ways to cell cycle progression and directly to mitotic entry ( Meisner and Czech , 1991; Brockman et al . , 1992; Hanna et al . , 1995; Ho et al . , 1997 ) . Therefore , it was important to test whether the effect of CKI inhibition on mechanical strain-induced entry of cells into mitosis required β-catenin transcriptional activity . Previously , we showed that iCRT3 , a small molecule inhibitor of β-catenin/TCF interactions ( Gonsalves et al . , 2011 ) , completely blocked β-catenin transcriptional activity and transition of cells from G1 into S following mechanical strain ( Benham-Pyle et al . , 2015 ) . Significantly , addition of both D4467 and iCRT3 to mechanically strained monolayers blocked the increase in Geminin-positive cells following mechanical strain and the number of cells entering mitosis observed with D4476 alone ( Figures 5 and 6 , Videos 5 and 6 ) . These results indicate that the effects of CKI inhibition on cell cycle progression and mitotic entry involve increased β-catenin transcriptional activity . In vivo , Wnt signaling blocks β-catenin degradation , resulting in β-catenin accumulation in the cytoplasm and nucleus and induction of β-catenin transcriptional activity ( Clevers and Nusse , 2012; Korinek et al . , 1997; Clevers , 2006; Chen et al . , 2012; Chen et al . , 2001; Reya and Clevers , 2005 ) . Therefore , we tested if β-catenin stabilization and transcriptional activation by Wnt , similar to CKI inhibition , could act synergistically with mechanical strain to drive cell cycle progression and entry into mitosis . Conditioned media was collected from control L cells or L cells expressing Wnt3A , as previously described ( Lyons et al . , 2004 ) . Both unstrained and strained monolayers treated with Wnt3A conditioned media had high levels of nuclear TOPdGFP compared to similarly strained monolayers treated with control conditioned medium ( Figure 7—figure supplement 1 ) . Mechanical strain in the absence of Wnt3A stimulation induced an increase in Geminin-positive S/G2 cells after an 8-hour delay compared to unstrained monolayers ( Figure 7A; Figure 7C , Video 7 and Video 8 ) , similar to results observed with DMSO treated monolayers . Despite the increase in nuclear TOPdGFP in monolayers treated with Wnt3A conditions media , the number of Geminin-positive cells in unstrained monolayers in the presence of control or Wnt3A condition medium was similarly low ( Figure 7A , B , Videos 7 and Video 9 ) , indicating that in the absence of mechanical strain Wnt3A and increased β-catenin transcription activity were insufficient to drive cells into S; note that mechanical strain-induced Yap1 transcriptional activity is required first for quiescent cell re-entry into G1 ( and hence progression to S ) , even if β-catenin transcription activity is high ( Benham-Pyle et al . , 2015 ) . 10 . 7554/eLife . 19799 . 029Figure 7 . Mechanical strain induced increase in the progression from G1 into S/G2 in the presence of Wnt3A . ( A ) Distribution of mAG-Geminin in Fucci MDCK monolayers treated with control or Wnt3A-conditioned media 0 hr or 24 hr after the application of No Strain or High Strain ( ~8 . 5% ) using the biaxial live cell stretcher . Scale bar: 150 μm . Quantification of percent cells Geminin positive in Fucci-MDCK monolayers after No Strain ( B ) or High Strain ( 15% ) ( C ) ; percent cells Geminin positive in Wnt3A treated monolayers were statistically significant ( p<0 . 05 ) relative to control monolayers following mechanical strain at 14–24 hr . ( D ) Single cell tracking quantification of number of cell objects accumulated in S/G2 ( red to green fluorescence , left ) during 24 hr . All quantifications were from 2–3 independent experiments and included analysis of at least 15 , 000 cells . Quantifications were mean +/- SEM; unpaired t-test ( B , C ) or Kolmogorov-Smirnoff ( D ) test p values<0 . 05 ( * ) , <0 . 01 ( ) , and <0 . 001 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 02910 . 7554/eLife . 19799 . 030Figure 7—source data 1 . Data used to construct graphs in Figure 7 and Figure 7—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 03010 . 7554/eLife . 19799 . 031Figure 7—figure supplement 1 . Wnt3A induces increased β-catenin transcriptional activity , independent of mechanical strain . Distribution of TOPdGFP at 8 hr ( A ) and pY654 β-catenin at 8 hr ( C ) in MDCK monolayers after No Strain or High Strain ( 15% ) using the ISA , treated with either control or Wnt3A-conditioned media . Quantification of percent cells TOPdGFP-positive ( B ) and quantification of average pY654 β-catenin intensity per pixel ( D ) . Quantifications were from at least 3 independent experiments and , for the TOPdGFP quantifications , included analysis of 1004–1479 cells per experiment . Quantifications were mean +/- SEM; unpaired t-test ( B ) or Kolmogorov-Smirnoff ( D ) test p values<0 . 05 ( * ) , <0 . 01 ( ) , and <0 . 001 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 03110 . 7554/eLife . 19799 . 032Video 7 . Representative movie of a Fucci-MDCK monolayer treated with control-conditioned media after no strain . Same data as in Figures 7 and 8 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 032 Significantly , monolayers under mechanical strain and treated with Wnt3A-conditioned media accumulated more Geminin-positive S/G2 cells than mechanically strained monolayers in the presence of control conditioned media ( Figure 7A , C , Video 10 ) . Moreover , single-cell tracking of Geminin-positive cells showed an increase in cells transitioning from G1 to S ( red to green fluorescence , Figure 7D , Videos 7–10 ) in mechanically strain monolayers treated with Wnt3A-conditioned media , compared to monolayers treated with either Wnt3A-conditioned media or mechanical strain alone . 10 . 7554/eLife . 19799 . 033Video 8 . Representative movie of a Fucci-MDCK monolayer treated with control-conditioned media after mechanical strain . Same data as in Figures 7 and 8 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 03310 . 7554/eLife . 19799 . 034Video 9 . Representative movie of a Fucci-MDCK monolayer treated with Wnt3A-conditioned media after no strain . Same data as in Figures 7 and 8 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 03410 . 7554/eLife . 19799 . 035Video 10 . Representative movie of a Fucci-MDCK monolayer treated with Wnt3A-conditioned media after mechanical strain . Same data as in Figures 7 and 8 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 035 In unstrained monolayers , Wnt3A stimulation increased the number of mitotic events over 5 hr , which then gradually decreased over 24 hr ( Figure 8B , Video 7 ) . Mechanically strained monolayers treated with Wnt3A also had an initial peak in the number of mitotic events at ~5 hr , but , in contrast to the control , the number of mitotic events increased significantly after ~8 hr and continued to increase over 24 hr ( Figure 8C , Video 10 ) . Single-cell tracking of Geminin-positive cells confirmed that the number of cells that transitioned from G1 into S and then divided ( red to green to red fluorescence , Figure 8A , white arrows ) increased in mechanically strained monolayers stimulated with Wnt3A , but not in the presence of either Wnt3A or mechanical strain alone ( Figure 8D , Videos 7–10 ) . Note that these effects of Wnt3A in driving mechanically strained cells into mitosis were very similar to those induced by D4467 ( compare Figures 6 and 8 ) . 10 . 7554/eLife . 19799 . 036Figure 8 . Mechanical strain induced increased mitotic entry in the presence of Wnt3A . ( A ) Distribution of mitotic events ( white arrow heads ) in Fucci MDCK monolayers 12 hr or 24 hr after the application of No Strain or High Strain ( ~8 . 5% ) using the biaxial live cell stretcher in the presence of control or Wnt3A-conditioned media . Scale bar: 100 μm . ( B ) Number of mitotic events per hour in treated Fucci-MDCK monolayers following No Strain; mitotic events in Wnt3A treated monolayers were statistically significant ( p<0 . 05 ) compared to control monolayers from 4–14 hr ( C ) Number of mitotic events per hour in treated Fucci-MDCK monolayers following High Strain; mitotic events in Wnt3A treated monolayers were statistically significant ( p<0 . 05 ) compared to control monolayers from 4–24 hr . ( D ) Single cell tracking quantification of number of cell objects that passed through S/G2 and divided ( red to green to red fluorescence , right ) during 24 hr . All quantifications were from 2–3 independent experiments and included analysis of at least 15 , 000 cells . Quantifications were mean +/- SEM; Kolmogorov-Smirnoff test p values<0 . 001 ( *** ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 03610 . 7554/eLife . 19799 . 037Figure 8—source data 1 . Data used to construct graphs in Figure 8 . DOI: http://dx . doi . org/10 . 7554/eLife . 19799 . 037 Mechanical cues are critical for the normal development , morphology , and function of multicellular tissues . Numerous pathways and molecular scaffolds have been identified as mechano-responsive , including cadherin cell-cell adhesion complexes ( Liu et al . , 2010; Cai et al . , 2014 ) , integrin-mediated focal adhesions ( Wang et al . , 2001 ) , the actin cytoskeleton ( Hoffman et al . , 2011; Engl et al . , 2014 ) , and Yap1 and β-catenin ( Whitehead et al . , 2008; Benham-Pyle et al . , 2015; Aragona et al . , 2013; Kamel et al . , 2010; Dupont et al . , 2011; Heo and Lee , 2011 ) . Whether mechanical cues synergize with signaling pathways known to regulate development and homeostasis remains a relatively unexplored area of investigation . Previously , we reported that mechanical strain sequentially induced Yap1 and β-catenin transcriptional activity to drive cell cycle re-entry ( Yap1 ) and progression from G1 into S ( β-catenin ) ( Benham-Pyle et al . , 2015 ) . Here , we show that these cells are held in S/G2 and do not enter mitosis , and that further activation of the Wnt signaling pathway and β-catenin transcriptional activity are required to drive cell cycle progression through mitosis . The accumulation of mechanically strained cells in S/G2 did not appear to be caused by the activation of the DNA damage checkpoint , as the level of DNA damage and DNA repair pathway activation in those cells , measured by levels of phospho-γH2A . X , p53 and p53BP1 , was insignificant compared to that induced by a DNA damaging agent and present in actively cycling cells ( Figure 2 ) . In vivo , G2 delay prior to mitosis has been identified in select developmental niches , including early stages of Drosophila development ( Edgar , 1990 ) , the Drosophila zone of non-proliferating cells ( ZNC ) ( Johnston and Edgar , 1998 ) , and during Drosophila neural fate determination ( Nègre et al . , 2003 ) . Our results indicate that quiescent cells may have evolved regulatory mechanisms to arrest healthy cells in S/G2 under circumstances unfavorable for mitosis; for example , mitosis at high cell density might result in the extrusion of cells and the disruption of monolayer integrity . β-Catenin is a well-characterized regulator of cell cycle progression ( Nelson and Nusse , 2004; Orford et al . , 1999; Morkel et al . , 2003; Olmeda et al . , 2003 ) , and is responsive to mechanical cues ( Farge , 2003; Benham-Pyle et al . , 2015; Brunet et al . , 2013 ) . While mechanical strain-induced activation of β-catenin transcriptional activity requires E-cadherin trans adhesion interactions ( Benham-Pyle et al . , 2015 ) , it remains unclear whether mechanical strain induces β-catenin transcriptional activity through destabilizing the junctional pool of cadherin-bound β-catenin , or stabilizing cytoplasmic β-catenin , or a combination of both . Here , we showed that mechanical strain induced a Src-dependent increase in tyrosine phosphorylated ( pY654 ) β-catenin in the cytoplasm and nucleus . pY654 β-catenin has a weaker affinity for E-cadherin ( Huber and Weis , 2001; Zeng et al . , 2006 ) , and thus may be released from the cadherin complex into the cytoplasm as indicated by the increased cytoplasmic staining of pY654 β-catenin in mechanically strained monolayers ( Figure 3E ) . Importantly , SU6656 inhibition of Src activity completely blocked the strain-induced increases in Y654 β-catenin phosphorylation , β-catenin accumulation in the cytoplasm and nucleus , β-catenin transcriptional activity , and DNA replication ( Figure 3 ) . Nevertheless , we also tested the possibility that other tyrosine kinases were involved in cellular responses to mechanical strain ( Lilien and Balsamo , 2005; Tamada et al . , 2012; Bays et al . , 2014 ) . Indeed , inhibition of EGFR partially reduced the strain-induced increase in level of pY654 β-catenin , but did not significantly affect the strain-induced increase in β-catenin transcriptional activity or cell cycle re-entry ( Figure 3—figure supplement 4 and 5 ) . Since Src inhibition blocked strain-induced pY654 β-catenin accumulation , β-catenin transcriptional activity and cell cycle progression , we conclude that Src activation plays the predominant role in activating β-catenin signaling in response to mechanical strain . How Src is activated by strain is currently unknown . However , our earlier finding that E-cadherin trans adhesion interactions are required for mechanical strain-induced β-catenin transcriptional activity and cell cycle progression ( Benham-Pyle et al . , 2015 ) indicates that Src activation may be downstream of E-cadherin extracellular engagement . In contrast to the effects of inhibiting Src activity , inhibition of CKI activity increased β-catenin transcriptional activity ( Figure 4 ) independently of mechanical strain . Normally , CKI phosphorylation of β-catenin is the first step in targeting cytosolic β-catenin for degradation ( Amit et al . , 2002; Maher et al . , 2009 ) . Since CKI inhibition did not change the level of pY654 β-catenin following mechanical strain ( Figure 4E , F ) , the effects of CKI inhibition and mechanical strain likely act through different biochemical pathways . Live imaging of Fucci-MDCK monolayers revealed that increased β-catenin transcriptional activity upon CKI inhibition produced a distinct cell cycle response to mechanical strain compared to un-treated monolayers ( Figures 5 and 6 ) . Following mechanical strain , CKI inhibition increased both the number of cells that accumulated in S/G2 and the number of mitotic events . However , only cells exposed to both CKI inhibition and mechanical strain re-entered the cell cycle and divided within 24 hr . Importantly , this increase in mitotic events was dependent on β-catenin transcriptional activity . Components of the β-catenin degradation machinery are commonly mutated in tumors ( Morin et al . , 1997; Bottomly et al . , 2010; Mishra et al . , 2015 ) , and inactivation of β-catenin degradation is thought to be an important event in tumor development . However , it has remained unclear what additional factors may be important for metastasis and malignant growth . Our results indicate that mechanical strain may trigger accelerated growth within a mutation-induced , pre-malignant background of increased β-catenin transcriptional activity . CKI inhibition is a simple method to stabilize cytoplasmic β-catenin by short-circuiting the β-catenin degradation machinery , but it has the potential for off-target effects given the broad functions of CKI in cells . Wnt ligands are secreted signaling proteins that induce β-catenin transcriptional activity by inhibiting β-catenin phosphorylation by CKI/GSK3β ( Li et al . , 2012; Vinyoles et al . , 2014 ) . In the absence of mechanical strain , Wnt3A stimulation of quiescent MDCK monolayers was not sufficient to drive cell cycle re-entry or an accumulation of cells in S/G2 . Note also that levels of pY654 β-catenin were also unchanged in the presence of Wnt3A ( Figure 7—figure supplement 1 ) , similar to CKI inhibition ( Figure 4E , F ) , indicating a Wnt-independent pathway for Src activation . Upon mechanical strain , Wnt3A-stimulated monolayers had significantly higher numbers of cells both in S/G2 and entering mitosis compared to monolayers treated with Wnt3A or mechanical strain alone ( Figures 7 and 8 ) . The linear increase in mitotic events after an 8–10 hr delay following mechanical strain in the presence of Wnt3A is consistent with the hypothesis that monolayers exposed to both mechanical strain and Wnt3A exited quiescence ( G0 ) , proceeded through DNA replication and then divided without a delay in S/G2 . This is in sharp contrast to mechanically strained monolayers without Wnt3A stimulation that accumulated in S/G2 . Dose-dependent effects and signaling thresholds have been previously described in early development ( Green and Smith , 1990; Green et al . , 1992; Zhang et al . , 2013 ) , and several signaling pathways , including Wnt/β-catenin signaling , have been suggested to function as continuously variable signaling pathways ( Hazzalin and Mahadevan , 2002; Bardwell , 2008; Goentoro and Kirschner , 2009 ) . Our results showed that mechanical strain or Wnt3A addition activated β-catenin signaling to a level sufficient to drive some cells through G1 into S ( Figure 1 , Figure 7D ) . However , the combination of mechanical strain and Wnt3A addition induced higher β-catenin signaling levels ( Figure 7—figure supplement 1 ) , which drove more cells through G1 into S ( Figure 7 ) and through G2 into mitosis ( Figure 8 ) . This synergistic effect of Wnt3A and mechanical strain on both β-catenin transcriptional activity and cell cycle progression confirms that Wnt/β-catenin activity is not a stable binary switch , but rather involves a graded activation in which different levels of β-catenin transcriptional activity produce different cell cycle responses ( lower for G1 to S/G2 , and higher for S/G2 into mitosis ) . It has been a long-standing hypothesis that growth-induced local mechanical cues could influence morphogenesis by modulating growth rates , the direction of growth , or differentiation ( Henderson and Carter , 2002 ) . Indeed , a model integrating both force-sensing and signaling pathways accounted for experimentally observed patterns of growth , cell shape , and cell size In the Drosophila wing imaginal disc ( Aegerter-Wilmsen et al . , 2012 ) . This model was supported by the observation that a global pattern of stress in the wing imaginal disc coincided with polarized actomyosin contractility in the cell cortex and the alignment of the division plane with the main axis of cell stretch , thereby contributing to tissue elongation . However , very few studies have demonstrated how mechanical forces could synergize with signaling pathways to effect growth rates . How might Wnt signaling and mechanical forces synergize in vivo ? Canonical Wnt signaling can act as a locally restricted morphogen to regulate limb development , asymmetric stem cell divisions , and sensitivity to other effector pathways during development and adult tissue homeostasis . ( Ng et al . , 1996; Habib et al . , 2013; Lindsley et al . , 2006; Alexandre et al . , 2014 ) . In mammary and feather morphogenesis , local Wnt signaling is required for proper appendage bud formation and local morphogenesis from a flat epithelium; increasing Wnt activation results in randomized and expanded growth zones and ectopic bud formation ( Widelitz et al . , 1999; Chu et al . , 2004 ) . Similarly , Wnt signaling defines localized growth zones during liver development and regeneration ( Suksaweang et al . , 2004 ) and expression of a constitutively active β-catenin expands local growth zones and stem cell populations and increases total liver size ( Blanchard et al . , 2009 ) . Our results indicate that local Wnt signaling in a tissue experiencing moderate levels of global mechanical strain could result in local hotspots of mitosis through increased levels of Wnt/β-catenin signaling . Levels of strain higher than those applied here have been observed during dorsal closure , germband extension , and neurulation in Drosophila and zebrafish ( Blanchard et al . , 2009 ) . Thus , in tissues experiencing high levels of strain ( 30–50% strain ) , cell division and tissue expansion may not require Wnt signaling . Indeed , in quiescent MDCK monolayers , 50% uni-axial stretch was sufficient to increase the fraction of cycling cells and number of cell divisions Streichan et al . ( 2014 ) . Therefore , while high levels of Wnt signaling and mechanical force could independently drive proliferation and tissue expansion , growth-generated mechanical forces during development could also combine with local ( Wnt ) signaling to refine areas of growth during tissue morphogenesis and during adult tissue repair . The cell lines in this study include Madin-Darby canine kidney type II G ( MDCK ) cells ( Mays et al . , 1995 ) , an MDCK TOPdGFP reporter cell line ( Maher et al . , 2009 ) , and an MDCK FUCCI cell cycle reporter cell line ( Streichan et al . , 2014 ) . No cell lines were used from the International Cell Line Authentication committee and all cell lines are mycoplasma free . MDCK cells were grown in DMEM with low glucose and 200 μM G418 ( TOPdGFP ) to maintain stable expression of reporter constructs of interest . MDCK cells stably expressing the FUCCI cell cycle reporter ( Streichan et al . , 2014 ) were not kept under selection , and were only kept in culture for 8 weeks before thawing an early passage population . Very dense , quiescent monolayers of MDCK cells were formed using a calcium switch method , as described previously ( Benham-Pyle et al . , 2015 ) . Briefly , MDCK cells were plated on Collagen-I coated flexible PDMS substrates at ~1000 cells/mm in low calcium ( 5 μM ) DMEM with low glucose ( 200 mg/L ) . After 60 min , the medium was removed and replaced by DMEM with low glucose and a normal calcium level ( 1 . 8 mM ) , resulting in a super-confluent monolayer in which after 36–48 hr , when mechanical strain was applied , >95% of cells were in G0 ( Benham-Pyle et al . , 2015 ) . Live imaging was performed using the biaxial live cell stretcher , and immunofluorescence images of fixed monolayers was prepared from samples stretched on the integrated strain array ( see below , and also [Benham-Pyle et al . , 2015; Simmons et al . , 2011] ) . We used the ISA in this study so that we could directly compare results to our previous publication ( Benham-Pyle et al . , 2015 ) . The ISA applies a maximum of 15% strain to cell monolayers , and maximum strain was used in all experiments . The ISA is designed for high-throughput analysis of up to 5 different strain levels , each applied simultaneously to 5 wells; the surface area of silicone substrates in each PDMS well is 0 . 81 cm2 ( ~equal to the well of a 96-well plate ) but the surface area strained is less since the surface area of the pillar used to stretch the substrates is smaller , depending on the amount of strain applied ( Simmons et al . , 2011 ) . Thus the cell population includes a uniform area under strain , surrounded by a variable area , depending on the level of strain , outside the pillar that is not strained . The small surface area , and hence small number of cells , coupled with 2 populations of strained and non-strained cells precludes rigorous biochemical analysis of the entire cell population . To circumvent these limitations , the quantitative methods used to measure protein levels from fluorescence images only obtained data from areas of cells over the pillar and hence under strain , and the custom MATLAB scripts provided an unbiased analysis of all cells in those areas comprising ~500–15 , 000 cells . Exact cell numbers for each experiment have been detailed in the source data files uploaded to the eLife web-site . Live imaging of Fucci MDCK monolayers was achieved using a biaxial live cell stretching device ( Figure 1—figure supplement 1 ) . The silicon bi-axial device was made by mixing PDMS ( Sylgard 182; Dow Corning , Inc . ) with 10:1 mixing ratio of elastomer and curing agent that was poured over a 3-D printed mold . The mold was degassed for 1 hr and then baked at 65°C overnight . The device from the mold was smoothed by stamping on a thin amount of uncured PDMS and baked at 65°C for 3 hr . The device was then plasma bonded ( PDC-32G , Harrick Plasma Inc . ) to a prefabricated silicone membrane ( SMI Manufacturing Inc . , 125 μm thickness ) . The cell loading well was 8 . 5 mm in diameter and was separated from the 4 mm pneumatic chamber by a wall 2 mm thick and 3 mm high . Strain was applied to the cell culture membrane by applying vacuum to the pneumatic chamber to deflect the walls outward from the cell loading well and consequently stretched the suspended membrane and attached cell monolayer . Applied strain using the biaxial live imaging cell stretcher was measured experimentally from displacements of 0 . 5 μm fluorescent microspheres ( F8812 , Thermo Fisher Scientific Inc . , Waltham , MA ) placed on the PDMS surface , or imaging cell nuclei in a Fucci MDCK monolayer ( Figure 1—figure supplement 1C–F ) . A pressure regulator ( P3RA17132NNKN , Parker Valve , Inc . , Cleveland , OH ) was used to control pressure in the pneumatic channel and the pressure differential to atmospheric pressure was monitored using a pressure gauge ( Ashcroft 25 1009 SW 02L , Ashcroft gauge Inc . , Stratford , CT ) . Maximum strain to the device was measured at 65 kPa of applied pressure . This level of vacuum pressure resulted in 8 . 5% strain measured by microspheres ( Figure 1—figure supplement 1C , D ) and an average of 8 . 6% increase in distance to nearest neighbor of living cells in the monolayer ( Figure 1—figure supplement 1F ) . Distance to nearest neighbor was calculated for each cell in an image frame , and was equal to the average distance from the nucleus centroid to the centroids of the nearest 7 cell nuclei ( See Figure 1—figure supplement 1E ) . The live cell stretcher applies a maximum of 8 . 5% strain to cell monolayers , and maximum strain was used in all experiments . The DNA damaging agent methyl methanesulfonate ( MMS , Sigma-Aldrich ) was used as a positive control in DNA damage studies . Cells were treated with 0 . 1% ( v/v ) MMS for 2 hr prior to fixation and processing for imaging . The small molecule inhibitor iCRT3 ( Gonsalves et al . , 2011 ) ( SML0211; Sigma-Aldrich , St . Louis , MO ) was used to inhibit β-catenin/TCF interactions and their transcriptional activity . iCRT3 ( 25 µM ) and DMSO controls were added to strain array wells 15 min prior to the application of strain . Src Inhibitors PP2 ( 10 μM; EMD Millipore , Germany ) and SU6656 ( 10 μM , Sigma-Aldrich ) , CKI inhibitor D4476 ( 10 μM; Abcam ) , EGFR inhibitor PD153035 ( 2 . 5 μM , Abcam , United Kingdom ) , and DMSO controls were added to strain device wells 15 min prior to strain application . Fresh inhibitor was added to all experiments every 12 hr . Wnt3A-expressing mouse L cells and parental L cells were grown as indicated ( ATCC CRL-2647 and CRL-2648 ) , and conditioned media was obtained as indicated in the comments section of ATCC CRL-2647 . Conditioned media from Wnt3A-expressing and parental L cells was added at a 1:2 dilution to MCDK cell culture medium ( described above ) 15 min prior to strain application . MDCK cells , plated on collagen I-coated PDMS , were fixed in 4% ( v/v ) paraformaldehyde for 15 min , permeabilized in 0 . 5% ( v/v ) Triton X-100 for 7 min , and blocked in PBS containing 0 . 2% ( w/v ) BSA and 1% goat or donkey serum at room temperature for 60 min . Primary antibodies used for immunofluorescence staining were: β-catenin ( 610154; BD Biosciences , San Jose , CA ) , phospho-γH2A . X ( ab11174; Abcam ) , p53 ( 92825; Cell Signaling Technology , Danvers , MA ) , p53BP1 ( sc-10915; Santa Cruz Biotechnology , Dallas , TX ) , and pY654 β-catenin/mouse ( Developmental Studies Hybridoma Bank ) . EdU incorporation was performed using a Click-iT Plus EdU Alexa Fluor 647 Imaging Kit ( C10339; Molecular Probes/Invitrogen , Waltham , MA ) as directed by the manufacturer . Immunofluorescence images were acquired with a Zeiss ( Jena , Germany ) Axiovert 200 inverted microscope equipped with a Mercury lamp and a 63X objective ( Olympus , Tokyo , Japan ) , and acquired with MicroManager Microscopy Software . Fluorescence live-cell images were acquired on a custom-built Zeiss Axiovert 200 M inverted wide field epifluorescence microscope ( Intelligent Imaging Innovations ( 3i ) , Denver , CO , USA ) equipped with a Hamamatsu C11440 Digital camera ( Orca-flash4 . 0OLT ) , an x , y motorized stage with harmonic drive z-focusing , and a quad filter set for DAPI , FITC , Cy3 and Cy5 laser system ( Andor Technology , South Widsor , CT , USA ) . Live cells were imaged in DMEM without Phenol Red at 37°C with a 20X air objective for tracking cellular movement; images were acquired using Slidebook ( 3i ) software . The chosen cell cycle markers , the transcriptional reporter , and ( transcriptionally active ) β-catenin localized in the cell nucleus . Therefore , the strategy for image quantification was nuclear segmentation ( using Hoechst staining ) followed by pixel intensity calculation for each nucleus; this method excluded cytoplasmic and plasma membrane staining . The nuclear pixel intensity of all biomarkers ( β-catenin , TBSmCherry , TOPdGFP , EdU , and Ki67 ) was analyzed on a single cell basis using a previously described image-processing routine in MATLAB ( Benham-Pyle et al . , 2015 ) . The resulting data were exported from MATLAB to a text file for plotting and further analysis . Exported data for each nucleus included the object identifier , object area , and pixel intensity of up to 3 analyzed imaging channels . Cytoplasmic staining of pY654 β-catenin was also quantified using a custom image-processing routine in MATLAB . Briefly , nuclei were identified and segmented using images of Hoechst stained cells . Pixels associated with nuclear objects were eliminated from analysis and remaining pixels were quantified to produce a whole image cytoplasmic pixel intensity that was exported from MATLAB to a text file for plotting and further analysis . Single Cell Tracking of Geminin-positive cells in live imaging movies ( Figures 1 and 4−7 ) was performed using the TrackMate plugin ( FIJI ) . Exported data were processed using MATLAB to report the timing of an object’s appearance and disappearance following the start of a movie and the time spent by the cell in S/G2 . Image quantification for signaling responses was represented as percent cells positive for the relevant biomarker and the threshold intensity separating positive and negative cells was kept constant between compared conditions . Statistics compared the mean percent of positive cells between multiple independent experiments using an unpaired t-test and the Holm-Sidak method to correct for multiple comparisons . When reporting # of occurrences of cell cycle progression , statistics compared the number of occurrences per 0 . 43 mm2 ( movie frame ) over 24 hr between conditions using an unpaired t-test . In cases where the reported metric was an absolute distance to a nearest neighbor or a pixel intensity value relative to a no strain control , data were not assumed to be normally distributed and a Kolmogorov-Smirnoff ( KS ) test was used to compare distributions . All experiments on the ISA were repeated at least 3 times , and the data from all experiments used in the statistical analysis and figure presentations . For Fucci MDCK monolayers , at least 6 replicate movies were taken from at least 2 independent experiments and the data from all experiments used in the statistical analysis and figure presentations .	Tissues and organs can both produce and respond to physical forces . For example , the lungs expand and contract; the heart pumps blood; and bones and muscles grow or shrink depending on how much they are used . These responses are possible because cells contain proteins that can respond to physical forces . One of the best studied of these is a protein called β-catenin , which increases the activity of genes that trigger cells to divide to promote the expansion of tissues . β-catenin is over-active in many types of cancer cells where it contributes to tumor growth . In addition to being switched on by mechanical force , β-catenin is also activated when cells detect a signal molecule called Wnt . Cells cycle through a series of stages known as the cell cycle to ensure that they only divide when they are fully prepared to do so . Benham-Pyle et al . investigated if physical force and Wnt activate β-catenin in the same way or if they have different effects on cell division . The experiments were conducted on dog kidney cells that had left the cell cycle and had therefore temporarily stopped dividing . Physical forces , such as stretching , resulted in β-catenin being modified by an enzyme called SRC kinase , which allowed the cells to re-enter the cell cycle . On the other hand , Wnt stabilized β-catenin and temporarily increased the number of cell divisions . When mechanical stretch and Wnt signaling were combined , the cells were more likely to re-enter the cell cycle and divide compared to either stimulus alone . These data suggest that physical force and Wnt signaling affect β-catenin differently and that they can therefore have a greater effect on cell or tissue growth when they act together than on their own . The findings of Benham-Pyle et al . show that β-catenin is not simply switched on or off , but can have different levels of activity depending on the input the cells are receiving . Future experiments will test whether these mechanisms also exist in three-dimensional tissues , which will help us understand how organs develop .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology" ]	2016	Increasing β-catenin/Wnt3A activity levels drive mechanical strain-induced cell cycle progression through mitosis
Evolutionary innovations that enable organisms to colonize new ecological niches are rare compared to gradual evolutionary changes in existing traits . We discovered that key mutations in the gltA gene , which encodes citrate synthase ( CS ) , occurred both before and after Escherichia coli gained the ability to grow aerobically on citrate ( Cit+ phenotype ) during the Lenski long-term evolution experiment . The first gltA mutation , which increases CS activity by disrupting NADH-inhibition of this enzyme , is beneficial for growth on the acetate and contributed to preserving the rudimentary Cit+ trait from extinction when it first evolved . However , after Cit+ was refined by further mutations , this potentiating gltA mutation became deleterious to fitness . A second wave of beneficial gltA mutations then evolved that reduced CS activity to below the ancestral level . Thus , dynamic reorganization of central metabolism made colonizing this new nutrient niche contingent on both co-opting and overcoming a history of prior adaptation . Evolutionary descent with modification has produced organisms with complex metabolic and gene regulatory networks . When a microbial population encounters a new environment , mutants encoding more effective variants of these networks that improve nutrient utilization or reveal latent metabolic capabilities may evolve ( Ryall et al . , 2012 ) . While there are many degrees of freedom that evolution can potentially access in altering cellular networks , only those mutational pathways that do not include deleterious intermediate steps are likely to be realized in most populations ( Bridgham et al . , 2006 , 2009; Weinreich et al . , 2006 ) . Laboratory studies have characterized mutational pathways that enable microorganisms to access new enzyme activities when there is strong selection for a new trait ( Hall , 2003; Näsvall et al . , 2012 ) . Combinations of metabolic and regulatory mutations that enable microorganisms to evolve toward optimal growth rates under defined conditions have also been exhaustively mapped at a whole-genome level ( Conrad et al . , 2011; Tenaillon et al . , 2012 ) . To this point , however , we have rarely had the opportunity to observe the interplay of these two regimes of optimization and innovation as they must occur over longer evolutionary timescales in nature ( Barrick and Lenski , 2013 ) . The Lenski long-term evolution experiment ( LTEE ) with E . coli provides a unique opportunity to study how metabolic and regulatory networks have changed over a history spanning more than 25 years of microbial adaptation ( Lenski and Travisano , 1994; Lenski et al . , 1991 ) . In particular , cells in one of the twelve LTEE populations evolved a qualitatively new metabolic capability—aerobic citrate utilization ( Cit+ phenotype ) —that enabled them to colonize a previously unoccupied ecological niche ( Blount et al . , 2008 ) . This Cit+ innovation is highly beneficial because it grants access to an abundant and previously untapped nutrient . Yet , it is also very rare . So far , a Cit+ variant has evolved in just one of the twelve LTEE populations , and then only after ∼15 years of evolution . The rarity of the Cit+ innovation suggests that accessing this new metabolic trait is contingent on a multi-step mutational pathway . The evolution of aerobic citrate utilization in the LTEE involved three stages: potentiation , actualization , and refinement . Actualization refers to the first appearance of phenotypically Cit+ cells . This transition was caused by a duplication that activated expression of the citT citrate:succinate antiporter gene through promoter capture ( Blount et al . , 2012 ) . However , on its own this mutation confers only extremely weak citrate utilization . Subsequently , this rudimentary Cit+ trait was refined to a stronger phenotype , Cit++ , when cells that were capable of fully exploiting citrate during each 24 hr growth cycle evolved , coincident with a large increase in cell density in this LTEE population ( Blount et al . , 2008 ) . Chief among the refining mutations was a promoter mutation that activated expression of the dctA C4-dicarboxylate:H+ symporter gene ( Quandt et al . , 2014 ) . Strains reconstructed with just the citT duplication and this dctA* mutation are capable of fully utilizing citrate ( i . e . , they are Cit++ ) . However , this simple two-step mutational pathway was apparently inaccessible without the prior evolution of one or more unknown mutations that created a potentiated genetic background ( Blount et al . , 2008 ) . A whole-genome phylogeny of this LTEE population has provided candidates for other mutations that contributed to the potentiation and refinement steps of Cit++ evolution ( Blount et al . , 2012 ) . We show here that one target of interest is the gltA gene , which encodes the enzyme citrate synthase ( CS ) . CS catalyzes the first irreversible step in the tricarboxylic acid ( TCA ) cycle: the aldol condensation of oxaloacetate ( OAA ) and acetyl-CoA to form citrate . Multiple mutations in one gene are rare in LTEE lineages that have retained the low ancestral mutation rate ( Barrick et al . , 2009; Wielgoss et al . , 2011 ) , yet the gltA gene was mutated twice in most Cit++ isolates , once before and once after Cit+ evolved . Due to the conspicuous metabolic function of CS and the appearance of the later gltA mutations specifically in Cit+ isolates , it was previously hypothesized that these mutations refined the Cit+ phenotype ( Blount et al . , 2012 ) . In this study , we characterized the effects of gltA mutations on competitive fitness , mRNA expression levels , and enzyme activity . Integrating this information with molecular dynamics simulations and genome-scale models of metabolism provided further insight into the molecular effects of these mutations and how they impacted cellular networks . We conclude that mutations affecting CS activity , and more broadly flux through the TCA cycle and glyoxylate shunt , were instrumental for potentiating the evolution of Cit+ , and then for refining the Cit++ phenotype . Our results underscore two principles of the evolution of complex systems that operate on long timescales . First , certain mutational paths that are immediately adaptive because they improve fitness in the current niche may be fortuitously co-opted to make future innovative leaps to new traits possible . Second , evolutionary innovations will often have disruptive effects on cellular networks , inverting epistatic relationships and prompting new waves of adaptation that may even reverse the effects of mutations that were necessary for accessing an innovation in the first place . In a previous study , genome sequencing and phylogenetic analysis of clones isolated from the Ara–3 LTEE population revealed multiple mutations in the gene for citrate synthase ( gltA ) ( Blount et al . , 2012 ) . The earliest evolved allele ( gltA1 ) is a single base change that causes an amino acid substitution ( A258T ) in this enzyme ( Figure 1A ) . This mutation was present in every Cit+ strain and in earlier Cit– clones from the clade that gave rise to Cit+ . Therefore , the gltA1 mutation arose before the Cit+ phenotype evolved . Three secondary gltA mutations ( gltA2 alleles ) were also found , separately , in the genomes of different Cit+ clones ( gltA2-4 , gltA2-6 , or gltA2-7 ) . Each of these base substitutions causes an additional single amino acid change in the citrate synthase protein sequence ( A124T , V152A , or A162V , respectively ) . 10 . 7554/eLife . 09696 . 003Figure 1 . Diversity and dynamics of gltA mutations in the E . coli population that evolved citrate utilization during the Lenski long-term experiment ( LTEE ) . ( A ) Mutations observed in the citrate synthase protein-coding sequence or upstream intergenic region . The gltA1 mutation evolved first in the LTEE . Many secondary gltA mutations later evolved independently in the LTEE ( numbered gltA2 alleles ) or in a previous genetic study ( gltA2-R ) . All gltA2 mutations occurred in genetic backgrounds that included gltA1 and the citT and dctA* mutations that confer robust citrate utilization ( Quandt et al . , 2014 ) . The effects of starred gltA mutations on citrate synthase activity were experimentally characterized in this study . Transcription start sites are labeled gltAp1 and gltAp2 ( Wilde and Guest , 1986 ) . See Figure 1—source data 1 for the DNA and protein sequence changes caused by each gltA mutation . ( B ) Muller plot of evolved allele frequencies over time constructed by using metagenomic DNA sequencing to profile archived samples of the LTEE population . Shaded regions correspond to the frequencies of genetically diverged subpopulations distinguished by different evolved alleles . When a colored sector arises within another region , it is a descendant of that genotype with at least this one new mutation and all of the mutations present in the earlier genotype because these populations are strictly asexual . Clades 1–3 correspond to a phylogenetic tree previously constructed by sequencing the genomes of clonal isolates ( Blount et al . , 2012 ) , which also constrains the order of certain mutations ( e . g . , gltA1 < citT < dctA* < any gltA2 ) . Each of the gltA2 alleles ( shaded as in panel A ) evolved independently and defines a separate evolved genotype and all of its descendants . Sequencing was performed on population samples from the times indicated with tick marks , and allele dynamics are shown as linearly interpolated between these points . The cell density of the entire LTEE population increased just prior to the dashed line at 33 , 500 generations ( Blount et al . , 2008 ) when refined Clade 3 Cit++ genotypes containing the citT and dctA* mutations evolved ( Blount et al . , 2012 ) . Figure 1—source data 2 shows the mutations used to track the different clades . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 00310 . 7554/eLife . 09696 . 004Figure 1—source data 1 . Details for all gltA alleles in this study . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 00410 . 7554/eLife . 09696 . 005Figure 1—source data 2 . Mutations used to track clade dynamics in the LTEE . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 005 To better understand the timing and diversity of gltA mutations in the LTEE , we performed metagenomic sequencing on Ara–3 population samples spanning a period from 2000 to 45 , 000 generations ( Figure 1B ) . The earliest we detected the gltA1 mutation was at 25 , 000 generations . Curiously , this allele dropped to a frequency within the population below the level of detection ( ∼1–5% ) in the next sample analyzed at 30 , 000 generations . As expected , the frequencies of mutations characteristic of diverged clades that never evolved Cit+ proportionally increased during this time . The gltA1 allele emerged again at 33 , 000 generations and reached nearly 100% frequency in the population by 33 , 500 generations . This resurgence coincided with the rise of the newly evolved gltA1-containing Cit++ subpopulation that had also accumulated the citT and dctA* mutations necessary for robust citrate utilization by this time ( Quandt et al . , 2014 ) . These gltA1 allele dynamics suggest that the clade that gave rise to Cit+ was rare within the population for several thousand generations prior to the evolution and refinement of this metabolic innovation that enabled it to achieve numerical dominance . During and after the expansion of the Cit++ subpopulation , a diverse array of secondary gltA alleles arose ( Figure 1 ) . Two of the three gltA2 mutations found in the sequenced clones and six other gltA2 mutations were present in ≥5% of the population at some point . All of these additional gltA2 alleles are also nonsynonymous mutations , except for one that is a single-base substitution 14 base pairs upstream of the gltA start codon . The first gltA2 mutation ( gltA2-1 ) was detected at 33 , 000 generations . This mutation was apparently completely displaced within ∼1000 generations by competition with various other genetically diverged subpopulations , some of which had other gltA2 alleles . This period of coexistence lasted ∼5000 generations with the gltA2-3 and gltA2-5 alleles each dominating for a time . By 38 , 000 generations , the gltA2-4 mutation had swept to fixation within the population , and no new gltA mutations were observed after 40 , 000 generations . In a separate experiment , an early Cit+ 33 , 000-generation LTEE clone with the gltA1 mutation , but no gltA2 mutation , was genetically backcrossed with the LTEE ancestor strain using recursive genome-wide recombination and sequencing ( REGRES ) to determine which evolved alleles were necessary for the Cit+ phenotype ( Quandt et al . , 2014 ) . This procedure involved several rounds of selecting for colony growth on agar containing citrate as the only carbon source . We found a de novo point mutation 182 base pairs upstream of the gltA start codon ( gltA2-R allele ) in every one of the sequenced REGRES clones that retained the gltA1 mutation present in the initial Cit++ clone ( Quandt et al . , 2014 ) . These results suggest that a secondary gltA2 mutation was also needed for robust citrate utilization under these conditions . The prevalence of gltA mutations , and in particular of so many secondary gltA mutations in Cit++ isolates , suggests that altering citrate synthase activity was beneficial during the LTEE . To directly test the effects of the evolved gltA alleles on growth , we created isogenic strains in which we introduced only the gltA1 mutation or both the gltA1 and gltA2-R mutations into the genome of strain EQ119 . This strain ( EQ119 ) was created by reconstructing the dctA* refinement mutation required for Cit++ in the LTEE ancestor strain REL607 . EQ119 is not able to grow on citrate on its own , but it becomes capable of robust growth on citrate when supplied with the genetic module containing the citT gene and its captured promoter on a low copy plasmid ( pCitT ) ( Quandt et al . , 2014 ) . We compared growth of these strains in DM25 , the citrate-containing glucose minimal medium used throughout the LTEE . Without the pCitT plasmid , and therefore without access to citrate , cells containing the gltA1 mutation grew indistinguishably from EQ119 cells with a fully wild-type citrate synthase sequence ( gltAwt allele ) ( Figure 2 ) . However , cells with both the gltA1 and gltA2-R mutations had somewhat slower growth rates and reduced final cell densities compared to the strain with only the gltA1 mutation , indicating that addition of the gltA2-R is deleterious when citrate cannot be used as a carbon and energy source . 10 . 7554/eLife . 09696 . 006Figure 2 . Effects of gltA alleles on growth depend on the nutrient utilization niche . ( A ) Growth curves of Cit– strain EQ119 containing just the evolved dctA* mutation and EQ119-derived strains reconstructed with evolved gltA alleles . Growth was measured in the DM25 medium used in the LTEE , which contains 0 . 0025% glucose ( w/v ) and 0 . 032% citrate ( w/v ) . These results show that gltA1 allele has no significant effect on growth when placed into a Cit– genetic background . Further addition of the gltA2-R mutation that evolved in a Cit++ genetic background greatly inhibits growth in this genetic background that cannot utilize citrate . Error bars are the S . D . of at least three replicates . ( B ) Growth curves of the same EQ119-derived strains transformed with plasmid pCitT in DM25 medium . pCitT contains the activated rnk-citT promoter configuration that evolved in the LTEE; it enables strains like EQ119 with the dctA* mutation to fully utilize citrate ( Cit++ phenotype ) ( Quandt et al . , 2014 ) . In the Cit++ genetic context , the gltA1 allele that evolved in a Cit– genetic background in the LTEE is very deleterious to growth . However , adding the gltA2 allele to this genetic background suppresses the deleterious effect of the gltA1 allele and results in further improved growth characteristics compared to the strain containing the gltAwt allele . Error bars are the S . D . of at least three replicates . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 006 When made Cit++ via transformation with the pCitT plasmid , EQ119 cells with the gltA1 mutation grew noticeably worse than those with the gltAwt allele , displaying both a longer lag phase and a slower exponential growth rate ( Figure 2 ) . Addition of the gltA2-R mutation in this context relieved the defect caused by gltA1 and even resulted in improved growth compared to cells with a wild-type citrate synthase sequence . Therefore , the gltA1 mutation , while seemingly having little effect on glucose growth , was strongly deleterious in Cit++ cells . These results suggest that other gltA2 alleles may similarly compensate for the gltA1-mediated growth defect and refine a Cit++ cell's ability to rapidly utilize citrate . Most mutations that accumulate during the LTEE improve fitness ( Barrick et al . , 2009 ) , so it was unexpected that the gltA1 mutation did not appear to appreciably impact growth in the initial growth curve experiment . However , the rate of adaptation in the LTEE slows over time , such that the typical beneficial mutations occurring at later generations only slightly improve competitive fitness ( Wiser et al . , 2013 ) . Therefore , it is possible that growth curves are not sensitive enough to detect a small , but highly relevant effect of the gltA1 mutation on fitness . Alternatively , the gltA1 mutation might only improve fitness in the specific genetic background or ecological context that existed when it evolved during the LTEE . To discriminate between these possibilities , we performed co-culture competition experiments with strains ZDB478 and ZDB483 , two Cit– clones with the gltA1 mutation that were isolated from the LTEE population at 25 , 000 generations ( Blount et al . , 2012 ) . Specifically , we pitted each of these strains against its respective isogenic derivative with the gltA gene sequence reverted to the ancestral allele ( gltAwt ) . In DM25 , the evolved gltA1 allele had no detectable effect on the fitness of strain ZDB478 , but it did improve the competitive fitness of strain ZDB483 by ∼3 . 5% ( Figure 3A ) . 10 . 7554/eLife . 09696 . 007Figure 3 . Evolved gltA1 , iclR , and arcB alleles improve acetate utilization . ( A ) Fitness measurements for two Cit– strains ( ZDB478 and ZDB483 ) isolated from the LTEE population at 25 , 000 generations with the gltA1 mutation relative to isogenic strains with this mutation reverted to the wild-type sequence ( gltAwt ) . The first set of co-culture competition assays was performed in DM25 under normal LTEE conditions . The second set was performed in DM25 supplemented with 0 . 0025% acetate ( Ac ) ( w/v ) to test whether gltA1 affected the component of fitness related to utilization of this overflow product of metabolism that transiently accumulates during E . coli growth on glucose . The presence of the gltA1 mutation is beneficial to fitness in DM25 in one of these two strains , each of which contains other mutations that evolved before and after gltA1 during the LTEE . With added acetate the evolved gltA1 allele is beneficial in both strains , suggesting that this mutation in citrate synthase is important for improving acetate utilization . Error bars are 95% confidence limits from six replicate assays . ( B ) Growth curves of the ancestral REL607 strain ( Anc ) and derivative strains constructed to contain evolved gltA1 , iclR , and arcB alleles . Strains were grown in DM25 media supplemented with 0 . 05% ( w/v ) acetate . The iclR and arcB genes encode transcriptional regulators of the glyoxylate and TCA cycles . These metabolic pathways are required for acetate assimilation in E . coli . The evolved gltA1 mutation is deleterious on its own in the ancestral strain background under these conditions , showing that its beneficial effect on acetate utilization in ( A ) is dependent on other mutations present in ZDB478 and ZDB483 . In contrast , mutations in iclR and arcB improve growth on acetate individually and in combination with one another in the ancestral genetic background . Thus , all three of these mutations likely evolved in the LTEE population because they specifically improved utilization of the acetate byproduct of glucose metabolism . Error bars are the S . D . of at least three replicates . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 007 E . coli excretes acetate as an overflow metabolite during growth on glucose and then switches to utilizing this acetate after glucose is depleted ( Wolfe , 2005 ) . Citrate synthase is a component of the glyoxylate pathway , the primary route by which acetate is assimilated in E . coli . Therefore , we reasoned that gltA mutations might specifically improve a component of fitness related to growth on this byproduct . We tested this hypothesis by measuring the effect of gltA1 on the fitness of strains ZDB478 and ZDB483 in DM25 medium supplemented with acetate to simulate an ecological context in which this resource transiently accumulates . We found that the gltA1 mutation increased fitness in both strains under these conditions ( Figure 3A ) . Other mutations present in these evolved clones are required for gltA1 to have these beneficial effects , as we found that adding gltA1 on its own to the ancestral REL606 strain led to a significant growth defect in DM25 supplemented with acetate ( Figure 3B ) . Altogether , these competition results suggest that the gltA1 mutation was beneficial in the genetic background and nutrient context in which it evolved , specifically because it improved acetate utilization . The dependence of the fitness effect of the gltA1 mutation on acetate and an evolved genetic background prompted us to search for other mutations that might have arisen as part of a larger adaptive network for acetate utilization . We identified mutations causing amino acid substitutions in IclR ( L201R ) and ArcB ( Q79L ) . IclR is a negative regulator of the aceBAK operon ( Cortay et al . , 1991; Maloy and Nunn , 1982 ) , which encodes enzymes for the bypass segment of the glyoxylate pathway ( Kornberg , 1966 ) . Derepression of this operon has been shown to increase acetate utilization ( Maloy and Nunn , 1981 ) , reduce acetate excretion ( Farmer and Liao , 1997 ) , and decrease lag time when switching from glucose to acetate in mixed media ( Spencer et al . , 2007 ) . ArcB is the sensor kinase of the ArcAB two-component system , which negatively regulates the expression of genes encoding enzymes in the TCA and glyoxylate cycles , including gltA and the aceBAK operon ( Iuchi and Lin , 1988; Waegeman et al . , 2011 ) . Introduction of these mutations into the LTEE ancestor strain REL607 greatly increased its proficiency for acetate assimilation , as judged by growth curves ( Figure 3 ) . Therefore , it seems likely that both the evolved iclR and arcB alleles are loss-of-function mutations that derepress expression of enzymes needed for acetate assimilation . To determine whether the initial gltA mutation impacted the evolution of Cit+ , we compared the effects of the citT mutation in otherwise isogenic pairs of strains with and without the gltA1 allele . Strain ZDB564 was previously isolated from generation 31 , 500 of the LTEE; it is an early and only weakly Cit+ clone containing the citT duplication , but not the dctA* refinement mutation . ZDB564 contains the evolved gltA1 allele shared by all Cit+ strains but has no gltA2 mutation . We isolated ZDB706 , a spontaneous Cit– revertant of ZDB564 in which its unstable citT duplication had collapsed back to the wild-type sequence . We next reverted the gltA1 mutation to the wild-type sequence ( gltAwt ) in both strain backgrounds . We were then able to compare differences in growth and fitness between ZDB564 and ZDB706 variants with and without the evolved gltA1 allele , and thereby test whether this citrate synthase mutation potentiated the evolution Cit+ by altering the effects of the citT actualizing mutation . The presence of the citT duplication had little effect on the growth of the Cit+ /Cit– strain pair containing the gltA1 allele ( Figure 4 ) . If anything , the citT mutation appeared to be slightly beneficial in terms of growth rate and final cell density . In stark contrast , the citT mutation greatly prolonged the lag phase before exponential growth in the pair of strains with the gltAwt allele ( Figure 4 ) . The long lag in growth would have significantly disadvantaged this strain within the LTEE population , even though it can reach a slightly higher final cell density when grown in isolation . Co-culture competition experiments between each pair of Cit+/Cit– strains confirmed that whereas the citT mutation was approximately neutral with respect to fitness in the gltA1 strain background , it was highly deleterious in the gltAwt background ( Figure 4 ) . We conclude that the gltA1 mutation was quantitatively necessary for potentiating the evolution of citrate utilization in the LTEE population . Epistatic interactions between gltA1 and the citT mutation in this evolved genetic background prevented a massive fitness defect that would have almost certainly led to the rapid extinction of any newly evolved Cit+ cells before this rudimentary trait could be refined to the advantageous Cit++ phenotype by further mutations . 10 . 7554/eLife . 09696 . 008Figure 4 . Evolution of citrate utilization was potentiated by the gltA1 mutation . ( A ) Growth curves for an early Cit+ clone ( ZDB564 ) from the LTEE which contains the gltA1 mutation and an isogenic Cit– revertant of this strain ( ZDB706 ) without the citT amplification , grown in DM250 medium . The presence of the citT mutation , which is sufficient for the rudimentary Cit+ trait on its own , slightly improves the growth dynamics and final cell density in this genetic background that includes gltA1 and other evolved alleles . Error bars are the S . D . of at least three replicates . ( B ) Growth curves of isogenic derivatives of strains ZDB564 and ZDB706 in which the gltA1 mutation has been reverted to the wild-type sequence , performed in DM250 medium . Addition of the citT amplification in this gltAwt genetic background now causes a large lag in growth dynamics although the final cell density achieved is still higher in the Cit+ strain . Error bars are the S . D . of at least three replicates . ( C ) Relative fitness values as determined by competition assays between Cit+ ( ZDB564 ) and Cit– ( ZDB706 ) isogenic strain pairs which contain either the gltA1 or gltAwt allele . Competitions were performed in DM25 medium , which was used throughout the LTEE . In accordance with the growth curves , these competitions show that the citT mutation would be highly deleterious to fitness if it evolved in a genetic background without the gltA1 mutation . In contrast , the citT mutation is neutral or possibly slightly beneficial to fitness when the gltA1 mutation is present . Thus , the gltA1 mutation potentiated the eventual evolution of robust citrate utilization ( Cit++ ) by preventing the citT mutation from having deleterious effects when it first appeared . Error bars are 95% confidence limits from six replicate assays . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 008 We next sought to characterize the effects of the gltA mutations on cellular physiology in order to understand their impacts on cellular fitness and interactions with other evolved mutations . We first examined whether the gltA1 mutation or the gltA2-R mutation , which is located in the upstream intergenic region , altered citrate synthase mRNA expression ( Figure 5 ) . We found that gltA1 had no effect on transcript levels in this near-ancestral genetic background . In contrast , addition of the gltA2-R mutation reduced gltA mRNA levels by approximately tenfold , whether or not the gltA1 mutation was also present . We conclude that the gltA2-R mutation targets the gltA promoter , and that the fitness effects of this mutation result from reduced gene expression . 10 . 7554/eLife . 09696 . 009Figure 5 . gltA mutations alter gene expression and allosteric regulation by NADH . ( A ) gltA mRNA expression levels as determined by qRT-PCR of EQ119-derived cells containing the specified ancestral or evolved alleles . Expression levels are shown relative to that of strain EQ119 which contains the gltAwt gene sequence . Error bars are 95% confidence intervals of biological triplicate samples . ( B ) NADH-mediated inhibition citrate synthase activity for the wild-type enzyme and evolved variants with combinations of gltA mutations . Fits to the hyperbolic model used to extract the binding and inhibition parameters in Table 1 are shown . Error bars are the S . E . M . of three replicates . ( C ) Molecular modeling predicts that the observed changes in allosteric regulation in evolved CS sequences are primarily caused by how mutations affect the orientation of histidine-110 in the NADH binding pocket . The gltA1 mutation is predicted to redirect this side chain into the binding pocket creating a steric clash with NADH ( red surface ) . The two characterized variants of citrate synthase with an additional gltA2 mutation are predicted to reorient histidine-110 back toward the wild-type conformation . The degree of this predicted structural change correlates with the relative levels of NADH inhibition experimentally measured for these CS variants ( Table 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 009 To understand the effects of the other gltA mutations that evolved in the Ara–3 population , we examined the enzymatic activity of citrate synthase ( GltA ) . We characterized His6-tagged GltA protein with just the gltA1 mutation ( A258T ) , as well as variants also containing secondary gltA2 amino acid substitutions . We found that the A258T substitution did not significantly alter the kinetic parameters of the enzyme ( Table 1 ) . In contrast , further addition of the gltA2-7 ( A162V ) or gltA2-4 ( A124T ) mutations reduced citrate synthase activity . Specifically , the A162V substitution increased the Km values for oxaloacetate ( OAA ) ( 143 . 4 ± 11 . 1 μM vs 84 . 2 ± 10 . 4 μM ) and acetyl-CoA ( AcCoA ) ( 374 . 1 ± 41 . 2 μM vs 138 . 6 ± 17 . 6 μM ) , the two substrates of citrate synthase . The A124T amino acid substitution resulted in an increased Km for acetyl-CoA ( 295 . 1 ± 52 . 6 μM vs 138 . 6 ± 17 . 6 μM ) and a reduction in the kcat value ( 33 . 9 ± 2 . 7 s-1 vs 51 . 1 ± 2 . 5 s-1 ) . We conclude that gltA2 mutations likely impact cellular metabolism in a similar manner , as they all reduce citrate synthase activity . This reduction is achieved either by affecting the Michaelis-Menten parameters of the enzyme ( gltA2-7 and gltA2-4 ) or by lowering the steady-state level of the gltA transcript , and thus the expression of the protein ( gltA2-R ) . 10 . 7554/eLife . 09696 . 010Table 1 . Kinetic and regulatory properties of evolved citrate synthase variants . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 010Evolved allelesSubstitutionskcat ( s-1 ) Km OAA ( μM ) Km AcCoA ( μM ) Kd NADH ( μM ) Ki NADH ( μM ) Maximum% inhibitionDocking energy ( kcal/mol ) gltAwt none51 . 1 ± 2 . 584 . 2 ± 10 . 4138 . 6 ± 17 . 61 . 2 ± 0 . 11 . 7 ± 0 . 180 . 5 ± 1 . 0–80 . 3gltA1A258T49 . 1 ± 5 . 390 . 3 ± 22 . 5148 . 0 ± 41 . 1N . D . 30 . 2 ± 4 . 683 . 2 ± 3 . 1–28 . 8gltA1 gltA2-7A258T , A162V55 . 0 ± 3 . 0143 . 4 ± 11 . 1374 . 1 ± 41 . 21 . 5 ± 0 . 41 . 9 ± 0 . 168 . 7 ± 0 . 7–63 . 8gltA1 gltA2-4A258T , A124T33 . 9 ± 2 . 788 . 1 ± 12 . 8295 . 1 ± 52 . 69 . 5 ± 1 . 717 . 1 ± 6 . 264 . 6 ± 5 . 1–47 . 8Data are represented as fit mean ± S . E . Significant differences from the wild-type enzyme are marked with an asterisk ( two-tailed t-test , p-value < 0 . 05 ) . N . D . indicates no detectable binding . Given that the gltA1 mutation ( A258T ) did not significantly alter mRNA levels or Michaelis-Menten enzyme parameters , we hypothesized that it might affect allosteric regulation of the enzyme . E . coli GltA is a type II citrate synthase that is inhibited by NADH , the primary product of the TCA cycle ( Weitzman , 1966a , 1966b; Weitzman and Jones , 1968 ) . NADH binding to purified GltA proteins was measured via changes in NADH fluorescence that occur upon associating with the enzyme ( Duckworth and Tong , 1976 ) , and allosteric inhibition was measured by analyzing kinetic parameters determined in the presence of different NADH concentrations . Wild-type GltA was found to exhibit a Kd and a Ki for NADH of ∼1 μM ( Table 1 and Figure 5 ) , in excellent agreement with earlier reports ( Anderson and Duckworth , 1988; Pereira et al . , 1994; Stokell et al . , 2003 ) . However , the gltA1 ( A258T ) mutation greatly diminished NADH binding , resulting in an inability to saturate the enzyme within the range of detection in the binding assay , which extends up to ∼10 μM NADH ( Dickinson , 1970 ) . Likewise , the gltA1 mutant displayed a Ki for NADH that was higher by a factor of ∼30 relative to wild-type enzyme . Each gltA2 mutation restored NADH binding and allosteric inhibition , either partially or fully . Notably , GltA containing both the gltA1 and the gltA2-7 mutations ( A258T , A162V ) displayed near wild-type Kd and Ki values for NADH ( Table 1 ) . To gain further insight into how these mutations affected allosteric inhibition by NADH , we performed molecular dynamics simulations . Mutations were introduced into the structure of E . coli citrate synthase ( Maurus et al . , 2003 ) . After energy minimization , we observed subtle differences in the predicted conformation of each enzyme variant , most notably around the NADH binding site . The most pronounced changes were in the orientation of histidine-110 ( Figure 5 ) , even though this amino acid is distant from every gltA mutation . In the wild-type GltA structure , histidine-110 adopts an upward conformation that allows for accommodation of NADH in the binding pocket . The A258T gltA1 mutation is predicted to reorient the histidine-110 side chain toward the binding pocket , presumably creating an unfavorable steric barrier to NADH binding . Addition of secondary gltA2 mutations ( A162V or A124T ) resulted in simulated structures with histidine-110 oriented between these two extremes . Computational docking of NADH to these mutant CS structures predicted relative binding energies that were correlated with the experimentally determined NADH binding affinities ( Table 1 ) . Having determined the molecular consequences of gltA mutations , we next sought to use flux balance analysis ( FBA ) ( Orth et al . , 2010 ) to evaluate how changes in citrate synthase activity would affect cellular growth rates . When the native regulatory pathways of bacteria cannot adjust enzyme activity to achieve optimal reaction fluxes , mutations—like those we observed in gltA—that break these constraints may be necessary to maximize growth rates ( Ibarra et al . , 2002; Lewis et al . , 2010; Teusink et al . , 2009 ) . We used the metabolic model for the LTEE ancestor strain ( Monk et al . , 2013 ) to predict reaction fluxes that are optimal for growth on glucose , citrate , or acetate ( Figure 6 ) . In addition , we also examined how constraining CS flux would impact growth on each of these substrates ( Figure 7 ) . 10 . 7554/eLife . 09696 . 011Figure 6 . Optimal reaction fluxes on carbon sources present in the E . coli LTEE . Flux balance analysis ( FBA ) was used to predict the reaction fluxes in the E . coli B REL606 ancestral strain of the LTEE that optimize the rate of biomass accumulation when utilizing a single carbon source: either glucose , acetate , or citrate . Glucose is the primary carbon source for cells grown under LTEE conditions , while acetate , a metabolic overflow product excreted during E . coli growth on glucose , can also be utilized by the ancestral strain ( Yoon et al . , 2012 ) . Citrate can only be utilized under the aerobic conditions of the LTEE after the Cit+ innovation ( Blount et al . , 2008 , 2012 ) . Flux values derived from FBA for key reactions in central metabolism are shown for modeling growth on each carbon source: glucose ( orange bars ) , acetate ( red bars ) , and citrate ( blue bars ) . Error bars show the full range of possible fluxes for each reaction that are consistent with globally optimal FBA solutions , as predicted by flux variability analysis , and the colored bars show an intermediate value in each range . Gene names for all enzymes that contribute to each reaction flux are displayed . Genes whose expression is controlled by IclR and ArcAB are marked with symbols indicating the direction of transcriptional regulation , and the key citrate synthase ( CS ) reaction catalyzed by GltA is boxed in green . The results of the FBA modeling agree with our experimental observations that the combined effects of derepressing the IclR and ArcAB regulons via the iclR and arcB mutations and alleviating NADH-mediated allosteric inhibition of CS via the gltA1 mutation are beneficial for growth on acetate because they increase flux values for these reactions toward levels that are optimal for this substrate . This metabolic program is thought to potentiate the evolution of Cit+ by increasing the production of C4-dicarboxylates ( succinate , fumarate , and malate ) which can be exported in exchange for citrate uptake by the CitT antiporter . Under Cit++ conditions in which citrate is the primary carbon source FBA predicts that drastically reducing flux through the CS reaction is required to achieve an optimal growth rate which also agrees with our finding that beneficial gltA2 mutations that decrease CS activity evolved at this point in the LTEE . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 01110 . 7554/eLife . 09696 . 012Figure 7 . Effects of constrained citrate synthase flux on utilizing different carbon sources available during the LTEE . Rates of biomass accumulation on different substrates were calculated by using FBA to optimize metabolic fluxes subject to a defined constraint on citrate synthase flux . Glucose/citrate represents a growth condition with the molar ratio of those two nutrients that is present in the DM25 medium used in the LTEE . Curves for other carbon sources are colored as in Figure 7 . Relative biomass accumulation rates were normalized within each curve to the maximum value achieved on the specified substrate or substrate mixture . Relative citrate synthase flux was normalized to the value that resulted in maximum biomass accumulation rate for glucose growth . Optimal relative flux values for each carbon source are indicated with dashed lines . Arrows above each graph indicate the changes in CS activity expected from each successive mutation in gltA that evolved in the LTEE . This analysis shows how mutations in gltA appear to have arisen because they enable flux through CS to achieve values that are more optimal for growth , first on the acetate byproduct of glucose metabolism ( gltA1 ) and then on the glucose/citrate mixture that can be metabolized once robust aerobic citrate utilization has evolved ( gltA1 + gltA2 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 012 For growth on glucose , FBA predicts that low citrate synthase flux will limit the rate of biomass accumulation ( Figure 7 ) . This observation agrees with the known importance of citrate synthase in the biosynthesis of glutamate in glucose minimal media ( Davis and Gilvarg , 1956 ) . However , when citrate is the sole carbon source , FBA predicts optimal growth when there is no flux through CS ( Figure 7 ) . In fact , any CS activity is detrimental because sufficient flux to synthesize glutamate ( and other compounds ) from TCA cycle intermediates is already available . Under these conditions , oxaloacetate ( OAA ) is diverted into gluconeogenesis for the production of essential glycolytic intermediates and sugars ( Sauer and Eikmanns , 2005 ) . This shift in metabolism leads to FBA predicting an increase in flux through the phosphoenolpyruvate ( PEP ) carboxykinase ( pck ) and malic enzyme ( maeA/maeB ) reactions ( Figure 6 ) . Eliminating CS flux also preserves its other CS substrate , acetyl-CoA , so that it can be used for other biosynthetic processes , rather than to produce unneeded TCA intermediates . Cit++ cells must balance using both glucose and citrate , so the optimal level of CS activity in these cells is predicted to be low but not zero ( Figure 7 ) , consistent with the effects of the gltA2 mutations we studied . Since acetate is initially excreted during glucose growth and then subsequently utilized after glucose is depleted , it can represent a distinct resource niche in the LTEE , as described above . FBA indicates that optimal growth on acetate occurs when the flux through citrate synthase is approximately 30% higher than the level that is optimal for growth on glucose ( Figure 7 ) . This increase is expected because CS is needed for the glyoxylate cycle , the major pathway for acetate assimilation in E . coli ( Kornberg , 1966 ) . Thus , FBA predicts that the gltA1 mutation that increased CS flux improved the component of competitive fitness in the LTEE related to acetate utilization . Flux through the bypass portion of the glyoxylate pathway , comprised of the isocitrate lyase ( aceA ) and malate synthase ( aceB ) reactions , is needed for optimal growth on acetate but not for growth on glucose ( Figure 6 ) . This FBA prediction supports our hypothesis that the LTEE mutations in iclR and arcB , which we showed improve growth on acetate-enriched media ( Figure 3 ) , do so because they derepress transcription of aceA and aceB . The FBA predictions motivated us to look for other mutations in central metabolism that may have contributed to refining the Cit++ phenotype . Loss of isocitrate lyase ( aceA ) activity is predicted to be beneficial for growth on citrate ( Figure 6 ) because it eliminates unnecessary input of acetyl-CoA into the TCA cycle via the glyoxylate shunt . We found that a nonsense mutation in aceA was present in ∼15% of the LTEE population in the 33 , 500-generation sample and ∼97% in the next 34 , 000-generation sample . Thus , nearly all Cit++ cells had either the aceA , gltA2-1 , or gltA2-9 allele at 33 , 500 generations ( Figure 1 ) . By 34 , 000 generations the aceA mutation had nearly swept to fixation within the Cit++ clade and all subsequent gltA2 alleles occurred in that genetic background . An aceA mutation may be especially necessary to adjust isocitrate lyase flux because , as discussed earlier , these strains already contain mutations in the iclR and arcB transcriptional regulators that appear to compromise the regulatory mechanisms that would normally repress aceA gene expression . We have established that mutations in the gltA gene encoding citrate synthase ( CS ) were critical for both potentiating the evolution of aerobic citrate utilization in the Lenski LTEE and for the subsequent refinement of this new metabolic capability . A key insight from our studies was the importance of fitness components in the LTEE related to utilizing both glucose , the sole carbon source added to the growth medium , and acetate , a transient overflow byproduct of E . coli growth on glucose ( Figure 8 ) . We found evidence that the clade in which Cit+ would eventually evolve had previously evolved a suite of mutations that improved the utilization of acetate . We assert that this particular mutational trajectory in the glucose-acetate fitness landscape reshaped metabolic fluxes in a way that facilitated the transition to aerobic citrate utilization . 10 . 7554/eLife . 09696 . 013Figure 8 . Model for how successive mutations in gltA are important for first potentiating the evolution of citrate utilization and then for refining this new trait . ( A ) Different carbon sources can be utilized by E . coli during each stage in the evolution of aerobic citrate utilization in the Lenski LTEE . Initially only glucose and acetate , a byproduct of glucose metabolism , are accessible to Cit– cells . The rudimentary Cit+ trait enabled very limited utilization of some of the abundant citrate present in the growth medium . Once this ability was refined to the Cit++ phenotype by further mutations , all of the citrate present was fully exploited . The areas of each box roughly reflect the relative amounts of each of the three carbon sources . ( B ) The initial gltA1 mutation potentiated the evolution of Cit+ . This mutation and additional mutations in iclR and arcB all improve acetate utilization in Cit– cells by increasing flux through the TCA and glyxolyate cycles . The order of accumulation of these mutations , interspersed with many other mutations during the LTEE , is represented by arrows on the left side of the figure . These mutations may potentiate the evolution of the weak Cit+ phenotype because they create a potentiated version of central metabolism that is capable of replenishing succinate and/or other C4-dicarboxylates that must be exported in exchange for citrate import by the CitT antiporter . In accordance with this model , the addition of the CitT-activating mutation ( citT ) to an evolved genetic background that crucially contains the gltA1 mutation results in a rudimentary Cit+ phenotype that is neutral or slightly beneficial to fitness and thus represents a path accessible to evolution , denoted by an arrow to mark the transition as shown in left bar graph . In contrast , adding the citT mutation to the same genetic background but without the gltA1 mutation is highly deleterious to fitness , rendering this path inaccessible to evolution within the LTEE population , as shown in right bar graph . ( C ) Fitness effects of gltA alleles show sign epistasis with nutrient utilization niche . The gltA1 mutation that increased citrate synthase activity was beneficial to fitness when it evolved prior to the Cit+ innovation when only glucose and acetate could be utilized , as shown in the bar graph on the left . While necessary for the evolution of the rudimentary Cit+ phenotype by citT activation , the effect of gltA1 mutation is suboptimal for fitness once the dctA mutation evolves and the citrate utilization niche can be fully exploited . Under this condition , the gltA1 mutation was deleterious to fitness and gltA2 mutations evolved that reversed the effect of the gltA1 mutation and even further reduced citrate synthase activity below the ancestral level to improve fitness in this niche , as shown in bar graph on the right . Note that relative fitness is on a different scale in each of the fitness diagrams , as all Cit++ strains are considerably more fit than any Cit– strain due to the abundant citrate in the growth medium . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 013 Acetate accumulation has previously been shown to reliably lead to the appearance and co-existence of glucose and acetate specialists in shorter E . coli evolution experiments ( Herron and Doebeli , 2013; Spencer et al . , 2007; Treves et al . , 1998 ) . Rather than give rise to this type of stable ecological diversification , the much lower concentration of glucose ( and therefore acetate ) in the LTEE appears to have largely favored the success of generalists that incorporate mutations that improve growth on both substrates . For example , mutations in the transcriptional regulators , iclR and arcB , arose in the population that evolved citrate utilization before 25 , 000 generations ( Blount et al . , 2012 ) . These mutations are expected to derepress the transcription of the mRNAs encoding enzymes in the TCA and glyoxylate cycles , which are necessary for assimilating acetate via acetyl-CoA . Similar iclR and arcB/arcA mutations are found in acetate specialists in other evolution experiments ( Herron and Doebeli , 2013; Spencer et al . , 2007 ) . Interestingly , changes affecting acetate metabolism in the LTEE were not unique to the population that evolved Cit+ . Strains isolated at 50 , 000 generations from all LTEE populations excreted 50% more acetate , on average , than the ancestral strain ( Harcombe et al . , 2013 ) . Mutations in both iclR and arcB are also present by 15 , 000 generations in a population that has not evolved Cit+ ( Barrick et al . , 2009 ) , and arcB/arcA mutations have been found in 11/12 of the LTEE populations ( Plucain et al . , 2014 ) . As expected from the widespread appearance of mutations in iclR and arcB/arcA , there was universal improvement in acetate growth for 20 , 000-generation isolates from all LTEE populations ( Leiby and Marx , 2014 ) . In contrast , mutations in gltA are rare in the LTEE . Only one mutation in citrate synthase was found among 16 clones isolated at generations 20 , 000 to 40 , 000 from 7 other LTEE populations ( Wielgoss et al . , 2011 ) . Flux through CS is highly regulated in wild-type E . coli , both at the level of transcription ( Gosset et al . , 2004; Iuchi and Lin , 1988; Park et al . , 1994 ) and via allosteric feedback inhibition by NADH ( Weitzman , 1966a , 1966b ) , as is typical of gram-negative bacteria ( Maurus et al . , 2003; Weitzman and Jones , 1968 ) . The initial gltA1 mutation in the population that evolved Cit+ disrupted allosteric repression of CS by NADH ( Figure 9 ) . Increasing CS activity in this way is predicted to improve E . coli growth on acetate . We found that the gltA1 mutation did indeed improve competitive fitness when it was added to a clone isolated from the LTEE population around the time when it appeared ( Figure 8B ) , particularly in growth medium supplemented with acetate . As the lineage with gltA1 seems to have come close to extinction in the Ara–3 population before it evolved efficient citrate utilization , it is possible that specializing towards better acetate utilization gave this lineage a frequency-dependent fitness advantage when rare against other competitors in this population that helped preserve it until Cit++ evolved . 10 . 7554/eLife . 09696 . 014Figure 9 . Summary of the molecular effects of evolved gltA mutations . The molecular effects of the initial gltA1 mutation and of the various gltA2 mutations were characterized and the mechanisms by which they produce changes in cellular citrate synthase activity are depicted in the series of gene and protein structure diagrams . Approximate locations of mutations in the gltA gene are shown as shaded triangles in the series of gene diagrams and likewise the locations of the resulting amino acid changes are shown as similarly shaded circles on the CS structure diagrams . Reduced transcription , enzyme activity , and allosteric inhibition as compared to wild-type are indicated with dashed lines . The inset shows the approximate relative levels of citrate synthase activity representative of each gltA allelic state . DOI: http://dx . doi . org/10 . 7554/eLife . 09696 . 014 We hypothesize that the gltA1 mutation critically affected the fitness consequence of the pivotal evolutionary step toward innovation: the citT mutation that enabled the first citrate import into cells and the weak Cit+ phenotype . An allosterically deregulated citrate synthase enzyme , which continuously inputs increased carbon flux into the TCA cycle during growth on glucose/acetate , coupled with overall transcriptional derepression of the TCA and glyoxylate cycles , could replenish the intracellular supply of succinate and/or other C4-dicarboxylates that are excreted in exchange for citrate by the CitT antiporter . The unbalanced loss of these important biosynthetic precursors might explain the detrimental fitness effect of becoming Cit+ via the citT mutation in cells lacking gltA1 ( Figure 8C ) . Thus , the gltA1 mutation was necessary for potentiating the evolution of Cit+ because it converted the citT duplication from a prohibitive step downward into a valley in the fitness landscape into a step in an upward mutational route . After strong citrate utilization ( Cit++ phenotype ) evolved in the LTEE due to the activation of the DctA transporter by the dctA* promoter mutation , multiple secondary gltA2 alleles reached high frequencies in this population in separate lineages vying for dominance . These gltA2 mutations were beneficial for growth on citrate as the primary carbon source ( Figure 8B ) , and they share a common overall effect: all are expected to decrease citrate synthase activity . When growing on citrate as a carbon source , the CS reaction is expected to be detrimental in that it consumes acetyl-CoA and diverts OAA that is needed for gluconeogenesis back into the TCA cycle , a futile-cycle under these conditions . While allosteric regulation of wild-type CS may have been able to adjust flux through this reaction to the very low levels that are optimal under these conditions , this was apparently not possible with the gltA1 mutation already present . Therefore , gltA2 mutations emerged that reverse the change in enzyme activity caused by gltA1 , either by decreasing mRNA expression levels , by reducing the catalytic proficiency of this enzyme , and/or by restoring allosteric inhibition by NADH ( Figure 9 ) . To a first approximation , this LTEE population can be thought of as having evolved through three metabolic epochs: first , glucose utilization was optimized , leading to greater acetate accumulation; second , acetate utilization was optimized in conjunction with further improvements in glucose growth; third , citrate utilization was discovered and optimized . As a whole , the LTEE populations have explored numerous variations on the complex metabolic and regulatory networks of E . coli as they have adapted . This diversity has allowed some lineages of cells to explore new nutrient niches , in particular citrate utilization . While rudimentary citrate utilization via activation of the citT gene could presumably have arisen at any time and in any of the LTEE populations , we have shown that it would have been at a selective disadvantage if it appeared in a non-potentiated genetic background . Moreover , if the structure of the metabolic and regulatory networks and their component genes yielded relatively few genetic pathways with which to improve growth on glucose , it is unlikely that the multi-step mutational pathway to Cit+ that first required mutations that improve acetate utilization would ever have been realized . Therefore , complexity in both the resource environment and in the genetic architecture of the cell conspired to make this metabolic innovation possible . More broadly , our results demonstrate that evolutionary innovations may rely not only on the acquisition of novel genes or the co-option of molecular machinery for entirely new purposes , but also on the inherent malleability of core cellular processes . The components of an organism's metabolic , regulatory , and developmental networks have evolved to interact in complex ways that are attuned to its current niche . Yet , these networks are also poised such that they can be dynamically reorganized toward new purposes by only a few mutations in key enzymes and regulatory proteins . As we observed for changes in citrate synthase activity at different stages in the emergence of citrate utilization during the Lenski LTEE , it may often be case that evolution must fine-tune essential links in these networks as it traverses epistatic turns and switchbacks on the tenuous mutational paths that lead to the successful colonization of new niches . Citrate synthase activity of purified His6-tagged GltA variants was measured using a 5′ , 5′-dithiobis- ( 2-nitrobenzoate ) ( DTNB ) colorimetric assay ( Srere , 1969 ) . Readings at 412 nm were taken in 96-well plates at 25°C using a Synergy HT plate reader . Standard CS assay buffer consists of 20 mM Tris·Cl ( pH 7 . 8 ) , 100 mM KCl , and 1 mM EDTA ( Duckworth and Tong , 1976 ) . Enzyme was present at a concentration at least hundredfold lower than both substrates in all assays . Under these conditions , E . coli CS has been shown to conform to the ordered bisubstrate mechanism ( Anderson and Duckworth , 1988 ) . Kinetic data was fit to the Ordered Bi Bi equation using SigmaPlot 10 ( Systat Software , San Jose , CA ) . NADH equilibrium binding assays were performed as previously described in CS buffer lacking KCl ( Duckworth and Tong , 1976 ) . Briefly , enzymes were equilibrated with varying concentrations of NADH ( 0–6 . 4 µM ) for 1 hr at 25°C . Fluorescent measurements were made with excitation at 340 nm and emission reading at 425 nm in a M200 plate reader ( Tecan , Männedorf , CHE ) . The observed changes in fluorescence versus NADH concentration were fit to a hyperbolic ligand-binding curve using SigmaPlot 10 . Inhibition assays were performed essentially as described elsewhere ( Stokell et al . , 2003 ) . Varying concentrations of NADH were equilibrated with enzyme in CS buffer lacking KCl at 25°C for 1 hr . Substrates OAA and AcCoA were both added at 100 μM and initial reaction rates were measured using the DTNB assay described above . Enzyme activities were normalized to the activity of wild-type GltA in the absence of NADH . Percent inhibition was plotted versus NADH concentration and fit to a hyperbolic model in SigmaPlot 10 . Flux balance analysis was performed with the COBRA Toolbox v2 . 0 ( Schellenberger et al . , 2011 ) and MATLAB v7 . 10 . 0 ( The MathWorks , Inc . , Natick , MA ) using the glpk solver . The genome-scale model of E . coli strain REL606 metabolism , iECB_1328 ( Monk et al . , 2013 ) , was used for this analysis . The model incorporates 2 , 750 reactions and 1954 metabolites . Default media conditions and reaction bounds were used . Carbon source uptake fluxes were set at 10 mmol per gram dry cell weight per hour unless otherwise stated . To simulate combined utilization of citrate and glucose , the uptake fluxes for each carbon source were adjusted to match the molar ratio at which they are present in DM25 ( 10:1 ) . We used flux variability analysis to predict the full ranges of flux values for reactions in central metabolism that exist within the set of optimal global solutions . There was no variability in the optimal flux predicted for the citrate synthase reaction under any of the conditions we tested .	Bacteria and other organisms are constantly under pressure to survive in the face of ever-changing environmental challenges . They generally adapt to these challenges through genetic mutations that modify features they already have . However , occasionally a species may acquire an entirely new characteristic – known as an evolutionary innovation – that allows it to colonize a new environment or adopt a new mode of life . The Lenski Experiment , which began in 1988 , is an ongoing study of the evolution of bacteria grown in the laboratory . The experiment started with twelve identical populations of bacteria and has so far tracked the genetic mutations that have been acquired by the populations over tens of thousands of generations . Fifteen years into the experiment , bacteria in one of the populations evolved the ability to exploit a new food source , a molecule called citrate . The bacteria in this population have multiple mutations in a gene called gltA , which encodes an enzyme called citrate synthase . However , it was not clear how these mutations contributed to the ability of the bacteria in this population to use citrate . Here , Quandt et al . have used a variety of genetic and biochemical techniques to examine the mutations in gltA . They found that one mutation occurred before the bacteria evolved the ability to use citrate , and others occurred afterward . The first mutation in gltA increased the activity of the citrate synthase enzyme , which paved the way for a key mutation affecting citrate transport into cells that allowed the bacteria to consume the new food source . However , once the bacteria evolved the ability to use citrate , and more mutations in other genes refined this process , the increased citrate synthase activity became detrimental . At this point , the bacteria acquired a second gltA mutation that lowered citrate synthase activity to a level below what it had been in the original bacteria before the first gltA mutation . The Lenski Experiment presents a rare opportunity to examine the complete history of an evolutionary innovation . Quandt et al . findings show that evolutionary 'reversals' may be necessary to adjust cell processes in different ways as an innovation first evolves and is further refined . A challenge for future work is to identify the other mutations that , together with the first gltA mutation , were necessary for the bacteria to evolve the ability to use citrate .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "evolutionary", "biology" ]	2015	Fine-tuning citrate synthase flux potentiates and refines metabolic innovation in the Lenski evolution experiment
In Heliconius butterflies , wing colour pattern diversity and scale types are controlled by a few genes of large effect that regulate colour pattern switches between morphs and species across a large mimetic radiation . One of these genes , cortex , has been repeatedly associated with colour pattern evolution in butterflies . Here we carried out CRISPR knockouts in multiple Heliconius species and show that cortex is a major determinant of scale cell identity . Chromatin accessibility profiling and introgression scans identified cis-regulatory regions associated with discrete phenotypic switches . CRISPR perturbation of these regions in black hindwing genotypes recreated a yellow bar , revealing their spatially limited activity . In the H . melpomene/timareta lineage , the candidate CRE from yellow-barred phenotype morphs is interrupted by a transposable element , suggesting that cis-regulatory structural variation underlies these mimetic adaptations . Our work shows that cortex functionally controls scale colour fate and that its cis-regulatory regions control a phenotypic switch in a modular and pattern-specific fashion . Butterfly wing pattern diversity provides a window into the ways genetic changes underlie phenotypic variation that is spatially limited to specific parts or regions of the organism ( McMillan et al . , 2020; Orteu and Jiggins , 2020; Rebeiz et al . , 2015 ) . Many of the underlying genetic loci controlling differences in colour patterns have been mapped to homologus ‘hotspots’ across disparate taxa . In some cases , this repeated adaptation has occurred through the alteration of downstream effector genes , such as pigment biosynthetic enzymes with functions clearly related to the trait under selection , for example , the genes tan and ebony that control insect melanin pigmentation ( reviewed in Massey and Wittkopp , 2016 ) . In other cases , upstream regulatory genes are important , and these are typically either transcription factors ( e . g . optix , MITF , Sox10 ) or components of signalling pathways such as ligands or receptors ( e . g . WntA , MC1R , Agouti ) . These ‘developmental toolkit genes’ influence pigment cell fate decisions by modulating gene regulatory networks ( GRNs ) ( Kronforst and Papa , 2015; Martin and Courtier-Orgogozo , 2017; Prud'homme et al . , 2007 ) , and are commonly characterised by highly conserved functions , with rapid evolutionary change occurring through regulatory fine-tuning of expression patterns . One gene that has been repeatedly implicated in morphological evolution but does not conform to this paradigm is cortex , a gene implicated by mapping approaches in the regulation of adaptive changes in the wing patterning of butterflies and moths . Cortex is one of four major effect genes that act as switch loci controlling both scale structure and colour patterns in Heliconius butterflies , and has been repeatedly targeted by natural selection to drive differences in pigmentation ( Nadeau , 2016; Van Belleghem et al . , 2017 ) . Three of the four major effect genes correspond to the prevailing paradigm of highly conserved patterning genes; the signalling ligand WntA ( Martin et al . , 2012; Mazo-Vargas et al . , 2017 ) and two transcription factors optix ( Lewis et al . , 2019; Reed et al . , 2011; Zhang et al . , 2017 ) and aristaless1 ( Westerman et al . , 2018 ) . Cortex , on the other hand , is an insect-specific gene showing closest homology to the cdc20/fizzy family of cell cycle regulators ( Chu et al . , 2001; Nadeau et al . , 2016; Pesin and Orr-Weaver , 2007 ) . The lepidopteran orthologue of cortex displays rapid sequence evolution and has acquired novel expression domains that correlate with melanic wing patterns in Heliconius ( Nadeau et al . , 2016; Saenko et al . , 2019 ) . It therefore seems likely that the role of cortex in regulating wing patterns has involved a major shift in function , which sits in contrast to the classic model of regulatory co-option of deeply conserved patterning genes . The genetic locus containing cortex was originally identified in the genus Heliconius as controlling differences in yellow and white wing patterns in H . melpomene and H . erato ( Figure 1a ) and the polymorphism in yellow , white , black , and orange elements in H . numata . This was inferred using a combination of association mapping and gene expression data ( Joron et al . , 2006; Nadeau et al . , 2016 ) . The same locus has also been repeatedly implicated in controlling colour pattern variation among divergent Lepidoptera , including the peppered moth Biston betularia and other geometrids , the silkmoth Bombyx mori , and other butterflies such as Junonia coenia , Bicyclus anynana , and Papilio clytia ( Beldade et al . , 2009; van der Burg et al . , 2020; Ito et al . , 2016; VanKuren et al . , 2019; Van't Hof et al . , 2019; Van't Hof et al . , 2016 ) . This locus therefore contains one or more genes that have repeatedly been targeted throughout the evolutionary history of the Lepidoptera to generate phenotypic diversity . In Heliconius butterflies , population genomic data suggest that cis-regulatory modules surrounding cortex underlie adaptive variation of yellow and white colour pattern elements ( Enciso-Romero et al . , 2017; Van Belleghem et al . , 2017 ) . These studies predict the existence of modular elements that compartmentalise expression of colour pattern genes across developing wings . However , developmental genes have complex regulatory domains and recent work has suggested that pleiotropy among different enhancers may be more common than is currently appreciated ( Lewis et al . , 2019; Murugesan et al . , 2021; Nagy et al . , 2018 ) . Further dissection of the regulatory elements controlling wing pattern variation is thus necessary to assess the relative contribution of pleiotropy versus modularity at colour pattern loci ( Lewis and Van Belleghem , 2020 ) . While fantastically diverse , most of the pattern variation in Heliconius is created by differences in the distribution of just three major scale cell types: Type I ( yellow/white ) , Type II ( black ) , and Type III ( red/orange/brown ) ( Aymone et al . , 2013; Gilbert et al . , 1987 ) . Each type has a characteristic nanostructure and a fixed complement of pigments . Type I yellow scales contain the ommochrome precursor 3-hydroxykynurenine ( 3-OHK ) ( Finkbeiner et al . , 2017; Koch , 1993; Reed et al . , 2008 ) , whereas Type I white scales lack pigment , and the white colour is the result of the scale cell ultrastructure ( i . e . structural white ) ( Gilbert et al . , 1987 ) ( see Figure 9f ) . Structurally , Type I scales are characterised by the presence of a lamina covering the scale windows and by microribs joining the larger longitudinal ridges . In contrast , Type II scale cells are pigmented with melanin , have larger crossribs and lack a lamina covering the scale windows . Quantitative variation in scale structures between populations ( but not within individuals ) can cause Type II scales to range from matte black to iridescent blue ( Brien et al . , 2019; Parnell et al . , 2018 ) . Finally , Type III scale cells contain the red ommochrome pigments xanthommatin and dihydroxanthommatin and are characterised by larger spacing between crossribs and ridges . Here we focus on the role of cortex in specifying these scale types in Heliconius butterflies , an adaptive radiation with over 400 different wing forms in 48 described species ( Jiggins , 2017; Lamas , 2004 ) and where diversity in wing patterns can be directly linked to the selective forces of predation and sexual selection ( Brown , 1981; Turner , 1981 ) . Specifically , we combine expression profiling using RNA-seq , ATAC-seq , in situ hybridisation and antibody staining experiments , as well as CRISPR/Cas9 gene knockouts to determine the role that this locus plays in pattern variation of two co-mimetic morphs of H . melpomene and H . erato ( Figure 1b ) . We focus on two mimetic morphs differing specifically in the presence/absence of a yellow hindwing bar , whose phenotypic switch has been mapped to non-coding regions surrounding the gene cortex . We also test its function in diverse patterning morphs , including ones differing in the presence of white margin elements spanning the hindwing , as well as species displaying divergent and complex phenotypes such as the tiger striped silvaniform Heiconius hecale . Despite cortex not following the prevailing paradigm of patterning loci , we demonstrate that the gene plays a fundamental role in pattern variation by modulating a switch from Type I scale cells to Type II and Type III scale cells . Moreover , we show that while the phenotypic effects of cortex extend across the entire fore- and hindwing surface , modular enhancers have evolved in two distantly related Heliconius species that control spatially restricted , pattern-specific expression of cortex . Our findings , coupled with recent functional experiments on other Heliconius patterning loci , are beginning to illuminate how major patterning genes interact during development to determine scale cell fate and drive phenotypic variation across a remarkable adaptive radiation . To identify genes associated with the yellow bar phenotype , we performed differential gene expression ( DGE ) analysis using developing hindwings sampled from colour pattern morphs in H . erato and H . melpomene differing only in the presence or absence of the hindwing yellow bar ( Figures 1b and 2a ) . In total , we sequenced 18 samples representing three developmental stages ( larval , 36 h ± 1 . 5 h [Day 1 pupae] and 60 h ± 1 . 5 h [Day 2 pupae] ) from two morphs in each of the two species , with hindwings divided into two parts for the pupal stages ( Figure 2a ) . We focused our attention on genes centred on a 47-gene interval on chromosome 15 previously identified as the minimal associated region with yellow band phenotypes by recombination and population genetic association mapping ( Nadeau et al . , 2016 , Supp Table 1; Joron et al . , 2006; Moest et al . , 2020; Van Belleghem et al . , 2017 ) . Both our initial expression analysis and recent analysis of selective sweeps at this locus ( Moest et al . , 2020 ) indicate that three genes show differential expression and are likely targets of selection: cortex , domeless ( dome ) , and washout ( wash ) ( Figure 2c ) . In Heliconius , dome appears to have duplicated in the ancestor of H . erato and H . melpomene , resulting in a full-length copy ( referred to here as domeless ) and a further copy exhibiting truncations at the C-terminus ( domeless-truncated [dome-trunc] ) ( Figure 1—figure supplements 1–2 ) . Transcriptomic and previous evidence ( Lewis et al . , 2020 ) also indicates that dome and wash are transcribed as a single bi-cistronic gene ( Figure 1—figure supplements 3–4 ) . Differential expression analysis was thus performed with dome/wash as a single annotation . The two species were analysed separately , with both showing only cortex and dome/wash as significantly differentially expressed between morphs among the 47 genes in the candidate region , with cortex differential expression occurring earlier in development . In fifth-instar larvae , cortex is differentially expressed in both species between the two colour pattern morphs , with cortex showing the highest adjusted p-value ( Benjamini and Hochberg correction ) for any gene in the genome at this stage in H . erato ( Figure 2c ) . Interestingly , cortex transcripts were differentially expressed in opposite directions in the two species , with higher expression in the melanic hindwing morph in H . melpomene and in the yellow banded morph in H . erato . We confirmed this pattern of expression through a SNP association analysis ( Figure 2—source data 1–3 ) and RT-qPCR ( Figure 2—figure supplement 1 ) . This pattern is reversed for dome/wash in Day 1 pupae , where a statistically higher proportion of transcripts are detected in H . melpomene rosina ( yellow ) and in H . erato hydara ( melanic ) ( Figure 2—figure supplements 2–4 ) . No differential expression of these genes was found at Day 2 pupae . When comparing across hindwing sections differing for the yellow bar phenotype , 22 genes of the associated 47-gene interval were differentially expressed at Day 1 between relevant wing sections in H . melpomene , including cortex and dome/wash ( Figure 2—figure supplements 2–4 ) . In contrast , in H . erato Day 1 pupae , only dome/wash was differentially expressed . At Day 2 pupae , there were no differentially expressed genes in either species between relevant wing sections at this locus . Given the strong support for the involvement of cortex in driving wing patterning differences , we re-analysed its phylogenetic relationship to other cdc20 family genes with more extensive sampling than previous analyses ( Nadeau et al . , 2016 ) . Our analysis finds strong monophyletic support for cortex as an insect-specific member of the cdc20 family , with no clear cortex homologs found outside of the Neoptera ( Figure 2—figure supplement 5 ) . Branch lengths indicate cortex is evolving rapidly within the lineage , despite displaying conserved anaphase promoting complex ( APC/C binding motifs , including the C-Box and IR tail Figure 2—figure supplement 6; Chu et al . , 2001; Pesin and Orr-Weaver , 2007 ) . In summary , cortex is the most consistently differentially expressed gene and showed differential expression earlier in development as compared to the other candidate dome/wash . We therefore focused subsequent experiments on cortex , although at this stage we cannot rule out an additional role for dome/wash in yellow pattern specification . Two studies have reported that cortex mRNA expression correlates with melanic patches in two species of Heliconius ( Nadeau et al . , 2016; Saenko et al . , 2019 ) . To further assess this relationship between cortex expression and adult wing patterns , we performed in situ hybridisation on developing wing discs of fifth-instar larvae , where we observed largest cortex transcript abundance , in both the yellow-barred and plain hindwing morphs of H . erato and H . melpomene . Cortex transcripts at this stage localised distally in forewings and hindwings of both species ( Figure 3—figure supplement 1 ) . In H . erato demophoon hindwings , expression was strongest at the intervein midline , but extends across vein compartments covering the distal portion of both forewing and hindwing ( Figure 3a ) . By contrast , in H . erato hydara hindwings , cortex transcripts are more strongly localised to the intervein midline forming a sharper intervein expression domain ( Figure 3c ) . Expression in H . melpomene rosina is similar to H . erato demophoon at comparable developmental stages , again with stronger expression localised to the intervein midline but extending further proximally than in H . erato demophoon ( Figure 3b ) . In H . melpomene melpomene , hindwing cortex expression extends across most of the hindwing , and does not appear to be restricted to the intervein midline ( Figure 3c ) . Given that cortex has been implicated in modulating wing patterns in many divergent lepidoptera , we also examined localisation in a Heliconius species displaying distinct patterns: H . hecale melicerta ( Figure 3e ) . Interestingly , in this species , transcripts appear strongest in regions straddling the wing disc veins , with weak intervein expression observed only in the hindwings . Previous data has shown that variation in yellow spots ( Hspot ) is also controlled by a locus located a chromosome 15 ( Huber et al . , 2015 ) . Expression in H . hecale melicerta forewings corresponds to melanic regions located in between yellow spots at the wing margins , indicating cortex may be modulating Hspot variation in H . hecale . Overall , our results suggest a less clear correlation to melanic elements than reported expression patterns ( Nadeau et al . , 2016; Saenko et al . , 2019 ) where cortex expression in fifth-instar caterpillars is mostly restricted to the distal regions of developing wings , but appears likely to be dynamic across fifth-instar development ( Figure 3—figure supplement 1 ) . To assay the function of cortex during wing development , we generated G0 somatic mosaic mutants using CRISPR/Cas9 knock outs . We targeted multiple exons using a combination of different guides and genotyped the resulting mutants through PCR amplification , cloning , and Sanger sequencing ( Figure 4—figure supplement 1 ) . Overall KO efficiency was low when compared to similar studies in Heliconius ( Concha et al . , 2019; Mazo-Vargas et al . , 2017 ) , with observed wing phenotype to hatched eggs ratios ranging from 0 . 3% to 4 . 8% . Lethality was also high , with hatched to adult ratios ranging from 8 . 1% to 29 . 8% ( Figure 4—source data 1–2 ) . Targeting of the cortex gene in H . erato morphs produced patches of ectopic yellow and white scales spanning regions across both forewings and hindwings ( Figure 4—figure supplements 2–4 ) . All colour pattern morphs were affected in a similar manner in H . erato . Mutant clones were not restricted to any specific wing region , affecting scales in both proximal and distal portions of wings . The same effect on scale pigmentation was also observed in H . melpomene morphs , with mutant clones affecting both distal and proximal regions in forewings and hindwings ( Figure 5a–c ) . In H . erato hydara , we recovered mutant individuals where clones also spanned the red forewing band ( Figure 4b , Figure 4—figure supplement 5 ) . Clones affecting this region caused what appears to be an asymmetric deposition of pigment across the scales , as well as transformation to white , unpigmented scales ( Figure 4—figure supplement 5 ) . As this locus has been associated with differences in white hindwing margin phenotypes ( Jiggins and McMillan , 1997; Figure 1b ) , we also targeted cortex in mimetic morphs that display the same phenotype in the two species , H . erato cyrbia and H . melpomene cythera . Mutant scales in these colour pattern morphs were also localised across both wing surfaces , with both white and yellow ectopic scales ( Figures 4c and 5c ) . Both the white and blue colouration in these co-mimics is structurally derived , indicating that cortex loss-of-function phenotype also affects the scale ultrastructure . Furthermore , we observed a positional effect , where ectopic scales in the forewing and anterior compartment of the hindwing shifted to yellow , and posterior hindwing scales became white ( Figure 4c , Figure 4—figure supplement 5d ) . This positional effect likely reflects differential uptake of the yellow pigment 3-OHK across the wing surface , which may be related to cryptic differential expression of the yellow-white switch aristaless-1 ( Reed et al . , 2008; Westerman et al . , 2018 ) . To further test the conservation of cortex function across the Heliconius radiation , as well as nymphalids as a whole , we knocked out cortex in H . charithonia and H . hecale melicerta , outgroups to H . erato and H . melpomene , respectively , and Danaus plexippus as an outgroup to all Heliconiini . Again , ectopic yellow and white scales appeared throughout the wing surface in all species , suggesting a conserved function with respect to scale development among butterflies ( Figure 5d–f , Figure 5—figure supplements 1–5 ) . In summary , cortex mKOs appear to not be restricted to any specific wing pattern elements and instead affect regions across the surface of both forewings and hindwings . Mutant scales are always Type I scales , with differing pigmentation ( 3-OHK , yellow ) or structural colouration ( white ) depending on morph and wing position . The high rate of mosaicism combined with high mortality rates suggests cortex is likely developmentally lethal . Mutant clones also appear aggregated , suggesting the cortex mKO may affect early phases of cell division or communication during development and produce a growth disadvantage or differential adhesion relative to WT cells that result in grouping effects . The cortex mRNA expression patterns in larval imaginal disks suggest a dynamic progression in the distal regions , and in a few cases ( Figure 3; Nadeau et al . , 2016; Saenko et al . , 2019 ) , a correlation with melanic patterns whose polymorphisms associate with genetic variation at the cortex locus itself . We thus initially hypothesised that like for the WntA mimicry gene ( Martin et al . , 2012; Mazo-Vargas et al . , 2017 , Concha et al . , 2019 ) , the larval expression domains of cortex would delimit the wing territories where it is playing an active role in colour patterning . However , our CRISPR based loss-of-function experiments challenge that hypothesis because in all the morphs that we assayed , we found mutant scales across the wing surface . This led us to re-examine our model and consider that post-larval stages of cortex expression could reconcile the observation of scale phenotypes across the entire wing , rather than in limited areas of the wing patterns . To test this hypothesis , we developed a Cortex polyclonal antibody and found nuclear expression across the epithelium of H . erato demophoon pupal hindwings without restriction to specific pattern elements ( Figure 6 ) . In fifth-instar larvae , Cortex protein was detected in a similar pattern to mRNA , with expression visible at the intervein midline of developing wings ( Figure 6a ) . Cortex was then detected across the entire wing surface from 24 hr after pupal formation ( a . p . f ) , until 80 hr a . p . f in our time series ( Figure 6b–d , Figure 6—figure supplement 1 ) . Localisation remained nuclear throughout development and appears equal in intensity across hindwing colour pattern elements ( Figure 6e ) . Given the broad effect observed for cortex across both wing surfaces , we next tested whether specific expression might be under the control of pattern-specific cis-regulatory elements ( CREs ) . In order to look for potential CREs , we performed an Assay for Transposase-Accessible Chromatin using sequencing ( ATAC-seq ) in fifth-instar hindwings of both co-mimetic morphs differing in the presence of the yellow bar in H . erato and H . melpomene ( Figure 7—source data 1 ) . We observed many accessible chromatin ‘peaks’ surrounding cortex ( Figure 7—figure supplement 1 ) , each of which could represent a potentially active CRE . To narrow down candidate peaks that could be regulating cortex in a pattern-specific manner , we overlayed association intervals with the ATAC-seq signals , which indicate evolved regions between populations of H . melpomene differing in the hindwing yellow bar phenotype . Specifically , we applied the phylogenetic weighting strategy Twisst ( topology weighting by iterative sampling of subtrees; Martin and Van Belleghem , 2017 ) to identify shared or conserved genomic intervals between sets of H . melpomene populations ( obtained from Moest et al . , 2020 ) with similar phenotypes around cortex . This method identified a strong signal of association ~8 kb downstream of the annotated cortex stop codon , that overlapped with a clear ATAC-seq peak ( Figure 7a , b , Figure 7—figure supplement 1 ) . We next sought to knock out this CRE , by designing a pair of sgRNA guides flanking the ATAC-seq signal . We reasoned that since cortex controlled the switch to melanic scales across the entire wing , knocking-out an enhancer in the melanic morph ( H . melpomene melpomene ) , or in F1 hybrids between H . melpomene rosina and H . melpomene melpomene , should result in the appearance of yellow scales in a yellow bar-like pattern . Indeed , upon KO of this CRE we recovered mKOs consistent with a modular enhancer driving cortex expression in a yellow bar-specific pattern , with no clones exhibiting yellow scales extending out of the region that forms the yellow bar ( Figure 7c , Figure 7—figure supplement 2 ) . To test whether the same element was driving the evolution of the yellow bar phenotype in the co-mimetic morph of H . erato , we first targeted the homologous peak , which shares both 95% sequence identity with H . melpomene , as well as the presence of an accessible chromatin mark ( Figure 7a ) . While none of our CRISPR trials resulted in a visible phenotype at this locus ( number of injected adults eclosed = 36 ) , we did observe the presence of a further accessible region ~10 kb 3’ of the H . melpomene conserved CRE . We reasoned that a different but positionally close peak could be driving the yellow bar phenotype in H . erato . Remarkably , targeting of this CRE resulted in a yellow bar phenotype in the melanic H . erato hydara , with no clones containing yellow scales extending beyond the region that forms the yellow bar ( Figure 7c , Figure 7—figure supplement 3 ) . Deletions at each of the loci were confirmed by PCR amplification , cloning , and Sanger sequencing ( Figure 7—figure supplement 4 ) . Finally , to confirm that the CREs were interacting with the cortex promoter , we took advantage of a previously published set of Hi-C samples in H . erato populations ( Lewis et al . , 2019 ) , to check for enhancer/promoter interactions through the implementation of virtual 4C plots . For both CREs , we found a statistically significant interaction between CRE and promoter , indicating the observed effect is likely due to the CRE interacting with the cortex promoter , and not a different gene at the locus ( Figure 7—figure supplement 5 ) . Given that we were able to induce a yellow bar phenotype by the deletion of a modular CRE , we next asked whether natural populations with this phenotype might also show a similar deletion . To test for the presence of deletions at the candidate CRE , we used extensive publicly available whole-genome re-sequence data for geographically isolated populations differing in the presence of the hindwing yellow bar ( Darragh et al . , 2019; Enciso-Romero et al . , 2017; Kozak et al . , 2018; Martin et al . , 2019; Van Belleghem et al . , 2017 ) . In total , we assayed 16 geographically isolated subspecies across central and south America and looked for signature coverage drops at the targeted ATAC-seq peak , which could be indicative of deletions ( Chan et al . , 2010; Kemppainen et al . , 2021; Figure 8—source data 1 ) . We observed a characteristic drop in coverage at the targeted CRE in all H . melpomene and H . timareta morphs exhibiting a yellow bar phenotype , while no drop was detected in morphs with a melanic hindwing ( Figure 8a ) . Given this characteristic signature associated with the presence of a yellow bar in the sequencing data , we next genotyped across the putative deletion using Sanger sequencing in H . melpomene rosina and H . melpomene melpomene individuals . Surprisingly , we found two transposable element ( TE ) insertions in H . melpomene rosina with a Helitron-like TE found spanning the CRE peak , suggesting that the coverage drop is instead due to an insertion of repetitive sequence , rather than a deletion . Enhancer disruption is therefore likely caused by TE sequence in yellow bar morphs ( Liu et al . , 2019 ) . We next assayed three other yellow-barred morphs ( H . melpomene bellula , H . melpomene amaryllis , and H . timareta tristero ) and found the same TE signatures in all three populations ( Figure 8—figure supplement 2 ) , suggesting the TE insertions are likely shared through introgression . No similar signature of reduced coverage was observed in co-mimetic morphs of H . erato , suggesting that sequence divergence is responsible for the evolution of the yellow bar CRE in this species ( Figure 8—figure supplement 2 ) . Previous studies have shown an association between scale ultrastructure and pigmentation in Heliconius butterflies ( Concha et al . , 2019; Gilbert et al . , 1987; Janssen et al . , 2001; Zhang et al . , 2017 ) . In particular , it has been reported that perturbation by wounding transforms both the pigment content and structure of scales in a tightly coupled way ( Janssen et al . , 2001 ) . We thus asked whether ectopic yellow/white scales generated through cortex knockout were accompanied by structural transformations using scanning electron microscopy ( SEM ) in the same way as ectopic colour scales generated through wounding or WntA knockouts ( Janssen et al . , 2001; Concha et al . , 2019 ) . To account for known positional effects on scale structure , we compared wild-type and mutant scales from homologous locations across the wing surface . We observed ultrastructural shifts that are consistent with partial homeosis in cortex mutant scales in both H . melpomene and H . erato ( Figure 9 , Figure 9—figure supplement 1 ) . In all cases where a yellow or white ( Type I ) clone was present in a region that would otherwise be black or blue ( Type II ) in the wild type , the ultrastructure of the scale was notably different . Wild-type blue and black scales have crossribs at a spacing of ~0 . 6 µm , lack lamina between ridges and crossribs , and have no prominent microribs , while both wild-type and mutant Type I scales have no prominent crossribs , lamina that fills the spaces between the microribs and ridges , and prominent microribs at a spacing of ~0 . 2 µm ( Figure 9a–d , Figure 9—source data 1–2 ) . A consistent difference between all Type I scales ( mutant and wild type ) is the presence of a lamina covering the inter-ridge space ( Figure 9f ) . These ultrastructural shifts suggest that the perturbation of cortex affects scale fate decision , not only shifting pigmentation type , but also scale morphology . Red scales ( Type III ) that are within a coding KO clone take on an aberrant structure and pigmentation . Scales were frequently found to be curled up laterally , and while ommochrome pigment is sometimes visibly deposited in the scale , it is granular in appearance rather than diffuse throughout the scale ( Figure 9e , Figure 9—figure supplement 2 ) . These ‘granular’ pigment accumulations could not be observed as a distinct structure by SEM , suggesting that they are under the surface of the scale . As with wild-type and mutant Type I scales , prominent microribs can also be observed on these rolled scales , but due to the topological deformity of these scales , it was not possible to take accurate measurements . The genetic locus containing the gene cortex represents a remarkable case of convergent evolution , where repeated and independent mutations surrounding the gene are associated with shifts in scale pigmentation state in at least nine divergent species of Lepidoptera ( Beldade et al . , 2009; van der Burg et al . , 2020; Nadeau et al . , 2016; Van Belleghem et al . , 2017; VanKuren et al . , 2019; Van't Hof et al . , 2019; Van't Hof et al . , 2016 ) . While these studies have linked putative regulatory variation around cortex to the evolution of wing patterns , its precise effect on scale cell identity and pigmentation has remained speculative until now . Here , we demonstrate that cortex is a causative gene that specifies melanic and red ( Type II and Type III ) scale cell identity in Heliconius and acts by influencing both downstream pigmentation pathways and scale cell ultrastructure . We also show that cortex is under the control of modular enhancers that appear to control the switch between mimetic yellow bar phenotypes in both H . melpomene and H . erato . Our combination of expression studies and functional knockouts demonstrate that this gene acts as a key scale cell specification switch across the wing surface of Heliconius butterflies , and thus has the potential to generate much broader pattern variation than previously described patterning genes . While we have shown that cortex is a key scale cell specification gene , it remains unclear how a gene with homology to the fizzy/cdc20 family of cell cycle regulators acts to modulate scale identity . In Drosophila , Fizzy proteins are known to regulate APC/C activity through the degradation of cyclins , leading to the arrest of mitosis ( Raff et al . , 2002 ) . In particular , fizzy-related ( fzr ) induces a switch from the mitotic cycle to the endocycle , allowing the development of polyploid follicle cells in Drosophila ovaries ( Schaeffer et al . , 2004; Shcherbata et al . , 2004 ) . Similarly , cortex has been shown to downregulate cyclins during Drosophila female meiosis , through its interaction with the APC/C ( Pesin and Orr-Weaver , 2007; Swan and Schüpbach , 2007 ) . Immunostainings show that Cortex protein localises to the nucleus in Heliconius pupal wings , suggesting a possible interaction with the APC/C in butterfly scale building cells . Ploidy levels in Lepidoptera scale cells have been shown to correlate with pigmentation state , where increased ploidy and scale size lead to darker scales ( Cho and Nijhout , 2013; Iwata and Otaki , 2016 ) . cortex may thus be modulating ploidy levels by inducing endoreplication cycles in developing scale cells . However , we currently have no direct evidence for a causal relationship between ploidy state and pigmentation output , and a mechanistic understanding of this relationship and any role cortex may be playing in modulating ploidy levels will require future investigation . A curious result reported from our RNA-seq dataset is that differential expression appears to occur in opposite directions between the two co-mimetic morphs . While this could represent some difference in the precise role of cortex between H . melpomene and H . erato , the pattern may also be explained by the limited sampling during fifth-instar development in both species . The dynamic expression observed during fifth-instar wing development suggests that levels of cortex expression may be changing in a precise and variable manner of short periods of development . A more precise time series across fifth-instar and early pupal development may thus be needed to reveal the precise difference in cellular function of cortex between these species . In H . melpomene , we were able to narrow down a clear peak of association with the presence of accessible chromatin marks and showed that KO of this region results in the appearance of a yellow bar phenotype in black hindwing morphs ( Figure 7 ) . Interestingly , when targeting the homologous peak in H . erato , we failed to recover any type of phenotype , but were able to induce the appearance of a yellow bar through the targeting of an adjacent peak , not present in the H . melpomene datasets , indicating that an independently evolved CRE is driving this phenotype in H . erato . These results , coupled with the coding KOs , suggests that the CREs are enhancers that are able to drive cortex expression in a yellow bar specific manner . It is therefore puzzling that both the in situ hybridisation and antibody experiments failed to recover an association between Cortex localisation and the yellow bar phenotype . One possibility is that , because cortex is expressed throughout the wing , the differences in cortex expression that drive the pattern difference are either highly discrete in time and therefore hard to observe , or are the consequence of subtle changes in concentration that we could not detect with immunofluorescence . Moreover , cortex is known to have complex patterns of alternative splicing ( Nadeau et al . , 2016 ) , suggesting that perhaps both our polyclonal antibody and in situ probes lack the specificity to detect localisation of specific alternatively spliced variants . This lack of a conspicuous link between expression and function is a puzzling result that will require further investigation in future . The ideal experiments would utilise the identified enhancers as enhancer traps , to show they are able to drive expression in a pattern-specific manner , as well as perform knock-in experiments in the reciprocal co-mimetic morph , to show that these regions are sufficient to drive the phenotypic switches . In H . melpomene , we found a clear association between the absence of an accessible chromatin peak in yellow-barred populations with a characteristic drop in coverage over the same region , that overlaps with both the targeted CRISPR and association intervals . The mapped profiles show that this drop in coverage is explained by phenotype , rather than geography , in contrast to other adjacent regions . Upon further investigation , we found a large 690 bp TE insertion 5’ of the peak of interest as well as a Helitron-like sequence overlapping the peak in H . melpomene rosina . This raises the interesting possibility that this portion of the enhancer might contain the binding sites necessary to drive cortex in a yellow bar specific manner , and that recurrent TE insertions across this region are driving the evolution of this phenotype in H . melpomene populations . We also note that this insertion is observed in mimetic morphs of a different species , H . timareta , with which H . melpomene has previously been described to share regulatory regions at other patterning loci via adaptive introgression ( Morris et al . , 2019; Wallbank et al . , 2016 ) . Thus , adaptive introgression of this region and its structural variants is likely facilitating mimicry in this system ( Heliconius Genome Consortium et al . , 2012 ) . Functional knockouts now exist for all the four major loci known to drive pigmentation differences in Heliconius ( Mazo-Vargas et al . , 2017; Westerman et al . , 2018; Zhang et al . , 2017 ) . These loci represent the major switching points in the GRNs that are ultimately responsible for determining scale cell identity . This work underscores the importance of two patterning loci , cortex and WntA , as master regulators of scale cell identity . Both are upregulated early in wing development and have broad effects on pattern variation ( Concha et al . , 2019; Nadeau et al . , 2016 ) . The signalling molecule WntA modulates forewing band shape in Heliconius by delineating boundaries around patterns elements , and is expressed in close association with future pattern elements ( Concha et al . , 2019; Martin et al . , 2012 ) . Unlike cortex mutants , WntA KOs shift scale cell identity to all three cell types ( I , II , and III ) , depending on genetic background . Thus , WntA acts as a spatial patterning signal inducing or inhibiting colour in specific wing elements , in contrast to cortex , which acts as an ‘on-off’ switch across all scales on the butterfly wing . Interestingly , cortex knockouts lead to shifts in scale fate irrespective of WntA expression . This suggests either that cortex is required as an inductive signal to allow WntA to signal further melanisation , or that two , independent ways to melanise a scale are available to the developing wing . The latter hypothesis is supported by certain H . erato colour pattern WntA mutants , where even in putatively cortex-positive regions , scales are able to shift to Type I in the absence of WntA alone ( Concha et al . , 2019 ) . This indicates that while under certain conditions cortex is sufficient to induce the development of black scales , WntA is also required as a further signal for melanisation in some genetic backgrounds . Under this scenario , colour pattern morphs may be responding epistatically to different WntA/cortex alleles present in their respective genetic backgrounds . This is also consistent with genetic evidence for epistasis between these two loci seen in crossing experiments , whereby the yellow bar in H . erato favorinus results from an interaction between the Cortex and WntA loci ( Mallet , 1989 ) . Under a simple model ( Figure 10 ) , cortex is one of the earliest regulators and sets scale differentiation to a specific pathway switches between Type I ( yellow/white ) and Type II/III ( black/red ) scales . Thus , we can envision a differentiating presumptive scale cell ( PSC ) receiving a Cortex input as becoming Type II/III competent , with complete Type III differentiation occurring in the presence of optix expression ( Zhang et al . , 2017 ) . This is consistent with our data , which shows cortex is also required as a signal for Type III ( red ) scales to properly develop . Several cortex mutant individuals had clones across red pattern elements and failed to properly develop red pigment . The development of red scales in Heliconius butterflies is also dependent on expression of the transcription factor optix during mid-pupal development ( Lewis et al . , 2019; Reed et al . , 2011; Zhang et al . , 2017 ) . Therefore , cortex expression is required for either downstream signalling to optix or to induce a permissive scale morphology for the synthesis and deposition of red pigment in future scales . Cortex is thus necessary for the induction of Type III scale cells but insufficient for their proper development . Conversely , a PSC lacking a Cortex input differentiates into a Type I scale , whose pigmentation state depends on the presence of the transcription factor aristaless1 ( al1 ) , where al1 is responsible for inducing a switch from yellow to white scales in Heliconius by affecting the deposition of the yellow pigment 3-OHK ( Westerman et al . , 2018 ) . The uptake of 3-OHK from the haemolymph occurs very late in wing development , right before the adult ecloses ( Reed et al . , 2008 ) . Our cortex mKOs revealed a shift to both yellow and white scales , with their appearance being positionally dependent; more distally located scales generally switch to white , while more proximal scales become yellow . This pigmentation state is likely controlled by differences in al1 expression varying between wing sections in different ways across morphs . However , the switch induced by Cortex under this model is likely not a simple binary toggle , and is perhaps dependent on a given protein threshold or heterochrony in expression rather than presence/absence , as we find that Cortex protein also localises to the presumptive yellow bar in developing pupal wings . Moreover , the RNA-seq data presented suggests other linked genes may also be playing a role in controlling pattern switches between Heliconius morphs . In particular , we report the presence of a bi-cistronic transcript containing the ORFs of the genes dome/wash , which are differentially expressed during early pupal wing development . While a precise role for dome/wash in wing patterning remains to be demonstrated , it raises the possibility that multiple linked genes co-operate during Heliconius wing development to drive pattern diversity . It is noteworthy that in the locally polymorphic H . numata , all wing pattern variation is controlled by inversions surrounding cortex and dome/wash , both of which are also differentially expressed in H . numata ( Saenko et al . , 2019 ) . This raises the interesting possibility that evolution has favoured the interaction of multiple genes at the locus that have since become locked into a supergene in H . numata . The utilisation of ‘hotspots’ in evolution has become a recurring theme of evolutionary biology , with several examples in which independent mutations surrounding the same gene have driven adaptive evolution ( e . g Pitx1 , Scute ) ( Stern and Orgogozo , 2009 ) . One proposed facilitator of such hotspots is through the action of genes acting as ‘input-output’ modules , whereby complex spatio-temporal information is translated into a co-ordinated cell differentiation program , in a simple switch-like manner . One prediction of the nature of such genes would be a switch-like behaviour such as that observed for cortex in this study , as well as the presence of a complex modular cis-regulatory architecture surrounding the gene that is able to integrate the complex upstream positional information into the switch-like output . A conserved feature of the cortex locus in Lepidoptera is the presence of large intergenic regions surrounding the gene , with evidence these may be acting as modular cis-regulatory switches in Heliconius ( Enciso-Romero et al . , 2017; Van Belleghem et al . , 2017 ) , fitting the predicted structure of input-output genes . Unlike canonical input-output loci however , cortex expression appears not to be restricted to any particular colour pattern element in any given species/morph , and yet is capable of producing a switch-like output ( Type I vs Type II/III scales ) . Furthermore , our work shows that two independent CREs in H . melpomene and H . erato evolved to control the presence/absence of a yellow hindwing bar . However , it is still unclear how cortex mechanistically affects pigmentation differences , and given its widespread usage throughout Lepidoptera , it is of general interest to understand its role in driving scale pigmentation . Heliconius butterflies were collected in the tropical forests of Panama and Ecuador . Adults were provided with an artificial diet of pollen/glucose solution supplemented with flowers of Psiguria , Lantana , and/or Psychotria alata according to availability . Females were provided with Passiflora plants for egg laying ( P . menispermifolia for H . melpomene , P . biflora for H . erato and H . charithonia , and P . vitifolia for H . hecale ) . Eggs were collected daily , and caterpillars reared on fresh shoots of P . williamsi ( melpomene ) , P . biflora ( erato and charithonia ) , and P . vitifolia for H . hecale . Late fifth ( final ) instar caterpillars were separated into individual pots in a temperature-monitored room for RNA-seq experiments , where they were closely observed for the purpose of accurate developmental staging . To identify orthologues of dome across the Lepidoptera , we performed tBLASTn searches using the previously annotated H . melpomene Hmel2 ( Hm ) and H . erato demophoon V1 ( Hed ) dome sequences against the genomes of Operophtera brumata V1 ( Ob ) , Trichoplusia ni Hi5 . VO2 ( Tn ) , Bombyx mori ASM15162v1 ( Bm ) , Manduca sexta 1 . 0 ( Ms ) , Plodia interpunctella V1 ( Pi ) , Amyeolis transitella V1 ( At ) , Phoebis sennae V1 . 1 ( Ps ) , Bicyclus anynana V1 . 2 ( Ba ) , Danaus plexippus V3 ( Dp ) , Dryas iulia helico3 ( Di ) , Agraulis vanillae helico3 ( Av ) , Heliconius erato lativitta V1 ( Hel ) genomes found on Lepbase ( Challi et al . , 2016 ) . As a trichopteran outgroup , we used a recently published Pacbio assembly of Stenopsyche tienmushanensis ( St ) ( Luo et al . , 2018 ) . Recovered amino acid translations were aligned using clustal omega ( Madeira et al . , 2019 ) . The resulting alignments were used to produce a phylogenetic tree using PhyML ( Guindon et al . , 2010 ) , based on a best fit model using AIC criterion ( selected model was JTT + G + I+F ) . The tree was visualised and re-rooted to the Trichopteran outgroup using FigTree . To confirm cortex as a cdc20 gene , we retrieved full-length protein homologs from TBLASTN searches and used them to generate a curated alignment with MAFFT/Guidance2 with a column threshold of 0 . 5 . Guidance2 is an alignment reliability method that parses aligned residues while also maximising tree robustness ( Sela et al . , 2015 ) , thus ruling out biases introduced from paralog-specific domains . We then constructed a maximum-likelihood tree using W-IQ-TREE with the ‘Auto’ function to find a best-fit model of substitution . H . melpomene rosina and H . erato demophoon butterflies were collected around Gamboa , Panama; H . melpomene melpomene and H . erato hydara butterflies were collected around Puerto Lara , Darien , Panama . Methodology for sample preparation and sequencing was performed as previously descri Hanly et al . , 2019 . The datasets generated and/or analysed during the current study are available in the SRA repository ( PRJNA552081 ) . Reads from each species were aligned to the respective genome assemblies Hmel2 ( Davey et al . , 2016 ) and Herato_demophoon_v1 ( Van Belleghem et al . , 2017 ) , using Hisat2 with default parameters ( Kim et al . , 2019 ) . The genomes and annotations used are publicly available at http://www . lepbase . org . Reads were counted with HTSeq-count in union mode ( Anders et al . , 2015 ) and statistical analysis performed with the R package DESeq2 ( Love et al . , 2014 ) . Comparisons for larvae were for whole hindwings , grouping samples by pattern form . Samples for pupal stages included wings that were dissected into anterior and posterior compartment as in Hanly et al . , 2019 , and were analysed in DESeq2 using the GLM:∼individual+compartment∗morph ( Compartments: anterior hindwing [HA] , posterior hindwing [HPo] ) . H . melpomene and H . erato were analysed separately; homology between genes was determined by reciprocal BLAST . The fold-changes and adjusted p-values given in Figure 2 reflect the primary contrast , showing the effect of pattern form given the effect of compartment . Read counts were determined for whole hindwings at all stages . The expression level of cortex in larval hindwings was further analysed by qPCR in H . e . demophoon and H . e . hydara . Three individuals were used for each morph . Each individual was an independent replicate ( i . e . no pooling of samples ) . RNA was extracted from the hindwing tissue of larva using Trizol followed by DNase-treatment . An mRNA enrichment was performed using the Dynabeads mRNA purification kit ( Thermo Fisher ) . The mRNA was then converted to cDNA by reverse transcription using the iScript cDNA synthesis kit ( Bio-Rad ) . All reactions had a final cDNA concentration of 2 ng µl−1 and a primer concentration of 400 nM . The RT-qPCR was carried out using Brilliant III Ultra-fast SYBR green qPCR master mix ( Agilent Technologies ) , on a AriaMx Real-time PCR system ( Agilent Technologies ) according to manufacturer’s instructions . The PCR programme consisted of 95°C for 2 min followed by 40 cycles of 95°C for 5 s , 58°C for 30 s , and 70°C for 30 s . qPCR experiments were performed using three biological replicates , three technical replicates , and a no template control . Expression levels were normalised using the geometric mean of three housekeeping genes , eF1α , rpL3 and polyABP that have previously been validated for Heliconius numata ( Piron Prunier et al . , 2016 ) . The relative expression levels were analysed using the R = 2−ΔΔCt method ( Livak and Schmittgen , 2001 ) . Primer specificity was confirmed using melting curve analysis and the PCR products were checked on a 2% ( w/v ) agarose gel . The primer efficiency of each gene was calculated using the standard curve given by a 10-fold serial dilution of cDNA ( 1 , 10−1 , 10−2 , 10−3 , 10−4 ) and regression coefficient ( R2 ) values . Fifth-instar larval wing disks and whole mount in situ hybridisations were performed following a published procedure ( Martin and Reed , 2014 ) and imaged using a Leica S4E microscope ( Leica Microsystems ) . Riboprobe synthesis was performed using the following primers from a fifth-instar wing disc cDNA library extracted from H . melpomene: Forward primer 5’-CCCGAGATTCTTTCAGCGAAAC-3’ and Reverse primer 5’- ACCGCTCCAACACCAAGAAG-3’ . Templates for riboprobes were designed by attaching a T7 promoter through PCR and performing a DIG labelled transcription reaction ( Roche ) . The same H . melpomene probe was used in all in situ hybridisation experiments . The resulting probe spanned from Exon two to Exon 7 and was 841 bp long . Pupal wings were dissected around 60–70 hr post-pupation in PBS and fixed at room temperature with fix buffer ( 400 µl 4% paraformaldehyde , 600 µl PBS 2 mM EGTA ) for 30 min . Subsequent washes were done in wash buffer ( 0 . 1% Triton-X 100 in PBS ) before blocking the wings at 4°C in block buffer ( 0 . 05 g bovine serum slbumin , 10 ml PBS 0 . 1% Triton-X 100 ) . Wings were then incubated in primary antibodies against Cortex ( 1:200 , monoclonal rabbit anti-Cortex ) at 4°C overnight , washed , and added in secondary antibody ( 1:500 , donkey anti-rabbit lgG , AlexaFlour 555 , ThermoFisher Scientific A-31572 ) . Before mounting , wings were incubated in DAPI with 50% glycerol overnight and finally transferred to mounting medium ( 60% glycerol/40% PBS 2 mM EGTA ) for imaging . Z-stacked two-channelled confocal images were acquired using a Zeiss Cell Observer Spinning Disk Confocal microscope . Guide RNAs were designed corresponding to GGN20NGG sites located within the cortex coding region and across putative CREs using the program Geneious ( Kearse et al . , 2012 ) . To increase target specificity , guides were checked against an alignment of both H . melpomene and H . erato re-sequence data at the scaffolds containing the cortex gene ( Moest et al . , 2020; Van Belleghem et al . , 2017 ) , and selected based on sequence conservation across populations . Based on these criteria , each individual guide was checked against the corresponding genome for off-target effects , using the default Geneious algorithm . Guide RNAs with high conservation and low off-target scores were then synthesised following the protocol by Bassett and Liu , 2014 . Injections were performed following procedures described in Mazo-Vargas et al . , 2017 , within 1–4 hr of egg laying . Several combinations of guide RNAs for separate exons at different concentrations were used for different injection experiments . For H . charithonia , we used the H . erato-specific guides , and for H . hecale , we used the H . melpomene guides . DNA was extracted from mutant leg tissue and amplified using oligonucleotides flanking the sgRNAs target region . PCR amplicons were column purified , subcloned into the pGEM-T Easy Vector System ( Promega ) , and sequenced on an ABI 3730 sequencer . H . melpomene rosina and H . erato demophoon butterflies were collected around Gamboa , Panama; H . melpomene melpomene and H . erato hydara butterflies were collected around Puerto Lara , Darien , Panama . Caterpillars of each species were reared on their respective host plants and allowed to grow until the wandering stage at fifth instar . Live larvae were placed on ice for 1–2 min and then pinned and dissected in 1× ice cold PBS . The colour of the imaginal discs , as well as the length of the lacunae , gradually change throughout the larva’s final day of development , so that they can be used to confirm the staging inferred from pre-dissection cues ( Reed et al . , 2008 ) . All larvae used for this project were stage 3 . 5 or older . ATAC-seq protocol was based on previously described methodology ( Lewis and Reed , 2019 ) and edited as follows . The imaginal discs were removed and suspended whole in 350 µl of sucrose solution ( 250 mM d-sucrose , 10 mM Tris–HCl , 1 mM . MgCl2 , 1× protease inhibitors [Roche] ) inside labelled 2 ml dounce homogenisers ( Sigma-Aldrich ) for nuclear extraction . Imaginal discs corresponding to the left and right hindwings were pooled . After homogenising the tissue on ice , the resulting cloudy solution was centrifuged at 1000 rcf for 7 min at 4 °C . The pellet was then resuspended in 150 µl of cold lysis buffer ( 10 mM Tris–HCl , 10 mM NaCl , 3 mM MgCl2 , 0 . 1% IGEPAL CA-630 [Sigma-Aldrich] , 1× protease inhibitors ) to burst the cell membranes and release nuclei into the solution . Samples were then checked under a microscope with a counting chamber following each nuclear extraction , to confirm nuclei dissociation and state and to assess the concentration of nuclei in the sample . Finally , based on these observations , a calculation to assess number of nuclei , and therefore DNA , to be exposed to the transposase was performed . This number was fixed on 400 , 000 nuclei , which is the number of nuclei with ~0 . 4 Gb genomes ( H . erato genome size ) required to obtain the amount of DNA for which ATAC-seq is optimised ( Buenrostro et al . , 2013 ) . For H . melpomene , this number was 500 , 333 , since the genome size of H . melpomene is 0 . 275 Gb . ( Buenrostro et al . , 2013 ) . For quality control , a 15 µl aliquot of nuclear suspension was stained with trypan blue , placed on a hemocytometer and imaged at 64× . After confirmation of adequate nuclear quality and assessment of nuclear concentration , a subsample of the volume corresponding to 400 , 000 nuclei ( H . erato ) and 500 , 333 ( H . melpomene ) was aliquoted , pelleted 1000 rcf for 7 min at 4°C and immediately resuspended in a transposition mix , containing Tn5 in a transposition buffer . The transposition reaction was incubated at 37°C for exactly 30 min . A PCR Minelute Purification Kit ( Qiagen ) was used to interrupt the tagmentation and purify the resulting tagged fragments , which were amplified using custom-made Nextera primers and a NEBNext High-fidelity 2× PCR Master Mix ( New England Labs ) . Library amplification was completed between the STRI laboratory facilities in Naos ( Panama ) and Cambridge ( UK ) . The amplified libraries were sequenced as 37 bp paired-end fragments with NextSeq 500 Illumina technology at the Sequencing and Genomics Facility of the University of Puerto Rico . We applied the phylogenetic weighting strategy Twisst ( topology weighting by iterative sampling of subtrees; Martin and Van Belleghem , 2017 ) to identify shared or conserved genomic intervals between sets of Heliconius melpomene and Heliconius cydno populations with similar phenotypes around the cortex gene locus on chromosome 15 . Given a tree and a set of pre-defined groups Twisst determines a weighting for each possible topology describing the relationship of groups or phylogenetic hypothesis . Similar to Enciso-Romero et al . , 2017 we evaluated the support for two alternative phylogenetic hypotheses using genomic data obtained from Moest et al . , 2020 . Hypothesis one tested for monophyly of samples that have a dorsal yellow hindwing bar . This comparison included the geographic colour patterns morphs with a dorsal hindwing bar H . m . rosina , H . c . weymeri weymeri , H . c . weymeri gustavi and H . pachinus versus the all-black dorsal hindwing morphs H . m . vulcanus , H . m . melpomene ( French Guiana ) , H . m . cythera , H . c . chioneus and H . c . zelinde . Hypothesis two tested for monophyly of samples that have a ventral yellow hindwing bar versus an all-black ventral hindwing . This comparison included the geographic colour patterns morphs with a ventral hindwing bar H . m . rosina , H . m . vulcanus , H . m . cythera , H . c . weymeri weymeri , H . c . weymeri gustavi and H . pachinus versus the all-black ventral hindwing morphs H . m . melpomene ( French Guiana ) , H . c . chioneus and H . c . zelinde . Maximum-likelihood trees were built from sliding windows of 50 SNPs with a step size of 20 SNPs using PhyML v3 . 0 ( Guindon et al . , 2010 ) and tools available at https://github . com/simonhmartin/twisst ( Martin , 2020 ) . Only windows were considered that had at least 10 sites for which each population had at least 50% of its samples genotyped . Twisst was run with a fixed number of 1000 subsampling iterations . Analysis of chromatin contacts between distal cis-regulatory loci and the cortex promoter region was performed as previously described ( Lewis et al . , 2019 PNAS , Lewis and Van Belleghem , 2020 ) . In brief , Hi-C data produced from day three pupal H . e . lativitta wings was used to generate an empirical expected distribution and read counts for Hi-C contacts between 5 kb windows centred on the distal CRE and cortex promoter were used to determine the observed contacts between loci . Fisher’s exact test was then performed to determine significance of the observed contacts relative to those expected from the background model . Virtual Hi-C signal plots were generated using a custom python script ( Ray et al . , 2019 ) . High-depth whole-genome sequences of 16 H . melpomene , H . timareta , and H . erato subspecies were obtained from the European Nucleotide Archive , accession numbers can be found in Figure 8—source data 1 . To assess structural variation putatively affecting the yellow phenotype , reads were mapped to the reference genomes of subspecies that lacked the yellow bar stored in the genome browser Lepbase , ‘hmel2 . 5’ for H . melpomene and H . timareta , and ‘Heliconius_erato_lativitta_v1’ for H . erato ( Challi et al . , 2016 ) with BWA mem ( Li and Durbin , 2009 ) . Median sequencing depths across the scaffold containing cortex were computed for all individuals ( n = 79 ) in 50 bp sliding windows and a mapping quality threshold of 30 with the package Mosdepth ( Pedersen and Quinlan , 2018 ) . Window median depths were normalised by dividing them by the mean depth for the full scaffold per individual . We then averaged the normalised median depths of all individuals per subspecies , to visualise deviations from the mean sequencing depth across the region in geographically widespread subspecies with and without the yellow bar . We then genotyped across the putative H . melpomene deletion using the primers employed for CRISPR genotyping ( See Figure 4—source data 2 ) . The products were then cloned into the pGEM-T Easy Vector System ( Promega ) and sequenced them on an ABI 3730 sequencer from both directions using T7 forward and M13 reverse primers . Sequencing was performed from three separate colonies , and a consensus sequence was created based on an alignment of the three replicates from populations of H . m . melpomene and H . m . rosina . Individual scales from wild-type and mutant regions of interest were collected by brushing the surface of the wing with an eyelash tool , then dusted onto an SEM stub with double-sided carbon tape . Stubs were then colour imaged under the Keyence VHX-5000 microscope for registration of scale type . Samples were sputter-coated with one 12 . 5 nm layer of gold for improving sample conductivity . SEM images were acquired on a FEI Teneo LV SEM , using secondary electrons and an Everhart-Thornley detector using a beam energy of 2 . 00 kV , beam current of 25 pA , and a 10 μs dwell time . Individual images were stitched using the Maps 3 . 10 software ( ThermoFisher Scientific ) . Morphometric measurements of scale widths and ridge distances were carried out on between 10 and 20 scales of each type , using a custom semi-automated R pipeline that derives ultrastructural parameters from large SEM images ( Day et al . , 2019 ) . Briefly , ridge spacing was assessed by Fourier transforming intensity traces of the ridges acquired from the FIJI software ( Schindelin et al . , 2012 ) . Scale width was directly measured in FIJI by manually tracing a line , orthogonal to the ridges , at the section of maximal width .	Heliconius butterflies have bright patterns on their wings that tell potential predators that they are toxic . As a result , predators learn to avoid eating them . Over time , unrelated species of butterflies have evolved similar patterns to avoid predation through a process known as Müllerian mimicry . Worldwide , there are over 180 , 000 species of butterflies and moths , most of which have different wing patterns . How do genes create this pattern diversity ? And do butterflies use similar genes to create similar wing patterns ? One of the genes involved in creating wing patterns is called cortex . This gene has a large region of DNA around it that does not code for proteins , but instead , controls whether cortex is on or off in different parts of the wing . Changes in this non-coding region can act like switches , turning regions of the wing into different colours and creating complex patterns , but it is unclear how these switches have evolved . Butterfly wings get their colour from tiny structures called scales , which each have their own unique set of pigments . In Heliconius butterflies , there are three types of scales: yellow/white scales , black scales , and red/orange/brown scales . Livraghi et al . used a DNA editing technique called CRISPR to find out whether the cortex gene affects scale type . First , Livraghi et al . confirmed that deleting cortex turned black and red scales yellow . Next , they used the same technique to manipulate the non-coding DNA around the cortex gene to see the effect on the wing pattern . This manipulation turned a black-winged butterfly into a butterfly with a yellow wing band , a pattern that occurs naturally in Heliconius butterflies . The next step was to find the mutation responsible for the appearance of yellow wing bands in nature . It turns out that a bit of extra genetic code , derived from so-called ‘jumping genes’ , had inserted itself into the non-coding DNA around the cortex gene , ‘flipping’ the switch and leading to the appearance of the yellow scales . Genetic information contains the instructions to generate shape and form in most organisms . These instructions evolve over millions of years , creating everything from bacteria to blue whales . Butterfly wings are visual evidence of evolution , but the way their genes create new patterns isn't specific to butterflies . Understanding wing patterns can help researchers to learn how genetic switches control diversity across other species too .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology", "evolutionary", "biology" ]	2021	Cortex cis-regulatory switches establish scale colour identity and pattern diversity in Heliconius
Eliminating malaria from a defined region involves draining the endemic parasite reservoir and minimizing local malaria transmission around imported malaria infections . In the last phases of malaria elimination , as universal interventions reap diminishing marginal returns , national resources must become increasingly devoted to identifying where residual transmission is occurring . The needs for accurate measures of progress and practical advice about how to allocate scarce resources require new analytical methods to quantify fine-grained heterogeneity in malaria risk . Using routine national surveillance data from Swaziland ( a sub-Saharan country on the verge of elimination ) , we estimated individual reproductive numbers . Fine-grained maps of reproductive numbers and local malaria importation rates were combined to show ‘malariogenic potential’ , a first for malaria elimination . As countries approach elimination , these individual-based measures of transmission risk provide meaningful metrics for planning programmatic responses and prioritizing areas where interventions will contribute most to malaria elimination . A key need is for new quantitative methods and practical operational advice to guide the final stages of malaria elimination , when few local cases remain . Elimination programs have repeatedly demonstrated the critical importance of identifying areas where transmission continues in order to make the most of limited resources . The architects of the smallpox eradication strategy , for example , credit the campaign’s ultimate success to a shift from universal to targeted vaccination ( Foege et al . , 1971 ) . Successful elimination of malaria during the GMEP similarly demonstrated that , in resource-constrained environments , a shift is required away from a focus on universal coverage for endemic malaria towards heightened surveillance , case investigation , identification of areas where transmission risk remains high ( i . e . , residual transmission foci ) , and highly targeted interventions ( Moonen , 2010 ) . Despite progress ( Ja and Hg , 2009 ) , little guidance is available on what specific methods to use where and which metrics are appropriate for measuring progress in today's eliminating countries . Since 1999 , Swaziland’s National Malaria Control Program reports that the incidence of malaria has declined from 2 . 9 to 0 . 07 malaria cases per 1 , 000 people per year ( Roll Back Malaria , 2012 ) . Between 2010 and June 2014 , Swaziland confirmed only 2 , 129 malaria cases , with case investigation of 1 , 524 of them . Of these , 870 ( 57% ) were classified as imported , with the proportion of cases likely having acquired their infection elsewhere increasing since 2010 . At a national level , the decreasing ratio of local to imported malaria cases since 2010 is suggestive of an reproductive number under control , Rc , much less than 1 on average , which would mean that elimination of endemic transmission may have already been achieved or is imminent ( Churcher et al . , 2014 ) . Analysis of national trends in reporting data provides a useful measure of overall progress but potentially masks local spatial heterogeneity in transmission , and it leaves unanswered the question of how to stratify Swaziland to allocate resources most efficiently within the country . Ideally , programs would focus attention on places and at times where and when the risk of malaria transmission is highest . Programs might direct aggressive interventions , such as focal mass drug administration , towards residual endemic foci where transmission would persist even in the absence of importation , but might opt for less aggressive maintenance of reduced risk in places where transmission is driven only by continual replenishment from imported infections . Determining whether locally acquired malaria infections are the result of endemic transmission or result from transmission chains stemming from importation requires analyzing transmission at an individual level . National risk maps have been developed based on the household locations of local malaria cases ( Cohen , 2013 ) but these maps do not describe how much transmission is likely occurring , only whether or not there is risk of any local infections in a given location as narrowly defined by the location of infected individuals . Furthermore , they do not explain the relative importance of importation as a driver of transmission and thus cannot inform intervention selection . Improving assessment of progress and making action maps requires developing individual-based assessment of risk to link cases together , determine the magnitude of transmission as measured by RC , and evaluate where transmission may be occurring endemically versus where it is driven only by ongoing importation . Most of the imported cases have been identified in and around the large cities of Mbabane and Manzini ( Figure 1A ) , but proportionally fewer locally acquired cases were found in these cities ( Cohen , 2013 ) . Malaria cases imported into the major cities of Swaziland are likely only responsible for causing a few local cases ( i . e . , Rc in the large cities is very low ) . Areas outside the major cities—specifically those in the east and north of Swaziland—appear to have a higher ratio of local to imported cases , suggesting that though on average each malaria case generates less than one other case ( i . e . Rc < 1 ) , there are some focal areas where endemic transmission may continue to smolder . Measuring progress and achieving elimination requires properly characterizing and quantifying heterogeneity in this residual transmission . Here , to address these needs , we have defined vulnerability , receptivity , and malariogenic potential in quantitative terms based on a continuous point-process and developed fine-grained maps . 10 . 7554/eLife . 09520 . 003Figure 1 . Consensus network plot of causal links . Panel A: Swaziland imported and local malaria cases ( green squares and orange diamonds , respectively ) are plotted spatially . Local case pairs identified as putative orphaned chains are indicated by red diamonds . A solitary local case also identified as an orphan is identified as a red diamond within a circle . Panel B: Swaziland imported ( green line ) and local ( orange line ) malaria cases are plotted in time , aggregated by month . Panel C: The final consensus network plot is displayed . Local cases are plotted as diamonds and imported cases as green circles . The color of each link corresponds to the “strength” of the connection as measured by the number of parameter sets where that link was identified as optimal . Imported cases that were not found to be the “most likely” parent of a local case are not displayed . DOI: http://dx . doi . org/10 . 7554/eLife . 09520 . 003 Elimination programs in the endgame have long been advised to categorize cases as imported , introduced ( the result of first degree transmission from an imported infection ) , or indigenous ( the result of second or more degree transmission ) , yet no method for making this distinction has ever been formally described ( Pampana , 1969 ) . Methods for doing so are largely based on case investigations , but such methods could be validated and augmented with genetic and computational methods for linking cases . Understanding which cases are linked to other cases is also important because it allows direct measurement of RC , an important measure of the need for additional intervention . We developed computational methods for assessing malaria transmission ( see Methods ) that quantifies the most likely “parent” of each local case . We do not distinguish between potential parents in terms of their status as “imported” or “local” . In this , we are estimating causal links that can be counted in space-time to get measurements of receptivity . Further , these links build transmission chains that can be used to gain a deeper understanding of the spatial variation in Rc . This approach uses an understanding of malaria transmission dynamics to redefine “proximity” of two malaria cases as a generalized probabilistic measure based on distance and time separating two cases to evaluate potential causality . Malaria transmission directly links two individuals through two bites from the same mosquito . The time elapsed between detection of two linked cases will be bound by the serial interval ( Fine , 2003 ) ( i . e . , the length of a complete malaria transmission cycle ) , mosquito mortality , and the timing of case detection relative to infection . These aspects of malaria ecology provide probabilistic bookends to temporal proximity , as even with detection ( and assuming all detected cases are treated promptly ) , cases are ever more unlikely to be linked as the chance of a mosquito living long enough to link them diminishes . In space , the simple but common Gaussian diffusion-based approximation of movement approximates the combined spread of mosquitoes and humans , and is governed by a single constant . Given the use of continuous distribution functions , the algorithm was forced to always find a link even if it was inconceivable . As such , we incorporated a minimum threshold that dichotomized links into “plausible” ( with a level of plausibility ) and “implausible . ” By accounting for potential differences in the distribution of the serial interval for human malaria cases and timing of case identification , multiplying the temporal processes with spatial diffusion , and finally sweeping across a suite of potential diffusion constants ( see Methods ) we linked local cases to a “most likely” parent case . By combining “most likely” links , we arrived at a single weighted network that represents the consensus linkages ( Figure 1 ) . As was suspected by visual inspection of the spatial distribution of the data , most of the imported cases within the major cities of Swaziland did not appear to be responsible for ongoing transmission . However , both imported and locally transmitted cases in the north and east did , infrequently , initiate extended transmission chains . Elsewhere , no identified case was a likely parent , so these cases are classified as “orphans . ” Taking the output of the transmission network , the number of direct ‘offspring’ arising from each case is its estimated Rc . Using zero-inflated negative binomial regression models on a set of ecological , social and demographic covariates , likely values of Rc were extrapolated spatially from observed case locations to all of Swaziland at 100-meters squared resolution ( Figure 2A ) . The resulting map of Rc illustrates the estimated heterogeneous distribution of current malaria receptivity within Swaziland . To the west , Rc is close to 0 . Within Mbabane and Manzini , Rc is estimated at 0 . 08 and 0 . 12 respectively . Conversely , in the northeast near the Mozambican border , Rc estimates increase up to 1 . 70 . As suspected , while endemic transmission does not appear to occur within the urban centers of Swaziland , it does not yet appear eliminated from the entirety of the country . The smoothed Rc map was found to be statistically significantly different from a flat map ( p-value <2e-16 ) but it remained unclear the statistical significance of each pixel . Further analysis is required to assess the robustness of isolated locations where Rc appears elevated . This will be an important step to appropriately interpret these maps for the purpose of resource allocation given constraints on the number of individual locations that can be visited . 10 . 7554/eLife . 09520 . 004Figure 2 . Vulnerability , receptivity and malariogenic potential . Panel A: Extrapolated Rc values for Swaziland using a zero-inflated negative binomial regression . Areas in orange to red indicate locations where Rc is greater than unity . The legend doubles as a histogram indicating the number of individuals ( on a log10 scale ) that live within each range of Rc values . Panel B: Extrapolated importation probabilities for Swaziland using a logistic regression . Panel C: Malariogenic potential for Swaziland calculated as the product of Rc and the probability of importation . DOI: http://dx . doi . org/10 . 7554/eLife . 09520 . 004 The potential for local transmission will not result in actual transmission unless malaria parasites are present . As Swaziland successfully extirpates these final foci of endemic transmission , local transmission in the country will increasingly arise only around imported malaria cases . The total number of locally acquired malaria cases in Swaziland is the product of importation ( vulnerability ) and onward transmission ( receptivity ) , with the ongoing operational challenge of maintaining gains greatest in regions with both higher receptivity ( i . e . RC ) and higher vulnerability ( i . e . , the number of malaria infections imported each year that could seed new transmission ) : the product of these quantities has been defined as the ‘malariogenic potential’ ( Pampana , 1969 ) . Malariogenic potential was mapped as the product of Rc and vulnerability ( Figure 2c ) . The resulting map illustrates where locally acquired/transmitted cases are most likely to occur , and thus where resources may need to be prioritized to prevent reestablishment of malaria given the joint risks of importation and subsequent transmission . We assessed the stability of our malariogenic potential maps as well as our other output by splitting the data into two halves ( before and after July 1 , 2012 ) . The malariogenic potential maps for the two halves appear very similar ( Figure 3C versus Figure 4C ) , as are the importation maps ( Figure 3B versus 4B ) . There are some differences in the Rc maps ( Figure 3A versus 4A ) , but both analyses identified regions in the northeast where Rc was larger than 1 . Both analyses identified a larger ‘max’ Rc ( 35 . 36 for the first half and 3 . 13 for the second half ) as well as a larger percent of the population living in the highest Rc category ( Rc>1 . 4 ) . 10 . 7554/eLife . 09520 . 005Figure 3 . Vulnerability , receptivity and malariogenic potential ( 2010-6/2012 ) . Panel A: Extrapolated Rc values for Swaziland using a zero-inflated negative binomial regression . Areas in orange to red indicate locations where Rc is greater than unity . The legend doubles as a histogram indicating the number of individuals ( on a log10 scale ) that live within each range of Rc values . Panel B: Extrapolated importation probabilities for Swaziland using a logistic regression . Panel C: Malariogenic potential for Swaziland calculated as the product of Rc and the probability of importation . DOI: http://dx . doi . org/10 . 7554/eLife . 09520 . 00510 . 7554/eLife . 09520 . 006Figure 4 . Vulnerability , receptivity and malariogenic potential ( 7/2012-2014 ) . Panel A: Extrapolated Rc values for Swaziland using a zero-inflated negative binomial regression . Areas in orange to red indicate locations where Rc is greater than unity . The legend doubles as a histogram indicating the number of individuals ( on a log10 scale ) that live within each range of Rc values . Panel B: Extrapolated importation probabilities for Swaziland using a logistic regression . Panel C: Malariogenic potential for Swaziland calculated as the product of Rc and the probability of importation . DOI: http://dx . doi . org/10 . 7554/eLife . 09520 . 006 Ideally , every case of malaria in Swaziland would be detected . In reality , case detection is imperfect , and it is likely unnecessary to find every case to create circumstances that lead to elimination . In this analysis , missed cases could matter if they biased the estimates of Rc , depending on whether locally transmitted or imported cases were more likely to be missed . If a case that is missed results in local transmission and if those future cases are captured by surveillance , those future cases may appear to have no plausible cause . The identification of such “orphan” cases can help indicate places where Swaziland must work to implement or strengthen active infection detection . Within this analysis , due to the inclusion of temporal uncertainty to account for potential differences between detection times and time of onset , rare links can be formed between a case and a second case where the second case was picked up at the same time or even later . These rare links would only form if no other case that occurred earlier were close in space-time as judged by the spatio-temporal kernels . In these circumstances , due to the flexibility of the spatio-temporal kernel , a pair of cases could be identified as being the most likely parent of each other . These “loops” identify two orphaned cases that were identified close in time-space to each other but whose actual parent was not captured by surveillance . Within this analysis , there were 22 pairs of local cases that formed loops ( Figure 1A , Figure 1C , red diamonds ) as well as a single case that was not linked to any other case across any of the potential parameter sets ( Figure 1A , circled red diamond , Figure 1C , red circle ) . Prioritizing enhanced surveillance in the areas surrounding these orphan chains would help narrow uncertainty about residual transmission within Swaziland . The majority of these loops occurred during the months with the least transmission ( Figure 5 ) , which reflects the decreased chance of any cases being detected in the month prior . The method described here yields estimates of transmissibility that can guide interventions to places where occult transmission is most likely to be happening even in the absence of knowledge of specific infected individuals . Nevertheless , the more complete the surveillance effort , the more accurate these mapping efforts will become . 10 . 7554/eLife . 09520 . 007Figure 5 . Timing of ‘orphan’ cases . The average number of cases per month and total occurrence of looped ( or ‘orphaned’ cases ) are plotted against month . DOI: http://dx . doi . org/10 . 7554/eLife . 09520 . 007 Asymptomatic infections are another possible explanation for the orphaned cases . Missing asymptomatic cases could either result in an overestimate or underestimate of Rc ( if they are likely parents or likely offspring of other cases ) . For this analysis , we did not account for asymptomatic or inapparent cases . Although it was not done universally , intensive infection detection around cases resulted in very few additional infections ( 53/7307 between July 2014 and June 2015 ) consistent with the assumption that there is not a large pool of unreported infections that would greatly bias our results . Communicable disease policies require different approaches than policies for non-communicable diseases , as each case presents both medical and public health challenges ( Smith et al . , 2005 ) . For infectious diseases , reproductive numbers provide a theoretical basis for strategic planning and programmatic evaluation , such as critical vaccine coverage levels and outbreak responses ( Anderson and May , 1991 ) . Estimating individual reproductive numbers by linking up malaria infections is a special case of a method that has be used more widely for other diseases ( Walker et al . , 2010 ) . Malaria , like other diseases with an environmental component , represents a special challenge because of spatial heterogeneity in the risk of transmission ( Bejon et al . , 2010; Bousema et al . , 2012; Bejon et al . , 2014; Bousema et al . , 2010 ) . In the end phases of elimination , population-level measures become inefficient and inadequate , so as countries approach the goal of eliminating malaria , individual-based estimates of transmission must identify putative foci where transmission remains high and where resources should be targeted ( Hay et al . , 2008 ) . Case counts alone do not necessarily convey information about transmission , since many of those cases may have been acquired far from where they were detected . Aggregate ratios of local to imported cases in time ( or in space ) alone , while more representative of overall progress , could obscure localized transmission if , for example , most cases failed to transmit but some pockets of transmission remained . This analysis , which identifies places and times where cases are most likely to be transmitted , confirms that there has been dramatic progress towards elimination overall , but it also identified substantial heterogeneity in progress within Swaziland . The Swaziland National Malaria Control Program ( NMCP ) will need to manage imported malaria as long as endemic transmission continues in neighboring countries , so directing and optimizing limited resources is crucial . Combining assessments of receptivity with assessments of vulnerability provide actionable intelligence to support malaria programs in designing targeted intervention strategies in the most relevant places; for example , the NMCP may consider targeting travelers with prophylaxis in places with high vulnerability , while focal mass drug administration or other aggressive measures might be most appropriate in places with evidence of endemic transmission and low vulnerability . Our approach provides spatiotemporally relevant and resolved metrics of transmission that can be used to identify future cases as either critical or relatively unimportant for overall elimination efforts . Further , and perhaps most important , our approach can be used to stratify future control responses by differentiating between locations where elimination would be a consequence of merely decreasing effective importation versus where elimination of endemic transmission is needed through reduction in local receptivity . Predictions generated by our approach will also be useful as a baseline for in-development genetic testing and molecular typing models ( Greenhouse and Smith , 2015 ) , and will remain pertinent as a proxy for such methods in places where resources are limited to enable universal parasite typing . These methods can help Swaziland reassess its needs and remain malaria-free as surrounding countries control transmission and make further progress . Through regional elimination , economic growth , and efficient use of existing resources , malaria elimination can perhaps become as stable in Swaziland as it has been elsewhere ( Smith , 2013; Chiyaka et al . , 2013 ) . Swaziland implemented the first stage of a national malaria elimination policy in 2011 , and local malaria transmission dropped to extremely low levels . There was a 25-fold decline in the average malaria case-load , from 10 . 0 cases per 1000 in 1995 at the peak of an epidemic to 0 . 4 cases per 1000 in 2010 ( Roll Back Malaria , 2012 ) . Swaziland also benefitted from a regional malaria control effort called the Lubombo Spatial Development Initiative ( LSDI ) established as a partnership with neighboring countries . The LSDI sharply reduced the number of malaria cases imported from its neighbors , and now most of the remaining cases appear to originate in Mozambique . At low transmission intensity , the methods and metrics used elsewhere in Africa to assess the risk of further local transmission initiated from imported malaria become inadequate , so Swaziland adopted a surveillance system based on some combination of passive and reactive case detection . Household locations of confirmed malaria cases were identified by passive or reactive case detection and georeferenced by the NMCP . Infected individuals reporting no travel , whether abroad or within Swaziland were categorized as locally acquired cases . Infected individuals who reported travel abroad to endemic countries within biologically meaningful windows were categorized as imported cases . Initially , a travel history in the previous two weeks was collected , this was updated to four weeks in August 2012 and to eight weeks in July 2013 . From January 2010 to June 2014 , Swaziland investigated 1 , 524 cases collecting information about household location , case demographics ( age , gender , occupation ) , use of malaria prevention measures , dates of symptoms and of diagnosis , treatment . Of all investigated cases , 592 ( 38 . 8% ) were categorized as locally-acquired based on a lack of a recent travel history to endemic regions . This national average of 592 local cases to 870 imported cases suggests that on average , the reproductive number under present levels of control is approximately 0 . 4 . This estimate represents a national average figure , however , and it could disguise undetected ongoing transmission in some hotspots . What is needed is a tool that can simultaneously assess transmission dynamics in a low-transmission setting such as Swaziland , identify locations that systematically produce unobserved cases , and provide internal feedback to improve the surveillance system . As a first step towards accomplishing this larger purpose , we developed algorithms to reconstruct putative transmission chains . Using comprehensive case data from the Swaziland NMCP from 2010 through 2013 , these estimated chains—based on identifying likely causal links between successive cases through the use of spatio-temporal kernels—provide insight on the frequency and length of chains of local transmission . To evaluate the relative chance that one locally acquired case arose from any other case , we would optimally like to calculate the probability that an older case was fed on and initiated a transmission cycle in a mosquito that subsequently fed on and infected the second case . This measure of propensity would combine the epidemiology of malaria within a mosquito , the mosquito’s lifespan as well as movement probabilities for both mosquitoes and humans . Given the complexities of both human and mosquito movement , we assessed the likelihood using a family of probability distribution functions . By varying the unknown space and time parameters for each component , we can produce a putative single space-time distribution of locally acquired cases based on the time and location of any other case . The approximate likelihood will be a product of mosquito lifespan , mosquito movement , human movement , and malaria epidemiology . We assume that the contributions of movement act independently of the contributions of mosquito and disease ecology . Following previous approaches , we approximate movement with simple diffusion . This movement kernel aggregates both the movement of the mosquito as well as the movement of the individuals . We assess the importance of this assumption by substituting diffusion within our algorithm with a long-tailed Pareto distribution , discussed in Appendix 2 . Simple diffusion assumes that there is no trend in time ( i . e . the most likely outcome is that there is no movement ) , and that movement is Gaussian . Specifically , if we have two individuals who became infectious t days apart and x meters apart , the chance , denoted M ( x , t ) , that the pathogen had moved so far in such a time given a specified diffusion constant , D>0 , would beM ( x , t ) =12πDte−x22Dt The mosquito and transmission dynamics component is more complex . Due to the disease ecology of malaria , incubation periods within both the mosquito ( known as the extrinsic incubation period ) and the second host ( known as the intrinsic incubation period ) definitionally separate onset of infectiousness in secondary infections from the onset of infectiousness in the initial case ( the time between the onset of infectiousness in causally linked infections is known as the serial interval ) . The serial interval is the sum of the extrinsic incubation period , the time elapsed while infectious mosquitoes quest for blood and infect humans and the intrinsic incubation period . We estimate the intrinsic incubation period as minimally 18 days ( approximately six days in the liver plus 12 days for mature gametocytes to be produced in sufficient densities ) and the extrinsic incubation period as minimally 12 days . The brief lifespan of the mosquito acts as an opposing force to the extrinsic incubation period . One increases the serial interval while the other decreases the likelihood of extended time between subsequent cases . Estimates of Anopheles mosquitos’ lifespan is between 10 and 14 days . Though somewhat simplistic , the exponential distribution is frequently used for the lifespan of mosquitoes . Combining mosquito and disease ecology , if we have two individuals who became infectious t days apart and x meters apart , the chance , denoted G ( x , t ) , that the serial interval was so long would beG ( x , t ) ={0 , if t<30112e1−112 ( t−18 ) , e∫∑o . w . Note that there is zero chance that the serial interval is less than the sum of the two incubation periods . Also , when t≥30 , the effect of mosquito mortality does not apply to the portion of the serial interval attributed to the intrinsic incubation period . As noted above , we treat movement independently from mosquito and disease ecology , and as such , the approximate likelihood of a causal link between two cases can be broken into it constituent parts . If we have two individuals who became infectious t days apart and x meters apart , the spatio-temporal transmission distribution of malaria , denoted P ( x , t ) , is given as:P ( x , t ) =M ( x , t ) ∗G ( x , t ) It is important to acknowledge that case data recorded are not in fact the times of onset of infectiousness within the hosts , and there is no guarantee that the time between two sequential cases being identified corresponds identically with the time between onset of infectiousness between the two individuals . To account for the non-negligible difference between the difference between two detection times and the serial interval , we add temporal noise to t . specifically , for a given level of temporal noise , denoted σt , we convolve P with Normally distributed noise . Thus , the spatio-temporal transmission distribution that we analyze the data with , denoted K ( x , t ) , is ( for a given diffusion constant D and temporal noise coefficient σt ) K ( x , t ) =∫−∞∞P ( x , t+εt ) 12πσt2e−εt22σt2dεt Thus , with the exception of D and σt , the entire spatio-temporal distribution of transmission is specified . Since both the movements of mosquitoes and humans is encapsulated by D , it is unclear exactly what its value should be . However , as will be discussed below , by sweeping across a variety of values , we can actually use the uncertainty in D to understand which links are relatively strong and which are relatively tenuous . Similarly , the true difference between onset and detection is unknown and we will likewise sweep across potential values of σt . The model described above computes the likelihood that a mosquito infected by a putative index case at a particular point in space and time later infected an individual identified as a locally acquired case at a different point in space and time . The assumption that movement can be approximated by diffusion ( and the use within the `likelihood’ of the exact locations where the two cases were identified ) will necessarily force the computed chance of causal infection to be extremely low . As such , any probability computed using the above spatio-temporal distribution would often yield inappropriately low values—if only because every particular place and time was unlikely . What is more useful , given the uncertainty , is to compare likelihood measures . All locally acquired cases can be assigned to a “parent” that gives the highest likelihood , even though it may be only marginally more likely than some other link . We assume due to the extremely low transmission intensity of malaria in Swaziland that each infection was only caused by a single parent and superinfection did not occur . A threshold value was chosen and tested to help identify cases that were “orphans , ” or unlikely to be associated with any other identified case . The most likely parent or orphan status was computed for each combination of a mesh on the constants ( i . e . , D , σt and a threshold value ) . The aggregate data from all these assessments was used to estimate the strength of some particular connection . Links that only occur in the rarest of cases ( i . e . when the diffusion constant is extremely high ) are tenuous . For the Swaziland data , we compute the set of potential links for 400 different scenarios corresponding to 20 different values of the diffusion constant and 20 different values of σt . We vary D in even steps from 40 m to 1000 m and vary σt from 0 to 19 days . For display purposes we will indicate the link that occurred the most times , although in most cases the link that occurred the most frequently was the only potential link identified for a given case . Further , we vary the relative probability threshold from 1% to 50% , also investigating the intermediary value of 10% . The number of direct offspring at each case location was explained by a set of spatial covariates , which described weather , geography , population density , and urbanicity ( Figure 6 ) . Elevation and topography have been demonstrated to influence risk through their effects on temperature and suitability for mosquito breeding ( Cohen , 2008 ) . The topographic wetness index ( TWI ) , a measure representing the amount of water that should enter a given spatial unit divided by the rate at which the water should flow out of that unit , was calculated from elevation as a measure for suitability for mosquito breeding habitat ( Cohen , 2008; Cohen , 2010a;2010b ) . Suitability for mosquito habitat was also described using remotely sensed imagery ( Hay et al . , 1998 ) . The normalized difference vegetation index ( NDVI ) ( Rouse Jr et al . , 1974 ) and enhanced vegetation Index ( EVI ) were calculated from averaged Landsat Enhanced Thematic Mapper ( ETM ) images from 2010 till 2013 with spatial resolution 100 m . Densely populated areas may face substantially different malaria risks from very sparsely populated , rural areas ( Hay et al . , 2005 ) . 10 . 7554/eLife . 09520 . 008Figure 6 . Spatial covariates for malaria receptivity regression . The four significant covariates for the malaria receptivity regression were ( A ) distance from paved roads , ( B ) distance from unpaved roads , ( C ) distance from feeder roads , and ( D ) distance from Mozambique . All distances were in meters . DOI: http://dx . doi . org/10 . 7554/eLife . 09520 . 008 We used spatial zero-inflated negative binomial regression model to extrapolate the number of direct offspring from the cases locations to all points across Swaziland , producing a map of malaria receptivity at 100-meter resolution . For model selection purposes , due to the small total number of covariates for the zero-inflated negative binomial regression ( 12 ) , we assessed the model fit through AIC for every sub-model ( 4095 models ) and selected the one with the best AIC . The resulting model ( Table 1 ) retained covariates that were not found to be significant , but since we were not interested in the impact of any given covariate , but rather interpolating the observed RC values across Swaziland , backwards selection would have been inappropriate . All analysis was conducted using R , version 3 . 1 . 1 ( Team RDC , 2011 ) . It is important to note here that there are numerous other models that are ‘almost’ as good as the best . The regression algorithm is a middle step between linking cases together and making operational recommendations . We have not reported all AIC values , and there are other models that give similar AIC values and result in similar maps of receptivity . The particular parameters chosen ( and their particular coefficients ) are only a step of our algorithm , which would be rerun at any time-point in the future given new data and which would likely result in a different “best” model linking spatial covariates to the output of the first part of our algorithm . 10 . 7554/eLife . 09520 . 009Table 1 . Zero-inflated negative binomial regression summary . DOI: http://dx . doi . org/10 . 7554/eLife . 09520 . 009Factor ( source ) Count model coefficientZero-inflated coefficientIntercept7 . 6864071642 . 7199Elevation ( m ) ( http://www . worldclim . org/bioclim ) −0 . 0026−0 . 65197Population ( http://www . worldpop . org . uk/ ) −0 . 017571−0 . 01190Annual Mean Temperature ( 0 . 1°C ) ( http://www . worldclim . org/bioclim ) 0 . 14197930 . 70232Max Temperature of Warmest Month ( 0 . 1°C ) ( http://www . worldclim . org/bioclim ) −0 . 113297−23 . 66837Min Temperature of Coldest Month ( 0 . 1°C ) −0 . 029091−10 . 04161Precipitation of Wettest Month ( mm ) ( http://www . worldclim . org/bioclim ) 0 . 0080320 . 50592Precipitation of Driest Month ( mm ) ( http://www . worldclim . org/bioclim ) −0 . 10876712 . 04175TWI−0 . 024820−4 . 02392NDVI ( https://landsat . usgs . gov/ ) 2 . 461314−159 . 39390EVI ( https://landsat . usgs . gov/ ) −3 . 73279582 . 72595Log ( theta ) −0 . 613861NA The risk of importing malaria from endemic countries to Swaziland is assumed to be a function of population density , distance to Mozambique and distance to roads ( Figure 7 ) . Values for each of the covariates were compared between the locations of the households of patients identified with imported acquired infections and randomly selected “background” points from across Swaziland . Background points do not necessarily indicate the absence of transmission , but instead characterize the environment of the country ( Anderson , 2006 ) . A sample of 10 , 000 background points ( Anderson , 2006; Phillips and Dudík , 2008 ) was selected randomly across Swaziland . The observed importation points as well as the 10 , 000 background points were combined in a GAM logistic regression ( Table 2 , Figure 7 ) . GAMs were implemented using the ‘mgcv’ package in R ( Wood , 2011 ) and fit by maximizing the restricted maximum likelihood to reduce bias and over-fitting of the smooth splines . 10 . 7554/eLife . 09520 . 010Figure 7 . Spatial covariates for malaria importation regression . The ten significant covariates for the malaria importation regression were ( A ) elevation , ( B ) population , ( C ) annual mean temperature ( bio1 - http://www . worldclim . org/bioclim ) , ( D ) maximum temperature of the warmest month ( bio5 - http://www . worldclim . org/bioclim ) , ( E ) minimum temperature of coldest month ( bio6 - http://www . worldclim . org/bioclim ) , ( F ) precipitation of the wettest month ( bio13 - http://www . worldclim . org/bioclim ) , ( G ) precipitation of driest month ( bio14 - http://www . worldclim . org/bioclim ) , ( H ) TWI , ( I ) normalized difference vegetation index , and ( J ) enhanced vegetation index . DOI: http://dx . doi . org/10 . 7554/eLife . 09520 . 01010 . 7554/eLife . 09520 . 011Table 2 . GAM logistic regression summary . DOI: http://dx . doi . org/10 . 7554/eLife . 09520 . 011FactoredfChi . sqp-valuePopulation ( http://www . worldpop . org . uk/ ) 6 . 729688 . 01<2e-16Paved roads ( source: country ) 5 . 909172 . 49<2e-16Unpaved roads1 . 00215 . 886 . 88e-5Feeders roads6 . 49950 . 373e-8Distance to Mozambique ( http://www . fao . org/geonetwork/srv/en/main . home ) 7 . 51675 . 271 . 04e-12 The relevant concept for malaria transmission in elimination setting was named “vulnerability” by the World Health Organization ( WHO ) and defined qualitatively as “the frequent influx of infected individuals or groups and/or infective anophelines”; quantitatively , the rate of malaria importation includes all parasites that cross the border in humans and vectors . The impact of vulnerability depends on an area's “receptivity” to malaria which reflects the conditions of transmission “through the abundant presence of vector anophelines and the existence of other ecological and climatic factors” . Receptivity is defined quantitatively as the effective reproduction number RC , which describes the expected number of secondary human infections originating from a single , untreated infected human taking into account vector control measures . The product of the receptivity and vulnerability was named malariogenic potential .	Swaziland has set a national goal of eliminating malaria transmission in the very short term , which would make it the first country in sub-Saharan Africa to do so . More than half of the cases of malaria that are observed in Swaziland are caused by infections picked up by travelers while they were in other countries where the disease is much more prevalent . The other cases – people who became infected in Swaziland – are the cases that the government of Swaziland is trying to prevent . If Swaziland is going to eliminate malaria , it will need to identify any places where the malaria parasites are still spreading throughout the population so it can target those communities with effective prevention measures . It will also need to manage the risk that infections imported from abroad may re-start transmission in places where it has been stopped . To work out how likely it is that a malaria infection will be transmitted by mosquitoes in a particular place , researchers can look at past malaria data and calculate how many new infections are caused by each case . Reiner et al . have now produced a computer model that estimates how this number varies across Swaziland , highlighting places where the government is going to need to focus efforts to eliminate malaria . The model shows that in some rural areas near Mozambique , each individual infected with malaria is causing more than one other person to become infected . This confirms that the disease has not yet been eliminated from these areas . However , in other regions of the country , malaria rarely spreads between individuals . The detailed regional information from the model may help public health authorities in Swaziland better target their anti-malaria resources . In large cities where most cases are imported , Reiner et al . suggest focusing resources on providing preventive treatment to travelers who plan on visiting places where malaria is spreading . However , in rural areas where malaria continues to spread , preventively treating the whole population or providing them with tools to protect them from mosquitoes might be more appropriate . Similar considerations of regional differences in the spread of malaria could also help other countries to more effectively combat the disease .	[ "Abstract", "Introduction", "Discussion", "Methods" ]	[ "epidemiology", "and", "global", "health", "medicine" ]	2015	Mapping residual transmission for malaria elimination
Decision-makers often arrive at different choices when faced with repeated presentations of the same evidence . Variability of behavior is commonly attributed to noise in the brain’s decision-making machinery . We hypothesized that phasic responses of brainstem arousal systems are a significant source of this variability . We tracked pupil responses ( a proxy of phasic arousal ) during sensory-motor decisions in humans , across different sensory modalities and task protocols . Large pupil responses generally predicted a reduction in decision bias . Using fMRI , we showed that the pupil-linked bias reduction was ( i ) accompanied by a modulation of choice-encoding pattern signals in parietal and prefrontal cortex and ( ii ) predicted by phasic , pupil-linked responses of a number of neuromodulatory brainstem centers involved in the control of cortical arousal state , including the noradrenergic locus coeruleus . We conclude that phasic arousal suppresses decision bias on a trial-by-trial basis , thus accounting for a significant component of the variability of choice behavior . Decision-makers often arrive at different choices in the face of repeated presentations of the same evidence ( Glimcher , 2005; Gold and Shadlen , 2007; Shadlen et al . , 1996; Sugrue et al . , 2005; Wyart and Koechlin , 2016 ) . This intrinsic behavioral variability is typically attributed to spontaneous fluctuations of neural activity in the brain regions computing decisions ( Glimcher , 2005; Shadlen et al . , 1996 ) ( but see [Beck et al . , 2012; Brunton et al . , 2013] ) . Indeed , fluctuations of neural activity are ubiquitous in the cerebral cortex ( Faisal et al . , 2008; Glimcher , 2005; Lin et al . , 2015 ) . One candidate source of these fluctuations in cortical activity is systematic variation in central arousal state . Central arousal state is controlled by the neuromodulatory systems of the brainstem , which have widespread projections to cortex and tune neuronal parameters governing the operating mode of their cortical target circuits ( Aston-Jones and Cohen , 2005; Harris and Thiele , 2011; Lee and Dan , 2012 ) . Importantly , these neuromodulatory systems operate at different timescales ( Aston-Jones and Cohen , 2005; Parikh et al . , 2007 ) . Some , in particular the noradrenergic locus coeruleus ( LC ) , are rapidly recruited , in a time-locked fashion , during elementary decisions ( Aston-Jones and Cohen , 2005; Bouret and Sara , 2005; Dayan and Yu , 2006; Parikh et al . , 2007 ) . Pupil diameter , a reliable peripheral marker of central ( cortical ) arousal state ( McGinley et al . , 2015b ) , also increases during decisions ( Beatty , 1982; de Gee et al . , 2014; Gilzenrat et al . , 2010; Lempert et al . , 2015; Nassar et al . , 2012 ) . These observations point to an important role of phasic ( i . e . , fast ) pupil-linked arousal signals in decision-making ( Aston-Jones and Cohen , 2005; Dayan and Yu , 2006 ) . Yet , the precise nature of this role has remained unknown . Here , we investigated how phasic , task-related arousal interacts with decision computations in the human brain . We combined pupillometry , fMRI , and computational modeling to probe into the interplay between task-related arousal and decision computations underlying elementary sensory-motor choice tasks . Sensory-motor decisions entail the gradual accumulation of noisy ‘sensory evidence’ about the state of the world towards categorical decision states governing behavioral choice ( Bogacz et al . , 2006; Brody and Hanks , 2016; Gold and Shadlen , 2007; Ratcliff and McKoon , 2008 ) . A large-scale network of regions in frontal and parietal cortex seems to accumulate stimulus responses provided by sensory cortices towards choices of motor movements ( Gold and Shadlen , 2007; Siegel et al . , 2011 ) ( but see [Brody and Hanks , 2016; Katz et al . , 2016] ) . We here aimed to elucidate the interaction between pupil-linked arousal responses , evidence accumulation , and decision processing across several ( cortical and subcortical ) brain regions . Large task-evoked pupil responses were consistently accompanied by a reduction in perceptual decision bias in different sensory modalities ( visual and auditory ) and task protocols ( detection and discrimination ) . Decision bias reflects the degree to which an observer’s choice deviates from the objective sensory evidence . Using fMRI for one of these tasks revealed that the bias reduction was accompanied by a modulation of choice-encoding pattern signals in prefrontal and parietal cortex . Further , the bias reduction was predicted by task-evoked , pupil-linked responses in a network of neuromodulatory brainstem nuclei controlling cortical arousal state . We conclude that phasic neuromodulatory signals reduce biases in the brain’s decision-making machinery . As a consequence , phasic arousal accounts for a significant component of the variability of choice behavior , over and above the objective evidence gathered from the outside world . The main task used in this study was detection ( ‘yes-no’ , simple forced choice protocol ) of a low-contrast grating ( Figure 1A ) . The grating contrast was titrated to the 75% correct level , and subjects did not receive trial-by-trial feedback . As observed previously ( de Gee et al . , 2014 ) , TPR amplitudes during this task fluctuated widely from trial to trial ( Figure 1B , C; see Materials and Methods for quantification of TPR ) . To illustrate , pooling trials into two bins containing the lowest and highest 40% of TPR amplitudes ( Figure 1B ) yielded , on average , the commonly observed task-evoked pupil dilations for the high TPR bin , but pupil constrictions for the low TPR bin ( Figure 1C ) . We used a previously established model to estimate the time course of the neural input driving the measured TPRs ( GLM; see Materials and methods; Figure 1—figure supplement 1A–C ) . This revealed that the difference between the low and high TPR bins was primarily due to the difference in a sustained component that spanned the entire interval from cue to behavioral choice ( Figure 1D ) . The difference of the sustained component between low and high TPR was significantly larger than the corresponding difference for two components at cue or choice , respectively ( 2-way repeated measures ANOVA with factors temporal component and TPR bin; interaction: F2 , 26 = 79 . 00 , p<0 . 001 ) . 10 . 7554/eLife . 23232 . 003Figure 1 . Behavioral task and task-evoked pupil responses . ( A ) Yes-no contrast detection task . Top: schematic sequence of events during a signal+noise trial . Subjects reported the presence or absence of a faint grating signal superimposed onto dynamic noise . Bottom left: the signal , if present , was oriented clockwise or counter clockwise on different blocks ( known to the subject beforehand ) . Signal contrast is high for illustration only . Bottom right: trial types . ( B ) Quantifying task-evoked pupillary response ( TPR ) amplitude . Top: mean TPR time course of an example subject . Green box , interval for averaging TPR values on single trials . Bottom: trials were pooled into three bins of TPR amplitudes ( lowest/highest 40% and intermediate 20% ) . ( C ) TPR time course for the three bins . ( D ) Mean beta weights of transient ( cue , choice ) and sustained input components under low vs . high TPR , estimated with a general linear model ( see Materials and methods; Figure 1—figure supplement 1A , B ) , separately for low and high TPR trials . Panels C , D: group average ( N = 14 ) ; shading , s . e . m . ; data points , individual subjects; stats , permutation test . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 00310 . 7554/eLife . 23232 . 004Figure 1—figure supplement 1 . Linear modeling of TPR . The peripheral system ( i . e . , nerves and the muscles of the iris [McDougal and Gamlin , 2008] ) that transforms central neural inputs originating from the brainstem into TPR is sluggish and acts as a low-pass filter ( Hoeks and Levelt , 1993; Korn and Bach , 2016 ) . ( A ) Linear modeling of TPR ( see Materials and methods ) . We used a previously established general linear model ( GLM ) to estimate the relative contribution of three putative underlying neural input components ( see Materials and methods; [de Gee et al . , 2014] ) . Gray-shaded interval , decision interval ( between cue , i . e . , decision onset , and button press ) . The three beta weights are the best fitting parameter estimates for the subject from panel B . IRF , impulse response function . ( B ) TPR time course from example subject , aligned to cue ( left ) and button press ( right ) . Gray line , mean TPR ( n = 624 trials ) . Black line , model prediction . The model provided a good fit of the shape of the TPR time course in most subjects . ( C ) Mean beta weights for all temporal components . As observed previously ( de Gee et al . , 2014 ) , the predominant input ( i . e . , largest beta-weight ) was a sustained component that spanned the interval between cue- and choice-components . Data points , individual subjects; *p<0 . 001 . ( D ) Correlation between TPR and reaction time ( RT ) ( 5 bins ) . Shading or error bars , s . e . m . All panels: Group average ( N = 14 ) ; stats , permutation test . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 004 In sum , TPR amplitude exhibited substantial trial-to-trial fluctuations , which were predominantly driven by changing levels in sustained input during decision formation . Given the prolonged nature of the decision ( median of subject-median reaction time , RT: 2 . 11 s ) , the sustained , intra-decisional arousal boost might have interacted with the decision computation . To test for such an interaction between arousal boost and decision computation , we next modeled subjects’ choice behavior as a function of TPR amplitude . We found a robust and consistent relationship between TPR and decision bias . This effect was present in two independent data sets using an analogous contrast detection task: the newly collected fMRI data set , and a re-analysis of an existing data set ( de Gee et al . , 2014 ) ) ( Figure 2A , D , middle and right panels ) . Decision bias was quantified in two ways ( for details , see Materials and methods ) . First , we computed signal detection-theoretic ( SDT ) criterion ( Figure 2A , D , middle panels ) . Second , we computed the fraction of ‘yes’-choices ( right panels ) , after balancing the number of signal+noise and noise trials within each TPR bin . We did not find a consistent relationship between phasic arousal , as measured by TPR , and perceptual sensitivity , quantified by SDT d’ ( Figure 2A , D , left panels ) . 10 . 7554/eLife . 23232 . 005Figure 2 . Phasic arousal predicts reduction of choice bias . ( A ) Perceptual sensitivity SDT d’ ( left ) , decision bias , measured as SDT criterion ( middle ) or fraction of ‘yes’-choices ( right ) , for low and high TPR . For the fraction of ‘yes’-choices analysis , we ensured that each TPR bin consisted of an equal number of signal+noise and noise trials ( see Materials and methods ) . Data points , individual subjects . ( B ) Relationship between TPR and d’ or criterion ( 5 bins ) . Linear fits are plotted wherever the first-order fit was superior to the constant fit ( see Materials and methods ) . Quadratic fits are plotted wherever the second-order fit was superior to first-order fit . ( C ) Sliding window linear correlation between TPR and SDT criterion ( 5 bins ) , aligned to button press . Dashed line , median decision onset ( cue ) . The group average pupil response time course is plotted for reference in blue . ( D–F ) As panels A-C , for an independent data set ( de Gee et al . , 2014 ) . All panels: group average ( N = 14 and N = 21 ) ; shading or error bars , s . e . m . ; stats , permutation test . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 00510 . 7554/eLife . 23232 . 006Figure 2—source data 3 . Table with variable identifiers used in Figure 2—source data 1 and 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 00610 . 7554/eLife . 23232 . 007Figure 2—source data 1 . This csv table contains the data for Figure 2 panel A . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 00710 . 7554/eLife . 23232 . 008Figure 2—source data 2 . This csv table contains the data for Figure 2 panel D . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 00810 . 7554/eLife . 23232 . 009Figure 2—figure supplement 1 . Phasic arousal predicts reduction of choice bias . ( A–D ) As main Figure 2 panels A , B , D , E , but now after splitting trials by ( or as a function of ) baseline pupil diameter , quantified as the mean pupil size in the 0 . 5 s before decision interval onset . ( E–J ) As main Figure 2 panels A-F , but now without removing trial-to-trial variations from TPR due to reaction time ( RT ) . The pattern of results is qualitatively identical to the one in Figure 2 . All panels: group average ( N = 14 ) ; shading or error bars , s . e . m . ; stats , permutation test . Panels A , C , E , H: data points , individual subjects . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 009 The negative association between TPR and decision bias ( SDT criterion ) was approximately linear across a range of five TPR-defined bins ( Figure 2B , E , right panels ) . In all cases , here and below , we tested whether fits of second-order polynomials , reflecting non-monotonic relationships between TPR and behavior , were superior to the linear fits ( via sequential polynomial regression analysis; Materials and methods ) . We found a non-monotonic relationship between TPR and sensitivity in the behavioral data set from de Gee et al . ( 2014 ) , but not in the fMRI dataset ( Figure 2B , E , left panels ) . This non-monotonic ( inverted U-shape ) relationship between pupil diameter and sensitivity is consistent with previous animal work on correlations between baseline arousal and behavior ( Aston-Jones and Cohen , 2005; McGinley et al . , 2015a ) . However , it was less consistent across the data sets analyzed in this paper than the negative linear effect of TPR on decision bias . The consistent effect of TPR on decision bias has not been reported before in previous studies of slow fluctuations of baseline pupil diameter . In what follows , we focus on the negative effect of TPR on decision bias . Most subjects were overall ( i . e . , without splitting trials by TPR ) intrinsically biased to respond ‘no’: 10 out of 14 subjects exhibited a significantly conservative criterion ( within-subject permutation tests; p<0 . 05 ) in the fMRI data set , and 14 out of 21 subjects in the data set from de Gee et al . ( 2014 ) . Because signal+noise and noise trials were equally frequent in both experiments , this bias was always maladaptive . Critically , this maladaptive bias was particularly pronounced under low TPR; but under high TPR the bias was nearly neutralized , especially in the fMRI data set ( criterion around zero , and fraction of ‘yes’-choices around 0 . 5 for highest TPR bins , Figure 2A , B ) . A number of control analyses and experiments supported the idea that the negative correlation between TPR amplitude and decision bias reflected a specific effect of phasic arousal on the decision computation that generalized across perceptual choice tasks . First , the effect emerged during , not after , decision formation: a sliding-window correlation between TPR and criterion became negative from decision onset onwards , and reached statistical significance before button press ( Figure 2C , F ) . In the fMRI data set , this correlation was highly significant more than 800 ms before button press ( Figure 2C ) . Given the sluggish nature of the pupil response ( see above ) , the underlying central arousal transients must have occurred even earlier than that , leaving substantial time for shaping the decision outcome . Second , there was no robust association between baseline pupil diameter and decision bias ( Figure 2—figure supplement 1A–D ) . This ruled out possible concerns that the effect might be due to corresponding ( opposite ) associations between baseline pupil diameter and behavior , ‘inherited’ by TPR through its negative correlation with baseline pupil diameter ( de Gee et al . , 2014 ) . Third , the effect of TPR on decision bias was robust with respect to the details of the analysis approach . For Figure 2 , as for all other analyses reported in the main text , we removed ( via linear regression ) components explained by RT . The rationale was to specifically isolate variations in the amplitudes of the neural responses driving TPR , irrespective of RT , variations of which might also cause variations of TPR amplitude without changes in the underlying neural response amplitudes ( for details see Materials and methods ) . We observed the same linear effect of TPR on bias without removing trial-to-trial variations in TPR that were due to RT ( Figure 2—figure supplement 1E–J ) . Fourth , the effect of TPR on decision bias shown in Figure 2 generalized to other perceptual choice tasks , which differed on several dimensions from the main contrast detection task used in this paper ( Figure 3 ) . In one follow-up experiment , we measured pupil-linked behavior during an auditory yes-no ( tone-in-noise ) detection task near psychophysical threshold using the same stimuli as in ( McGinley et al . , 2015a ) ( see Materials and methods ) . The only visual stimulus was a stable fixation dot . The decision interval contained only auditory noise ( the same as in ( McGinley et al . , 2015a ) ) on half the trials , and a pure sine wave superimposed onto the noise on the other half of the trials . Again , TPR predicted a significant ( linear ) reduction in conservative decision bias , and an increased tendency to respond ‘yes’ ( Figure 3A , B ) . TPR also exhibited a non-monotonic relationship with sensitivity , as observed in rodents for baseline pupil diameter in ( McGinley et al . , 2015a ) . 10 . 7554/eLife . 23232 . 010Figure 3 . Arousal-linked bias reduction generalizes to other choice tasks . ( A ) Perceptual sensitivity ( d’; left ) and decision bias , measured as criterion ( middle ) or fraction of ‘yes’-choices ( computed as for Figure 2A , right ) , for low and high TPR . Data points , individual subjects . ( B ) Relationship between TPR and d’ or criterion ( 5 bins ) . Linear fits were plotted wherever the first-order fit was superior to the constant fit ( see Materials and methods ) . Quadratic fits were plotted wherever the second-order fit was superior to first-order fit . ( C ) Perceptual sensitivity ( d’ , left ) and decision bias , measured as absolute criterion ( middle ) or fraction of non-preferred choices ( right ) , for low and high TPR . For the fraction of non-preferred choices analysis , we ensured that each TPR bin consisted of an equal number of motion up and down trials ( see Materials and methods ) . ( D ) Relationship between TPR and d’ or absolute criterion ( 4 bins instead of 5 , because of fewer trials per subject , see Materials and methods ) . All panels: group average ( N = 24 and N = 15 ) ; shading or error bars , s . e . m . ; stats , permutation test . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 01010 . 7554/eLife . 23232 . 011Figure 3—source data 3 . Table with variable identifiers used in Figure 3—source data 1 and 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 01110 . 7554/eLife . 23232 . 012Figure 3—source data 1 . This csv table contains the data for Figure 3 panel A . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 01210 . 7554/eLife . 23232 . 013Figure 3—source data 2 . This csv table contains the data for Figure 3 panel C . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 013 Another follow-up experiment assessed whether the pupil-linked bias reduction observed above may have been due to the asymmetric nature of the detection tasks ( i . e . , discriminating the presence from the absence of a signal ) or due to the absence of single-trial feedback . Symmetric two-alternative forced choice tasks are commonly associated with weaker biases than yes-no detection tasks ( Green and Swets , 1966 ) . We used a symmetric visual random dot motion ( up vs . down ) discrimination task near psychophysical threshold with feedback after each trial ( see Materials and methods ) . Although many subjects exhibited clear biases for reporting one or the other direction , these were more evenly distributed around zero than in the above yes-no tasks , in which the sign of the bias was largely consistent across individuals . Therefore , we here analyzed subjects’ absolute criterion values ( i . e . , overall bias regardless of sign ) and fraction of non-preferred choices ( i . e . , the choice opposite to their general bias , irrespective of TPR ) . Again , TPR predicted a reduction in absolute decision bias , and an increase in the fraction of non-preferred choices ( Figure 3C , D ) , analogous to the effects observed for the detection tasks above . In sum , a number of analyses and experiments showed that pupil-linked , phasic arousal was consistently associated with a monotonic reduction in perceptual decision biases in different sensory modalities and task protocols . To further pinpoint the nature of the TPR-induced bias suppression , we fitted the drift diffusion model , an established dynamic model of two-choice decision processes ( Figure 4A; [Ratcliff and McKoon , 2008] ) to subjects’ RT distributions from the main task ( contrast detection ) . The drift diffusion model posits the perfect accumulation of noisy sensory evidence towards one of two decision bounds , here for ‘yes’ and ‘no’ ( Figure 4A ) . 10 . 7554/eLife . 23232 . 014Figure 4 . Phasic arousal predicts reduction of accumulation bias . ( A ) Schematic and simplified equation of drift diffusion model accounting for RT distributions for ‘yes’- and ‘no’-choices ( ‘stimulus coding’; see Materials and methods ) . Notation: dy , change in decision variable y per unit time dt; v•dt , mean drift ( multiplied with 1 for signal+noise trials , and −1 for noise trials ) ; dc•dt , drift criterion ( an evidence-independent constant added to the drift ) ; and cdW , Gaussian white noise ( mean = 0 , variance = c2 dt ) . ( B ) RT distributions of one example subject for ‘yes’- and ‘no’-choices , separately for signal+noise and noise trials and separately for low and high TPR . RTs for ‘no’-choices were sign-flipped for illustration purposes . Straight lines , mode ( i . e . , maximum ) of the fitted RT distributions . Please note that TPR predicts an increased fraction of ‘yes’-choices with only a minor change of the mode of the RT distribution , consistent with a drift criterion effect rather than a starting point effect ( Figure 4—figure supplement 1 ) . ( C ) Group-level posterior probability densities for means of parameters . To maximize the robustness of parameter estimates ( Wiecki et al . , 2013 ) , two data sets were fit jointly ( the current fMRI and our previous study ( de Gee et al . , 2014 ) ; N = 35 ) . Starting point ( z ) is expressed as a proportion of the boundary separation ( a ) . ( D ) Drift criterion point estimates for low and high TPR trials , separately for both data sets ( N = 14 and N = 21 , respectively ) . Data points , individual subjects; stats , permutation test . ( E ) Change in fraction of ‘yes’-choices for low vs . high TPR trials , plotted against change in drift criterion . Data points , individual subjects . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 01410 . 7554/eLife . 23232 . 015Figure 4—source data 2 . Table with variable identifiers used in Figure 4—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 01510 . 7554/eLife . 23232 . 016Figure 4—source data 1 . This csv table contains the data for Figure 4 panel D . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 01610 . 7554/eLife . 23232 . 017Figure 4—figure supplement 1 . Effects of starting point vs . drift criterion on RT distributions . Analytical RT distributions sorted by the four SDT categories: ‘yes’- and ‘no’-choices , as well as signal+noise and noise trials . Within each category , RT distributions are shown separately for a biased model ( black ) and an unbiased model ( green ) , whereby biased model refers to a model producing unequal fractions of ‘yes’- and ‘no’-choices . RTs for ‘no’-choices were sign-flipped for illustration purposes . A conservative choice bias was implemented in two separate mechanisms in the two panels , both producing the same change on the fraction of choices . These mechanisms have distinguishable effect on the shape of the RT distributions , most evident in the mode . ( A ) Conservative choice bias through setting the starting point of the accumulation process closer to the ‘no’-bound . Straight lines , mode ( i . e . , maximum ) of the analytical RT distributions . A shift in starting point produced a substantial shift of the mode of the RT distribution ( compare black and green vertical lines ) . ( B ) Conservative choice bias through changing drift criterion in the direction of the ‘no’-bound . This shift in drift criterion had a negligible effect on the mode of RT distributions ( green and black vertical lines are on top of one another ) . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 01710 . 7554/eLife . 23232 . 018Figure 4—figure supplement 2 . Phasic arousal predicts reduction of accumulation bias . ( A ) As main Figure 4C , but now after splitting trials by baseline pupil diameter , quantified as the mean pupil size in the 0 . 5 s before decision interval onset . ( B–D ) As main Figure 4C–E , but now without removing trial-to-trial variations from TPR due to reaction time ( RT ) . The pattern of results is qualitatively identical . Data points , individual subjects; stats , permutation test . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 018 We fitted the model separately for low and high TPR trials ( see Figure 4B for an individual example ) . Within the model , the TPR-induced reduction of conservative bias , evident in Figures 2 and 3 , may have been brought about by two distinct mechanistic scenarios: ( i ) the evidence accumulation process started from a level closer to the ‘yes’-bound ( i . e . , a change in the ‘starting point’ parameter ) ; or ( ii ) the accumulation process was driven more towards the ‘yes’-bound ( i . e . , a change in the ‘drift criterion’ parameter ) . The drift criterion is equivalent to an evidence-independent constant added to the drift . A non-zero drift criterion results in a bias of the decision variable that grows linearly with time . Although clearly distinct in nature , both mechanisms ( starting point and drift criterion ) would have resulted in an increase in the fraction of ‘yes’-choices , and thus a reduction of decision bias . Critically , both mechanisms were distinguishable through their distinct effects on the shape of the RT distribution ( Figure 4—figure supplement 1 ) . To dissociate between these alternative mechanisms we fitted the model , while allowing several model parameters ( boundary separation , non-decision time , mean drift rate , starting point , and drift criterion ) to vary with TPR . The model fits ( see Materials and methods and [Wiecki et al . , 2013] ) supported the second mechanism: a change in drift criterion . An individual example is shown in Figure 4B , and group data are shown in Figure 4C . Drift criterion was generally negative , indicating an overall conservative accumulation bias towards the bound for ‘no’-choices . But drift criterion was pushed closer towards zero under high TPR , indicating an unbiased drift , as optimal for the current task ( Figure 4B , C ) . The other main parameters ( including starting point and mean drift rate ) were not significantly affected by TPR . The TPR-linked effect on drift criterion was also evident in the individual point estimates from the fMRI sample only ( Figure 4D ) . Again , we we found no evidence for an effect on any parameter of the drift diffusion model when comparing trials with low and high baseline pupil diameters ( Figure 4—figure supplement 2A ) , and we obtained qualitatively identical results without removing trial-to-trial variations of RT from the TPR amplitudes ( Figure 4—figure supplement 2B–D; Materials and methods ) . As a control of the significance of the TPR-dependent effect on drift criterion , we re-fitted the model , but now fixing drift criterion with TPR , while still allowing all other of the above parameters to vary with TPR . In this variant of the model , we again found no TPR-dependent change in any of the other parameters ( boundary separation: p=0 . 428; non-decision time: p=0 . 370; starting point: p=0 . 117; mean drift rate: p=0 . 361 ) . Critically , model comparison favored the complete version of the model with TPR-dependent variation in drift criterion ( deviance information criterion , 50437 vs . 50528 , respectively; see Materials and methods ) . This implies that the TPR-dependent variability in accumulation bias was essential to account for the TPR-dependent effects on behavior . The individual changes in drift criterion between low vs . high TPR trials established by means of diffusion modeling accounted for a substantial fraction of the individual differences in TPR-predicted changes in the fraction of ‘yes’-choices ( Figure 4E ) obtained in the model-free analyses ( Figure 2A , D , right panels ) . TPR-related changes in starting point had a weaker , and statistically not significant , effect on the fraction of ‘yes’-choices ( fMRI data set: r = −0 . 345 , p=0 . 227; de Gee et al . ( 2014 ) data set: r = −0 . 419 , p=0 . 059 ) . In sum , in the decision task studied here , pupil-linked , phasic arousal predicted a reduction of conservative bias , specifically in the evidence accumulation , and was neither reflected in the baseline level of the decision variable at the start of the accumulation nor its mean drift . In other words , TPR accounted for a portion of the trial-to-trial variability in the drift unrelated to the objective sensory evidence . This correlate of phasic arousal at the algorithmic level was in line with the notion that phasic arousal shapes decision outcome by interacting with the evidence accumulation computation that lies at the heart of the decision process . Taken together , the behavioral modeling results reported in Figures 2–4 put strong constraints on the expected changes in cortical decision processing due to phasic arousal . Specifically , changes in the encoding of the incoming evidence by sensory cortical areas , as observed in previous work on fluctuations in baseline arousal levels ( McGinley et al . , 2015a; Reimer et al . , 2014; Vinck et al . , 2015 ) , would be associated with changes in perceptual sensitivity . However , we found that TPR was not associated with any robust change in sensitivity ( measured as d’ or as mean drift rate ) in the fMRI dataset , thus , predicting no TPR-linked modulation of sensory responses in visual cortex . Instead , the observed effect of TPR on choice bias ( criterion , drift criterion ) predicted a directed shift ( towards ‘yes’ ) in neural signals encoding subjects’ choices , in downstream cortical regions . We next tested these predictions by assessing the relationship between TPR and ( i ) stimulus-specific responses in early visual cortex , and ( ii ) choice-specific responses in downstream cortical regions . The fMRI response in early visual cortex ( areas V1 , V2 , and V3 ) during near-threshold visual tasks is made up of distinct components , including a ( weak and focal ) stimulus-specific component and a ( large and global ) task-related , but stimulus-independent , component ( Cardoso et al . , 2012; Donner et al . , 2008; Ress et al . , 2000 ) . We used an approach based on multi-voxel pattern analysis analogous to previous work ( Choe et al . , 2014; Pajani et al . , 2015 ) to isolate the stimulus-specific response component . Because the majority of visual cortical neurons encoding stimulus contrast are also tuned to stimulus orientation , orientation-tuning could serve as a ‘filter’ to separate the cortical stimulus response from stimulus-unrelated signals . Specifically , the low contrast signal in our task should have evoked a small response in each visual cortical neuron selective for the orientation of the target signal ( 45° or 135° , on different experimental runs , Figure 1A ) across a substantial part of the retinotopic map . Thus , the presence or absence of the target signal should be reliably encoded in the orientation-specific component of the cortical population response , within the retinotopic sub-region corresponding to the signal . We first individually delineated these retinotopic sub-regions within each of V1-V3 ( see Figure 5A for an example subject ) and then quantified the orientation-specific response component therein as the spatial correlation of multi-voxel response patterns with an orientation-specific ‘template’ ( Materials and methods ) . 10 . 7554/eLife . 23232 . 019Figure 5 . Phasic arousal does not boost sensory responses in visual cortex . ( A ) Map of fMRI responses during stimulus localizer runs ( see Materials and methods ) ; example subject . V1-V3 borders were defined based on a separate retinotopic mapping session . ‘Stimulus sub-regions’ , regions with positive stimulus-evoked response; ‘surround sub-regions’ , regions with negative stimulus-evoked response . ( B ) Orientation-specific fMRI responses in ‘center’ sub-regions of V1-V3 , separately for signal+noise and noise trials , and separately for low and high TPR trials . Statistical tests are reported in main text . Data points , individual subjects ( N = 14 ) ; stats in main text . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 01910 . 7554/eLife . 23232 . 020Figure 5—figure supplement 1 . Quantifying single-trial reliability of stimulus-specific responses . ( A ) The stimulus-predictive index was calculated from the receiver operating characteristic ( ROC ) curve , separately for ‘yes’- ( left ) and ‘no’-choices ( right ) . Each panel shows distributions ( taken from area V1 of one example subject ) of single-trial pattern responses ( see Materials and methods ) for signal+noise and noise conditions . ROC curves ( insets ) were constructed by shifting a criterion across both distributions , and plotting against one another , for each position of the criterion , the fraction of trials for which responses were larger than the criterion . The area under the resulting ROC curve ( AUC; grey shading ) , here referred to as ‘predictive index’ , quantifies the probability with which an ideal observer can predict the signal presence from the single-trial fMRI response . Predictive indices calculated separately for ‘yes’- and ‘no’-trials were pooled ( averaged ) into a single stimulus-predictive index . The predictive index in this example was 0 . 63 . ( B ) Stimulus-predictive indexes for orientation-specific responses ( signal+noise vs . noise , irrespective of choice; see Materials and methods and panel A ) . Data points , individual subjects; stats , permutation test . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 020 As expected , this orientation-specific response component differed robustly between signal+noise and noise trials ( Figure 5B ) . A 2-way repeated measures ANOVA with factors stimulus and TPR bin yielded a highly significant main effect of stimulus for V1 , V2 , and V3 ( V1: F1 , 13 = 303 . 5 , V2: F1 , 13 = 646 . 3 , V3: F1 , 13 = 316 . 6; all p<0 . 001 ) . The orientation-specific response component also reliably discriminated between signal+noise and noise trials on a single-trial basis ( Figure 5—figure supplement 1 ) . Consequently , we henceforth refer to this component as the ‘stimulus-specific response’ . However , the stimulus-specific response was not boosted under high TPR ( Figure 5B , no significant main effect of TPR , nor stimulus x TPR interaction in any of V1-V3 ) . The above analysis focused on the stimulus-specific response in early visual cortex . To avoid missing TPR-dependent modulations of sensory responses in higher cortical regions , we also mapped out modulations of fMRI responses by TPR across cortex ( see Materials and methods ) . Various regions including visual , parietal , prefrontal , and motor cortices exhibited robust task-evoked overall fMRI responses ( i . e . , difference between the decision interval and baseline; Figure 6A ) , as well as robust modulations by TPR ( Figure 6B ) , whereby TPR-induced boosts only partly overlapped with the task-positive responses . 10 . 7554/eLife . 23232 . 021Figure 6 . Cortex-wide fMRI correlates of phasic arousal and stimulus . ( A ) Functional map of task-evoked fMRI responses computed as the mean across all trials . ( B ) As panel A , but for the contrast high vs . low TPR trials . ( C ) As panel A , but for the contrast signal+noise vs . noise . ( D ) As panel A , but for the interaction between TPR ( 2 levels ) and stimulus ( 2 levels ) . All panels: functional maps are expressed as t-scores computed at the group level ( N = 14 ) and presented with cluster-corrected statistical threshold ( see Materials and methods ) . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 021 However , in no single region did the overall fMRI responses differ between signal+noise and noise trials ( Figure 6C ) . This indicates that our multi-voxel pattern approach described above was , in fact , essential for detecting the weak cortical response to the near-threshold target signals . Critically , in no region did we find a significant interaction between the factors stimulus ( signal+noise vs . noise ) and TPR ( low vs . high TPR; Figure 6D ) . Taken together , both complementary analyses showed that phasic , task-evoked arousal signals did not modulate cortical responses encoding the presence of the low-contrast signal . This is in line with the lack of TPR-linked change in perceptual sensitivity in the fMRI dataset ( Figure 2A , Figure 4D ) . We then sought to test for directed shifts in neural signals encoding subjects’ choices under high TPR , which would be in line with the changes in decision biases identified by behavioral modeling . Here , we use the term ‘choice-specific’ to refer to fMRI-signals that reliably discriminated between subjects’ choice ( ‘yes’ vs . ‘no’ ) . Two complementary approaches delineated several cortical regions that exhibited such choice-specific signals ( Figure 7 ) . The first approach ( Figure 7A ) was based on the lateralization of fMRI responses with respect to the motor effector used to report the choice ( i . e . , response hand; see ( de Lange et al . , 2013; Donner et al . , 2009 ) and Materials and methods ) . In addition to the hand area of primary motor cortex ( henceforth referred to as M1 ) , this approach yielded reliable effector-specific lateralization also in two regions of posterior parietal association cortex: the junction of the intraparietal and postcentral sulcus ( IPS/PostCeS ) and the anterior intraparietal sulcus ( aIPS1; Figure 7A and Figure 7—figure supplement 1A , B ) . The second approach ( Figure 7B ) was based on multi-voxel pattern classification of choice , using a ‘searchlight’ procedure that scanned the entire cortex for choice information ( see ( Hebart et al . , 2012 , 2016 ) and Materials and methods ) . The underlying rationale was to identify cortical regions encoding choice in other formats ( e . g . , in terms of more fine-grained patterns ) than the hemispheric lateralization of response amplitudes . The second approach revealed robust ( and reproducible ) choice-specific response patterns in a number of additional regions in bilateral posterior parietal cortex and ( right ) prefrontal cortex: superior and inferior parietal lobule ( SPL and IPL , respectively ) , a second region within aIPS ( aIPS2 ) , posterior insula ( pIns ) , the junction of precentral sulcus and right inferior frontal gyrus ( PreCeS/IFG ) and right medial frontal gyrus ( MFG; Figure 7B and Figure 7—figure supplement 1C , D ) . In both approaches , choice specific regions were delineated after factoring out the physical stimulus ( see Materials and methods ) . 10 . 7554/eLife . 23232 . 022Figure 7 . Phasic arousal predicts change of cortical decision signals . ( A ) Conjunction of session-wise maps of logistic regression coefficients of choice against fMRI lateralization ( see Figure 7—figure supplement 1A for individual sessions ) . Tested against 0 . 5 at group level; red outlines , ROIs used for further analyses . ( B ) Conjunction of session-wise maps of searchlight choice classification precision scores ( see Figure 7—figure supplement 1C for individual sessions ) . Tested against 0 . 5 at group level; red outlines , ROIs used for further analyses . ( C ) Choice-predictive indexes for choice-specific responses ( ‘yes’ vs . ‘no’ , irrespective of stimulus; see Materials and methods and Figure 7—figure supplement 1G ) . Dashed line , index for M1 , which can be regarded as a reference given the measurement noise . Data points , individual subjects . ( D ) Choice-specific responses , obtained through mapping lateralization ( M1 and the combined ‘lateralization signal’ , i . e . , regions from Figure 7A excluding M1; see Materials and methods ) and through searchlight classification ( combined ‘searchlight signal’ , i . e . , all regions from Figure 7B ) , for low and high TPR trials . Data points , individual subjects . ( E ) Correlation between TPR and M1 ( left ) , or the combined ‘lateralization signal’ ( middle ) , or the combined ‘searchlight signal’ ( right ) ( 5 bins ) . In all cases , the effect of the physical stimulus was removed ( see Materials and methods ) . Shading or error bars , s . e . m . All panels: group average ( N = 14 ) ; stats , permutation test . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 02210 . 7554/eLife . 23232 . 023Figure 7—figure supplement 1 . Identifying choice-specific cortical signals . ( A ) Session-wise maps map of logistic regression coefficients ( choice against fMRI lateralization ) . ( B ) Overlay of both maps from panel A , to identify robust and replicable choice-specific responses . ( C ) Session-wise maps of searchlight choice classification precision scores . ( D ) Overlay of both maps from panel C , to identify robust and replicable choice-specific responses . A-D: all tested against 0 . 5 at group level; red outlines , ROIs used for further analyses . ( E , F ) Choice-specific responses in ROIs from panels B and D , respectively , separately for ‘yes’- and ‘no’-choices . ( G ) Quantifying the reliability of choice-specific cortical responses , using single-trial , lateralized M1-responses of an example subject . As Figure 5—figure supplement 1A , but now for prediction of choice , rather than of signal presence . To remove effects of signal presence , we averaged ROC indexes for choice computed separately for signal+noise and noise conditions . The resulting measure is analogous to ‘choice probability’ employed in monkey electrophysiology . The predictive index in this example was 0 . 82 . All panels: group average ( N = 14 ) ; data points , individual subjects; stats , permutation test . aIPS , anterior intraparietal sulcus; IPS , intraparietal sulcus; PostCeS , postcentral sulcus; M1 , primary sensorimotor cortex ( hand area ) ; SPL , superior parietal lobule; IPL , inferior parietal lobule; pINS , posterior insular cortex; PreCeS , precentral sulcus; IFG , inferior frontal gyrus; MFG , medial frontal gyrus . ( H ) Stimulus-predictive indexes for choice-specific responses ( ‘yes’ vs . ‘no’ , without first taking out effects of stimulus as in panel G ) . ‘Combined lateralization’ signal , weighted sum of choice-specific responses across ROIs obtained through mapping significant lateralization with respect to the hand movement ( except M1; from Figure 7A; see Materials and methods ) . ‘Combined searchlight’ signal , weighted sum of choice-specific responses across ROIs obtained through searchlight classification ( from Figure 7B ) . ( I ) As panel H , but for choice-predictive indexes ( signal+noise vs . noise , without first taking out effects of choice as in Figure 5—figure supplement 1A ) . ( J ) As panel I , but after removing effects of physical stimulus via linear regression ( see Materials and methods ) . All panels: group average ( N = 14 ) ; data points , individual subjects; *p<0 . 001; stats , permutation test . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 023 In all the above choice-encoding regions , responses ( estimated in a cross-validated fashion , see Materials and methods ) reliably differentiated between ‘yes’- and ‘no’-choices – both on average ( Figure 7—figure supplement 1E , F ) and at the single-trial level ( Figure 7C , see also Figure 7—figure supplement 1G ) . As expected , the single-trial reliability of the choice-specific responses differed between cortical regions ( 1-way repeated measures ANOVA with factor region of interest ( 9 levels ) : F8 , 104 = 30 . 20 , p<0 . 001 ) , with the strongest reliability for M1 ( dashed horizontal line in Figure 7C ) , the region closest to the subjects’ motor output . For analysis of the association with TPR , we pooled the choice-specific signals of these different regions into three groups ( Figure 7—figure supplement 1A ) : the motor end stage of the decision process M1 , the combined ‘lateralization signal’ ( i . e . , regions from Figure 7A excluding M1 ) , and the combined ‘searchlight signal’ ( i . e . , all regions from Figure 7B ) . Critically , as predicted , the combined choice-specific signals , but not the M1 response , were significantly pushed towards the ‘yes’-choice ( i . e . , more positive in Figure 7D ) for high compared to low TPR . The effect of TPR differed by cortical signal ( 2-way repeated measures ANOVA with factors signal type ( 3 levels ) and TPR bin ( 2 levels ) ; interaction: F2 , 26 = 7 . 30 , p=0 . 003 ) . Specifically , the difference of the choice-specific signals between low and high TPR was significantly larger for the combined lateralization signal and the combined searchlight signal than for M1 ( combined lateralization signal vs . M1: p=0 . 015; combined searchlight signal vs . M1: p=0 . 004; permutation tests ) . Because subjects’ mean accuracy was about 74% correct , their choices were partially correlated with the physical stimulus ( i . e . , signal+noise vs . noise trials ) . Consequently , the choice-specific cortical responses were also ( weakly ) predictive of the stimulus ( Figure 7—figure supplement 1H ) . To isolate variations in the amplitude of the choice-specific response that were independent of the stimulus , we removed ( via linear regression ) components explained by the stimulus and quantified the effect of TPR on the residual choice-specific cortical signals . Fitting the linear model to the combined choice-specific responses yielded highly significant TPR coefficients , for both the combined lateralization and combined searchlight signals ( Figure 7E , middle and right panel ) . By contrast , the TPR-linked modulation was absent in the end stage region M1 ( Figure 7E , left panel ) . In sum , a number of fronto-parietal cortical regions exhibited signals that reliably encoded subjects’ behavioral choice and were robustly modulated by phasic arousal , with a larger tendency towards the ‘yes’-choice under high TPR . This was true even when factoring out the effect of the sensory evidence ( i . e . presence of the target signal ) . Finally , we aimed to identify brainstem regions whose task-evoked responses were ( i ) linked to the trial-to-trial fluctuations of TPR , and ( ii ) accounted for the trial-to-trial modulation of subjects’ evidence accumulation bias , and the resulting tendency to choose ‘yes’ . Previous work from monkey physiology has implicated three brainstem nuclei in particular in the control of TPR: the locus coeruleus ( LC ) , the inferior colliculus ( IC ) , and the superior colliculus ( SC ) , respectively ( Joshi et al . , 2016; Varazzani et al . , 2015; Wang et al . , 2012 ) . Here , we exploited the wide coverage of our fMRI measurements to concurrently monitor responses across a wider brainstem network , including a number of other nuclei implicated in central arousal: the dopaminergic substantia nigra ( SN ) and ventral tegmental area ( VTA ) , as well as the ( partly ) cholinergic basal forebrain ( BF ) . We further sub-divided the BF region into the part including cell groups within the septum and the horizontal limb of the diagonal band ( BF-sept ) and the sublenticular part ( BF-subl ) . BF-subl contains cholinergic neurons with widespread ascending projections ( Zaborszky et al . , 2008 ) , which are involved in the regulation of cortical arousal state ( Lee and Dan , 2012; McGinley et al . , 2015b ) . Our analysis approach minimized the effect of physiological noise on the brainstem fMRI responses , including removal of the fourth ventricle signal ( see Materials and methods ) . We also verified that the fourth ventricle signal was unrelated to TPR ( Figure 8—figure supplement 1D , E ) . The LC region of each subject was delineated through independent structural scans ( Figure 8A , and Figure 8—figure supplement 1A; for details see Materials and methods ) . 10 . 7554/eLife . 23232 . 024Figure 8 . Pupil responses reflect responses of a network of brainstem nuclei . ( A ) Delineation of LC by structural scan . The LC corresponds to two hyper-intense spots; example subject ( see Figure 8—figure supplement 1 for all subjects ) . Left inset , magnification of yellow box with LC ROI . Right inset , three-dimensional representation of signal intensity levels in yellow box . ( B ) Task-evoked LC responses for low and high TPR . Red bar , high TPR time course significantly different from zero; green bar , high TPR time course significantly different from low TPR time course ( p<0 . 05; cluster-corrected ) . Grey box , time window for computing scalar response amplitudes . ( C ) As panel B , but split by signal+noise and noise trials . ( D ) As panel B , but for the 2 voxels with highest probability of containing the LC . ( E ) As panel B , but for SN , VTA , and two BF-ROIs . ( F ) Map of single-trial correlation between TPR and evoked fMRI responses ( tested against 0 at group level ) . Yellow outlines , brainstem nuclei from probabilistic atlases . ( G ) Matrix of correlations between evoked brainstem fMRI responses . Stats corrected with false discovery rate ( FDR ) . ( H ) Partial correlation of evoked fMRI responses and TPR . For each ROI , responses of all other ROIs were first removed via linear regression . ( I ) Correlation between fMRI responses in ACC and TPR and LC . All panels: group average ( N = 14 ) ; shading , s . e . m . ; data points , individual subjects; stats , permutation test . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 02410 . 7554/eLife . 23232 . 025Figure 8—figure supplement 1 . TPR-linked brainstem responses . ( A ) As Figure 8A for all subjects . Green outline , subject presented in Figure 8A . ( B ) Task-evoked responses in SC and IC , separately for low and high TPR trials . ( C ) As panel B , but for LC , SN , VTA , BF-sept , and BF-subl . Data correspond to Figure 8 panels B and E , but is now represented as bar graphs of task-evoked fMRI response scalar measures ( see Materials and methods ) . ( D ) Remaining task-evoked fMRI response in the fourth ventricle ( after RETROICOR ) . The fourth ventricle ROI was delineated in the TSE scan , and its response computed by averaging across all voxels covering the ventricle . Gray-shaded interval , task-evoked fMRI response measure time window . Residual signal fluctuations in the fourth ventricle were uncorrelated to task events . Shading , s . e . m . ( E ) Task-evoked fMRI responses in the fourth ventricle plotted against TPR . Data were binned for display ( 50 bins ) . Panels B-E: group average ( N = 14 ) . Panels B , C: data points , individual subjects; stats , permutation test . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 025 The LC region exhibited a robust positive response on high TPR trials and a trend towards deactivation on low TPR trials ( Figure 8B–D , and Figure 8—figure supplement 1C ) . The same pattern was evident for both signal+noise and noise trials separately ( Figure 8C ) . The association to TPR was also highly significant in the most spatially specific definition of the LC region afforded by our measurements: evaluating only the two fMRI voxels with the largest probability of containing the individual LC region ( Figure 8D , and see Materials and methods ) . Fluctuations of task-evoked fMRI responses measured in the LC were also robustly coupled to fluctuations in TPR amplitude at the single trial level ( Figure 8F , H ) . Similar to the LC region , we found a robust difference between low and high TPR conditions for fMRI responses in the SC and VTA regions ( Figure 8E , F , and Figure 8—figure supplement 1B , C ) . Mapping the trial-to-trial correlations between TPR and brainstem fMRI responses at the single-voxel level yielded robust coupling to TPR in the LC , SC , VTA and as well as in BF-subl regions ( Figure 8F ) . As expected from the anatomical connectivity between brainstem centers ( España and Berridge , 2006; Sara , 2009; Wang and Munoz , 2015 ) , the trial-to-trial fluctuations of the task-evoked responses were significantly correlated among a number of these brainstem nuclei ( Figure 8G ) . Removing components of the trial-to-trial fluctuations in TPR and fMRI responses shared with the other ROIs yielded significant residual ( i . e . , partial ) correlations between TPR and responses in SC , LC region , VTA and BF-subl ( Figure 8H ) . This indicates robust and unique contributions of these four nuclei to TPR . Phasic brainstem responses during decision tasks might be driven by top-down signals from anterior cingulate cortex ( ACC ) , which sends descending projections to the LC ( Aston-Jones and Cohen , 2005 ) and other brainstem nuclei . In line with this notion , trial-to-trial fluctuations of both LC responses and TPR were robustly correlated to trial-to-trial fluctuations of task-evoked responses of the ACC ( Figure 8I ) . The task-evoked responses in the neuromodulatory nuclei , but not the colliculi , were tightly linked to the inferred decision computation and subjects’ overt choice behavior . We computed the combined ‘neuromodulatory brainstem signal’ as the linear combination of responses from LC , VTA , SN , and BF that maximized the correlation to TPR ( Materials and methods; correlation coefficient across subjects , 0 . 146 ( ±0 . 014 s . e . m . ) ) . The amplitude of this combined signal predicted a significant reduction in conservative decision bias ( Figure 9A ) , and an increased tendency to choose ‘yes’ ( Figure 9B ) , but no change in sensitivity ( Figure 9—figure supplement 1A ) . This pattern of effects was absent for the combined ‘colliculi signal’ ( Figure 9A , B ) , a linear combination of responses from SC and IC that maximized the correlation to TPR ( correlation coefficient across subjects , 0 . 092 ( ±0 . 011 s . e . m . ) ) . Further , the trial-to-trial variations in the strength of the combined neuromodulatory ( but not colliculi ) response robustly pushed the trial-to-trial drift towards the ‘yes’-boundary , in effect reducing the overall negative drift criterion ( Figure 9D , see Materials and methods for details ) . 10 . 7554/eLife . 23232 . 026Figure 9 . Brainstem neuromodulatory nuclei predict reduction of choice bias . ( A ) Correlation between decision bias ( criterion ) and the combined neuromodulatory brainstem signal ( linear combination of responses in LC , SN , VTA , BF-sept , and BF-subl maximizing the correlation to TPR; see Materials and methods; left ) , and the combined colliculi signal ( linear combination of responses in SC and IC maximizing the correlation to TPR; right ) ( 5 bins ) . Stats , permutation test . ( B ) As panel A but for the correlation to fraction of ‘yes’-choices . ( C ) Group-level posterior probability densities for means of parameters in the DDM regression model , through which we assessed the trial-by-trial , linear relationship between single-trial drift and the combined neuromodulatory response or the combined colliculi response ( see Materials and methods; see Figure 9—figure supplement 1 for the remaining parameters ‘starting point’ , ‘boundary separation’ and ‘non-decision time’ ) . All panels: group average ( N = 14 ) ; shading or error bars , s . e . m . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 02610 . 7554/eLife . 23232 . 027Figure 9—figure supplement 1 . Brainstem responses are not associated to sensitivity . ( A ) Correlation between perceptual sensitivity ( d’ ) and the combined neuromodulatory brainstem response ( linear combination of responses in LC , SN , VTA , BF-sept , and BF-subl maximizing the correlation to TPR; see Materials and methods ) , and the combined colliculi response ( linear combination of responses in SC and IC maximizing the correlation to TPR ) ( 5 bins ) . Stats , permutation test . ( B ) Group-level posterior probability densities for means of the remaining parameters in the DDM regression model , through which we assessed the linear relationship between the trial-by-trial drift and the trial-by-trial combined neuromodulatory response and the combined colliculi response ( see Materials and methods ) . All panels: group average ( N = 14 ) ; shading or error bars , s . e . m . DOI: http://dx . doi . org/10 . 7554/eLife . 23232 . 027 In sum , trial-to-trial fluctuations in TPR were predicted by fluctuations in the task-evoked responses of a network of brainstem regions , most notably the LC , VTA and SC . Despite the expected coupling between these and other brainstem regions ( Figure 8G ) , TPR carried robust LC- , SC- , and ( less strongly ) VTA-specific components ( Figure 8H ) . But only the responses of the neuromodulatory ROIs , not of the colliculi , accounted for the concomitant reduction of the bias in evidence accumulation and the resulting behavioral choice patterns . These results establish a tight link between phasic neuromodulator release and the dynamics of evidence accumulation . Imaging the brainstem with fMRI is challenging ( Astafiev et al . , 2010; Beissner , 2015; Brooks et al . , 2013; Forstmann et al . , 2017 ) because this region is prone to physiological noise artifacts ( Brooks et al . , 2013 ) , and brainstem nuclei tend to be small relative to the spatial resolution of standard fMRI measurements . For example , although the adult human LC is an elongated structure of approximately 15 mm length along the rostro-caudal axis , its diameter is only a few millimeters , as assessed by high-resolution MRI ( Figure 8A and Figure 8—figure supplement 1A ) ( Keren et al . , 2009 , 2015 ) . Our study addressed these challenges by following the recommendations of Eckert and colleagues ( Eckert et al . , 2010 ) : We ( i ) delineated the LC in each brain , based on individual ( neuromelanin-sensitive ) structural MRI scans; ( ii ) performed fMRI tailored to the anatomical layout of the LC while maximizing functional signal-to-noise ratio ( SNR ) , by using an in-plane spatial resolution of 2 × 2 mm and 3 mm thick slices that were oriented perpendicular to the longitudinal extent of the LC; ( iii ) performed no spatial smoothing of these functional data; and ( iv ) rigorously removed measured cardiac and respiratory signal components , as well as residual fourth ventricle signal , which have been identified as a major source of uncertainty regarding previous fMRI work on the LC ( Astafiev et al . , 2010 ) . The resulting time-course of task-evoked fMRI responses exhibited the standard features of hemodynamic responses ( Figure 8B–E ) , and correlations to pupil responses that are largely consistent with single-unit physiology in monkeys ( see below ) . Taken together , the brainstem responses in Figures 8 and 9 likely reflect true neural signal from brainstem nuclei , rather than physiological noise . However , there is some inevitable uncertainty regarding the spatial specificity of our measurements . Due to the lower spatial resolution of fMRI images , the co-registration between functional and structural images , and the point spread of the hemodynamic response , each fMRI voxel is likely to sample activity from brain tissue neighboring the nuclei depicted as the regions of interest ( e . g . , LC ) . Consequently , we do not conclude that the LC responses in Figure 8B–D reflect the activity of noradrenergic neurons only; such a conclusion would require single-unit measurements . The focus of our conclusions instead lies on the distribution of pupil and behavioral correlations across different brainstem structures , which provides an important complement to targeted single-unit measurements . Despite the above-mentioned limitations , the overall distribution of pupil-linked brainstem responses shown in Figure 8F meaningfully follows the outlines of key candidate structures , in a fashion that is largely consistent with monkey physiology , and a previous human study on fMRI correlates of fluctuations in baseline pupil diameter ( Murphy et al . , 2014a ) . Our approach also identified so-far unknown effects . Previous monkey physiology has established significant coupling of pupil responses to responses of the LC , SC , and IC ( Joshi et al . , 2016; Varazzani et al . , 2015; Wang et al . , 2012 ) , but not yet for dopaminergic and cholinergic structures ( i . e . , SN , VTA , and BF ) . The ability to monitor all of the above brainstem regions at once enabled quantification of their trial-to-trial correlation structure , and hence isolating the contributions that were unique to each region . This revealed that ( i ) many brainstem nuclei co-fluctuated during the decision task and that ( ii ) not only the LC and SC , but also the VTA and sublenticular part of the BF each made robust and specific contributions to task-evoked pupil dilations , over and above those shared with other brainstem centers ( Figure 8H ) . Thus , the noradrenergic , cholinergic and dopaminergic systems are all phasically , and to some extent independently , recruited during challenging decision tasks , and jointly shape the concomitant changes in arousal state . Our findings provide a basis for a more comprehensive neurophysiological interpretation of results from cognitive pupillometry studies in humans . Most importantly , we also established that only a subset of those brainstem nuclei exhibiting robust correlations with pupil responses were also predictive of the trial-by-trial suppression in decision bias . The latter effect was solely accounted for by responses in the ( noradrenergic , dopaminergic , and cholinergic ) neuromodulatory nuclei with diffuse projections to cortex , but not by responses in the superior or inferior colliculi . This indicates that the phasic release of neuromodulators in the brain , possibly a combination of different neuromodulators , is key for behavioral correlates of phasic arousal identified here . A number of recent studies have characterized the relationship between tonic arousal levels ( measured through baseline pupil diameter ) and cortical state ( McGinley et al . , 2015a; Reimer et al . , 2014; Vinck et al . , 2015; Warren et al . , 2016 ) . Other studies have characterized the relationship between tonic arousal levels and behavioral performance ( McGinley et al . , 2015a; Murphy et al . , 2014b ) . The comparison between this previous work and ours points to possible differences between the functional correlates of tonic arousal levels and phasic , task-evoked changes in arousal . We found that phasic , task-evoked arousal responses were primarily linked to decision bias , at both , the algorithmic and cortical levels . By contrast , the above studies of tonic arousal levels have revealed effects on the quality of sensory cortical responses and behavioral sensitivity to sensory evidence ( McGinley et al . , 2015b ) . While we also found some evidence for non-monotonic ( inverted U-shape ) relationships between phasic arousal and sensitivity , the dominant and most consistent link was a monotonic and approximately linear relationship between phasic arousal and decision bias . Candidate factors accounting for these apparent differences between the functional correlates of phasic and tonic arousal might be the dynamics of the underlying neuromodulatory effects on cortical circuits , or the different combination of neuromodulatory systems involved . It will be instrumental to track TPR-linked changes in brainstem and cortical state in real time in future work . One account holds that the phasic arousal signals ( specifically , phasic responses of the noradrenergic LC ) are triggered by the bound-crossing in one of the cortical accumulator circuits; the resulting transient and cortex-wide neuromodulator release then facilitates the translation of the choice into a motor act ( Aston-Jones and Cohen , 2005 ) . An alternative idea ( Dayan and Yu , 2006 ) , supported by indirect evidence ( Cheadle et al . , 2014; de Gee et al . , 2014 ) , is that arousal systems are already recruited before the bound-crossing , throughout the evidence accumulation process . In line with the latter notion , we found that task-evoked pupil responses are driven most strongly by a sustained central input throughout decision formation , not only after commitment to a choice . This finding has potentially important implications for the functional role of phasic arousal in decision processing . The finding indicates that at least one of the brainstem nuclei linked to pupil responses was , likewise , activated in a sustained fashion throughout decision formation . The resulting neuromodulatory transients might alter the state of brain regions involved in decision computations as the decision unfolds , provided that the accumulation operates on timescales of seconds or longer . Because the tasks used in previous animal physiology studies of task-related LC responses involved much faster decision processes than the one studied here ( reaction times of about 0 . 5 s vs . 2 s , respectively ) , it remains unknown whether the more sustained , task-evoked responses also occur in noradrenergic neurons ( but see [Varazzani et al . , 2015] ) . Sustained responses encoding reward uncertainty have been observed in dopaminergic neurons in the VTA ( Fiorillo et al . , 2003 ) , one of the structures whose task-evoked responses predicted pupil responses . Future electrophysiological studies should determine the time course of task-related activation in the different nuclei of the brain’s arousal network during sensory-motor decisions involving protracted evidence accumulation ( Nomoto et al . , 2010 ) . One notable aspect of our findings is that the functional correlates of neuromodulatory responses were specific for a particular choice option ( see Figure 9 ) . Also in the context of learning , the interplay between pupil-linked arousal and competitive cortical circuitry has been found to translate into specific effects on cognition and behavior ( Eldar et al . , 2013 ) . A scenario consistent with our results is that phasic neuromodulator release alters the relative strength of information flow between cortical processing stages , suppressing ‘top-down’ relative to ‘bottom-up’ signals ( Friston , 2010; Gil et al . , 1997; Hsieh et al . , 2000; Kimura et al . , 1999; Kobayashi et al . , 2000 ) . In perceptual decisions like the ones studied here , early sensory cortices provide bottom-up sensory likelihood signals , while top-down signals might encode prior beliefs ( Friston , 2010; Pouget et al . , 2013 ) . Thus , through a relative suppression of ‘top-down’ signal flow , phasic arousal might reduce the weight of the prior ( reflecting subjects’ intrinsic bias ) relative to the likelihood . Specifically , in our yes-no task , the prior may have been a conservative bias for choosing ‘no’ . Reducing its weight would reduce this bias . Such an increase in the relative weight of bottom-up signals might be implemented by synaptic gain modulation through neuromodulators . This gain modulation , in turn , might depend on the precision ( inverse of uncertainty ) of incoming sensory data ( Friston , 2010; Moran et al . , 2013 ) . The above scenarios postulate a uni-directional effect of neuromodulatory transients on cortical decision computations . However , this interaction may also be bi-directional , with trial-to-trial fluctuations of cortical decision signals driving fluctuations of phasic arousal responses ( Aston-Jones and Cohen , 2005; Dayan and Yu , 2006 ) . Specifically , phasic LC responses may be driven by specific cortical regions ( e . g . , the ACC ) , which compute the ratio of the posterior probability of target presence over the ( estimated ) prior probability of target occurrence ( Dayan and Yu , 2006 ) . The resulting phasic norepinephrine release across cortex might reset cortical networks ( Bouret and Sara , 2005 ) and interrupt the ( default ) state encoding the prior ( Dayan and Yu , 2006 ) . In a yes-no task such as ours , a tendency towards the ‘no’-option may correspond to the default state for conservative subjects , and a phasic arousal signal is generated when decision-related neural activity ramps towards ‘yes’ , facilitating the transition of the entire cortical system towards that non-default state . Our findings establish that phasic task-evoked pupil responses during the formation of sensory-motor decisions reflect responses of a network of neuromodulatory brainstem centers including the noradrenergic LC . Phasic , pupil-linked arousal alters choice-encoding population signals in parietal and prefrontal association cortices . Phasic arousal in general , and neuromodulatory brainstem responses in particular , explain a dynamic reduction in decision-makers’ bias towards one particular choice . The resulting trial-to-trial variability of decision bias accounts for a significant component of the intrinsic behavioral variability: when decisions are made in the face of uncertainty , tracking phasic arousal signals may be just as important for predicting choice behavior as tracking the objective evidence gathered from the outside world . We report analyses of four independent data sets , from behavioral tasks described in the subsequent section . All subjects had normal or corrected-to-normal vision and gave written informed consent . Subjects received €15 per hour ( all visual tasks ) or research credit ( auditory task ) for their participation . The ethics committee of the Psychology Department of the University of Amsterdam approved the experiments . Fifteen healthy subjects ( 5 females; age range , 22–35 y ) participated in the main experiment of this study , entailing concurrent pupillometry and brainstem as well as cortical fMRI recordings . Here , each subject participated in several fMRI sessions: one to define retinotopically organized visual cortical areas ( 75 min ) and two sessions ( three for one subject ) for the main experiment ( about 2 hr per session ) . Three subjects were authors , and the remaining 12 subjects were naive to the purpose of the study . The results were unchanged when excluding the three authors ( see Author response , online ) and the one subject who performed three sessions ( and more trials; see section Behavioral tasks ) of the main experiment . One ( male ) subject was excluded from the analyses because the stimulus software did not receive the triggers from the MRI scanner in two sessions ( the age range remained the same ) . We also re-analyzed the 21 subjects from an existing behavioral data set , for which we had previously published different analyses ( de Gee et al . , 2014 ) ( Figure 2 , Figure 2—figure supplement 1 , Figure 4 and Figure 4—figure supplement 1 ) . In that experiment , 23 subjects had performed a yes-no visual contrast detection task with trial structure analogous to that of the fMRI experiment , enabling joint fitting of the drift diffusion model to both data sets using a hierarchical Bayesian procedure ( see below ) . To this end , we excluded the two subjects from the ( de Gee et al . , 2014 ) data set who had also participated in the current fMRI experiment , keeping the two samples independent . Finally , 24 subjects ( 20 females; age range , 19–23 y ) performed an auditory tone-in-noise detection task ( Figure 3A , B ) , and 15 subjects ( six females; age range , 23–37 y ) performed a visual random dot motion discrimination task ( Figure 3C , D ) . The sample sizes were determined based on a number of criteria: ( i ) the assessment of the behavioral correlates of TPR obtained in a previous study ( de Gee et al . , 2014 ) ; ( ii ) the need to obtain as many trials as possible from each individual ( necessary for detailed modeling of choice behavior as a function of TPR within subjects at a first level , before second-level statistics; Figures 2–4 ) ; and , in the case of fMRI , ( iii ) the need to obtain detailed retinotopic maps per individual from a separate scanning session ( Figure 5 ) , as well as robust maps of choice-specific activity by means of conjunction across two sessions of the main experiment ( Figure 7 , and Figure 7—figure supplement 1 ) . Taken together , these criteria prioritized obtaining a large amount of data ( and experimental sessions ) from each participant , which was traded off against the total number of participants . For the main experiment , MRI data were acquired on a 3T Philips Achieva XT MRI scanner using a 32-channel head coil in two types of sessions: retinotopic mapping sessions ( for defining the borders of visual cortical areas V1-V3 , see section Definition of regions of interest ) and main experimental sessions . In all sessions , cardiac cycle was monitored with a pulse oximeter attached to the left index finger , and respiratory activity was recorded with a chest belt , for physiological noise removal . Both physiological signals were recorded at a sampling rate of 496 Hz . During the main experimental sessions , EPI images were acquired in 35 slices ( thickness: 3 . 0 mm , no gaps ) oriented perpendicular to the floor of the fourth ventricle ( i . e . , perpendicular to the longitudinal extent of the locus coeruleus ( Keren et al . , 2009 ) , with the following parameters: TR = 2 s , TE = 27 . 62 ms , flip angle = 76 . 1° , SENSE acceleration factor = 3 . 0 . Images were acquired at an in-plane resolution of 2 . 0 × 2 . 06 mm and were reconstructed at a resolution of 1 . 79 × 1 . 79 mm . A structural T1 scan was acquired with an MPRAGE sequence for anatomical co-registration and cortical surface reconstruction ( voxel size: 1 × 1 × 1 mm , TR = 8 . 2 ms , TE = 3 . 73 ms , flip angle = 8° ) . An additional structural scan was acquired with a T2-weighted sequence and higher resolution than the EPI scans ( 1 × 1 × 1 . 5 mm , TR = 5114 ms , TE = 12 . 5 ms , flip angle = 90° ) to facilitate co-registration of EPI images and the high-resolution structural T1 scan . Two turbo spin echo ( TSE ) neuromelanin-sensitive structural scans were acquired for delineation of the LC ( Keren et al . , 2015 , 2009; Shibata et al . , 2007 ) , again oriented perpendicular to the floor of the fourth ventricle . The first ( partial field-of-view ) TSE scan was obtained with the following parameters: 20 slices ( 1 . 5 mm , no gaps ) , in-plane resolution: 0 . 7 × 0 . 88 ( reconstructed at: 0 . 35 × 0 . 35 mm ) , TR = 500 ms , TE = 10 ms , flip angle = 90° , covering the brainstem only . The second ( whole-brain ) TSE scan was obtained with the following parameters: 35 slices ( 3 mm , no gaps ) , in-plane resolution: 1 . 96 x 2 . 0 ( reconstructed at: 0 . 47 × 0 . 47 mm ) , TR = 500 ms , TE = 10 ms , flip angle = 90° . Finally , field maps were acquired using two separate acquisitions ( voxel size: 2 × 2 × 2 mm3 , TR = 11 ms , TE1 = 3 . 0 , TE2 = 3 . 5 , ms , flip angle = 8° ) During retinotopy sessions , EPI scans were acquired in 29 slices ( thickness: 2 . 5 mm , with 0 . 25 mm slice gaps ) with the following parameters: in-plane resolution: 2 . 5 × 2 . 58 mm ( reconstructed at 2 . 5 × 2 . 5 mm ) , TR = 1 . 5 s , TE = 27 . 62 ms , flip angle = 70° , SENSE acceleration factor = 3 . 0 . An additional structural scan was acquired with a T2-weighted sequence and higher resolution than the EPI scans ( 1 . 25 × 1 . 25 × 1 . 25 mm with 0 . 12 mm slice gaps , TR = 8390 ms , TE = 100 ms , flip angle = 90° ) to facilitate co-registration of EPI images and the high-resolution structural T1 scan used for cortical surface reconstruction ( see above ) . Concurrently with the fMRI recordings , the left eye’s pupil was tracked ( via the mirror attached to the head coil ) at 1000 Hz with an average spatial resolution of 15 to 30 min arc , using an EyeLink 1000 Long Range Mount ( SR Research , Osgoode , Ontario , Canada ) . The MRI-compatible ( non-ferromagnetic ) eye tracker was placed outside the scanner bore , and it was calibrated once at the start of each scanning session . The purely behavioral experiments were conducted in a psychophysics laboratory . Here , the left eye’s pupil was also tracked at 1000 Hz with an average spatial resolution of 15 to 30 min arc , using the same EyeLink 1000 system ( SR Research , Osgoode , Ontario , Canada ) . The first trial from each block and trials in which subjects failed to respond within the time limit of 3 . 5 s ( see section Stimuli , task and procedure ) were excluded from all analyses . RT was defined as the time from decision interval onset ( cued by tone ) until the button press . In a model-free analysis , we computed the fraction of ‘yes’-choices separately for two TPR bins ( Figure 2 , Figure 2—figure supplement 1 , and Figure 3 . To ensure that each TPR bin consisted of the same number of signal+noise and noise trials , we ( i ) sorted all trials of a subject into of four ‘cells’ defined by the factors TPR ( low and high ) and stimulus ( signal+noise and noise ) , ( ii ) determined the lowest trial count across the four cells , ( iii ) randomly sampled the same number of trials ( without replacement ) from the remaining cells , and ( iv ) computed the fraction of ‘yes’-choices separately for the two TPR bins . We then repeated this procedure 1000 times and averaged the results across all repetitions . The fraction of non-preferred choices ( Figure 3C ) was computed in the same way , with the exception that the non-preferred choice was defined as the choice opposite to the subject’s overall bias ( towards up or down ) calculated across all trials ( i . e . , irrespective of TPR ) . We then modeled the effects of phasic arousal ( as indexed by TPR ) on choice behavior using two approaches , which yielded converging results . MRI data were analyzed using custom-made software written in Python ( de Gee , 2017a , https://github . com/jwdegee/2017_eLife; a copy is archived at https://github . com/elifesciences-publications/2017_eLife ) . A number of processing steps relied on FSL ( RRID:SCR_002823 , Smith et al . , 2004 ) and FreeSurfer ( RRID:SCR_001847 , Dale et al . , 1999; Fischl et al . , 1999 ) . We used nonparametric paired permutation tests to test for significant differences between behavioral estimates ( Figure 2 , Figure 2—figure supplement 1 , Figures 3 and 4 , and Figure 4—figure supplement 2 ) , task-evoked fMRI responses ( Figure 7 , Figure 7—figure supplement 1 , Figure 8 , and Figure 8—figure supplement 1 ) , stimulus- / choice-predictive indices ( Figure 5—figure supplement 1 , Figure 7 , and Figure 7—figure supplement 1 ) , and regression beta weights / coefficients ( Figures 1 , 2 , Figure 2—figure supplement 1 , Figures 3 , 7 , 8 , Figure 8—figure supplement 1 , Figure 9 , and Figure 9—figure supplement 1 ) from different trial categories , or to test them against zero ( against 0 . 5 in the case of stimulus- / choice-predictive indices ) . Statistical tests were performed at the group level , using the individual subjects’ mean parameters as observations . For each comparison , we randomly permuted the labels of the observations ( e . g . , the regressor label of the beta estimates ) , and recalculated the difference between the two group means ( 10 , 000 permutations ) . The p-value was the fraction of permutations that exceeded the observed difference between the means . We used nonparametric permutation tests within the FSL Randomise implementation to test cluster-corrected task-evoked fMRI responses against 0 ( Figure 5 ) , linear regression coefficients against 0 ( Figure 8 ) , logistic regression beta weights against 0 . 5 ( Figure 7 , Figure 7—figure supplement 1 ) , and searchlight classification precision scores against 0 . 5 ( Figure 7 , Figure 7—figure supplement 1 ) . Randomise implemented 10 , 000 randomly generated permutations of the data to perform a Monte Carlo-style permutation test . This procedure was robust with respect to inflated false-positive rates ( Eklund et al . , 2016 ) . In the majority of cases , we used a cluster correction threshold of p<0 . 01 . For the logistic regression of binary choice against lateralized task-evoked fMRI responses ( Figure 7 , Figure 7—figure supplement 1 ) , and in case of the brainstem TPR correlation ( Figure 8F; accounting for the comparably low sensitivity of fMRI measurements in the brainstem ) , we used a cluster correction threshold of p<0 . 05 . The analyses of fMRI signals focused on a number of ROIs , which were defined in each individual brain using a variety of criteria described in this section . The data are publicly available on Figshare ( de Gee et al . , 2017a , https://doi . org/10 . 6084/m9 . figshare . 4806562; de Gee et al . , 2017b , https://doi . org/10 . 6084/m9 . figshare . 4806559 ) . Analysis scripts are publicly available on Github ( de Gee , 2017a , https://github . com/jwdegee/2017_eLife ( with a copy archived at https://github . com/elifesciences-publications/2017_eLife ) ; de Gee , 2017b , https://github . com/jwdegee/2014_PNAS ) .	When asked to make repeated decisions we will often choose differently each time even when we are given the same information to inform our choice . A stock trader , for example , will typically be more inclined to buy on some days and sell on others even if the financial markets remain unchanged . Fluctuations in the brain’s level of alertness or excitability , otherwise known as its arousal , are thought to contribute to this variability in decision-making . An area at the base of the brain called the brainstem – and in particular one of its subregions , the locus coeruleus – helps shape arousal levels by releasing chemicals called neuromodulators . For reasons that remain unknown , activation of the locus coeruleus also causes the pupil of the eye to suddenly increase in size . Now , de Gee et al . have exploited this link to unravel how changes in brain arousal lead to systematic changes in decision-making . Volunteers were asked to judge whether a faint pattern was embedded in flickering noise on a computer screen , and to report their judgment by pressing one of two buttons to indicate “yes” or “no” . Although the decision was comparatively simple , it did involve evaluating changing information over time before making a choice – like when considering the stock market . As the volunteers performed the task , de Gee et al . measured their brain activity and the size of their pupils . Most of the volunteers had a tendency to respond “no” even when the pattern was present . However , whenever their locus coeruleus was particularly active , and their pupils increased in size , their decision process was changed so that this unhelpful choice bias decreased . This suggests that by boosting arousal , the locus coeruleus reduces existing biases in our decision-making . Varying levels of locus coeruleus activity may thus explain why we can reach different conclusions when considering the same information on multiple occasions . The next challenge is to identify what it is about the decision-making process that activates the locus coeruleus on some occasions but not others .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2017	Dynamic modulation of decision biases by brainstem arousal systems
Optogenetic techniques enable precise excitation and inhibition of firing in specified neuronal populations and artifact-free recording of firing activity . Several studies have suggested that optical stimulation provides the precision and dynamic range requisite for closed-loop neuronal control , but no approach yet permits feedback control of neuronal firing . Here we present the ‘optoclamp’ , a feedback control technology that provides continuous , real-time adjustments of bidirectional optical stimulation in order to lock spiking activity at specified targets over timescales ranging from seconds to days . We demonstrate how this system can be used to decouple neuronal firing levels from ongoing changes in network excitability due to multi-hour periods of glutamatergic or GABAergic neurotransmission blockade in vitro as well as impinging vibrissal sensory drive in vivo . This technology enables continuous , precise optical control of firing in neuronal populations in order to disentangle causally related variables of circuit activation in a physiologically and ethologically relevant manner . Feedback is essential for controlling complicated systems . It can be used to define system dynamics and decouple causal interactions . Recently , a diverse set of specialized techniques that employ elements of feedback control have emerged for studying adaptation in neuronal micro-circuits ( Ahrens et al . , 2012 ) , using electrical stimulation to control spike latency ( Wallach et al . , 2011 ) and firing levels ( Wagenaar et al . , 2005; Newman et al . , 2013 ) , improving brain-computer interfaces ( Velliste et al . , 2008; Cunningham et al . , 2011 ) , inducing motor plasticity ( Jackson et al . , 2006 ) , and controlling intracellular firing rate ( Miranda-Domínguez et al . , 2010 ) . True feedback control has been most broadly applied in neuroscience research using the voltage clamp , which is used to decouple the membrane potential from causally related voltage-dependent conductances . This approach has provided the foundation for our understanding intracellular electrochemical signaling and demands extension to other features of neural activity . The neuronal firing rate is a basic feature of codes for motor action ( Georgopoulos et al . , 1988 ) , vision ( Steinmetz et al . , 1987 ) , and place ( Zhang et al . , 1998 ) . Changes in thalamic firing tone can alter cortical receptive fields ( Stoelzel et al . , 2009 ) and the nature of temporal coding within the thalamocortical pathways ( Wang et al . , 2010 ) . Long-term changes in network firing levels can trigger a multitude of homeostatic processes that regulate circuit excitability and stability ( Turrigiano et al . , 1998; Corner et al . , 2002; Turrigiano , 2011 ) . A system analogous to the voltage clamp , but capable of precise , bidirectional control of circuit firing levels could be used to identify the independent role of firing rate in downstream processes in spite of changes to causally-related variables . For instance , long-term changes in population firing have long been thought to initiate compensatory homeostatic mechanisms , but a causal link has remained elusive . Direct bidirectional control over population firing rates would allow us to test this hypothesis directly . Optogenetic tools are routinely used to provide genetically specified , millisecond time-scale stimulation or suppression of neural activity with light ( Mattis et al . , 2011 ) during simultaneous , artifact-free electrical recording . The ability to simultaneously perturb and measure neural activity form the basic elements of a feedback loop , which can be exploited to control firing . Although several studies have presented closed-loop optogenetic stimulation techniques ( Leifer et al . , 2011; Stirman et al . , 2011; Paz et al . , 2012; Krook-Magnuson et al . , 2013; O'Connor et al . , 2013; Siegle and Wilson , 2014 ) , no approach yet permits feedback control of neuronal firing levels . Here we describe and quantify optogenetic feedback control ( ‘optoclamping’ ) , a method enabling continuous , bi-directional , closed-loop firing rate control both in vitro and in vivo . We show that the optoclamp allows precise control of population firing levels in dissociated cortical networks over timescales ranging from seconds to days . We characterize the effects of different control schemes , algorithm parameters , and optical waveforms on the precision of feedback control and higher-order statistics of population activity . We show that firing rate control can be achieved over many hours and used to restore pre-drug firing levels during chronic blockade of excitatory or inhibitory synaptic transmission . Using this approach , we decouple the effects of suppressed neurotransmission from the indirect effects on network firing , and find that changes in firing levels are not required to induce homeostatic alterations in network excitability . Finally , we show how optogenetic feedback can be used to control firing activity in vibrissal somatosensory thalamus of rats . We find that background thalamic activity levels can be controlled during ongoing sensory input without corrupting the fine-scale temporal structure of whisker-evoked spike trains . Together , our results demonstrate that the optoclamp is an effective general tool for decoupling neural firing from other variables that would normally affect network excitability . To characterize the range of evoked firing levels that could be achieved using multimodal optical stimulation in dissociated cortical networks , we stimulated excitatory cells expressing channelrhodopsin-2 ( H134R ) ( ChR2R ) ( Nagel et al . , 2005 ) and enhanced halorhodopsin-3 . 0 ( eNpHR3 . 0 ) ( Gradinaru et al . , 2008 ) while recording spiking activity using a 59-channel microelectrode array ( MEA; Figure 1A ) ( Newman et al . , 2013 ) . For ChR2R activation , a single dimensionless excitatory control variable , UC , simultaneously modulated the pulse width , frequency , and intensity of homogeneous 465 nm stimuli ( Equations 7–9 , ‘Materials and methods’; Figure 1—figure supplement 1 and Figure 1—figure supplement 2 ) . For eNpHR3 . 0 activation , we defined a second control variable , UH , proportional to the continuous intensity of a 590 nm LED ( Equation 10 ) . We applied UC and UH ranging from 0 to 1 , for randomly interleaved , 60-s stimulation epochs ( 2 cultures , 50 trials/culture ) . Evoked population firing rates were positively correlated with UC and negatively correlated with UH ( Figure 1B ) . UC-evoked firing levels saturated at approximately 12 . 5 Hz/unit corresponding to UC = 0 . 47 ( freq . = 14 . 7 Hz , pulse-width: 2 . 4 ms , power at 465 nm: 6 . 9 mW·mm−2 ) . Firing rate suppression saturated at 0 . 04 Hz/unit corresponding to UH = 0 . 15 ( power at 590 nm: 1 . 8 mW mm−2 ) . The monotonic relationships between UC and UH and network firing levels indicated their applicability as closed-loop control signals . 10 . 7554/eLife . 07192 . 003Figure 1 . Optogenetic modulation of network activity in vitro . ( A ) Multichannel recording , processing , and stimulation system . A 59-channel amplifier detects spiking activity produced by cells close to electrodes ( white outline ) . Neurons express ChR2R-mCherry ( red ) and eNpHR3 . 0 under the CaMKllα promoter ( green: immunoreactivity for CaMKllα; scalebar: 20 μm ) . Electrode voltages are processed in real-time and can be used to update an LED stimulator feeding a homogeneous Köhler illuminator below the MEA . An optical feedback circuit ( blue line ) ensures distortion free blue stimulus waveforms . ( B ) Time-averaged firing rates of two cultures ( △ and ○ ) in response to 60-s applications of randomly valued UC and UH during different forms of synaptic blockade . Black horizontal bars indicate the cultures' spontaneous firing levels . Blue and yellow symbols indicate the mean firing level over a single trial at the corresponding value of UC and UH , respectively . The dotted lines are least-squares fits used to estimate the UC and UH saturation points provided in the text . ( C ) PSTH of individual units ( grey scale ) and the unit-averaged PTSH in response to 1 millisecond 5 mW mm−2 blue light pulses for each drug condition . Scale bars , 50 Hz/unit . ( D ) Raster plots for 87 detected units during 60-s applications of UC and UH . The firing rate evoked by stimulation using a particular value of UC and UH decays over the course of the protocol . ( E ) The trial-averaged firing rate profiles for the stimulus levels presented in ( D ) across drug conditions . Black horizontal lines indicate the 60 s stimulation period . Dotted lines indicate spontaneous firing levels . Note the log scale . In ( C ) and ( E ) , line colors indicate the drug conditions above each panel in ( B ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 00310 . 7554/eLife . 07192 . 004Figure 1—figure supplement 1 . Optical characteristics of the in vitro stimulator and in vivo fiber . ( A , B ) Optical power density at the culture as a function of the reference voltage sent to the LED driver from NeuroRighter's digital to analog converters for the in vitro stimulator . ( A ) For the blue ( 465 nm ) LED , we used optical feedback to completely linearize the relationship between the reference voltage and optical power ( Figure 1A in the main text; https://github . com/jonnew/cyclops; ‘Materials and methods’ ) . This enabled the delivery of complex , distortion-free stimulus waveforms , such as sinusoidal and triangle waves ( Figure 4 ) . ( B ) For the yellow LEDs ( 590 nm ) we used current regulation mode to control optical intensity . ( C ) Colormap indicating the uniform spatial light intensity profile projected onto the MEA surface using a Köhler illuminator ( Figure 1A in the main text ) . The black lines show cross sectional intensity profiles through the horizontal and vertical center of the illuminated region ( white lines ) . The MEA image is superimposed on the profile to provide an indication of scale . ( D ) Power density at the tip of the three 125 μm diameter fibers used for in vivo stimulation as a function of a reference voltage provided by the real-time controller . Differences in power across fibers are due to disparities in fiber coupling efficiency along with circuit tuning that was performed to ensure linearity over the reference voltage range prior to each experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 00410 . 7554/eLife . 07192 . 005Figure 1—figure supplement 2 . Expression time course of AAV2-CaMKIIα-ChR2 ( H134R ) -mCherry . ( A ) Phase-contrast and confocal imaging of a single region of interest ( ROI ) , containing 4 microelectrodes , performed over the first 26 days in vitro ( DIV ) . Cultures were transduced at 1 DIV . ( B ) To quantify the expression time course , three or four ROIs were imaged in three cultures over the first 26 DIV . For each ROI and DIV , the integrated intensity of 600–690 nm light through the emission filter was calculated and then normalized by the maximal integrated intensity over the 26 day imaging period ( black dots ) . A sigmoid of the form a1+exp ( bx+c ) was fit to the resulting data using nonlinear regression ( r2 = 0 . 98; MATLAB curve-fit toolbox ) . The half maximal expression point occurred at ∼12 DIV . ( C ) The time-course of ChR2R function was measured by recording the evoked network spiking response in three networks over the first 16 DIV . Each experiment applied 140 trains of 30 s stimulation periods , each consisting of a random combination of pulse frequency ( 1 , 5 , 10 , 20 , 30 , 40 , and 50 Hz ) , pulse width ( 0 . 1 , 0 . 5 , 1 . 0 , and 5 . 0 ms ) , and 465 nm LED intensity ( 0 . 2 , 0 . 4 , 0 . 6 , 0 . 8 , and 1 . 0 Amps; current regulation was used because these experiments were performed prior to the creation of the linear LED driver shown in Figure 1—figure supplement 1 ) . Stimulus bouts were separated by 30 s and were applied in random order . Colored lines show the average neuronal firing rate , across all three networks , at a set value for particular stimulation parameters . For example , the average network firing rate , 〈f[t]〉 , for a stimulus frequency of 20 Hz is . 〈f[t]〉=〈[Firing Rate ( Stim . Freq , Pulse Width , ILED ) \|Stim . Freq=20Hz]〉 , where 〈·〉indicates the average over time and units . The legend indicates the number of units used to produce each line for each DIV . The monotonicity of these functions across development ( except for high stimulus frequencies ) indicates the achievable evoked firing levels at different developmental points and the potential of each of the stimulus parameters to be effective control inputs . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 005 We also tested the robustness of UC and UH for modulating firing levels during blockade of AMPAergic , NMDAergic , or GABAergic transmission using CNQX , AP5 , or bicuculline , respectively ( ‘Materials and methods’ ) . Synaptic blockade strongly affected mean spontaneous firing levels ( CNQX: −74 . 2% , AP5: −66 . 6% , bicuculine: +357% ) and network-level signal propagation ( Figure 1C ) . In spite of this , firing in CNQX- and AP5-treated networks could be driven over the same dynamic range as the drug free condition ( Figure 1B; CNQX: 0 . 057 to 13 . 3 Hz/unit , AP5: 0 . 088 to 12 . 6 Hz/unit ) , indicating that optogenetic input could compensate for depressed excitatory transmission . Bicuculline greatly reduced the dynamic range of evoked network activity indicating a loss of reliable activity modulation ( Figure 1B; 0 . 095 to 5 . 1 Hz/unit ) . Although time-averaged firing levels were monotonically related to UC and UH ( Figure 1B ) , open-loop stimuli lost effectiveness throughout each 60-s trial causing significant second-to-second drift in evoked activity levels ( Figure 1D , E ) . This effect was consistent across synaptic blocker conditions . Decreases in stimulus efficacy using ChR2R were likely due to network adaptation , rather than changes in ChR2R-mediated photocurrents ( Mattis et al . , 2011 ) . Aside from network adaptation , deceased efficacy of eNpHR3 . 0-mediated firing suppression likely resulted from a loss of synaptic inhibition due to intracellular Cl− accumulation ( Raimondo et al . , 2012 ) and decreased outward photocurrents due to pump desensitization ( Mattis et al . , 2011 ) . We developed a proportional-integral ( PI ) feedback controller to clamp network population activity in dissociated cortical networks in the face of uncontrolled fluctuations in neuronal excitability and opsin dynamics ( Equations 1–6 ) . The PI algorithm updated UC and UH in real-time in order to minimize the difference ( ‘error’ ) between the measured network firing rate and a target level ( ‘Materials and methods’ ) . We tested the controller using 60-s , randomly-ordered targets ranging from 0–10 Hz/unit ( Figure 2A; 7 cultures , 11 trials/culture ) , and successful control was achieved in over 90% of trials ( Figure 2B; 71/77 trials; mean RMS tracking error 0 . 14 ± 0 . 091 Hz/unit ) . Tracking error increased with target rate , and occasionally the stimulator saturated before the trial was complete ( e . g . , Figure 2A , grey line ) . Control settling time varied across preparations and was not correlated with the target firing rate ( mean ± SD , 7 . 83 ± 6 . 07 s; Figure 2—figure supplement 1 ) . Although we generally used discrete steps in the target firing rate , the controller was also capable of tracking continuously varying targets ( Figure 2—figure supplement 2 ) . 10 . 7554/eLife . 07192 . 006Figure 2 . PI optical feedback allows precise control of network firing levels over 1-min epochs . ( A ) ( Top ) Network firing rate during different trials ( colors ) . Target firing levels ( black lines ) ranged from 0 to 10 Hz and were applied in random order . ( Bottom ) Control signals , UC and UH , required during closed-loop control . For this network , the controller saturated while attempting to clamp network firing at 10 Hz/unit , resulting in a control failure ( grey trace ) . ( B ) Time-averaged firing rates for seven different networks during PI control ( left axis , colors ) . The dotted line is identity representing perfect closed-loop control . The spontaneous firing rates of each network are indicated by black arrows . The RMS error between the measured and target firing for each network is shown as a function of the target rate ( right axis , black markers ) . A trial was considered successful if the RMS error between the target and achieved firing rate was less than 0 . 5 Hz/unit ( red line ) . ( C ) Time- and culture-averaged successful control signals vs target firing rates . The shaded areas indicate the minimum and maximum value across networks . All temporal averages in this figure were taken over the final 30 s of the control epoch . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 00610 . 7554/eLife . 07192 . 007Figure 2—figure supplement 1 . PI settling time in vitro . ( A ) The settling time was defined as the time-point at which the smoothed firing rate ( grey line; LOWESS with a 2 . 5 s smoothing window and a tri-cube weight function ) entered and stayed within the boundaries defined by the target rate ± 0 . 25 Hz/unit ( dotted red lines ) . ( B ) The settling time did not have a strong relationship with the target firing rate and was variable across cultures ( mean ± SD: 7 . 8 ± 6 . 0 s , 7 cultures ) . Colors denote different cultures and the dotted black line indicates the mean across cultures for each target rate . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 00710 . 7554/eLife . 07192 . 008Figure 2—figure supplement 2 . PI feedback permits control during a continuously changing target rate . ( A ) Firing rate of detected units . Each row displays the firing rate of a particular unit , encoded by the grey-scale to the right ( 1 s bins ) . ( B ) The average firing rate of the network ( black ) and the target firing rate ( red ) and the error signal during different control periods . The target firing rate was moved up and down manually by the experimenter via mouse clicks on NeuroRighter's graphical interface for the duration of the 5-min control epoch . ( C ) Optical control signals delivered by the PI controller during the control epoch . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 00810 . 7554/eLife . 07192 . 009Figure 2—figure supplement 3 . Effects of proportional gain ( K ) on closed-loop stability of PI control in vitro . ( A ) The network firing rate is shown during control using different values of K ( Ti = 1 . 0 s and τ = 2 . 5 s ) . Different values of K were used in random order and correspond to the colors shown in ( C ) . Firing rates were calculated using a bin size of 1 s instead of the exponential moving average used by controller ( Equation 1 ) to facilitate comparison with Figure 2—figure supplement 5 since the firing-rate filter time constant is manipulated in that experiment . ( B ) The median network firing rate ( black lines ) is shown with the interquartile range ( colored bars ) taken over each firing rate time series . Values of K greater than ∼1 cause the controller to become unstable , leading to large firing rate variance . ( C ) Firing rasters for individual units along with corresponding control signals for all values of K tested . Ineffective values of K are printed in red . ( D ) Mean control signals ± standard deviation over time . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 00910 . 7554/eLife . 07192 . 010Figure 2—figure supplement 4 . Effects of integral time-constant ( Ti ) on closed-loop stability and accuracy of PI control in vitro . ( A ) The network firing rate is shown during control using different values of Ti ( K = 0 . 1 and τ = 2 . 5 s ) . Different values of Ti were used in random order and correspond to the colors shown in ( C ) . Firing rates were calculated using a bin size of 1 s instead of the exponential moving average used by controller ( Equation 1 ) to facilitate comparison with Figure 2—figure supplement 5 since the firing-rate filter time constant is manipulated in that experiment . ( B ) The median network firing rate ( black lines ) is shown with the interquartile range ( colored bars ) taken over each firing rate time series . Values of Ti less than ∼1 s caused the controller to become unstable , leading to large firing rate variance . Values of Ti greater than ∼25 s introduced an offset . ( C ) Firing rasters for individual units along with corresponding control signals for all values of Ti tested . Non-functional values of Ti are printed in red . ( D ) Mean control signals ± standard deviation over time . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 01010 . 7554/eLife . 07192 . 011Figure 2—figure supplement 5 . Effects of the firing-rate estimation time-constant ( τ ) on closed-loop stability of PI control in vitro . ( A ) The network firing rate is shown during control using different values of τ ( K = 0 . 1 and Ti = 1 . 0 s ) . Different values of τ were used in random order and correspond to the colors shown in ( C ) . Firing rates were calculated using a bin size of 1 s so that different firing rate time-constants could be compared using a common time-scale . ( B ) The median network firing rate ( black lines ) is shown with the interquartile range ( colored bars ) taken over each firing rate time series . Values of τ less than ∼0 . 5 s caused the controller to become unstable , leading to large firing rate variance . Values of Ti greater than ∼10 s introduced large , slow oscillations that caused significant target overshoot . ( C ) Firing rasters for individual units along with corresponding control signals for all values of τ tested . Non-functional values of τ are printed in red . ( D ) Mean control signals ± standard deviation over time . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 01110 . 7554/eLife . 07192 . 012Figure 2—figure supplement 6 . PI control of firing levels during synaptic blockade in vitro . ( A ) Network firing rate for different trials ( colors ) in the presence of various competitive neurotransmitter receptor antagonists ( ○ no drug , △20 μM CNQX , □50 μM AP5 , ▽20 μM bicuculline ) . Trials were presented in a random sequence that was repeated across drug conditions . ( B ) Time-averaged network firing rates during PI control , for each drug tested . Spontaneous firing rates of the network during each pharmacological condition are represented by black symbols to the left of the ordinate axis . The dotted line represents perfect clamping of mean activity to the target rate . ( C ) RMS tracking error between the measured and target firing for each pharmacological condition as a function of the target rate . Control failure occurred for each point above the red line , which was defined as RMS tracking error > 0 . 5 Hz/unit . CNQX , which blocks AMPARs , destabilized the network somewhat , likely though the removal of recurrent inhibition , and resulted in a control failure for the 2 Hz/unit target . AP5 , which blocks NMDARs , reduced the dynamic range of evoked activity and slowed the rise-time of the population response ( compare to control onset in ( A ) ) . Bicuculline , which blocks GABAARs , strongly destabilized network activity and resulted in control failure for all but two target rates: 0 and 2 Hz/unit . In the presence of bicuculline , average network firing levels could not be pushed higher than ∼2 Hz/unit . Interestingly , the only successful non-zero target rate was the one closest to the spontaneous network firing rate in the presence of bicuculline . ( D ) Time-averaged control signals and ( E ) settling times ( Figure 2—figure supplement 1 ) vs target rate for each pharmacological condition . All data in this figure are from a single culture . All temporal averages in this figure were taken over the final 30 s of the control epoch . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 012 We explored the parametic sensitivity of the controller by changing the value of either the proportional gain ( K ) , the integral-error time constant ( Ti ) , or the firing rate filter time constant ( τ ) , while the remaining two parameters were held at their nominal values ( K = 0 . 1 , Ti = 1 s , τ = 2 . 5 s ) . Functional control was achieved at K < 1 . 0 , 1 . 0 s < Ti <10 s , and 0 . 5 s <τ <10 s . Outside of these bounds , closed-loop dynamics were unstable and/or firing levels exhibited significant target offsets ( Figure 2—figure supplement 3 through Figure 2—figure supplement 5 ) . The control signals , UC and UH , were highly variable across networks , even for the same target rate ( Figure 2C ) . This variability likely reflects heterogeneous network excitability , opsin expression , synaptic connectivity , and developmental processes ( Wagenaar et al . , 2006 ) and suggests that the controller continuously adapts to ongoing changes in network excitability in order to precisely clamp firing levels . To test this possibility , we delivered open-loop replay of successful closed-loop control signals and found they were incapable of controlling firing ( Figure 4 , Figure 2 cultures ) . This demonstrates the necessity of real-time feedback to achieve precise control of neural firing , even for same target rate within single preparations . Next , we used the PI controller to clamp network firing levels to targets between 0 and 10 Hz/unit during blockade of AMPAergic and NMDAergic transmission ( Figure 2—figure supplement 6A; ‘Materials and methods’ ) . Control performance was equivalent to the drug-free case and control failure was isolated to high target rates ( 9–10 Hz/unit ) due to stimulus saturation ( Figure 2—figure supplement 6B–E ) . We also tested PI control during blockade of GABAergic transmission and found that reliable control was not possible ( Figure 2—figure supplement 6 ) . This is likely due to the destabilizing effects of bicuculline , which caused the controller to oscillate . This result is consistent with studies indicating a general role of reduced inhibition in diseases of circuit instability such as temporal lobe epilepsy ( Kobayashi and Buckmaster , 2003 ) and Dravet syndrome ( Dutton et al . , 2013 ) . To demonstrate PI control over more extended time periods , we clamped firing at a set of randomly selected , 5-min long target firing levels which switched without downtime ( 50 min . total clamp time; Figure 3 ) . During each 5-min step , the controller made rapid , second-to-second adjustments in stimulus intensity to maintain the instantaneous target rate , while slower changes in stimulus intensity occurred over minutes . Minute-to-minute changes in the control signal intensity likely reflect short-term synaptic depression and changes in cellular excitability that accrued over each control epoch . Therefore , the control signal can be used as a readout of network excitability , analogous to how injected current from a voltage clamp amplifier can be used to asses cellular excitability . 10 . 7554/eLife . 07192 . 013Figure 3 . PI feedback control to track a changing target rate . ( A ) Firing rate of detected units . Each row displays the firing rate of a particular unit , encoded by the grey-scale to the right ( 1 s bins ) . ( B ) The average firing rate of the network ( black ) , the target firing rate ( red ) , and the error signal during different control periods . The pre-control firing rate is indicated by a dotted line . ( C ) Optical control signals delivered by the PI controller during the control epoch . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 013 To test how different stimulus waveforms affected network response correlations during PI control , we mapped UC onto triangular , sinusoidal , pseudorandom binary sequence ( PRBS ) , and direct intensity modulation inputs ( Tchumatchenko et al . , 2013 ) . Each permitted successful closed-loop control , but notably , the choice of stimulus waveform significantly affected the peak firing correlation ( P = 6 . 5 × 10−24 ) and synchrony ( P = 10−22 ) of the population response ( Figure 4 ) . The square pulse trains typically used for ChR2-based stimulation ( Mattis et al . , 2011 ) resulted in periodic , highly correlated population firing ( Figure 4A ) . Compared to square pulses , sinusoidal stimuli decreased peak unit-to-unit firing correlations ( −10 . 6% , p = 0 . 028 ) , but did not affect synchrony . PRBS stimuli reduced peak correlations ( −24 . 0% , p = 6 . 8 × 10−13 ) and firing synchrony ( −17 . 9% , p = 4 . 6 × 10−8 ) . Continuous light modulation increased both correlations ( +21 . 3% , p = 4 . 3 × 10−5 ) and synchrony ( +41 . 4% , p = 2 . 5 × 10−9 ) . Triangular pulses did not affect correlation or synchrony . Importantly , while periodic input signals produced a periodic response , PBRS input and continuous intensity modulation resulted in non-periodic firing . Therefore , altering the temporal characteristics of excitatory stimulus waveforms resulted in remarkably different higher-order firing statistics while still enabling successful PI control . This emphasizes the fact that , in its current form , the optoclamp only controls population firing levels and leaves more complex features of neural activity unconstrained and subject to the influence of network connectivity , network dynamics , and the nature of the stimulus signal . 10 . 7554/eLife . 07192 . 014Figure 4 . A diverse set of optical input signals can be used to clamp network firing rate , and accurate firing rate control requires closed-loop stimulation . ( A ) Left , network firing rates during closed-loop control ( black ) and during replay of closed-loop control signals in open-loop ( grey ) , along with corresponding control inputs UC ( blue ) and UH ( yellow ) . These time series data were derived from culture 1 . Note that in all instances , open-loop replay of input signals recorded from previous successful closed-loop control failed to clamp firing levels and resulted in erratic activity levels over the control epoch . Middle , time-averaged firing rates for both cultures during closed-loop control ( black ) and during subsequent replay of control signals in open-loop ( grey ) . Right , average unit-to-unit cross-correlogram for both cultures ( top , bin = 5 ms ) and unit-to-unit synchronization structure for culture 1 ( bottom ) during optogenetic feedback control . Synchronization was defined as , Synci , j=Ncc ( Ni2+Nj2 ) /2 , where Ncc is number of correlated events within ± 10 ms , and Ni and Nj are the number of spikes from units i and j used to calculated the cross-correlogram . ( B ) Same as ( A ) for triangle optical stimuli modulated according to . Pulse freq . 465 nm=10 Hz , Rising slope . 465 nm=0 . 22 mWms·mm2 , Peak power 465 nm=13 . 4UC mWmm2 . ( C ) Same as ( A ) for sinusoidal optical stimuli modulated according to . Optical power 465 nm=13 . 4UCsin ( 2π10t ) mWmm2 . ( D ) Same as ( A ) for pseudo-random binary sequence of optical pulses modulated according to . Update freq . 465 nm=150 Hz , Peak power 465 nm=13 . 4UC mWmm2 . ( E ) Same as ( A ) for continuous optical stimuli modulated according to . Optical power 465 nm=13 . 4UC mWmm2 . Each protocol was performed in the same culture . Periodic stimuli ( panels A–C ) were applied at 10 Hz so that the periodicity of evoked activity would be apparent in the correlation functions . In all cases , the 590 nm light was modulated according the standard control scheme ( Equation 10 of ‘Materials and methods’ ) . Note that each input type evokes a unique correlation and synchronization structure while still achieving accurate firing rate control . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 014 Planar MEAs allow stable extracellular recordings over long timescales . Previous studies have used MEAs to monitor spiking activity over many hours and correlated recorded activity with homeostatic or developmental changes in network properties ( Wagenaar et al . , 2006; Minerbi et al . , 2009 ) . Compared to simply measuring spiking activity , controlling mean firing rates over long timescales would enable investigations of causal , rather than correlative , relationships between spiking and long-term , activity-dependent processes . To this end , we developed an ‘on-off’ controller to clamp network activity across many hours . To clamp firing rates above spontaneous levels , this controller delivered a blue light pulse ( 5 ms , 13 . 4 mW mm−2 , 465 nm ) when the integral error exceeded zero ( Equation 12 ) . We tested on-off control by clamping network firing rates within a single culture to seven elevated setpoints ( ranging from 0 . 75 to 6 Hz/unit ) for 12-hr epochs ( Figure 5 ) . The on-off controller successfully held firing rates at six of these targets , saturating only at 6 Hz/unit after ∼7 hr ( Figure 5A , B ) . Notably , the stimulation frequency required to maintain each target firing rate was better correlated to the difference between the target and the pre-clamp firing rate than the target rate alone ( R2 = 0 . 63 vs 0 . 52; Figure 5—figure supplement 1 ) . This indicates that alterations in network excitability across different experimental days were reflected in the intensity of the control inputs ( Figure 5C , D; Figure 5—figure supplement 1A ) and suggests that optogenetic feedback control can be used to study changes in neuronal network dynamics over developmental timescales . 10 . 7554/eLife . 07192 . 015Figure 5 . On-off feedback control of population firing rate over 12-hr epochs . ( All data presented in this figure were obtained from a single culture over the course of ∼3 weeks . ) ( A ) Firing rates of detected units during 12-hr control periods are represented using the grey-scale to the right . At time 0 , the on-off controller was engaged and the average network rate was clamped firing to the target rate indicated to the left of each chart . The day and hour of each protocol , relative to the first experiment , is shown to the right . Units are sorted by their mean firing rate during the 3-hr period prior to closed-loop control . ( B ) The network firing rate during each control epoch ( 5-min bins ) . The color map corresponds to the target rates shown in ( A ) . ( C ) Closed-loop stimulation frequency over the course of the 12-hr clamp . For a target rate of 6 Hz/unit , the controller saturated at the maximal frequency of 10 Hz at around 7 hr into the control epoch , and target tracking failed as a result . ( D ) Time- and unit-averaged firing rates ( colors , left axis ) and control signal ( black , right axis ) across each 12-hr clamping period . The dotted line is identity . ( E ) The average cross-correlation function between 30 randomly selected units during on-off or PI control are plotted for each target rate . The correlation function for spontaneous activity is shown in black . When low stimulation frequencies were required , the unimodal correlation structure of spontaneous activity was preserved using on-off control . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 01510 . 7554/eLife . 07192 . 016Figure 5—figure supplement 1 . Characteristics of on-off control over weeks in vitro . ( A ) Spontaneous , pre-stimulation firing rates over the course of 7 long-term excitatory on-off control experiments with a single culture . Spontaneous excitability changes smoothly across the ∼20 days during which 12-hr firing rate control experiments were conducted . ( B ) The average stimulation frequency required to achieve firing rate control is plotted against the target rate ( ○ ) and the difference between the target rate the pre-stimulation spontaneous firing level ( ● ) . A linear fit is improved when the spontaneous excitability is taken into account , indicating that ongoing changes in network excitability influence the intensity of stimulation required to achieve firing rate control . ( C ) The spontaneous firing rate before each 12-hr protocol vs the spontaneous firing rate following each protocol exhibits a strong linear relationship ( black line ) that is not significantly different from identity ( dashed line ) . This indicates the absence of a homeostatic decrease in network activity that might have resulted from chronically elevated firing levels in the absence of pharmacological agents . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 01610 . 7554/eLife . 07192 . 017Figure 5—figure supplement 2 . In vitro inhibitory on-off control using Arch3 . 0 . ( A ) Summary of unit spiking activity . ( Top ) Rastergrams show zoomed portions of spiking activity taken from the pre-clamp , clamp , and post-clamp periods of the experiment . Arrows indicate the time during the experiment that each reastergram was derived from . ( Bottom ) Firing rate histogram for the duration of the 7-hr recording for each unit , using 5-min bins . Firing levels are indicated by the grey-scale to the right . ( B ) Summary of network firing rates and the corresponding control signal . ( B . i ) Network firing rate calculated using 1 s bins and ( B . ii ) 5-min bins . The red line indicates the 0 . 75 Hz/unit target rate . ( B . iii ) Raw on-off control waveform . ( B . iv ) On-to-off transition frequency histogram calculated using 5-min bins . ( C ) Time-averaged firing rates for each epoch of the experiment with numerical values written above the bars . Note that post-clamp firing levels did not show a homeostatic increase due to 3-hr of firing rate suppression . ( D ) Average unit-to-unit spike correlogram during the pre-clamp and clamp period . Note that the correlation time and structure were very similar in both conditions . The correlogram derived from the clamp period appears to be a scaled version of the pre-clamp correlogram due to decreased firing levels . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 017 To clamp firing rates below spontaneous levels , the on-off controller delivered continuous yellow light ( ∼11 mW mm−2 , 590 nm ) when the integral error signal fell below zero ( Equation 13; ‘Materials and methods’ ) . Because chronic activation of eNpHR-3 . 0 produced relatively weak photocurrents and induced a depolarizing shift in the chloride reversal potential ( Raimondo et al . , 2012 ) , we found that it was inadequate for long-term control . For this reason , archaerhodopsin-3 . 0 ( Arch-3 . 0 ) was used for multi-hour firing rate suppression ( Chow et al . , 2010; Mattis et al . , 2011 ) ( ‘Materials and methods’ ) . To validate this strategy , we clamped a culture's spiking activity to ∼60% of its spontaneous firing rate ( from 1 . 23 to 0 . 75 Hz/unit ) over a 3-hr epoch ( Figure 5—figure supplement 2 ) . The on-off and PI control schemes have distinct advantages and disadvantages . PI control provided rapid response times and low RMS error over small time windows , but imposed strong , short timescale ( ∼50 ms ) response correlations between units ( Figure 5C; Figure 4 ) . This correlation structure contrasts the aperiodic , network bursting activity that is a common feature of developing neural circuits in vivo and in vitro ( O'Donovan et al . , 1998; Feller , 1999; Wagenaar et al . , 2006 ) . We found that both excitatory and inhibitory on-off control were better able to preserve spontaneous activity correlations than PI control ( Figure 5E; Figure 5—figure supplement 2C ) . However , for targets that required higher excitatory stimulation rates during on-off control , a periodic correlation structure emerged ( Figure 5E ) . Previous in vitro studies have shown that chronic elevation in network activity using GABAergic transmission blockers leads to a homeostatic reduction in firing rate following relief from blockade ( Turrigiano et al . , 1998 ) . Conversely , chronic reductions in activity using glutamatergic blockers elicit homeostatic increases in firing rate ( Corner et al . , 2002 ) . Interestingly , we did not observe homeostatic changes in firing rate following prolonged increases or decreases in network spiking activity during on-off feedback control ( Figure 5; Figure 5—figure supplement 1C and Figure 5—figure supplement 2B ) . This suggests that altered synaptic transmission and altered spiking activity may have distinct effects on homeostatic regulation of network excitability . To test this possibility , we used on-off control to decouple the effects of prolonged glutamatergic or GABAergic blockade from changes in firing rate . We treated networks with CNQX ( 2 cultures ) , AP5 ( 2 cultures ) , or bicuculline ( 1 culture ) , and then used on-off control to restore pre-drug firing rates during 24-hr ( CNQX or AP5 ) or 3-hr ( bicuculline ) periods . In all cases , the controller effectively clamped firing rates to pre-drug levels ( Figure 6A . v , B . v , C . v ) . 10 min of activity were recorded in the presence of each drug just before and after each clamp period . As expected , application of CNQX or AP5 caused marked reductions in network spiking activity compared to the pre-drug levels and these reduced activity levels were sustained upon relief from on-off control ( Figure 6A . v , B . v ) . Meanwhile , bicuculline greatly increased firing rate , both before and after the clamp period ( Figure 6C . v ) . 10 . 7554/eLife . 07192 . 018Figure 6 . Decoupling spiking and neurotransmission using on-off feedback control . ( A ) Summary of a 24-hr AMPAergic neuortransmission/spiking decoupling protocol . ( A . i ) Rastergrams show zoomed portions of spiking activity taken from discrete times during the experiment . Top color bars indicates recording epoch . Blue bars beneath indicate stimulus times . Horizontal scale bar , 1 s ( A . ii ) Firing rate histogram for the duration of the 33-hr recording for each unit , using 5-min bins . Firing levels are indicated by the grey-scale to the right . CNQX ( AMPAergic receptor antagonist ) was added at time 0 and removed 24 hr and 10 min later . Closed-loop stimulation began 5 min after CNQX addition and lasted 24 hr . Colored boxes indicate the location of the data used in the zoomed rastergrams , crosscorrelograms , and burst profiles . ( A . iii ) The average unit firing rate using 1-s bins and 5-min bins . The red line indicates the target rate . ( A . iv ) Closed-loop stimulation frequency . ( A . v ) Time- and unit-averaged firing rates for each epoch , normalized to the pre-drug firing level . The ‘post’ firing rate was evaluated over 6 hr following the drug wash . ( A . vi ) The average unit to unit crosscorrelogram for each control epoch . ( A . vii ) Example burst ratergrams , average burst profiles , and burst-triggered stimulus optical intensity for each control epoch . The location of the data used to calculate ( A . vi ) and ( A . vii ) is indicated by the matching colored boxes in ( A . ii ) . ( B , C ) Same as ( A ) but using AP5 ( B ) or bicuculline ( C ) to block NMDAergic and GABAergic neurotransmission , respectively . For bicuculline , the firing rate was clamped over a 3 hr period . The changes in spontaneous firing levels before on-off control for each culture were: CNQX , −93 . 8 and −56 . 3%; AP5 , −87 . 3 and −84 . 5%; bicuculline , +116 . 2% , and upon relief from on-off control: CNQX , −78 . 7 and −80 . 0%; AP5 , −73 . 0 and −80 . 4%; bicuculline: +81 . 3% . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 018 The mean stimulation frequency remained low during excitatory on-off control in the presence of CNQX or AP5 ( Figure 6A . iv , B . iv ) ; CNQX: 0 . 72 and 0 . 19 Hz , AP5: 0 . 21 and 0 . 71 Hz ) . For CNQX , pre-drug network activity correlations and burst shape were largely maintained during on-off control ( Figure 6A . vi–vii ) . For AP5 , the unit-to-unit correlation time and burst duration were shorter than pre-drug levels ( Figure 6B . vi–vii ) . This is likely due to the prominent role of NMDA receptors in signal propagation in dissociated cortical networks ( Nakanishi and Kukita , 1998 ) . Following drug removal , spontaneous firing was elevated compared to pre-drug levels ( CNQX: +86 . 4 and +21 . 1% , AP5: +157 . 4 and +273 . 3% ) . Inhibitory on-off control using Arch3 . 0 was used to clamp firing to pre-drug levels during bicuculline treatment ( ‘Materials and methods’ ) . Bicuculline greatly increased network firing correlations and burst duration compared to spontaneous activity levels . During the clamp epoch , ‘on’ to ‘off’ control transitions reliably triggered large rebound bursts . A rapid closed-loop response truncated rebound bursts via reactivation of Arch-3 . 0 ( Figure 6C . vi–vii ) . After washing bicuculline , firing was reduced by 47 . 2% compared to pre-drug levels . Notably , homeostatic changes in spiking levels were observed following prolonged glutamatergic or GABAergic blockade even though network firing rates were maintained at pre-drug levels during the treatment windows . This indicates that changes in firing rate were not required to drive compensatory alterations in network excitability . Instead , homeostatic alterations of network excitability were triggered directly by suppressed synaptic activity . In line with this result , on-off control has recently been used to show that upward synaptic scaling , the most widely studied form of homeostatic plasticity , is directly induced via reductions in AMPA receptor activation ( Fong et al . , 2015 ) . We next evaluated the functionality of optogenetic feedback control in the intact rodent brain . The rat vibrissal pathway is a widely studied model of sensory information transduction due to its well defined discrete feed-forward anatomy . Recent findings have revealed the importance of network activity state for gating sensory information in thalamic networks ( Halassa et al . , 2014 ) . We used optogenetic feedback to control background firing state in single units of the ventral posteromedial nucleus ( VPm ) in anesthetized rats during ongoing vibrissa stimulation . Extracellular recordings of ChR2-expressing thalamocortical units ( TCUs ) were used to update an integral controller ( Equation 15 ) ( parameters: Ti = 1 s , τ = 0 . 8 s ) , which determined the continuous intensity of 470 nm light delivered to VPm using an optical fiber ( Figure 7A; ‘Materials and methods’ ) . 10 . 7554/eLife . 07192 . 019Figure 7 . Firing rate control of isolated thalamic units , in vivo . ( A ) Single unit extracellular recordings were performed in thalamic VPm and used to update the optical power of the LED stimulator . The primary vibrissa could be deflected along the rostral–caudal plane using a galvanometer-based scanning motor to provide sensory perturbations during closed-loop control . A representative TCU waveform is shown ( black line is the mean and the shaded region is ± 1 SD ) . Vertical and horizontal scale bars represent 100 μV and 1 ms , respectively . ( B ) Single-trial closed-loop firing rate control in the absence of sensory input . Traces show the target firing rate ( red ) , measured firing rate ( black ) , and light power ( blue ) . Inset spike trains display 1 s of activity for each target rate . ( C ) Time-averaged firing rates vs target rates for 10 TCUs ( symbols ) . Data points are color coded according to the target rate . Black symbols at left indicate spontaneous firing levels prior of closed-loop control . Grey symbols indicate control failure . Data points derived from a single TCU are connected with a line . Shaded areas are peak-normalized histograms of spontaneous firing rates ( black ) and successfully controlled firing rates ( blue ) across units . ( D ) RMS tracking error for each target rate . ( E ) Average light power required for each target rate . ( F ) Mean vs standard deviation of the ISI distribution for each target rate . The dotted identity line indicates Poisson firing statistics . Inset bar chart shows the mean CVISI across units during spontaneous and clamped firing . p = 0 . 043; t-test . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 01910 . 7554/eLife . 07192 . 020Figure 7—figure supplement 1 . Open-loop application of precisely defined optical stimuli results in highly variable , non-stationary evoked firing levels in the intact rat vibrissa system . ( A ) Continuous , 30-s optical stimuli ( blue bar ) of linearly increasing intensity across trials ( scale bar at right ) were applied to 13 TCUs . Although evoked firing , as measured by the optrode in VPm thalamus , tended to increase monontonically with light power ( as indicated by the preservation of color ordering in the time-series overlay ) , evoked firing was non-stationary and highly variable during each 30-s stimulation epoch . ( B ) Time-averaged evoked firing rates were highly variable for a given light intensity across TCUs , reflecting differences in channel expression , depth of anesthesia , and other uncontrolled variables affecting neuronal excitability . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 02010 . 7554/eLife . 07192 . 021Figure 7—figure supplement 2 . Continuous real-time update of optical intensity is required for accurate firing rate control in the intact rat vibrissa system . ( A ) 15 s into each 30-s control epoch , the control signal ( blue ) was locked at its most recent value . ( Top ) The firing rate of a single TCU , encoded by the scalebar at right , is shown for 10 control trials ( columns ) . ( Middle ) The trial-averaged firing rate ( black ) line , and target rate ( red line ) . Dotted lines indicate ± standard deviation . Bin size , 1 s ( Bottom ) Trial-averaged optical control signal . Shading indicates ± standard deviation . ( Right ) The RMS tracking error during functional integral control and during the locking period are shown for 6 TCUs ( symbols ) . Black symbols represent individual trials and red symbols are trial-averages . Red lines connect means derived from the same unit . The rightmost column indicates the percent change in RMS tracking error during the control lock compared to the functioning integral control for each trial . The mean RMS error increased 204 ± 227% during the locking period compared to integral control ( p = 1 . 07 × 10−15 , Wilcoxon signed-rank test ) . ( B ) Same as ( A ) except that the control signal was locked at the average value taken during the first 15 s of control . The mean RMS tracking error increased 240 ± 145% during the locking period compared to integral control ( * p = 1 . 25 × 10−17 , Wilcoxon signed–rank test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 02110 . 7554/eLife . 07192 . 022Figure 7—figure supplement 3 . Effects of firing-rate filter time-constant ( τ ) and integral time-constant ( Ti ) on performance of integral control in vivo . ( A ) The firing rate was controlled in three TCUs using 3 different values of τ: 0 . 16 s , 0 . 8 s ( nominal ) , and 1 . 6 s ( Top ) The firing rate of a single unit , encoded by the scalebar at right , is shown for 10 optoclamp trials ( rows ) . ( Middle ) The trial-averaged firing rate ( black ) line , and target rate ( red line ) . Dotted lines indicate ± standard deviation . Bin size , 1 s ( Bottom ) Trial-averaged optical control signal . Shading indicates ± standard deviation . ( Right ) The RMS tracking error during integral control for each value of τ is shown for each unit ( symbols ) . Black symbols represent individual trials and red symbols are trial-averages . Red lines connect means derived from the same unit . p = 0 . 010 , p = 1 . 4 × 10−6 , and *p = 1 . 2 × 10−10 . ( B ) Same as ( A ) except that firing rate was controlled in two TCUs using 3 different values of Ti: 0 . 1 s , 1 . 0 s ( nominal ) , and 10 s . p = 0 . 019 . For both ( A ) and ( B ) , a Kruskal–Wallis one-way analysis of variance was followed by post-hoc Mann–Whitney U tests , using a Bonferroni correction to control the familywise error rate . Adjusted p values are reported . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 022 We used optogenetic feedback to clamp firing rates in TCUs at increasing target levels for 30-s epochs ( Figure 7B ) . For > 75% of TCUs , the controller effectively clamped firing over a range of target rates , which varied across cells ( e . g . , 4–40 Hz vs 18–22 Hz; Figure 7C ) . As in our in vitro PI experiments , the RMS tracking error trended upward with increasing target rates ( Figure 7D ) . Because firing rate estimates were derived from single cells instead of population activity , the RMS tracking error was larger than for in vitro control ( mean ± SD , 2 . 0 ± 0 . 8 Hz ) . Controller settling time was not correlated to the target firing rate and was shorter than for in vitro control ( mean ± SD , 3 . 3 ± 3 . 2 s ) . The optical power required for successful control varied greatly from cell to cell ( Figure 7E ) . For instance , the mean optical intensity required for successful control at 16 Hz , the most widely achieved target in our sample , spanned nearly two orders of magnitude ( 0 . 66–22 . 92 mW mm−2; Figure 7E ) . This suggests that open-loop optical stimuli would not result in consistent firing rates . Indeed , we found that linear increases in optical intensity resulted in extremely variable and temporally non-stationary firing over trials and units ( Figure 7—figure supplement 1 ) . Further , we examined whether successful control could be achieved by locking the stimulator at a static optical power once the controller stabilized . Halfway through each 30-s trial we locked the light signal either at the last output taken by the controller or the average control signal during first half of the trial ( 6 TCUs; Figure 7—figure supplement 2 ) . Locking optical power at the mid-trial or trial-averaged level significantly increased RMS tracking error ( +204 ± 227% and +238 ± 145% , respectively , p < 10−14 for both ) . This indicates that continuous stimulus updates are required to exert precise control over neural activity in vivo . Next we examined how changes to the firing rate filter time-constant , τ , or the integral time-constant , Ti affected control performance ( Figure 7—figure supplement 3 ) . Increasing τ ( from 0 . 8 to 1 . 6 s ) introduced lag into the control loop , causing overshoot , decreased stability , and a significant increase in RMS tracking error ( +57% , p = 1 . 4 × 10−6 ) . Conversely , lowering τ ( from 0 . 8 to 0 . 16 s ) decreased overshoot and significantly reduced RMS tracking error ( −46 . 9% , p = 1 . 2 × 10−10 ) . Increasing Ti ( from 1 to 10 s ) did not significantly affect RMS tracking error ( p = 0 . 088 ) , but caused over-damping and increased controller settling time . Reducing Ti ( from 1 to 0 . 1 s ) significantly decreased RMS tracking error ( −18% , p = 0 . 011 ) and introduced overshoot during control onset . Together , these results indicate that low-latency feedback and a short integral time constant improve the performance of firing rate control in vivo . In this experiment , τ = 0 . 16 s and Ti = 1 s gave the best performance in terms of RMS error . In the awake animal , sensory thalamic spike trains tend to exhibit irregular firing with interval statistics close to those of a Poisson process ( Poggio and Viernstein , 1964 ) . Drugs used for anesthesia have profound effects on evoked and background firing ( Simons et al . , 1992 ) , receptive field properties ( Friedberg et al . , 1999 ) , and subthreshold voltage statistics ( Constantinople and Bruno , 2011 ) in the vibrissal pathway . We calculated the coefficient of variation of the interspike interval ( ISI ) distribution ( CVISI ) for each TCU . Across target rates and units , we found a CVISI close to 1 , indicating a Poisson-like spiking process ( mean ± SD , 1 . 31 ± 0 . 33 across targets and units; Figure 7F ) . In comparison , the CVISI of spontaneous unit activity was significantly elevated ( Figure 7F , inset ) mean ± SD , 1 . 72 ± 0 . 43; p = 0 . 043 ) , likely due to an increased propensity for burst firing in sensory thalamus during anesthesia and non-alert states ( Stoelzel et al . , 2009 ) . This suggests that optogenetic feedback control using continuously modulated input can be used to mimic alert , Poisson-like spiking statistics in anesthetized animals . Finally , we tested whether the controller could clamp firing during ongoing sensory drive . We recorded from TCUs that were responsive to punctate deflections of the corresponding primary vibrissa ( 5 TCUs , 8° at ∼1600 deg . s−1 , 10 Hz; Figure 8—figure supplement 1; ‘Materials and methods’ ) . Vibrissa deflections evoked stimulus-locked spike trains both in the presence and absence of closed-loop control ( Figure 8 , Figure 8—figure supplement 1 ) . However , whisker stimuli resulted in little or no performance degradation of firing rate control in terms of mean TCU firing rate ( Figure 8B; p = 0 . 09 ) or RMS tracking error ( Figure 8C; p = 0 . 73 ) compared to control without whisker input . To maintain control during sensory stimulation , the controller automatically decreased LED stimulus intensity to accommodate sensory drive ( Figure 8D; mean ± SD , 8 . 4 ± 5 . 34 to 4 . 9 ± 4 . 8 mW mm−2 , p = 0 . 024 ) . 10 . 7554/eLife . 07192 . 023Figure 8 . Using optogenetic feedback to control thalamic activity state during ongoing sensory input . ( A ) Real-time control of thalamic firing levels during external sensory drive . The firing rate of a single TCU cell ( grey lines: single trials; black lines , average ) is shown for three interleaved protocol types: 15-s trains of whisker stimuli ( yellow ) , 45-s closed-loop control periods in the absence of whisker input ( grey ) , and closed-loop control during concurrent whisker stimulation ( green ) . ( Top ) 10 Hz whisker deflections occurred from 15–30 s within each trial ( black triangles ) . Inset raster plot shows spikes times for 4 trials at the onset of whisker stimulation . ( Middle ) TCU firing was clamped at 30 Hz ( red line ) for the duration of each trial . Blue lines show the optical control signal ( light blue: single trials; dark blue: average ) . ( Bottom ) TCU firing was clamped at 30 Hz ( red line ) for the duration of each trial and whisker stimuli were delivered from 15–30 s within each trial . Horizontal scale bars on the firing rasters indicate 100 ms . ( B ) mean relative ( measured/target ) firing rates , ( C ) mean RMS tracking errors , and ( D ) mean optical power across trials and units . Values are shown for each of the 5 trial epochs indicated on the abscissa axis of each time series in ( A ) . Error bars indicate ± SEM . ( E ) Spike-triggered average ( STA ) optical power and ( F ) STA whisker position for the TCU shown in ( A ) . Note the difference in time scales between ( E ) and ( F ) . ( G ) FWHM of the STA for each TCU across trial types . Sample sizes: 5 TCUs , 3 to 5 applications of each protocol type per unit . p = 0 . 024 , *p = 0 . 0079; Mann–Whitney U Test . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 02310 . 7554/eLife . 07192 . 024Figure 8—figure supplement 1 . Spike waveforms and autocorrelograms of TCUs used for concurrent optogenetic feedback control and whisker stimulation in intact rats . Shown for each of the 5 TCUs are the spike waveform and autocorrelogram of spiking activity during different portions of the control epoch . For the spike waveform , the thick black line represents the mean and shading is ± 1 standard deviation . The bin size used to calculate the autocorrelogram was 5 ms . The autocorrelogram histogram was normalized by the bin size and number of spikes to arrive at a firing rate . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 024 Precise temporal spiking patterns carry information in the vibrissal sensory pathway ( Curtis and Kleinfeld , 2009; Wang et al . , 2010; Bruno , 2011 ) . To characterize the timescales over which the optical controller and whisker stimuli affected firing , we calculated the spike-triggered average ( STA ) of optical power and whisker position ( Figure 8E , F ) . During concurrent whisker stimulation and closed-loop control , the full-width at half-maximum ( FWHM ) of the STA for optical power and whisker position differed by more than an order of magnitude , indicating that sensory input and optical control signals affected firing on distinct timescales ( Figure 8G; mean ± SD FWHM of STA: optical power , 0 . 46 ± 0 . 2 s vs whisker position , 18 . 3 ± 5 . 5 ms , p = 0 . 0079 ) . Therefore , optogenetic feedback control using continuously modulated input provides a means to control baseline firing state without distorting the fine-scale temporal structure of sensory-evoked spike trains . Punctate vibrissa stimuli rearrange spike times instead of introducing additional spikes , allowing the temporal correlations in the firing rate to dictate the downstream response rather than the magnitude of the firing rate . This effect is reminiscent of firing modulation by free air whisking in vibrissa primary sensory cortex ( Curtis and Kleinfeld , 2009 ) and contrasts the effect of pulsed optogenetic stimulation , which transiently overrides the influence of sensory input on the temporal structure of spike trains . All previous methods involving closed-loop or activity-guided optogenetic stimulation have used a physiological measurement to trigger static optical stimuli without corrective action or modification of stimulus parameters following stimulus onset ( Leifer et al . , 2011; Stirman et al . , 2011; Paz et al . , 2012; Krook-Magnuson et al . , 2013; O'Connor et al . , 2013; Siegle and Wilson , 2014 ) . In contrast , the optoclamp continuously updates stimulus intensity and frequency in real-time to enable precise control of neural firing in cultured networks and single cells in vivo and therefore provides a foundation for techniques aiming to achieved true closed-loop optical control of neural activity ( Grosenick et al . , 2015 ) . We have shown that this form of optogenetic feedback control is capable of clamping network firing rates using different stimulation protocols ( Figure 4 ) and control algorithms ( Equations 4 , 12 , 13 , 14 ) over a wide range of controller parameters ( Figure 2—figure supplement 3 through Figure 2—figure supplement 5 , Figure 7—figure supplement 3 ) and timescales ( Figure 2 vs Figure 5 through Figure 6 ) . The optoclamp's ability to control firing levels even during various synaptic ( Figure 6; Figure 2—figure supplement 6 ) and sensory ( Figure 8 ) perturbations demonstrates its robustness and provides evidence of the method's suitability in a range of experiments . Finally , we found that even when using the same controller and target firing rate , successful control often required vastly different intensities of optical stimulation across experiments and experimental preperations ( Figure 9 ) . This suggests that closed-loop regulation of optical stimulation intensity may be a requirment , rather than a benefit , for evoking consistent activity levels across experiments . 10 . 7554/eLife . 07192 . 025Figure 9 . A wide range of optical power was required during successful closed-loop control in vitro and in vivo . The time-averaged power density of blue ( left plot ) and yellow ( right plot ) light vs the corresponding target firing rate is shown for all control algorithms and experimental preparations used in the paper . Lines connect data points derived from the same culture ( in-vitro data ) or unit ( in-vivo data; note the log scales ) . Only successful control trials are shown . The light intensity required during closed-loop control varied across orders of magnitude and depended on the target rate , control algorithm , stimulus waveform , type of neural preparation being controlled , and variability in cell-to-cell and culture-to-culture excitability . This highlights the ability of closed-loop control to compensate for the experimental variability across preparations , equipment , and algorithms , as well as the intrinsic variability in neural circuits , to achieve a target activity level . DOI: http://dx . doi . org/10 . 7554/eLife . 07192 . 025 Like the optoclamp , the voltage clamp relies on a continuously updated feedback loop to control neural activity . Although the scale of neural activity controlled by these two methods differs greatly ( population firing rate vs membrane potential ) , comparing the optoclamp to the voltage clamp is useful for illustrating the power , potential uses , and shortcomings of optogenetic feedback control in its current form , and to highlight how the technique might be extended to allow control over features of neural activity other than firing levels . Two capabilities of the voltage clamp technique have made it a ubiquitous tool for characterizing neuronal excitability and synaptic dynamics . First , by continuously servoing the current injected into a cell in order to maintain a target membrane potential , the injected current mirrors underlying synaptic or intrinsic conductances that are difficult to measure directly . Second , because the voltage clamp can precisely control the membrane potential , it can decouple strong causal relationships that exist between the membrane voltage and other variables . For example , measuring the open-loop relationship between the membrane voltage and voltage-dependent conductances is impossible in the unclamped cell since these variables feed back onto one another . The voltage clamp breaks this circular dependence in order to systematically examine the strength of conductances at fixed voltage levels . The optoclamp affords analogous capabilities for examining network dynamics . For instance , population-level excitability in an unclamped network must be inferred through measures of firing activity , which in turn affects network excitability . By continuously updating the intensity of optical stimulation to clamp network firing at set levels , information about network excitability and afferent input can be read directly from optical control signals ( Figure 3C ) . One shortcoming of this feature is that it is difficult to identify the origin of changes in network excitability that appear in optical control signals since they result from the aggregate effect of many different processes . However , parallel limitations also exist for the voltage clamp . For example , when using voltage clamp , it can be difficult to distinguish contributions from different neurotransmitter systems and intrinsic conductances or to deduce the relationship between synaptic currents measured at the recording site vs at the site of receptor binding . Another issue with both optical clamping and the voltage clamp is that the control signal can be contaminated by non-ideal aspects of the recording or stimulation setup . For instance , opsin desensitization could affect the intensity of light required for the optoclamp to maintain firing levels . Likewise , space clamp and seal stability issues that arise during voltage clamp influence the amount of current required to hold a target membrane potential . In the case of the voltage clamp , these issues are partially compensated for by using secondary measurements of seal quality and by employing the voltage clamp in concert with drugs or genetic manipulations that help to isolate particular synaptic and intrinsic conductances . Different neural circuits have distinct population-level dynamics , connectivity , neurotransmitter systems , cellular constituents , and opsin expression properties . Therefore , analogous to the voltage clamp , the optoclamp should be used in combination with auxiliary techniques that can compensate for imperfections in the control system and allow the mechanistic underpinnings of changes in excitability that occur during clamping to be isolated . For instance , the effects of opsin desensitization and bandwidth can be factored out of optical control signals such that the control signals accurately reflect changes in network excitability during clamping ( Tchumatchenko et al . , 2013 ) . Further , the optoclamp can be combined with existing pharmacological , genetic , or electrophysiological tools to isolate specific mechanisms that influence network excitability following clamping ( Fong et al . , 2015 ) . In addition to providing a means to quantify network excitability , like the voltage clamp , optogenetic feedback control is capable of decoupling causally related variables of circuit activation . Using two case studies , operating on very different time-scales , we have demonstrated that the optoclamp is capable of decoupling thalamocortical cell baseline state from fine-scale sensory-evoked firing patterns in vivo ( Figure 8 ) and decoupling network firing levels from various forms of neurotransmission ( Figure 6 ) . The former is especially relevant given recent focus on the state-dependent nature of thalamic coding ( Halassa et al . , 2014 ) , and the need for stimulation technologies capable of controlling non-stationary neural dynamics in order to inject meaningful sensory information into damaged circuits ( Stanley , 2013 ) . Decoupling firing levels from variables to which they are normally causally intertwined can also help clarify causal relationships between firing and other factors that influence network excitability . For instance , determining the independent roles of neurotransmission and spiking on various forms of plasticity has proven challenging since manipulation of neurotransmission invariably affects spiking levels , and vice versa . To overcome this , Fong et al . recently used the optoclamp to decouple network firing levels from long-term AMPAergic neurotransmission blockade to show that upward synaptic scaling is directly triggered by reduced AMPAergic transmission without relying on changes in spiking activity ( Fong et al . , 2015 ) . This provides a plasticity mechanism to explain the increases in network firing levels we witnessed following long-term CNQX treatment even when firing was clamped to pre-drug levels for the duration of drug application ( Figure 6A ) . The control algorithms and technologies presented here are simple , straightforward , and widely available . Our implementations of the optoclamp are quite reliable for controlling neural activity in cultured networks and thalamic cells in vivo and the technique's simplicity lowers the barrier for its adoption . However , there are several avenues for further development of the method we have presented . For instance , we used a pre-sorting procedure to identify neurons used for real-time firing rate estimation in subsequent clamping periods . During periods of optical stimulation , neurons that were silent during the pre-sorting routine may be activated . If these cells had systematically different firing characteristics the sorted units used for firing rate estimation , then our procedure would lead to a biased estimate of network firing levels . Although we found no evidence that this was the case in our networks , the situation might differ for other neural preparations or brain regions . If this issue were to arise , spike sorting could be removed entirely and firing could be normalized by the number of recording channels , provided that the channel count is sufficiently high . Further , the incorporation of more sophisticated activity measurement techniques , stimulation technologies , and control algorithms will enable improvements in control performance and broaden the applicability of optogenetic feedback control to different experimental contexts ( Grosenick et al . , 2015 ) . For example , the incorporation of spatial light modulation would allow optical inputs to be steered towards the spike initiation zones of individual cells in order to minimize light exposure ( Figure 9 ) and abnormal conductances , and potentially enable complex system identification alogrithms to be introduced into the feedback loop ( Grosenick et al . , 2015 ) . Additionally , decreased feedback latency or the addition of predictive elements to the feedback loop may enable control over rapid sensory or motor events . There are two options to obtain accurate firing rate measures over very short timescales ( e . g . , that of individual whisker perturbations ) . The first option is to sample a very large population of neurons such that small time bins will have an adequate number of spikes for accurate estimation of population firing levels . This large population measure would need to be combined with a sub-millisecond feedback loop and opsins with extremely fast kinetics . Alternatively , if an accurate model of feedforward network dynamics could be incorporated into the controller , reliable control over fast events might be possible with a modestly sized population of cells , a slower feedback loop , and standard opsin variants . This is a viable approach in circuits for which predictive models of feedforward network dynamics are available , such as early visual , auditory , and vibrissal pathways ( Wu et al . , 2006; Millard et al . , 2013 ) , or , for which accurate input/output relationships can be deduced in situ using real-time system identification ( Grosenick et al . , 2015 ) . Additionally , control algorithms that incorporate models of feed-forward neural dynamics will be more capable of stabilizing firing in unstable circuits , such as epileptic networks , without total cessation of ongoing activity . We demonstrated that optical waveforms with very different temporal characteristics could be used to successfully control population firing rate in vitro while having markedly different effects on spiking correlation and synchrony across individual units ( Figure 4 ) . However , in the optoclamp's current form , higher-order temporal characteristics , such as the unit-to-unit firing correlation , are not actively controlled features of neural activity . Therefore , during clamping periods , these features of population firing will be dependent on network architecture , the identity and percentage the cells expressing opsins , and opsin dynamics . Improvements on our technique might treat higher order features of network activity , such as unit-to-unit correlation and synchrony , as secondary control targets . In this case , the controller would use real-time measures of higher order firing statistics to adjust the spatial and temporal characteristics of optical stimuli in order to enforce a particular firing structure , such as the heterogeneity of activity levels across cells and/or temporal variance of firing activity ( e . g . , regular firing vs bursting ) . Optogenetic feedback control could also be incorporated into more complex experimental contexts . For example , firing rate control could be made contingent on specific behaviors or complex spatiotemporal activity patterns associated with specific behaviors ( Zhang et al . , 1998 ) , in order to introduce fictive reward or neuromodulatory signals to influence learning or alleviate pathological activity . In particular , optoclamping cortical activity to replace lost neuromodulatory tone is a potentially exciting future avenue for treating Parkinson's disease ( Beuter et al . , 2014 ) . Recently , several ‘all-optical’ electrophysiology techniques have been introduced to simultaneously measure neural activity via genetically encoded calcium sensors and optogenetically inject currents at single cell resolution ( Packer et al . , 2014; Rickgauer et al . , 2014 ) . If combined with real-time control , these techniques could offer the ability to optically clamp activity levels in specified subnetworks with far greater specificity than is afforded using electrodes . Perhaps most exciting , recent improvements in microbial rhodopsins for simultaneous voltage indication and optogenetic stimulation provide a means for all-optical measurement and perturbation of the membrane voltage at subcellular resolution and millisecond timescales ( Flytzanis et al . , 2014; Hochbaum et al . , 2014 ) . These tools even allow simultaneous sensing and actuation using a single opsin . Using opsins to both measure and actuate voltage within a feedback control loop will open the possibility of voltage clamping arbitrary populations of cells without puncturing their cell bodies . This would enable an unprecedented improvement of fine-scale control and measurement of neural circuit activation in vitro and , with specialized optics , in vivo . In summary , we have performed a systematic and extensive investigation of how optogenetic feedback control can be used to precisely control neuronal firing levels during perturbations that strongly affect network excitability , across time scales ranging from seconds to days , both in vitro and in vivo . The functionality of our technique across control parameters , algorithms , preparations , and firing rate measures ( network vs single units ) indicates the robustness and general applicability of the technique to different experimental contexts . When combined with secondary genetic , pharmacological , or behavioral manipulations and tailored to particular experimental contexts using suitable control algorithms , we envision the use of optogenetic feedback control in a multitude of experimental and clinical contexts requiring precise control of neuronal activity . For these reasons , we believe that the optoclamp is a powerful addition to the expanding optogenetic toolbox and will improve and accelerate the study of neural control of motor action , sensory encoding and adaptation , neuromodulation , and activity homeostasis . All statistical analyses were performed using MATLAB ( MathWorks , Natick , MA ) . For tests between two groups , we first used a Lilliefors test ( α = 0 . 05 ) to determine if sample distributions were normally distributed . If the null hypothesis of normality was rejected for one or both sample distribution ( s ) , we performed a Wilcoxon signed-rank test ( α = 0 . 05 ) . Otherwise we used a paired t-test ( α = 0 . 05 ) . We used paired tests because our samples were ‘matched’ ( i . e . , the same culture or cell was examined in two different experimental conditions ) . For tests involving multiple comparisons across three or more groups , we first used a Lilliefors test ( α = 0 . 05 ) to determine if the sample distributions were normally distributed . If the null hypothesis of normality was rejected for one or more sample distribution ( s ) , we performed a Kruskal–Wallis one-way analysis of variance . Otherwise , we performed standard one-way ANOVA . Post-hoc hypothesis testing was performed using the Bonferroni correction to control the familywise error rate in order to determine which pairs were significantly different . We used t-tests if sample distributions were Gaussian and Mann–Whitney U tests otherwise . Adjusted p-values are reported in figure captions and the text .	Cells called neurons use electrical signals to rapidly carry information around the body . When a neuron is activated , it generates ( or ‘fires’ ) a short electrical impulse that travels along the cell to relay a message to other neurons , muscles or organs . Optogenetics is a technique that allows scientists to genetically modify neurons to produce proteins that make them light sensitive . One of the most commonly used light-sensitive proteins is called channelrhodopsin-2 . It is activated by blue light and increases the electrical activity of neurons . Another protein is called halorhodopsin , which responds to yellow light and inhibits the firing of neurons . By shining light of particular colors onto neurons that produce these and other light-sensitive proteins , it is possible to manipulate the activity of large populations of neurons . Most previous optogenetic experiments have involved altering the activity of neurons and then observing the outcome at a later point in time . However , it would be very useful to be able to alter the amount of optical stimulation to achieve particular levels of neuron activity in real time . To achieve this , the level of neuron activity at any point in time would need to be quickly compared to the desired level , so that optogenetics could be used to increase or decrease the firing of neurons as appropriate . Newman et al . have now developed an optogenetic system called ‘optoclamp’ that can control the activity of neurons in real time . In neurons grown in cell culture , the optoclamp is able to hold the level of neuron activity at particular values for periods of time ranging from 60 seconds to 24 hours . It can be used to restore and maintain the baseline level of neuron activity in the presence of drugs that would otherwise produce large increases or decreases in the firing of neurons . Moreover , in anaesthetized rats , the optoclamp can prevent some neurons from being activated even when the rats' whiskers move , which would normally change their firing level . Newman et al . 's findings open the door to a new type of neuroscience experiment where it is possible to manipulate activity patterns as they are produced by the brain . This will help researchers to understand how particular patterns of brain activity are linked to learning , memory , and behavior .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "neuroscience" ]	2015	Optogenetic feedback control of neural activity
The gradient of Decapentaplegic ( Dpp ) in the Drosophila wing has served as a paradigm to characterize the role of morphogens in regulating patterning . However , the role of this gradient in regulating tissue size is a topic of intense debate as proliferative growth is homogenous . Here , we combined the Gal4/UAS system and a temperature-sensitive Gal80 molecule to induce RNAi-mediated depletion of dpp and characterise the spatial and temporal requirement of Dpp in promoting growth . We show that Dpp emanating from the AP compartment boundary is required throughout development to promote growth by regulating cell proliferation and tissue size . Dpp regulates growth and proliferation rates equally in central and lateral regions of the developing wing appendage and reduced levels of Dpp affects similarly the width and length of the resulting wing . We also present evidence supporting the proposal that graded activity of Dpp is not an absolute requirement for wing growth . Decapentaplegic ( Dpp ) , the Drosophila BMP homolog , has served as a paradigm to characterize the role of morphogens in regulating patterning of developing tissues ( Affolter and Basler , 2007; Restrepo et al . , 2014 ) . In the developing Drosophila wing disc , Dpp is expressed in a central stripe that corresponds to the anterior-posterior ( AP ) compartment boundary , and its gradient provides a series of concentration thresholds throughout the tissue that set the transcriptional state of target genes in discrete domains of gene expression as a function of their distance from the source ( Lecuit et al . , 1996; Nellen et al . , 1996 ) . Graded activation of the Dpp transducer MAD and the inverse gradient of Brinker , a transcriptional repressor negatively regulated by Dpp , contribute to the transcriptional regulation of Dpp target genes in discrete domains ( Affolter and Basler , 2007; Restrepo et al . , 2014 ) . These domains are ultimately used to locate the patterning elements of the adult wing [e . g . longitudinal veins , ( de Celis et al . , 1996; Sturtevant et al . , 1997 ) ] . Thus , the participation of the Dpp gradient in specifying cell identities in a concentration-dependent manner is well accepted . By contrast , the role of the Dpp gradient in regulating tissue size has been a topic of intense debate as proliferative growth is homogenous in the developing wing ( Milán et al . , 1996 ) . The classical ‘steepness model’ proposes that the juxtaposition of cells sensing disparate levels of the morphogen promotes proliferative growth ( Lawrence and Struhl , 1996; Rogulja and Irvine , 2005 ) . This model is questioned by the observation that the Dpp-gradient scales with the size of the wing primordium and that the slope of the gradient does not change ( Wartlick et al . , 2011 ) . These same authors propose a new model - the ‘temporal rule model’ - that posits that cells divide when Dpp signaling levels have increased by 50% . However , cells lacking MAD and Brinker activities - in which the increase of Dpp signaling levels is genetically blocked - were shown to grow at rates comparable to those of wild-type cells ( Schwank et al . , 2012 ) . The alternative ‘growth equalisation model’ suggests that Dpp controls growth of the central region of the wing by repressing Brinker ( Schwank et al . , 2011 ) . The recent use of membrane-tethered-GFP nanobodies against Dpp-GFP to modulate Dpp spreading proposes that Dpp emanating from the AP boundary is mainly required for the growth of the center of the wing disc , the region with the highest levels of Dpp activity ( Harmansa et al . , 2015 ) . A recent report questions the temporal and spatial requirement of Dpp expression and activity in promoting wing growth . CRISPR-Cas9-mediated genome editing of the dpp locus combined with the FLP-FRT recombination system to temporally control the removal of Dpp from its endogenous stripe domain has come to the conclusion that Dpp emanating from the AP boundary is not required for wing growth and that the growth-promoting role of Dpp is restricted to the early stages of wing development ( Akiyama and Gibson , 2015 ) . In order to carefully characterize the spatial and temporal requirement of Dpp in promoting proliferative growth of the different regions of the wing disc , here we combine the use of the conventional Gal4/UAS system and a temperature-sensitive Gal80 molecule together with a collection of RNAi hairpins to deplete dpp expression in the developing wing . We have carried out tissue size measurements in developing primordia and adult wings , FRT-mediated mitotic recombination clones and Gal4-based cell lineage analysis to present evidence that Dpp emanating from the AP compartment boundary is required throughout development to promote growth of the developing wing appendage by regulating cell number and tissue size . We also present experimental evidence supporting the proposal that Dpp promotes proliferative growth simply by maintaining Brinker levels below a growth-repressing threshold ( Schwank et al . , 2008 ) , as nearly wild-type sized adult wings can be obtained by co-depleting Dpp and Brinker . Remarkably , our results indicate that Dpp regulates growth and proliferation rates equally in central and lateral regions of the developing wing appendage and that reduced levels of Dpp has an impact in both width and length of the wing . Altogether , our results together with previous observations on the impact of Dpp spreading on wing growth ( Ferreira and Milán , 2015 ) indicate that regional differences in Dpp signaling activity do not have a direct impact on growth rates , that graded activity of Dpp is not an absolute requirement for wing growth , and that the range of Dpp spreading emanating from the AP compartment boundary can regulate the final size of the wing appendage . We first drove expression of five RNAi hairpins , one long and four short , targeting various regions of the first dpp coding exon ( Perkins et al . , 2015 ) under the control of the nubbin-gal4 ( nub-gal4 ) driver . We selected this driver as its expression begins at the time the wing is being specified , namely in late second instar wing primordia ( Ng et al . , 1995 , 1996 ) , and it is restricted to those cells that will give rise to the adult wing blade and the distal hinge ( Zirin and Mann , 2007 ) . Larvae were raised at 25°C and the resulting adult wings were analysed . Control individuals expressing an RNAi hairpin targeting GFP and containing the nub-gal4 driver were raised in parallel in this and subsequent experiments . All individuals expressing the different dpp-RNAi hairpins produced rudimentary wings resembling those caused by the classical dpp-disk alleles in which the enhancers driving dpp expression to the imaginal tissues are removed [Figure 1A , A’; ( Masucci et al . , 1990 ) ] . We observed that dpp-RNAi-expressing wings maintained largely well-formed and grown distal hinge structures ( eg . alula and costa , Figure 1A ) , as occurs in dpp-disk wings ( Zecca et al . , 1995 ) . In order to address the temporal requirement of Dpp activity , we next used the temperature-sensitive Gal80 molecule , which represses Gal4 transcriptional activity at low temperatures ( 18°C , [McGuire et al . , 2004] ) . Larvae were raised at three temperatures ( 18°C , 25°C and 29°C ) during larval and pupal development . Individuals raised at 18°C showed no overt wing phenotype ( Figure 1B , B’ ) , while those raised at 25°C showed only a mild growth phenotype ( Figure 1C , C’ ) . This observation reinforces the robust repression of Gal4 transcriptional activity by Gal80ts at low temperatures . All individuals raised at 29°C and expressing the different dpp-RNAi hairpins produced rudimentary wings ( Figure 1D , D’ ) . Based on the phenotypes at 25°C , we chose the strongest dpp-RNAi hairpin ( BL 36779 ) for further experiments . We next characterised the effects of dpp depletion on the size of the developing wing appendage and monitored Dpp expression and activity levels . The developing appendage , labelled by the expression of a Nubbin ( Nub ) antibody , was drastically reduced in size when raised at 29°C ( Figure 1E ) . We also used antibodies to Wingless ( Wg ) and Patched ( Ptc ) to label the dorsal-ventral ( DV ) and AP compartment boundaries and analyse the impact on the size of each compartment . The expression of Wg in two concentric rings ( the outer , OR , and inner , IR , rings ) in the proximal region of the wing disc helped us to delimit the wing pouch ( the region that will give rise to the adult wing blade , inside the IR ) from the surrounding wing hinge ( between IR and OR , Figure 1E ) . In dpp-depleted wing primordia , the sizes of the D and V compartments were reduced in a proportional manner , which is consistent with the symmetric reduction in size of the D and V surfaces of the adult wing ( Figure 1D ) . We observed that the size of the A compartment was reduced to a larger extent than the size of the P compartment ( Figure 1E ) . The size of the wing hinge ( the distance between the IR and OR , Figure 1E , bottom panels ) was largely unaffected . As expected , dpp mRNA levels and Dpp activity levels , visualised with an antibody against the phosphorylated form of the Dpp transducer MAD ( p-MAD ) and by the expression of the Dpp target genes spalt and optomotor-blind [omb , ( de Celis et al . , 1996; Lecuit et al . , 1996; Nellen et al . , 1996 ) ] , decreased in the developing wing appendage ( Figure 1F–I ) . Brinker ( Brk ) , a transcription factor whose expression is repressed by the activity of Dpp and restricted to the lateral sides of the wing disc ( Campbell and Tomlinson , 1999; Jaźwińska et al . , 1999; Minami et al . , 1999 ) , was de-repressed in all wing pouch cells ( Figure 1G–I ) . 10 . 7554/eLife . 22013 . 003Figure 1 . Independent RNAi hairpins to induce temporally controlled depletion of Dpp . ( A–D ) Cuticle preparations of male adult wings expressing the indicated RNAi hairpins under the control of nub-gal4 and grown at the indicated temperatures . In B-D , flies carry the tub-gal80ts transgene . High magnification of rudimentary wings are shown in A and D , and percentages of wing size with respect to control GFP-RNAi expressing wings are indicated in A , C , and D . Wing blade ( wb , pink ) and hinge structures ( alula and costa , blue ) are shaded in A . Scale bars in A-D , 300 µm . Scale bars in the squared wings in A , D , 150 µm . ( A’–D’ ) Histograms plotting tissue size of the wing blade with the indicated genotypes normalized as a percent of the control wings . Error bars show standard deviation . Number of wings per genotype and temperature >15 . ns , not significant; **p<0 . 001 , p<0 . 05 . ( E–I ) Late third instar wing discs of the indicated genotypes , grown at 29°C , and stained for Wg and Ptc ( E , green ) , DAPI ( E , blue ) , Nub ( E , red ) , dpp mRNA ( purple , F ) , p-MAD ( G , red ) , Spalt ( H , red ) , Omb ( I , red ) and Brk ( G-I , green ) . Scale bars , 50 µm ( E–I ) or 25 µm ( higher magnifications in E , F ) . Higher magnifications of the dorsal hinge region are shown below each wing disc in panel E . In E , inner ( IR ) and outer ( OR ) rings of Wg , and dorsal ( D ) , ventral ( V ) , anterior ( A ) and posterior ( P ) compartments are marked . Note that the width of the hinge is largely unaffected by Dpp depletion . In F , dpp mRNA levels are reduced in the wing pouch ( wp ) when compared to the hinge region ( black arrows ) . Higher magnifications of the wing pouch are shown below each wing disc in panel F . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 00310 . 7554/eLife . 22013 . 004Figure 1—source data 1 . Summary of tissue size quantifications . This file contains numerical data on tissue size quantifications of Figure 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 004 We next performed temperature-shift experiments ( from 18°C to 29°C ) to initiate depletion of dpp at different times of larval and pupal development and to characterise the impact on the size of the resulting adult wings . We started our temperature shifts in the mid-second instar stage , as the wing appendage is specified and nubbin expression is initiated in late second instar wing discs [grey arrow in Figure 2A , ( Ng et al . , 1995 , 1996 ) ] . Depletion in pupal stages , when Dpp is expressed along the presumptive vein regions ( de Celis , 1997; Yu et al . , 1996 ) , had a mild impact on final wing size ( Figure 2B , C ) . The earlier the initiation of Gal4-dependent expression of the dpp-RNAi hairpin in larval wing primordia , the stronger the effects on wing size ( Figure 2B , C ) . Remarkably , the effects on tissue size increased in a gradual manner . As expected , cell size was largely unaffected in dpp-depleted wings ( Figure 2E ) . In all cases , we observed that dpp depletion gave rise to a reduction in both the width and length of the wing resulting in smaller but largely well-proportioned adult structures ( Figure 2D ) . The distal hinge structures were well formed and grown . At longer exposures , the size of the A compartment was reduced to a larger extent than the size of the P compartment ( Figure 2C ) . All together , these observations indicate that dpp expression in wing cells is continuously required since the wing appendage is being specified and during the whole third instar stage to promote proliferative growth of the wing blade . 10 . 7554/eLife . 22013 . 005Figure 2 . Dpp is continuously required for growth of the wing blade . ( A ) Cartoon depicting developmental timing in hours ( h ) and days ( d ) at 29°C and 18°C , respectively . Grey arrow marks the developmental timing at which the wing is specified . L1-L3 , larval stages . ( B ) A series of cuticle preparations of male adult wings carrying the tub-gal80ts transgene and the nub-gal4 driver and shifted from 18°C to 29°C at the developmental time points ( red arrows ) indicated in the corresponding cartoons to initiate expression of GFP- or dpp-RNAi hairpins until adulthood . The percentages of wing size with respect to control GFP-RNAi expressing wings subjected to the same temperature shifts are indicated . Anterior , A , and posterior , P , compartments are marked by blue lines based on the characteristic anterior-posterior pattern of bristles at the wing margin . Scale bars , 300 µm . ( C–E ) Histograms plotting tissue size ( C ) , proportions ( width and length , D ) , and cell size ( E ) of adult wings carrying the tub-gal80ts and the UAS-dpp-RNAi transgenes and the nub-gal4 driver , shifted from 18°C to 29°C at the developmental time points TS1-TS6 indicated in the cartoons in B and normalized as a percent of the GFP-RNAi expressing control wings . Error bars show standard deviation . Number of wings per temperature >15 . *p<0 . 001; ns , not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 00510 . 7554/eLife . 22013 . 006Figure 2—source data 1 . Summary of tissue size and width and length quantifications . This file contains numerical data on tissue size and cell size , and width and length quantifications of Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 006 CRISPR-Cas9-mediated genome editing of the dpp locus has enabled the conditional removal of the dpp gene from its endogenous stripe domain and contributed to the proposal that the Dpp morphogen gradient emanating from the AP compartment boundary is not continuously required to induce wing growth ( Akiyama and Gibson , 2015 ) . In order to revisit this proposal , we used the dppdisk-gal4 ( dpp-gal4 ) driver to induce targeted expression of the dpp-RNAi hairpin along the compartment boundary . This Gal4 driver contains the 4 kb DNA fragment within the 3’disk region called the blk enhancer fragment , which is expressed in all dpp-producing cells in imaginal tissues ( Masucci et al . , 1990 ) and known to rescue dpp-disk alleles when driving Dpp expression ( Staehling-Hampton et al . , 1994 ) . Once again , we used the tub-gal80ts transgene to modulate dpp depletion over time . We first characterised the kinetics of Dpp depletion by shifting the larvae from 18°C to 29°C at different time points of the third instar stage and analysing dpp expression and Dpp activity levels in late third instar wing discs ( Figure 3 and Figure 3—figure supplement 1 ) . We used wing discs grown at 18°C as controls and monitored dpp mRNA and p-MAD , Brk , Spalt and Omb protein levels . p-MAD levels and Brk repression in the central part of the wing pouch were the first to be altered upon induction of dpp-RNAi expression ( Figure 3 ) . After 12 hr of induction , p-MAD levels were visibly reduced , and Brk initiated expression in the central part of the wing pouch . Twenty-four hours of induction were sufficient to completely remove p-MAD levels and obtain robust expression of Brk in all wing pouch cells . Longer exposures ( at least 36 hr ) to dpp-RNAi expression were required to remove the expression of the Dpp target genes Spalt and Omb from wing pouch cells ( Figure 3 ) . The levels of dpp mRNA were already decreased after a period of 12–24 hr of induction but longer exposures ( 36–48 hr ) were required to induce a more robust , though not complete , reduction of dpp mRNA ( Figure 3—figure supplement 1 ) . These results indicate that the wing pouch levels of Brk and p-MAD proteins are highly sensitive to reductions in Dpp , and that Spalt and Omb either require stronger or longer reduction in Dpp activity levels or they are highly stable proteins , which might contribute to the robustness of Dpp-mediated patterning of the wing . 10 . 7554/eLife . 22013 . 007Figure 3 . Changes in Dpp signaling and target gene expression upon temporal depletion of boundary Dpp . Late third instar wing discs of the indicated genotypes , raised at 18°C or 29°C throughout development ( left or right panels ) , or shifted from 18°C to 29°C at the indicated developmental times ( red arrows ) , and stained for Wg and Ptc ( green ) , p-MAD ( red or grey ) , Brk ( blue or grey ) , Spalt ( grey ) and Omb ( grey ) . A proxy of the wing pouch was marked by a red line . The inner ( IR ) and outer ( OR ) rings of Wg , and the hinge ( hg ) are marked in some wing discs . Note that the width of the hinge is largely unaffected by Dpp depletion . Scale bars , 50 µm . L2 , L3 , second and third instar . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 00710 . 7554/eLife . 22013 . 008Figure 3—figure supplement 1 . dpp mRNA expression upon temporally controlled expression of dpp-RNAi with the dpp ( blk ) -gal4 driver . Late third instar wing discs of the indicated genotype , raised at 18°C throughout development ( left panel ) , or shifted from 18°C to 29°C at the indicated developmental times ( red arrows ) , and stained to visualize dpp mRNA ( purple ) . All samples were processed in parallel . Lower panels show high magnification of the dpp stripe . Scale bars , 50 µm ( upper panels ) , or 25 µm ( bottom panels ) . L2 , L3 , second and third instar . While dpp mRNA levels are gradually reduced in the wing pouch ( wp ) , they remain largely unaffected in the ventral hinge ( arrows ) , most probably as a consequence of dpp ( blk ) -gal4 not driving robust expression of dpp-RNAi in this region . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 008 Unfortunately , adult wings expressing dpp-RNAi under the control of dpp-gal4 could not be recovered due to early pupal lethality . We thus analysed and quantified the effects on tissue size on larval wing primordia and monitored both the size of the whole wing disc and of the developing wing appendage ( wing pouch ) . We performed two protocols to deplete Dpp levels at the AP boundary and quantified the effects on tissue size ( Figure 4A , B and Figure 5A ) . We first analysed the effects on the size of late third instar wing discs upon induction of dpp-RNAi expression at the AP boundary during the last 12 , 24 , 36 and 48 hr of larval development . We stained the wing primordia with Wg and Ptc antibodies to label the DV and AP compartment boundaries , respectively . Control individuals expressing GFP and containing the dpp-gal4 driver and the tub-gal80ts transgene were subjected to the same temperature shifts and quantified in parallel . In this experimental setup , the folds of the wing hinge were used as a proxy to delimit the wing pouch ( depicted in Figure 4A ) . The effects on the size of the wing pouch and of the whole wing disc were already observed after 24 hr of dpp-RNAi expression and longer exposures gave rise to a gradual reduction in wing pouch and wing disc size ( Figure 4A–D ) . The impact on the size of the wing pouch was stronger than on the size of the whole wing disc , thereby indicating that Dpp plays a major role in promoting the growth of the wing appendage and a minor role in the growth of the body wall ( see below ) . The size of the A compartment was also reduced to a larger extent than the size of the P compartment ( Figure 4—figure supplement 3 ) . Similar results were obtained with other dpp-RNAi lines ( Figure 4—figure supplement 3 ) . The overall proportions of the wing pouch ( width versus length , Figure 3 and Figure 4A , B ) and the width of the wing hinge ( the distance between the IR and the OR , Figure 3 , Figure 4B and Figure 4—figure supplement 1 ) were largely unaffected by dpp depletion . Temporally controlled expression of dpp-RNAi with ptc-gal4 , which is restricted to the AP compartment boundary , also caused a stronger reduction of the wing pouch than of the wing disc ( Figure 4—figure supplement 2 ) . In all cases , at 18°C , the size of the wing pouch and the wing disc was unaffected in dpp-RNAi-carrying discs . 10 . 7554/eLife . 22013 . 009Figure 4 . Effects on the size of the wing pouch and wing disc upon temporal depletion of boundary Dpp . ( A , B ) Late third instar wing discs of the indicated genotypes , raised at either 18°C or 29°C throughout development ( left and right panels ) or shifted from 18°C to 29°C at the indicated developmental times ( red arrows ) . Discs were stained for Wg and Ptc ( A , green or B , white ) , and DAPI ( A , blue ) . In A , wing pouch and disc contours are marked by a dotted line . In B , the outer , OR , and inner , IR , rings of Wg are marked by arrowheads and used to delimit the hinge ( between the rings ) . Note the width of the hinge is largely unaffected by Dpp depletion . Scale bars , 100 µm . ( C , D ) Scatter plots showing the size ( normalized to GFP ) of the wing pouch ( C ) and wing disc ( D ) of the indicated genotypes , raised at either 18°C or 29°C throughout development or shifted from 18°C to 29°C at the indicated developmental times shown in A . Average wing pouch or wing disc areas of dpp-RNAi-expressing individuals ( normalized to GFP ) are shown in blue . Error bars show standard deviation . Number of wing discs per experiment: n ( GFP ) =45–75; n ( dpp-i ) =40–70 . p<0 . 001 , p<0 . 05; ns , not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 00910 . 7554/eLife . 22013 . 010Figure 4—source data 1 . Summary of tissue size quantifications . This file contains numerical data on tissue size quantifications of Figure 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 01010 . 7554/eLife . 22013 . 011Figure 4—figure supplement 1 . Different effects of Dpp-depletion on the size of the wing pouch and hinge . ( A , B ) Late third instar larval wing discs of the indicated genotypes , raised at 29°C during the last 48 hr of larval development and stained for Wg and Ptc protein ( green or white ) , Teashirt ( Tsh , red or white , ( A ) , Homothorax ( Hth , red or white , ( B ) and DAPI ( blue ) . Tsh is expressed in the body wall region and contributes to delimit the region that will give rise to the adult wing hinge ( hg ) and wing pouch ( wp ) structures . Hth is expressed in the body wall and hinge regions and contributes to delimit the region that will give rise to the adult wing pouch ( wp ) . The two concentric rings of Wingless expression ( Outer Ring , IR , and Inner Ring , IR ) are marked by arrowheads and are used to delimit the hinge ( between the rings ) . Note that the width of the hinge is largely unaffected by Dpp depletion . Scale bars , 100 µm . L2 , L3 , wL3: second , third , and late third larval stages . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 01110 . 7554/eLife . 22013 . 012Figure 4—figure supplement 2 . Boundary Dpp is required for growth of the wing disc . ( A ) Larval wing discs of the indicated genotypes grown at 18°C or 29°C during whole larval development ( left and right examples ) or shifted from 18°C to 29°C at the developmental times indicated in the cartoons ( red arrows ) . Discs were dissected in late third instar ( wL3 ) and stained for Wg and Ptc ( green ) , p-MAD ( red ) , and Brinker ( Brk , blue ) . Scale bars , 100 µm . L2 , L3 , second and third larval stages . ( B ) Higher magnification of the Dpp-depleted wing pouches of panel A ) are shown to visualise p-MAD and Brinker ( Brk ) in grey . Scale bars , 50 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 01210 . 7554/eLife . 22013 . 013Figure 4—figure supplement 3 . Effects on the size of the wing pouch , wing disc and anterior and posterior compartments upon temporal depletion of boundary Dpp with different dpp-RNAi lines . ( A ) Larval wing discs of the indicated genotypes , shifted from 18°C to 29°C 48 hr before dissection ( see cartoons with the red arrows ) , dissected in late third instar ( wL3 ) and stained for Wg and Ptc ( green ) , and Brinker ( Brk , red or white ) and labelled with DAPI ( blue ) . Scale bars , 100 µm . L2 , L3 , second and third larval stages . Wing pouch and wing discs contours are marked by dotted lines in the upper panels . ( B , C ) Scatter plots showing the size ( normalized to GFP ) of the wing pouch ( B ) and wing disc ( C ) of the same genotypes as in A ) and subject to the same induction protocol . Average wing pouch or wing disc areas of dpp-RNAi-expressing individuals ( normalized to GFP ) are shown in blue . Error bars represent standard deviation . Number of wing discs per experiment , n = 24–33 . *p<0 . 001 . ( D ) Histograms plotting the size of the anterior and posterior compartments regions ( normalised to those of control discs expressing GFP ) of late third instar wing pouches of the same genotypes as in A ) and subject to the same induction protocol . Error bars represent standard deviation , p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 01310 . 7554/eLife . 22013 . 014Figure 4—figure supplement 3—source data 1 . Summary of tissue size quantifications . This file contains numerical data on tissue size quantifications of Figure 4—figure supplement 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 01410 . 7554/eLife . 22013 . 015Figure 5 . Effects on growth rates in the wing pouch and wing disc upon boundary depletion of Dpp . ( A ) Larval wing discs of the indicated genotypes shifted from 18°C to 29°C at the L2/L3 transition ( red arrows ) and dissected 0 , 12 , 24 , 36 and 48 hr thereafter . Discs stained for Wg ( red ) and Ci ( green ) , and DAPI ( blue ) . Scale bars , 100 µm . L2 , L3 , eL3 , mL3 , lL3 , wL3: second , third , early-third , mid-third , late-third and wandering-third larval stages . In the right panel , the outer , OR , and inner , IR , rings of Wg are marked by arrowheads and used to delimit the hinge ( between the rings ) . Note the width of the hinge is affected by Dpp depletion to a lesser extent than the pouch . ( B , C ) Fold change increase in the size of the wing pouch and wing disc ( with respect to the one at the L2/L3 transition ) of individuals expressing dpp-RNAi ( blue ) or GFP ( grey ) shifted from 18°C to 29°C at the L2/L3 transition ( red arrows ) . The average fold change increases in the area of the wing pouch ( B ) and wing disc ( C ) with respect to the values at the beginning of the temperature shift at the L2/L3 transition are indicated . Error bars represent standard deviation . Number of wing discs per developmental point: n ( GFP ) = 25–45; n ( dpp-i ) = 30–40 . *p<0 . 001; ns , not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 01510 . 7554/eLife . 22013 . 016Figure 5—source data 1 . Summary of tissue size quantifications . This file contains numerical data on tissue size quantifications of Figure 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 01610 . 7554/eLife . 22013 . 017Figure 5—figure supplement 1 . Changes in Dpp signalling in early wing discs upon temporal depletion of boundary Dpp . ( A , B ) Larval wing discs of the indicated genotypes raised at 29°C throughout development and dissected at the L2/L3 transition ( left column ) or shifted from 18°C to 29°C at the L2/L3 transition ( red arrows ) and dissected 0 , 12 , 24 and 36 hr thereafter ( second to fifth columns ) . Discs were stained for Wg and Ptc ( green ) , p-MAD ( red or grey ) , and Brk ( blue or grey ) . Higher magnifications of wing pouches are shown to visualize p-MAD and Brk in grey . Scale bars , 50 µm . L2 , L3 , eL3 , mL3 , lL3: second , third , early-third , mid-third , and late-third larval stages . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 017 The second approach used was to initiate dpp-RNAi expression at the AP boundary in early third instar wing discs and to quantify the impact on tissue size 12 , 24 , 36 and 48 hr thereafter ( Figure 5A ) . Control individuals expressing GFP and containing the dpp-gal4 driver and the tub-gal80ts transgene were subjected to the same temperature shifts and quantified in parallel . Expression of Wg at the inner ring ( IR ) of the wing hinge helped us to delimit the wing pouch in young and mature wing discs ( Figure 5A ) . After 12 hr of induction , p-MAD was completely lost , and Brk initiated expression in the central part of the wing pouch ( Figure 5—figure supplement 1 ) . Twenty-four hours of induction were sufficient to obtain robust expression of Brk in all wing pouch cells . Again , the effects of temporally controlled boundary expression of dpp-RNAi on the size of the wing pouch were stronger than on the size of the wing disc ( Figure 5A–C ) . The wing pouch of control wing discs expressing GFP showed an eleven-fold increase in size during the first half of the third instar but only a two-fold increase during the last 24 hr of larval development ( Figure 5B ) . Interestingly , the impact of Dpp depletion on the size of the wing pouch and wing disc was still observed during this later developmental period ( Figure 5A–C ) . These results reinforce the requirement of Dpp for wing pouch growth throughout the third instar larval period ( Figure 2 ) and suggest that the minor impact on wing pouch size observed in the first protocol upon 12-h depletion of Dpp ( Figure 4A , B ) should be explained by insufficient reduction of dpp mRNA levels ( Figure 3—figure supplement 1 ) . Dpp is a target of Hedgehog ( Hh ) coming from the P compartment ( Capdevila et al . , 1994; Zecca et al . , 1995 ) . In mature third instar wing discs , Dpp is expressed in a narrow stripe at the AP boundary . In young third instar discs , the Dpp stripe has a similar width but it proportionally occupies one third of the disc ( Weigmann and Cohen , 1999 ) . Thus , cells lose expression of Dpp when they are displaced out of range of the Hh signal by growth of the disc . It has recently been shown that transient exposure of cells to short hairpins can reduce gene function in their descendants , due to the high stability of these hairpins ( Bosch et al . , 2016 ) . In order to rule out the possibility that the effects on tissue size are caused by targeted depletion of dpp in a broader domain than the one expressing dpp at the time of visualisation , we used the G-TRACE technique to label the domain that has been exposed to dpp-RNAi expression during the induction process ( Evans et al . , 2009 ) . In wing discs carrying the tub-gal80ts transgene and expressing a dpp-RNAi hairpin in the dpp-gal4 domain during 12 , 24 , 36 and 48 hr , the presence of an UAS-FLP transgene together with an FRT-mediated FLP-out cassette driving EGFP expression ( ubi-FRT-stop-FRT-EGFP ) , and an UAS-RFP construct , allowed us to irreversibly label , in green , all cells born in the dpp-gal4 domain during the induction process , and to compare this domain with the one expressing the transgenes , in red , at the time of visualisation ( Figure 6A , B ) . Control individuals not expressing a dpp-RNAi hairpin were subjected to the same protocol and analysed in parallel . As expected , the longer the induction , the broader the domain labelled by EGFP , thereby reinforcing the notion that cells lose Dpp expression when they are displaced out of range of the Hh signal by growth of the disc ( Figure 6A , B ) . In the case of induction periods of 24 hr or 36 hr , this domain occupied a small fraction of the central region of the wing primordium , and the non-autonomous effects on tissue size were significant , visualised by the impact on the size of the P compartment and on the size of the A compartment not labelled by EGFP ( Figure 6B , C ) . Although it is still possible that some low level of Dpp made outside the stripe might be knocked down in our experiments , our G-TRACE and temperature shifts results ( Figures 4 , 5 and 6 ) support the proposal that Dpp emanating from the AP compartment boundary is the one required to induce wing growth since the wing is being specified . Our experimental observation that incomplete reductions in the levels of dpp mRNA at the AP compartment boundary ( Figure 3—figure supplement 1 ) can have a clear impact on the size of the wing pouch ( Figure 4 ) is in conflict with the previously proposed role of low levels of non-boundary Dpp as the ones promoting wing growth ( Akiyama and Gibson , 2015 ) , and it strongly suggests that wing growth requires high levels of boundary Dpp . 10 . 7554/eLife . 22013 . 018Figure 6 . Cell lineage analysis of dpp-RNAi expressing cells and effects on growth rates upon temporal depletion of boundary Dpp . ( A ) G-TRACE-mediated cell lineage analysis to irreversibly label all cells born in the dpp-gal4 expressing domain during the last 12 , 24 , 36 and 48 hr of larval development . Larvae were shifted from 18°C to 29°C at the developmental times indicated in the cartoons ( red arrows ) to express dpp-RNAi , and late third instar wing discs were stained for RFP ( red ) , EGFP ( green ) and DAPI ( blue ) . As a proof of concept , larval wing discs were raised at 18°C ( left panel ) . Scale bars , 100 µm . L2 , L3 , second and third larval stages . ( B ) G-TRACE to label all cells born in the dpp-gal4-expressing domain during the last 24–48 hr of larval development . Larvae were shifted from 18°C to 29°C at the indicated developmental times ( red arrows ) and late third instar wing discs were stained for RFP ( red ) , EGFP ( green ) and DAPI ( blue ) . Wing pouch contours are marked by a dotted line . Scale bars , 50 µm . L2 , L3 , second and third instar . A , anterior; P , posterior compartments . ( C–E ) Histograms plotting the size of the indicated regions ( normalised to those of control discs not expressing dpp-RNAi , ( C , E ) or the size ratio between the EGFP- and RFP-expressing domains ( D ) in late third instar wing pouches ( inside dotted line ) expressing dpp-RNAi during the last 24–48 hr of larval development . Number of discs per genotype and induction time = 20–35 . Error bars represent standard deviation , and p<0 . 05; **p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 01810 . 7554/eLife . 22013 . 019Figure 6—source data 1 . Summary of tissue size and EGFP/RFP ratio quantifications . This file contains numerical data on tissue size and EGFP/RFP ratio quantifications of Figure 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 019 We made use of the G-TRACE results to characterise the requirement of Dpp emanating from the AP boundary on the growth of the different regions of the wing pouch . We analysed wing discs subjected to dpp depletion for 24 , 36 and 48 hr . The impact on the size of the A compartment of the wing pouch was stronger than on the size of the P compartment ( Figure 6B , C ) . The ratio between the EGFP and RFP expression domains , a proxy of the expansion of the medial region , was reduced in dpp-depleted wing discs when compared to controls and did not increase over time ( Figure 6D ) . Within the A compartment of the wing pouch , we observed that the size of the EGFP-expressing domain was reduced to a similar extent than the size of the domain not expressing EGFP ( Figure 6E ) . These results suggest that Dpp is equally required for the growth of both medial and lateral regions of the developing wing appendage . In order to further address the impact of Dpp depletion on the growth rates of medial and lateral regions of the developing wing appendage , we induced neutral clones of cells in early-second instar , initiated dpp-RNAi expression in early-third instar wing discs and examined the size of these clones 42 hr later in mature wing discs . Clone size ( in arbitrary units ) and number of cells per clone were measured in hs-FLP , RFP , FRT19A/FRT19A; tub-gal80ts; dpp-gal4/UAS-dpp-RNAi wing discs , and these two measurements were compared to those of control clones induced in hs-FLP , RFP , FRT19A/FRT19A; tub-gal80ts; dpp-gal4/UAS-EGFP-RNAi wing discs and grown in parallel . In dpp-depleted wing discs , the size of the clones and the number of cells per clone in the wing pouch was , as expected , significantly smaller than the size of clones quantified in the wing pouch of EGFP-RNAi-expressing discs ( Figure 7A , B ) . The size reduction of posterior clones was slightly less than the one observed in anterior clones ( Figure 7B ) . Both medial and lateral clones were smaller to a similar extent , thus reinforcing the notion that Dpp is equally required for the growth of both medial and lateral regions of the developing wing pouch ( Figure 7C ) . We observed that the elongated shape of the clones along the proximal-distal axis of the wing pouch was largely unaffected by depletion of dpp ( Figure 7A ) . The size of clones of cells in the body wall and hinge regions of dpp-depleted wing discs was reduced to a smaller extent than those located in the wing pouch ( Figure 7D and Figure 7—figure supplement 1 ) . In order to further characterize the impact of reduced Dpp on the growth of medial and lateral regions of the wing , we also examined the size distribution of clones in pupal wings . In this case , we generated neutral clones of cells and initiated dpp-RNAi expression in mid-third instar wing discs of hsFLP; ubi-FRT-stop-FRT-GFP/nub-gal4; tub-gal80ts /UAS-dpp-RNAi individuals , and the number of cells per clone was compared to that of control clones examined in hsFLP; ubi-FRT-stop-FRT-GFP/nub-gal4; tub-gal80ts/UAS-EGFP-RNAi pupal wings . Interestingly , Dpp depletion caused a similar reduction in the size distribution of clones located in medial and lateral regions of the wing ( Figure 7E , F , F’ ) . We also observed that the size distribution of clones was nearly identical in medial and lateral regions of both control and dpp-depleted pupal wings ( Figure 7E , F , F’ ) . All together , these results indicate that Dpp emanating from the AP boundary is equally required to promote proliferative growth of medial and lateral regions of the developing wing . 10 . 7554/eLife . 22013 . 020Figure 7 . Boundary Dpp regulates growth and proliferation rates equally in medial and lateral regions of the developing wing . ( A , E ) Late third instar wing discs ( A ) or pupal wings ( E ) expressing the indicated transgenes for 42 hr ( A ) or 48 hr ( E ) , and bearing neutral clones ( labelled by the absence of RFP in red or white in A , or by the expression of GFP in white in E ) induced in early second ( A ) or mid-third ( E ) instars . In A , discs were stained for Wg and Ptc ( green ) and DAPI ( blue ) . In E , high magnification of the regions in the white boxes are shown on the right panels . Scale bars , 50 µm ( A ) or 100 µm ( E ) . L2 , L3 , second and third instar . A , anterior; P , posterior compartments; wp , wing pouch; hg , hinge; APF , after puparium formation . ( B–D ) Histograms plotting clone size ( in arbitrary units , B–D ) or cells per clone ( B , right ) in the indicated regions of late third instar wing discs expressing the indicated transgenes . Clones were induced in early second instar , transgenes were expressed for 42 hr , and wing discs were dissected in late third instar . Number of clones: n ( pouch ) = 70–85 , n ( hinge/body wall ) =35–40 . Error bars represent standard deviation , and p<0 . 05; *p<0 . 001 . ( F , F’ ) Distribution of cells per clone in the indicated regions of pupal wings expressing the indicated transgenes . Clones were induced in mid-third instar , transgenes were expressed for 48 hr , and pupal wings were dissected 24 hr APF . Number of clones in F: n ( lateral ) = 427 , n ( medial ) = 855; number of clones in F’: n ( lateral ) = 337 , n ( medial ) = 404 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 02010 . 7554/eLife . 22013 . 021Figure 7—source data 1 . Summary of clone size and number of cells per clone quantifications . This file contains numerical data on clone size and number of cells per clone quantifications of Figure 7 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 02110 . 7554/eLife . 22013 . 022Figure 7—figure supplement 1 . Different effects of Dpp-depletion on the growth rates of wing pouch and hinge cells . ( A , B ) Late third instar wing discs ( A ) and 24 h-after puparium formation ( APF ) pupal wings ( B ) of the indicated genotypes and shifted from 18°C to 29°C at the developmental times indicated in the cartoons ( red arrows ) . Discs were stained for Brinker ( Brk , green or white , ( A ) and DAPI ( blue in A , white in B ) . The lateral region of a wild type wing pouch was labelled by the expression of Brinker and covered 23% ( in the anterior compartment ) or 30% ( in the posterior compartment ) of the wing pouch width . These numbers were used to demarcate the lateral regions of dpp-depleted wing pouches . The lateral regions of a wild-type pupal wing correspond to the regions located anterior to vein L2 and posterior to vein L5 , and covered 15% ( in the anterior compartment ) or 30% ( in the posterior compartment ) of the pupal wing width along the axes depicted in panel B . These numbers were used to demarcate the lateral regions of dpp-depleted pupal wings . ( C ) Late third instar wing discs expressing the indicated transgenes during the last 42 hr of larval development and stained for RFP ( red ) , Wg and Ptc ( green ) , and DAPI ( blue ) . Neutral clones labelled by the absence of RFP were induced in early second instar . Scale bars , 50 µm ( A , C ) and 100 µm ( B ) . L2 , L3: second and third larval stages in the cartoons shown in A-C . L2-L5 , longitudinal veins in the pupal wing shown in B . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 022 Our experimental observations showing that temporally controlled depletion of Dpp gives rise to smaller but largely well-proportioned adult wings ( Figure 2B , D ) and that Dpp is equally required to promote proliferative growth of medial and lateral regions of the wing primordium ( Figure 7E , F , F’ ) suggest that regional differences in Dpp signaling activity do not have a direct impact on growth rates . We next tested whether our results are consistent with the model proposed in Schwank et al . ( 2008 ) that indicates that Dpp regulates wing growth by simply counteracting the growth-repressive activity of Brinker . In this work , authors focused on the developing wing imaginal disc to present evidence that a hypomorphic allele of brinker was able to largely rescue the tissue size defects caused by a hetero-allelic and hypomorphic combination of dpp alleles . We took advantage of our experimental setup based on expression of dpp-RNAi hairpins under the control of the nub-gal4 driver to co-express one or two independent brk-RNAi hairpins and analyse the impact on the size of the resulting adult wings . Consistent with the results obtained by Schwank et al . ( 2008 ) , co-depletion of brk and dpp gave rise to nearly wild type-sized wing pouch primordia ( Figure 8A ) . As expected , p-MAD levels were not rescued in these experimental conditions , and Spalt and Omb expression was restored , reinforcing the Dpp-Brinker double repression mechanism involved in Dpp target gene expression ( Affolter and Basler , 2007; Restrepo et al . , 2014 ) . The use of two brk-RNAi lines at the same time was able to rescue to a larger extent the expression levels of Spalt ( Figure 8A ) . We observed , although , that Spalt and Omb were expressed at lower levels than in wild-type primordia and that their expression profiles did not show their characteristic graded expression along the anterior-posterior axis ( Figure 8A ) . Most interestingly , size but not patterning of the resulting adult wings was rescued by co-depletion of dpp and brk , and this rescue was almost complete by the use of two brk-RNAi lines at the same time ( Figure 8B , C ) . Similar results on Dpp signaling and tissue size were obtained with the rotund-gal4 driver , whose expression is also restricted to the developing wing blade ( Figure 8—figure supplement 1 ) . These observations indicate that graded activity of Dpp is required for wing patterning but not for wing growth , and reinforce the proposal that Dpp promotes growth of the wing blade mostly by reducing Brinker levels . We noticed that the shape of the wing pouch of dpp and brk co-depleted wing discs was elongated along the anterior-posterior axis ( Figure 8A and Figure 8—figure supplement 1 ) . This elongation was not observed in the resulting adult wings ( Figure 8B and Figure 8—figure supplement 1 ) , raising the possibility that cell re-arrangements during metamorphosis might contribute to the correction of the final wing shape . 10 . 7554/eLife . 22013 . 023Figure 8 . Wing growth in the absence of graded activity of Dpp . ( A ) Late third instar wing discs of the indicated genotypes , and stained for Wg and Ptc ( green ) , and Brk , p-MAD , Spalt and Omb ( grey ) . Scale bars , 100 µm . ( B ) A series of cuticle preparations of male adult wings of the indicated genotypes . Percentages of wing size with respect to control GFP-expressing wings are indicated . Scale bars , 300 µm . ( C ) Histograms plotting tissue size of adult wing blades ( wb ) carrying the indicated transgenes and the nub-gal4 driver , and normalized as a percent of the GFP-expressing control wings . Error bars show standard deviation . Number of wings per temperature = 17–30 . p<0 . 001; ns , not significant . Individuals were grown at 25°C . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 02310 . 7554/eLife . 22013 . 024Figure 8—source data 1 . Summary of tissue size quantifications . This file contains numerical data on tissue size quantifications of Figure 8 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 02410 . 7554/eLife . 22013 . 025Figure 8—figure supplement 1 . Wing growth in the absence of graded activity of Dpp . ( A ) Late third instar wing discs of the indicated genotypes , and stained for Wg and Ptc ( green ) , GFP ( purple ) , and Brk , p-MAD , Spalt and Omb ( grey ) . Scale bars , 100 µm . ( B ) A series of cuticle preparations of male adult wings of the indicated genotypes . Percentages of wing size with respect to control GFP-expressing wings are indicated . Scale bars , 300 µm . ( C ) Histograms plotting tissue size of adult wing blades ( wb ) carrying the indicated transgenes and the rn-gal4 driver , and normalized as a percent of the GFP-expressing control wings . Error bars show standard deviation . Number of wings per temperature = 12–20 . p<0 . 001; ns , not significant . Individuals were grown at 29°C . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 02510 . 7554/eLife . 22013 . 026Figure 8—figure supplement 1—source data 1 . Summary of tissue size quantifications . This file contains numerical data on tissue size quantifications of Figure 8—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 02610 . 7554/eLife . 22013 . 027Figure 8—figure supplement 2 . Dpp spreading and wing growth . ( A ) A series of cuticle preparations of male adult wings of the indicated genotypes . Percentages of wing size with respect to control GFP-expressing wings are indicated . Scale bars , 300 µm . ( B ) Histograms plotting tissue size of adult wing blades ( wb ) carrying the indicated transgenes and the nub-gal4 driver , and normalized as a percent of the GFP-expressing control wings . Error bars show standard deviation . Number of wings per temperature >15 . p<0 . 001; ns , not significant . Individuals were grown at 25°C . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 02710 . 7554/eLife . 22013 . 028Figure 8—figure supplement 2—source data 1 . Summary of tissue size quantifications . This file contains numerical data on tissue size quantifications of Figure 8—figure supplement 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 22013 . 028 Here , we have used a collection of dpp-RNAi hairpins and the GAL4/UAS system combined with the temperature-sensitive Gal80 molecule to control the depletion of dpp expression in time and space and characterize the spatial and temporal requirement of Dpp in promoting the growth of the different regions of the wing primordium: the wing blade , the wing hinge and the body wall . We present evidence that the impact of Dpp depletion on the growth rates and size of the hinge and body wall regions was mild . Our data indicate that Dpp emanating from the AP compartment boundary is absolutely required throughout development to promote the growth of the developing wing blade by regulating cell proliferation and growth rates . Dpp depletion gave rise to a reduction in the size of both the anterior-posterior and proximal-distal axes of the wing . We observed that growth rates in wild-type primordia were similar in those regions with the highest ( medial ) and lowest ( lateral ) levels of Dpp activity , and that Dpp depletion caused an identical impact on the growth rates of these two regions . These results argue against a direct relationship between Dpp signalling activity levels and the rates of proliferative growth within the developing wing blade . The fact that co-depletion of Dpp and Brinker in developing wing blade cells gives rise to nearly wild type-sized adult wings indicate that graded activity of Dpp is not an absolute requirement for growth and that Dpp promotes proliferative growth by maintaining Brk expression levels below a growth-repressive threshold ( Schwank et al . , 2008 ) . This proposal is consistent with the observation that wing blade cells unable to transduce the Dpp signal and mutant for brk proliferate at the same rate as wild-type cells ( Schwank et al . , 2012 ) and that loss of Brinker in clones of cells induces overgrowths mainly in the wing hinge region ( Campbell and Tomlinson , 1999; Jaźwińska et al . , 1999; Martín et al . , 2004; Minami et al . , 1999 ) . These results question the classical ‘steepness’ model that proposes that juxtaposition of cells sensing disparate levels of the morphogen promotes proliferative growth ( Lawrence and Struhl , 1996; Rogulja and Irvine , 2005 ) and the more recent ‘temporal rule’ model that postulates that cells divide when Dpp signaling levels have increased by 50% ( Wartlick et al . , 2011 ) . How is then the size of the wing controlled by Dpp ? Of remarkable interest is the capacity of overexpression of Dally , a proteoglycan that contributes to Dpp stability ( Akiyama et al . , 2008 ) , to induce the expansion and flattening of the Dpp gradient and to cause an increase in wing size [ ( Ferreira and Milán , 2015 ) , Figure 8—figure supplement 2] . The observation that wing primordia overgrow even though the Dpp gradient is flattened also questions the classical ‘steepness’ model . A reduction in Dally expression levels induces smaller but well-proportioned wings in which the patterning elements are correctly located [ ( Ferreira and Milán , 2015 ) , Figure 8—figure supplement 2] . These results support the proposal that the range of Dpp spreading emanating from the AP boundary can regulate , in an instructive manner , the final size of the developing wing appendage . We noticed that the effects of Dpp depletion on tissue size and growth rates were always stronger in the A compartment . Whether the differential response of P and A cells to Dpp depletion relies on the expression of the selector genes conferring compartment identity ( Tabata et al . , 1995 ) or on the fact that Hedgehog is expressed in P cells but only sensed in A cells ( Domínguez et al . , 1996 ) remains to be elucidated . The Cre-Lox and FLP-FRT recombination systems are widely used in developmental biology to induce conditional null alleles and address gene function . The recent use of an FRT-dependent conditional null allele of dpp came to the unexpected conclusion that Dpp emanating from the AP boundary is not required for wing growth and that the growth-promoting role of Dpp is restricted to the early stages of wing development ( Akiyama and Gibson , 2015 ) . Although authors conclude , in line of our results , that the Dpp gradient does not play an instructive role in wing growth , our results , based on the use of RNAi hairpins targeting dpp , do not validate their conclusions on the temporal and spatial requirement of Dpp expression and their impact on growth . It is interesting to note in this context that our work has demonstrated an important role of Dpp in promoting wing blade growth , and a minor role in promoting the growth of wing hinge and body wall . Unfortunately , Akiyama and Gibson based their conclusions only on the impact of Dpp removal on the size of the whole wing primordium and not specifically on the size of the developing wing blade . It is also certainly possible that RNAi-mediated depletion of gene activity might bypass the potential stability of the dpp mRNA , thus inducing a more efficient and rapid removal of Dpp activity than the FRT-dependent conditional null allele of dpp . The following strains were provided by the Bloomington Drosophila Stock Center: UAS-dppRNAi ( RRID:BDSC_25782 , RRID:BDSC_33628 , RRID:BDSC_33767 , RRID:BDSC_33618 , RRID:BDSC_36779 ) , UAS-brkRNAi ( RRID:BDSC_51789 ) , UAS-GFPRNAi ( RRID:BDSC_9331 ) , UAS-EGFPRNAi ( RRID:BDSC_35782 ) , UAS-GFP ( RRID:BDSC_35786 ) , UAS-Red , UAS-FLP , ubi-FRT-stop-FRT-EGFP ( G-TRACE , RRID:BDSC_28282 ) , ubi-FRT-stop-FRT-GFP ( RRID:BDSC_32250 ) , nubbin-gal4 ( RRID:BDSC_25754 ) , and UAS-dallyRNAi ( RRID:BDSC_33952 ) , or by the Vienna Drosophila RNAi Center: UAS-brkRNAi ( # 2919 ) , and UAS-dallyRNAi ( #14136 ) . rotund-gal4 is described in Colombani et al . ( 2012 ) , dppdisk-gal4 ( dpp-gal4 in the text ) in Staehling-Hampton et al . ( 1994 ) , and UAS-dally in Ferreira and Milán ( 2015 ) . Other stocks are described in Flybase . Mouse anti-Wg ( 1:50 , 4D4 , DSHB ) ; mouse anti-Ptc ( 1:50 , ( Apa1 , DSHB ) ; rabbit anti-Tsh ( 1:600 , gift from S . Cohen ) ; rabbit anti-Nub ( 1:600 , gift from X . Yang ) ; rabbit anti-Spalt ( 1:500 , gift from R . Barrio ) ; rabbit anti-p-MAD ( 1:200 ) and guinea pig anti-Brk ( 1:1000; gift from G . Morata ) ; rabbit anti-Omb ( 1:1000 , gift from G . Pflugfelder ) ; rat anti-Ci ( 1:10 , 2A1 , DSHB ) ; and goat anti-Hth ( 1:50 , sc-26187 , Santa Cruz ) . Secondary antibodies Cy2 , Cy3 , Cy5 and Alexa 647 ( 1:400 ) were obtained from Jackson Immuno-research . A digoxigenin ( DIG ) -labelled antisense probe was transcribed by T3 RNA polymerase from an EcoRI digested full-length dpp cDNA clone RE20611 ( gift from C . Estella ) , using the DIG RNA Labelling Kit ( Roche ) according to the manufacturer's instructions . In situ hybridisation was performed as in Milán et al . ( 1996 ) . We used the Gal4/UAS system ( Brand and Perrimon , 1993 ) , combined with the thermo-sensitive version of Gal80 ( Gal80ts , [McGuire et al . , 2004] ) , a repressor of Gal4 protein activity , to precisely control , in time and space , the expression of dpp-RNAi . Adult flies carrying a Gal4 driver , the tub-gal80ts construct and the UAS-dpp-RNAi ( experimental condition ) were allowed to lay eggs in plates at 18°C over a period of 12 hr . Flies not carrying the UAS-dpp-RNAi transgene were also allowed to lay eggs in parallel ( control condition ) . The progeny of both the experimental and control conditions was then raised at 18°C to maintain the Gal4/UAS system switched off and then transferred to 29°C for different periods during larval development to induce Gal4/UAS-dependent gene expression . Larvae were staged at the second to third larval stage transition to target dpp-RNAi expression for defined periods of time during third instar . Experimental conditions and control individuals were grown in parallel . In the case of targeted expression of dpp-RNAi in the dpp-gal4 domain , G-TRACE was performed as described in Evans et al . ( 2009 ) to trace the lineage of all the cells that have expressed dpp-RNAi during the induction period at 29°C . For this purpose , the following genotypes were used: ( 1 ) UAS-Red , UAS-FLP , ubi-FRT-stop-FRT-EGFP/tub-gal80ts; dpp-gal4/+ and ( 2 ) UAS-Red , UAS-FLP , ubi-FRT-stop-FRT-EGFP/tub-gal80ts; dpp-gal4/UAS-dpp-RNAi . The size of adult wings and their anterior and posterior compartments ( in µm2 ) , wing proportions ( width and length , in µm ) and cell size ( in µm2 ) were all measured using Fiji Software ( NIH , USA ) . Wing proportions ( width and length ) were measured along the red lines depicted in Figure 2B . The length corresponded to the distance between the tip of the anterior-posterior compartment boundary and the confluence of the anterior and posterior vein trunks in the hinge . The width corresponded to the distance between the anterior and posterior wing margins along a line orthogonal to the anterior posterior compartment boundary and depicted in the middle of the length's line . Cell size was measured as follows . Two conserved regions of a defined size between veins L4 and L5 ( P compartment ) and veins L2 and L3 ( A compartment ) were used to quantify the number of hairs ( each wing cell differentiates a hair ) . Cell size was measured as the ratio between the size of the region and the number of hairs . The final values of dpp-RNAi-expressing wings were normalized as a percent of the control gal4-driver; UAS-GFP-RNAi values . At least 15 adult wings coming from different individuals were scored per genotype . Only adult males were scored . The average values and the corresponding standard deviations were calculated , and a Student t-test was carried out . Experimental conditions and control individuals driving the expression of control UAS-GFP-RNAi transgenes were grown in parallel . The AP boundary of adult dpp-RNAi-expressing wings in Figure 2 was identified by the distinct pattern of bristles at the wing margin . The sizes of the wing disc and of the wing pouch were measured using Fiji Software ( NIH , USA ) . Specified cell populations ( notum , hinge and wing blade ) are separated by epithelial folds , which are initiated by the apical shortening of cells at the early to mid-L3 stage . In order to delimit the wing pouch region , we always imaged apical sections of the wing disc and used the dorsal and ventral blade/hinge fold ( according to DAPI staining ) to delimit our wing pouch area . Only in Figure 5 , we strictly followed the inner ring of Wg to delimit the wing pouch area . 25–75 wing discs were scored per genotype and experiment . The corresponding standard deviation was calculated , and a Student’s t-test was carried out . Experimental conditions and individuals driving the expression of control UAS transgenes were grown in parallel . In Figure 6 , after limiting the wing blade region of interest ( represented by a dot line ) , quantification of the A , P , RFP-labelled , EGFP-labelled and non-expressing areas during the G-TRACE experiment were measured using Fiji Software ( NIH , USA ) manually ( for the A and P ) or by adjusting the signal threshold ( RFP , EGFP ) . At least 20 wing discs per genotype and experiment were scored . The corresponding standard deviation was calculated , and the Student’s t-test was carried out . Larvae from the following genotypes: ( 1 ) hs-FLP , ubi-nls-RFP , FRT19A/FRT19A; tub-gal80ts/+; dpp-gal4/UAS-EGFP-RNAi and ( 2 ) hs-FLP , ubi-nls-RFP , FRT19A/FRT19A; tub-gal80ts/+; dpp-gal4/UAS-dpp-RNAi were grown at 18°C , heat-shocked at 38°C for 45 min in early-second instar ( 96–108 hr AEL ) , returned to 18°C and transferred to 29°C in early third instar ( 156–168 hr AEL ) . Wing discs were dissected 42 hr thereafter , and the size of clones was quantified from confocal images with Fiji Software ( NIH , USA ) . At least 80 clones in the wing blade and 35 clones in the hinge and body wall regions were quantified , and only clones/twin spots that did not fuse with neighboring clones/twin spots were used for the statistical analysis . Average values and the corresponding standard deviations were calculated , and a Student’s t-test was carried out . Clones in the two different genotypes were induced and dissected always in parallel . Medial and lateral regions of control and dpp-depleted wing pouches were marked as depicted in Figure 7—figure supplement 1 . Larvae from the following genotypes: ( 1 ) hs-FLP; ubi-FRT-stop-FRT-GFP , tub-gal80ts; nub-gal4/UAS-dpp-RNAi and hs-FLP; ubi-FRT-stop-FRT-GFP/nub-gal4; tub-gal80ts/UAS-EGFP-RNAi were grown at 18°C , heat-shocked at 38°C for 9 min in mid-third instar ( 8 days AEL ) and transferred to 29°C . Those animals that entered puparium formation 24–30 hr thereafter were selected and pupal wings were dissected 24–30 hr after puparium formation ( APF ) . At least 300 clones in the lateral and 400 clones in the medial regions from a minimum of 12 different pupal wings were analysed . The number of cells per clone was quantified from confocal images . Clones in the two different genotypes were induced and dissected in parallel . Medial and lateral regions of control and dpp-depleted pupal wings were marked as depicted in Figure 7—figure supplement 1 . Standard deviation were calculated , and a Student’s t-test was carried out in all cases with the help of Excel . p<0 . 05; p<0 . 01; *p<0 . 001 . Graphical representations of data were done using GraphPad Prism version 6 . 07 or Microsoft Excel .	From the wings of a butterfly to the fingers of a human hand , living tissues often have complex and intricate patterns . Developmental biologists have long been fascinated by the signals – called morphogens – that guide how these kinds of pattern develop . Morphogens are substances that are produced by groups of cells and spread to the rest of the tissue to form a gradient . Depending on where they sit along this gradient , cells in the tissue activate different sets of genes , and the resulting pattern of gene activity ultimately defines the position of the different parts of the tissue . Decades worth of studies into how limbs develop in animals from mice to fruit flies have revealed common principles of morphogen gradients that regulate the development of tissue patterns . Morphogens have been shown to help regulate the growth of tissues in a number of different animals as well . However , how the morphogens regulate tissue size and what role their gradients play in this process remain topics of intense debate in the field of developmental biology . In the developing wing of a fruit fly , a morphogen called Dpp is expressed in a thin stripe located in the center and spreads to the rest of the tissue to form a gradient . Barrio and Milán have now characterized where and when the Dpp morphogen must be produced to regulate both the final size of the fly’s wing and the number of cells the wing eventually contains . The experiments involved preventing the production of Dpp in the developing wing in specific cells and at specific stages of development . This approach confirmed that Dpp must be produced in the central stripe for the wing to grow . Matsuda and Affolter and , independently , Bosch , Ziukaite , Alexandre et al . report the same findings in two related studies . Moreover , Barrio and Milán and Bosch et al . also conclude that the gradient of Dpp throughout the wing is not required for growth . Further work will be needed to explain how the Dpp signal regulates the growth of the wing . The answer to this question will contribute to a better understanding of the role of morphogens in regulating the size of human organs and how a failure to do so might cause developmental disorders .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology" ]	2017	Boundary Dpp promotes growth of medial and lateral regions of the Drosophila wing
Protein folding homeostasis in the endoplasmic reticulum ( ER ) requires efficient protein thiol oxidation , but also relies on a parallel reductive process to edit disulfides during the maturation or degradation of secreted proteins . To critically examine the widely held assumption that reduced ER glutathione fuels disulfide reduction , we expressed a modified form of a cytosolic glutathione-degrading enzyme , ChaC1 , in the ER lumen . ChaC1CtoS purged the ER of glutathione eliciting the expected kinetic defect in oxidation of an ER-localized glutathione-coupled Grx1-roGFP2 optical probe , but had no effect on the disulfide editing-dependent maturation of the LDL receptor or the reduction-dependent degradation of misfolded alpha-1 antitrypsin . Furthermore , glutathione depletion had no measurable effect on induction of the unfolded protein response ( UPR ) ; a sensitive measure of ER protein folding homeostasis . These findings challenge the importance of reduced ER glutathione and suggest the existence of alternative electron donor ( s ) that maintain the reductive capacity of the ER . Disulfide bonds have a critical role in stabilizing correctly folded secreted and membrane proteins ( Fass , 2012 ) . Dedicated enzymatic machinery , consisting of disulfide exchange catalysts of the protein disulfide isomerase ( PDI ) class , rapidly introduces disulfide bonds into nascent polypeptide chains . The recycling of PDIs back to their oxidized form is mediated by upstream oxidases , exemplified by ERO1 and back-up enzymes , such as PRDX4 and VKOR that exploit O2 , H2O2 , and vitamin K epoxide , respectively , as downstream electron acceptors . Defects in components of this electron transfer chain markedly affect protein folding homeostasis in the ER ( Sevier and Kaiser , 2008; Rutkevich and Williams , 2012; Zito , 2013 ) . Many disulfides introduced by the oxidative machinery are not native and must be broken down in an editing process that is catalyzed by reduced forms of PDIs ( Hatahet and Ruddock , 2009 ) . This editing entails attack by the reduced N-terminal cysteine of PDI's active site on the misplaced disulfide following resolution of the mixed disulfide . The latter proceeds either by way of a zero-sum re-shuffling process entailing an attacking second reduced cysteine in the client polypeptide , which releases reduced PDI and establishes a new disulfide , or by a net reductive process involving an attack on the mixed disulfide by the C-terminal active site cysteine , in which PDI ‘escapes’ in its oxidized form , having reduced its client protein ( Walker et al . , 1996 ) . Both mechanisms require a pool of reduced PDI , the maintenance of which is not trivial , given the oxidative environment in the ER . This challenge is especially great in case of the second mechanism , which is a set-up for iterative cycles of net oxidation-reduction-oxidation ( Schwaller et al . , 2003 ) . The crucial role of reduced PDI to oxidative protein folding is supported by in vitro experiments in which PDI's ability to accelerate folding is not monotonically increased by its oxidized fraction , but rather is optimal in a redox buffer that also contains a high concentration of reduced glutathione ( Lyles and Gilbert , 1991 ) . Yeast genetics reveal that disulfide shuffling ( which requires reduced PDI ) is PDI1's essential function , as an active-site mutant that has lost the ability to produce disulfides ( but has selectively retained activity as a disulfide reductase ) is nonetheless able to rescue the lethal phenotype of pdi1 nullizygosity ( Laboissiere et al . , 1995 ) . The reductive facet of oxidative protein folding in the ER is especially important to the maturation of large proteins such as the low density lipoprotein receptor ( LDL-R ) , in which it has been estimated that most disulfides that form early during biogenesis are non-native and must be rearranged before the protein clears ER quality control and traffics to the Golgi ( Jansens et al . , 2002 ) . This editing process appears to involve a specific PDI family member , ERdj5 ( Oka et al . , 2013 ) . ERdj5 may have specialized in disulfide reduction , as its redox function also accelerates the clearance of misfolded ER proteins such as the null Hong Kong mutant α1-antitrypsin ( NHK-A1AT ) ( Ushioda et al . , 2008; Hagiwara et al . , 2011 ) , but the identity of ERdj5's reductase remains unknown . Reductive editing of disulfides is also observed in the E . coli periplasm . Where the transfer of electrons from reduced thioredoxin in the cytosol maintains a reduced pool of the periplasmic isomerases , DsbC and DsbG . Electrons are conveyed across the inner-membrane space by a specialized transmembrane protein DsbD . This protein relay-based mechanism enables DsbC/DsbG-dependent disulfide shuffling despite the absence of a soluble small molecule redox buffer in the periplasmic space of gram negative bacteria ( reviewed in Cho and Collet , 2013 ) . By contrast , the mammalian ER contains up to 15 mM glutathione ( Montero et al . , 2013 ) whose reduced form is widely believed to fuel the reductive aspects of secreted protein metabolism in eukaryotes , by serving as a terminal electron donor to reduce PDI family members ( reviewed in Kojer and Riemer , 2014 ) . To critically examine the role of ER glutathione in the reductive re-shuffling of non-native disulfides and in the reductive steps believed to be associated with degradation of misfolded ER proteins , we devised a method to selectively deplete the ER of glutathione and examined the consequences on the organelles' capacity to handle well-characterized sentinel proteins . Kumar et al . recently reported that the mammalian pro-apoptotic gene ChaC1 encodes a glutathione-specific γ-glutamyl cyclotransferase that efficiently degrades glutathione ( Kumar et al . , 2012 ) . We confirmed their observations by measuring the ability of purified murine ChaC1 ( expressed in E . coli ) to degrade glutathione in vitro: At submicromolar enzyme concentrations , recombinant ChaC1 was able to degrade a 10 mM solution of reduced glutathione within 1 hr ( Figure 1A ) . The enzymatic activity was selective towards reduced glutathione ( GSH ) ( Figure 1B ) . The inability of oxidized glutathione ( GSSG ) to serve as a substrate for degradation correlated with its inability to bind a Chac1-based optical probe whose fluorescent resonance energy transfer ( FRET ) signal reflects substrate binding ( Figure 1C–D and Figure 1—figure supplement 1 ) . An E116Q mutation abolished all enzymatic activity ( Figure 1E ) , as observed previously ( Kumar et al . , 2012 ) . 10 . 7554/eLife . 03421 . 003Figure 1 . ChaC1 efficiently and selectively degrades reduced glutathione . ( A ) A bar-graph representation of residual glutathione levels following incubation of 10 mM glutathione with the indicated concentrations of bacterially expressed mouse ChaC1 . Varying concentrations of enzymes were assayed at a single time point ( left panel ) and varying initial concentrations of glutathione were assayed at different time points ( right panel ) . ( B ) Comparison of the ability of ChaC1 to eliminate reduced ( GSH ) and oxidized glutathione ( GSSG ) . Note that ChaC1 effectively eliminated reduced glutathione , but had no effect on oxidized glutathione . ( C ) Cartoon of the fluorescent resonance energy transfer ( FRET ) probe , OG-ChaC1-Cherry , used to detect substrate binding to ChaC1 . Shown is a model of murine ChaC1 ( UniProt Q8R3J5 ) residues 31–204 , created by Phyre2 ( Kelley and Sternberg , 2009 ) based on the crystal structure of γ-glutamyl cyclotransferase ( PDB 2RBH ) . The side chain of Cys 92 , which has been modified with the Oregon Green ( OG ) donor , is highlighted , as is the C-terminus of the protein , site of the fused mCherry fluorescent acceptor . ( D ) Time-resolved FRET signal ( expressed as the ratio of the emission signal at 532 nm and 670 nm upon excitation at 480 nm ) of the OG-ChaC1-Cherry probe [2 . 5 µM] following exposure to 10 mM reduced ( GSH ) or oxidized glutathione ( GSSG ) . Where indicated , the sample was injected with dithiotreitol ( DTT ) to reduce the GSSG and convert it to a substrate for ChaC1 . The biphasic change in FRET signal upon exposure to GSH is consistent with binding followed by breakdown of GSH by the probe , which retains its enzymatic activity . ( E ) Comparison of glutathione elimination by purified bacterially expressed wild-type and E116Q mutant ChaC1 in vitro . DOI: http://dx . doi . org/10 . 7554/eLife . 03421 . 00310 . 7554/eLife . 03421 . 004Figure 1—figure supplement 1 . Analysis of the substrate binding properties of ChaC1 . ( A ) Absorbance spectrum of ChaC1-mCherry , Oregon Green ( OG ) , and Oregon green-labeled ChaC1-mCherry . ( B ) Absorbance profile of size-exclusion chromatogram of Oregon green-labeled ChaC1 mCherry . Note the coincidence of the absorption peak for protein ( 280 nm ) , Oregon Green ( 496 nm ) and mCherry ( 587 nm ) ( C ) FRET signal of an enzymatically inactive OG-ChaC1E116Q-Cherry probe upon exposure to varying concentrations of reduced glutathione . Note the mono-phasic change in FRET signal , consistent with inability of the mutant enzyme to break down glutathione and the contrast with wild-type OG-ChaC1-mCherry ( Figure 1D ) . ( D ) Graph of the relationship between the steady-state FRET signal of the OG-ChaC1E116Q-Cherry probe and the concentration of reduced glutathione ( GSH ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03421 . 004 To exploit ChaC1 as a tool to purge the ER of glutathione , we targeted expression of this cytosolic enzyme to the ER , by fusing the coding sequence to an N-terminal cleavable signal peptide and a C-terminal KDEL ER retention signal . An N-terminal FLAG-M1 peptide tag was included , to facilitate detection of the enzyme . Cells transfected with a plasmid encoding ER-FLAG-ChaC1 expressed a protein of the expected mobility on reducing SDS-PAGE that reacted with the anti-FLAG antibody ( Figure 2A ) and resulted in a granular staining pattern that overlapped with that of the ER marker calreticulin ( Figure 2B ) . However , unlike cytosolic ChaC1 , which migrates at a position expected of the reduced monomer on non-reducing SDS-PAGE ( Figure 2—figure supplement 1 ) , ER-localized ChaC1 migrated as a heterogenous collection of species , consistent with inappropriate disulfide bond formation ( compare the reducing and non-reducing SDS-PAGE in Figure 2A and Figure 2—figure supplement 1 ) . 10 . 7554/eLife . 03421 . 005Figure 2 . Cysteine-free ChaC1 is an active enzyme that can be targeted to the endoplasmic-reticulum . ( A ) Immunoblot of FLAG M1-tagged ER-localized wild type ( WT ) , cysteine-free ( CtoS ) , and compound CtoS; E116Q enzymatically dead ChaC1 in N-ethyl maleimide-blocked lysates of transfected HeLa cells . Lanes 1–4 are from a reducing and lanes 5–8 are from a non-reducing SDS-PAGE . Note the presence of high-molecular weight disulfide linked FLAG-tagged ChaC1 in cells transfected with the WT ER-targeted protein that is absent from those transfected with the cysteine-free , CtoS mutants . ( B ) Fluorescent photomicrographs of HeLa cells transfected with the indicated expression plasmids and immunostained for the FLAG tag ( marking ChaC1 ) and calreticulin ( CALR ) as an ER marker . The merge panels show an overlap of the FLAG , CALR , and Hoechst 33 , 258 signal ( to reveal the nuclei ) at 630X with a close-up view in the right-most panel . ( C ) A bar-graph representation of residual glutathione levels following 60-min incubation of 10 mM glutathione with the indicated concentrations of bacterially expressed wild-type ChaC1 , its cysteine free derivative , ChaC1CtoS , and its inactive mutant ChaC1CtoS;E116Q . DOI: http://dx . doi . org/10 . 7554/eLife . 03421 . 00510 . 7554/eLife . 03421 . 006Figure 2—figure supplement 1 . Replacement of cysteines with serines circumvents aberrant disulfide bond formation in ER-localized ChaC1 . Immunblot of FLAG-tagged authentic , cytosolic mouse ChaC1 , ER-localized ChaC1 and ER-localized cysteine-free ChaC1CtoS in lysates of stably-transfected Flp-In T-REx HEK 293T cells expressing the proteins under the control of a tetracycline-inducible promoter . Shown are samples blocked with N-ethyl maleimide at the time of lysis and resolved by reducing and non-reducing SDS-PAGE . Where indicated the cells had been exposed to doxycycline to induce expression of the heterologous protein . DOI: http://dx . doi . org/10 . 7554/eLife . 03421 . 006 A model of mouse ChaC1 , based on the crystal structure of the homologous γ-glutamyl cyclotransferase ( PDB 2RBH ) ( Kumar et al . , 2012 ) , suggested that none of its four cysteines is likely to play an important role in substrate recognition or catalysis . As expected , conversion of all four cysteines to serines resulted in a protein that no longer formed disulfides when introduced into the ER ( Figure 2A , compare lanes 6 and 7 and Figure 2—figure supplement 1 ) . Importantly , the cysteine-free enzyme ( ChaC1CtoS ) purified from E . coli retained its enzymatic activity ( Figure 2C ) and specificity for reduced glutathione ( Figure 1D and Figure 1—figure supplement 1C ) . There are no predicted N-linked glycosylation sites in ChaC1CtoS to further corrupt protein structure when targeted to the ER , therefore , it seemed possible that ER-localized ChaC1CtoS might retain its enzymatic activity and breakdown glutathione in the ER . Measuring the impact of ER-ChaC1CtoS on glutathione levels required an assay that would be selectively sensitive to the ER pool of glutathione . Glutaredoxin ( Grx1 ) has been shown to dramatically accelerate the interaction of a linked redox-sensitive green fluorescent protein ( roGFP ) with glutathione , both in vivo and in vitro ( Gutscher et al . , 2008; Birk et al . , 2013 ) ( cartooned Figure 3A ) . We confirmed the reported ability of a linked Grx1 to accelerate the equilibration of roGFP with a glutathione buffer: alone , reduced roGFP2 was only slowly oxidized by glutathione ( Figure 3B ) , but the linked Grx1 markedly accelerated the oxidation of Grx1-roGFP2 ( Figure 3 , compare the red traces in panels B and C ) . The rate of probe oxidation by glutathione was concentration-dependent , with half-saturation ( Kmax0 . 5 ) attained at ∼18 µM GSSG ( Figure 3D ) . Importantly , the presence of oxidized PDI had a minor role in further accelerating the oxidation of Grx1-roGFP2 , but dominated the oxidation kinetics of roGFP2 alone ( Figure 3B , C ) . 10 . 7554/eLife . 03421 . 007Figure 3 . ER-targeted ChaC1 purges the organelle of its glutathione content . ( A ) Cartoon contrasting the slow coupling of roGFP2 with the glutathione redox buffer ( dashed lines ) and the rapid coupling of Grx1-roGFP2 with the glutathione redox buffer ( after Gutscher et al . , 2008 ) . ( B ) Trace of time-dependent changes in the ratio of reduced to oxidized roGFP2 detected optically as the ratio between the emission signal ( at 535 nm ) upon excitation at 405 nm vs 488 nm ( ex 405/488 ) following introduction of the fully reduced probe into the indicated solutions of oxidized glutathione ( GSSG ) or PDI and GSSG . ( C ) Similar trace of reduced Grx1-roGFP2 . Note the indifference of roGFP2 and the marked responsiveness of Grx1-roGFP to oxidized glutathione . ( D ) Graph of the initial velocity of Grx1-roGFP2 oxidation as a function of GSSG concentration , fitted to Linweaver–Burk plot . Half-maximal velocity is observed at 18 µM GSSG . ( E ) Trace of time-dependent changes in the ratio of oxidized and reduced roGFP2 and Grx1-roGFP2 probes expressed in the ER of HeLa cells following a brief ( 1 min ) reductive pulse with dithiothreitol ( DTT , 2 mM ) followed by a washout . ( F ) Bar diagram of the half-time to recovery of oxidized roGFP2 and Grx1-roGFP2 following the reductive DTT pulse . Shown are means ±SD ( N = 4 , p<0 . 01 ) . ( G ) Trace of time-dependent changes in the ratio of oxidized to reduced probes expressed in the ER of HeLa cells alongside active or inactive ChaC1 ( tagged at its C-terminus with mCherry to allow visualization of cells co-expressing the redox probes and the glutathione-depleting enzyme ) following a brief reductive pulse with dithiothreitol and a washout . Note that the expression of active ChaC1 in the ER eliminates the kinetic advantage of Grx1-roGFP2 over roGFP2 in re-oxidation during the recovery from a DTT reductive pulse . ( H ) Bar diagram of the half-time to recovery of oxidized roGFP2 and Grx1-roGFP2 following the reductive DTT pulse in cells co-expressing active or inactive CHaC1 . Shown are means ±SEM ( N = 20 , p<0 . 01 ) . ( I ) Bar diagram of cellular glutathione levels following 36 hr of doxycycline ( DOX ) induction of cytosolic and ER localized active and inactive ChaC1 in the absence and presence of concomitant exposure to buthionine-sulfoxide ( BSO , 50 µM ) . Also shown is a time course of total cellular glutathione following induction of active and inactive mCherry-KDEL-tagged ChaC1 . DOI: http://dx . doi . org/10 . 7554/eLife . 03421 . 007 Oxidized glutathione levels recover rapidly following a reductive pulse in cultured mammalian cells ( Appenzeller-Herzog et al . , 2010 ) . Therefore , the aforementioned in vitro observations indicating tight coupling of Grx1-roGFP2 to glutathione ( and relative indifference to PDI ) suggested that the rate of re-oxidation of an ER-localized Grx1-roGFP2 probe following a reductive pulse might be affected by the presence of glutathione in the ER lumen . To examine this further , we first compared the rate of re-oxidation of ER-localized roGFP2 with that of ER-localized Grx1-roGFP2 following a reductive pulse of dithiothreitol ( DTT ) and its washout . At steady-state both probes were highly oxidized and both were similarly reduced by the DTT pulse . However the recovery of ER-Grx1-roGFP2 was accelerated compared with ER-roGFP2 alone , with a half-time to recovery of 75 . 3 ± 7 . 7 s in case of the former and 167 . 3 ± 16 . 8 s in case of the latter ( Figure 3E , F ) . This observation is consistent with a direct contribution of lumenal glutathione to the kinetics of ER-Grx1-roGPF2 re-oxidation in vivo . Next , we compared the effect of ER-localized enzymatically-active ER-ChaC1CtoS and its catalytically dead counterpart , ER-ChaC1CtoS−E116Q on the rate of oxidation of ER-roGFP2 and ER-Grx1-roGFP2 following a reductive pulse and washout . To focus the measurements on cells co-expressing the roGFP probe and the ChaC1 enzyme , the latter was tagged at its C-terminus with mCherry . Oxidation of ER-roGFP2 was unaffected by the presence of active ChaC1 , but the kinetic advantage of ER-Grx1-roGFP2 over the ER-roGFP2 alone was abolished by expression of an active , glutathione-degrading enzyme in the ER lumen ( Figure 3G , H ) . We were unable to reproducibly measure glutathione concentrations in microsomal fractions of cultured cells; however , the effect of ChaC1 over-expression on total cellular glutathione was quantifiable . At similar levels of over-expression , cytosolic ChaC1 led to a marked depletion of total cellular glutathione levels , whereas ER-localized ChaC1CtoS had a more modest effect . However , ER-localized ChaC1CtoS markedly enhanced glutathione depletion by low concentrations of buthionine-sulfoxide ( BSO , an inhibitor of the rate limiting step of glutathione biosynthesis , Figure 3I ) . To avoid the corrupting effect of an untransfected pool of cells , these ensemble measurements were conducted in stable clones homogenously and conditionally-expressing ChaC1 from a doxycycline-inducible transgene . They point to relatively slow equilibration of cytosolic and ER pools of glutathione in mammalian cells . Given that Grx1-roGFP2 reacts with glutathione with a Kmax0 . 5 in the 10−5 M range ( Figure 3D ) the effacement of its kinetic advantage over roGFP2 in the DTT washout experiment , indicated a profound and selective depletion of lumenal glutathione by the ER-targeted expression of active ChaC1 , with modest effects on other cellular pools of glutathione . Maturation of the LDL-R in the ER entails significant rearrangement of its 30 disulfide bonds ( Jansens et al . , 2002 ) and involves the reduced form of the PDI family member ERdj5 ( Oka et al . , 2013 ) . Maturation of the LDL-R can be tracked by the conversion of the relatively high mobility , glycosylated , ER form , to the lower-mobility post-ER form ( reflective of Golgi sugar modifications ) in pulse-chase labeling followed by immunoprecipitation . We thus compared the effects of ER expression of active ChaC1CtoS and the catalytically inactive ChaC1CtoS;E116Q mutant on the rate of maturation of co-expressed LDL-R tagged on its C-terminal , cytosolic facing domain , with a triple FLAG-tag . Conversion of the LDL-R from its ER to post-ER form was unaffected by the presence of either active or inactive form of ER-ChaC1 and proceeded with a half-life of ∼1 hr in presence or absence of an active glutathione-depleting enzyme in the ER ( Figure 4A ) . The non-reducing gel revealed identical accelerated mobility of LDL-R at the earliest time point , regardless of expression of ChaC1 , indicating that glutathione depletion also had no drastic effect on the earlier oxidative phase of LDL-R maturation ( Figure 4A , B ) . 10 . 7554/eLife . 03421 . 008Figure 4 . Maturation of the LDL-R receptor is unaffected by depletion of ER glutathione . ( A ) Autoradiograph of metabolically labeled LDL receptor ( LDL-R ) immunopurified from HeLa cells co-expressing the FLAG-tagged LDL-R and ER-localized enzymatically active ( ER-ChaC1CtoS ) or inactive ChaC1 ( ER-ChaC1CtoS;E116Q ) and resolved on a reducing SDS-PAGE . Cells were lysed at the end of a 30-min labeling pulse ( lanes 1 and 6 ) or after an additional chase period ( indicated ) . The mobility of the ER and Golgi forms of the LDL-R on reducing ( left ) and non-reducing ( right ) SDS-PAGE is indicated , as is the labeled ChaC1 , which is also recovered in this anti-FLAG immunoprecipitation . ( B ) Graphic presentation of the conversion of the ER to Golgi form of the LDL-R from ‘A’ above . Shown is a representative experiment reproduce three times with similar outcome . ( C ) Autoradiograph of an experiment similar in layout to that depicted in ‘A’ above . Where indicated , the cells were exposed to the glutathione synthesis inhibitor buthionine-sulfoxide ( BSO , 100 µM , 20 hr ) before the pulse-chase labeling . ( D ) Graphic presentation of the conversion of the ER to Golgi form of the LDL-R from ‘C’ above . Shown is a representative experiment reproduce three times with similar outcome . ( E ) Bar graph of total cellular glutathione in cells manipulated as in the experiment described in ‘C’ ( shown is the mean ±SD , n = 3 , p<0 . 05 ) . ( F ) Trace of time-dependent changes in the ratio of oxidized and reduced roGFP2 expressed in the ER of mouse embryonic fibroblasts with genetic lesions compromising disulfide bond formation ( Ero1am/m; Ero1bm/m; Prdx4m/y ) following a brief reductive pulse with dithiothreitol followed by a washout . The cells co-expressed ER-localized enzymatically active ( ER-ChaC1CtoS ) or inactive ChaC1 ( ER-ChaC1CtoS;E116Q ) marked at its C-terminus with an mCherry fluorescent probe . ( G ) Bar diagram of the half-time to recovery of oxidized roGFP2 following the reductive DTT pulse in ‘F’ above . Note that expression of active ChaC1 in the ER did not affect the rate of recovery of the sentinel disulfide in ER-localized roGFP2 . DOI: http://dx . doi . org/10 . 7554/eLife . 03421 . 008 LDL-R maturation was also unaffected by further global depletion of cellular glutathione , effected by the combined expression of ER-ChaC1CtoS and exposure to buthionine sulfoxide ( BSO ) , which depleted glutathione globally ( Figure 4C–E ) . These experiments , conducted in HeLa cells , where reductive editing of LDL-R disulfides was first discovered ( Jansens et al . , 2002 ) , reinforces the dispensability of glutathione to that process . To further explore the potential role of ER glutathione in the early oxidative steps of protein folding , we used highly sensitized mouse cells genetically deficient in the upstream thiol oxidases ERO1 and PRDX4 ( Zito et al . , 2010b , 2012 ) Expression of active ChaC1 in their ER had no effect on the kinetics of disulfide re-formation on ER-localized roGFP2 following a reductive DTT pulse ( Figure 4F–G ) , further attesting to the dispensability of glutathione to thiol oxidation in the mammalian ER . The degradation of misfolded NHK-A1AT requires the action of the reduced form of a specialized PDI , ERdj5 ( Ushioda et al . , 2008; Hagiwara et al . , 2011 ) . To determine if an ER pool of glutathione contributes to this process , we compared the half-life of C-terminally FLAG-tagged NHK-A1AT in cells expressing active ER-ChaC1CtoS and the catalytically inactive ER-ChaC1CtoS;E116Q . The stability of NHK-A1AT was unaffected by ChaC1 both in HeLa cells ( Figure 5A , D ) and in 293T cells ( Figure 5B , E ) . Furthermore , depletion of total cellular pools of glutathione by coincidental exposure to BSO had no effect on NHK-A1AT half-life ( Figure 5C , F ) , complementing the evidence for the dispensability of glutathione for ER-associated degradation of this redox-dependent substrate . 10 . 7554/eLife . 03421 . 009Figure 5 . Clearance of misfolded null Hong Kong mutant alpha 1 anti-trypsin ( NHK-A1AT ) is unaffected by depletion of ER glutathione . ( A ) Autoradiograph of metabolically labeled NHK-A1AT immunopurified from HeLa cells co-expressing C-terminally FLAG-tagged NHK-A1AT and ER-localized enzymatically active ( ER-ChaC1CtoS ) or inactive ChaC1 ( ER-ChaC1CtoS;E116Q ) and resolved on a reducing or non-reducing SDS-PAGE . Cells were lysed at the end of a 30-min labeling pulse ( lanes 1 and 5 ) or after an additional chase period ( indicated ) . The mobility of the labeled NHK-A1AT , and the ER-chaperone BiP that co-purifies with it , are indicated , as is the labeled ChaC1 , which is also recovered in this anti-FLAG immunoprecipitate . ( B ) Autoradiograph of samples recovered from 293T cells in an experimental design as in ‘A’ . ( C ) Autoradiograph of samples recovered from HeLa cells in an experimental design as in ‘A’ . Where indicated , cells were exposed to the glutathione synthesis inhibitor buthionine-sulfoxide ( BSO , 100 µM , 20 hr ) before the pulse-chase labeling . ( D–F ) Plot of time-dependent change in NHK-A1AT ( monomer ) signal from the reducing gels A–C above . Shown are representative experiments reproduced twice with similar outcome . DOI: http://dx . doi . org/10 . 7554/eLife . 03421 . 009 Thiol redox reactions contribute to protein folding homeostasis in the endoplasmic reticulum . This is reflected in enhanced signaling in the endoplasmic reticulum unfolded protein response ( UPR ) in cells impaired in ER thiol redox ( Frand and Kaiser , 1998; Pollard et al . , 1998 ) . Therefore , to gain a more global view on the impact of ER glutathione depletion on ER protein folding homeostasis , we compared the effect on UPR activity of ER-targeted active and inactive ChaC1 . These experiments made use of mCherry-tagged ChaC1CtoS , which retains its enzymatic activity , purges the ER of glutathione ( Figures 1D and 3G ) and marks the ChaC1-expressing cells . Dual-channel FACS analysis revealed that neither active nor inactive ER-ChaC1CtoS-mCherry-KDEL measurably affected the basal activity of a stably integrated mammalian UPR reporter , CHOP::GFP ( Novoa et al . , 2001 ) ( reflected in the absence of a shift to the right in the mCherry positive population of cells , Figure 6A , left column ) . Furthermore , the activity of the CHOP::GFP reporter , which was increased by tunicamycin , a toxin that perturbs protein folding homeostasis in the ER , was unaffected by ChaC1 ( Figure 6A ) . The indifference of the UPR to ER-ChaC1CtoS is observed over a broad range of unfolded protein stress and over a broad range of ER-ChaC1CtoS-mCherry-KDEL expression , and suggests that the lesson learned from the sentinel proteins , LDL-R and NHK-A1AT likely extend to the bulk of proteins that fold oxidatively in the ER under normal cell culture conditions . 10 . 7554/eLife . 03421 . 010Figure 6 . Activity of the unfolded protein response ( UPR ) is unaffected by depletion of ER glutathione . ( A ) Two-dimensional plots ( FACScans ) of fluorescent intensity of individual CHO cells containing a stably-integrated UPR reporter , CHOP::GFP ( Novoa et al . , 2001 ) , following transfection with plasmids encoding ER-localized , active ( ER-ChaC1CtoS-mCherry-KDEL ) or inactive ChaC1 ( ER-ChaC1CtoS;E116Q-mCherry-KDEL ) . GFP fluorescent intensity , reporting on the activity of the UPR ( X-axis ) , was detected at 530 ± 30 nm following excitation at 488 nm , whereas mCherry fluorescent intensity , reporting on the level of ChaC1-mCherry-KDEL in the ER ( Y axis ) , was detected at 610 ± 20 nm following excitation at 561 nm . Where indicated the cells were exposed to the ER stress-inducing agent tunicamycin for the indicated period of time . ( B ) Three-color FACScans as in ‘A’ of CHOP::GFP cells co-transfected with expression plasmids for a hyperactive mutant of ERO1 ( C104A , C133A; ERO1 ) tagged by the cell-surface marker CD2 ( decorated with an AF647-tagged antibody and detected by excitation at 640 nm and emission at 670 nm ) with a wildtype or inactive mutants of ChaC1 fused to mCherry , as noted above . The axis of the scans are labeled with the cognate signals and panels numbered for ease of reference in the text . DOI: http://dx . doi . org/10 . 7554/eLife . 03421 . 01010 . 7554/eLife . 03421 . 011Figure 6—figure supplement 1 . Activity of the unfolded protein response ( UPR ) as measured by an XBP1-splicing reporter is also unaffected by depletion of ER glutathione . FACScans as in ‘6B’ of CHO cells stably transduced with an XBP1 splicing reporter linked to the expression of the fluorescent protein Venus ( Iwawaki et al . , 2004 ) . Where indicated cells were co-transfected with expression plasmids for a hyperactive mutant of ERO1 ( C104A , C133A; ERO1* ) tagged by the cell-surface marker CD2 and wild-type or inactive mutants of ChaC1 fused to mCherry . The axis of the scans is labeled with the cognate signals . DOI: http://dx . doi . org/10 . 7554/eLife . 03421 . 011 The role of ER glutathione was further explored under conditions in which ER redox balance was perturbed by co-expression of a deregulated allele of the ER oxidase ERO1 ( Sevier et al . , 2007 ) , which has been shown to hyper-oxidize the mammalian ER ( Baker et al . , 2008 ) and modestly activate the UPR ( Hansen et al . , 2012 ) . Introduction of the C104A; C133A human ERO1L ( ERO1* ) , expressed from a plasmid tagged with the CD2 surface marker indeed modestly activated the UPR , whether measured in cells stably expressing the CHOP::GFP transcriptional reporter ( Figure 6B , reflected in the higher GFP levels of cells co-expressing the CD2 marker which tags the ERO1-expressing cells , panels 2 , 4 , and 7 ) or an XBP1::Venus splicing reporter ( Iwawaki et al . , 2004 ) ( Figure 6—figure supplement 1 ) . However , co-expression of active ChaC1CtoS had no evident synergistic effect with ERO1 on UPR activity beyond that observed with the enzymatically inactive E116Q mutant enzyme . This is evident by first noting that in this co-transfection experiment most ERO1* expressing cells ( CD2 positive ) are also co-expressing the Cherry-tagged ChaC1CtoS ( Figure 6B , panels 3 and 6 ) and then noting that the relationship between ERO1* expression ( for which CD2 is a surrogate ) and the CHOP::GFP signal is indistinguishable in cells co-expressing wild-type and enzymatically inactive ChaC1CtoS ( Figure 6B panels 4 and 7 ) . Similarly , the co-expression of ERO1* does not impart sensitivity to active ChaC1 , as reflected by the observation that CHOP::GFP levels are unaffected by ChaC1CtoS in these double positive cells ( Figure 6B panels 5 and 8 ) . These findings indicate that glutathione is dispensable for the function of the ER even under hyperoxidizing conditions . Adapting a cytosolic enzyme that breaks down glutathione to function in the ER has afforded a means to selectively purge the organelle of glutathione and thereby assess the role of this otherwise abundant tri-peptide in ER protein redox . Nearly complete depletion of ER glutathione had no measurable effect on the rate of disulfide bond formation in the highly sensitized experimental setting of cells deficient in ER thiol oxidases . More surprising was the lack of any measurable effect of glutathione depletion on processes that require a pool of reduced PDI enzymes: the disulfide shuffling-dependent maturation of nascent LDL-R and the degradation of a folding incompetent mutant NHK-A1AT . Furthermore , depletion of ER glutathione was without effect on two sensitive , broad-spectrum UPR reporters , arguing that glutathione is indeed dispensable to a broad range of processes required for protein folding homeostasis in the ER of cultured mammalian cells . The dispensability of glutathione to reductive processes in the ER has been hinted at previously: in yeast deletion of GSH1 ( encoding gamma glutamylcysteine synthetase that performs the rate-limiting step in glutathione biosynthesis ) does not adversely affect the maturation of the disulfide-bonded lysosomal hydrolyze CPY ( Frand and Kaiser , 1998 ) . However , the extent of ER glutathione depletion attained by this genetic manipulation is unclear and , given the essential role of cellular glutathione to yeast growth , may have been limited by toxicity . Similar reservations apply to the use of enzyme inhibitors , such as BSO , which subject the cell to the consequences of glutathione depletion in other compartments where it has an essential role ( Kumar et al . , 2011 ) , but whose effect on the ER pools of glutathione may be partial . How complete was depletion of ER glutathione by ChaC1 ? In vitro the re-oxidation of Grx1-roGFP2 is half-maximal at 18 µM glutathione ( Figure 3D ) . Thus , the abolition of the kinetic advantage of Grx1-roGFP2 over roGFP2 in cells expressing enzymatically active ChaC1 argues that , despite the enzyme's relative low affinity for its substrate ( in the millimolar range , Kumar et al . , 2012; and Figure 1—figure supplement 1C , D ) , glutathione levels were purged to micromolar levels by the high concentration of the enzyme . This conclusion is also supported by the effects of ER ChaC1 on total cellular glutathione levels: At similar levels of expression , cytosolic ChaC1 led to near complete depletion of total cellular glutathione , whereas ER ChaC1 had a more modest effect on total cellular pools . Given the 10-fold greater volume of the cytosol over the ER ( Stefan et al . , 1987 ) , the aforementioned observations indicate substantially higher local concentration of ER vs cytosolic ChaC1 . Thus , the preservation of total cellular glutathione levels in cells expressing high levels of ChaC1 in their ER confirms that transport of glutathione into the ER , from its site of synthesis in the cytosol , is slow ( as suggested by Kumar et al . , 2011 ) , and therefore , that depletion of ER stores by ChaC1 is profound . The redundancy of glutathione could be explained by other small molecule thiols fueling reduction in the ER . These may even include cysteinyl-glycine , one of the glutathione breakdown product of ChaC1 action . Alternatively , eukaryotes may have a protein-driven apparatus akin to bacterial DsbD for ferrying reducing equivalents from the cytosol to reduce PDI family members . Despite its apparent dispensability , ER glutathione does equilibrate with protein thiols ( Appenzeller-Herzog et al . , 2010 ) , indicating that it is a redox buffer in the organelle . However , it is possible that despite seemingly rapid equilibration of glutathione to protein thiol redox , the kinetics are insufficient to render glutathione essential and that other , even faster processes , maintain an adequate pool of reduced and oxidized PDIs in the mammalian ER , with glutathione following passively . It is noteworthy , in this vein , that ERO1-deficient cells , with compromised ER thiol oxidative power , have an elevated ratio of oxidized to reduced glutathione ( Appenzeller-Herzog et al . , 2010; Rutkevich and Williams , 2012 ) . It has been speculated , reasonably , that this reflects the action of an alternative oxidative pathway that kicks-in when the major oxidases are compromised and exploits oxidized glutathione to couple to protein thiol oxidation . However , our finding that ER glutathione depletion does not further compromise protein thiol oxidation kinetics in oxidase-deficient cells suggests that here too glutathione follows passively the rearrangements in redox pathways and does not participate actively in their implementation . Across distant phyla , ER redox is not indifferent to cellular glutathione levels . Depletion of glutathione , by GSH1 deletion , rescues the oxidative defect in ERO1-deficient yeast ( Cuozzo and Kaiser , 1999 ) , and cytosolic glutathione influences disulfide-bonding of glutenin in the ER of wheat endosperm ( Lombardi et al . , 2012 ) . These observations , which fall short of directly indicting glutathione in reducing ER disulfides , nonetheless argue for participation of glutathione in a shared economy of reducing equivalents across the ER membrane . Prolonged ( 48 hr ) depletion of glutathione by high concentrations of BSO ( 1 mM ) synergize with ER hyperoxidation to degrade cell viability ( Hansen et al . , 2012 ) . Further support for the idea of a shared economy of glutathione is provided by the phenotype of yeast over-expressing a plasma membrane glutathione transporter HGT1 . Glutathione over-load in these cells compromises protein folding in the ER and triggers an unfolded protein response , the basis of which appears to be unregulated re-cycling of PDI from its oxidized to its reduced state imposed by the excessive glutathione ( Kumar et al . , 2011 ) . While our state of ignorance in regard to the genes responsible for glutathione transport into the yeast ER preclude assigning a role to luminal glutathione in this consequences of cellular glutathione overload , the observations of Kumar et al . clearly argue that glutathione may participate in ER redox . In mammalian cells too , glutathione stands to impact the maturation of some secreted or membrane proteins: In a study agnostic of its enzymatic activity , over-expression of wild-type cytosolic ChaC1 in mouse ganglionic eminence cells profoundly inhibited the maturation of the Notch precursor to its furin-cleaved form ( Chi et al . , 2012 ) . Whereas anti-oxidants that restore impaired glutathione metabolism to normality have been shown to improve the capacity of liver cells to secrete factor VIII , a heavily disulfide bonded serum protein ( Malhotra et al . , 2008 ) . Thus , conservatively interpreted , our observations lead to the conclusion that ER glutathione is not generally required to maintain protein thiol redox nor folding homeostasis in the ER of cultured mammalian cells . It may now be informative to examine the impact of an ER glutathione purge elicited by ER-localized ChaC1CtoS on the fate of specialized secretory cells and their specialized secretory cargo proteins . Table 1 lists the plasmids used , their lab names , description , published reference , and a notation of their appearance in the figures . 10 . 7554/eLife . 03421 . 014Table 1 . List of the plasmids used in this study , their unique lab identifier , lab name , description , PMID of the relevant reference ( if available ) , figure in which they first appear and cognate label in figure legendDOI: http://dx . doi . org/10 . 7554/eLife . 03421 . 014IDPlasmid nameDescriptionReferenceFirst appearanceLabel in figure15pFLAG-CMV1Mammalian expression bovine trypsinogen signal peptide-FLAGM1 fusionPMID: 80247962AER-FLAG_vector242roGFP2_pRSETBBacterial expression of 6X His-tagged roGFP2PMID: 147220623AroGFP2836mChac1_1-224-H6-pET30aBacterial expression of mouse Chac1 C-terminal His-taggedThis paper1AChaC1888pFLAG_mCherry_KDEL_CMV1ER localised FLAGM1-mCherry-KDEL in pFLAG-CMV1This paper6AER-vector915mChaC1_1-224_CtoS_pET30aBacterial expression mouse Chac1 CtoS ( C92S , C169S , C190S , C212S ) C-terminal His-taggedThis paper2CChaC1_CtoS932mChaC1_1-224_E116Q_pET30aBacterial expression of E116Q mutant mouse Chac1 C-terminal His-taggedThis paper1EChaC1_E116Q934mChaC1_3XFLAG_pCDNA5_FRT_TOMammalian expression of C-term FLAG-tagged mouse ChaC1This paper3HCyto-ChaC1937mChaC1_CtoS_E116_pET30aBacterial expression of E116Q cysteine to serine mutant mouse Chac1 C-terminal His-taggedThis paper2CChaC1_CtoS_E116Q945mChaC1_E116Q_3XFLAG_pCDNA5_FRT_TOMammalian expression of C-term FLAG-tagged mouse ChaC1 E116Q mutant ( Cyto-ChaC1_E116Q ) This paper3HCyto-ChaC1_E116Q950FLAGM1_mChaC1_CtoS_pCDNA5_FRT_TOMammalian expression ER-localised FLAG M1 tagged mouse CHAC1 CtoS ( C92S , C169S , C190S , C212S ) KDELThis paper2AER-FLAG_ChaC1_CtoS951FLAGM1_mChaC1_CtoS_E116Q_pCDNA5_FRT_TOMammalian expression ER-localised FLAG M1 tagged mouse CHAC1 CtoS ( C92S , C169S , C190S , C212S ) E116Q KDELThis paper2AER-FLAG_ChaC1_CtoS_E116Q974FLAGM1_mChaC1_WT_pCDNA5_FRT_TOMammalian expression ER-localised FLAG M1 tagged mouse CHAC1 KDELThis paper2AER-FLAG_ChaC1988FLAGM1_mChaC1_CtoS_mCherry_pCDNA5_FRT_TOMammalian expression ER-localised FLAG M1 tagged mouse CHAC1 mCherry-KDEL , CtoS ( C92S , C169S , C190S , C212S ) This paper3FER-ChaC1_CtoS_mCherry993mChaC1_CtoS_92C_mCherry-pET30aBacterial expression of mouse ChaC1-mCherry fusion , C-terminal His-tagged , ( C169S , C190S , C212S ) This paper1DOG-ChaC1-Cherry probe1028FLAGM1_mChaC1_CtoS_E116Q_mCherry_pCDNA5Mammalian expression ER-localised FLAG M1 tagged mouse CHAC1 mCherry-KDEL , CtoS , E116QThis paper3FER-ChaC1_CtoS_E116Q_mCherry1037mChaC1_CtoS_S92C_E116Q_mCherry-pET30aBacterial expression of mouse ChaC1-mCherry fusion , C-terminal His-tagged , ( C169S , C190S , C212S ) , E116QThis paperS1COG-ChaC1_E116Q-Cherry1052ER_roGFP2_pCDNA3 . 1ER localized roGFP2 KDELThis paper3DER-roGFP21063ER_Grx1_roGFP2_KDEL_pCDNA3 . 1ER localized Grx1 fused to roGFP2 KDELPMID:234241943DER-Grx1-roGFP21181hLDLR_3XFLAG_pCDNA5_FRTMammalian expression plasmid of human LDL receptor , cytosolic tail tagged with a 3X FLAG tagPMID:124939184AFLAG-tagged LDL-R1204A1AT_NHK_3XFLAG_pCDNA5_FRT_TOMammalian expression plasmid of null Hong-Kong mutant a1-antitrypsin C-terminally tagged with 3XFLAGPMID:127362545AFLAG-tagged NHK-A1AT1206Grx1_roGFP2_pET30aBacterial expression of Grx1-roGFP2 fusion proteinPMID: 184698223BGrx1-roGFP21239pCAX-F-XBP1ΔDBD-venusXBP1 mini-cDNA with Venus fused the post-IRE1 spliced open reading framePMID: 147026396CXBP1-Venus1273hERO1A_C104A_C131A_pCDNA3-CD2Mammalian expression plasmid encoding human hyperactive ERO1L ( ERO1a ) and a co-expressed human CD2 FACS markerPMID: 230278706BERO1* The mouse ChaC1 cDNA ( IMAGE clone 4483043 ) was purchased from Source Bioscience . Bacterial expression vectors encoding C-terminally His 6X tagged wild type and E116Q mutant ChaC1 were constructed by PCR amplification . A mutant ChaC1 in which all four cysteines were converted to serines , CtoS ( C92S , C169S , C190S , C212S ) , was synthesized as an artificial gene and shuttled into the mammalian and bacterial expression plasmids described in Table 1 . Bacterial expression plasmids for roGFP2 ( Hanson et al . , 2004 ) and the glutaredoxin 1 fusion to roGFP2 ( Gutscher et al . , 2008 ) were gifts of James Remington ( U Oregon ) and Tobias Dick ( DKFZ , Heidelebrg ) . ER localized mammalian expression plasmids of their counterparts were generated by deleting the E147b insertion and introducing an S65T mutation into ER_HA_Grx1_roGFP1iE_KDEL_pcDNA3 . 1 ( Birk et al . , 2013 ) ( a gift of Christian Appenzeller-Herzog , University of Basel ) . ER_roGFP2 was produced by deleting E147b from FLAGM1_roGFP2_iE_pCDNA3 . 1 ( Avezov et al . , 2013 ) . Mammalian expression plasmids encoding ER localized FLAG M1-tagged ChaC1 with a KDEL ER retention signal at the C-terminus and mutant derivatives thereof were constructed by shuttling the ChaC1 coding sequence into the relevant plasmid backbone . Mammalian expression plasmids for ER-localized ChaC1 fused to the mCherry fluorescent protein were prepared using similar techniques . The hyperactive C104A: C133A allele of human ERO1L ( ERO1A ) was expressed from a modified pCDNA3 plasmid in which the coding sequence of the Neor marker had been replaced by human CD2 . C-terminally 6X His-tagged mouse ChaC1 was expressed in BL21 pLysS E . coli at 30°C with 4 hr of induction with 1 mM IPTG and purified from the lysate by nickel affinity chromatography followed by size exclusion chromatography on Superdex 200 ( GE Healthcare , Chalfont St Giles , United Kingdom ) in 20 mM Tris pH7 . 4 , 100 mM NaCl , 10% Glycerol , 1 mM Tris ( 2-carboxyethyl ) phosphine hydrochloride ( TCEP ) . Glutathione degradation by purified wild-type ChaC1 and its mutant derivatives was assayed using the 5 , 5′-dithiobis- ( 2-nitrobenzoic acid ) ( DTNB ) recycling assay of Tietze ( 1969 ) as modified by Griffith ( 1980 ) . Mutant ChaC1 ( C169S , C190S , C212S ) fused at its C-terminus to an mCherry 6X His fluorescent protein was purified from E . coli and labeled at its single remaining cysteine ( C92 ) with thiol reactive Oregon Green 488 iodoacetamide ( Life Technologies ) , according to the manufacturer's instructions . Fluorescent resonance energy transfer ( FRET ) between the Oregon Green donor and the mCherry acceptor was measured at room temperature on TECAN 500 plate reader as the ratio between their fluorescence emission at 535 nm and 670 nm respectively when they were excited at 485 nm and 590 nm . The binding reaction was initiated by addition of reduced glutathione ( G4251 , Sigma ) or oxidized glutathione ( G4626 , Sigma ) . Human PDI ( PDIA1 18–508 ) and roGFP variants were expressed in the E . coli BL21 ( DE3 ) strain , purified with Ni-NTA affinity chromatography , dialyzed into the reaction buffer , reduced by incubation with 20 mM of DTT , and then buffer exchanged on a PD-10 gel filtration column ( GE Healthcare ) , as described previously ( Avezov et al . , 2013 ) . Reduced PDI ( 5 μM ) was equilibrated in 100 mM Tris–HCl , 150 mM NaCl , pH 7 . 5 degassed buffer before being added to samples containing roGFP variants in the reduced state followed by oxidized glutathione ( 100 μM; Sigma ) . The ratio of fluorescence emission at 535 nm of samples sequentially excited at 405 nm and 488 nm was measured using a Synergy 4 microplate reader ( BioTek Instruments ) . Kinetic parameters were extracted by fitting the data to a Linweaver–Burk plot . HeLa and HEK293T cells were cultured in DMEM supplemented with 10% FBS and maintained at 37°C with 5% CO2 . Cells were electroporated with the indicated plasmids ( 5 μg of DNA/1 × 106 cells ) utilizing the Neon transfection system or Lipofectamine LTX ( both from Life Technologies ) following the manufacturer's protocols . The effects of cytosolic and ER-localized ChaC1 on cellular glutathione pools were analyzed in stable clones of doxycycline-inducible Flp-In™ T-REx™ HEK 293T cells by the 5 , 5′-dithiobis- ( 2-nitrobenzoic acid ) ( DTNB ) recycling assay of Tietze ( 1969 ) as modified by Griffith ( 1980 ) . The effects of ChaC1 on the activity of the unfolded protein response were studied by transient transfection of stable CHOP::GFP reporter Chinese Hamster Ovary cells ( C30 clone of CHO K1 cells [Novoa et al . , 2001] ) or a stable clone of CHO K1 cells ( XV8-1 ) expressing a Venus reporter of XBP1 splicing activity ( Iwawaki et al . , 2004 ) followed by multi-channel FACS analysis on an LSRFortessa ( BD Bioscience ) of the GFP or Venus signals , which reports on the intensity of the unfolded protein response and mCherry expression that reports on the level of expression of ER localized ChaC1 . Where indicated , a CD2-marked expression plasmid encoding a hyper-active mutant of human ERO1L ( C104A; C133A , Hansen et al . , 2012 ) or CD2-marked empty vector was co-transfected and CD2 expression detected by surface staining with an AlexaFluor 647 conjugated mouse anti-human CD2 antibody , clone LT2 ( MCA1194A647 , AbD Serotec/Biorad ) . 24 hr after transfection , cells were washed twice with PBS and lysed for 30 min on ice in 1% Triton X-100 , 20 mM Tris–HCl ( pH 7 . 4 ) , 150 mM NaCl , 1 mM EDTA , 0 . 1 mM PMSF , 3–7 TIU/L aprotinin , and 20 mM N-ethylmaleimide ( NEM ) . The cellular lysate was centrifuged at 15 , 000 ×g for 15 min , and the supernatant was used for protein assay using a BCA protein assay reagent . Total proteins ( 30 μg ) were separated on 12% SDS–polyacrylamide gels and electroblotted onto PVDF membrane . Primary antibodies to the FLAG tag ( Sigma Cat #F1804 , 1:1000 dilution ) or a rabbit antiserum to bacterially expressed H6-tagged mouse ChaC1 ( residues 1–224 , lab number UC8166 , 1:1000 dilution ) followed by IR800 conjugated secondary antibody and by scanning on a Licor Odyssey scanner . Transfected HeLa cells were grown on coverslips . 24 hr after transfection , the cells were washed with PBS , fixed with 4% paraformaldehyde for 30 min at room temperature , permeabilized in PBS containing 0 . 1% Triton X-100 for 30 min at room temperature , and blocked in 1% BSA in PBS for 30 min at room temperature . Anti-FLAG antibodies ( 1:1000 dilution in PBS ) in combination with rabbit anti-calreticulin antibodies ( a gift of Steven High , University of Manchester , 1:1000 dilution in PBS ) followed by goat anti-mouse and goat anti-rabbit secondary antibodies conjugated with DyLight 543 and DyLight 488 ( 1:1000 dilution in PBS , Jackson ImmunoResearch Laboratories ) , respectively . Nuclei were counter stained with Hoechst 33 , 342 ( 2 μg/ml in PBS ) for 30 min at room temperature for counter-staining . Cells co-transfected with the redox reporter ( roGFP ) and ChaC1-mCherry were analyzed by laser-scanning confocal microscopy system ( 510 Meta; Carl Zeiss ) with a Plan-Apo- chromat 63× oil immersion lens ( NA 1 . 4 ) , coupled to a microscope incubator , maintaining standard tissue culture conditions ( Okolab ) . Fluorescence ratiometric intensity images ( 512 × 512 points , 16 bit ) of live cells were acquired . A diode 405 nm and Argon 488 nm lasers ( 2 and 0 . 5% output respectively ) were used for excitation of the ratiometric probes in the multitrack mode with an HFT 488/405 beam splitter , the signal was detected with 518–550 nm filters , the detector gain was arbitrary adjusted to yield an intensity ratio of the two channels approximating one . The recovery half-time was extracted from fitting the intensity ratio changes over time to an exponential equation I ( t ) =A ( 1−e−τt ) , where I is intensity , t is time , τ is recovery half-time . Cells were co-transfected with ER_mChaC1 and the FLAG-tagged LDL receptor or mutant alpha1 antitrypsin NHK plasmids . 24 hr later , pulse-chase labeled , followed by anti-FLAG immunoprecipitation ( Sigma A2220 ) , SDS-PAGE and autoradiography were conducted as previously described ( Zito et al . , 2010a ) . Where indicated , the glutathione synthesis inhibitor buthionine-sulfoxide ( BSO , 100 µM , 20 hr ) was added before the pulse-chase labeling . Purified protein intensities were quantified using ImageJ software . Images of gels were scaled to fit the page dimensions .	Proteins are basically strings of amino acids that have folded into a specific three-dimensional shape , and this shape is often important for the protein's function . Some proteins have bonds between pairs of cysteines—an amino acid that contains sulfur—in different parts of the protein to maintain its correct shape . In eukaryotes , such as plants and animals , these so-called ‘disulfide bonds’ are formed inside a structure within each cell called the endoplasmic reticulum , which is where many proteins are folded . Occasionally , disulfide bonds form in the wrong place in a protein , so they need to be broken and re-positioned—a process sometimes called editing—for the protein to fold correctly . It was widely assumed that a chemical called ‘reduced glutathione’ fuels the breaking of disulfide bonds in the endoplasmic reticulum , but to date few researchers have tried to test this assumption . Tsunoda et al . have now taken an enzyme that degrades glutathione elsewhere in the cell and modified it in a way that allows it to work inside the endoplasmic reticulum . When this modified enzyme was produced in human cells grown in the laboratory , it purged the endoplasmic reticulum of glutathione . However , the lack of glutathione had no effect on the folding of a large protein with 30 disulfide bonds , many of which need to be edited at one time or another for the protein to fold correctly . The destruction of a poorly folded protein , via a process that also needs this protein's disulfide bonds to be broken down , was also not affected by a lack of reduced glutathione in the endoplasmic reticulum . Furthermore , decreasing these levels of glutathione did not affect the unfolded protein response: a stress response in cells that are experiencing a build-up of unfolded or poorly folded proteins within the endoplasmic reticulum . As such , the findings of Tsunoda et al . challenge the importance of reduced glutathione in the endoplasmic reticulum and suggest that other chemical processes might be involved in editing disulfide bonds . Further work is now needed to investigate the other known processes that might complete this task instead to see which , if any , are involved .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology" ]	2014	Intact protein folding in the glutathione-depleted endoplasmic reticulum implicates alternative protein thiol reductants
Oriented cell division is one mechanism progenitor cells use during development and to maintain tissue homeostasis . Common to most cell types is the asymmetric establishment and regulation of cortical NuMA-dynein complexes that position the mitotic spindle . Here , we discover that HMMR acts at centrosomes in a PLK1-dependent pathway that locates active Ran and modulates the cortical localization of NuMA-dynein complexes to correct mispositioned spindles . This pathway was discovered through the creation and analysis of Hmmr-knockout mice , which suffer neonatal lethality with defective neural development and pleiotropic phenotypes in multiple tissues . HMMR over-expression in immortalized cancer cells induces phenotypes consistent with an increase in active Ran including defects in spindle orientation . These data identify an essential role for HMMR in the PLK1-dependent regulatory pathway that orients progenitor cell division and supports neural development . The development and homeostasis of the brain require that neural stem cells , termed neuroepithelial progenitors ( NPs ) , balance expansion with differentiation ( Gönczy , 2008; Knoblich , 2008 ) . During neurogenesis , NPs primarily undergo divisions that produce the various types of cells required for proper brain development . During these divisions , the orientation of the axis of division and mitotic spindle position determines the placement of the progeny cells within the tissue and , as a consequence , imbalances in the spindle positioning pathway can lead to defects in brain morphology ( Lancaster and Knoblich , 2012 ) . Mitotic spindle position is controlled by forces applied on astral microtubules by dynein molecular motor complexes , which are anchored at the cell cortex by complexes of nuclear mitotic apparatus protein ( NuMA ) - G-protein signaling modulator 2 ( GPSM2 , aka LGN ) ( Konno et al . , 2008; Morin et al . , 2007; Peyre et al . , 2011 ) . In mammalian cultured cells , a gradient of Ran-GTP at chromosomes excludes LGN-NuMA complexes from lateral regions of the cell cortex to establish cortical asymmetry of these complexes and to position the spindle ( Kiyomitsu and Cheeseman , 2012 ) . The activity of dynein complexes anchored at polar regions of the cortex are regulated by the proximity of the spindle pole and its associated polo-like kinase 1 ( PLK1 ) gradient ( Kiyomitsu and Cheeseman , 2012 ) and complexes of dynein light chain 1 ( DYNLL1 ) - hyaluronan mediated motility receptor ( HMMR ) ( Dunsch et al . , 2012 ) . If one pole moves too close to the cortex , the centrosomal components of this pathway strip dynein from the cortex to reduce pulling forces and reposition the mitotic spindle . PLK1 activity , for example , stabilizes astral microtubules that are needed to strip dynein-dynactin complexes from the cortex ( Zhu et al . , 2013 ) . HMMR is not proposed to be part of the NuMA-PLK1 spindle positioning pathway but rather to regulate cortical dynein through a complex containing CHICA ( aka FAM83D ) and DYNLL1 ( Dunsch et al . , 2012 ) . However , HMMR and PLK1 activity are known to be interconnected during mitosis: during spindle assembly , PLK1 activity as measured by a kinetochore-localized , FRET-reporter construct is reduced following treatment of cells with siRNA targeting HMMR ( Chen et al . , 2014 ) and HMMR-threonine 703 is a putative substrate for PLK1 identified through mitotic phosphoproteome analysis ( Nousiainen et al . , 2006 ) . During cell division , HMMR acts downstream from Ran-GTP to localize targeting protein for XKLP2 ( TPX2 ) and promote spindle assembly ( Groen et al . , 2004; Joukov et al . , 2006; Chen et al . , 2014; Scrofani et al . , 2015 ) . This action is reliant upon a carboxy-terminal basic leucine zipper ( bZip ) motif in HMMR , which targets it to the centrosome ( Maxwell et al . , 2003 ) , and enables the activation of Aurora A by TPX2 ( Chen et al . , 2014 ) ( Scrofani et al . , 2015 ) . Aurora A can directly phosphorylate PLK1 on Thr210 ( Macůrek et al . , 2008 ) , which suggests that HMMR may impact the PLK1-dependent positioning pathway . Consistent with a potential role establishing the planar cell division exhibited in neural progenitors , HMMR has been shown to regulate apicobasal polarity in mammary epithelia ( Maxwell et al . , 2011 ) , while expression of a truncated Hmmr ( 1–317 of 794 aa , termed Hmmr m/m ) impairs planar cell division in ovarian follicle cells ( Li et al . , 2015 ) , and germ cells in the testes ( Li et al . , 2016 ) . HMMR is expressed in the developing nervous system ( Casini et al . , 2010 ) and proliferative regions of the adult mouse brain ( Lindwall et al . , 2013 ) , Recently , HMMR has been shown to be required for anterior neural tube closure and morphogenesis in Xenopus , where HMMR reduction leads to the absences of ventricular lamina and increased intraocular distance , olfactory bulb size , and forebrain width ( Prager et al . , 2017 ) . The N-terminal microtubule binding region in HMMR is needed for neural tube morphogenesis in Xenopus ( Prager et al . , 2017 ) and the very terminal region is similar to that of Miranda ( Chang et al . , 2011 ) , a regulator of asymmetric NP cell division in Drosophila ( Ikeshima-Kataoka et al . , 1997; Shen et al . , 1997 ) . Hmmr mutant mice models are viable , including when central exons are targeted in Hmmr−/− mice ( Tolg et al . , 2003 ) and Hmmrm/m mice ( Li et al . , 2015 ) , which result in the expression of truncated Hmmr transcript and protein ( exons 1–7 or exons 1–10 , respectively ) . Here , we studied the requirement of HMMR during oriented NP cell division and nervous system development through the creation of Hmmr-deficient mice with targeted disruption of Hmmr following exon 2 . We find that HMMR is needed for neonatal survival and proper brain development . Our studies using cultured primary fibroblasts , directed differentiation of embryonic stem cells , and immortalized cancer cell lines , including neuroblastoma-like cells , uncovered a role for HMMR in the PLK1-dependent positioning pathway at mitotic spindle poles . We generated mice encoding a targeting construct following Hmmr exon 2 , termed Hmmrtm1a ( EUCOMM ) Hmgu ( hereafter Hmmrtm1a/tm1a , Figure 1A , Figure 1—figure supplement 1A–B ) . Western blot analysis of lysates from tissues known to have elevated levels of HMMR expression ( spleen and testes ) ( Line et al . , 2002 ) using antibodies targeting the N-terminal peptide in HMMR revealed that HMMR expression was completely lost in Hmmrtm1a/tm1a mice ( Figure 1B ) . Adult Hmmrtm1a/tm1a mice were rare , and those Hmmrtm1a/tm1a mice that did survive were smaller than their wild-type ( WT ) littermates ( Figure 1C ) . Similar to the phenotypes seen in Hmmrm/m mice attributed to misoriented germ cell divisions ( Li et al . , 2016 ) , we observed atrophic seminiferous tubules and an increase in apoptosis in the testes as indicated by TUNEL staining in Hmmrtm1a/tm1a mice ( Figure 1D–E ) . Additionally , Hmmrtm1a/tm1a mice were less fertile ( fewer litters and fewer pups per litter ) ( Figure 1F–G ) . Few adult Hmmrtm1a/tm1a mice survived despite no evidence of embryonic lethality at E14 . 5 and E18 . 5 ( Figure 1H ) . To identify when Hmmrtm1a/tm1a mice were dying , we monitored neonates for 2 days following birth . 12 . 5% of Hmmrtm1a/tm1a neonates were found dead within 3 hr of birth and 76 . 9% were found dead within the first 48 hr after birth ( Figure 1I ) . Necropsy samples from Hmmrtm1a/tm1a neonates ( P0-1 ) demonstrated morphological defects in the brain , including defects in overall structure and size ( Figure 2A ) . In multiple matching sections taken from WT or Hmmrtm1a/tm1a neonatal brains , we measured the area of the cerebrum and ventricles . We found large variation in the size of Hmmrtm1a/tm1a neonatal brains with three of the nine measuring two standard deviations smaller ( microcephaly ) than the mean brain size for age-matched ( P0-1 ) WT littermates ( Figure 2B ) . In addition , three of nine Hmmrtm1a/tm1a neonatal brains measured two standard deviations larger ( megalencephaly ) than the mean brain size for age-matched ( P0-1 ) WT littermates , but these larger areas correlated with increases in the ventricle area in two of these three Hmmrtm1a/tm1a brains ( Figure 2C ) . The putative reduced cortical area identified in Hmmrtm1a/tm1a relative to WT brains was not accompanied by increased levels of apoptosis , as measured by TUNEL staining , at either E18 . 5 or P0-1 ( Figure 2—figure supplement 1A ) . A putative reduction in cortical area and the potential manifestation of enlarged ventricles ( hydrocephalus ) in Hmmrtm1a/tm1a mice is consistent with phenotypes associated with defects in the orientation of NP cell division ( Noatynska et al . , 2012; Kielar et al . , 2014 ) . As HMMR is known to regulate the cell division axis in immortalized cancer cells ( Dunsch et al . , 2012 ) , we next examined the localization of HMMR and the axis of NP cell division during neurogenesis in E14 . 5 embryos . In WT embryos , HMMR localized to spindle microtubules in dividing NP cells lining the ventricle surface ( Figure 2D ) . The angle of NP cell divisions measured during anaphase was strongly biased to be planar with the ventricle surface ( <30 degrees off the axis of centrosomes lining the surface ) with an average spindle angle of 18 . 0 ± 19 . 5 degrees ( Figure 2E–F ) . In WT brains , 60 . 6% or 82 . 9% of divisions were oriented within 15 degrees or 30 degrees of the ventricular surface , respectively . In Hmmrtm1a/tm1a embryos , however , HMMR was absent from mitotic spindles ( Figure 2D ) and the axis for division was altered with an average angle of 24 . 8 ± 19 . 5 degrees ( p<0 . 05; unpaired t-test ) with only 38 . 5% or 66 . 6% of divisions oriented within 15 degrees or 30 degrees of the ventricular surface , respectively ( χ2 test , p<0 . 001 ) ( Figure 2E–F ) . The degree of misorientation observed in Hmmrtm1a/tm1a NP cells compares to that observed in LGN-mutant NP cells , which have 37 . 1% or 60% of divisions oriented within 15 degrees or 30 degrees of the ventricular surface , respectively ( Konno et al . , 2008 ) . We also observed a significant reduction in the number of mitotic Pax6+ radial glial cells at E14 . 5 in the brains of Hmmrtm1a/tm1a embryos ( Figure 2G–H ) and a consistent , significant reduction in Tbr2+ intermediate progenitor cells in Hmmrtm1a/tm1a brains ( Figure 2I–J ) . However , we observed no significant difference between WT and Hmmrtm1a/tm1a brains in the levels of TUNEL-positive cells in the cortex , the localization of Par3 and ZO-1 in cells lining the ventricles , or the levels of centrosome amplification in dividing NP cells ( Figure 2—figure supplement 1A–C ) . These data support the conclusion that the reduced cortical areas observed in Hmmrtm1a/tm1a neonatal mice , which are less able to successfully transition to extrauterine life , result from the reduced production of intermediate progenitor cells that correspond with impaired planar spindle orientation during NP cell division and an overall decreased mitotic rate of Pax6+ radial glial cells . To better understand the mechanisms through which HMMR controls spindle orientation , we isolated mouse embryonic fibroblasts ( MEFs ) from WT or Hmmrtm1a/tm1a mice and imaged these cells through mitosis ( Figure 3A ) . Hmmrtm1a/tm1a MEFs contained significantly higher levels of centrosome amplification ( defined as cells containing greater than two centrosomes , which were quantified using colocalization of pericentrin and γ–tubulin ) ( Figure 3B–C ) , and were less able to orient the cell division axis , measured at anaphase relative to their long cell axis in interphase ( Figure 3A , D–E ) . In addition , Hmmrtm1a/tm1a MEF progeny cells were more likely to display indications of genome instability , including increases in the frequency of mitotic death , binucleated progeny and micronuclei ( Figure 3F ) . To ensure that the observed spindle orientation defects were not due to changes in cell shape that may accompany the loss of HMMR , we confirmed that the width to long axis ratio of the cell did not differ between WT or Hmmrtm1a/tm1a MEFs ( Figure 3—figure supplement 1A ) . Thus , Hmmrtm1a/tm1a MEFs are less able to correctly orient the axis of cell division , which is similar to the defective spindle orientation that was observed following the treatment of HeLa cells with siRNA targeting HMMR ( Dunsch et al . , 2012 ) . To uncover the critical domain in HMMR needed to establish the cell division axis , we transduced GFP-HMMR ( human ) constructs into Hmmrtm1a/tm1a MEFs . We used truncations of the bZip domain , which is required to target HMMR to the centrosome ( Maxwell et al . , 2003 ) , including GFP- HMMRFL ( full-length , FL ) , HMMR1-679 ( truncation following the b-Zip domain ) , and HMMR1-623 ( truncation lacking the b-Zip domain ) . The transduction of HMMRFL or HMMR1-679 , gene products that are known to locate at centrosomes ( Figure 3—figure supplement 1B ) ( Maxwell et al . , 2003 ) , recovered the axis of cell division ( Figure 3E ) . However , the expression of HMMR1-623 failed to rescue the axis of cell division ( Figure 3E ) , implicating the centrosome targeting bZip motif ( amino acids 624–679 ) as an essential domain for the orientation of cell division . As the centrosome targeting domain was critical to HMMR function in spindle positioning , we next used time-lapse imaging of HeLa cells that stably expressed DHC-GFP and were previously treated with either scrambled control siRNA or siRNA targeting HMMR to determine whether the centrosome-localized , PLK1-dependent positioning pathway , which regulates the localization and activity of dynein complexes anchored at polar regions of the cortex ( Kiyomitsu and Cheeseman , 2012 ) , was functional in HMMR-silenced cells . As multipolar spindles occur at an elevated frequency in HMMR-silenced cells ( Maxwell et al . , 2005 ) , we restricted our analysis to mitotic cells with phenotypically normal bipolar spindles . Immediately prior to anaphase in control-treated cells with an off-center mitotic spindle , DHC-GFP was asymmetrically localized to the far polar region of the cortex and absent from the cortex near the proximal pole leading to the correction of spindle position ( Figure 4A , Video 1 ) as described previously ( Kiyomitsu and Cheeseman , 2012 ) . In HMMR-silenced cells with an off-center mitotic spindle , however , DHC-GFP remained on the polar cortex and its retention on the cortex predicted the direction of spindle rotation ( Figure 4A , Video 2 ) . We also measured the intensity of DHC-GFP recruited to the cortex relative to the cytoplasmic intensity and determined that overall DHC-GFP recruitment to crescents was not significantly different between scrambled control-treated and HMMR-silenced cells ( Figure 4—figure supplement 1A ) . In addition , the spindle was maintained in an off-centered position in HMMR-silenced cells as determined by the ratio of the distance between the cortex and the location of chromosomes in anaphase cells ( Figure 4B ) . A prolonged period of spindle rotation was frequently observed in HMMR-silenced HeLa cells expressing DHC-GFP ( Figure 4C ) and , as seen in separate experiments , in HMMR-silenced HeLa cells expressing mCherry histone H2B , eGFP-TUBA ( Figure 4—figure supplement 1B–C; Videos 3–4 ) . We next examined the localization of the dynein anchoring protein NuMA in fixed HMMR-silenced cells by immunofluorescence . When the fluorescence intensity of DHC-GFP or NuMA was measured along the cortex in control-treated cells with an off-center spindle , the ratio of intensities along the far polar cortex was elevated compared to that along the proximal polar cortex ( Figure 4D–E ) , as expected . However , NuMA was retained at the proximal polar cortex and the consequent ratio of intensities was significantly dampened in HMMR-silenced mitotic cells with an off-center metaphase spindle ( Figure 4D–E ) . Yet , when the metaphase spindle was centered , the asymmetric cortical localization of NuMA and DHC-GFP was indistinguishable in control-treated and HMMR-silenced cells ( Figure 4F–G ) . That is , the ratio of intensities along the polar cortex relative to the midzone cortex was elevated at the polar cortex for both NuMA and DHC-GFP , as reported ( Kiyomitsu and Cheeseman , 2012 ) . Our data indicate HMMR is dispensable for the loss of NuMA and DHC-GFP at the midzone cortical region , which is established by Ran-GTP gradient generated at chromosomes ( Kiyomitsu and Cheeseman , 2012 ) , but HMMR is needed for a centrosome-localized positioning pathway that strips NuMA and DHC-GFP when the pole is brought proximal to the cortex . We identified a requirement for HMMR in the removal of cortical NuMA-DHC-GFP , which is consistent with a prior report ( Dunsch et al . , 2012 ) . However , we observed spindle rotation in HMMR-silenced cells rather than fixed and misoriented spindles ( Dunsch et al . , 2012 ) . It was postulated that HMMR-DYNLL1 complexes at the centrosome functioned independent of the PLK1 intrinsic positioning pathway ( Dunsch et al . , 2012 ) . That is , spindle pole-localized HMMR may create a local increase of DYNLL1 at the spindle/spindle poles that binds to dynein in competition with dynactin and displaces dynein from the cortex ( Dunsch et al . , 2012 ) ; however , Dyn2 ( yeast DYNLL1 ortholog ) promotes , rather than impedes , the incorporation of dynactin into the dynein motor complex ( Stuchell-Brereton et al . , 2011 ) . So , we examined whether the loss of HMMR altered the composition of the dynein motor complex . HeLa cells expressing DHC-GFP were treated with control siRNA or siRNA targeting HMMR and DHC complexes were immunoprecipitated with antibodies recognizing GFP . In HMMR-silenced lysates , relative to control-treated lysates , the abundance of subunits for dynein ( DHC , dynein intermediate chain ( DIC ) , and DYNLL1 ) and dynactin ( p150glued ) were unchanged ( Figure 5A ) . While the amount of DHC-GFP precipitated and the levels of DIC co-precipitated with DHC-GFP remained unaffected , the level of co-precipitated DYNLL1 was reduced in HMMR-silenced lysates ( Figure 5A ) . Moreover , a corresponding reduction in p150glued co-precipitated with DHC-GFP was also observed in HMMR-silenced lysates ( Figure 5A ) . We also noted that both FLAG-DYNLL1 and CHICA were retained at spindle poles , although absent from spindle fibers , in HMMR-silenced cells ( Figure 5B–C ) . Therefore , HMMR appears dispensable for the spindle pole localization of CHICA and DYNLL1 and HMMR promotes , rather than restricts , the incorporation of p150glued into DHC complexes . As our data do not support the proposed PLK1-independent role for HMMR in spindle positioning , we investigated a putative role for HMMR in the PLK1-dependent positioning pathway . PLK1 activity at centrosomes and kinetochores enables the removal of LGN-NuMA-DHC complexes from the cortex ( Kiyomitsu and Cheeseman , 2012; Zhu et al . , 2013; Tame et al . , 2016 ) . As PLK1 activity is reduced at kinetochores in HMMR-silenced cells ( Chen et al . , 2014 ) , we measured the levels of phosphorylated-PLK1 , the active form of the kinase , by immunofluorescence in HMMR-silenced and control-treated mitotic cells . In HMMR-silenced mitotic cells , we observed a significant reduction in the fluorescence intensity of pPLK1 at spindle poles ( Figure 5D–E ) . Consistent with a putative reduction in PLK1 activity at spindle poles , we observed a significant decrease in the number of EB1-positive microtubule plus ends contacting the cortex in HMMR-silenced or BI2536-treated mitotic cells ( Figure 5F–G ) . As these data suggest HMMR enables PLK1-dependent processes , we tested if the concurrent inhibition of HMMR and PLK1 significantly altered the spindle positioning pathway when compared to the inhibition of either alone . We focused on the localization of NuMA to the cortical regions most proximal to the spindle pole in control treated , HMMR-silenced , BI2536-treated , or dual inhibited metaphase cells with off-center spindles ( Figure 5H ) . In control-treated cells , NuMA was uniformly absent from the cortex , while NuMA was largely retained in HMMR-silenced cells with the exception of a discreet loss in the inner region directly proximal to the pole ( Figure 5H–I ) . In mitotic cells treated with BI2536 , the localization of NuMA mirrored that observed in HMMR-silenced cells and was not additively altered in dual inhibited cells ( Figure 5H–I ) consistent with a requirement for PLK1 and HMMR in a shared positioning pathway . Thus , our data supports the conclusion that astral microtubule density is dampened in HMMR-silenced mitotic cells in a PLK1-dependent manner , which disturbs the spindle pole-localized positioning pathway and results in spindle rotation due to the retention of NuMA-DHC complexes at the proximal cortex . As PLK1 phosphorylates HMMR at threonine 703 ( pHMMR ) ( Nousiainen et al . , 2006 ) , we used mass spectrometry to identify proteins that are co-precipitated with antibodies targeting pHMMR as a means to discover putative pathways through which centrosome-localized HMMR may regulate spindle position . In pHMMR immunoprecipitates from mitotic or G2-phase synchronized HeLa cell lysates , we identified a number of actin- , myosin- , and dynein-binding proteins ( Figure 6—figure supplement 1; Figure 6—source data 1 ) , including known interactors FAM83D/CHICA and DYNLL1 ( Dunsch et al . , 2012 ) . We filtered out proteins that were also co-precipitated with antibodies targeting a non-phosphorylated N-terminal peptide in HMMR ( unpublished results ) and found that proteins related to small GTPases , such as ARHGAP17 , RACGAP1 , and RanBP2 , were uniquely co-precipitated with pHMMR antibodies ( Figure 6A ) . As Ran regulates cortical LGN-NuMA-dynein localization during mitosis ( Kiyomitsu and Cheeseman , 2012 ) , we focused on the putative pHMMR-RanBP2 interaction and confirmed this interaction by western blot analysis ( Figure 6B ) . RanBP2 binds specifically to Ran-GTP ( Vetter et al . , 1999 ) , so we postulated that HMMR may affect Ran-GTP levels or localization . To test this postulate , we first measured the levels of Ran-GTP in scrambled siRNA-treated or HMMR-silenced HeLa cell lysates with a commercially available assay that pulls-down the active form of Ran ( Ran-GTP ) . In control experiments , scrambled siRNA-treated HeLa cell lysates were treated with either non-hydrolysable GTP ( GTPγS ) or with GDP to switch all Ran to Ran-GTP or Ran-GDP , respectively , prior to immunoprecipitation . As expected , the level of co-precipitated Ran was significantly augmented in cell lysates treated with GTPγS and lost in cell lysates treated with GDP ( Figure 6C ) . Having verified our control conditions , we then compared the levels of Ran-GTP in control-treated and HMMR-silenced cell lysates . In HMMR-silenced cell lysates , the level of Ran-GTP precipitated was slightly reduced relative to scrambled control-treated cell lysates ( Figure 6C ) . When we confirmed the knockdown efficacy of HMMR in these experiments , we noted that HMMR was also co-precipitated when lysates were pretreated with GTPγS suggesting HMMR may interact specifically with Ran-GTP , which is greatly increased by GTPγS treatment . To titrate a putative HMMR-Ran-GTP interaction , we precipitated HMMR from mitotic cell lysates previously treated with increasing amounts of GTPγS . While the levels of precipitated HMMR remained constant , we found that the level of Ran co-precipitated with HMMR was increased in a GTPγS dose-dependent manner ( Figure 6D ) . Thus , HMMR interacts with Ran-GTPγS in cell lysates . We predicted that HMMR may affect the localization of active Ran in mitotic cells . To test this , we expressed constitutively active Ran ( Ran Q69L , RanCA ) ( Kazgan et al . , 2010 ) in HeLa cells treated with scrambled siRNA or siRNA targeting HMMR . In scrambled siRNA-treated cells , a fraction of RanCA colocalized with the spindle pole demarked by γ-tubulin ( Figure 6E ) , consistent with the identification of Ran in the centrosome proteome ( Andersen et al . , 2003 ) . However , the fraction of RanCA that colocalized with γ-tubulin , as measured by the ratio of intensities for RanCA and γ-tubulin , was significantly reduced in HMMR-silenced cells ( Figure 6E–F ) . Similarly , inhibition of PLK1 activity , through treatment with a small-molecule inhibitor BI2536 , also reduced the fraction of RanCA that colocalized with γ-tubulin ( Figure 6G–H ) . This data shows that reducing PLK1 activity or silencing HMMR reduces the localization of constitutively active Ran at mitotic centrosomes and suggests the phosphorylation of HMMR ( pHMMR ) by PLK1 may promote pHMMR-RanBP2-Ran-GTP complexes at spindle poles . To investigate the HMMR-Ran pathway in neural cells and tissues , we first utilized the neuroblastoma cell line , SHSY5Y , which is known to polarize NuMA during cell division ( Izumi and Kaneko , 2012 ) . Following the transduction of shRNA targeting HMMR or nonhairpin control shRNA , we confirmed the loss of HMMR immunofluorescence at mitotic spindles ( Figure 7—figure supplement 1A ) . In HMMR-silenced cells compared to control-treated cells , we observed a decrease in the immunofluorescence intensity detected for endogenous Ran colocalized with pericentrin , a centrosome marker ( Figure 7—figure supplement 1B–C ) . We then investigated mitotic spindle structure and position as well as the localization of Ran in sections derived from WT and Hmmrtm1a/tm1a E14 . 5 brains . In these sections stained for the spindle marker beta-tubulin ( Figure 7A ) , we measured the position and length of the spindle in dividing neural progenitor cells that lined the ventricles . When compared to spindles within wild-type progenitor cells , spindles in Hmmrtm1a/tm1a neural progenitors were less centered ( a/b measurement ) and significantly shorter ( c measurement ) ( Figure 7B ) . Additionally , the density of spindle fibers , as measured by the intensity of beta-tubulin fluorescence , was significantly lower in Hmmrtm1a/tm1a neural progenitors than those in WT cells ( Figure 7C ) . Similar to our observations in HMMR-silenced HeLa or SH-SY5Y cells , we found a fraction of endogenous Ran , as measured by immunofluorescence , colocalized with a centrosome marker ( γ-tubulin , Figure 7D ) ; this fraction of Ran , as measured by the ratio of intensities for Ran and γ-tubulin , was significantly reduced in Hmmrtm1a/tm1a relative to WT neural progenitors ( Figure 7E ) . Taken together , our data obtained from studies of cells , tissues and animals that are deficient for HMMR indicate a critical role for the protein in establishing the correct position of the mitotic spindle during cell division . Complete loss of HMMR is sufficient to alter the position and orientation of NP cell division and , in the case of Hmmrtm1a/tm1a mice , disturb brain development and impair the animal’s ability to successfully transition to extrauterine life . Our in vitro data support a model where HMMR supports PLK1 activity at the centrosome , which stabilizes astral microtubules and through the phosphorylation of HMMR localizes Ran to mitotic spindle poles . These PLK1-dependent spindle pole positioning processes are critical to reduce cortical localization of NuMA-DHC in off-center spindles and prevent spindle rotation ( Figure 7—figure supplement 2A–B ) . To compare and contrast the mitotic consequences that follow HMMR deletion to those that follow ectopic GFP-HMMR expression , we studied spindle positioning and NuMA localization using HeLa cells with doxycycline-inducible expression of GFP-HMMR ( tet-HMMR ) ( Figure 8A ) , which express GFP-HMMR along the spindle microtubules in mitotic cells and undergo spindle tumbling when grown on L-shaped micropatterned substrates ( He et al . , 2017 ) . In subconfluent cultures of induced tet-HMMR cells , metaphase spindles were more frequently off-center ( Figure 8B ) . We next examined whether induced tet-HMMR expression altered the localization of co-expressed RanWT , RanCA , or RanDN constructs tagged with mCherry . In HeLa cells , these constructs localized as previously reported ( Hutchins et al . , 2009 ) : RanWT was cytoplasmic , RanCA localized to the mitotic spindle , and RanDN localized to the chromosomes ( Figure 8C ) . In induced tet-HMMR cells , however , RanWT localized to the spindle similar to that of RanCA ( Figure 8C ) . Induction of GFP-HMMR expression also caused NuMA to be lost entirely from the cortex ( Figure 8D–E ) . We observed a similar effect on cortical NuMA localization in HeLa cells overexpressing RanCA ( Figure 8F–G ) . Taken together , these data indicate that expression of GFP-HMMR induces defects in the spindle positioning pathway that are consistent with an ectopic localization of Ran-GTP on mitotic spindles ( Figure 7—figure supplement 2B ) . HMMR is classified as a non-motor spindle assembly factor ( Manning and Compton , 2008 ) and has been shown to be a critical cell division gene product in immortalized cancer cells ( Neumann et al . , 2010 ) . In order to study HMMR functions in vivo , we generated Hmmrtm1a/tm1a mice , which encode a targeting construct following Hmmr exon 2 . Our approach is in contrast to previous published Hmmr mutant mice . An initial murine model , termed Hmmr−/− , was generated by targeted disruption at exon 8 ( Tolg et al . , 2003 ) while an alternative model , termed Hmmrm/m , was generated by targeted disruption at exon 10 ( Li et al . , 2015 ) ; thus , truncated N-terminal Hmmr transcript or protein is expressed in both of these published Hmmr models . Hmmr−/− animals exhibited fertility defects ( Tolg et al . , 2003 ) , while Hmmrm/m mice demonstrated deficient spindle orientation during gametogenesis ( Li et al . , 2016; Li et al . , 2015 ) . However , Hmmrm/m and Hmmr−/− animals are both viable and phenotypically normal . This contrasts with the severely diminished survival we observed in Hmmrtm1a/tm1a neonates and , for the few animals that survived the transition , the reduced body sizes we observed in rare , adult Hmmrtm1a/tm1a mice . We observed in Hmmrtm1a/tm1a mice previously unseen neural defects such as enlarged ventricles and microcephaly; both of these congenital conditions can arise from defects in spindle orientation and planar cell division within the progenitor population ( Lancaster and Knoblich , 2012 ) . In accordance , we observed a reduced proportion of mitotic Pax6+ cells that did not properly align their division axis in the subventricular zone ( SVZ ) and a decrease in the proportion of Tbr2+ progenitors at E14 . 5 , suggesting an overall decreased neuronal production in these animals . These cellular phenotypes and reduced body size , are similar to those that accompany microcephalic brains observed in MagohMos2/+ mice ( Silver et al . , 2010 ) , which are haploinsufficient for a regulator of the levels of expression of the dynein adaptor protein , Lis1 . We also observed enlarged ventricles , which augmented the gross brain size in a proportion of Hmmrtm1a/tm1a mice . The etiology of these phenotypes may relate to impaired cerebrospinal fluid flow , as seen with mutation of dynein components and related to primary cilia dyskinesis ( Ibañez-Tallon et al . , 2002; Ostrowski et al . , 2010 ) . As HMMR is a non-motor adaptor for dynein , a more detailed analysis of the effect on CSF flow is Hmmrtm1a/tm1a mice is warranted . A recent study identified misoriented division planes in apical neuroprogenitors and postnatal granule cell precursors , without changes to the cell division rate , during brain development of Hmmrm/m mice expressing truncated N-terminal Hmmr protein ( Li et al . , 2017 ) . Transient megalencephaly was observed in Hmmrm/m brains during the neuronal differentiation period , up to PND7 but normalized by PND14 , and attributed to misoriented divisions in apical neuroprogenitors leading to an increased number of Tbr+ intermediate progenitors contributing to cerebral cortex enlargement ( Li et al . , 2017 ) . In Hmmrtm1a/tm1amice , however , the predominant phenotype observed was microcephaly , which may be explained by a decreased mitotic rate for Pax6+ radial glial cells; consistent with the recent observations in Hmmrm/m brains , we did observe rare Hmmrtm1a/tm1a brains at E14 . 5 with cortex enlargement . The different neural phenotypes between Hmmrm/m and Hmmrtm1a/tm1a mice imply the expression of truncated N-terminal Hmmr protein retains critical functions needed for neural development and neonatal survival . In support of this , the microtubule-binding domain of HMMR ( Assmann et al . , 1999; Maxwell et al . , 2003 ) has recently been shown to be important for neural development in Xenopus ( Prager et al . , 2017 ) . Microinjection of morpholinos targeting Hmmr in Xenopus laevis embryos resulted in gross alterations in forebrain morphogenesis and anterior neural tube closure and these defects were rescued by the expression a truncated HMMR construct that preserved the microtubule-binding domain ( Prager et al . , 2017 ) . These defects were attributed to the loss of a non-mitotic role for HMMR in the promotion of neural cell polarization and radial intercalation concomitant with neural tube closure ( Prager et al . , 2017 ) . While we observed no changes in cell morphology in Hmmrtm1a/tm1a MEFs and no defects in the localization of Par3 or ZO-1 in Hmmrtm1a/tm1a NSCs lining the ventricles , future studies should investigate the disruption of Hmmr function in non-mitotic Hmmrtm1a/tm1a tissues and cells , such as during directed cell migration needed for corticogenesis and neural tube closure ( Marín and Rubenstein , 2003 and Ayala et al . , 2007 ) . Directed smooth muscle cell migration following balloon catheter injury in rats is reliant upon HMMR ( Silverman-Gavrila et al . , 2011 ) , and HMMR is expressed in cells within the SVZ and rostral migratory stream in the adult mouse brain , where neural progenitor cells persist throughout life ( Lindwall et al . , 2013 ) . While a detailed investigation of non-mitotic processes are needed in Hmmrtm1a/tm1a mice , we focused on mitotic processes due to our analysis of Hmmr promoter activity and HMMR expression during the directed neural differentiation of mouse embryonic stem cells ( ESC ) . When we derived neural rosette-like structures from a mouse mouse HmmrBB0166/+ ESC line ( Figure 7—figure supplement 1D–E ) , in which β-geo is inserted following Hmmr exon 7 on one allele , or from WT ESCs ( Figure 7—figure supplement 1F ) , we found Hmmr/HMMR expression was restricted to apical-positioned , cyclin B1-positive rosette-neural stem cells ( r-NSCs ) ; moreover , spindle position was disturbed in apical-positioned r-NSCs derived from HmmrBB0166/+ mESCs ( Figure 7—figure supplement 1G–H ) . Thus , Hmmr shows cell cycle-restricted expression in G2 phase and mitotic cells during the process of neural differentiation , and our data are consistent with the defects we observed in Hmmrtm1a/tm1a mice being attributed to alterations in neural progenitor expansion and differentiation as determined by changes to the control of spindle orientation and cell division . Hmmrtm1a/tm1a mice exhibited the following phenotypes that are known to arise from defects in spindle orientation and planar cell division: microcephaly ( Gai et al . , 2016 ) , enlarged ventricles ( Dietrich et al . , 2009; Godin et al . , 2010 ) , and testicular atrophy and fertility defects ( Li et al . , 2016; Li et al . , 2015 ) . Moreover , in HeLa cells , HMMR is critical to spindle positioning ( Dunsch et al . , 2012 ) . However , other mitotic or post-mitotic processes may also be disturbed in Hmmrtm1a/tm1a mice independent of spindle orientation , such as an increased rate of apoptosis , centrosome amplification ( Marthiens et al . , 2013 ) , and decreased division rate ( Caviness et al . , 2003; Caviness et al . , 1995 ) . In our study , we focused our efforts on addressing many of these additional factors and specifically those related to cell division . TUNEL analysis revealed no increase in apoptosis in Hmmrtm1a/tm1a mice at both embryonic and neonate stage . An elevated occurrence of supernumerary centrosomes , as induced for example through overexpression of PLK4 , results in embryonic lethality ( Vitre et al . , 2015 ) . Our analysis revealed no evidence of multipolar spindles or centrosome amplification in neural tissues , but we did note an increased frequency of centrosome amplification in Hmmrtm1a/tm1a MEFs . In addition , Hmmrtm1a/tm1a MEFs require more time to complete the spindle assembly checkpoint when followed through cell division independent of spindle orientation ( Unpublished results ) . However , whether such delays in cell division kinetics are present in Hmmrtm1a/tm1a mice , or if an associated reduced rate of cell division contributed to the observed neural defects remain unknown . A loss of control of the cell division axis , however , has been consistently observed in HMMR-silenced HeLa cells ( Dunsch et al . , 2012 ) and , as described here , in r-NSCs derived from HmmrBB1066/+ mESCs , Hmmrtm1a/tm1a MEFs , and neural progenitor cells lining the SVZ in brains isolated from Hmmrtm1a/tm1a mice . The intrinsic spindle positioning pathway regulates components of the dynein motor complex on the cortex to generate forces that center spindles . It is well recognized that the PLK1 gradient at both centrosomes and kinetochores is essential in regulating the spindle positioning pathway ( Kiyomitsu and Cheeseman , 2012; Tame et al . , 2016 ) . Here , we find HMMR is a critical component of a PLK1-dependent positioning pathway , which contrasts with the findings of a prior report that predicts HMMR-CHICA-DYNLL1 play a PLK1-independent role that regulates the composition of cortical dynein complexes ( Dunsch et al . , 2012 ) . While DYNLL1 and CHICA were identified in our mass spectrometry analysis of pHMMR immunoprecipitates and we found that the incorporation of DYNLL1 into DHC-GFP complexes was dampened in HMMR-silenced cell lysates , our data are consistent with HMMR-DYNLL1 enabling the incorporation of p150-glued into DHC-GFP complexes . This role is similar to that reported for Dyn2 ( yeast DYNLL1 ortholog ) ( Stuchell-Brereton et al . , 2011 ) but contrary to the prediction that DYNLL1-bound form of dynein cannot bind to dynein adaptors required for cortical targeting ( Dunsch et al . , 2012 ) . HMMR acts downstream from Ran-GTP to localize TPX2 ( Groen et al . , 2004; Joukov et al . , 2006; Chen et al . , 2014; Scrofani et al . , 2015 ) , which relies upon a carboxy-terminal bZip motif in HMMR ( Maxwell et al . , 2003 ) and enables the activation of Aurora A by TPX2 ( Chen et al . , 2014 ) ( Scrofani et al . , 2015 ) . As Aurora A can directly phosphorylate PLK1 on Thr210 ( Macůrek et al . , 2008 ) , our observed reduction in pPLK1 ( T210 ) in HMMR-silenced cells can be mechanistically explained through reduced Aurora A activity . Consistent with reduced PLK1 activity in HMMR-silenced cells , we find that astral microtubules are reduced and cortical NuMA is retained , which is phenocopied and not additively augmented by PLK1 inhibition . Indeed , the growth rate of EB1-marked microtubule comets was previously shown to be dampened in HMMR-silenced cells although the conclusion of these experiments was that astral microtubule organization was similar to control-treated cells ( Dunsch et al . , 2012 ) . We note that the density of astral microtubules is reduced in HMMR-silenced cells , which provides an alternative mechanistic explanation for the retention of cortical DHC-GFP in HMMR-silenced cells with off-center spindles . Ran is a component of the centrosome proteome ( Andersen et al . , 2003 ) , although FRET-based assessment of Ran activity has not been seen at the centrosome ( Kaláb et al . , 2006 ) . Here , we find evidence for the location of Ran-GTP at mitotic centrosomes reliant upon PLK1 activity and HMMR . Mass spectrometry analysis discovered RanBP2 as a novel interactor with pHMMR and we show that HMMR precipitates Ran-GTPγS in a dosage-dependent manner and is needed to locate active Ran at mitotic centrosomes . Moreover , in HeLa cells induced to express GFP-HMMR we also observe ectopic localization of Ran to mitotic spindles and the loss of cortical NuMA localization similar to those induced by expression of RanCA . However , HMMR appears dispensable for the Ran-GTP gradient generated at chromosomes , which strips NuMA and DHC-GFP at the midzone cortical region ( Kiyomitsu and Cheeseman , 2012 ) . Therefore , our data supports a spatially restricted role for pHMMR in the localization of Ran-GTP at centrosomes and spindle fibers downstream of PLK1 activity . However , the relative contribution in the control of spindle positon for pHMMR function with Ran at centrosomes versus the role HMMR plays in promoting pPLK1 ( T210 ) , potentially through modulation of Aurora A activity , warrants further study . Taken together , the results of our study identify HMMR as a key component of the PLK1-dependent mitotic spindle positioning pathway that is needed for neural development and neonatal survival . We suggest that the loss of spindle positioning identified here , and by others ( Dunsch et al . , 2012; Li et al . , 2016; Li et al . , 2015 ) ( Li et al . , 2017 ) , in HMMR-silenced and HMMR-deleted or mutated cells and tissues , the occurrence of pleiotropic phenotypes observed in Hmmrtm1a/tm1a and Hmmrm/m mice , and the requirement of HMMR during meiotic ( Groen et al . , 2004; Joukov et al . , 2006; Scrofani et al . , 2015 ) and mitotic spindle assembly ( Maxwell et al . , 2005; Maxwell et al . , 2003; Dunsch et al . , 2012; Chen et al . , 2014 ) together indicate that the physiological function for HMMR is as a homeostasis , meiosis , and mitosis regulator . Hmmrtm1a/+ ES cell strains with the L1L2_Bact_P cassette inserted after Hmmr exon 2 were purchased from The European Conditional Mouse Mutagenesis Program ( HEPD0778_4_B11; EUCOMM ) . Hmmrtm1a/+ ES cells were introduced into blastocyst stage embryos by microinjection and resulting male chimera mice were bred to C57BL/6J female to obtain Hmmrtm1a/+ mice . Hmmrtm1a/+ mice were then intercrossed to generate Hmmrtm1a/+ mice on a C57BL/6J background . All mice were maintained in the pathogen-free Centre for Molecular Medicine and Therapeutics animal facility on a 6 am-to-8 pm light cycle , 20 ± 2°C , with 50 ± 5% relative humidity , and had food and water ad libitum . All procedures involving animals were in accordance with the Canadian Council on Animal Care ( CCAC ) and UBC Animal Care Committee ( ACC ) ( Protocol no . A13-0168 ) . Tail clips from embryos and ear notches from weaned animals were lysed in GB buffer ( 100 mM Tris , pH8 . 8 , 100 mM ( NH4 ) 2SO4 , 100 mM MgCl2 , 1% β-mercaptoethanol , 0 . 5% triton X-100 and 1 . 6 mg/ml protease K ) at 50°C for 3–5 hr . Protease K was inactivated at 95°C for 10 min . PCR was performed using AccuStart II PCR mix ( Quanta Biosciences , Beverly , MA ) . Primers used for PCR1 were: Hmmr-for , 5’-AGATACAACCTTGCTTGCTTCGGC-3’ , loxR , 5’-TGAACTGATGGCGAGCTCAGACC-3’ ( mutant 507 bp ) ; Primers used for PCR2 were: Hmmr-5'-arm , 5’-CAGGCCTTAGAAGCTGACATGAGC-3’ , Hmmr-3'-arm , 5’-TCCAAACTTCTCACTGCAGACAGC-3’ , LAR3 , 5’-CAACGGGTTCTTCTGTTAGTCC-3’ ( WT 515 bp , mt 339 bp ) . Mouse tissues were harvested , snap frozen , ground into powder , lysed in RIPA buffer supplemented with protease inhibitor ( Roche , Switzerland ) and sonicated . Lysates were clarified by centrifugation at 16 , 000xg for 20 min at 4°C , and concentration was determined by BCA protein assay kit ( Thermo Scientific , Waltham , MA ) . Lysates were mixed with SDS sample buffer , separated by SDS-PAGE , and blotted with the indicated antibodies: Actin ( rabbit ( rb ) , Sigma , 1:2500 ) and HMMR ( rb , Abcam , 1:500 ) . HmmrBB0166/+ mouse ES cells ( BB0166; MMRRC: 026467-UCD ) and the parental control mouse ES cells ( E14TG2a; MMRRC: 015890-UCD ) were purchased from a Mutant Mouse Regional Resource Center ( University of California , Davis ) . ES cells were maintained on mitomycin C-treated MEFs prepared as previously described ( Conner , 2001 ) . ES cells were cultured and neural induction was initiated as previously described ( Barberi et al . , 2003 ) . To generate Hmmrtm1a/tm1a MEFs , heterozygous mice were interbred as previously described ( Johnson et al . , 1995 ) with the following exceptions: embryos were collected at day 13 . 5 ( day 1 ) and homogenized using an 18-gauge needle and a 10cc syringe , cultures were incubated for two days , trypsinized on day 3 , and frozen once confluent . HeLa cells ( ATCC: CCL-2 ) were maintained as previously described ( Chen et al . , 2014 ) . HeLa cells expressing mouse DHC-GFP were obtained from Mitocheck and maintained as described ( Hutchins et al . , 2010 ) . HeLa cells expressing mCherry-Histone H2B , eGFP-TUBA were obtained from the Gruneberg lab ( University of Liverpool ) ( Zeng et al . , 2010 ) and maintained in media with 0 . 3 μg/ml puromycin ( Invitrogen ) and 0 . 5 μg/ml blasticidin S ( Invitrogen ) . Live imaging was performed using Leibovitz’s L-15 media supplemented with 10% FBS . BI2536 ( Selleck Chemicals ) treatment was performed for 2 hr at a concentration of 20 nM . HeLa cells with tet-on inducible expression of enhanced GFP fused in frame with full-length HMMR ( GFP-HMMR ) were obtained from Dr . LM Pilarski ( University of Alberta ) and produced as described ( He et al . , 2017 ) . For experiments , expression of HMMR was induced using 2 μg/ml of doxycycline ( Clontech , Mountain View , CA ) for 10 hr . SH-SY5Y ( Sigma 94030304; ECACC validated prior to purchase ) were maintained as previously described ( Izumi and Kaneko , 2012 ) . Neural rosettes and MEFs were grown on coverslips coated with 0 . 1% gelatin and fixed with 4% paraformaldehyde ( PFA ) for 15 min . For pericentrin , cells were fixed with 4% PFA followed by MeOH for 15 min at −20°C . HeLa cells were fixed in ice-cold MeOH for 15 min at −20 °C Cells were blocked with 0 . 3% triton X-100 , 10% donkey serum , 0 . 1% BSA in PBS ( rosettes ) or 0 . 3% triton X-100 , 1 . 0% BSA , in PBS . Cells were incubated with primary antibodies for 2 hr at RT or overnight at 4°C and secondary antibodies for 1 hr at RT . Mouse tissues were fixed in 4% PFA overnight and stored in 70% EtOH . Tissues were paraffinized , embedded , and sectioned at 5 µm intervals . Deparaffinization and antigen-retrieval were performed and sections were processed for immunostaining as previously described ( Li et al . , 2015 ) . Primary Antibodies Used: α-tubulin ( rabbit ( rb ) , Abcam , 1:1000 ) ; β-tubulin ( mouse ( ms ) , Sigma ) ; β-tubulin-647 ( rb , Cell Signaling , 1:500 ) ; EB1 ( rat , Abcam , 1:500 . ) ; γ-tubulin ( ms , Sigma , 1:2000 ) ; GFP ( ms , Abcam , 1:500 ) ; HMMR ( rb , Abcam , 1:500; [Li et al . , 2015] ) ; NuMA ( rb , Abcam , 1:500–1000 ) ; Par3 ( rb , Millipore , 1:100 ) ; Pax6 ( rb , Covance , 1:300 ) ; Pericentrin ( rb , Covance , 1:500 ) ; pPLK1 ( Thr210 ) ( rb , Abcam , 1:200 ) ; PLZF ( ms , EMD chemical , 1:100 ) ; Ran ( ms , Cell Biolabs , 1:500 ) , Ran ( rb , Abcam , 1:50 ) ; Tbr2 ( rb , Abcam , 1:100 ) ; TPX2 ( rb , Novus , 1:500 ) ; ZO-1 ( rb , Invitrogen , 1:1000 ) ; ZO-1 ( ms , Invitrogen , 1:100 ) . Secondary Antibodies Used: AlexaFluor 488 , AlexaFluor 549 , and AlexaFluor 647 ( Invitrogen ) . For TUNEL , samples were stained with the In Situ cell death detection kit , Fluorescein ( Roche ) following the manufacturer’s instructions . Coverslips were mounted with Prolong Gold antifade reagent with DAPI ( Invitrogen ) and images were acquired with confocal microscopy ( FluoView Fv10i , Olympus ( Japan ) or Axiovert 200 , Zeiss ( Germany ) ) . Image analysis was performed using ImageJ . For DHC-GFP HeLa , imaging was performed using a Perkin Elmer Ultraview VOX spinning disk confocal microscope using a Leica DMI6000 inverted microscope equipped with a Hamamatsu 9100–02 camera . Images were taken at two intervals . MEFs , Tet-HeLa cells , and eGFP-TUBA HeLa cells were imaged using an ImageXpress Micro High Content Screening System ( Molecular Devices , Inc . , Sunnyvale , CA ) for up to 24 hr at 15 min intervals . Prior to imaging cells were stained with Hoechst . Image analysis was performed using ImageJ . HMMR constructs were delivered and expressed using the Gateway system ( Invitrogen ) . Briefly , HEK293FT cells ( Invitrogen R7007 ) were transfected using Lipofectamine 2000 ( Invitrogen ) with vectors containing , HMMRFL , HMMR1-623 , or HMMR1-679 , for 72 hr . Supernatant was collected and concentrated using Lenti-X concentrator ( Clontech ) . Virus was added to MEFs with polybrene ( 8 μg/ml ) . After 24 hr , the media was replaced and cells grown for 24 hr prior to imaging . On-target plus siRNA ( Dharmacon ) and scrambled siRNA as previously described ( Chen et al . , 2014 ) . pmCherry-C1-RanQ69L was a gift from Jay Brenman ( Addgene plasmid # 30309 ) ( Kazgan et al . , 2010 ) . pcDNA-RanWT-mRFP1-polyA was a gift from Yi Zhang ( Addgene plasmid # 59750 ) ( Inoue and Zhang , 2014 ) . The mCherry-RanDN construct was graciously provided by Dr . Iain Cheeseman ( Kiyomitsu and Cheeseman , 2013 ) . Transfection of DNA and siRNA used JetPrime ( Polyplus Transfection , France ) following the manufacturer’s protocols . Cells were harvested 96 hr post-transfection of siRNA . HeLa cells were treated with siRNA and synced with a double thymidine block and treated with MG132 for 2 hr starting 8 hr post-release . Ran activity assays were performed using the Ran Activation Assay kit ( Cell Biolabs , Inc ) as per manufacturer’s protocols . DHC-GFP HeLa cells treated with siRNA and synced with a double thymidine block were homogenized with lysis buffer ( Dunsch et al . , 2012 ) with a phosphatase inhibitor ( Phosphostop ( Roche ) ) and Protease Inhibitor cocktail ( Roche ) . HeLa cells were treated with indicated plasmids and synced with a nocodazole block and then released into mg-132 . Cells were lysed with lysis buffer ( 25 mM HEPES , pH 7 . 5 , 150 mM NaCl , 1% NP-40 , 10 mM MgCl2 , 1 mM EDTA , 2% glycerol ) . GTPγS was loaded prior to immunoprecepitation by adding EDTA ( 10 µM ) and GTPγS at the indicated concentration for 30 min at 30°C . The reaction was stopped with MgCl2 ( 65 µM ) . The supernatants were used for immunoprecipitation with IgG ( ms and rb , Sigma ) , GFP ( ms , Abcam ) , HMMR ( rb , Abcam ) , or pHMMR ( Maxwell et al . , 2011 ) , overnight at 4°C , followed by incubation with protein A/G beads overnight at 4°C ( Santa Cruz Biotechnology ) . Protein A/G beads were washed with lysis buffer three times . Bound proteins were separated by SDS-PAGE and analysed by western blotting . Actin ( rb , Sigma , 1:2500 ) ; mCherry ( rb , Abcam , 1:1000 ) ; HMMR ( rb , Abcam , 1:500 ) ; GFP ( ms , Abcam , 1:1000 ) ; DIC ( ms , Millipore , 1:500 ) ; DynLL1 ( rb Abcam , 1:1000 ) ; p150Glued ( ms , Abcam , 1:500 ) ; Ran ( ms , Cell Biolabs Inc , 1:1000 ) ; and RanBP2 ( Abcam , 1:5000 ) . Cells were lysed at 0 . 5–1 . 0 × 107 cells/ml in immunoprecipitation buffer ( 50 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl , 1 mM EDTA , 0 . 5% NP-40 ) supplemented with protease and phosphatase inhibitors ( Roche ) . Cell lysates were clarified by centrifugation at 16 , 000 X g for 10 min at 4°C and protein concentration was determined using the BCA protein assay kit ( Thermo Fisher ) . For immunoprecipitation , lysates were precleared with protein G or A/G PLUS-Agarose beads ( Santa Cruz ) . Protein complexes were isolated by incubation with the indicated antibodies at 4°C on rotation , and then with protein G or A/G PLUS-Agarose beads for 6 hr at 4°C on rotation . Isolated complexes were washed four times with lysis buffer . Following IP , protein samples on beads were eluted twice with 50 µL of 100 mM citric acid , pH 2 . 6 at 50°C for 10 min shaking at 1300 rpm , followed by centrifugation , collection of the supernatant and neutralization with 125 µL of 1 M HEPES , pH 8 . 5 . Proteins were reduced by adding 5 µL of 200 mM DTT and incubating at 37°C for 60 min , followed by alkylation by adding 10 µL of 400 mM IAA and incubation at room temperature for 60 min in the dark . The reaction was quenched by adding 10 µL of 200 mM DTT . Proteins were digested with Trypsin/Lys-C mix ( Promega , Madison , WI ) at an enzyme:protein ratio of 1:100 at 37°C for 16 hr . For stable isotope labeling by reductive dimethylation formaldehyde and heavy formaldehyde ( C13D2O ) was added to 40 mM final concentration to IgG control and HMMR IP samples , respectively . Sodium cyanoborohydride was added to a final concentration of 20 mM immediately after to both samples and incubated at 21°C for 60 min . Both conditions were combined , acidified to pH 2 . 5 with TFA , and the peptides purified with C18-STAGE tips as described ( Rappsilber et al . , 2003 ) . Liquid chromatography tandem mass spectrometry analysis was performed with the Easy nLC ultra-high-pressure LC system ( Thermo Fisher Scientific ) couple to a Q Exactive HF mass spectrometer with an EASY-Spray source . An EASY-Spray C18 column ( Thermo-Fisher , 50 cm long , 75 µm inner diameter ) heated to a temperature of 50°C was used for separation . Dried Stage-tip eluates were resuspended in 10 µL buffer A ( 0 . 1% FA ) and 2 µL was used for injection . The peptides were loaded at a back pressure of 550 bar and separated with a gradient of 3–25% buffer B ( 0 . 1% FA in 80% ACN ) over 105 min followed by 25–40% buffer B over 20 min at a flow rate of 300 nL/min . The chromatography method ends with a ramp from 40 to 100% buffer B over 3 min then a hold at 100% buffer B for 12 min . A column equilibration step using 11 µL buffer A was included prior to the next sample loading step . MS data were acquired using a data-dependent top 12 method with a dynamic exclusion of 20 s . MS1 was performed at a resolution of 60 , 000 at m/z 200 with an AGC target of 3E6 and a maximum ion injection time of 75 ms over the m/z range 400 to 1800 . HCD fragmentation of peptides was performed with an isolation range of 1 . 4 m/z and normalized collision energy set to 28 . A resolution of 15 , 000 at m/z 200 , an AGC target of 5E4 and a maximum ion injection time of 50 ms was set for fragment spectra acquisition . Acquired spectra from two separate experiments and multiple injections were searched using Proteome Discoverer 2 . 1 ( Thermo Fisher Scientific ) . Database search was performed against the Homo sapiens reference proteome including isoforms downloaded from UniProt in June 2016 . Main search parameters: enzyme: Trypsin ( full ) ; missed cleavages: 2; precursor mass tolerance: 10 ppm; fragment mass tolerance: 0 . 02 Da; static modifications: +57 . 021 Da on C; variable modifications: +15 . 995 Da on M , +28 . 031 or +34 . 063 on K and peptide N terminus , +42 . 011 Da on protein N terminus . Identifications were filtered for 1% FDR at the peptide and protein level . Differential abundance of proteins between pHMMR and control IP was calculated based on the area of heavy and light dimethyl precursor peaks . Common contaminants and decoy identifications were filtered out . To identify proteins commonly identified in affinity purification experiments the identified proteins were searched against the CRAPOME database ( www . crapome . org , version 1 . 1 , H . sapiens ) . Proteins that were found in less than 30% of reported experiments were classified as ‘rare’ , the reminder as ‘common’ . Proteins not matched in the CRAPOME database were classified as ‘unknown’ . Full Proteome Discoverer results are available in Figure 6—source data 1 and raw data is available through the PRIDE Archive . Neural rosettes were stained with TPX2 , ZO-1 , and TUBB and images were acquired using confocal microscopy . In rosettes , spindle orientation was measured as the angle of the cleavage plane ( anaphase or telophase cells ) relative to the apical surface . For analysis of the division orientation of apical NP cells , E14 . 5 brain sections were stained for pH3 and γ-Tubulin . The long spindle axis of anaphase cells , defined by a line bisecting the two centrosomes , was used to indicate the cell division plane . For each progenitor , the angle between the long spindle axis and the apical surface ( defined by a line along the centrosomes of apically localized interphase cells ) was determined . In cultured MEFs , spindle orientation was measured between the long axis of G2 phase cells ( 15 mins prior to mitosis ) and the angle of the mitotic spindle ( anaphase cells ) . All replicates were biological replicates . Statistical analysis was performed using GraphPad Prism v5 . 01 for Windows ( Graphpad Software , La Jolla , CA ) . Pairwise comparisons were made using two-tailed , paired or unpaired student’s t-test . Comparisons of multiple groups were made using one-way analysis of variance ( ANOVA ) with a Bonferroni post-test .	As an embryo develops , its cells divide , grow and change into many different types of cells that eventually build our body . When cells divide , they first need to duplicate their genetic material . A structure called the spindle then distributes the two copies of the genetic information between the new cells . Cells must position their spindle precisely , and the way the spindle is oriented helps to determine what type of cell will develop . If the spindle fails to align properly , it can disrupt the development of specific tissues and organs and even lead to diseases such as cancer . Numerous proteins help to position the spindle . For example , a protein called Ran-GTP ensures that motor proteins are anchored on opposite sides of the dividing cell , which tug on the spindle and position it between them . If the spindle gets pulled too closely to one side , a protein called PLK1 changes parts of the motor proteins to reduce the pulling force and to reposition the spindle towards the center . Previous research has shown that non-motor proteins , such as a protein called HMMR are also part of the motor-protein complex . However , until now it was not known how HMMR was involved in repositioning the spindle during this process . Now , Connell et al . have used mice that lacked HMMR to find out if it helps the cells in the brain to develop . The results show that without HMMR , very few mice were able to survive and many suffered from deformed and underdeveloped brains . In these mice , the orientation of the spindle changed and fewer cells of the correct type could be formed . Connell et al . then analyzed different types of cells grown in the laboratory to better understand how HMMR controls the position of the spindle . In all cases , HMMR formed a complex with Ran-GTP and was needed for the cells to orient their spindle correctly . When HMMR was absent , PLK1 could not work properly , and the spindle was positioned incorrectly . This suggests that HMMR is essential for the spindle to align properly and is needed to help brain cells develop and become specialized . The next step will be to understand how HMMR , Ran and PLK1 work together during cell division . Studying mice that survive without HMMR offer an opportunity to examine how poorly aligned spindles affect their development .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology", "cell", "biology" ]	2017	HMMR acts in the PLK1-dependent spindle positioning pathway and supports neural development
Small nucleolar RNAs ( snoRNAs ) are a diverse group of non-coding RNAs that direct chemical modifications at specific residues on other RNA molecules , primarily on ribosomal RNA ( rRNA ) . SnoRNAs are altered in several cancers; however , their role in cell homeostasis as well as in cellular transformation remains poorly explored . Here , we show that specific subsets of snoRNAs are differentially regulated during the earliest cellular response to oncogenic RASG12V expression . We describe a novel function for one H/ACA snoRNA , SNORA24 , which guides two pseudouridine modifications within the small ribosomal subunit , in RAS-induced senescence in vivo . We find that in mouse models , loss of Snora24 cooperates with RASG12V to promote the development of liver cancer that closely resembles human steatohepatitic hepatocellular carcinoma ( HCC ) . From a clinical perspective , we further show that human HCCs with low SNORA24 expression display increased lipid content and are associated with poor patient survival . We next asked whether ribosomes lacking SNORA24-guided pseudouridine modifications on 18S rRNA have alterations in their biophysical properties . Single-molecule Fluorescence Resonance Energy Transfer ( FRET ) analyses revealed that these ribosomes exhibit perturbations in aminoacyl-transfer RNA ( aa-tRNA ) selection and altered pre-translocation ribosome complex dynamics . Furthermore , we find that HCC cells lacking SNORA24-guided pseudouridine modifications have increased translational miscoding and stop codon readthrough frequencies . These findings highlight a role for specific snoRNAs in safeguarding against oncogenic insult and demonstrate a functional link between H/ACA snoRNAs regulated by RAS and the biophysical properties of ribosomes in cancer . Non-coding RNAs ( ncRNAs ) encompass a large group of functionally diverse non-protein coding transcripts that are emerging as important regulators of biological processes ( Cech and Steitz , 2014; Esteller , 2011 ) . Small nucleolar RNAs ( snoRNAs ) are abundant , often intron-encoded , short ncRNAs classified based on specific sequence and secondary structure features ( Kiss , 2002; Matera et al . , 2007 ) . The most well-characterized functions of snoRNAs relate to their roles in ribosome biogenesis , wherein structurally distinct C/D and H/ACA snoRNAs directly base pair to complementary regions of ribosomal RNA ( rRNA ) ( Filipowicz and Pogacić , 2002 ) . In doing so , C/D and H/ACA snoRNAs modulate the chemical landscape of the ribosome by directing ribonucleoprotein complexes to modify up to two hundred site-specific ribose methylations ( 2’-O-Me ) and pseudouridine ( Ψ ) modifications , respectively ( Sloan et al . , 2017; Watkins and Bohnsack , 2012 ) . Unlike nucleotide modifications performed by stand-alone RNA modifying enzymes , the function of the vast majority of RNA-directed modifications such as those guided by snoRNAs , remain poorly studied . Recent discoveries have shown that dysregulations in ribosome activity and protein synthesis are hallmarks of many cancer types ( Freed et al . , 2010; Marcel et al . , 2013; Pelletier et al . , 2018; Robichaud and Sonenberg , 2017; Sulima et al . , 2017; Truitt and Ruggero , 2016 ) . Emerging evidence suggests that the expression and activity of snoRNAs is also altered in a variety of human diseases , including cancer ( Belin et al . , 2009; Bellodi et al . , 2013; Ferreira et al . , 2012; Gong et al . , 2017; Mei et al . , 2012; Ronchetti et al . , 2013; Sahoo et al . , 2008; Valleron et al . , 2012; Williams and Farzaneh , 2012 ) . SnoRNA expression profiles have also been proposed as ‘predictors’ of specific cancer subtypes and clinical outcomes ( Ronchetti et al . , 2013; Valleron et al . , 2012 ) . Altered snoRNA expression in a variety of human cancers open several questions as to how snoRNAs may be regulated downstream of key oncogenic drivers in human tumors . However , it has yet to be examined whether snoRNA dysfunction plays a direct causative role in specific stages of cancer progression . While a loss of individual snoRNAs in single-celled organisms appears to be compatible with life ( Lowe and Eddy , 1999; Ni et al . , 1997 ) , the precise biological impact of distinct snoRNA-directed modifications within defined regions of the ribosome in cancer development remains poorly understood . Here , we find that specific subsets of H/ACA snoRNAs , that mediate pseudouridine modifications , are selectively regulated upon activation of oncogenic RAS . Upon oncogenic insult , primary cells normally activate a tumor suppressive response to counteract cellular transformation , known as oncogene-induced senescence ( OIS ) ( Collado et al . , 2007 ) . We show that loss of only one distinct RAS-induced snoRNA , SNORA24 ( or H/ACA snoRNA 24 ) , leads to the bypass of OIS in a liver model of RAS-induced senescence in vivo . SNORA24 , which mediates two distinct pseudouridine modifications in the small , 40S subunit of the ribosome is also decreased in human hepatocellular carcinoma ( HCC ) . We further show that loss of Snora24 cooperates with RASG12V to promote the development of liver cancer in vivo resembling a subtype of HCC characterized by lipid deposition , with similar features , as described in human steatohepatitic HCC ( SH-HCC ) ( Salomao et al . , 2010 ) . Changes in the biophysical properties of ribosomes in cancer cells arising from loss of specific snoRNAs has not previously been tested . Employing single-molecule Fluorescence Resonance Energy Transfer ( smFRET ) imaging , we demonstrate that ribosomes isolated from human HCC cells specifically lacking SNORA24-guided pseudouridine modifications within the small ribosomal subunit , differ in the efficiency of aminoacyl-transfer RNA ( aa-tRNA ) selection , consistent with downstream reductions in translation accuracy , and in the dynamic properties of the pre-translocation ribosome complex . These findings reveal an important function for specific snoRNAs in RAS-mediated oncogenic activity and provide evidence that ribosomes lacking site-specific rRNA modifications exhibit physical alterations in the translation machinery . To investigate the role of H/ACA snoRNAs during the earliest cellular response to oncogene activation , we interrogated the expression of ~90 H/ACA snoRNAs in primary human skin fibroblasts using a snoRNA quantitative PCR ( qPCR ) array , in the context of oncogene-induced senescence ( OIS ) by expression of RASG12V ( Pylayeva-Gupta et al . , 2011 ) . While the levels of the vast majority of H/ACA snoRNAs appeared unchanged following RASG12V expression in primary fibroblasts , we observed a dynamic change in the expression of 28 H/ACA snoRNAs compared to control cells ( Figure 1A and Figure 1—source data 1 ) ( for example SNORA23 , SNORA24 , SNORA26 , SNORA48 , and SNORA67 ) . The majority of these H/ACA snoRNAs were predominately upregulated ( FDR < 0 . 1 ) , with the exception of 3 snoRNAs that were downregulated ( SNORA36C , SNORA53 , and SNORA70B ) downstream of oncogenic RAS . The increase in H/ACA snoRNA levels upon RASG12V expression is not associated with elevated global protein production . On the contrary , we observe a pronounced decrease in overall protein synthesis as a consequence of RAS-induced senescence detected by monitoring O-propargyl-puromycin ( OPP ) incorporation into newly synthesized proteins ( Figure 1B and Figure 1—figure supplement 1A , top panel ) . This change in global protein synthesis is consistent with the cell cycle arrest that occurs upon induction of senescence . We next investigated whether changes in the expression of specific H/ACA snoRNAs is selective to RAS activation or whether it is similarly induced upon other oncogenic signals . Interestingly , downregulation of the tumor suppressor PTEN ( Figure 1—figure supplement 1A , bottom panel ) , a known oncogenic event that also promotes OIS in primary cells , had no obvious effect on the expression of selective RAS-induced H/ACA snoRNAs in primary fibroblasts ( Figure 1—figure supplement 1B and highlighted in Figure 1A ) . These findings suggest that distinct oncogenic lesions alter a unique snoRNA expression pattern during OIS . To extend the broader implications of our findings to human cancer etiology , we analyzed H/ACA snoRNA expression in ~300 human cancers using previously published microarray gene expression datasets ( Hao et al . , 2011; Jima et al . , 2010; Kabbout et al . , 2013; Skrzypczak et al . , 2010; Zhang et al . , 2012 ) designed to capture small RNA species , such as snoRNAs , that are often excluded during RNA sequencing library preparation . By assessing the average expression level of individual H/ACA snoRNAs in cancer specimens and matched control samples from five major tumor types ( lymphoma [Jima et al . , 2010] , pancreatic [Zhang et al . , 2012] , colon [Skrzypczak et al . , 2010] , lung [Kabbout et al . , 2013] , and liver [Hao et al . , 2011] ) , we identified H/ACA snoRNAs displaying changes in expression greater than ± 20% ( p<10−5 ) in tumor samples compared to control samples ( Figure 1—figure supplement 2 and Figure 1—figure supplement 2—source data 1 ) . In the case of liver cancer , we observed an upregulation of 4 snoRNAs ( SNORA14B , SNORA17 , SNORA72 , and SNORA81 ) and decreased expression of 2 H/ACA snoRNAs ( SNORA24 and SNORA67 ) ( p<10−5 ) in 91 primary HCC specimens compared to matched adjacent non-tumor hepatic tissue ( Figure 1C , left panel , Figure 1—figure supplement 3 , and Figure 1—figure supplement 2—source data 1 ) . Interestingly , two H/ACA snoRNAs , SNORA24 and SNORA67 , identified in our expression analysis upon RASG12V-induced senescence ( Figure 1A and Figure 1—figure supplement 1B ) , were significantly decreased in HCC specimens compared to matched adjacent non-tumor tissue ( p<1 . 35×10−9 and p<4 . 6×10−7 , respectively ) . These findings suggest that SNORA24 and SNORA67 may act as tumor suppressors downstream of the early steps of oncogenic activation and may therefore be lost or downregulated during tumor progression . We next probed the clinical significance of altered H/ACA snoRNA expression in the same liver cancer microarray gene expression dataset , where survival data was available from 91 patients ( Hao et al . , 2011; Kan et al . , 2013 ) . After separating HCC patients into high or low snoRNA expression using mean ±1 standard deviation ( SD ) of snoRNA levels as a cutoff point , Kaplan-Meier curve analysis indicated that low levels of SNORA24 were associated with worse overall survival compared to patients with high SNORA24 expression ( p=0 . 03 , log-rank test ) ( Figure 1C , right panel ) . Interestingly , other snoRNAs assessed ( including SNORA17 , SNORA67 , and SNORA72 ) did not demonstrate evidence for an association between expression levels and patient survival ( Figure 1—figure supplement 3A–C , right panels ) . Importantly , we confirmed that SNORA24 was decreased in an independent patient cohort consisting of primary matched tumor and adjacent non-tumor tissue from 13 HCC patients ( Supplementary file 1 , samples 1–13 ) using qPCR ( Figure 1—figure supplement 4A ) . Similar to our observations from microarray gene expression analysis , SNORA24 was dramatically decreased in all HCC tumor specimens compared to non-tumor adjacent tissue by qPCR ( p<0 . 0001 ) ( Figure 1—figure supplement 4A ) . In the same matched tumor and adjacent non-tumor tissue , we detected no change in the expression of the SNORA24 host gene , SNHG8 ( Small Nucleolar RNA Host Gene 8 ) ( Figure 1—figure supplement 4B ) . Together , these findings suggest that SNORA24 may exert a tumor suppressor role in HCC . As SNORA24 was significantly decreased in HCC and low SNORA24 levels were associated with poor patient survival , we next sought to assess the tumor suppressor activity of Snora24 in liver cancer development in vivo . To this end , we employed a previously described mouse model of oncogenic RAS-induced senescence ( Carlson et al . , 2005; Kang et al . , 2011 ) whereby hydrodynamic tail vein injection allows stable delivery of oncogenic RAS to hepatocytes using the sleeping beauty ( SB ) transposase system ( Figure 1—figure supplement 5A ) . This model permits mosaic expression of RASG12V in the mouse liver ( SB ( + ) NRASG12V mice ) which activates an anti-tumor program ( OIS ) to halt tumor development ( Kang et al . , 2011 ) . We confirmed that RASG12V promoted senescence in vivo after 6 days in SB ( + ) NRASG12V mice livers by measuring two well-characterized markers of senescence , senescence-associated ( SA ) β-galactosidase ( Bandyopadhyay et al . , 2005 ) and a cell cycle inhibitor p21 ( Brown et al . , 1997 ) , both of which are activated downstream of mitogenic signals to halt pre-malignant cells ( Collado et al . , 2005; Kuilman et al . , 2010 ) ( Figure 1D , top row ) . To test whether Snora24 was implicated in OIS in SB ( + ) NRASG12V mice , we used tail vein injection of locked nucleic acid ( LNA ) to target Snora24 ( LNA-24 ) for degradation . In so doing , we observed a specific reduction in Snora24 levels in the mouse liver employing LNA-24 compared to a non-targeting scrambled sequence LNA ( LNA-ctrl ) ( Figure 1—figure supplement 5B ) , with no observed impact on the levels of the corresponding snoRNA host gene , Snhg8 ( Figure 1—figure supplement 5C ) . Treatment with LNA-ctrl or LNA-24 had no obvious effects on hepatic tissue architecture or function ( data not shown ) . Strikingly , we observed a bypass of OIS upon Snora24 reduction in this model , as evident by reduced p21 expression and a lack of β-galactosidase staining in liver tissue from LNA-24 treated SB ( + ) NRASG12V mice compared to LNA-ctrl treated SB ( + ) NRASG12V mice ( Figure 1D , right panel , p<0 . 005 , n = 3 mice per condition ) . Importantly , LNA-24 decreased pseudouridine modifications on 18S rRNA at positions U609 and U863 ( Figure 1—figure supplement 5D ) , without impacting modifications at other sites on 18S rRNA ( U105 ) or 28S rRNA ( U1731 ) that not guided by SNORA24 ( Figure 1—figure supplement 5E ) . Having identified that cancer-associated changes in Snora24 lead to bypass of OIS in the context of oncogenic RAS expression in vivo , we next sought to determine whether reduction of this single H/ACA snoRNA was sufficient to promote tumor development in this model . To do this , we analyzed the liver phenotype of SB ( + ) NRASG12V mice treated with either LNA-ctrl or LNA-24 for four months . In contrast to LNA-ctrl treated SB ( + ) NRASG12V mice , which have normal livers and do not show any signs of HCC ( Figure 2A , bottom left panel , representative image from n = 8 mice ) , we found that a reduction in Snora24 cooperates with RASG12V to promote the development of liver cancer in all LNA-24 treated SB ( + ) NRASG12V mice examined ( Figure 2A , bottom right panel , representative image from n = 8 mice ) . Pathological analysis of liver specimens revealed the presence of HCC in LNA-24 treated SB ( + ) NRASG12V mice , with evidence of dramatic lipid accumulation and swollen hepatocytes suggestive of hepatocyte balloons , the pathology of which resembles human steatohepatitic HCC ( SH-HCC ) ( Figure 2B ) . Interestingly , SH-HCC is a subtype of liver cancer characterized by increased fat accumulation , the etiology and genetic makeup of which is poorly understood ( Salomao et al . , 2010 ) . The presence of lipids in liver tumors derived from LNA-24 treated SB ( + ) NRASG12V mice was confirmed by Oil Red O ( ORO ) staining ( Figure 2C and Figure 2—figure supplement 1 , n = 3 mice , p=0 . 0107 ) . Taken together , these findings indicate that loss of Snora24 cooperates with oncogenic RAS to promote the development of HCC in vivo . These data also indicate that cancer-associated changes in SNORA24 , identified in HCC patient expression data ( Figure 1C ) , may play a direct role in HCC pathogenesis . To investigate the importance of Snora24 in established HCC , we turned to a genetically engineered mouse model of liver cancer driven by the expression of oncogenic KrasG12D in the mouse liver using albumin-cre ( Alb-cre;KrasG12D ) . In this genetically engineered mouse model , oncogenic RAS is ubiquitously expressed in the fetal liver and therefore these animals develop HCC within 8 months of age ( O'Dell et al . , 2012; Xu et al . , 2019 ) . In line with human HCC patient expression data ( Figure 1C and Figure 1—figure supplement 4A ) , we found that in established liver tumors derived from Alb-cre;KrasG12D mice , Snora24 was significantly decreased in tumor tissue compared to liver tissue from wild-type mice ( Figure 2D , n = 3 mice per condition ) . To test the tumor suppressive activity of Snora24 in a more aggressive liver cancer , we orthotopically injected primary tumor cells derived from Alb-cre;KrasG12D liver tumor ( Xu et al . , 2019 ) , following CRISPR-Cas9 gene editing of the Snora24 loci using two distinct single guide RNA ( sgRNA ) targeting Snora24 ( sgRNA-24 ) ( Figure 2—figure supplement 2A ) , into the livers of wild-type mice . sgRNA-24 KrasG12D HCC cells exhibit reduced Snora24 expression ( Figure 2E , right panel ) with no obvious impact on the levels of the corresponding host gene ( Figure 2—figure supplement 2B ) . Following orthotopic injection of control ( Ctrl ) KrasG12D HCC cells or sgRNA-24 KrasG12D HCC cells into the subcapsular region of the median liver lobe of wild-type mice , we monitored survival over a period of 4 weeks and found that Snora24 reduction , decreased the overall survival of mice compared to controls ( p=0 . 017 ) ( Figure 2E , left panel n = 4 mice per arm ) . Snora24 reduction in this mouse model of liver cancer significantly decreased Snora24-guided pseudouridine modifications on 18S rRNA ( Figure 2—figure supplement 2C ) as shown using SCARLET ( Site-specific Cleavage And Radioactive-labeling followed by Ligation-assisted Extraction and Thin-layer chromatography ) ( Li et al . , 2015; Liu and Pan , 2015 ) . These findings reveal a previously uncharacterized role for Snora24 in the maintenance of RAS-driven HCC in vivo . Given that loss of Snora24 cooperates with oncogenic RAS to promote the development of liver cancer resembling human SH-HCC ( Figure 2A–C ) , we sought to explore the clinical relationship between SNORA24 expression and lipid content in human HCC . To do this , we took advantage of available primary tissue specimens from 62 HCC patients ( Supplementary file 1 ) . SNORA24 expression was determined by qPCR and HCC specimens were stratified into high or low SNORA24 using SNORA24 levels greater than ±1 SD from the mean as a cut-off . Strikingly , we found that tumors with low SNORA24 levels had higher lipid content compared to tumors with high SNORA24 levels using ORO staining ( Figure 2F , p=0 . 0453 , n = 17 ) . These findings confirm a significant correlation between SNORA24 levels and lipid content in HCC . We also found that SNORA24 reduction by CRISPR-Cas9 gene editing in an established , well-differentiated human HCC cell line , HuH-7 , ( HuH-7 sgRNA-24 ) ( Figure 2—figure supplement 3A ) , also enhanced lipid droplet formation compared to isogenic control ( ctrl ) HuH-7 cells ( HuH-7 sgRNA-ctrl ) ( Figure 2—figure supplement 3B ) . Altogether , these findings highlight a previously unidentified connection between SNORA24 and a particular feature of lipid accumulation associated with human SH-HCC . The impact of SNORA24 and SNORA24-directed rRNA modifications on ribosome activity and global protein production remains unknown . SNORA24 guides two pseudouridine modifications on the 18S rRNA component of the small ( 40S ) ribosomal subunit ( Boccaletto et al . , 2018 ) , one at position uridine ( U ) 609 ( U609 ) within helix 18 ( h18 ) and one at U863 at the base of expansion segment 6 ( ES6 ) ( Figure 3A , highlighted in red ) . While the ES6 modification is distal to known functional centers of the ribosome , the h18 modification is located within the functionally important , so-called ‘530 loop’ region of the shoulder domain ( Figure 3A , highlighted in pale green ) . The shoulder domain closes towards the body of the small ribosomal subunit during the process of aminoacyl-tRNA ( aa-tRNA ) selection , bringing residue G530 ( G626 in human rRNA ) into direct contact with the messenger RNA ( mRNA ) codon-tRNA anticodon pair such that it directly contributes to mRNA decoding ( Demeshkina et al . , 2012; Fislage et al . , 2018; Loveland et al . , 2016; Ogle et al . , 2001; Ogle et al . , 2002 ) . We therefore next sought to investigate the role of these SNORA24-guided modifications in modulating ribosome function in HCC . To address this question , we employed a human HCC cell line , HuH-7 , with a stable reduction in SNORA24 ( HuH-7 sgRNA-24 ) ( Figure 2—figure supplement 3A ) and quantified SNORA24-mediated pseudouridylation in h18 and ES6 using SCARLET ( Li et al . , 2015; Liu and Pan , 2015 ) . As expected , SCARLET revealed the amount of uridine ( U ) and pseudouridine ( Ψ ) present at both residue 609 and 863 on 18S rRNA and confirmed diminished pseudouridine levels at both sites in HuH-7 sgRNA-24 cells compared to isogenic HuH-7 sgRNA-ctrl cells ( >90% reduction ) ( Figure 3B and Figure 3—figure supplement 1 ) . Interestingly , we did not detect any significant change in global protein production in cells lacking SNORA24-guided modifications as measured by OPP incorporation into newly synthesized protein ( Figure 3C ) . Furthermore , we saw no detectable difference in the abundance of ribosome subunits or the abundance and distribution of polysome in HuH-7 sgRNA-24 cells compared to isogenic HuH-7 sgRNA-ctrl cells by sucrose gradient fractionation trace ( Figure 3D ) . These results indicate that SNORA24 and SNORA24-guided rRNA modifications are likely dispensable for ribosome biogenesis and global protein production in HCC and instead may harbor specific functions during translation . To gain mechanistic insights into how Ψ609 and Ψ863 influence ribosome activity , we harnessed the power of smFRET imaging to capture the functional dynamics of ribosomes on mRNA ( Ferguson et al . , 2015; Juette et al . , 2016 ) . Ribosome transit along mRNA is a highly coordinated , multistep process involving the selection of the correct aa-tRNA in the ribosomal aminoacyl ( A ) site for each codon , peptide bond formation , and the translocation of the ribosome , codon-by-codon along the mRNA . We first imaged aa-tRNA selection or the process by which aa-tRNAs are decoded at the ribosomal A site to extend the nascent polypeptide by one amino acid using an established in vitro smFRET assay ( Blanchard et al . , 2004; Ferguson et al . , 2015; Geggier et al . , 2010 ) . Briefly , translation initiation complexes ( 80S ICs ) carrying Cy3-labeled Met-tRNAfMet in the peptidyl ( P ) site and displaying a UUC codon ( encoding Phenylalanine ( Phe ) ) in the ribosomal A site were formed from isolated ribosomal subunits purified from HuH-7 sgRNA-ctrl or sgRNA-24 cells ( Figure 4A ) . The 80S ICs were immobilized in a passivated microfluidic flow cell and imaged as described previously ( Juette et al . , 2016 ) . Ternary complex , consisting of LD655-labeled Phe-tRNAPhe , eukaryotic translation elongation factor 1A ( eEF1A ) , and GTP , was then injected into the flow cell and the process of aa-tRNA selection was followed via the time-evolution of the FRET signal between the P-site tRNA and the aa-tRNA decoded at the A site , at a time resolution of 15 milliseconds ( ms ) . Ribosomes purified from both HuH-7 sgRNA-ctrl and sgRNA-24 cells , proceeded through the aa-tRNA selection mechanism , as expected ( Ferguson et al . , 2015; Geggier et al . , 2010 ) , via a stepwise progression through states of increasing FRET efficiency ( Figure 4B ) . These FRET states correspond to initial Phe-tRNAPhe binding or codon recognition events ( FRET ~0 . 2 ) , from which aa-tRNAs can either rapidly dissociate or proceed to the GTPase-activated state ( FRET ~0 . 4 ) . GTP hydrolysis within the GTPase-activated state enables eEF1A to dissociate and allows aa-tRNAs to transition into a fully accommodated state , where Phe-tRNAPhe in the A site undergoes peptide bond formation ( FRET ~0 . 7 ) ( Figure 4B ) . We estimated the rate of peptide bond formation in this system and calculated the catalytic efficiency ( kcat/KM ) of tRNA selection ( Figure 4C and D ) . In doing so , we found that ribosomes purified from HuH-7 sgRNA-24 cells were ~50% more efficient in aa-tRNA selection of tRNAPhe compared to ribosomes isolated from sgRNA-ctrl cells ( p<10−6 , Welch’s t-test ) ( kcat/KM of 50 ± 7 µM−1 s−1 vs . 71 ± 4 µM−1 s−1 , respectively ) ( Figure 4D ) . We further assessed ribosomes displaying a different codon , AAA ( encoding Lysine ( Lys ) ) , in the A site reacting with ternary complex containing a cognate LD650 labeled Lys-tRNALys using the same assay . In this context , we found that ribosomes lacking Ψ609 and Ψ863 were also more efficient in selection of tRNALys , however , the magnitude of the change was smaller than that observed for tRNAPhe . Specifically , ribosomes from HuH-7 sgRNA-24 cells were ~30% more efficient in selection of tRNALys compared to their wild-type counterparts ( p<10−6 , Welch’s t-test ) ( kcat/KM of 27 ± 5 µM−1 s−1 vs . 36 ± 5 µM−1 s−1 ) ( Figure 4D ) as opposed to the ~50% differences observed in the case of tRNAPhe . These findings indicate that SNORA24-directed rRNA pseudouridylation acts to regulate decoding on actively translating ribosomes . Ribosomes undergo large-scale conformational rearrangements during the elongation phase of translation ( Voorhees and Ramakrishnan , 2013 ) . For example , in pre-translocation ribosome complexes , deacylated tRNA within the P site and peptidyl-tRNA within the A site rapidly and dynamically exchange between so-called ‘classical’ and ‘hybrid’ states of tRNA binding ( Ferguson et al . , 2015 ) . Classical ( C: A/A , P/P ) and hybrid states ( H1: A/P , P/E; H2: A/A , P/E ) can be distinguished by smFRET imaging based on their distinct FRET efficiency values ( Ferguson et al . , 2015 ) ( Figure 4E and F ) . To assess the potential impacts of SNORA24-guided modifications on ribosome dynamics , we imaged pre-translocation complexes generated in the tRNA selection experiments described above bearing Met-Phe-tRNAPhe in the A site . These analyses revealed that ribosomes lacking SNORA24-guided modifications exhibit a modest preference for classical tRNA configurations compared to control ribosomes ( 15 ± 1% vs . 20 ± 1% Classical , p<10−6 , Welch’s t-test ) ( Figure 4G ) . In the case of ribosomes bearing Met-Lys-tRNALys in the A site , we instead observed little to no change in tRNA configurations between the two types of ribosomes ( Figure 4—figure supplement 1 ) . These findings indicate that pseudouridine modifications at rRNA residues 609 and 863 have the capacity to alter the dynamic properties of pre-translocation ribosome complexes in a way that likely depends on the tRNA species in the P and A sites . Taken together , these smFRET data provide compelling evidence that ribosomes lacking these two specific pseudouridine modifications exhibit functionally relevant physical distinctions in regard to dynamic structural features within the pre-translocation complex , that appear to depend on the mRNA coding sequence . The observed impacts on aa-tRNA selection and pre-translocation complex dynamics lead us to investigate the sensitivity of HuH-7 sgRNA-ctrl or sgRNA-24 cells to ribosome-targeting drugs ( Figure 4H and Figure 4—figure supplement 2 ) . In doing so , we found that HCC cells lacking SNORA24 displayed a specific and increased tolerance to Anisomycin ( ANS ) , a drug that binds to the ribosomal A site ( Hansen et al . , 2003 ) and inhibits peptidyl transfer ( Figure 4H ) . This result is in perfect agreement with our smFRET observations that loss of SNORA24-guided modifications increase the efficiency of aa-tRNA selection , which would be expected to reduce the time window during each elongation cycle that the ribosome is sensitive to ANS . Collectively , these investigations provide compelling evidence that snoRNA-mediated changes in the chemical composition of mammalian ribosomes , have the potential to affect tRNA selection , ribosome dynamics , and sensitivity of cancer cells to specific translation inhibitors . Given that reduced SNORA24 expression had no observed impact on overall protein production ( Figure 3C and D ) , we predicted that the differences in aa-tRNA selection and conformational dynamics observed by smFRET in ribosomes lacking SNORA24-guided pseudouridine modifications may impact the accuracy of translation , in a codon-specific manner . To examine to what extent SNORA24-guided modifications impact decoding accuracy or translation fidelity in HCC cells , we employed established luciferase reporter systems , consisting of the Renilla luciferase ( Rluc ) gene fused to the Firefly luciferase ( Fluc ) gene , to monitor both amino acid misincorporation ( Kramer et al . , 2010 ) and stop codon readthrough ( Jack et al . , 2011 ) . We first evaluated decoding accuracy in HCC cells using two luciferase reporters , in which one of two codons in Fluc , either codon 245 ( CAC , His ) or codon 529 ( AAA , Lys ) , had been mutated to near-cognate codons ( CGC and AAU , respectively ) ( Figure 5A , top panel ) . These mutations are known to reduce Fluc activity ( Kramer et al . , 2010 ) . Misreading of these codons can therefore lead to the incorporation of the original amino acid , to restore Fluc activity , enabling comparative estimates of miscoding error . For both codons tested , we found that HCC cells with reduced SNORA24 expression display a 10–20% increased level of amino acid misincorporation compared to control cells ( Figure 5A , bottom panel ) . We also tested the ability of cells lacking SNORA24-guided pseudouridine modifications to terminate translation at stop codons using a similar luciferase reporter system in which a stop codon is placed between Rluc and Fluc ( Figure 5B , top panel ) . Interestingly , in the presence of paromomycin , an antibiotic known to induce translation error and stop codon readthrough ( Kramer et al . , 2010 ) , we observe a SNORA24-dependent increase ( ~15% ) in UGA stop codon readthrough ( Figure 5B , bottom panel ) . By contrast , we detected no difference in UAG stop codon readthrough in the same cells ( Figure 5B , bottom panel ) . These findings suggest that Ψ609 and Ψ863 within the small ribosomal subunit function to enhance translational accuracy and that reduced expression and activity of SNORA24 in HCC may lead to errors in the translation of specific mRNAs . Ribosome dysfunction and alterations in translation are linked to cancer development ( Pelletier et al . , 2018; Silvera et al . , 2010; Sulima et al . , 2017; Truitt and Ruggero , 2016 ) . The function of the vast majority of conserved RNA modifications within the ribosome ( Decatur and Fournier , 2002 ) , in this context remain largely unexplored . For decades , snoRNAs have been thought to exert minor functions , largely due to findings that in unicellular organisms , depletion of individual snoRNAs yield little to no growth defect . In this respect , the biological influence of snoRNA-directed rRNA modifications in mammalian physiology , and in disease states has been poorly understood ( Williams and Farzaneh , 2012 ) . We provide evidence that one H/ACA snoRNA plays a direct role in specific steps of cancer development . Our studies reveal that SNORA24 is implicated in a tumor suppressor program in vivo to halt cellular transformation and with compelling human and mouse data supports a role for SNORA24 dysfunction in tumor initiation and in maintenance of RAS-driven cancer . Interestingly , in the context of HCC , SNORA24 appears dispensable for overall protein synthesis , suggesting that cancer-associated changes in SNORA24 may have more selective functions , for example towards controlling translation of specific mRNAs . This is supported by the apparent codon specificity in aa-tRNA selection and pre-translocation complex dynamics observed by smFRET analysis of ribosomes from cancer cells lacking SNORA24-dependent pseudouridine modifications . One intriguing possibility is that SNORA24-guided modifications directly impinge on the production of proteins involved in , for example , lipid metabolism and signaling , due to the strong association between SNORA24 and tumor lipid deposition identified in this study . It is interesting to note a previous link between distinct C/D snoRNAs and response to metabolic stress ( Jinn et al . , 2015; Michel et al . , 2011 ) . Although the roles of C/D snoRNAs in modulating ribosome activity or translation in this context was not examined , it will be important to determine whether dysfunction of specific snoRNAs and site-specific ribosome modifications are implicated in human lipid metabolic disorders and/or in the progression of chronic liver disease to HCC . Our studies employing smFRET imaging represent , to our knowledge , the first report of biophysical changes in ribosomes from cancer cells lacking specific rRNA nucleotide modifications . Ψ609 is located in a highly structured element ( the so-called ‘530 loop' in bacteria ) , a region of rRNA directly implicated in codon-anticodon pair recognition in the ribosomal A site . Previous studies implicate this region as contributing to the process of domain closure , a large-scale movement within the small subunit that aids mRNA codon-tRNA anticodon pairing required for accurate decoding at the A site ( Fislage et al . , 2018; Loveland et al . , 2016; Ogle et al . , 2001; Ogle et al . , 2002 ) . Our data demonstrating that loss of Ψ609 and Ψ863 enhances the efficiency of aa-tRNA accommodation , imply that SNORA24-guided modifications , and Ψ609 in particular , may function to alter domain closure strength . In contrast to Ψ609 , Ψ863 is located in an rRNA expansion segment ( ES ) that is distal to the decoding center of the ribosome . Interestingly , a recent study has established a connection between ribosome expansion segments and translation fidelity ( Fujii et al . , 2018 ) . Thus , it seems plausible that the functional alterations observed in ribosomes lacking Ψ609 and Ψ863 may arise from both direct and indirect mechanisms that alter how the small subunit of the ribosome recognizes and responds to aa-tRNA binding at the A site . In purified ribosomes lacking SNORA24-guided modifications , the observed differences in aa-tRNA selection of tRNAPhe compared to tRNALys can have several plausible explanations . For instance , studies have shown that the efficiency of aa-tRNA selection varies depending on the tRNA species and the codon in the A site ( Pavlov and Ehrenberg , 2018; Zhang et al . , 2016 ) . These and other observations indicate that the stability of intermediates in the decoding process are affected by even small differences in interaction energy between the ribosome and the tRNA substrate . As noted above , decoding intermediates also exhibit conformational changes in the small subunit proximal to the sites of SNORA24-guided modifications . We therefore speculate that pseudouridylation of U609 and U863 may fine tune decoding in a manner that results in small , but significant codon-dependent effects on tRNA selection efficiency . In addition , it seems likely that factors such as tRNA abundance , codon-anticodon base pair interactions , and codon context , that are known to influence ribosome elongation rate and translation efficiency ( Gardin et al . , 2014; Goodarzi et al . , 2016; Pop et al . , 2014; Quax et al . , 2015; Riba et al . , 2019 ) , may lead to gene-specific alterations in translational control in cancer cells with altered SNORA24 expression . Our findings that a distinct snoRNA impacts translational accuracy is in line with observations in other organisms , demonstrating that clusters of snoRNA-directed modifications close to the decoding center influence translational fidelity ( Baudin-Baillieu et al . , 2009 ) . However , unlike single-celled organisms that can tolerate relatively high levels of translation errors , minor defects in translation accuracy arising from snoRNA dysfunction in mammals may yield more severe cellular consequences in certain contexts , for example in response to oncogenic stress . For instance , alterations in aa-tRNA selection efficiency or fidelity during translation elongation may lead to changes in the abundance of specific proteins beneficial to cancer cell survival . Differences in translation fidelity may also alter stop codon readthrough during translation termination , as well as the rate at which ribosomes translocate through a given mRNA open reading frame due to impacts on translocation ( Alejo and Blanchard , 2017 ) . Elucidating the structural-functional roles of modified residues within the ribosome will undoubtedly aid in the development of a new generation of cancer therapeutics focused on targeting ribosomes with differential modification patterns in cancer and will shed significant new light on the role of ancient RNA modifications in directing ribosome activity and cellular integrity . Expression of NRASG12V in mouse liver was performed as previously described ( Kang et al . , 2011 ) , with minor modifications . Briefly , C57BL/6 wild-type mice 8–12 weeks old were injected with a 5:1 molar ratio of transposon ( NRASG12V ) to transposase ( SB13 ) encoding plasmids ( 35 µg total DNA ) by hydrodynamic tail vein injection . As a control , mice were injected with the transposase ( SB13 ) encoding plasmid ( 35 µg total DNA ) by hydrodynamic tail vein injection ( SB ( - ) NRASG12V ) . Plasmid DNA were prepared using a Qiagen Endo Free Maxi Kit . DNA was suspended in Normal Saline solution and administrated at a final volume of 10% of the animal’s body weight . Mice treated with locked nucleic acid ( LNA-ctrl or LNA-24 ) ( Exiqon , MA , USA ) were tail vein injected with 20 mg/kg LNA three days prior to hydrodynamic delivery of NRASG12V ( SB ( + ) NRASG12V ) or control ( SB ( - ) NRASG12V ) and received LNA treatment every 10 days for the duration of the study . The LNA sequences were as follows: LNA-ctr 5’-AACACGTCTATACGC-3’ and LNA-24 5’-GCTCTTCCATGGCTAG-3’ . For determination of senescence , mouse livers were harvested 6 days after NRASG12V administration . Orthotopic injections of Alb-cre;KrasG12D mice liver tumor cells into the subcapsular region of the median liver lobe of C57BL/6 wild-type mice were performed as previously described ( Xu et al . , 2019 ) . All mice were maintained under specific pathogen-free conditions . Experiments were performed in compliance with guidelines approved by the Institutional Animal Care and Use Committee ( IACUC ) with assistance from the Laboratory Animal Resource Center ( LARC ) of UCSF . Primary human skin fibroblast ( GM00730 ) were obtained from Coriell Cell Repositories ( Coriell Institution for Medical Research , NJ , USA ) and maintained in ( Dulbecco’s Modified Eagle Medium ( DMEM ) supplemented with 10% Fetal Bovine Serum and Penicillin/Streptomycin ( DMEM , 10% FBS , P/S ) . HuH-7 are an established cell line obtained from the Japanese Collection of Research Bioresources Cell Bank ( JCRB0403 ) of the National Institutes of Biomedical Innovation , Health and Nutrition , Japan and maintained in DMEM , 10% FBS , P/S . Generation of mouse liver cancer cell lines ( from Alb-cre;KrasG12D mice ) was previously described ( Xu et al . , 2019 ) and maintained in DMEM , 10% FBS , P/S . 293 T cells were obtained from ATCC and maintained in DMEM , 10% FBS , P/S . All cell lines used in this study were found to be negative of mycoplasma contamination using a MycoAlert mycoplasma detection kit ( Lonza , Allendale , NJ , USA ) . Retroviral and lentiviral particles were produced in 293 T cells by transfection with the appropriate expression and packaging plasmids using PolyFect Transfection Reagent ( Qiagen ) and filtering cultured supernatants through a 0 . 45 μM filter . Early passage primary skin fibroblasts ( P9 ) were infected with PTEN shRNA or HRASG12V expression constructs followed by selection with puromycin ( 2 μg/ml ) . Retroviral vectors were obtained from Addgene ( pBabe puro HRASG12V ( #9051 ) ) . Lentiviral vector harboring a shRNA targeting PTEN ( pLKO . 1 backbone ) was previously described ( Nguyen et al . , 2018 ) . All chemicals used in this study were purchased from Sigma‐Aldrich unless otherwise stated . All sgRNAs targeting mouse or human SNORA24 were designed using the Zhang Lab design tool ( crispr . mit . edu ) . Chemically modified synthetic sgRNAs were purchased from Synthego ( Menlo Park , CA , USA ) and Cas9-NLS purified protein was from the QB3 MacroLab ( UC Berkeley , CA , USA ) . Cas9 RNP was prepared immediately prior to nucleofection by incubating Cas9 protein with sgRNA at 1:1 . 3 molar ratio in 20 mM HEPES ( pH 7 . 5 ) , 150 mM KCl , 1 mM MgCl2 , 10% glycerol and 1 mM TCEP at 37°C for 10 min . Cells were dissociated using trypsin , pelleted by centrifugation , and washed once with D-PBS . Nucleofection of human HuH-7 cells and mouse KrasG12D tumor cell line was performed using Amaxa Cell Line Nucleofector Kit V ( Lonza , Allendale , NJ , USA ) and program H-022 on an Amaxa Nucleofector II system . Each nucleofection reaction consisted of ~4×105 cells in 50 μl of nucleofection reagent mixed with two distinct 10 μl RNP mixtures containing different sgRNA ( to allow specific deletion within the SNORA24 gene locus [sgRNA-24] ) . Cas9 alone or a set of non-targeting control sgRNA ( sgRNA-ctrl ) were used in a separate RNP reaction . Two days following nucleofection , gene editing was confirmed by extracting genomic DNA ( gDNA ) from cells using Quick Extraction ( Lucigen Corporation , WI , USA ) , performing PCR of the SNORA24 loci using gene specific primer , and sequencing the PCR product . The following sgRNA sequences were use; SNORA24 human sgRNA #1 5’-GGATATGCTCTTCCATGGCT-3’ , SNORA24 human sgRNA #2 5’-CAAAGCTGTCACCATTTAAT-3’ , non-targeting sgRNA #1 5’-AACGACTAGTTAGGCGTGTA-3’ , non-targeting sgRNA #2 5’-CGCCAAACGTGCCCTGACGG-3’ , Snora24 mouse sgRNA #1 5’-TCTTTGGGACCTGCCGCCTG-3’ , Snora24 mouse sgRNA #2 5’-CACTTGCTCAAGTCAGAATC-3’ . HuH-7 sgRNA-ctrl and sgRNA-24 cells were incubated with 100 µg/ml cycloheximide ( Sigma ) in the growth media for 5 min at 37°C and 5% CO2 . Cells were washed once in ice-cold PBS containing 100 µg/ml cycloheximide . Cells were then scraped in 5 ml of ice-cold PBS containing 100 µg/ml cycloheximide and pelleted . Cell pellets were lysed in buffer containing 10 mM Tris-HCl ( pH 8 ) , 150 mM NaCl , 1 . 5 mM MgCl2 , 1% Triton X-100 , 20 mM DTT , 150 µg/ml cycloheximide , and 640 U/ml Rnasin for 30 min on ice . Lysates were centrifuged at 10 , 000 x g for 5 min at 4°C . The supernatant ( ~300 ul ) were adjusted by OD260 ( to OD260 of ~15 ) and loaded onto a 10–50% sucrose gradient before centrifugation at 37 , 500 rpm for 2 . 5 hrs at 4°C in a Beckman L8-70M ultracentrifuge . Samples were separated on an ISCO gradient fractionation system to evaluate polysome profiles . Western blot analysis was performed on samples lysed in RIPA buffer ( 50 mM Tris pH 8 , 150 mM NaCl , 0 . 2% Na deoxycholate , 0 . 5% TritonX-100 ) with the addition of PhosSTOP and Complete Mini proteasome inhibitors ( Roche ) using standard procedures with commercial antibodies for RAS ( Cell Signaling Technology ) , PTEN ( Cell Signaling Technology ) and β-actin ( Sigma ) . Immunohistochemistry analysis was performed on OCT embedded frozen tissue using standard protocols and the following primary antibodies: NRAS ( Santa Cruz Biotechnology ) and p21 ( BD Biosciences ) . Cellular senescence was assayed 15 days after retroviral expression of HRASG12V in primary human skin fibroblasts using a senescence detection kit ( Calbiochem ) according to manufacturer’s instructions . Cells were imaged using a Nikon TE2000E inverted microscope . Determination of senescence in liver sections or whole liver lobes was carried out as described previously ( Kang et al . , 2011 ) . OCT embedded frozen tissue was prepared using standard protocols and following equilibrated at room temperature for 10 min , sections were fixed in formalin for 5 min . Following wash in tap water , slides were stained in ORO working solution for 10 min at room temperature . ORO stock and working solution were prepared as previously described ( Mehlem et al . , 2013 ) . Slides were washed in tap water for 10 min and counterstained with Mayer’s hematoxylin by submerging the slides in hematoxylin for 3 min . Slides were rinsed under running tap water for ~10 min and mounted with AquaSlip from AMTS Inc ( Lodi , CA , USA ) . Hematoxylin and Eosin ( H and E ) stained frozen sections were imaged on an Aperio Versa slide scanner ( Leica Biosystems ) , equipped with a HC PL Fluotar 10X/ . 32 objective . ORO stained frozen sections were imaged on an Axio Scan . Z1 slide scanner ( Carl Zeiss Microscopy ) , equipped with a Plan-Apochromat 10x/0 . 45 objective . ORO quantification was performed by selecting four regions of interest ( ROIs ) from each H and E . scn image file and extracted as a TIFF at 1000 × 1000 pxl ( 551 × 551 microns; scale = 551 nm/pxl ) in Aperio ImageScope v . 12 . 3 . 2 software . The analogous region from the ORO stained serial adjacent section . czi image file ( scale = 442 nm/pxl ) was extracted in Zen , converted to TIFF and downsampled to yield the same spatial scaling for analysis . The two resulting TIFF files were spatially registered using ImageJ TrackEM2 . An ORO RGB image was used as input to tune the threshold parameters in the Zen Pro Image Analysis software module for quantifying lipid droplets . Three classes were created; ORO ( Oil Red O positive ) , All ( all tissue ) , and White ( empty ) with the following colormetric parameters: ORO: R 94–158 , G 2–98 , B – 109; All: R 90 , G 10–177 , B 19–182; White: R 191–220 , G 177–210 , B 181–224 . The percentage ( % ) ORO positive stained area per total area examined from four different ROIs of each tissue section was calculated and summed . For each condition tested , the mean ± SD ORO positive area ( % ) was plotted , with the Y-axis label representing ‘ORO positive area ( % ) ” . For mouse tissue sections ( n = 3 mice ) , ROIs were identified within the H and E images that could be classified as abnormal or normal tissue under the guidance of a pathologist . Abnormal or normal tissue ROIs ( fixed area of 7400 pixels ) were applied to the analogous region within the registered ORO image ( as shown in Figure 2—figure supplement 1 ) . For human HCC specimens , patient samples were dichotomized into high or low SNORA24 expression by identifying samples with SNORA24 expression more extreme than ±1 SD from the mean ( n = 17 HCC specimens ) and ORO staining and quantification was performed as described above . OP-Puro ( Medchem Source LLP , WA , USA ) was reconstituted in PBS , adjusted to pH 6 . 5 , and stored in aliquots at −20°C . Cells were treated with 30 μM OP-Puro or PBS ( mock to subtract background signal during analysis ) . Two hours following OP-Puro addition to the media , cells were dissociated using trypsin , pelleted by centrifugation , washed in PBS , and fixed in paraformaldehyde ( PFA ) in PBS for 15 min on ice . After washing in cold PBS , samples were permeabilized in the dark using PBS with 3% FBS and 0 . 1% saponin . Click-iT reaction ( Invitrogen ) was performed according to manufacturer’s instructions with cycloaddition conjugation to Alexa555 for 30 min at room temperature with light protection . Data was acquired using a BD LSRII and analyzed with FlowJo to calculate the fluorescence intensity of each sample . For quantification , the relative rates of protein synthesis depicted by OP-Puro signals were calculated as mean fluorescence intensity ( MFI ) , subtracting the auto-fluorescence background from mock ( PBS control ) . Normalized MFI for each cell sample was plotted with SD of the mean . Liver tissue specimens were obtained from patients undergoing treatment for HCC at the University of California , San Francisco ( San Francisco , CA , USA ) . A summary of patient demographics and staging is presented in Supplementary file 1 . Microarray gene expression was obtained from the NCBI GEO database , accession GSE25097 ( HCC ) , accession GSE22898 ( Diffuse large B-cell lymphoma ) , accession GSE20916 ( Colorectal cancer ) , accession GSE28735 ( Pancreatic ductal adenocarcinoma ) , and accession GSE43458 ( Lung adenocarcinoma ) . Expression data from probes corresponding to H/ACA snoRNAs were extracted , analyzed for fold change in expression in tumor and control samples , and statistical significance was calculated using paired or unpaired Student’s t-test as indicated . Microarray gene expression and clinical data were obtained from the NCBI GEO database , accession GSE25097 ( Hao et al . , 2011; Kan et al . , 2013 ) . Calculations were performed in R ( R Development Core Team , 2008 ) . Patient samples were dichotomized into high or low by identifying samples with SNORA24 expression more extreme than ±one SD from the mean ( n = 24 ) . Kaplan-Meier survival curves were fit , and statistical significance was calculated using the log-rank test , with p<0 . 05 used as a threshold for statistical significance . Similar analyses were performed for SNORA14B , SNORA17 , SNORA67 , SNORA72 , and SNORA81 . All smFRET experiments were conducted at 37° C in human polymix buffer ( 50 mM Tris pH 7 . 5 , 5 mM MgCl2 , 50 mM NH4Cl , 2 mM spermidine , 5 mM putrescine ) containing a mixture of triplet-state quenchers ( 1 mM Trolox , 1 mM 4-nitrobenzyl alcohol ( NBA ) , 1 mM cyclooctatetraene ( COT ) ) and an enzymatic oxygen scavenging system ( 2 µM 3 , 4-Dihydroxybenzoic acid ( PCA ) , 0 . 02 Units/ml protocatechuate 3 , 4-dioxygenase ( PCD ) ) . Ribosomes from HuH-7 sgRNA-ctrl or sgRNA-24 cells were prepared using the protocol described in Flis et al . ( 2018 ) . Elongation factor eEF1A and fluorescence labeled tRNAs were prepared as in Flis et al . ( 2018 ) . Pre-formed 80S initiation complexes made with ribosomes from either HuH-7 sgRNA-ctrl or sgRNA-24 cells , containing Cy3-labeled Met-tRNAfMet in the P site and displaying either a UUC or AAA codon in the A site , were surface-immobilized on passivated quartz coverslips ( Blanchard et al . , 2004 ) in a home-built total internal reflection-based fluorescence microscope ( Juette et al . , 2016 ) . To initiate tRNA selection , 10 nM ternary complex , consisting of eEF1A , GTP and either Phe-tRNAPhe labeled with LD655 or Lys-tRNALys labeled with LD650 was stop flow delivered to the immobilized ribosomes . Imaging of the pre-translocation complexes was carried out by washing ternary complex from the flow cell with polymix buffer 30 s after injection . smFRET data were recorded at a time resolution of 15 ms at ~0 . 25 kW/cm2 laser ( 532 nm ) illumination . Donor and acceptor fluorescence intensities were extracted from the recorded movies and FRET efficiency traces were calculated . FRET traces were selected for further analysis according to the following criteria: a single catastrophic photobleaching event , at least 8:1 signal/background-noise ratio and 6:1 signal/signal-noise ratio , less than four donor-fluorophore blinking events and a correlation coefficient between donor and acceptor <0 . 5 . smFRET traces were analyzed using hidden Markov model idealization methods as implemented in the SPARTAN software package ( Juette et al . , 2016 ) . The idealization model for tRNA selection traces included four separate FRET values accounting for unbound , initial binding , GTPase activation , and accommodated states during tRNA selection ( Ferguson et al . , 2015; Geggier et al . , 2010 ) with FRET values of 0 . 0 ± 0 . 05 , 0 . 2 ± 0 . 075 , 0 . 46 ± 0 . 075 and 0 . 72 ± 0 . 075 . The idealization model for the pre-translocation state included three FRET states with FRET values of 0 . 22 ± 0 . 075 , 0 . 42 ± 0 . 075 and 0 . 72 ± 0 . 075 ( 0 . 61 ± 0 . 05 in case of LD650 labeled tRNALys containing ribosomes ) accounting for the hybrid 1 , hybrid two and classical tRNA binding states ( Pellegrino et al . , 2019 ) . To generate cumulative distributions for estimation of apparent reaction rates during tRNA selection the number of traces that had achieved the 0 . 72 FRET state prior to each movie frame were summed . An exponential function containing two exponential terms and a term accounting for the initial delay due to the stop flow delivery dead time was then fit to the data . All distributions contained two phases , a fast phase accounting for >85% of events and a slower phase accounting for the remainder . In all cases the reaction rate of the dominant , fast , phase was used for further analysis . To take the effect of donor photobleaching into account for estimation of accurate tRNA selection kcat/KM values , donor photobleaching rates estimated from the total dataset were subtracted from the apparent reaction rates . To estimate the fraction of time the ribosomal pre-translocation complexes spend in each tRNA binding state , state dwell times were extracted directly from the hidden Markov-model idealizations . All experimental uncertainties were estimated from bootstrap analysis of two to five smFRET datasets . Significance testing of the difference in tRNA selection efficiency and tRNA state occupancy between ribosomes isolated from HuH-7 sgRNA-ctrl and sgRNA-24 cells was carried out by a bootstrap implementation of Welch’s t-test . Briefly , the t statistic was calculated from the bootstrap distributions of the estimated reaction rate constants or fractional state occupancies . This was then compared to 106 t statistics calculated from bootstrap samples picked from null distributions generated by shifting the mean of both original distributions to their pooled mean . This generated the upper bound for the P value of p<10−6 reported in the text . HuH-7 sgRNA-ctrl or sgRNA-24 cells plated on glass coverslips and treated with Oleic Acid ( diluted 1:10 in media ) 24 hrs after plating . 6 hrs following Oleic Acid addition , cells were fixed using 4% PFA for 30 min at room temperature , followed by a PBS wash , and LipidTOX green neutral lipid staining ( Thermo ) ( 1:200 dilution in PBS ) for 1 hr . Coverslips were mounted on glass slides using Prolong anti-fade mounting solution with DAPI . Imaging was performed on a Zeiss Cell Observer Spinning Disc Confocal Microscope and quantification was performed using ImageJ . HuH-7 sgRNA-ctrl or sgRNA-24 cells were plated at 2 , 000 cells per well in 96-well plates . 24 hrs after plating , cells were treated with the indicated concentration of translation inhibitor or DMSO and incubated for 48 hrs . CellTiter-Glo Luminescent Cell Viability Assay ( Promega , WI , USA ) was performed following manufacturer’s instructions with luminescence measurements made using a Glomax 96-well plate luminometer ( Promega ) . Proliferation data were generated by first normalizing luminescence intensity in each well to that of the DMSO-treated wells and normalized luminescence data was plotted ( ± SD ) from at least three independent experiments . SCARLET was performed essentially as previously described ( Li et al . , 2015; Liu and Pan , 2015 ) on 10 μg of total RNA isolated using TRIzol ( Invitrogen ) from the indicated human HuH-7 cells or mouse KrasG12D tumor cell lines using the following oligonucleotides , where Nm = 2’-O-Me modified nucleotide; U609 18S rRNA chimera: 5’-CmAmGACTUmGmCmCmCmUmCmCmAmAmUm-3’ , U609 18S rRNA splint: 5’-AGCTGGAATTACCGCGGCTGCTGGCACCACTATTAACTCACAGGACCGGCGATGGCTG-3’ , U863 18S rRNA chimera: 5’-UmCmCmAmUmUmAmTTCCUmAmGmCmUmGmCm-3’ , U863 18S rRNA splint: 5’-CAAAATAGAACCGCGGTCCTATTCCATTACTATTAACTCACAGGACCGGCGATGGCTG-3’ . Site-specific detection of pseudouridine modifications in LNA-S and LNA-24 treated samples was performed 48 hrs post-transfection as described ( Karijolich et al . , 2010 ) using the following oligonucleotides , where Nm = 2’-O-Me modified nucleotide; U609 18S rRNA: 5’-CmAmGACTUmGmCmCmCmUmCmCmAmAmUm-3’ , U863 18S rRNA: 5’-UmAmTTCCUmAmGmCmUmGmCmGmGmUmAm-3’ , U1731 28S rRNA: 5’-CmAmTTCGCmUmUmUmAmCmCmGmGmAmUm-3’ , U105 18S rRNA: 5’-GmAmTTTAAmUmGmAmGmCmCmAmUmUmCm-3’ . Results were visualized by a phosphor imager and quantification of uridine or pseudouridine in a given sample was determined using ImageJ . HuH-7 sgRNA-ctrl and HuH-7 sgRNA-24 cells were seeded in 12 well plates at 30 , 000 cells/well . 24 hrs later cells were transfected using Lipofectamine 2000 ( Invitrogen ) with 0 . 1 μg per well of the indicated luciferase reporter construct . Cells were lysed after 24 hrs in passive lysis buffer and Rluc and Fluc activity was assessed using the Dual-luciferase Reporter Assay System ( Promega ) according to the manufacturer’s instructions and using a Glomax microplate luminometer ( Promega ) . For stop codon readthrough experiments , performed in the presence of paromomycin , 1 mg/ml paromomycin ( Sigma ) was added to cells 8 hrs post-transfection . To measure stop codon readthrough ( % ) , normalized Fluc activity ( Fluc/Rluc ) from UAG or UGA stop codon readthrough luciferase reporters was further normalized to a control construct , which does not have a stop codon between Rluc and Fluc as described ( Jack et al . , 2011 ) . To measure amino acid misincorporation ( % ) , normalized Fluc activity ( Fluc/Rluc ) from CGC or AAU amino acid misincorporation luciferase reporters was normalized to a control construct , which does not contain a point mutation in Fluc as described ( Fujii et al . , 2018 ) . The amino acid misincorporation ( % ) or stop codon readthrough ( % ) values from the indicated number of independent experiments in HuH-7 sgRNA-ctrl and sgRNA-24 cells are shown . RNA was isolated using TRIzol ( Invitrogen ) purification on Direct-zol RNA Microprep columns ( Zymo Research , CA , USA ) according to manufacturer’s instructions with DNase treatment . cDNA samples were diluted 1:10 and 1 μl of template was used in a PowerUP SYBR Green master mix reaction run on an Applied Biosystems QuantStudio 6 Flex Real-Time PCR System ( Thermo Fisher ) . qPCR primer sequences are listed in Supplementary file 2 . For snoRNA qPCR array , 2 μg Dnase treated ( Turbo DNAse ) RNA was reverse transcribed using an Arraystar rtStar First-strand cDNA Synthesis kit . The Arraystar nrStar snoRNA PCR Array was performed following manufacturer’s instructions using Arraystar SYBR Green Real-time qPCR master mix and run on an Applied Biosystems QuantStudio 6 Flex Real-Time PCR System ( Thermo Fisher ) . Unless otherwise stated data is presented as mean ± SD . Statistical tests and specific P values used for experiments are listed in the figure legends and were generated using GraphPad Prism six software unless otherwise stated . Results are representative of at least three independent experiments . For survival analysis , a log-rank test was used . p<0 . 05 was considered significant and the exact P values are indicated in the figures and the corresponding figure legends .	Ribosomes are cellular machines responsible for translating the genetic code into proteins . Research has shown that changes in ribosome activity can contribute to healthy cells becoming cancerous . Ribosomes consist of proteins and other molecules known as ribosomal RNAs ( or rRNAs for short ) . Before they can become part of a ribosome , another type of molecule called snoRNAs must modify new rRNAs . Indeed , many of the modifications that allow rRNAs to accurately translate genetic information into proteins are introduced by snoRNAs . As such , it is possible that changes to snoRNAs could contribute to the creation of cancerous cells by affecting how ribosomes operate . To explore this possibility , McMahon , Contreras et al . examined snoRNAs in healthy cells grown in the laboratory that have been given pro-cancer signals , in cancer from mice , and in samples from human cancer patients . The investigation revealed that the activation of growth signals – a hallmark of many cancers – affects the abundance of some snoRNAs and changes the pattern of rRNA modifications they make on ribosomes . Reducing the levels of one such snoRNA called SNORA24 led mice to develop fatty liver cancer when combined with cancer-promoting growth signals . Analyzing why reducing the levels of SNORA24 led to liver cancer , McMahon , Contreras et al . found that ribosomes lacking rRNA modifications introduced by SNORA24 made more mistakes when producing proteins coded for by certain genes . These results contribute to the view of ribosomes as a key hub for the transformation of healthy cells into cancer cells . Increasing the error rate of ribosomes could be a key driver in further changes that drive cancer development . This study also highlights the role of snoRNAs in responding to growth signals , particularly in cancer . These findings identify snoRNAs as new potential diagnostic factors and treatment targets .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "chromosomes", "and", "gene", "expression", "cancer", "biology" ]	2019	A single H/ACA small nucleolar RNA mediates tumor suppression downstream of oncogenic RAS
mRNA translation decodes nucleotide into amino acid sequences . However , translation has also been shown to affect mRNA stability depending on codon composition in model organisms , although universality of this mechanism remains unclear . Here , using three independent approaches to measure exogenous and endogenous mRNA decay , we define which codons are associated with stable or unstable mRNAs in human cells . We demonstrate that the regulatory information affecting mRNA stability is encoded in codons and not in nucleotides . Stabilizing codons tend to be associated with higher tRNA levels and higher charged/total tRNA ratios . While mRNAs enriched in destabilizing codons tend to possess shorter poly ( A ) -tails , the poly ( A ) -tail is not required for the codon-mediated mRNA stability . This mechanism depends on translation; however , the number of ribosome loads into a mRNA modulates the codon-mediated effects on gene expression . This work provides definitive evidence that translation strongly affects mRNA stability in a codon-dependent manner in human cells . Messenger RNA ( mRNA ) degradation and mRNA translation represent two fundamental steps in the regulation of gene expression . Stability of the mRNA affects mRNA levels ( Herzog et al . , 2017 ) which in turn , impact protein production ( Ingolia , 2016 ) . Alterations in mRNA degradation leads to developmental defects ( Giraldez et al . , 2006 ) and human disease ( Goodarzi et al . , 2016 ) . Likewise , aberrant mRNA translation has been implicated in protein misfolding and neurodegenerative disease ( Kapur et al . , 2017; Pechmann and Frydman , 2013 ) , viral infection ( Walsh et al . , 2013 ) and developmental defects ( Gonskikh and Polacek , 2017; Kong and Lasko , 2012 ) . Recent studies have shown that translation impacts mRNA stability in a codon-dependent manner in yeast ( Harigaya and Parker , 2016; Presnyak et al . , 2015; Radhakrishnan et al . , 2016 ) , E . coli ( Boël et al . , 2016 ) , zebrafish ( Bazzini et al . , 2016; Mishima and Tomari , 2016 ) , Xenopus ( Bazzini et al . , 2016 ) , Trypanosoma brucei ( de Freitas Nascimento et al . , 2018; Jeacock et al . , 2018 ) and Drosophila melanogaster ( Burow et al . , 2018 ) . This mechanism , termed codon optimality , refers to the ability of a given codon to affect mRNA stability in a translation-dependent manner ( Presnyak et al . , 2015 ) . mRNAs enriched in ‘optimal’ codons tend to be more stable , display greater abundance , higher translation efficiency and longer poly ( A ) -tails . Conversely , mRNAs enriched in ‘non-optimal’ codons tend to be unstable , display lower homeostatic RNA levels , poor translation efficiency and shorter poly ( A ) -tails ( Bazzini et al . , 2016; Mishima and Tomari , 2016; Radhakrishnan et al . , 2016; Webster et al . , 2018 ) . Most efforts aimed at identifying cis-regulatory elements affecting mRNA stability have focused on sequences within 3’ untranslated regions ( UTRs ) . MicroRNAs and RNA binding proteins repress translation and/or destabilize target mRNAs through recognition of regulatory elements located primarily in the 3’UTR ( Bazzini et al . , 2012; Despic and Neugebauer , 2018; Giraldez et al . , 2006; Meyer et al . , 2012; Ray et al . , 2013; Tadros et al . , 2007 ) . However , on average , the coding sequence is approximately twice as long as the 3’UTR and is recognized by the most abundant RNA-binding complex in the cell: the ribosome ( Doudna and Rath , 2002 ) . While translating the information from the mRNA into protein is crucial , translation is also involved in quality control mechanisms of mRNAs ( Shoemaker and Green , 2012 ) . The mechanism of codon optimality represents a novel regulatory function of the ribosome upon properly processed mRNAs ( Richter and Coller , 2015; Schikora-Tamarit and Carey , 2018; Wu and Bazzini , 2018 ) . Translation elongation rates underlie codon-mediated mRNA stability in yeast ( Hanson et al . , 2018; Presnyak et al . , 2015 ) . While less clear in higher organisms , optimal and non-optimal codons tend to be decoded by tRNAs that are highly or poorly expressed , respectively ( Bazzini et al . , 2016 ) . This suggests that the supply and/or demand for specific tRNAs affects translation elongation , which in turn , affects mRNA stability ( Despic and Neugebauer , 2018; Richter and Coller , 2015 ) . The tRNA repertoire can fluctuate in different cell types ( Gingold et al . , 2014; Goodarzi et al . , 2016 ) and/or following stress ( Torrent et al . , 2018 ) . For example , proliferating cells undergoing differentiation contain tRNA profiles that correlate with codon usage of the specific transcriptome of each cell type ( Gingold et al . , 2014 ) . Altering tRNA availability can lead to neurodegenerative diseases ( Ishimura et al . , 2014 ) and upregulation of specific tRNAs drives metastasis by enhancing stability of transcripts enriched in their cognate codons ( Goodarzi et al . , 2016 ) . To investigate whether translation affects mRNA stability in a codon-dependent manner in humans , we have measured the ability of each codon to affect mRNA stability . We have conducted analyses in four different human cell lines , using three independent methods to measure mRNA stability . We demonstrate that the regulatory information affecting mRNA stability is encoded specifically within codon identity , rather than other nucleotide sequence information . Destabilizing codons tend to have lower respective tRNA levels , as well as lower ratios of charged-tRNA ( with amino acid ) compared to stabilizing codons . Genes enriched in stabilizing codons also tend to possess longer poly ( A ) -tails than genes enriched in destabilizing codons . However , the poly ( A ) -tail is not essential for affecting mRNA stability in a codon-dependent manner . We demonstrate that codon-mediated effect on gene expression can also be modulated by tuning the translational level ( number of ribosomes on mRNA ) through either global change in translation efficiency ( e . g . viral infection ) or specific UTRs sequences . Our studies reveal that in human cells , the ribosome interprets two codes within the mRNA: the genetic code , which specifies the amino acid sequence , and a ‘codon-optimality-code’ , which shapes mRNA stability . To determine whether codon composition affects mRNA stability in human cells , we treated 293T , HeLa and RPE cells with Actinomycin D to block transcription ( Figure 1—figure supplement 1A ) and measured decay of existing mRNAs by performing time-course mRNA-seq ( Figure 1A ) . We calculated the codon stability coefficient ( CSC ) as the Pearson correlation coefficient between mRNA stability and codon occurrence . Based on this approach , we and others have previously determined the properties of each of 61 codons in mRNA stability in yeast ( Presnyak et al . , 2015 ) , zebrafish , and Xenopus embryos ( Bazzini et al . , 2016 ) ( Figure 1A ) . Codons displaying a positive correlation were referred to as ‘optimal’ codons because mRNAs enriched in those codons were more stable . Conversely , codons displaying a negative correlation were referred to as ‘non-optimal’ codons because mRNAs enriched in those codons were less stable ( Figure 1A , B ) . These codon regulatory properties ( optimal , non-optimal ) correlate well across the three analyzed human cell types and zebrafish ( Figure 1C and Figure 1—figure supplement 1B ) , suggesting that the regulatory activity of some codons is shared between humans and zebrafish . The CSC scores do not present strong correlation with codon usage ( Figure 1—figure supplement 1C ) . These results also imply that codon composition has effects on mRNA stability in humans that are analogous to those found in other species . mRNA stability can be influenced by cis-regulatory sequences within 5’ and 3’ UTRs of mRNAs which are targeted by microRNAs , RNA-binding proteins and RNA modification ( m6A ) ( Despic and Neugebauer , 2018; Meyer et al . , 2012 ) . To separate the effects on stability related to codon composition from those due to cis-elements in the UTRs , we have previously developed a method to measure the regulatory properties of the coding sequence in a UTR-independent manner ( Bazzini et al . , 2016 ) . We compared the decay of millions of exogenous mRNAs in zebrafish and Xenopus embryos which possess similar 5’ and 3’UTRs but differ in codon composition ( Bazzini et al . , 2016 ) . To adapt this strategy to human cells , we developed a method using a vector-based library , termed ORFome ( Yang et al . , 2011 ) , containing ~17 , 000 different human coding sequences fused to common promoter , 5’ and 3’ UTRs ( Figure 1D ) . ORFome library infection into 293T and K562 cells was followed by transcriptional inhibition ( Actinomycin D treatment ) and ORFome mRNA levels were monitored over time using small barcodes in the 3’UTR ( Figure 1D ) . The distribution of mRNA level in the more than ten thousand ORFome genes is narrower than the endogenous mRNAs , likely because all ORFome mRNAs share the same strong promoter , 5’ and 3’ UTRs ( Figure 1E ) . Nonetheless , the CSC scores derived from ORFome mRNAs correlated with respective endogenous mRNAs , further supporting that codon composition affects mRNA stability ( Figure 1B , C and Figure 1—figure supplement 1B ) . To further explore that stability of the ORFome-derived mRNAs are indeed independent of UTR-mediated regulation , we selected mRNAs predicted to be targets of methylation ( m6A ) and compared that to a control group ( Meyer et al . , 2012 ) . As expected , the endogenous m6A target mRNAs are more unstable than a control group ( Meyer et al . , 2012; Yue et al . , 2015 ) . However , there was significantly less difference between the same mRNAs when derived from ORFome , most likely due to the absence of methylation-sensitive 3’UTR regulatory elements ( Figure 1F ) . Together , these observations suggest that this approach enables the dissection and measurement of regulatory activities embedded within coding regions and/or UTRs . The above analyses depend on blocking transcription by Actinomycin D treatment , which may have unintended consequences . To circumvent this problem and to measure mRNA decay without blocking transcription , we employed SLAM-seq method ( Herzog et al . , 2017 ) . This method measures endogenous mRNA half-lives based on 4-thiouridine ( s4U ) incorporation and orthogonal-chemistry-based RNA sequencing , where incorporated s4U is read as cytosine ( C ) rather than uracil ( U ) ( Herzog et al . , 2017 ) . Cells were ‘fed’ with s4U , followed by washout and unlabeled chase ( Figure 1G and Figure 1—figure supplement 1D , E , F , G and methods ) . We observed a strong CSC score correlation using the SLAM-seq approach akin to the exogenous ORFome ( Figure 1B , C ) approach as well as sequencing of endogenous mRNAs in different cell types ( Figure 1B , C and Figure 1—figure supplement 1B ) . Interestingly , we also observed strong half-lives correlation between our SLAM-seq data and a similar methodology , TimeLapse-seq ( Schofield et al . , 2018 ) in K562 cell ( Figure 1—figure supplement 1H ) and so , strong CSC scores correlation ( Figure 1—figure supplement 1I ) . Furthermore , SLAM-seq has also been done in mouse embryonic stem cells ( Herzog et al . , 2017 ) , we calculated CSC scores based on their data , it also correlated with our human SLAM-seq data ( Figure 1—figure supplement 1I ) , indicating a similar codon optimality code in mouse embryonic stem cells . And similar to our endogenous mRNA , mRNAs previously defined as targets of the m6A pathway display increased decay compared to a control set of mRNAs in our SLAM-seq ( Figure 1F ) . In sum , we have measured mRNA decay using three independent approaches in different human cells ( 293T , HeLa , RPE and K562 ) to score codon-optimality-code . Our results suggest that codon composition affects mRNA stability in all the tested human cell lines , and that the regulatory properties of each codon are similar between cells . The results above indicate that regulatory information is embedded in the coding sequence . To determine whether this sequence information is ‘read’ in a codon-dependent manner or simply nucleotide composition , we compared a pair of reporters that differed by a single nucleotide insertion ( 1nt frame-shift ) , causing a frameshift that converts an ‘optimal’ coding sequence ( enriched in optimal codons ) into a ‘non-optimal’ sequence ( enriched in non-optimal codons ) ( Figure 2A ) , keeping the nucleotide composition otherwise almost identical ( Figure 2A ) . We redeployed a reporter system where mCherry ( red fluorescent protein ) or GFP ( green fluorescent protein ) was followed by a ribosome skipping sequence ( P2A ) ( de Felipe et al . , 2006; Donnelly et al . , 2001 ) and a coding region enriched in either optimal or non-optimal codons ( due to 1 nucleotide frameshift ) ( Figure 2A ) . The P2A sequence allowed the analysis of protein production in vivo independent of potential folding effects from the optimal or non-optimal peptide . If the regulatory information is ‘read’ in a manner that was dependent on codon identity , there should be a correlation between codon-optimality scores and expression levels . We found that mRNA derived from the non-optimal reporter was less stable than mRNA derived from its optimal counterpart after blocking transcription with Actinomycin D ( Figure 2B ) . Further , this difference was observed at RNA level 24 hr post-transfection ( Figure 2C ) , suggesting that codon composition can affect homeostatic mRNA levels ( Bazzini et al . , 2016 ) . In addition to higher mRNA abundance , we also observed higher fluorescence intensity from the optimal reporter vs the non-optimal reporter ( Figure 2D ) . No significant differences were observed in the co-transfection control ( GFP ) , ruling out potential global expression changes due to toxicity of the non-optimal peptide ( Figure 2D ) . These changes due to the codon composition were independent of the nucleotide used to cause the frameshift ( G , C , U or A ) ( Figure 2—figure supplement 1A ) , fluorescence protein used in the reporter assay ( mCherry-based , Figure 2D; GFP-based reporters , Figure 2—figure supplement 1B ) or coding sequence of the frame-shifted reporter ( different frame-shifted coding sequence pair , Figure 2—figure supplement 1C ) . Likewise , similar outcomes were observed in HeLa , RPE and K562 cells ( Figure 2—figure supplement 1D ) , consistent with strong correlation between their respective codon optimality scores ( Figure 1 ) . Taken together these results indicate that regulatory information is dependent on codon assignment rather than other nucleotide sequence information . To assess whether active translation in cis is required to confer the regulatory effects on stability , we generated different paired reporters ( Extremes ) with single nucleotide mutation to create a premature stop codon , preventing the region enriched in either optimal or non-optimal to be translated ( Figure 2E ) . Differently to the 1nt-out of frame reporters ( Figure 2A ) , the ‘extreme’ reporters enriched in optimal or non-optimal codons do not share the same nucleotide composition but contain higher optimality differences . Therefore , if translation in cis is important , translation of a coding region enriched in either optimal or non-optimal codons should increase or decrease mRNA stability , respectively , when compared to their untranslated counterparts ( referred to as Mut ) ( Figure 2E ) . Indeed , a translation-competent reporter enriched in optimal codons resulted in , increased mRNA stability ( Figure 2F ) , mRNA abundance ( Figure 2G ) and fluorescent intensity ( Figure 2H ) compared to its translation-deficient counterpart in 293 T cells . Conversely transfection of a translation-competent reporter enriched in non-optimal codons resulted in decreased mRNA stability , mRNA abundance , as well as fluorescence intensity , when compared to its translation-deficient counterpart in 293 T cells ( Figure 2F , G , H ) . Similar fluorescent intensity differences were observed in HeLa cells ( Figure 2—figure supplement 1E ) . Furthermore , codon-mediated effects on endogenous mRNAs were dampened in both directions upon inhibition of translation through cycloheximide treatment . Specifically , the absolute correlation coefficient between mRNA stability and codon occurrence , CSC , were smaller in 293 T cells treated with cycloheximide ( Figure 2—figure supplement 1F ) , when compared to untreated 293 T cells ( Figure 1 ) . In the presence of cycloheximide , mRNAs enriched in optimal codons were less stable and mRNAs enriched in non-optimal were less unstable when compared to untreated cells ( Figure 2—figure supplement 1G ) . This data demonstrates that mRNA stability and expression are influenced by the codon composition in a translation-dependent manner . The above results indicate that codon identity impacts mRNA stability in human cells . In yeast , translation elongation speed is related to codon optimality , where ‘non-optimal’ codons are more slowly translated compared to ‘optimal’ codons . The decoding rate can be influenced by both amino acid identity ( Artieri and Fraser , 2014; Gardin et al . , 2014 ) and tRNA levels . To address whether amino acid identity influences mRNA stability , we first assessed whether synonymous codons behaved in the same way . We hypothesized that synonymous codons should share the same optimality behavior if amino acid identity was the main determinant . For example , in zebrafish and Xenopus , we found that some amino acids share either optimal or non-optimal codons ( Figure 3A ) ( Bazzini et al . , 2016 ) . Of the 20 different amino acids , we found that in human , only histidine possessed synonymous codons that were exclusively non-optimal across all three assays ( Figure 3A ) . For a few other amino acids ( e . g . glycine , serine ) , synonymous codons were largely encoded by optimal or non-optimal codons in most of the assays ( Figure 3A ) . For the majority of amino acids , synonymous codons possessed different regulatory properties . However , a preprint recently proposed that amino acid identity also acts as a driver of translation-dependent decay regulation using similar dataset in human ( Forrest et al . , 2018 ) , which shows strong correlation with ours CSC scores ( Figure 3—figure supplement 1A ) . Therefore , we calculated the amino acid stabilization coefficient ( ASC ) , as the Person correlation coefficient between mRNA stability and amino acid occurrence ( Bazzini et al . , 2016 ) . Similar to the preprint ( Forrest et al . , 2018 ) , amino acids presented correlations with mRNA decay ( ASC ) ( Figure 3—figure supplement 1B ) . However , in human those correlations are strongly affected after removing the most abundance synonymous codon for most of the amino acids ( Figure 3—figure supplement 1B ) . For example , Leucine and Isoleucine displayed a positive ASC score suggesting that both amino acids might be optimal ( also mentioned in Forrest et al . , 2018 ) ; however , in all the datasets both amino acids are encoded by optimal and non-optimal codons and the most abundance codon dominates the ASC calculation ( Figure 3—figure supplement 1B ) . Actually , both amino acids would be referred as non-optimal after removing the single most abundant synonymous codon ( Figure 3—figure supplement 1B ) . However , as mentioned before , there are few amino acids like Histidine or Serine ( Figure 3—figure supplement 1B ) , where all the synonymous codons temp to share the same optimality trend ( Figure 3A and Figure 3—figure supplement 1B ) , and so those few amino acids might have an implication on mRNA stability . To further test that codons , and less likely the amino acid they encode , affect mRNA stability , we sought to determine the effect of synonymous codons in mRNA stability using two different approaches . First , using reporter genes differing in silent mutations ( Figure 3B ) , we observed that mRNA levels ( Figure 3C ) and fluorescent intensity ( Figure 3D ) were higher in the reporters enriched in optimal codons compared to counterparts enriched in non-optimal codons . These results suggested that synonymous codons can have different effects on gene expression . Second , we designed individual reporters ( Mini-gene ) that contained coding sequences , in which all even positions ( 61 of 122 codons ) after the P2A releasing sequence possessed a single codon type ( Figure 3E ) . As before , we measured expression levels between translation-competent and translation-deficient reporters ( due to premature stop codon , Mut ) ( Figure 3E ) . Due to the enrichment on a single codon in each mini-gene reporter , the Mini-gene reporters provide a way to analyze the regulatory properties at a single codon resolution . Consistent with our observations of endogenous mRNAs , we found that several amino acids possessed synonymous codons that differed in optimality identity ( Figure 3F ) . Specifically , mini-genes enriched in optimal codons displayed increased expression when compared to translation-deficient counterparts , whereas mini-genes enriched in non-optimal displayed either decreased expression , or no significant change when compared to respective translation-deficient counterparts ( Figure 3F ) . These observations further validate the differences in optimality between synonymous codons for leucine , cysteine and isoleucine ( Figure 3F and Figure 3—figure supplement 1B ) . As predicted , in the case of Histidine ( Figure 3A and Figure 3—figure supplement 1B ) , both mini-gene reporters presented the same non-optimality trend found for endogenous profiles ( Figure 3F ) ( with CAT being consistently more non-optimal than CAC , ( Figure 3A ) ) . These several results together suggest that codon identity affects mRNA stability in a translation-dependent manner . To explore how synonymous codons display different regulatory behaviors , we examined their respective tRNA levels , which have been proposed to be an important determinant of codon optimality ( Bazzini et al . , 2016; Presnyak et al . , 2015 ) . tRNA levels measured by two independent techniques in 293T cells , Hydro-tRNA-seq and TGIRT-seq ( Evans et al . , 2017; Gogakos et al . , 2017 ) , correlates with 293T CSCs ( Figure 3G ) , supporting the idea that tRNA levels affect mRNA stability . However , these correlations are not strong , and the two methods do not strongly correlate ( ( Figure 3G and Figure 3—figure supplement 1C ) , evidencing that measuring tRNA level is challenging . Beside the level of total tRNA , we investigate the relation between tRNA quality and codon optimality . Interestingly , the ratio of charged tRNA ( with amino acid ) to total tRNAs ( with and without amino acid ) in 293T cells also correlated with the CSC ( Figure 3H ) , suggesting that the ratio of charged tRNA might also affect mRNA stability . In particular , the four encoded nuclear tRNAs for Serine were the most uncharged tRNAs and Serine is one of the amino acids that appears to be non-optimal in human , zebrafish and Xenopus ( Figure 3A ) ( Bazzini et al . , 2016 ) . This suggests that particular amino acids may contribute to codon-mediated mRNA decay ( Bazzini et al . , 2016; Frumkin et al . , 2017 ) . Our results suggest that both tRNA level and quality contribute to the available ‘tRNA ready to go’ ( Rak et al . , 2018 ) , that might dictate the codon-mediated regulation of mRNA stability . Poly ( A ) -tail length and alternative polyadenylation influences the translation and stability of mRNAs ( Geisberg et al . , 2014; Lima et al . , 2017; Moqtaderi et al . , 2018; Subtelny et al . , 2014; Tian and Manley , 2013; Tian and Manley , 2017 ) . Consistent with this , in zebrafish and yeast endogenous and reporter mRNAs enriched in optimal codons tend to possess longer poly ( A ) -tails than mRNAs enriched in non-optimal codons ( Bazzini et al . , 2016; Mishima and Tomari , 2016; Radhakrishnan et al . , 2016; Webster et al . , 2018 ) . In the present study , two independent observations indicate that in humans , mRNAs enriched in optimal codons tend to have longer poly ( A ) -tails than genes enriched in non-optimal codons . First , we calculated polyadenylation status by comparing levels of poly ( A+ ) -containing mRNA against total RNA depleted of ribosomal RNA . Similar to zebrafish , genes enriched in optimal codons presented higher polyadenylation status ( poly ( A ) /total RNA ratio ) compared to those enriched in non-optimal codons in RPE cells ( Figure 4A ) . Second , we analyzed two independent datasets measuring the poly ( A ) -tail length in a transcriptome-wide manner , PAL-seq ( Subtelny et al . , 2014 ) and TAIL-seq ( Chang et al . , 2014 ) , in 293T and HeLa cells , respectively . In both datasets , mRNAs enriched in optimal codons displayed longer poly ( A ) -tails than mRNAs enriched in non-optimal codons ( Figure 4B ) . Together , these results indicate that in human cells poly ( A ) -tail length correlates with codon composition within mRNAs . Although poly ( A ) -tails length corelates with codon-optimality and stability ( Figure 4A , B ) ( Bazzini et al . , 2016; Mishima and Tomari , 2016; Radhakrishnan et al . , 2016; Webster et al . , 2018 ) , no causal relationship between them has been established . Therefore , we next set out to determine whether codon effects on stability are mediated through the modulation of poly ( A ) -tail length . Toward this end , we generated reporter genes that possess a histone tail ( Figure 4C ) , with unique 3’ end structure found on canonical non-polyadenylated histone genes ( Marzluff et al . , 2008 ) . The histone-tailed reporter mRNAs displayed precise 3’ end ( Figure 4C , D and Figure 4—figure supplement 1B ) . Despite invariable length and the absence of a poly ( A ) -tail , histone-tailed reporter mRNAs enriched in optimal genes displayed higher RNA levels ( Figure 4D ) and protein intensity ( Figure 4E and Figure 4—figure supplement 1B ) compared to counterparts enriched in non-optimal codons . Further , we wondered whether this dissociation between poly ( A ) -tail and codon optimality is specific to human or a more general phenomenon , so we injected the reporters into zebrafish embryos . Injected mRNAs into zebrafish embryos , possessing either a cytoplasmic poly ( A ) signal or a histone tail displayed similar trends of fluorescence intensity change based on codon composition ( Figure 4C , F and Figure 4—figure supplement 1C , D ) . These results demonstrate that modulation in poly ( A ) -tail length is not required for codon-mediated changes in mRNA stability in human and zebrafish embryos . This implies that poly ( A ) -tail shortening in non-optimal mRNAs likely occurs in parallel to or as a consequence of mRNA destabilization rather than as the primary cause of codon-mediated destabilization . Codon-mediated regulation of mRNA stability depends on translation in human cells ( Figure 2F , G , H and Figure 2—figure supplement 1E , F , G ) as well as in other model organisms ( Bazzini et al . , 2016; Mishima and Tomari , 2016; Presnyak et al . , 2015 ) . Our data and published studies indicate that translational elongation , which may be influenced by tRNA abundance , is correlated with stability kinetics . Based on these data , we hypothesized that the ribosome is a key regulatory molecular factor , therefore , we proposed that increasing the number of ribosomes in a mRNA population would enhance the codon-meditated effects on gene expression . To explore this hypothesis , we generated paired reporters ( 1nt frame-shift ) possessing a battery of different 5’ and 3’ UTR sequences from mRNAs with different levels of translation based on zebrafish ribosome profiling ( Figure 5A ) ( Bazzini et al . , 2014 ) . The ability of these sequences to affect protein production was confirmed in human cells ( Figure 5B and Figure 5—figure supplement 1A ) . We also generated reporters possessing upstream open-reading frame ( uORF ) elements within the 5’UTR , known to repress translation of the canonical reading frame ( Figure 5A and Figure 5—figure supplement 1C ) ( Johnstone et al . , 2016 ) . In all cases , we found that the impact of codon composition on mRNA stability was enhanced in reporters that displayed a higher baseline rate of translation . Specifically , optimal and non-optimal paired reporters with high translation rates displayed greater differences at both RNA and protein levels when compared to similarly paired reporters with lower translation efficiencies ( Figure 5B , C- and Figure 5—figure supplement 1A , B , C ) . These results indicate that the level of translation , and therefore , the number of ribosomes loaded onto an mRNA , influences codon-mediated effects on gene expression . Since the number of ribosomes on mRNA can modulate codon-mediated effects on gene expression , we hypothesized that trans-regulatory elements and physiological conditions where mRNA translation is globally affected , may also impact the codon-mediated effects on gene expression . Viruses are known to reduce endogenous mRNA translation ( Walsh et al . , 2013 ) . For example , Herpes simplex virus 1 ( HSV-1 ) infection reduces the translation efficiency of endogenous mRNAs globally ( Figure 5—figure supplement 1D ) ( Rutkowski et al . , 2015 ) . Therefore , we compared homeostatic levels of endogenous mRNAs before and after HSV-1 infection . We observed that genes enriched in optimal codons presented higher mRNA level than genes enriched in non-optimal codons at homeostasis in all cell lines analyzed ( Figure 5D and Figure 5—figure supplement 1E ) . Interestingly , the differences in mRNA levels between genes enriched in optimal versus non-optimal codons were dampened after infection ( Figure 5D ) . In agreement with our reporter results ( Figure 5C ) , when translation was reduced during virus infection , genes enriched in optimal codons were downregulated and mRNA enriched in non-optimal codons upregulated compared to control set of genes ( Figure 5—figure supplement 1F ) . These results suggest that the impact of codon optimality on gene expression is conditional upon other cis- ( e . g . UTRs ) and trans- ( e . g . virus ) regulatory mechanisms within the cell that modulate translation level . The main function of the ribosome is to translate the mRNA nucleotide sequences into the amino acid sequence . However , translation is also important for mRNA quality control , targeting defective mRNAs for degradation ( Shoemaker and Green , 2012 ) . Translation has also been shown to strongly affect mRNA stability of non-defective transcripts depending on the codon composition in model organisms ( Bazzini et al . , 2016; Boël et al . , 2016; Burow et al . , 2018; de Freitas Nascimento et al . , 2018; Harigaya and Parker , 2016; Jeacock et al . , 2018; Mishima and Tomari , 2016; Presnyak et al . , 2015; Radhakrishnan et al . , 2016 ) . Here , we demonstrate that , in human cells ( also suggested in Hanson and Coller , 2018 ) , translation also strongly affects mRNA stability in a codon-dependent manner , thus dictating mRNA and protein levels at homeostasis ( Figure 6 ) . These observations highlight the wealth of regulatory information residing within the coding sequence , the largest fraction of the human transcriptome . Further , this study provides insight on the regulatory role of core components , such as tRNAs and ribosomes ( Figure 6 ) . These have long been under appreciated in regulation despite their relatively high levels of expression , their abundant interactions with mRNAs , and evolutionary conservation . Future studies of this relatively unexplored mechanism of post-transcriptional regulation may be relevant to human diseases . Here , we used diverse approaches to measure mRNA decay of both exogenous and endogenous mRNA in different human cell lines . We observed that particular codons were enriched within stable mRNAs and other codons were enriched within unstable mRNAs , optimal and non-optimal codons , respectively ( Figure 1 ) . In particular , we developed a method ( ORFome ) to distinguish mRNA decay regulation between regulation coming from codon composition versus other regulatory information ( UTRs ) . In addition to highlighting codon effects , this method proved to be a viable strategy to identify other cis regulatory features impacting stability . For example , by comparing endogenous decay profiles with ORFome profiles , we found that predicted m6A targets displayed considerable different destabilization ( Figure 1F ) . We demonstrated that the regulatory information was based on codon identity rather than other sequence information , by using reporter gene pairs differing in the codon composition due to a single nucleotide insertion causing a framed shift ( Figure 2 ) . While most synonymous codons affect mRNA stability differently , a few amino acids ( e . g . Histidine , Serine ) ( Figure 3 ) , appear to have an effect at the amino acid level by either impacting the rate of peptidyl transfer or the amino-acyl tRNA charging step ( potentially , Serine ) ( Figure 6 ) . Comparing to human , in zebrafish and Xenopus embryos ( Bazzini et al . , 2016 ) , few more amino acids could be proposed to be optimal or non-optimal , suggesting potential multilayer regulatory differences between species or cell stage ( embryogenesis ) . In human cells , a variety of data indicate that codons rather than the amino acid they encode—affects mRNA stability in a translation-dependent manner . First , most amino acids ( e . g Leucine , Isoleucine , Cysteine , Glutamic acid , Arginine , Asparagine , Phenylalanine , Threonine , Tyrosine , Valine ) display both optimal and non-optimal codons in most decay profiles ( Figure 3 ) . Second , two set of reporters having synonymous mutations confirmed that synonymous codon affects mRNA stability differently , especially the mini-genes we designed have high resolution at single codon level ( Figure 3 ) . Third , we observed , that non-optimal codons tend to be decoded by lowly expressed tRNAs , and optimal codons by highly expressed tRNAs ( Figure 3 ) . This suggests that tRNA supply/demand might be an important determinant of codon-mediated mRNA stability ( Figure 6 ) ( Bazzini et al . , 2016; Despic and Neugebauer , 2018; Frumkin et al . , 2018; Richter and Coller , 2015 ) . Furthermore , we observed that non-optimal codons also displayed a low ratio of charged to total tRNA ( Figure 3 ) . Interestingly , in the case of serine , defined as non-optimal , all nuclear-encoded tRNAs were the most poorly charged , suggesting that charging tRNA may be another crucial point impacting codon-mediated gene regulation ( Figure 6 ) . Therefore , the ‘tRNA ready to go’ level ( Rak et al . , 2018 ) may be modulating translation elongation rates and serving as a determinant of codon optimality ( Figure 6 ) . In addition of aminoacyl charging , tRNA quality is also related to tRNA modifications , therefore , it will be worthwhile to examine the effects of tRNA modifications on mRNA stability ( Chou et al . , 2017; Duechler et al . , 2016 ) . Our data support the idea that tRNAs are master gene regulators ( Gingold et al . , 2014; Goodarzi et al . , 2016 ) and highlight the need to fully understand how tRNA expression and processing are regulated in different cells and different conditions , including human diseases ( Ishimura et al . , 2014; Kirchner and Ignatova , 2015; Torrent et al . , 2018 ) . Therefore , genes involve in tRNA expression regulation and processing , would be strong candidates to explain a part of the codon optimality molecular mechanism . With respect to the function of the ribosome in mRNA quality control , it was proposed that codon optimality might be a Slowness-mediated decay ( SMD ) ( Rak et al . , 2018 ) because it is similar to a slow-go-decay ( SGD ) pathway where the elongation speed of the ribosome might be the trigger for regulation . This is distinct from no-go-decay ( NGD ) , where stalling ribosomes trigger mRNA degradation ( Simms et al . , 2017 ) . Therefore , codon optimality may be related to mRNA quality control mechanisms for: recognition , labeling and cleaning of the mRNA . However , the molecular mechanisms of how translation affects mRNA stability in a codon-dependent manner remains poorly characterized . While Dhh1p in yeast senses ribosome elongation speed ( Radhakrishnan et al . , 2016 ) and affects mRNA stability in a codon-dependent manner , the role of the vertebrate ortholog , DDX6 , with respect of codon optimality is not clear . In human cells , there is a clear correlation between mRNA enriched in non-optimal codons and shortening of the poly ( A ) -tail ( Figure 4 ) similar to that observed in yeast and zebrafish ( Bazzini et al . , 2016; Mishima and Tomari , 2016; Radhakrishnan et al . , 2016; Webster et al . , 2018 ) . However , reporter genes possessing a histone tail and therefore lacking a poly ( A ) -tail were still affected by codon composition in both human and zebrafish embryos ( Figure 4 ) . Therefore , these results indicate that the poly ( A ) -tail is not required and likely , the shortening of the poly ( A ) -tail in genes enriched in non-optimal codons is an indirect consequence of decreased stability rather than a required step in the codon optimality mechanism ( Figure 6 ) . Future work should explore the molecular mechanism of how the translation affects mRNA destabilization . In addition to altered dynamics of mRNA stability due to specific misregulation of tRNA levels ( Goodarzi et al . , 2016; Kirchner and Ignatova , 2015; Torrent et al . , 2018 ) , we showed that codon-mediated mRNA stability can be affected by altering the translational level through either global changes in translation efficiency ( during viral infection ) or specific 5’ and 3’ UTRs sequences ( Figure 5 ) . This suggests an alternative way for the cell to differentially regulate gene expression ( Figure 6 ) . Therefore , it will be important to integrate the evolutionary and functional relationship between translation initiation driven by the UTRs and translation elongation dictated by the coding sequence with respect to codon-mediated gene regulation ( Chu et al . , 2014 ) as well as the effects of codon composition and translation speed on protein folding ( Yu et al . , 2015 ) ( Figure 6 ) . The observation that codon optimality effects can be modulated by altering baseline translation ( Figure 5 ) , leads us to speculate that , global reductions in translation efficiency might explain the predicted attenuated codon optimality effect in neural-specific mRNA decay in Drosophila ( Burow et al . , 2018 ) . Therefore , molecular factors such microRNAs or RNA-binding protein regulating mRNA translation could potentially modulate the impact of codon-mediated regulation ( Figure 6 ) . Hence , it is worth exploring how codon-mediated regulation is impacted in disease states where translation rates are globally affected , such as in neurodegenerative disease ( Gao et al . , 2017 ) , ribosomopathies ( Danilova and Gazda , 2015 ) , virus infection ( Walsh et al . , 2013 ) and other cellular stresses ( Gonskikh and Polacek , 2017 ) . In addition to the cell-specific translational competency ( ribosome specificity , translation level ) , the ready to go tRNA repertoire ( level , charged ) and intrinsic mRNA properties ( mRNA localization , codons distribution along the coding region ) need to be integrated to understand the differential gene expression during development , cell reprogramming , as well as identify underlying causes of misregulated genes in human diseases . 293T , HeLa and RPE cells were cultured with DMEM media , supplied with 10% FBS . K562 cells were cultured with IMDM media with 10% FBS . The cells were ordered from tissue culture facility from the Stowers Institute , at relative low passage , lower than passage 15 . 293T , HeLa and RPE cells were transfected with lipofectamine 3000 based on manufacture’s instruction in 24-well plate . K562 cell was transfected with Trans-X2 ( from Mirus company ) in 96-well plate . 24 hr post transfection , cells are collected for cytometry or RNA extract . The florescent reporter intensity of the cells was quantified in ZE5 equipment using GFP ( 488/510 ) and mCherry ( 587/610 ) , cells were suspended in DMEM with 10%FBS . Cells were not fixed . Cytometry data was analyzed with FlowJo , median intensity of the cells was used to represent fluorescent intensity . Cells were sub-cultured in 24-well plate overnight , so the cells were at around 80% coverage before treatment . Actinomycin D was added into the well , with final concentration of ( 5 μg/ml ) , in 0 . 1% DMSO . Cycloheximide was added at 2 μg/ml , in H2O . Cells were directly collected with Trizol , at desired time-point for RNA extract . RNA was extracted using Trizol reagent and quantified with Qubit RNA broad range kit for RNA-seq or RT and qPCR . For reverse transcription , superscript IV kit was used from Invitrogen . qPCR was done using Perfect SYBR Green FastMix Reaction Mixes , QuantaBio . RNA gel running was following the protocol from Lonza Bioscience . Shortly . Total RNA was resuspended with 1xMOPS buffer , formaldehyde and deionized formamide . heat at 70°C for 10 min , chill on ice , and add loading buffer before running . Then RNA was migrated using 1X MOPS at 100V for 3 hr and transferred with 10X SSC overnight . Oligonucleotide DNA probes with 3’biotin were ordered from IDT with HPLC purification . Probing and detection were done following the protocol of North2South Chemiluminescent Hybridization and Detection Kit from ThermoFisher , using Streptavidin-HRP . The ORFome library was bought from Sigma-Aldrich company named:TRC3 ORF Puromycin Arrayed Glycerol Library , the commercial version already contains specific barcode in each mRNA . The 96 well format ORFome library was pool together using an automatic robot . First in 20 bins were created based in ORF size , then those 20 were pooled in five groups , grown , DNA maxi-preparation done . After quantification , the five groups were mixed into a single cocktail . The ORFome cocktail was transfected with lentivirus vector: Pax2 and VSV-G into 293 T cells for virus packing . Media containing virus were filtered and ultracentrifuged to enrich virus for infection . 293 T cells and k562 cells were infected and selected with puromycin for a week , at the concentration of 293T ( 2 μg/ml ) , K562 ( 0 . 75 μg/ml ) . Then , cells were treated with Actinomycin D ( 5 μg/ml ) at six-well plate and samples were collected in duplicate every hour 0–6 hr for RNA-seq . Specific oligos were used to target the surrounding barcode region of ORFome to generate library and sequenced , we tested different PCR cycles to amply the library and finally used 15 cycles . The ORFome reads were trimmed using cutadapt 1 . 16; the resulting trimmed reads were mapped to the ORF-ome barcodes using salmon 0 . 9 . 1 . To calculate mRNA stability , transcripts were selected with the Bioconductor zFPKM package , a cut-off of zFPKM > −3 was applied . The decay rate was estimated using a linear model according to the integrated rate law for a first-order reaction . SLAMseq Kinetics Kit - Catabolic Kinetics Module ) from Lexogen was performed . Basically , k562 cells were feed with 100 uM 4sU for 24 hr , fresh media containing s4U was changed every 3 hr . For chasing , old media are removed and cells were washed with PBS three times before adding media without s4U , but 10 mM UTP instead , samples were collected at 0 , 2 , 4 , 6 hr in triplicates for RNA extract and IAA treatment . All the operations were done in red light source to protect 4sU from crosslink . QuantSeq 3’mRNA-seq library were generated and sequenced , by PCR 12 cycles for library amplification based on the protocol . The SLAM-seq data was processed with the slam dunk pipeline ( https://github . com/t-neumann/slamdunk 0 . 3 . 3 . ) The reads were aligned to the human genome GRCh38 . The half-life was estimated according to the SLAM-seq paper; for downstream analyses a cut-off of p-value<0 . 05 was applied to the decay rate estimates . Seven set of evidences indicated that we have successfully performed SLAM-seq and Quant-seq ( Herzog et al . , 2017 ) . ( 1 ) As expected , the majority of sequenced reads come from the 3’UTR . ( 2 ) From all possible nucleotide transitions across the transcriptome , T-to-C changes were only significant in cells treated with s4U ( Figure 1—figure supplement 1D ) . ( 3 ) The rate of T-to-C transitions decrease with time after washing out the s4U from the medium , consistent with labeled ( ‘old’ ) mRNA being replaced with new mRNA ( unlabeled ) ( Figure 1—figure supplement 1E ) . ( 4 ) We observed a strong correlation between mRNA levels calculated with regular RNA-seq ( CPM ) and Quant-seq ( length-independent ) as well as between technical replicates ( Figure 1—figure supplement 1F ) . ( 5 ) The s4U incorporation does not affect global mRNA abundance when compared to untreated embryos ( Figure 1—figure supplement 1G ) . ( 6 ) The mRNA half-life we get from SLAM-seq highly correlates with the published mRNA half life from TimeLapse-seq ( Figure 1—figure supplement 1H ) . ( 7 ) As shown in Figure 1G , m6A mRNA targets presented lowered stability than a control mRNA group . The plasmid constructs containing optimal and non-optimal ORFs with poly A tail and Histone tail were linearized with Not1HF or Kpn1HF respectively , similarly GFP containing control plasmid was also linearized with Not1HF . Linearized plasmids were then in vitro transcribed using SP6 mMessage mMachine kit ( Life technology ) . The mRNA with polyA tail ( 150 ng/μl ) , GFP ( 100 ng/μl ) and mRNA with histone tail ( 200 ng/μl ) plus GFP ( 100 ng/μl ) were microinjected separately for each construct in zebrafish embryos at one-cell stage . Injected embryos were imaged after 24 hr using ‘Zeiss Lumar . V12 steREO’ microscope with same conditions for all the injected embryos . Raw images were processed using Fiji software separately for poly ( A ) -tail and histone-tail constructs . The end of histone mRNA is detected by 3’RACE . Total RNA is ligated with the adaptor:/5rApp/AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC/3ddC/by T4 Rnl2tr K227Q . cDNA was produced with following oligo: GTGACTGGAGTTCAGACGTGTGCTCTTCCG , And PCR with specific forward oligo and the RT oligo . PCR product was inserted into TOPO vector , and miniprep for sanger sequencing . All the vector sequences , primers and probes can be accessed from the Stowers Original Data Repository at http://www . stowers . org/research/publications/libpb-1373 .	Proteins are made by joining together building blocks called amino acids into strings . The proteins are ‘translated’ from genetic sequences called mRNA molecules . These sequences can be thought of as series of ‘letters’ , which are read in groups of three known as codons . Molecules called tRNAs recognize the codons and add the matching amino acids to the end of the protein . Each tRNA can recognize one or several codons , and the levels of different tRNAs inside the cell vary . There are 61 codons that code for amino acids , but only 20 amino acids . This means that some codons produce the same amino acid . Despite this , there is evidence to suggest that not all of the codons that produce the same amino acid are exactly equivalent . In bacteria , yeast and zebrafish , some codons seem to make the mRNA molecule more stable , and others make it less stable . This might help the cell to control how many proteins it makes . It was not clear whether the same is true for humans . To find out , Wu et al . used three separate methods to examine mRNA stability in four types of human cell . Overall , the results revealed that some codons help to stabilize the mRNA , while others make the mRNA molecule break down faster . The effect seems to depend on the supply of tRNAs that have a charged amino acid; mRNA molecules were more likely to self-destruct in cells that contained codons with low levels of the tRNA molecules . Wu et al . also found that conditions in the cell can alter how strongly the codons affect mRNA stability . For example , a cell that has been infected by a virus reduces translation . Under these conditions , the identity of the codons in the mRNA has less effect on the stability of the mRNA molecule . Changes to protein production happen in many diseases . Understanding what controls these changes could help to reveal more about our fundamental biology , and what happens when it goes wrong .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "chromosomes", "and", "gene", "expression" ]	2019	Translation affects mRNA stability in a codon-dependent manner in human cells
Heritability of human lifespan is 23–33% as evident from twin studies . Genome-wide association studies explored this question by linking particular alleles to lifespan traits . However , genetic variants identified so far can explain only a small fraction of lifespan heritability in humans . Here , we report that the burden of rarest protein-truncating variants ( PTVs ) in two large cohorts is negatively associated with human healthspan and lifespan , accounting for 0 . 4 and 1 . 3 years of their variability , respectively . In addition , longer-living individuals possess both fewer rarest PTVs and less damaging PTVs . We further estimated that somatic accumulation of PTVs accounts for only a small fraction of mortality and morbidity acceleration and hence is unlikely to be causal in aging . We conclude that rare damaging mutations , both inherited and accumulated throughout life , contribute to the aging process , and that burden of ultra-rare variants in combination with common alleles better explain apparent heritability of human lifespan . Genome-wide association studies ( GWAS ) of human lifespan , including studies examining extreme lifespan , parental survival , and healthspan , produced a number of gene variants potentially associated with human aging . For example , GWAS on centenarians consistently demonstrate loci near APOE gene to be associated with extreme longevity , and loci near FOXO3A , HLA-DQA1 and SH2B3 genes to have population-specific associations ( Melzer et al . , 2020 ) . However , even in developed countries , centenarians represent less than 0 . 1% of the population , and the genetic determinants responsible for the survival of general population remain poorly understood . Release of massive genotype and phenotype data by UK Biobank ( UKB ) ( Bycroft et al . , 2018 ) allowed to investigate the relationship between genetics and several longevity proxies , such as parental lifespan ( Pilling et al . , 2017 ) and healthspan , within the general population ( Zenin et al . , 2019 ) . They confirmed most of the variants from centenarian studies and identified additional variants . However , the combined contribution of common variants could explain only a small fraction of the lifespan variation as most of the individuals lack any of the alleles previously associated with lifespan . We hypothesized that some of the remaining heritability could be explained by the combined burden of rare damaging gene variants as those are present in every genome . Until very recently , only common variants could be probed in genetic studies due to sample size limitations . However , large datasets such as gnomAD and UKB now allow assessing the effects of variants with minor allele frequency ( MAF ) lower than 0 . 1% ( Lek et al . , 2016 ) . These ultra-rare variants , most notably protein-truncating variants ( PTVs ) , are known to be enriched for damaging alleles . They tend to have larger effect sizes and dramatically change gene expression and function . An inverse relationship between variant's minor allele frequency ( MAF ) and effect size was recently demonstrated for type II diabetes , an archetypal age-related disease ( Mahajan et al . , 2018 ) . Multi-tissue gene expression outliers were enriched with rare variants in the GTEx dataset ( Li et al . , 2017 ) . Notably , PTVs represent a significant fraction of those variants . Most of the underexpressed outliers harbor rare PTVs , which are more likely to trigger nonsense-mediated mRNA decay ( NMD ) than common variants ( Rivas et al . , 2015 ) . Additionally , ExAC consortium demonstrated that the nonsense variants with a high Combined Annotation Dependent Depletion score ( Rentzsch et al . , 2019 ) , a widely used predictor of deleteriousness of single-nucleotide variants , were enriched in singletons ( Lek et al . , 2016 ) . Although missense and non-coding variants may also be damaging , PTVs are substantially enriched for deleterious alleles . They also alter gene expression more dramatically than missense and untranslated region ( UTR ) variants ( GTEx Consortium et al . , 2017 ) . Ultra-rare PTVs are usually eliminated by purifying selection , but the small effective population size of humans means that they are present in all human genomes . Increased rare PTV burden was associated with complex diseases , such as schizophrenia , epilepsy and autism ( Leu et al . , 2015; Singh et al . , 2017; Ji et al . , 2016 ) , whereas individual genetic variants exhibited small effects . The burden of rare PTVs in genes intolerant to such variants was tested for association across ExAC traits , wherein these variants were defined as PI-PTVs - protein-truncating variants in proteins intolerant to protein-truncating variants ( defined as having pLI score of 0 . 9 or above ) . This analysis revealed a negative association with years of schooling ( academic attainment ) and a positive association with intellectual disability , autism , schizophrenia and bipolar disorder ( Ganna et al . , 2018 ) . Notably , the age at enrollment was also negatively correlated with the burden of PI-PTVs , suggesting a possible association with lifespan . Rare variants emerged as a novel genetic component with profound effect on complex traits and fitness . In this study , we focused specifically on the association of germline PTV burden with lifespan and disease-free survival , and estimated the effect of somatic PTV accumulation on mortality and morbidity acceleration . We characterized the effects of inherited mutations burden on human traits associated with lifespan . For the UK Brain Bank Network ( UKBBN ) , we ran a survival analysis against the age at death ( Keogh et al . , 2017 ) . For UKB subjects , we tested the effects of mutations on lifespan and healthspan . For these analyses , we define lifespan as survival within a follow-up period of 11 years , and healthspan as the disease-free period before one of the following conditions is diagnosed for the first time ( Table 1 ) : cancer , diabetes , myocardial infarction , congestive heart failure , chronic obstructive pulmonary disease , stroke , dementia , and death ( Zenin et al . , 2019 ) . In addition , following the approach of Joshi et al . , 2016 , we studied the effect of mutation burden on parental survival ( separately for the age at death for mothers and fathers ) , a useful lifespan proxy in genetic studies . We selected a cohort of 40 , 368 individuals from UKB with sequenced exomes who self-reported ’White British’ and were of close genetic ancestry based on a principal component analysis of their genotypes ( Bycroft et al . , 2018 ) . Of those , 21 , 742 ( 54% ) were males with mean age of 58 . 1 years ( SD = 7 . 9 , age range 40 . 2 − 70 . 6 ) and 18 , 626 ( 46% ) were females with mean age of 57 years ( SD = 7 . 8 , age range 40 . 1 − 70 . 4 ) at the time of assessment . In the UKB cohort , 1 , 122 subjects died during the follow-up period of 11 years ( 2005 − 2016 ) , mostly of cancer ( Table 2 ) . The UKBBN cohort included 1 , 105 deceased subjects of European origin after we excluded cases of suicides , accidents , and cases of death with no abnormalities detected . Of those , 489 ( 44% ) were females with mean age of 71 . 2 years ( SD = 18 , age range 16 − 103 years ) and 616 ( 56% ) were males with mean age of 67 . 7 years ( SD = 17 , age range 17 − 105 years ) . The cause of death was reported for 359 out of 1 , 105 individuals used for downstream analysis . Most participants in this study were diagnosed with neurodegenerative diseases , for example Alzheimer’s , Parkinson’s , and Pick’s diseases ( Keogh et al . , 2017 ) . We used here the set of variants identified through whole-exome sequencing ( WES ) as part of the UKB and UKBBN projects ( Keogh et al . , 2017 ) . As in Ganna et al . ( 2018 ) , we limited our analysis to PTVs , defined as splice donors/acceptors , stop codon gains , and frameshifts , observed in canonical transcripts . To address the relationship between the PTVs allele frequency and their effects on lifespan , we binned the PTVs according to their minor allele frequency: ( 1 ) M⁢A⁢F<10-4; ( 2 ) 10-4<M⁢A⁢F<10-3; ( 3 ) 10-3<M⁢A⁢F<0 . 01; ( 4 ) 0 . 01<M⁢A⁢F<0 . 2 . For each allele frequency bin , we computed the PTV burden as the total number of PTVs per individual’s exome ( Figure 1—figure supplement 1 for PTV burden distribution in the MAF bins ) . We examined the association of PTV burden against lifespan traits ( i . e . survival in UKB and UKBBN , the chronic disease free survival ( healthspan ) , mother’s and father’s age at death in UKB ) using variations of Cox proportional hazards ( PH ) models . We used sex , assessment center , and genetic principal components as covariates to account for the effects related to the population heterogeneity . As we shall see below in the ‘Somatic mutations and mortality acceleration’ section , predicted effects of PTVs accumulation with age provides negligible contribution to mortality acceleration . Therefore , time-varying effects can be neglected and the age-independent PTV load contribution to the age-dependent mortality in UKB can be found by means of the standard Cox proportional hazards model ( hereinafter referred to as the ‘mortality risk’ or survival model ) using the follow-up survival information and the age of the first assessment as a covariate . The survival model involving the follow-up time and the explicit age as the regression parameter is a maximum likelihood estimator of probability of short-term survival for the individuals healthy enough to survive till the age of the first assessment . In this form , the survival model does not depend on the life history and hence should be robust with regard to enrollment bias effects . In Zenin et al . ( 2019 ) , we observed that the incidence of major chronic diseases ( such as Congestive Heart Failure ( CHF ) , Myocardial Infarction ( MI ) , Chronic Obstructive Pulmonary Disease ( COPD ) , stroke , dementia , diabetes , cancer , and death ) in UKB increases exponentially with age in accordance to Gompertz law . Therefore , the end of healthspan can also be naturally modeled with the help of PH models including risk estimates exponential in age variables . As much as 28% of UKB participants were diagnosed with at least one of the selected diseases by the time of the first assessment . Therefore , the chronic disease-free survival , also known as healthspan , cannot be studied with the help of the standard Cox PH model . Instead , in Zenin et al . ( 2019 ) we noted the very limited number of death events during the follow-up time in UKB and hence assumed that the incidence of diseases do not considerably affect enrollment . Accordingly , we suggested and employed here the maximum likelihood formulation of PH model ( hereinafter referred to as the ‘morbidity risk’ , simply ‘morbidity’ , or healthspan model ) involving the age at the first incidence chronic disease or the end of the follow-up time . The mortality and morbidity risk models returned Cox regression parameters that were consistent with well-established mortality and morbidity acceleration patterns . For example , the survival ( the remaining lifespan ) model in the UKB produced the regression coefficient Γ=0 . 087 ( 95 % CI 0 . 078–0 . 097 ) per year for the age of first assessment , very close to the mortality and morbidity acceleration rate of approximately 0 . 09 per year in UKB cohort ( Zenin et al . , 2019 ) . The characteristic time scale is t1/2=ln⁡ ( 2 ) /Γ=7 . 5 years and hence is nothing else but the mortality rate doubling time from Gompertz mortality law . The Cox regression coefficients for males were 0 . 47 ( 95% CI 0 . 35–0 . 59 ) in UKB and 0 . 26 ( 95% CI 0 . 13–0 . 38 ) in UKBBN . Under constant mortality acceleration , this would correspond to approximately 3 − 5 years of difference in life expectancy . Women in the UK ( the population relevant to this study ) live longer than men , although the gap between the sexes has decreased over time down to 3 . 7 years ( Sanders , 2017 ) . We found that , in both datasets , burden of ultra-rare ( M⁢A⁢F<0 . 0001 ) PTVs was negatively and significantly correlated with lifespan , and with healthspan in UKB ( Figure 1 ) . The proportional hazards effect estimations ( sign and order of magnitude ) were consistent , β=0 . 046 and 0 . 014 per mutation , for lifespan and healthspan in UKB , respectively . To estimate the effect of ultra-rare PTVs on lifespan and healthspan in years , we equate the contributions to log-hazards from the Gompertz term , Γ=0 . 093 per year , and the burden term , β per mutation: each additional ultra-rare PTV accounts for β/Γ years of reduction , that is roughly 0 . 5 and 0 . 16 years per mutation for lifespan and healthspan , respectively . Moreover , the Cox regression coefficients were very similar in UKBBN and UKB datasets , indicating consistency of the effect across populations despite differences in population structure and morbidity statistics ( Table 2 ) , tissue source ( blood in UKB and brain in UKBBN ) , sequencing methods and variant calling pipelines ( Keogh et al . , 2017 ) . We also observed a smaller but still significant effect of the ultra-rare PTV burden on mothers’ but not on fathers’ age at death in UKB . The effect size on mother’s age at death was smaller and less significant than that on the subject’s healthspan and lifespan . The difference between male and female longevity is one of the most conserved observations in human biology . In light of this , we ran analysis separately for men and women and found sex-specific effects for lifespan phenotypes ( Table 3 ) . Association with age at death was similar between the sexes . However , the associations with healthspan and mother’s age at death were almost entirely driven by women . On average , we identified 6 ( S⁢D=2 . 6 ) ultra-rare PTVs per genome ( Figure 1—figure supplement 1 , upper left corner ) . The variability of the burden of S⁢D=2 . 6 transforms into the variability in life- and healthspan reduction of 1 . 3 and 0 . 4 years , respectively . To visualize the effects of such PTVs on survival , we split deceased UKB subjects into five nearly equal groups corresponding to increasing PTV burden . The subjects in the first group had 0–3 ultra-rare PTVs per genome , in the second - 4 or 5 PTVs , in the third - 6 or 7 PTVs , in the forth - 8 or 9 PTVs and in the fifth - 10 or more PTVs ( Figure 2 , inset ) . Mean ages within the groups were 57 . 7 , 57 . 5 , 57 . 5 , 57 . 4 , and 57 . 4 years , respectively , with no difference in age distribution across the groups ( Kolmogorov-Smirnov test on two samples p-value > 1% ) . The Kaplan-Mayer ( KM ) survival curves for UKB individuals who harbor the lowest and the highest number of ultra-rare PTVs are shown in Figure 2 as a function of the follow-up time . The separation of the curves further illustrated elevated mortality of the subjects with high PTV burden , with the most significant difference between cohorts #1 and #5 ( log-rank test p=7 . 1×10-5 ) . Due to Gompertz mortality acceleration , most of the death events involve the oldest individuals . Accordingly , the KM analysis here is naturally limited to a relatively narrow age group representing those close to the maximum age in the UKB population . Having established the association of PTVs number with lifespan , we explored other types of genetic variants selected for incidence frequency and category: 3-prime and 5-prime UTR region variants , transcription factor ( TF ) binding sites , and structural interaction variants ( Table 4 ) . Among all tested PTV types , the most significant associations with lifespan and healthspan were observed for the ultra-rare ( M⁢A⁢F<0 . 0001 ) stop gain , splice donor , and frameshift variants ( Figure 3 ) . However , only stop gains were associated with mother’s age at death , and none of the categories were associated with father’s age at death . As a negative control , we also included the effects of neutral variants - synonymous variants , which showed no significant associations with lifespan phenotypes . Ultra-rare PTVs affect 89% of the sequenced genes in the UKB dataset . No ultra-rare PTVs were observed in the remaining 11% , which we refer as genes intolerant to rare PTVs ( iPTV ) . We compared these genes with those harboring at least one PTV ( n=16 , 495 ) within the same 4 MAF bins tested for the association with lifespan . iPTV genes , on average , were expressed in a higher number of tissues ( Figure 4a ) and had higher indispensability scores ( a metric to measure gene essentiality introduced by Khurana et al . , 2013; Figure 4b ) . As expected , iPTV genes in the UKB cohort are strongly enriched in genes intolerant to PTVs measured by pLI scores ( Figure 4—figure supplement 1b ) , confirming that genes intolerant to PTVs largely overlap between UKB and ExAC datasets . Accordingly , genes that harbored frequent PTVs had tissue-specific expressions and had lower indispensability scores , thus were less essential , in agreement with previously published results for ExAC cohort ( Lek et al . , 2016 ) . Ultra-rare stop gains were more likely to trigger nonsense-mediated mRNA decay ( NMD ) ( Figure 4c ) as predicted by snpEff by 50 base-pair rule ( Maquat , 2004 ) which was also demonstrated for the rare variants in GTEx dataset ( Li et al . , 2017 ) . We further hypothesized that subjects with the same number of ultra-rare PTVs may have different lifespan due to difference in the damaging effect of their PTVs . Thus , subjects dying earlier would harbor more deleterious alleles than those dying later . To test this idea , we compared characteristics of genes disrupted by PTVs in subjects with the same PTV number ( 5 PTVs per exome , n= 171 ) but different lifespan ( Figure 5a ) . Our analysis confirmed the idea that subjects dying younger harbored more damaging PTVs . Those variants affect more broadly expressed genes , based on GTEx gene expression data ( Figure 5c ) , and cause gene loss-of-function more frequently ( Figure 5e ) . PTVs in shorter-lived subjects also resided in genes less likely to maintain a wild-type phenotype when a single copy of the gene is inactivated , as evident by genome-wide haploinsufficiency score GHIS from Steinberg et al . ( 2015 ) ; Figure 5b . Also , these PTVs affected genes with stronger constraints against PTVs , based on the observed/expected LoF ( oe , gnomAD v2 . 1 ) scores , suggesting that these genes harbored fewer PTVs than expected in the general population . Mean values for constraints of the genes disrupted by PTVs showed association across lifespan tested by Cox PH model ( Figure 5a , inset ) . Percent of tissues expressing these genes , oe scores and loss-of-function occurrence were all significantly associated with lifespan of subjects with 5 PTVs ( Figure 5a , inset ) . We further tested whether some of the genes were more frequently affected by ultra-rare PTVs in UKB individuals depending on their lifespan and healthspan . For that , we 1 ) split the list of UKB subjects ordered by lifespan or healthspan into two groups of same size , 2 ) summed up a number of unique cases of the gene harboring ultra-rare PTV for each group , and 3 ) tested whether those numbers are biased towards one of the groups by Fisher’s exact test . Interestingly , none of the genes reached statistical significance under FDR p-value cut-off of 0 . 05 ( Supplementary file 1 and Supplementary file 2 ) . Previous efforts to characterize rare PTVs in large datasets demonstrated that some genes are more prone to be affected by rare PTVs than others ( Lek et al . , 2016 ) . However , it was unclear to what extent that applies to the UKB dataset . We first assessed the genome-wide distribution of ultra-rare PTVs and found it to be similar to the rest of the variants ( Figure 4—figure supplement 2 ) . To address the same question at the level of individual genes , we estimated the number of ultra-rare stop gains and frameshifts per gene in the UKB cohort , as well as the number of all synonymous variants per gene as a neutral read-out to normalize for coverage . By comparing the number of PTVs and synonymous variants per gene , to the overall number of those variants , we found 110 genes that were prone to ultra-rare PTVs , as well as 188 genes intolerant to those variants ( Supplementary file 3 , Figure 4—figure supplement 3 ) . Our estimations were in agreement with oe scores provided by gnomAD . As expected , genes prone to ultra-rare PTVs in UKB showed high oe scores in gnomAD , while genes intolerant to those variants had low oe scores ( Figure 4—figure supplement 4 ) . We excluded variants in splice donor and acceptor sites , as the number of variants per gene is directly affected by the number of introns . In addition to the germline burden of extremely rare PTVs , somatic cells accumulate new genetic variants ( Vijg , 2000; Milholland et al . , 2015; Zhang and Vijg , 2018 ) at a median mutation frequency of R≈10-8 per base pair per year ( Milholland et al . , 2017 ) . Thus , the negative effects on healthspan and lifespan due to germline burden should get gradually amplified with age in somatic cells . We quantitatively assessed whether the contribution of accumulated somatic PTVs is strong enough to explain the exponential growth of mortality with age , a . k . a . the Gompertz law . In doing so , we extrapolated the Cox PH model for germline PTV burden by taking into account the effects of acquiring new PTVs with age in somatic cells . The somatic PTV burden increases linearly with age and can be estimated as λ⁢L⁢R⁢t , where t is age , the genome size is L=3 Gbp , and the fraction of the genome covered with the extremely rare PTVs ( 10 kbp ) with M⁢A⁢F<10-4 is λ=0 . 33⋅10-5 . Overall , the somatic PTV burden contributed to the mortality log-hazard a linear ( in age ) term β⁢λ⁢L⁢R⁢t , where β=0 . 046 per year is the Cox PH coefficient , whereas the Gompertz contribution would be proportional to Γ⁢t . We estimate that the somatic PTV burden term β⁢λ⁢L⁢R≈4 . 6⋅10-6 per year is negligible in comparison to the Gompertz exponent Γ≈0 . 09 per year characterizing mortality and incidence of chronic disease acceleration with age ( Zenin et al . , 2019 ) . The estimated effect of somatic PTV accumulation is minor in comparison to the effect of germline PTV burden , and can account only for a minute fraction of mortality and morbidity acceleration . However , our prediction should be experimentally validated , for example , by testing the effect of somatic mutations on lifespan and healthspan in model organisms . We report that both lifespan and healthspan are negatively impacted by the burden of rarest variants that disrupt genes and are already present at birth . These mutations are found in most human subjects . Thus , disease occurrence and age of death are directly influenced by genotype . Our approach is radically different from the previous searches for variants that contribute to longevity , for example searches for alleles enriched in centenarians . In this regard , we find that the genetic component of lifespan is shaped by two mutation types: common variants , as found by previous studies , and ultra-rare PTVs , as shown here . These conclusions are based on the analysis of two large independent datasets that provide both whole exome sequencing data and lifespan traits . Previously released genotyping data were probed primarily for common variants , thus missing information on the most deleterious , rare variants . Here , we took advantage of the recent release of UKB exomes and revealed the relationship between the most damaging variants , ultra-rare PTVs , and lifespan traits . However , due to the limited follow-up , mortality in the UKB dataset reflects the progression rate of age-related chronic diseases in an individual; that is if a subject is deceased , he/she most probably had one or more age-related disease at the time of enrollment . As a result , UKB subjects included in lifespan analysis are biased toward shorter lifespan . A general UK population born at the same time had a life expectancy at birth of 71 years which is much longer compared to the average lifespan of 57 years in the deceased UKB cohort . On the other hand , UKBBN subjects had an average lifespan of 69 years which is closer to the actual lifespan in the UK . However , subjects in the UKBBN cohort were born in the period spanning the whole century from 1891 to 1996 . As one would expect , deleterious alleles might accumulate in recent generations due to advances in medicine . Thus , our UKBBN analysis of a mixture of a few generations could be biased . Both limitations can be addressed 30–40 years later when the average lifespan of UKB individuals will reach the average lifespan of the UK population . The association of ultra-rare PTV mutations with healthspan , however , reflects the effect of deleterious gene variants on the incidence of the first chronic disease and thus covers the accumulated effect of genotype on health and survival over a much longer time , effectively from birth up to the age of enrollment/death . The association of ultra-rare mutation burden and lifespan is consistent between the UKB and UKBBN cohorts . UKB has more subjects but a narrower age distribution , and UKBBN provides postmortem genotypes of individuals deceased at ages of 16 − 105 years old . By the nature of its design , the UKBBN cohort may be enriched for individuals prone to diseases and death at any given age . On the other hand , UKB subjects exhibit lower mortality and hence are probably healthier than the general population ( Ganna and Ingelsson , 2015 ) . It , therefore , appears that the association of ultra-rare mutations and lifespan traits is a general feature that applies to the UK population . However , there is still an open question whether our findings are translatable to the populations outside UK and Europe . The release of more ethnically diverse datasets accompanied by lifespan phenotypes would help to address this question in the future . It is also unclear if the association would be preserved beyond the 11 years of the follow-up period in UKB . Findings from a much older UKBBN cohort suggest that the effect size of ultra-rare PTVs on lifespan will remain significant . To fully understand the role of rare variants in human lifespan , we need to test its effects in an older UKB cohort as well as in large ethnically diverse datasets . We found no association between father’s age at death and PTV burden , while there was a modest effect for mother’s age at death . At the same time , a genetic correlation between longevity and father’s age at death was reported to be one of the strongest ( Deelen et al . , 2019 ) . One of explanations would be that ultra-rare PTV burden is more relevant for lifespan than for longevity . To address this hypothesis in the future , PTV burden can be compared between centenarians and an appropriate control group . If PTV burden is associated with longevity , we would expect centenarians to be severely depleted of ultra-rare PTVs . Interestingly , we observed sex-specific effects of PTV burden for healthspan and mother’s age at death . Both signals were mostly driven by women . At the same time , men had a much shorter healthspan compared to women ( Cox ph beta = 0 . 16 , p-value=1 . 55E-18 ) . We hypothesize that the genetic component , represented here by ultra-rare PTVs , may play a less important role in male healthspan due to the lifestyle choices such as smoking , drinking , risky behavior and unhealthy diet . Indeed , men are known to smoke more ( Peters et al . , 2014 ) , drink more , and exhibit higher BMI scores ( Wills et al . , 2017 ) than women in UKB . To investigate sex specificity in more detail , we ran an analysis of the X-chromosome in men , where mutations cannot be compensated by in the homologous chromosome . We found no associations of X-chromosome PTV burden with either lifespan or healthspan . However , the number of PTVs per individual specifically on X chromosome is extremely low; thus , we may be out of power to pick up the difference . Ultra-rare PTVs occur across the genome and affect 89% of sequenced genes in UKB . Intriguingly , we observed a subset of 1 , 496 genes that are free of ultra-rare PTVs in the whole UKB population sequenced so far . These genes are more essential as evidenced by high indispensability scores and are expressed more broadly throughout the body . Together , this findings indicate strong purifying selection against PTVs in these genes . Their disruption could lead to either childhood or embryonic lethality , the time periods that are not covered by UKB as well as other public datasets , for example ExAC . As expected , genes affected by rare and common PTVs ( M⁢A⁢F>0 . 0001 ) are less evolutionary conserved and more frequently disrupted in the general population ( Figure 4—figure supplement 1 ) , less essential ( based on indispensability scores ) , and expressed in fewer tissues ( Figure 4a , b ) . Moreover , fewer common nonsense variants were predicted to trigger nonsense-mediated mRNA decay; thus , they affect gene expression less than ultra-rare stop gains ( Figure 4c ) . Overall , common PTVs are expected to have a lower effect on fitness , which would explain the lack of the association between the burden of common PTVs and lifespan phenotypes . It is apparent that the ultra-rare PTVs are more damaging than common PTVs but not enough damaging to cause early life mortality . Notably , individuals sharing same PTV number still have diverse lifespan . This discrepancy might be explained by differences in the rate of age-related damage accumulation , regulated by environment and genetic factors . In addition , the impact of a PTV on phenotype depends on the gene it disrupts and its position within the gene body . For example , disruption of a more evolutionary conserved gene and with a broad expression would , intuitively , have a stronger effect on lifespan than the disruption of a less conserved gene with a tissue-specific expression . Indeed , our analysis confirmed that individuals with shorter lifespan were born with more deleterious alleles . Genes disrupted in short-lived individuals are broadly expressed in the body , are more likely to cause haploinsufficiency when inactivated , and are more intolerant to PTVs ( according to gnomAD oe scores ) . Additionally , PTVs in subjects with shorter lifespan were more likely to cause gene loss-of-function . Thus , both the degree of damage caused by ultra-rare PTVs and the number of these variants are important factors influencing human lifespan . Intriguingly , the effect size of ultra-rare PTVs on lifespan was comparable to the effect of known longevity alleles . For example , ϵ⁢4 allele in APOE/TOMM40 locus conferred an estimated 1 . 24 years of life shortening in women , as inferred from a large parental survival study ( Joshi et al . , 2016 ) . The PTV burden difference of 2 − 3 variants corresponds to the similar effect size ( 1 − 1 . 5 years ) on the lifespan variation at the standard deviation for M⁢A⁢F<0 . 0001 . Having established the mortality and morbidity risk association with PTVs , we were able to factor in the rates of somatic mutation accumulation over the lifespan . The dramatic discrepancy between the estimate for somatic PTV burden accumulation and the empirical mortality and morbidity acceleration does not support the hypothesis that random somatic mutations significantly reduce healthspan or lifespan . Moreover , the analysis shows that the effect of accumulation of somatic mutations is less profound than that of germline PTV burden . Thus , we found little evidence for a significant role of somatic mutations in aging ( Promislow and Tatar , 1998; Moorad and Promislow , 2008 ) . Somatic mutations may , however , play a role through high-order effects , such as clonal expansion and cell competition , and hence amplify the effects of other forms of damage ( Martincorena , 2019 ) . Taken together , the effects of common variants earlier implicated in longevity and the effects of ultra-rare variants reported here could help explain the apparent heritability of lifespan . Currently , this issue is not fully resolved . Twin studies ( Herskind et al . , 1996; Ljungquist et al . , 1998 ) suggest that lifespan could be as much as 23 − 33% genetically determined . A more recent study ( Ruby et al . , 2018 ) puts up a challenge to this conclusion and points to a much lower level of genetic determinism . We therefore expect that future investigations of the effects of ultra-rare genetic variants may turn to be crucial for quantitative understanding of lifespan heritability . These findings strengthen the case for complexity of aging , wherein aging is a systemic process resulting from the combined accumulation of age-related deleterious changes , none of which could cause aging on their own ( Gladyshev , 2016 ) . The advantage of mutations in aging studies , however , is that they can be quantified and their contribution estimated , which is something that is currently much more difficult to do for other forms of age-related damage . The first batch of UKB exome sequence group consists of 49 , 960 individuals who passed QC procedures by UKB . Exome sequencing cohort is enriched with samples with a higher rate of imaging and enhanced measurements such as retinal optical coherence tomography test , visual acuity , hearing test , and other . This cohort is not biased on any health condition , disease or physical measurement results from the UKB population of almost 500 , 000 individuals ( Hout et al . , 2019 ) . We selected a cohort of 41 , 250 individuals who self-reported ’White British’ and have very similar genetic ancestry based on a principal components analysis of the genotypes . Then , we made an effort to produce the maximal independent set of individuals based on computed kinship coefficients ( two individuals were considered related if they share relatedness of third degree or closer ) and selected 40 , 368 individuals for the analysis . Exome data consisted of 8 , 959 , 608 SNPs and short indels from human coding DNA . We selected 6 , 208 , 943 variants that are not monomorphic in UKB cohort and have a missing rate less than 10% and M⁢A⁢F<0 . 2 . We annotated these genetic variants for functional consequence using SNPeff ( Cingolani et al . , 2012 ) software and GRCh38 . 86 genome reference . UKBBN dataset was additionally annotated with ANNOVAR ( Wang et al . , 2010 ) to add ExAC MAFs . In downstream analysis we focused on protein-truncating variants annotated as: stop codon gained , frameshift variant , slice donor or splice acceptor site , this produced 152 , 790 and 11 , 393 SNPs and indels in UKB and UKBBN , correspondingly . PTV burden was defined as a number of ultra-rare ( MAF<0 . 0001 ) variants that disrupt open reading frame ( stop gain , frameshift , disruption of splice donor/acceptor site ) . PTV burden was tested for association with UKBBN lifespan using Cox PH model with sex and first 20 principal components ( obtained by clustering with 1000G dataset , see below ) as covariates in R ( R Development Core Team , 2018 ) . For UKB data we included sex , 40 genetic principal components and assessment centers as covariates for Cox PH analysis on lifespan , healthspan and mother’s and father’s age at death . For all types of survival data except for healthspan we have also added age at assessment as covariate . Genetic principal components were calculated on genotypes for 500 , 000 UKB participants ( Bycroft et al . , 2018 ) . First and second chromosome for all 1000G super populations and UKBBN dataset were clustered together . For that , 1000G vcf files were lifted over to hg19 using picard tools ( Broad Institute , 2018 ) combined with UKBBN vcf file by overlap variants using GATK tools ( Van der Auwera et al . , 2013 ) . Variants with MAF deviating between datasets over 30% were excluded . Eigen vectors were obtained from variants with M⁢A⁢F>10% pruned using 50 window size , step size of 5 and variance inflation factor threshold of 1 . 5 by Plink ( Purcell et al . , 2007 ) . We kept individuals that clustered with EUR superpopulation . PTVs in UKB were filtered using internal MAFs . Since UKBBN cohort is much smaller to get desired resolution we used ExAC MAFs for non-finish European population ( ExAC_NFE ) . We excluded ultra-rare variants absent in ExAC dataset ( ExAC_ALL = 0 ) from UKBBN analysis to reduce number of sequencing and variant calling errors . Analysis in both datasets was restricted to autosomal chromosomes to avoid sex bias . We restricted UKBBN cohort to natural causes of death ( i . e . excluding car accidents , poisoning and suicides ) and excluded deaths with no abnormalities detected . UKBBN vcf files were downloaded from EGA repository ( EGAS00001001599 , https://www . ebi . ac . uk/ega/studies/EGAS00001001599 ) . Transcripts per kilobase million ( TPM ) counts for 53 human tissues were downloaded from GTEx Portal , release v7 . Gene expression values within brain regions , two heart and two skin samples were averaged for subsequent analysis , and primary cell cultures were excluded , yielding a total of 37 tissues . Transcripts considered to be expressed in the tissue if T⁢P⁢M>10 . Oe ratios were downloaded from gnomAD repository ( gnomad . v2 . 1 . 1 . lof_metrics . by_gene . txt . bgz ) . GHIS values were obtained from Steinberg et al . ( 2015 ) and indispensability scores were downloaded from Khurana et al . , 2013 . d⁢S and d⁢N values for chimpanzee-human orthologs were downloaded from Ensembl Biomart . NMD and LoF predictions were obtained from snpEff annotation ( ’NMD . gene’ , ’LoF . gene’ ) ( Cingolani et al . , 2012 ) . All UK Biobank data are available upon application . Gene burden analysis was performed with assumptions that all ultra-rare PTVs would have the same effect direction and the same effect size . Following those assumptions , we summed up all cases of gene harboring ultra-rare PTV . Cohorts were defined by splitting UKB into two groups with equal number of subjects based on ordered lifespan or healthspan data . We tested the hypothesis that some genes harbor more ultra-rare PTVs in one cohort than another ( compared to the sum of PTV number in each cohort ) using Fisher’s exact test . To explore sex-specific effects , we separately run analysis for healthspan in males and females . In order to identify genes with a significantly deviated burden of ultra-rare PTVs in UKB , we performed a Fisher’s exact test using the number of ultra-rare PTVs and synonymous variants . For each gene , we build a 2 × 2 contingency table containing the number of ultra-rare PTVs observed in the gene and those observed in the rest of the population , and the number of synonymous variants observed in the gene and those observed in the rest of the population . The result of each test was an odds ratio and p-value , where genes with odds ratio <1 showed a disproportionately low number of rare PTVs . The Fisher test was performed using the fisher . test function in R , and the Bonferroni correction is performed using p . adjust function in R .	Most living things undergo biological changes as they get older , a process that we generally refer to as aging . Despite being a widespread phenomenon , scientists do not fully understand why we age , though it appears that a combination of genetics and lifestyle factors , such as diet , play a role in influencing lifespan . Aging increases the risk of developing a wide range of diseases , including cancer , Alzheimer’s disease and diabetes . As such , finding ways to slow the aging process would help to postpone the onset of illness and potentially improve health in old age . Genes are thought to be responsible for between one quarter and one third of the variation in human lifespans . The relationship between genes , aging and lifespan is complex and not well understood . One set of rare genetic changes that have been shown to have significant effects on diseases are called protein truncation variants ( PTVs ) . PTVs cause damage by altering the production of certain proteins . There are many possible PTVs and people can be born with them or they can develop them in some cells later in life . The full influence of PTVs on aging is not known . Shindyapina , Zenin et al . have now studied observational data collected from two groups of over 40 , 000 people in the UK . Both groups recorded over 1 , 000 deaths , and the study examined the influence of PTVs on natural lifespan . The results show that each person is born with an average of six PTVs , which can vary in the impact that they have on aging . Having more , or more severe , PTVs could reduce life expectancy on average by 1 . 3 years . PTVs affect both total lifespan and healthy lifespan , the period of time lived prior to developing the first age-related disease . While PTVs that people are born with have a significant effect on aging , this study also showed that PTVs that are acquired due to spontaneous mutations through a person’s life have much less of an impact . This is a key insight into the relationship between genes and aging . These discoveries could help in using genetics to anticipate future health , it also helps to identify some of the biological systems that have a role in aging . This could lead to new ways to delay the aging process and its effects on health .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "genetics", "and", "genomics" ]	2020	Germline burden of rare damaging variants negatively affects human healthspan and lifespan
Clustering of proteins into micrometer-sized structures at membranes is observed in many signaling pathways . Most models of clustering are specific to particular systems , and relationships between physical properties of the clusters and their molecular components are not well understood . We report biochemical reconstitution on supported lipid bilayers of protein clusters containing the adhesion receptor Nephrin and its cytoplasmic partners , Nck and N-WASP . With Nephrin attached to the bilayer , multivalent interactions enable these proteins to polymerize on the membrane surface and undergo two-dimensional phase separation , producing micrometer-sized clusters . Dynamics and thermodynamics of the clusters are modulated by the valencies and affinities of the interacting species . In the presence of the Arp2/3 complex , the clusters assemble actin filaments , suggesting that clustering of regulatory factors could promote local actin assembly at membranes . Interactions between multivalent proteins could be a general mechanism for cytoplasmic adaptor proteins to organize membrane receptors into micrometer-scale signaling zones . Numerous membrane proteins have been observed to organize into supramolecular clusters upon extracellular ligand binding and/or cell–cell adhesion . Examples include cadherins ( Yap et al . , 1997 ) , Eph receptors ( Nikolov et al . , 2013; Seiradake et al . , 2013 ) , immune receptors ( Goldstein and Perelson , 1984 ) , apoptotic signaling receptors ( Henkler et al . , 2005 ) , chemotaxis receptors ( Li et al . , 2011 ) , GPI anchored proteins ( Varma and Mayor , 1998 ) and components of T cell signaling pathways ( Balagopalan et al . , 2013 ) . A variety of mechanisms have been proposed to account for this higher-order organization . The extracellular domains of cadherins and Eph receptors have been postulated to interact laterally in homotypic fashion within the plasma membrane to produce large-scale assemblies at sites of cell–cell adhesion ( Himanen et al . , 2007; Wu et al . , 2011; Seiradake et al . , 2013 ) . Modeling studies have suggested that binding of divalent antibodies to the extracellular domain of trivalent Fcε receptors could lead to large networks , which could account for Fcε receptor puncta observed in cells ( Goldstein and Perelson , 1984 ) . An analogous mechanism has been proposed for intracellular interactions of the oligomeric receptor , Fas , with its oligomeric adaptor protein FADD ( Scott et al . , 2009; Wang et al . , 2010; Wu , 2013 ) to produce clusters of the receptors hundreds of nanometers in size ( Siegel et al . , 2004 ) . Similarly , dimeric bacterial chemoreceptors such as Tsr are linked together by their downstream partners CheA and CheW , forming trimers of dimers resulting in a highly ordered and conserved hexagonal array that is suggested to be the basic unit of polar clusters ( Briegel et al . , 2012 , 2014 ) . GPI-anchored proteins and lipid-anchored Ras have been shown to organize into dynamic clusters of 4–7 molecules through transient interactions with lipids and the cortical actin–myosin network ( Plowman et al . , 2005; Goswami et al . , 2008 ) . Such clusters of GPI-anchored proteins are also believed to play an important role in creation of dynamic nanometer scale cholesterol-rich lipid domains , which further contribute to organization of the plasma membrane ( Sharma et al . , 2004; Lingwood and Simons , 2010; Gowrishankar et al . , 2012 ) . Finally , data suggest that clustering of T cell receptors may arise in part from size differences , and consequent steric occlusion , between the extracellular domains of different membrane proteins found at contacts between T cells and antigen presenting cells ( James and Vale , 2012 ) . These models have proven powerful in describing the individual systems above . However , for several reasons most of them are difficult to generalize in a predictive manner to new systems . First , the models hinge on molecular interactions that are specific to the individual systems and that are not readily apparent from protein sequence features alone . Thus , with the exception of GPI-anchored proteins , which likely behave similarly as a group , in the absence of a fairly detailed physical characterization it is difficult to predict whether any new protein/system is likely to produce membrane clusters , and if so , which clustering models are most appropriate . Additionally , with the exception of the nanometer scale clusters of GPI-anchored proteins and lipid-modified Ras , the physical properties of most receptor clusters have not been extensively characterized . Clustering models have derived in many cases from molecular packing in crystal lattices and have been analyzed largely through cellular studies showing qualitative consistency with structural studies and theoretical analyses . But in general , the physical properties of the clusters ( e . g . , their thermodynamic and kinetic properties ) have not been correlated to the physical parameters of the molecules that compose them nor have the key molecular properties that influence cluster properties been identified . This shortcoming arises partly because the models have not been examined through in vitro biochemical reconstitution , where the parameters of the system can be tightly controlled and the physical properties of the clusters can be analyzed in detail . Finally , the functional consequences of macroscopic clustering ( as distinct from association to create defined oligomers–dimers , trimers , etc ) are not well understood . But it is notable that many clustered receptors signal to the actin cytoskeleton , and that many of their downstream targets , such as actin nucleation promoting factors in the WASP family , are also known to form micrometer sized clusters at the plasma membrane ( Yamaguchi et al . , 2005; Weiner et al . , 2007; Gomez and Billadeau , 2009 ) . These observations suggest that one function of receptor clustering may be to control the localization , structure , and/or dynamics of actin filament networks . We recently demonstrated that interactions between multivalent proteins and their multivalent ligands can lead to macroscopic phase separation . This occurs concomitant with assembly of the proteins into large polymers , through a sol–gel transition , as observed in many other multivalent systems in polymer science ( Li et al . , 2012 ) . In three-dimensional solution , this process produces phase separated protein polymers that organize into dynamic micron sized liquid droplets . These droplets are formed in a sharp transition as protein concentration in solution is increased . The critical concentration for droplet formation depends on valency and affinity of interacting species , and the proteins are highly concentrated within the droplets . We have studied this phenomenon in a variety of model multivalent systems , involving both protein–protein and protein–RNA interactions , and also in an actin regulatory signaling pathway involving the adhesion receptor , Nephrin , and its intracellular targets Nck and N-WASP ( Jones et al . , 2006 ) . In the latter , phase separation can be controlled by multivalent phosphorylation of Nephrin and results in enhanced signaling activity of N-WASP . These previous studies were performed in three-dimensional solution . But in vivo Nephrin is an integral membrane protein; therefore its cytoplasmic tail is attached to membranes ( Welsh and Saleem , 2010 ) . The behavior of multivalent–multivalent interaction systems in such a two-dimensional arrangement remained unresolved . In this study , we show that multivalency-induced polymerization and phase separation can also occur in two-dimensional systems , generating micrometer-size protein clusters at membranes . When phosphorylated Nephrin is attached to supported lipid bilayers of DOPC , addition of Nck and N-WASP induce formation of micron-sized concentrated puncta containing all three proteins . Puncta form abruptly when a critical concentration of Nck/N-WASP is reached and are highly dynamic . The critical concentration is appreciably lower for two-dimensional puncta formation than for three-dimensional droplet formation , and it depends on the phosphotyrosine and SH3 domain valencies of p-Nephrin and Nck , respectively , and also on the affinity of the Nck SH2 domain for p-Nephrin . These data suggest that puncta formation is driven by polymerization of the proteins in a plane adjacent to the membrane . In the presence of actin and the N-WASP target , the Arp2/3 complex , puncta formation causes focal actin assembly . Our biochemical approach has allowed us to control the clustering process and discover key parameters that control puncta formation . Our study demonstrates that specific protein–protein interactions result in the formation of macroscopic clusters without the necessity of lipid segregation or actin–myosin assembly . This clustering can be defined as phase separation of proteins on the surface of a membrane . Our observations here and previously ( Li et al . , 2012 ) suggest that polymerization and phase separation of multivalent macromolecules may represent a general mechanism to produce two- and three-dimensional dynamic and highly concentrated micron-sized structures in cells . Nephrin is a transmembrane protein expressed in the foot processes of kidney podocyte cells , where its extracellular domain is a critical component of the slit diaphragm , the final element of the kidney's glomerular filtration barrier ( Welsh and Saleem , 2010 ) . The integrity of the slit diaphragm requires intracellular assembly of actin filaments downstream of the Nephrin cytoplasmic tail ( Jones et al . , 2006 ) . When Nephrin is crosslinked by antibodies , its cytoplasmic tail can be phosphorylated by the Src family kinase , Fyn ( Jones et al . , 2006; Verma et al . , 2006 ) . Three phosphotyrosines ( pTyrs ) in the tail bind the SH2 domain of the Nck adaptor protein , which in turn uses its three SH3 domains to bind multiple proline-rich motifs ( PRMs ) in the actin regulatory protein , N-WASP . N-WASP then recruits and promotes activation of the Arp2/3 complex , which generates branched actin filament networks through nucleating new actin polymers . Mutations that disrupt this pathway in humans and mice result in disorganization of the slit diaphragm and defects in the glomerular filter that cause proteinuria ( Jones et al . , 2006 , 2009 ) . We previously reported that mixing Nck , N-WASP , and the phosphorylated cytoplasmic tail of Nephrin in solution produced phase separated liquid droplets ( Li et al . , 2012 ) . This observation suggested that if the Nephrin tail was attached to a membrane , as it is in vivo , Nck and N-WASP might induce it to condense into membrane clusters ( Figure 1A ) . To test this hypothesis , we began by generating the triply phosphorylated cytoplasmic tail of Nephrin ( amino acids 1174–1223 , phosphorylated at Tyr1176 , Tyr1193 , and Tyr1217 , and mutated from Tyr to Phe at residues 1183 and 1210 , sites not believed to bind Nck ( Jones et al . , 2006; Verma et al . , 2006 ) ; called p-Nephrin hereafter ) . The construct contained a His8 tag at its N-terminus , followed by a ( Gly-Gly-Ser ) 5 linker containing a cysteine , which was covalently labeled with maleimide Alexa488 fluorophore . We attached p-Nephrin to supported bilayers of DOPC lipid , doped with 1% of a nickel-chelating lipid ( Ni2+-NTA-DOGS ) . Through this approach we could control and quantify the surface density of p-Nephrin as detailed in the ‘Materials and methods’ section ( Galush et al . , 2008 ) . 10 . 7554/eLife . 04123 . 003Figure 1 . Reconstitution of p-Nephrin clusters on supported lipid bilayers . ( A ) Cartoon illustrating the interaction of triply-phosphorylated His8-tagged Nephrin ( p-Nephrin ) with its partners Nck and N-WASP . Top panel illustrates p-Nephrin attached to bilayers . Bottom panel illustrates the model for clustered p-Nephrin , upon Nck and N-WASP binding . ( B ) Top: TIRF image of Alexa488-labeled p-Nephrin attached to a supported DOPC lipid bilayer doped with 1% nickel-chelating lipid ( Ni2+-NTA DOGS ) , ( corresponding to panel A , top ) . Bottom: TIRF image of analogous membrane-attached Alexa488-labeled p-Nephrin after addition of 1 µM Nck and 1 µM N-WASP ( corresponding to panel A , bottom ) . ( C ) Line-scans of the images in panel B , at the positions depicted by the white dotted lines . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 00310 . 7554/eLife . 04123 . 004Figure 1—figure supplement 1 . p-Nephrin , Nck and N-WASP colocalize to clusters formed on fluid supported lipid bilayers . ( A ) Fluorescence recovery after photobleaching ( FRAP ) on a supported bilayer with p-Nephrin shows full recovery with exponential recovery time constant τ = 1 . 3 s . Line shows fit to a single-exponential . ( B ) Clusters do not form with only p-Nephrin on the membrane ( left-panel , p-Nephrin Alexa488 ) or with p-Nephrin ( Alexa488 ) + 1 μM Nck ( Alexa568 ) ( middle and right panels ) . The legend below each panel indicates the fluorophore imaged . ( C ) Three color imaging shows that p-Nephrin Alexa488 , Nck Alexa568 , and N-WASP Alexa647 co-localize at the clusters . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 004 Membrane-bound p-Nephrin is homogeneous and fluid on supported bilayers , as demonstrated by total internal reflection fluorescence microscopy ( TIRFM ) and rapid fluorescence recovery after photobleaching ( FRAP , exponential recovery time constant τ = 1 . 3 s ) ( Figure 1B , and Figure 1—figure supplement 1A ) . Addition of 1 µM Nck causes no change in the distribution of p-Nephrin on the membrane , despite clear association of Nck with the bilayer ( Figure 1—figure supplement 1B ) . Similarly , 1 µM of an N-WASP construct containing the basic proline-rich and VCA regions of the protein ( residues 183–193 , 273–501 , N-WASP hereafter ) does not change the p-Nephrin distribution . However , addition of 1 µM Nck and 1 µM N-WASP together causes p-Nephrin to organize into micron-sized clusters ( Figure 1B , Video 1 ) . Unphosphorylated Nephrin remains uniformly distributed under these conditions ( not shown ) , indicating that clustering requires binding the Nck SH2 domain to pTyr sites on Nephrin . Labeling of Nck or N-WASP with fluorophores ( Alexa 568 or Alexa 647 , respectively ) shows that the clusters contain all three protein components ( Figure 1—figure supplement 1C ) . Quantitative analysis indicated that the clustered regions contain up to fourfold higher density of p-Nephrin than the surrounding regions of the bilayer ( Figure 1C ) . Note that much higher concentrations of Nck and N-WASP ( ∼40 µM and ∼15 µM , respectively [Li et al . , 2012] ) are required to form phase-separated droplets in solution than to induce p-Nephrin clustering on membranes . Thus , clustering does not involve adhesion of pre-existing three dimensional Nck/N-WASP droplets to membrane-bound p-Nephrin , but rather de novo assembly of the proteins together on the bilayer surface . Further , the DOPC/DOGS lipids in our experiments do not phase-separate , indicating that clustering is independent of lipid phase separation . 10 . 7554/eLife . 04123 . 005Video 1 . Addition of Nck and N-WASP to p-Nephrin produces macroscopic clusters on supported bilayers . Time-lapse images taken immediately after adding 1 μM Nck and 1 μM N-WASP to p-Nephrin Alexa488 . Images were captured every minute . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 005 To understand the concentration dependence of cluster formation , we fixed the concentration of N-WASP at 500 nM and the density of p-Nephrin at 2700 ± 200 molecules/μm2 ( see density control and measurement in ‘Materials and methods’ , also Figure 2—figure supplement 1 , 2 ) and added increasing concentrations of Nck . We used two measures to determine the onset of clustering . First , we used a thresholding approach to identify and quantify bright regions of the membrane , which we define as clusters . As detailed in ‘Materials and methods’ , two different thresholding procedures gave virtually identical results in this approach . After thresholding , we calculated the fraction of total membrane fluorescence intensity that is present in the clusters . As a second independent approach , we determined the variance of the fluorescence signal across the bilayer image , which also increases as bright regions form . Using either approach , we found that p-Nephrin clusters appear in a highly non-linear fashion as Nck concentration in solution increases . Clusters are essentially absent at low concentrations of Nck but form quite sharply once a critical concentration is reached ( ∼200 nM , Figure 2A ) . We note that the sharp increase in variance and the coincidence of the critical concentration measured by both methods speak against the possibility that small clusters are forming in a more gradual fashion but are too dim to be recognized by the thresholding approach . The average density of p-Nephrin on the membrane does not change during the titration ( Figure 2—figure supplement 2 ) . We define the concentration at which fractional intensity and variance begin increasing as the clustering concentration . The highly cooperative nature of the cluster formation on bilayers is reminiscent of the sharp phase transitions observed in forming p-Nephrin/Nck/N-WASP liquid droplets in three-dimensional solutions ( Li et al . , 2012 ) . The clusters are distributed randomly ( Gaussian distribution ) across the membrane ( Figure 2B ) , consistent with a stochastic assembly process , where the clusters are nucleated and grow independent of one other ( Dill and Bromberg , 2003 ) . The clusters also show a broad range of sizes that can be fit well to an exponential distribution ( Figure 2C ) , similar to that observed for stochastically assembled chemotaxis receptors in bacteria ( Greenfield et al . , 2009 ) . These properties suggest a stochastic process of cluster formation in our system . In contrast , clusters of GPI-anchored proteins in cells do not show a Gaussian spatial distribution nor a broad size distribution , indicating their active control by the cortical actin cytoskeleton ( Goswami et al . , 2008 ) . 10 . 7554/eLife . 04123 . 006Figure 2 . Nephrin clusters are created via a two-dimensional phase-transition . ( A ) Fractional intensity in clusters ( blue symbols , left ordinate ) and signal variance ( red symbols , right ordinate ) of p-Nephrin fluorescence on a DOPC bilayer as a function of Nck concentration for 500 nM N-WASP and total p-Nephrin density of ∼2700 molecules/µm2 . ( B ) Relative frequency with which a given number of clusters are found within 93 randomly selected 56 × 56 µm regions of a bilayer formed using ∼2500 molecules/µm2 Alexa488-labeled p-Nephrin , 1 µM Nck , and 1 µM N-WASP . ( C ) Size distribution of clusters formed using ∼2500 molecules/µm2 Alexa488-labeled p-Nephrin , 1 µM Nck , and 1 µM N-WASP . ( D ) Puncta formed using 1 µM Nck , 1 µM N-WASP , and low ( left ) or 4 . 7-fold higher ( right ) density of p-Nephrin . Images were autocontrasted for clarity . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 00610 . 7554/eLife . 04123 . 007Figure 2—figure supplement 1 . Quantitative analysis of the measurement and control of His8-p-Nephrin density on supported lipid bilayers . ( A ) Fluorescence intensity as a function of fluorescent lipid ( OG-DHPE ) concentration for a solution of small unilamellar vesicles . Fluorescence of liposomes ( in solution ) containing OG-DHPE were measured at the indicated concentrations of the OG-DHPE concentrations in the x-axis . ( B ) Fluorescence intensity as a function of p-Nephrin Alexa488 concentrations . Blue points represent data for protein alone , red points represent data for p-Nephrin in the presence of 9 . 5 μM of Ni2+-NTA DOGS . Concentrations of the protein for this standard plot were kept similar to those of the concentration of OG-DHPE in panel A . ( C ) Fluorescence intensity as a function of OG-DHPE density on supported lipid bilayers . Upper and lower x-axis labels list density as percent total lipid and molecules/µm2 , respectively . Lines in ( A–C ) represent a linear fits . ( D ) Time course of His8-tagged p-Nephrin Alexa488 dissociation from supported lipid bilayers , monitored by TIRFM , following washes that left 2 . 8 nM protein in solution above the bilayer . ( E ) Fluorescence intensity of bilayers containing different percentages of p-Nephrin Alexa488 ( with total p-Nephrin density ∼2000 molecules/µm2 ) . The data suggest linearity up to ∼60% labeling . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 00710 . 7554/eLife . 04123 . 008Figure 2—figure supplement 2 . Quantification of average p-Nephrin density on the bilayer for every titration point shown in Figure 2A . Y-axis represents p-Nephrin density and x-axis represents the different Nck concentrations of the titration as in Figure 2A . Densities are averages of five different areas of each bilayer . Error bars representing standard deviations are smaller than the symbols . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 008 When experiments are performed at ∼fivefold higher initial density of p-Nephrin on the membrane , the morphology of the clusters changes significantly . Distinct puncta are no longer observed , and the clustered regions span the entire field of view ( Figure 2D ) . These data are consistent with low- and high-density p-Nephrin phase separating via nucleation and spinodal decomposition mechanisms , respectively ( Dill and Bromberg , 2003 ) , as observed in non-biological phase separating systems in material science ( Zinke-Allmang et al . , 1992 ) . Together , these data strongly suggest that the clustering of p-Nephrin occurs through a phase transition of the molecules on the surface of the membrane in response to binding of Nck and N-WASP . We next examined the dynamic behaviors of the p-Nephrin clusters . Individual clusters are irregularly shaped , indicating that they possess low line tension . On short timescales , the edges of clusters show substantial fluctuations , extending and retracting in seconds ( Video 2 ) . On timescales of minutes , these fluctuations lead to coalescence of small clusters into increasingly larger structures ( Figure 3A , Videos 1 , 2 ) . We also rarely observe apparent fission events , where a larger cluster seems to split into two smaller structures ( Video 2 ) . These behaviors suggest that p-Nephrin clusters are fluid-like . The size distribution of the clusters depends on the initial p-Nephrin density and time after Nck/N-WASP addition , reflecting variable contributions of nucleation , growth through monomer addition and coalescence , and Ostwald ripening throughout the process ( Zinke-Allmang et al . , 1992 ) . At lower density ( 2500 molecules/µm2 ) the distribution is exponential at all times we examined ( Figure 3—figure supplement 1 ) , while at higher density ( 4000 molecules/µm2 ) the distribution follows a power law ( Figure 3—figure supplement 2 ) . At a given time after Nck/N-WASP addition , higher density produces a larger average cluster size and correspondingly a larger fraction of total area covered by the clusters ( Figure 3—figure supplement 3A , B , respectively ) , most likely due to the larger degree of coalescence at higher cluster densities . A detailed mechanistic understanding of these behaviors will be goal of future efforts . 10 . 7554/eLife . 04123 . 009Video 2 . Clusters are dynamic . Time-lapse of clusters made from 1 μM Nck and 1 μM N-WASP with p-Nephrin Alexa488 on the membrane . Images were captured every 30 s . In addition to fusion events , the clusters also occasionally undergo fission . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 00910 . 7554/eLife . 04123 . 010Figure 3 . Clusters are dynamic . ( A ) Time-lapse TIRF imaging of bilayers containing ∼3100 molecules/µm2 Alexa488-labeled p-Nephrin after addition of 1 µM Nck and 1 µM N-WASP . Images represent time intervals of 2 min and show coalescence of clusters into larger structures . ( B ) Fluorescence recovery after photobleaching of Nck and N-WASP in clustered regions ( left panel , red and blue , respectively ) and p-Nephrin in clustered and unclustered regions ( right panel , green and black , respectively ) . FRAP experiments were performed in separate experiments using Alexa-488 labeled p-Nephrin , Nck , or N-WASP . Lines show bi-exponential fits of the data , except for unclustered p-Nephrin , which was fit using a single-exponential . Bars represent standard deviation from three FRAP experiments on a single bilayer . Bottom table lists the parameters obtained from the fitting . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 01010 . 7554/eLife . 04123 . 011Figure 3—figure supplement 1 . Cluster size-distribution analyses at different times suggest exponential behavior at lower densities . ( A ) Log-linear plot of cluster number vs size at p-Nephrin density of ∼2500 μm2 , 1 μM Nck , and 1 μM N-WASP . The distributions are plotted for times between 2 and 20 min . ( B ) Log–log plot of the same data . Lines in ( A and B ) represent the best linear fits of the data . The better fits in ( A ) than ( B ) indicate that the data are better described by exponential than power law functions . In other experiments ( not shown ) , the sizes remain exponentially distributed to times as long as 60 min . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 01110 . 7554/eLife . 04123 . 012Figure 3—figure supplement 2 . Cluster size-distribution analyses suggest power law behavior at higher densities . ( A ) Log-linear plot of the size distribution of clusters at a Nephrin density of ∼4000 μm2 , 1 μM Nck , and 1 μM N-WASP , recorded 60 min after clustering was initiated . ( B ) Log–log plot of the size distribution of clusters at a Nephrin density of ∼4000 μm2 , 1 μM Nck , and 1 μM N-WASP . Lines in ( A and B ) represent the best linear fits of the data . The better fits in ( B ) than ( A ) indicate that the data are better described by power law than exponential functions ( contrast with Figure 3—figure supplement 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 01210 . 7554/eLife . 04123 . 013Figure 3—figure supplement 3 . Average cluster size is dependent on molecular density . Samples contained either low ( ∼2000 molecules/µm2 , red dots ) or high ( ∼3500 molecules/µm2 , blue dots ) density of p-Nephrin . Clustering was initiated by addition of 1 μM Nck and 1 μM N-WASP . Images were taken every minute . ( A ) Average cluster size , ( B ) the fractional intensity in clusters . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 013 We next used fluorescence recovery after photobleaching ( FRAP ) to examine the dynamics of the three proteins that compose the clusters . In individual experiments , we labeled either p-Nephrin , Nck , or N-WASP with Alexa488 and examined FRAP behavior of the labeled component . Within the clusters , each of the proteins recovers nearly fully in tens to hundreds of seconds ( Figure 3B ) . Thus , even though the clusters themselves are persistent for hours , the individual components exchange with the surroundings on time-scales of seconds to minutes . The recovery profiles can all be fit to a double-exponential but do not fit to a single-exponential ( see ‘Materials and methods’ for F-test statistics ) . N-WASP shows recovery time constants of τ-fast = 2 . 6 s ( 37% ) and τ-slow = 43 s ( 63% ) . Nck recovers with τ-fast = 1 . 6 s ( 49% ) and τ-slow = 72 s ( 51% ) . p-Nephrin recovers with τ-fast = 86 s ( 76% ) and τ-slow = 526 s ( 24% ) . In the non-clustered regions , p-nephrin recovery can be fit well to a single-exponential , with τ = 31 s , similar to the fast phase in the clusters but appreciably slower than recovery in the absence of Nck/N-WASP , where τ = 1 . 3 s ( Figure 1—figure supplement 1A ) . In independent experiments we found that the dissociation of p-nephrin from the membrane occurs much more slowly than these rates ( τ = 2080 s , Figure 2—figure supplement 1D ) , indicating that the FRAP recovery of the protein is largely due to two-dimensional diffusion within the bilayer . By contrast , Nck and N-WASP likely recover through a combination of diffusion in the plane of the bilayer as well as binding and dissociation from the membrane . We recognize that the kinetic processes here must represent the convolution of multiple molecular processes , given the complex oligomeric/polymeric nature of the clusters ( see below ) . Nevertheless , the data suggest that both the clustered regions and non-clustered regions contain small assemblies that slow p-nephrin dynamics relative to its free diffusion in the bilayer . The clustered region likely contains additional assemblies that are larger and have greater degrees of crosslinking that appreciably slow dynamics further . Together , our data show that upon recruitment of Nck and N-WASP , membrane-bound p-nephrin undergoes a sharp thermodynamically controlled phase transition to produce dense dynamic puncta on the membrane . Our previous data suggested that three dimensional phase separation in the p-nephrin/Nck/N-WASP system occurred concomitantly with a sol–gel transition , producing macroscopic non-covalent polymers within the liquid phase boundary . Evidence for polymerization came in part from studies of the dependence of critical concentration and dynamics on the valencies and affinities of the interacting species . To examine whether such polymerization is also occurring in the two-dimensional system , we initially compared the critical concentrations of singly- , doubly- , and triply-phosphorylated nephrin ( Nephrin1pY , Nephrin2pY , and p-Nephrin , respectively; see ‘Materials and methods’ for specific phosphorylation sites ) . Previous studies showed that the three nephrin pTyr sites have essentially identical affinities for the Nck SH2 domain ( Blasutig et al . , 2008 ) . Thus , these constructs differ largely in pTyr valency , rather than inherent affinity for Nck . At a membrane density of 1000 molecules/μm2 and in the presence of 500 nM N-WASP , p-Nephrin begins to show clusters at 200–300 nM Nck . Under the same conditions , Nephrin2pY and Nephrin1pY do not cluster even at Nck concentrations greater than 10 µM ( Figure 4A ) nor with their own densities increased to 3000 molecules/μm2 . If the concentrations of N-WASP and Nck are increased to 2 µM and 5 µM , respectively , Nephrin2pY produces clusters ( Figure 4—figure supplement 1 ) . However , even at 5 µM N-WASP and 10 µM Nck , Nephrin1pY does not cluster ( Figure 4—figure supplement 1 ) . Thus , the valency of nephrin phosphorylation can control the critical concentration for puncta formation , as in the three-dimensional phase separation of this system ( Li et al . , 2012 ) . 10 . 7554/eLife . 04123 . 014Figure 4 . Clustering is dependent upon the valency of the interacting motifs . Plots show fractional intensity of fluorescent Nephrin proteins in clusters as a function of Nck protein concentrations for 500 nM N-WASP . ( A ) Top , middle , and bottom panels show data for p-Nephrin 3pY , 2pY , and 1pY , respectively . For these concentrations of N-WASP and Nck , only Nephrin 3pY shows clustering . At 2 µM N-WASP , Nephrin 2pY also clusters when Nck is added ( Figure 4—figure supplement 1 ) . ( B ) Top , middle , and bottom panels show data for p-Nephrin plus engineered Nck proteins containing 3 , 2 , or 1 repeat of the second SH3 domain of Nck . For these concentrations/densities of N-WASP/p-Nephrin , only the ( SH3 ) 3 protein can induce clustering . At 5 µM N-WASP , ( SH3 ) 3 also induces clustering ( Figure 4—figure supplement 1 ) . Note that the x-axis is Nck concentration in panel A but total SH3 domain concentration in panel B . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 01410 . 7554/eLife . 04123 . 015Figure 4—figure supplement 1 . Di-valent molecules are stronger clustering agents than mono-valent molecules . ( A ) In the presence of 5 µM Nck and 2 μM N-WASP , p-Nephrin 2pY forms clusters ( right panel ) . Even in the presence of 10 µM Nck and 5 μM N-WASP , p-Nephrin 1pY does not form clusters ( left panel ) . Bottom: line-scan of images in the top panels , with locations indicated by dotted lines . ( B ) At 5 μM N-WASP , 2 . 5 μM ( SH3 ) 2 ( 5 μM SH3 module concentration ) produces p-Nephrin clusters ( right panel ) , whereas ( SH3 ) 1 does not ( left panel ) . Bottom: line-scan of images in the top panels , with locations indicated by dotted lines . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 015 We also performed analogous studies of the SH3 valency of Nck . Since the different SH3 domains of Nck have different affinities for the individual PRM sites in N-WASP ( Qiong Wu , unpublished observations ) , we generated a series of Nck analogs , containing one , two , or three repeats of the second SH3 domain of the protein plus the natural SH2 domain [ ( SH3 ) 1 , ( SH3 ) 2 , and ( SH3 ) 3 , respectively] . The SH3 domains were separated by the natural linker between the first and second SH3 domains . At 500 nM N-WASP , the trivalent molecule ( SH3 ) 3 induces clustering at 200 nM ( SH3 module concentration ) , whereas the di-valent ( SH3 ) 2 and monovalent ( SH3 ) 1 molecules do not cluster even at concentrations above 10 µM of the SH3 module concentrations ( Figure 4B ) . Increasing N-WASP concentration to 5 µM and ( SH3 ) 2 concentration to 5 µM ( SH3 module concentration ) produced clusters , whereas clusters were absent even with 5 µM N-WASP and 5 µM ( SH3 ) 1 ( Figure 4—figure supplement 1 ) . These data demonstrate the strong dependence of clustering on valency of the interacting species . To determine the effect of SH2–pTyr affinity on the clustering concentrations , we replaced the three pTyr motifs of Nephrin with three repeats of the pTyr motif of the bacterial protein TIR ( p-TIR ) ( Campellone et al . , 2002 ) . The binding affinity of the p-Nephrin motif to the SH2 domain of Nck is 370 nM , as determined by isothermal titration calorimetry ( Figure 5—figure supplement 1 ) . For the p-TIR motif the affinity to the SH2 domain is 40 nM . In the presence of p-TIR , at a density of 2000 molecules/μm2 , the clustering concentration of the trivalent SH3 protein , ( SH3 ) 3 , is 100 nM , as opposed to 200 nM for p-Nephrin ( Figure 5A ) . The higher affinity interaction also slows the recovery of Nck , as FRAP data demonstrate ( Figure 5B ) . Fitting to a double-exponential , Nck shows recovery rate constants of τ-fast = 6 . 5 s ( 46% ) and τ-slow = 89 . 5 s ( 54% ) when the clusters of p-TIR/Nck/N-WASP were photobleached . However , Nck shows recovery rates of τ-fast = 1 . 6 s ( 49% ) and τ-slow = 73 . 2 s ( 51% ) when the clusters of p-Nephrin/Nck/N-WASP were photobleached . The data would be consistent with τ-fast being governed by processes based on dissociation of Nck from pTyr sites on Nephrin/TIR ( which are likely slower in the high affinity system ) and τ-slow being governed by diffusion of large assemblies in the membrane ( which are expected to be similar in the two cases ) . Together , the data show that both the clustering concentrations and the dynamics of the clusters can be affected by molecular affinities , as expected of a crosslinked polymer network . 10 . 7554/eLife . 04123 . 016Figure 5 . Molecular affinities affect macroscopic clustering . ( A ) Fractional intensity of fluorescent pTyr proteins in clusters as a function of SH3 ( module ) concentrations for 500 nM N-WASP . Left and right panels show data for a p-TIR and p-Nephrin , whose pTyr motifs bind the SH2 domain of Nck with KD values of 40 nM and 370 nM , respectively . ( B ) Fluorescence recovery after photobleaching ( FRAP ) for Alexa488-labeled Nck in p-Nephrin clusters ( blue ) and p-TIR clusters ( red ) . Nck recovers more slowly ( larger τ ) , can be bleached more strongly ( Y-intercept ) and recovers to a lower value ( plateau ) with p-TIR than with p-Nephrin , all indicating slower dynamics in clusters with the higher affinity SH2 binding partner . The bars represent standard deviation from three FRAP experiments on a single bilayer . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 01610 . 7554/eLife . 04123 . 017Figure 5—figure supplement 1 . Measurement of the affinity of Nck for p-TIR and p-Nephrin . Isothermal titration calorimetry analysis of Nck binding to p-Nephrin and p-TIR . Nck ( 150 µM ) in the syringe was titrated into 5 μM of either ( A ) p-Nephrin ( 3pY ) or ( B ) p-TIR ( 3pY ) . Both datasets could be fit well to a three-site binding model with a single affinity for Nck . In the p-Nephrin and p-TIR titrations , ∼6% and ∼11% of the proteins , respectively , were found to be incompetent to bind Nck . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 017 Additionally when a higher-affinity monovalent pTyr peptide is added in solution , the clusters dissipate . In the presence of clusters made from 1 μM ( SH3 ) 3 , 500 nM N-WASP and p-nephrin , we added singly phosphorylated TIR peptide ( without a His tag ) at 10 μM concentration ( Video 3 , Figure 6A ) . The clusters disappear within minutes after the addition of the monovalent peptide . The dissolution of the clusters occurs sharply , over a time-span of ∼2 min , starting ∼7 min after peptide addition ( Figure 6B ) . When TIR is titrated from 100 nM to 100 μM , the fractional intensity of the clusters also decreases sharply above 10 μM ( Figure 6C ) . These data suggest that the disassembly of the clusters ( similar to the formation ) is also cooperative . 10 . 7554/eLife . 04123 . 018Video 3 . Mono-valent peptide dissolves clusters . Addition of 10 μM 1pY—TIR causes the clusters of 1 μM ( SH3 ) 3 and 500 nM N-WASP to dissipate . Images were captured every 30 s . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 01810 . 7554/eLife . 04123 . 019Figure 6 . Mono-valent pTyr peptide can eliminate clusters . ( A ) Time course following addition of 10 µM of a monovalent pTyr peptide derived from TIR ( with KD of 40 nM for the Nck SH2 domain ) to clusters formed from p-Nephrin / ( SH3 ) 3/N-WASP . ( B ) Time course of the fractional p-Nephrin intensity in clusters after addition of the TIR peptide . ( C ) Equilibrium fractional intensity of the p-Nephrin clusters as a function of p-TIR peptide concentration , performed in the presence of 1 µM ( SH3 ) 3 and 500 nM N-WASP . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 019 The favorability of higher valency and higher affinity on clustering , as well as the disruption of the clusters by a mono-valent molecule , suggest that as in the three dimensional droplets the two dimensional clusters form through polymerization ( a sol–gel transition ) of p-Nephrin , Nck and N-WASP . We next asked whether the p-Nephrin/Nck/N-WASP clusters can direct actin assembly by the Arp2/3 complex at membranes . We added monomeric actin ( 10% rhodamine labeled ) to the solution above preformed clusters in the presence or absence of the Arp2/3 complex , under conditions that favor actin polymerization . Immediately after addition a small amount of actin , likely monomers , is recruited to the clusters in a relatively uniform fashion ( Figure 7—figure supplement 1A ) . After a lag of ∼6–15 min ( see below ) , actin filaments then assemble on the clusters over a time course of approximately 100 min , as visualized by phalloidin 647 staining ( Figure 7—figure supplement 1A ) . In the absence of the Arp2/3 complex , actin filaments are formed only sparsely in the field of view ( Figure 7—figure supplement 1B ) . In the presence of the Arp2/3 complex , actin appears on the clusters much more rapidly and to a much greater degree ( Figure 7A , Figure 7—figure supplement 1B ) . The lag time between the initial weak recruitment of actin and the appearance of robust actin fluorescence ( presumably representing filaments ) varies substantially between clusters ( Figure 7A , B ) . Some clusters show increased actin after only 6 min , while others remain devoid of additional actin until 10–15 min into the reaction . This behavior appears to be stochastic , and the lag time does not show any obvious correlation with size or density of the Nephrin clusters ( Figure 7B ) . Regardless of when filament assembly begins on a cluster , once it does begin , actin intensity rapidly increases , typically reaching a plateau in less than 10 min ( Figure 7C ) . This behavior likely reflects strong positive feedback due to activation of the Arp2/3 complex by actin filaments ( Machesky et al . , 1999 ) . 10 . 7554/eLife . 04123 . 020Figure 7 . Actin assembles specifically on p-Nephrin/Nck/N-WASP clusters . ( A ) Alexa488-labeled p-Nephrin ( 2200 molecules/µm2 ) was clustered by addition of 2 μM N-WASP and 1 μM Nck . Images show time course of p-Nephrin ( top row ) , actin ( middle row ) and merge ( bottom row ) after addition of 10 nM Arp2/3 complex and 1 µM actin ( 10% rhodamine labeled ) . ( B ) Half-times of actin assembly as a function of surface area ( left-panel ) and p-Nephrin intensity ( right-panel ) in individual clusters . Half-times were calculated using the data for the first 27 min of the time-lapse . ( C ) Fluorescence of rhodamine-actin on individual clusters as a function of time for 20 representative clusters . Individual curves represent average intensity across an individual cluster . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 02010 . 7554/eLife . 04123 . 021Figure 7—figure supplement 1 . Actin localizes to and assembles on the clusters in an Arp2/3 dependent manner . ( A ) Images of clusters formed by p-Nephrin ( 2173 molecules/μm2 on the supported lipid bilayer ) plus soluble 1 μM Nck , 2 μM N-WASP , 10 nM Arp2/3 complex , and 1 μM actin ( 10% rhodamine labeled ) at 0 and 3 min . Top panels show p-Nephrin ( Alexa 488 labeled ) , bottom panels show fluorescence for rhodamine-actin . Note that the actin images are contrast-enhanced relative to those in Figure 7A to illustrate weak , but relatively uniform actin recruitment to the p-Nephrin clusters at 3 min . ( B ) Actin assembly reactions as in panel A , except that of the lower row lacks the Arp2/3 complex , imaged at 105 min after actin addition . Left , middle , and right panels show p-Nephrin Alexa488 , rhodamine-actin , and phalloidin 647 staining , respectively . In the bottom row phalloidin stains only the morphologically elongated structures , suggesting that the actin filaments are formed only in the re-shaped structures on the membrane . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 02110 . 7554/eLife . 04123 . 022Figure 7—figure supplement 2 . Actin assembly reorganizes p-Nephrin clusters . Enlarged actin assembly images from Figure 7 , including more time points between 36 and 45 min . Top , middle , and bottom rows show p-Nephrin Alexa488 , rhodamine-actin and merge , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 022 As the reaction proceeds , the morphology of the Nephrin clusters changes dramatically , without appreciable changes on overall intensity ( except the slow decrease due to photobleaching ) . Actin fluorescence remains coincident with Nephrin throughout this process , indicating that the signaling molecules are reorganized by the assembling filaments . Shortly after the appearance of actin on a cluster , the structure changes from having relatively rounded edges to having many thin hair-like projections from its periphery . These projections coalesce over time to give the puncta star-like morphologies . For reasons we cannot currently explain , between 42 and 45 min , well after all of the clusters have recruited significant actin , there is a dramatic change in cluster morphology , such that the puncta appear to shatter into a large number of short linear structures ( Figure 7—figure supplement 2 ) . This change in Nephrin morphology coincides with a sharp increase in the total actin localized to the TIRF field/membrane but no change in total Nephrin fluorescence . These data demonstrate the p-Nephrin/Nck/N-WASP clusters can effectively promote actin filament assembly through the Arp2/3 complex ( which is presumably recruited to the membrane through binding N-WASP ) . As the filaments assemble , they cause substantial changes in the morphology of the clusters . This feedback between actin and the signaling proteins that promote its assembly can control the micron-scale morphology of the entire pathway . We have shown here that membrane-bound phosphorylated Nephrin can form micron-size clusters through interactions with Nck and N-WASP . The clusters form through a thermodynamic phase transition that is driven by oligomerization/polymerization of the proteins through their modular binding domains . The occurrence of a phase transition is supported by the sharpness with which clusters appear as Nck concentration is increased and the temporal sharpness of cluster disappearance after a monovalent competitor is added . The importance of polymerization/oligomerization is supported by the valency- and affinity-dependence of the critical concentration and also by the dissolution of clusters by a monovalent competitor . The clusters appear to be polymers/oligomers of the three proteins , as evidenced by the dependence of FRAP rate on the affinity of the Nck SH2 domain for the pTyr sites on Nephrin . The clusters assemble actin through the Arp2/3 complex and can themselves be dynamically remodeled by the resultant filament network . Our work demonstrates that , as in three-dimensional systems , multivalent polymerization and phase separation can control micron-scale spatial organization ( and likely biochemical activity ) of signaling pathways . This process may contribute generally to the organization of signaling receptors . The cytoplasmic tails of many receptors are rapidly phosphorylated on multiple tyrosine residues upon stimulation by extracellular ligands ( Roche et al . , 1996; Hunter , 2000; Palmer et al . , 2002; Schlessinger , 2000; Houtman et al . , 2006; Kaushansky et al . , 2008; Wagner et al . , 2013 ) . This often occurs concomitant with concentration of the receptors into micron-sized puncta ( Douglass and Vale , 2005; Salaita et al . , 2010 ) . Where examined , these puncta persist over many minutes , but exchange molecules in seconds with the surroundings , similar to the p-Nephrin/Nck/N-WASP puncta we have generated here . Further , many of these receptors have been shown , through combinations of biochemistry and genetics , to use the pTyr modifications to engage signaling networks composed of proteins with multiple modular binding domains , often ( but not exclusively ) combinations of SH2 domains , SH3 domains , PRMs , and additional pTyr sites . Examples of processes controlled by such pathways include T cell activation ( Lee et al . , 2003; Dustin et al . , 2010 ) , invadopodia formation ( Oser et al . , 2010; Bergman et al . , 2014 ) , myoblast fusion ( Abmayr and Pavlath , 2012 ) , neurite self-avoidance ( Chen and Maniatis , 2013 ) , and cell–matrix interactions through focal adhesions ( Hoffmann et al . , 2014 ) . The molecules that control these processes have the capacity to function analogous to the Nephrin/Nck/N-WASP system studied here . We hypothesize that coupled polymerization and phase separation may contribute to the formation of macroscopic puncta in these systems and others that are composed similarly . We note that polymerization does not strictly require multivalency at the level of an individual receptor tail . At high densities , a receptor containing a single motif behaves effectively in multivalent fashion and can then cluster through interactions with multivalent ligands . For example , proteins with multiple PDZ domains interact with voltage-gated Kv1 . 4 channels , which are found in clusters at the cell-surface ( Burke et al . , 1999 ) . Further , membrane receptors are often oligomeric in nature . For example , EGF receptors have been reported to form pre-formed oligomers in the absence of ligand ( Clayton et al . , 2008 ) , effectively increasing the valency of their cytoplasmic tails . Thus , there are a variety of ways that the basic concept of multivalent polymerization and phase separation could be manifested in specific signaling systems . This behavior appears to be particularly prominent in actin regulatory pathways . These often contain the adaptor proteins Nck or Crk/CrkL , linked upstream to pTyr-containing proteins and downstream to proline-rich proteins including members of the WASP family ( Buday et al . , 2002; Antoku et al . , 2008; Noy et al . , 2012; Chaki and Rivera , 2013; Kaipa et al . , 2013 ) . In this regard , it is notable that over half of the 29 known ligands of the Nck SH2 domain contain two or more ( up to 16 in p130 CAS ) predicted and/or demonstrated Nck binding sites ( Lettau et al . , 2009 ) . Further , almost all WASP proteins have large proline-rich regions with multiple SH3-binding PRMs ( Padrick and Rosen , 2010 ) . The only exception is WASH , which has a small proline-rich region . However , WASH is constitutively associated with the Fam21 protein , which has a large disordered tail that contains 21 so-called LFa peptide motifs that can bind the membrane-associated retromer complex ( Derivery et al . , 2009; Gomez and Billadeau , 2009; Jia et al . , 2010 , 2012 ) . Thus WASH may have a conceptually similar but molecularly distinct mechanism of assembling into large structures . This general behavior suggests that clustering may play an important role in spatial and temporal control of actin dynamics . Consistent with this idea , several groups have demonstrated that increased density of WASP proteins corresponds to increased activity towards the Arp2/3 complex . Pantaloni and colleagues have shown that the rate of actin-based motility of N-WASP-coated beads increases non-linearly with increasing WASP density ( Wiesner et al . , 2003 ) . Similarly , Ditlev et al . have shown through modeling and cell-based experiments that actin assembly activity scales with the square of N-WASP density at the plasma membrane ( Ditlev et al . , 2012 ) . Finally , Padrick et al . showed that a natural consequence of the 2:1 stoichiometry of the WASP:Arp2/3 complex during filament nucleation is that actin assembly activity should increase with the size of WASP clusters and thus the density of WASP at membranes ( Padrick et al . , 2008 ) . These observations , together with our data here , suggest that clustering of receptors and their proximal adaptors may provide a mechanism of concentrating WASP proteins into high-density puncta and thus increasing their activation of the Arp2/3 complex , providing local bursts of actin filament assembly . The mechanism we have described is not exclusive of , and in fact is expected to act cooperatively with , many other mechanisms that have been proposed to explain receptor clustering . Interactions between extracellular domains , as proposed for cadherins and Eph receptors ( Himanen et al . , 2007; Wu et al . , 2011; Seiradake et al . , 2013 ) , will be thermodynamically coupled to assembly of intracellular oligomers/polymers . Similarly , interactions of receptor transmembrane regions with specific lipids , which can promote concentration of receptors into nano-domains enriched in those lipids , should also be thermodynamically coupled to the clustering of receptor cytoplasmic tails ( Lingwood and Simons , 2010 ) . Further , ATP-dependent clustering of the cortical acto-myosin system , which promotes oligomerization of GPI-anchored proteins ( Sharma et al . , 2004; Goswami et al . , 2008 ) , could also promote assembly of a phase separated multivalent network if any components of that network can bind to the cytoskeleton . In this case , the dynamic rearrangements of the acto-myosin system could also control the properties of the signaling clusters ( e . g . , cluster size and/or lifetime of clusters ) and maintain them away from equilibrium . It is important to note that while weak interactions between extracellular domains or between transmembrane regions and lipids or between receptors and the cytoskeleton may not on their own produce significant oligomerization of receptors , these energies could have substantial effects when combined with energies of clustering . Phrased differently , these other interactions could have strong effects on the critical concentrations ( or the degree of receptor phosphorylation ) needed for phase separation/clustering through adaptor-based intracellular interactions . For any particular system , or for a single system under different conditions , these various mechanisms are likely to be used to different degrees to promote the organization of receptors into macroscopic structures . The ability of membrane receptors to cluster through multivalent phase separation could have a number of functional implications in cells . The process will generate a sharp switch between different states , which will depend on the concentrations of at least two ( and possibly several ) species , as well as the degree of receptor phosphorylation in pTyr-dependent cases . Thus , the switch could be tightly controlled , either through relatively slow changes in protein concentration or more rapidly through changes in receptor phosphorylation or oligomerization by extracellular ligands . The phase-separated state will have different density , composition , and dynamics from the surrounding regions of the membrane , each of which could have functional consequences . In the case of actin regulatory systems , we and others have shown that because WASP proteins bind ( and activate ) Arp2/3 complex in 2:1 fashion , increasing density of WASP proteins leads to non-linear increases in actin assembly activity ( Padrick et al . , 2008; Ditlev et al . , 2012 ) . Thus , clustering should provide not only spatial organization of the actin filament network ( decreasing spatial noise [Grecco et al . , 2011] ) but also increased biochemical signaling activity . This should be true for any signaling system that requires multiple simultaneous or sequential events to generate downstream outputs . The enhancement due to clustering would be particularly strong for systems with positive feedback , as in Arp2/3 complex–actin pathways . In addition to the polymer components themselves , other proteins and lipids could be concentrated into or excluded from the phase-separated structure . This partitioning could be dictated by both specific interactions ( e . g . , a monovalent SH3 protein could be recruited to the p-Nephrin/Nck/N-WASP clusters by binding the PRMs of N-WASP ) as well as non-specific electrostatic and/or hydrophobic interactions with the polymer matrix . The collection of these molecules would then produce a distinct biochemical environment from the surrounding regions , favoring or disfavoring certain reactions or afford specificity to signaling pathways . Since the clusters are temporally stable but readily exchange molecules with the surroundings , they could potentially act as sites of enzymatic modification and release . Finally , the structural and dynamic features of the polymer matrix could also influence the rates and/or specificities of reactions that occur within the clusters . Recent data have shown that Nephrin is constitutively phosphorylated in the slit diaphragm between podocytes of the kidney ( Jones et al . , 2009; New et al . , 2013 ) . Previous data showed that the loss of Nck disrupts the filtration capacity of the diaphragm , concomitant with the loss of cortical actin filaments ( Jones et al . , 2006 ) . These observations suggest that the pathway from p-Nephrin to actin , and by inference the polymeric network we have described here , is important in maintaining the slit diaphragm . The extracellular portion of Nephrin is composed of multiple IgG domains and FNIII domains . These have been suggested to self-associate , both in trans across the slit diaphragm and in cis within individual cells ( Gerke et al . , 2003 ) . The latter should promote polymerization and phase separation of the actin pathway components . Thus , this system may be a case where interactions on both sides of the plasma membrane act cooperatively to produce a polymeric structure with both extracellular functions ( the filtration barrier ) and intracellular functions ( signaling to actin ) . In summary , we have shown that interactions between multivalent proteins at membranes can lead to concomitant polymerization and phase separation , generating micron-size clusters . Although only demonstrated here for the p-Nephrin/Nck/N-WASP system , the analogous construction of many signaling pathways suggests that this behavior could be quite general , and relevant to many biological processes . Polymerization and phase separation at membranes could impart spatial organization on these pathways and afford them strongly non-linear activities . Further work in vitro and in vivo will be necessary to determine the extent to which these effects are important in specific biological processes . Information on different constructs is provided in Table 1 . Maltose binding protein ( MBP ) -tagged His8-Nephrin and its mutants were expressed in BL21 ( DE3 ) T1R cells at 18°C through overnight induction with 1 mM IPTG . Cells were collected by centrifugation and lysed by cell disruption ( Emulsiflex-C5 , Avestin , Ottowa , ON , Canada ) in 20 mM Tris , pH 8 , 20 mM imidazole , 150 mM NaCl , 5 mM βME , 0 . 01% NP-40 , 10% glycerol , 1 mM PMSF , 1 μg/ml antipain , 1 mM benzamidine and 1 μg/ml leupeptin . The cleared lysate was applied to Ni-NTA agarose ( Qiagen , Venlo , Netherlands ) , washed with the lysis buffer containing 300 mM NaCl and 50 mM imidazole , and eluted with the same buffer but containing 150 mM NaCl and 300 mM imidazole . The MBP was removed with TEV protease treatment at 4°C for 16 hr or at room-temperature for 2 hr . The protein was further purified using a Source 15Q column ( GE Healthcare , Pittsburgh , PA ) , evolved with a gradient of 150 → 300 mM NaCl in 20 mM imidazole , pH 8 , 1 mM EDTA , and 2 mM DTT , followed by an SD200 column ( GE Healthcare ) run in 25 mM Hepes , pH 7 . 5 , 150 mM NaCl , 1 mM MgCl2 , and 2 mM βME . Fractions containing His8-Nephrin were concentrated using an Amicon Ultra 3 K concentrator ( Millipore , Billerica , MA ) and flash frozen in aliquots at −80°C . 10 . 7554/eLife . 04123 . 023Table 1 . Information on the protein constructs used in this studyDOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 023ProteinsSequence informationNotesNckGHMAEEVVVVAKFDYVAQQEQELDIKKNERLWLLDDSKSWWRVRNSMNKTGFVPSNYVERKNSARKASIVKNLKDTLGIGKVKRKPSVPDSASPADDSFVDPGERLYDLNMPAYVKFNYMAEREDELSLIKGTKVIVMEKCSDGWWRGSYNGQVGWFPSNYVTEEGDSPLGDHVGSLSEKLAAVVNNLNTGQVLHVVQALYPFSSSNDEELNFEKGDVMDVIEKPENDPEWWKCRKINGMVGLVPKNYVTVMQNNPLTSGLEPSPPQCDYIRPSLTGKFAGNPWYYGKVTRHQAEMALNERGHEGDFLIRDSESSPNDFSVSLKAQGKNKHFKVQLKETVYCIGQRKFSTMEELVEHYKKAPIFTSEQGEKLYLVKHLSHuman , WT , residues 1–377N-WASP BPVCAGSEFKEKKKGKAKKKRAPPPPPPSRGGPPPPPPPPHSSGPPPPPARGRGAPPPPPSRAPTAAPPPPPPSRPGVVVPPPPPNRMYPHPPPALPSSAPSGPPPPPPLSMAGSTAPPPPPPPPPPPGPPPPPGLPSDGDHQVPASSGNKAALLDQIREGAQLKKVEQNSRPVSCSGRDALLDQIRQGIQLKSVSDGQESTPPTPAPTSGIVGALMEVMQKRSKAIHSSDEDEDDDDEEDFEDDDEWEDRat , residues 183–193 fused to 273–501Nck ( cysteine-modified ) GHMCMAEEVVVVAKFDYVAQQEQELDIKKNERLWLLDDSKSWWRVRNSMNKTGFVPSNYVERKNSARKASIVKNLKDTLGIGKVKRKPSVPDSASPADDSFVDPGERLYDLNMPAYVKFNYMAEREDELSLIKGTKVIVMEKSSDGWWRGSYNGQVGWFPSNYVTEEGDSPLGDHVGSLSEKLAAVVNNLNTGQVLHVVQALYPFSSSNDEELNFEKGDVMDVIEKPENDPEWWKARKINGMVGLVPKNYVTVMQNNPLTSGLEPSPPQSDYIRPSLTGKFAGNPWYYGKVTRHQAEMALNERGHEGDFLIRDSESSPNDFSVSLKAQGKNKHFKVQLKETVYSIGQRKFSTMEELVEHYKKAPIFTSEQGEKLYLVKHLSHuman , residues 1–377 , with mutations: C139S , C232A , C266S , C340SNephrin3YGGSLEHHHHHHHHGGSCGGSGGSGGSGGSHLYDEVERTFPPSGAWGPLYDEVQMGPWDLHWPEDTFQDPRGIYDQVAGDHuman , residues 1174–1223 , with mutations: Y1183F , Y1210FNephrin2YGGSLEHHHHHHHHGGSCGGSGGSGGSGGSHLFDEVERTFPPSGAWGPLYDEVQMGPWDLHWPEDTFQDPRGIYDQVAGDHuman , residues 1174–1223 , with mutations: Y1176F , Y1183F , Y1210FNephrin1YGGSLEHHHHHHHHGGSCGGSGGSGGSGGSHLFDEVERTFPPSGAWGPLYDEVQMGPWDLHWPEDTFQDPRGIFDQVAGDHuman , residues 1174–1223 , with mutations: Y1176F , Y1183F , Y1210F , Y1217FTIR3YGGSLEHHHHHHHHGGSCGGSGGSGGSGGSHMHIYDEVAADPPPSGAWGHIYDEVAADPWDLHWPEDTFQDPRHIYDEVAADPHuman Nephrin , with pTyr sites replaced by those in EPEC Tir protein ( underlined ) ( SH3 ) 3GHMPAYVKFNYMAEREDELSLIKGTKVIVMEKSSDGWWRGSYNGQVGWFPSNYVTEEGDSPLSARKASIVKNLKDTLGIGKVKRKPSVPDSASPADDSFVDPGERLYDLNMPAYVKFNYMAEREDELSLIKGTKVIVMEKSSDGWWRGSYNGQVGWFPSNYVTEEGDSPLSARKASIVKNLKDTLGIGKVKRKPSVPDSASPADDSFVDPGERLYDLNMPAYVKFNYMAEREDELSLIKGTKVIVMEKSSDGWWRGSYNGQVGWFPSNYVTEEGDSPLNNPLTSGLEPSPPQCDYIRPSLTGKFAGNPWYYGKVTRHQAEMALNERGHEGDFLIRDSESSPNDFSVSLKAQGKNKHFKVQLKETVYCIGQRKFSTMEELVEHYKKAPIFTSEQGEKLYLVKHLSHuman , three repeats of the second Nck SH3 domain , plus the Nck SH2 domain ( SH3 ) 2GHMPAYVKFNYMAEREDELSLIKGTKVIVMEKSSDGWWRGSYNGQVGWFPSNYVTEEGDSPLSARKASIVKNLKDTLGIGKVKRKPSVPDSASPADDSFVDPGERLYDLNMPAYVKFNYMAEREDELSLIKGTKVIVMEKSSDGWWRGSYNGQVGWFPSNYVTEEGDSPLNNPLTSGLEPSPPQCDYIRPSLTGKFAGNPWYYGKVTRHQAEMALNERGHEGDFLIRDSESSPNDFSVSLKAQGKNKHFKVQLKETVYCIGQRKFSTMEELVEHYKKAPIFTSEQGEKLYLVKHLSHuman , two repeats of the second Nck SH3 domain , plus the Nck SH2 domain ( SH3 ) 1GHMPAYVKFNYMAEREDELSLIKGTKVIVMEKSSDGWWRGSYNGQVGWFPSNYVTEEGDSPLNNPLTSGLEPSPPQCDYIRPSLTGKFAGNPWYYGKVTRHQAEMALNERGHEGDFLIRDSESSPNDFSVSLKAQGKNKHFKVQLKETVYCIGQRKFSTMEELVEHYKKAPIFTSEQGEKLYLVKHLSHuman , one repeat of the second Nck SH3 domain , plus the Nck SH2 domainTIR-1pYEEHIpYDEVAADPGGSWGGSCN-terminal rhodamine labeled single pTyr motif from EPEC Tir proteinLckANSLEPEPWFFKNLSRKDAERQLLAPGNTHGSFLIRESESTAGSFSLSVRDFDQNQGEVVKHYKIRNLDNGGFYISPRITFPGLHDLVRHYTNASDGLCTKLSRPCQTQKPQKPWWEDEWEVPRETLKLVERLGAGQFGEVWMGYYNGHTKVAVKSLKQGSMSPDAFLAEANLMKQLQHPRLVRLYAVVTQEPIYIITEYMENGSLVDFLKTPSGIKLNVNKLLDMAAQIAEGMAFIEEQNYIHRDLRAANILVSDTLSCKIADFGLARLIEDNEYTAREGAKFPIKWTAPEAINYGTFTIKSDVWSFGILLTEIVTHGRIPYPGMTNPEVIQNLERGYRMVRPDNCPEELYHLMMLCWKERPEDRPTFDYLRSVLDDFFTATEGQFQPQPHuman , 119–509 , Y505F Nephrin proteins were phosphorylated at 30°C with 20 nM Lck kinase overnight or with 500 nM Lck for 1 hr . The phosphorylation reaction was quenched with 10 mM EDTA . Kinase and incompletely phosphorylated Nephrin were removed using a source 15 Q column evolved with a gradient of 150 → 250 mM NaCl in 25 mM Hepes , pH 7 , and 2 mM βME . The phosphorylated product was further purified using an SD200 column ( GE Healthcare ) and labeled at its single cysteine residue with maleimide-Alexa 488 fluorophore ( Invitrogen , Carlsbad , CA ) . The labeled protein was separated from unreacted fluorophore using a Source 15 Q column and a Hi-trap desalting column ( GE Healthcare ) . Phosphorylation at one , two , or three sites , for Nephrin1Y , Nephrin2Y , or Nephrin3Y ( see Table 1 ) , respectively , was confirmed using mass-spectrometry . GST-Nck and His6-N-WASP were expressed in BL21 ( DE3 ) T1R cells at 18°C through overnight induction with 1 mM IPTG . Cells expressing GST-Nck were collected by centrifugation and lysed by sonication in 20 mM Tris , pH 8 , 200 mM NaCl , 1 mM EDTA , 1 mM DTT , 1 mM PMSF , 1 μg/ml antipain , 1 mM benzamidine , 1 μg/ml leupeptin , and 1 μg/ml pepstatin . The cleared lysate was applied to glutathione sepharose beads ( GE ) and washed with 10 column volumes of 200 mM NaCl , 20 mM Tris , pH 8 , 1 mM DTT , and 1 mM EDTA . The GST tag was removed with TEV protease treatment on the beads at 4°C for 16 hr or at room-temperature for 2 hr . Cleaved Nck was collected by 20 column washes with 20 mM imidazole , pH 7 , and 1 mM DTT and applied to a Source 15 Q column using a gradient of 0 → 200 mM NaCl in 20 mM imidazole , pH 7 , 1 mM DTT . Fractions containing Nck were pooled , concentrated using an Amicon Ultra 30 K concentrator ( Millipore ) , and passed through a Source 15 S column ( GE ) , using a gradient of 0 → 200 mM NaCl in 20 mM imidazole , pH 7 , 1 mM DTT . Fractions containing Nck were concentrated and run through an SD75 column ( GE ) . Pooled fractions were concentrated and flash-frozen in 25 mM Hepes , pH 7 . 5 , 150 mM NaCl , and 1 mM βME . The ( SH3 ) 1 , ( SH3 ) 2 , and ( SH3 ) 3 proteins were purified in the same way but excluding the Source 15 S column . His6-N-WASP expressing cells were collected by centrifugation and lysed by cell disruption ( Emulsiflex-C5 , Avestin ) in 20 mM imidazole , pH 7 , 300 mM KCl , 5 mM βME , 0 . 01% NP-40 , 1 mM PMSF , 1 μg/ml antipain , 1 mM benzamidine , and 1 μg/ml leupeptin . The cleared lysate was applied to Ni-NTA agarose ( Qiagen ) , washed with 300 mM KCl , 50 mM imidazole , pH 7 , 5 mM βME , and eluted with 100 mM KCl , 300 mM imidazole , pH 7 , and 5 mM βME . The elute was further purified over a Source 15 Q column using a gradient of 250 → 450 mM NaCl in 20 mM imidazole , pH 7 , and 1 mM DTT . The His6-tag was removed by TEV protease at 4°C for 16 hr or at room-temperature for 2 hr . Cleaved N-WASP was then applied to a Source 15 S column using a gradient of 110 → 410 mM NaCl in 20 mM imidazole , pH 7 , 1 mM DTT . Fractions containing N-WASP were concentrated using an Amicon Ultra 10 K concentrator ( Millipore ) , passed through an SD200 column , concentrated and flash-frozen in 25 mM Hepes , pH 7 . 5 , 150 mM NaCl , and 1 mM βME . N-WASP ( BPVCA with single cysteine ) and Nck ( cysteine-modified , see Table 1 ) were labeled with Alexa488/568/647 . For labeling purposes , the pure protein after Source15S was desalted into a buffer without reducing agent ( 25 mM Hepes , pH 7 , 150 mM NaCl ) and reacted with a maleimide-conjugated fluorophore for 2 hr at room temperature . The reaction was quenched with DTT and the fluorophore was removed using a Source15Q and SD75/Hi-trap desalting columns . His6-Lck kinase was expressed from baculovirus in Spodoptera frugiperta ( Sf9 ) cells . Cells were harvested in 50 mM Tris , pH 7 . 5 , 100 mM NaCl , 5 mM βME and 0 . 01% NP-40 , 1 mM PMSF , 1 μg/ml antipain , 1 mM benzamidine , and 1 μg/ml leupeptin . Cells were lysed by douncing on ice ∼10 times . The cleared lysate was applied to Ni-NTA agarose beads equilibrated with 20 mM Tris , pH 7 . 5 , 500 mM NaCl , 20 mM imidazole , 5 mM βME , and 10% glycerol ( Buffer A ) , washed with Buffer A containing 1 M NaCl , and then eluted with Buffer A containing 200 mM imidazole 7 . 5 and 100 mM NaCl . The elute was applied to a Source 15 Q column using a gradient of 100 → 300 mM NaCl in 25 mM Hepes , pH 7 . 5 , and 2 mM βME . Collected fractions were concentrated ( Amicon 10 K , Millipore ) and applied to an SD75 column in 25 mM Hepes , pH 7 . 5 , 150 mM NaCl , and 1 mM βME . Liposomes were prepared as follows . A mixture of 99% DOPC and 1% Ni2+-NTA DOGS ( Avanti Polar Lipids , Alabaster , Alabama ) was dried under argon and further dried under vacuum overnight . The dried mixture was hydrated with MilliQ water for 3 hr . Buffer ( 25 mM Hepes , pH 7 . 5 , 150 mM NaCl , 1 mM MgCl2 ) was added to the hydrated multi-lamellar vesicle solution . Small unilamellar vesicles ( SUVs ) were prepared by 21 passes through an extruder ( Avanti ) fitted with 80 nm and again seven times with a fresh 80 nm or 30 nm filter . In our hands , changing the filter and re-extruding produced more consistently homogeneous liposomes . SUVs made by this method were stored at 4°C and used within 2 days of extrusion . To make supported lipid bilayers , chambered glass coverslips ( Lab-tek , Cat #155409 ) were cleaned with 50% isopropanol , washed with Milli-Q water , and then incubated for 2 hr in 6 M NaOH . We found that cleaning the glass and using it within the few hours after cleaning was important to get consistent fluidity of the supported bilayers . Therefore , all experiments were performed within 8 hr of cleaning the glass substrate . After extensive further washes with Milli-Q water , 150 µl of room temperature SUV solution containing 0 . 5 to 1 mg/ml lipid was added to the coverslips and incubated for 10 min . Unadsorbed vesicles were removed by a three-step wash totaling a 216-fold dilution . BSA , 0 . 1% ( Sigma A3294 , protease-free , St . Louis , MO ) in 25 mM Hepes , pH 7 . 5 , 150 mM NaCl , 1 mM MgCl2 was used to block the surface for 45 min , yielding a total solution volume of 200 µl . The surface was washed again with 25 mM Hepes , pH 7 . 5 , 150 mM NaCl , 1 mM MgCl2 , and 0 . 1% BSA in two steps totaling a 36-fold dilution . His8-p-Nephrin was added to the bilayer at 100 nM and incubated for 1 hr and washed twice totaling a 36-fold dilution . This procedure yielded 200 µl solution above the bilayer containing 2 . 8 nM His8-p-Nephrin ( assuming a negligible fraction of the total protein binds the bilayer ) . Subsequent experiments were performed after waiting 30 min to allow the His8 attachment to the bilayer to stabilize ( Figure 2—figure supplement 1D ) . Precise control of the timing and dilution-factor of all wash steps was critical to obtaining consistent p-Nephrin densities on the bilayers ( quantified as described below ) . All experiments were performed in 25 mM Hepes , pH 7 . 5 , 150 mM NaCl , 1 mM MgCl2 , 1 mM BME , and 0 . 1% BSA . The density of His8-p-Nephrin on the supported lipid bilayers was quantified as previously described ( Galush et al . , 2008; Salaita et al . , 2010 ) . Briefly , SUVs containing fluorescent lipid ( OG-DHPE , Invitrogen ) were made as described above and were used to generate a standard curve of OG-DHPE concentration vs fluorescence intensity on a Nikon Eclipse Ti microscope using a 20× objective focusing deep into the solution and away from the glass ( Figure 2—figure supplement 1A ) . The slope of the standard curve was denoted as I-labeled SUV . Using the identical settings , a similar standard curve was made using His8-p-Nephrin-Alexa488 in solution , with slope I-labeled protein ( Figure 2—figure supplement 1B ) . I-labeled protein was identical in the presence or absence of Ni-NTA-containing SUVs at 9 . 5 µM Ni-NTA concentration ( minimum of 158-fold excess over His8-p-Nephrin ) , showing that the His8-p-Nephrin-Alexa488 fluorescence does not change upon binding lipid . The correction factor F , denoted by F = I-labeled protein/I-labeled SUV , represents the intrinsic brightness of and sensitivity of the microscope for His8-p-Nephrin-Alexa488 vs OG-DHPE . Since the OG and Alexa488 fluorophores have very similar excitation and emission spectra , F should be an instrument-independent parameter . The SUVs containing OG-DHPE were combined in different ratios with non-fluorescent SUVs to make supported bilayers with OG-DHPE densities between 0 . 05 and 0 . 4% . Assuming the surface area of the lipid head groups to be 69 Å2 ( Kucerka et al . , 2005 ) , this corresponded to OG-DHPE densities of 1430–11 , 440 molecules/µm2 . A standard curve of bilayer fluorescence intensity on a Nikon Eclipse Ti microscope and a 100× objective vs fluorophore density was then generated from these bilayers . To obtain the density of His8-p-Nephrin-Alexa488 on the supported bilayers , the measured fluorescence intensity was first divided by F , and the result was analyzed with the standard curve of bilayers with OG-DHPE ( Figure 2—figure supplement 1C ) . We note that this approach assumes that F is the same on the SLB as when His8-p-Nephrin-Alexa488 and OG-DHPE are associated with SUVs in free solution . To examine the potential changes in Alexa488 fluorescence as a function of p-Nephrin density , we generated supported bilayers as above with 10–100% Alexa488-labeled p-Nephrin . Intensity remained linear up to ∼60% labeling . Initial measurements suggested that the density change in p-bephrin upon clustering is fourfold . Therefore , we used p-Nephrin labeled with 15% or less Alexa488 for all quantitative image analyses . For critical concentration of clustering measurements , images were collected on a Nikon Eclipse Ti microscope equipped with an Andor iXon Ultra 897 EM-CCD camera , with a 100× objective in epi-fluorescence mode . Background was collected with supported bilayers containing non-fluorescent lipids and subtracted from all images before processing . Images were corrected for uneven illumination and detector sensitivity as previously described ( Wu et al . , 2008 ) . Briefly , pixel intensities across a homogeneous bilayer containing p-NephrinA488 were normalized to the maximum intensity of the image to obtain pixel-by-pixel correction factors ( in a 0 to 1 range ) . Experimental images were then corrected by dividing by these factors . Images were thresholded using the triangle algorithm in Image J . The fractional intensity of the clustered regions was then calculated by dividing the integrated intensity of the thresholded image by that of the non-thresholded image . Analyzing the clusters using the triangle algorithm or the Maximum Entropy algorithm yielded the same critical concentrations . Similar thresholding results were obtained using an iterative manual procedure to identify pixels with intensity greater than three standard deviations above the mean of the non-clustered regions . Thus , our calculation of fractional intensity in the clustered regions and our consequent determination of critical concentration are not dependent on the method used to identify clusters . For the data in Figure 2B , C , 512 by 512 pixel images were taken at 93 randomly selected areas of a sample with clusters made using p-NephrinA488 , 1 µM Nck , and 1 µM N-WASP . The images were background corrected as described above , flattened using the rolling-ball method in ImageJ , and thresholded using the triangle method . The clusters were binned according to size ( excluding those at the image edges ) and the distribution was fit to a single-exponential using Graph-pad Prism . The size distributions in Figure 3B were determined similarly from single images obtained at each time point . To analyze the spatial distribution of puncta , each thresholded image was divided into 25 boxes . In each box , the number of clusters was counted twice—excluding and including clusters at the edges . The average number of edge clusters was obtained from the difference in these values , averaged across all boxes in all images . To eliminate overcounting , for each box half of this value was subtracted from the number of clusters counted including edges . These data were plotted to obtain a frequency histogram using Graph-pad Prism and fit to a Gaussian distribution . FRAP was performed using a Nikon Eclipse Ti microscope equipped with an Andor iXon Ultra EM-CCD camera . A circle of 1-µm diameter was initially photobleached and recovery followed for up to 1000 s . The images were corrected for drift using the Sift-Align plugin in ImageJ ( Schneider et al . , 2012 ) . Background photobleaching was obtained by imaging under the same conditions , excluding the laser illumination used for photobleaching . Background corrected images were normalized to the intensities of the pre-bleached images and fit to either a single or a double-exponential using Graph-pad Prism . F-tests performed in Prism demonstrated that the double-exponential fits are most appropriate ( p-values for all experiments were <0 . 0001 , Table 2 ) . In the FRAP experiments , a glucose-oxidase scavenger system with trolox was used to reduce photobleaching during the recovery period . His8-p-NephrinA488 dissociation from the membrane was monitored by the decrease in total fluorescence measured in TIRF mode following washes that afforded a final solution concentration of 2 . 8 nM ( see ‘Supported lipid bilayers’ section above ) . To limit the effect of photobleaching , the images at each time point were taken at a different area of the bilayer . The data were fit to a single-exponential with time constant of 2080 s . 10 . 7554/eLife . 04123 . 024Table 2 . Statistics of fitting for FRAP dataDOI: http://dx . doi . org/10 . 7554/eLife . 04123 . 024p-NephrinNck ( with p-Nephrin ) Nck ( with p-TIR ) N-WASPNull hypothesisSingle Exp . Single Exp . Single Exp . Single Exp . Alternate hypothesisDouble Exp . Double Exp . Double Exp . Double Exp . p value<0 . 0001<0 . 0001<0 . 0001<0 . 0001Conclusion ( alpha = 0 . 05 ) Reject null hypo . Reject null hypo . Reject null hypo . Reject null hypo . Preferred modelDouble Exp . Double Exp . Double Exp . Double Exp . F ( DFn , DFd ) 64 . 16 ( 2282 ) 47 . 33 ( 2635 ) 46 . 72 ( 2379 ) 48 . 64 ( 2379 ) Actin and Arp2/3 complex were purified from rabbit muscle and bovine thymus , respectively , using established methods ( Doolittle et al . , 2013a; Doolittle et al . , 2013b ) . G-actin ( 1 µM , 10% rhodamine labeled ) was added to p-Nephrin clusters containing 1 μM Nck and 2 μM N-WASP , with or without 10 nM Arp2/3 complex . Images were collected in TIRF mode every 3 min . For quantitative analysis , images were background corrected and thresholded as described above . In the p-Nephrin clusters , the average intensities of p-Nephrin and rhodamine-actin were measured for times up to 27 min . For each cluster , t1/2 represents the time at which the average actin intensity reaches half its maximum value . ITC was performed using a VP-ITC 200 calorimeter ( GE Healthcare ) . Before the experiment , the proteins were dialyzed in the same buffer ( 25 mM Hepes , pH 7 . 5 , 150 mM NaCl , 1 mM MgCl2 , and 2 mM TCEP ) . Nck at 150 μM in the syringe was titrated to either triply phosphorylated Nephrin or triply phosphorylated TIR . We assumed that all the three sites in Nephrin were of equal affinity . Isotherms were fit well using NITPIC ( Keller et al . , 2012 ) and Sedphat ( Houtman et al . , 2007 ) , assuming that all three pTyr sites in p-Nephrin have equal affinity for Nck .	The membrane that surrounds a cell is made up of a mixture of lipid molecules and proteins . Membrane proteins perform a wide range of roles , including transmitting signals into , and out of , cells and helping neighboring cells to stick together . To perform these tasks , these proteins commonly need to bind to other molecules—collectively known as ligands—that are found either inside or outside the cell . Membrane proteins are able to move around within the membrane , and in many systems , ligand binding causes the membrane proteins to cluster together . Although this clustering has been seen in many different systems , no general principles that describe how clustering occurs had been found . Now , Banjade and Rosen have constructed an artificial cell membrane to investigate the clustering of a membrane protein called Nephrin , which is essential for kidneys to function correctly . When it is activated , Nephrin interacts with protein ligands called Nck and N-WASP that are found inside cells and helps filaments of a protein called actin to form . These filaments perform a number of roles including enabling cells to adhere to each other and to move . In Banjade and Rosen's artificial system , when a critical concentration of ligands was exceeded , clusters of Nephrin , Nck and N-WASP suddenly formed . This suggests that the clusters form through a physical process known as ‘phase separation’ . Banjade and Rosen found that this critical concentration depends on how strongly the proteins interact and the number of sites they possess to bind each other . Within the clusters , the three proteins formed large polymer chains . The clusters were mobile and , over time , small clusters coalesced into larger clusters . Even though the clusters persisted for hours , individual proteins did not stay in a given cluster for long and instead continuously exchanged back-and-forth between the cluster and its surroundings . When actin and another protein complex that interacts with N-WASP were added to the artificial membrane system , actin filaments began to form at the protein clusters . Banjade and Rosen suggest that such clusters act as ‘signaling zones’ that coordinate the construction of the actin filaments . Regions that are also found in many other signaling proteins mediate the interactions between Nephrin , Nck and N-WASP . Banjade and Rosen therefore suggest that phase separation and protein polymer formation could explain how many different types of membrane proteins form clusters .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cell", "biology", "physics", "of", "living", "systems" ]	2014	Phase transitions of multivalent proteins can promote clustering of membrane receptors
Transposable elements ( TEs ) are widespread genomic parasites , and their evolution has remained a critical question in evolutionary genomics . Here , we study the relatively unexplored epigenetic impacts of TEs and provide the first genome-wide quantification of such effects in D . melanogaster and D . simulans . Surprisingly , the spread of repressive epigenetic marks ( histone H3K9me2 ) to nearby DNA occurs at >50% of euchromatic TEs , and can extend up to 20 kb . This results in differential epigenetic states of genic alleles and , in turn , selection against TEs . Interestingly , the lower TE content in D . simulans compared to D . melanogaster correlates with stronger epigenetic effects of TEs and higher levels of host genetic factors known to promote epigenetic silencing . Our study demonstrates that the epigenetic effects of euchromatic TEs , and host genetic factors modulating such effects , play a critical role in the evolution of TEs both within and between species . Transposable elements ( TEs ) are genetic elements that can copy and transpose themselves into new genomic locations . Even though there are incidental reports of potentially adaptive TEs ( Daborn et al . , 2002; Schlenke and Begun , 2004; Aminetzach et al . , 2005; González et al . , 2008; Schmidt et al . , 2010; Mateo et al . , 2014; Hof et al . , 2016 ) , they generally lower host fitness ( Mackay , 1989; Pasyukova et al . , 2004 ) and are widely recognized as ‘genomic parasites’ . Despite the deleterious fitness consequences of TEs , they comprise appreciable and highly variable proportions of euchromatic genomes in all eukaryotes surveyed ( Biémont , 2010; Elliott and Gregory , 2015; Chalopin et al . , 2015 ) . However , fundamental questions remain about the mechanisms that restrict the selfish increases in TE copy number and contribute to the wide variation of TEs within and between species . Theoretical analyses predict that , in an outbreeding and meiotically recombining host population , copy number of TEs can be contained ( i . e . reach an equilibrium ) if the increase in copy number through transposition is counterbalanced by the removal of TEs ( Charlesworth and Charlesworth , 1983; Langley et al . , 1983 ) . One possible mechanism for the containment of TEs is to regulate the transposition rate to equal the removal rate . Small RNAs in Drosophila , mammals , and plants are enriched for TE sequences and regulate the transposition of TEs in host germlines ( Girard et al . , 2006; Gunawardane et al . , 2007; Brennecke et al . , 2007; Aravin et al . , 2007; Slotkin et al . , 2009 ) . These small RNAs guide the Ago and/or Piwi subfamilies of Argonaute proteins ( reviewed in [Hutvagner and Simard , 2008] ) to TE transcripts with complementary sequences , resulting in post-transcriptional silencing ( reviewed in [Klattenhoff and Theurkauf , 2008; Girard and Hannon , 2008; Senti and Brennecke , 2010] ) . In addition , TEs can be transcriptionally silenced through small-RNA guided enrichment of repressive epigenetic marks , which include DNA modifications ( such as methylation ) and post-translational histone modifications ( such as di- and tri-methylation of H3 lysine 9 ( H3K9me2/3 ) , [Klenov et al . , 2007; Aravin et al . , 2008; Sienski et al . , 2012; Le Thomas et al . , 2013] ) . Both post-transcriptional and transcriptional silencing mechanisms reduce the RNA and protein output from TEs , and accordingly lower TE transposition rate . However , despite the presence of small-RNA regulation , measured transposition rates of TEs are significantly higher than excision rates ( reviewed in [Charlesworth and Langley , 1989] ) . Furthermore , euchromatic TE insertions in multiple outbreeding species have low population frequencies ( Charlesworth and Langley , 1989; Dolgin et al . , 2008; Lockton et al . , 2008; González et al . , 2008; Lockton and Gaut , 2010; Cridland et al . , 2013; Kofler et al . , 2015 ) , and a reduction in transposition rate alone is unlikely to explain such observations . Alternatively , selection against the deleterious effects of TEs has been theoretically proposed and empirically supported as a major force that removes TE insertions from host populations and shapes the population dynamics of TEs ( reviewed in [Charlesworth and Langley , 1989; Lee and Langley 2010; Barrón et al . , 2014] ) . It is well-established that TEs can be deleterious through their genetic effects , such as inserting into and disrupting genes and other functional elements ( Cooley et al . , 1988; Finnegan , 1992 ) , acting as ectopic regulatory elements ( Feschotte , 2008 ) , and mediating ectopic recombination that results in detrimental chromosomal rearrangements ( Langley et al . , 1988; Montgomery et al . , 1991; Petrov et al . , 2003; Mieczkowski et al . , 2006 ) . On the other hand , TE insertions can also influence the epigenetic states of adjacent functional sequences , interfering with gene regulation ( ‘epigenetic effects’; reviewed in [Slotkin and Martienssen , 2007] ) . A genome-wide study in A . thaliana first established the associations between DNA methylation of TEs and lower transcript levels of adjacent genes ( Hollister and Gaut , 2009 ) . Later studies identified TEs as a major cause for DNA methylation-enriched regions in the A . thaliana genome ( Ahmed et al . , 2011; Schmitz et al . , 2013; Dubin et al . , 2015; Quadrana et al . , 2016; Kawakatsu et al . , 2016; Stuart et al . , 2016 ) , and several demonstrated that this association results from spreading of DNA methylation from epigenetically silenced TEs ( Ahmed et al . , 2011; Quadrana et al . , 2016; Stuart et al . , 2016 ) . Associations between TEs and enrichment of repressive epigenetic marks were also documented in mouse cell lines ( Rebollo et al . , 2012 ) and maize ( Eichten et al . , 2012; West et al . , 2014 ) . However , only ( Hollister and Gaut , 2009 ) explored the influences of these TE-induced enrichment of repressive epigenetic marks on the evolutionary dynamics of TEs . In Drosophila , TE-induced enrichment of repressive epigenetic marks at functional elements in euchromatin was first solidly supported by comparing the epigenetic states of reporter genes in constructs with and without adjacent TEs ( Sentmanat and Elgin , 2012 ) . The same study also found that epigenetic effects of TEs depend on small-RNA targeting , and thus on host-directed transcriptional silencing of TEs . This spreading of repressive epigenetic marks from epigenetically silenced euchromatic TEs is reminiscent of the well-studied position-effect variegation ( PEV ) , in which repressive epigenetic marks from pericentromeric or subtelomeric heterochromatin spread to juxtaposed euchromatic genes and cause stochastic gene silencing ( [Gowen and Gay , 1934] , reviewed in [Girton and Johansen , 2008; Elgin and Reuter , 2013] ) . The extent of PEV is influenced by several genetic factors , including the amount of heterochromatic DNA in a genome ( reviewed in [Girton and Johansen , 2008] ) and heterochromatic enzymatic and structural proteins whose hypomorphic or null mutations enhance or suppress PEV ( known as E ( var ) s and Su ( var ) s respectively , [Elgin and Reuter , 2013; Swenson et al . , 2016] ) . Likewise , the epigenetic effects of TEs on an adjacent reporter genes were observed to be contingent on the expression of two Su ( var ) genes ( Sentmanat and Elgin , 2012 ) . Previously , we used D . melanogaster modEncode epigenomic data ( Nègre et al . , 2011 ) and demonstrated that histone H3K9me3 , a key repressive epigenetic mark , is enriched around euchromatic TEs ( Lee 2015 ) . Importantly , TEs adjacent to genes that are highly enriched with H3K9me3 are more strongly selected against , supporting an important role for TE’s epigenetic effects in its own population dynamics ( Hollister and Gaut , 2009; Lee and Langley 2010 ) . Yet , several critical questions remain . For example , our previous study was based on the reference D . melanogaster strain ( Adams et al . , 2000 ) , which has been maintained as a laboratory stock for many years , and is unlikely to be representative of natural populations . More importantly , single-strain analysis precluded distinguishing whether the enrichment of H3K9me3 at genes was due to TE-induced enrichment of repressive epigenetic marks , or the preferential insertions of TEs into genomic regions already enriched in repressive marks ( Lee 2015 ) . To test the hypothesis that euchromatic TE insertions nucleate repressive epigenetic marks , here we exploit natural variation in the presence/absence of individual TE insertions in D . melanogaster populations . In this species , euchromatic insertions from most TE families segregate at low population frequencies ( Charlesworth and Langley , 1989; Kofler et al . , 2012 , 2015; Cridland et al . , 2013 ) . Accordingly , randomly selected , unrelated individuals usually share few TE insertions . This will provide a direct comparison of epigenetic states at homologous sequences with and without the presence of TEs , and allow distinguishing the causal relationship between the presence of TEs and the enrichment of repressive epigenetic marks . Importantly , TEs only comprise 5 . 4% of the D . melanogaster euchromatic genome ( Hoskins et al . , 2015 ) , and the epigenetic effects of individual TE insertions can thus be determined . In this study , analyses of the epigenomes of two recently established , wild-derived , inbred D . melanogaster strains showed that euchromatic TEs are responsible for the enrichment of repressive epigenetic marks in flanking regions . Further , analysis of individual insertions revealed that more than half of euchromatic TEs are associated with epigenetic effects on flanking sequences , demonstrating their pervasive impact on the Drosophila genome . Importantly , we found evidence supporting stronger selection against TE insertions with more extensive epigenetic effects . Comparisons between the closely related D . melanogaster and D . simulans revealed that the epigenetic effects of TEs also vary between species , and correlate with variation in host genetic factors that regulate epigenetic silencing . Our results support that the epigenetic effects of euchromatic TEs , and host genetic factors that modulate these effects , play an important role in the population dynamics of TEs within and between species . In Drosophila , repressive histone modifications H3K9me2/3 ( Kouzarides , 2007; Grewal and Elgin , 2007 ) and their cognate ‘reader’ protein Heterochromatin Protein 1a ( HP1a ) ( Eissenberg and Elgin , 2014 ) play a dominant role in the initiation and maintenance of repressive chromatin states in heterochromatin . Our previous study showed that euchromatic sequences flanking TEs have strong enrichment for H3K9me3 in the reference D . melanogaster strain ( Lee 2015 ) . Using modEncode ChIP-seq data generated from the Oregon-R strain , we observed that sequences flanking euchromatic TEs are also enriched for another key heterochromatic histone modification ( H3K9me2 ) and HP1a , while depleted for ‘active’ histone modifications H3K4me2 and H3K4me3 , which are enriched at transcribing promoters ( Kouzarides , 2007; Kharchenko et al . , 2011 ) ( Figure 1 ) . Interestingly , enrichment for repressive epigenetic marks around TEs is strongest at the embryonic stage and weaker at later developmental stages , consistent with our previous study of only H3K9me3 ( Lee 2015 ) . 10 . 7554/eLife . 25762 . 003Figure 1 . Epigenetic states of euchromatic sequences around TEs . Euchromatic sequences around TEs are enriched for ( A ) repressive epigenetic marks ( H3K9me2 , H3K9me3 , and HP1a ) , ( B ) and depleted for active epigenetic marks ( H3K4me2 and H3K4me2 ) in Oregon-R . Different colors represent different developmental stages . Plots were generated using LOESS smoothing ( span = 10% ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 003 To investigate if the enrichment of repressive epigenetic marks is TE-induced or results from the preferential insertion of TEs into regions already enriched for repressive epigenetic marks , we performed Chromatin Immuno-Precipitation and sequencing ( ChIP-seq ) on H3K9me2 using two inbred , wildtype D . melanogaster strains collected in North Carolina , USA ( RAL315 and RAL360 from Drosophila Genetic Reference Panel or DGRP [Mackay et al . , 2012] ) . These strains have been fully sequenced and annotated for the locations of euchromatic TE insertions ( Rahman et al . , 2015 ) , allowing direct comparison of the epigenetic status of allelic regions with and without TEs . Our ChIP-Seq analyses of H3K9me2 distributions ( see Materials and methods ) in 4–8 hr RAL315 and RAL360 embryos , which contain fully-formed heterochromatin ( Yuan and O'Farrell , 2016 ) , only included TE insertions annotated with high confidence and unique to either strain ( see Materials and methods ) . Importantly , we used highly conservative heterochromatin-euchromatin boundaries ( 0 . 5 Mb distal from epigenetically defined boundaries [Riddle et al . , 2011] ) . This ensures that only euchromatic sequences and TEs were included in the analysis , and prevents confounding effects from pericentromeric or subtelomeric heterochromatin . Because the ChIP-Seq data were generated using whole animals that contain multiple cell types , combined with the stochastic nature of heterochromatic silencing , H3K9me2 enrichment reflects the average epigenetic states of all cells in the samples . Accordingly , we analyzed the enrichment of H3K9me2 quantitatively , instead of as binary states ( see Materials and methods ) . We compared the epigenetic states of euchromatic sequences around all TE insertions present in one strain with those of homologous alleles lacking the TE insertions in the other strain . The presence of TEs correlated with substantially higher H3K9me2 enrichment ( Figure 2 ) , which strongly supports the conclusion that these repressive mark enrichments are due to TE insertions , and not pre-existing epigenetic states . To quantify the epigenetic effects of individual TEs , we compared H3K9me2 fold enrichment in strains with and without a TE using non-overlapping 1 kb windows around each TE insertion ( Figure 2—figure supplement 1 ) . A TE was counted as having epigenetic effects if the H3K9me2 enrichment level was significantly higher in the strain with the TE than the other strain in the 0–1 kb windows flanking the TE insertion . We also estimated the ‘extent of H3K9me2 spread’ from the TE insertion ( the farthest window in which H3K9me2 enrichment was consecutively and significantly higher in the strain with the TE ) and the ‘% increase in H3K9me2 enrichment’ ( the difference in H3K9me2 enrichment between the two strains in 0–1 kb windows; see Materials and methods and Figure 2—figure supplement 1 ) . 10 . 7554/eLife . 25762 . 004Figure 2 . Euchromatic sequences around TE insertions are enriched for H3K9me2 . Levels of H3K9me2 enrichment were compared between homologous sequences of two D . melanogaster strains . Left: sequences around TEs in strain RAL315 that are absent in RAL360 . Right: sequences around TEs in strain RAL360 that are absent in RAL315 . H3K9me2 fold enrichment was averaged over all euchromatic sequences flanking the analyzed TEs . Plots were generated using LOESS smoothing ( span = 10% ) . Upper figures show ±50 kb around TE insertions , while lower figures show expanded views of ±20 kb . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 00410 . 7554/eLife . 25762 . 005Figure 2—source data 1 . Estimates of epigenetic effects for D . melanogaster TE insertions . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 00510 . 7554/eLife . 25762 . 006Figure 2—figure supplement 1 . Three indexes describing the epigenetic effects of TE insertions . Taking one TE present in RAL360 ( green line ) and absent in RAL315 ( orange line ) as an example , we compared H3K9me2 fold enrichment in the two strains for 20 kb on each side of the insertion site ( 40 kb total ) , using 1 kb nonoverlapping windows ( dashed lines ) . A TE is counted as having epigenetic effects if the H3K9me2 enrichment is significantly higher ( determined by Mann-Whitney U test ) in the strain with the TE ( RAL360 here , green line ) than in the strain lacking the TE ( RAL 315 here , orange line ) in 0–1 kb windows around TE insertion site ( shaded gray area ) . The ‘extent of H3K9me2 spread’ is the farthest window in which H3K9me2 enrichment was consecutively and significantly higher in the strain with the TE . Note that H3K9me2 enrichment needs to be significantly higher in windows on both left and right sides of TE insertions . The ‘% increase in H3K9me2 enrichment’ is the difference in H3K9me2 enrichment between the two strains , divided by the enrichment level of the strain without TE , in 0–1 kb windows ( shaded gray area ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 00610 . 7554/eLife . 25762 . 007Figure 2—figure supplement 2 . The distribution of TE insertions with epigenetic effects . The number of TE insertions analyzed at each genomic window is represented with gray bars , while the number of such TE insertions with epigenetic effects is represented with blue bars . The start and end of euchromatic regions analyzed are labeled with vertical lines . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 00710 . 7554/eLife . 25762 . 008Figure 2—figure supplement 3 . IDR ( irreproducible rate ) analysis plots ( Li et al . , 2011 ) for replicates of D . melanogaster RAL strain ChIP samples . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 008 Surprisingly , more than half of the euchromatic TEs ( 54 . 2% of 419 TEs analyzed ) were associated with enrichment for H3K9me2 in at least 1 kb of adjacent sequences . The TE-induced spreading of H3K9me2 in flanking sequence extended for a mean of 4 . 50 kb ( standard deviation 4 . 59 kb ) , and an average of 79 . 8% increase in H3K9me2 enrichment at the TE insertion site ( standard deviation 78 . 8% , Figure 2—source data 1 ) . These observations revealed that the epigenetic effects of euchromatic TEs in D . melanogaster are not only pervasive and extensive , but also highly variable between TE insertions . Previous investigations using randomly inserted transgenic constructs in D . melanogaster found that the epigenetic effects of TEs depend on proximity to pericentromeric or subtelomeric heterochromatin ( Sentmanat and Elgin , 2012 ) , and on local repeat density ( Huisinga et al . , 2016 ) . Our analysis focused on regions far from these heterochromatic regions , and showed that TEs associated with H3K9me2 enrichment at flanking sequences are not concentrated around pericentromeric or subtelomeric heterochromatin ( Figure 2—figure supplement 2 ) . Also , local repeat density does not differ between TEs that are or are not associated with H3K9me2 spreading ( Mann-Whitney U test , p=0 . 55 ) . Similarly , we observed no correlations between the extent or magnitude of TE’s epigenetic effects and local repeat density ( Spearman rank correlation test , p=0 . 81 ( repeat density vs extent of H3K9me2 spread ) , 0 . 65 ( repeat density vs % increase in H3K9me2 ) ) . These results demonstrate that epigenetic influences of TEs are not restricted to specific genomic locations or contexts , and can be observed across diverse euchromatic regions . While our results demonstrate that euchromatic TEs have widespread epigenetic effects in D . melanogaster , we also found that the epigenetic effects of individual TE insertions vary significantly . In particular , there is substantial variation in the epigenetic effects of insertions from different TE families ( Figure 3 ) . Many biological properties differ between TE families , including transposition mechanism ( Wicker et al . , 2007 ) , genome abundance ( Kaminker et al . , 2002; Quesneville et al . , 2005 ) , and targeting by small RNAs ( Gunawardane et al . , 2007; Brennecke et al . , 2007 , 2008; Ghildiyal et al . , 2008; Czech et al . , 2008 ) . We investigated which properties are associated with stronger epigenetic effects of insertions from a TE family . 10 . 7554/eLife . 25762 . 009Figure 3 . Variation in the epigenetic effects of different TE families . There is substantial variation in the ( A ) proportion of TEs with epigenetic effects , ( B ) mean extent of H3K9me2 spread , and ( C ) mean % increase in H3K9me2 enrichment of the TE families analyzed . Different colors denote different types of TEs . The number of observations for each TE family is in parenthesis in ( A ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 009 Based on transposition mechanisms , there are three major types of TE families: Long Terminal Repeats ( LTR ) retrotransposons , non-LTR retroposons , and Terminal Inverted Repeats ( TIR ) transposons . An immediately obvious pattern is that LTR-type TE families seem to have the strongest epigenetic effects . The LTR copia family has the largest proportion of insertions with epigenetic effects , and LTR roo insertions display both the most extensive average spread of H3K9me2 and the largest average increase in H3K9me2 enrichment in flanking sequences ( Figure 3 ) . Similarly , eight of 11 TE families in which over half of analyzed insertions showed epigenetic effects are LTR-type , while the remaining three are TIR-type . The two TE families with >5 kb average spread of H3K9me2 and the four families that yield >50% mean increase in H3K9me2 enrichment are all LTR-type families . To formally test if LTR-type TE families have stronger epigenetic effects than other types of TE families , we estimated the proportion of TEs with epigenetic effects , the average extent of H3K9me2 spread , and average % increase in H3K9me2 enrichment of TE insertions from each TE family and compared these metrics between LTR-type and other types of TE families . Indeed , LTR-type TE families show a larger increase in H3K9me2 enrichment compared to other types of TEs ( Mann-Whitney U test p=0 . 00047 , median: 0 . 547 ( LTR ) vs 0 . 352 ( others ) , Figure 4 ) . The other two indexes are not significantly different , likely due to the high heterogeneity between LTR-type TE families ( Figure 4 ) . 10 . 7554/eLife . 25762 . 010Figure 4 . Quantitative analysis of the epigenetic effects of different types of TE families . While there are no significant differences in ( A ) the proportion of TEs with epigenetic effects and ( B ) the mean extent of H3K9me2 spread , ( C ) TE insertions of LTR-type families lead to significantly higher mean % increase of H3K9me2 enrichment in flanking sequences . Note that each data point represents one TE family . ( *** Kruskal-Wallis test p<0 . 005 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 01010 . 7554/eLife . 25762 . 011Figure 4—figure supplement 1 . Scatter plot for the abundance of a TE family ( X-axis ) and the proportion of TEs with epigenetic effects ( Y-axis ) . Outlier TE families are denoted in blue . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 011 In Drosophila , TEs are targeted by two types of small RNAs: piRNAs in the germline ( Gunawardane et al . , 2007; Brennecke et al . , 2007 ) and endo-siRNAs in the soma ( Ghildiyal et al . , 2008; Czech et al . , 2008 ) . The epigenetic silencing of TEs in the germline and early embryo , which is maintained through development ( Gu and Elgin , 2013 ) , depends on piRNAs ( Klenov et al . , 2007; Sentmanat and Elgin , 2012; Le Thomas et al . , 2013 ) , while the role of endo-siRNAs in epigenetic silencing of TEs is currently less clear . Consistently , we observed that TE families targeted by more piRNAs show more extensive H3K9me2 spreading and enrichment in flanking sequences ( Table 1 ) . It is worth noting that there is no difference in the amount of piRNAs targeting LTR-type TEs compared to other major types of TEs ( Mann-Whitney U test , p=0 . 19 ( wK ) and 0 . 39 ( w1118 ) ) , suggesting that the observed correlation between the amount of piRNAs and TE’s epigenetic effects was unlikely solely driven by stronger epigenetic effects of LTR-type TE families . On the other hand , we did not find significant associations between the epigenetic effects of TEs and targeting by endo-siRNAs ( Table 1 ) . 10 . 7554/eLife . 25762 . 012Table 1 . Spearman rank correlation tests between properties of TE families and the epigenetic effects of TEs . piRNA amounts were estimated from two studies ( two genotypes: w1118 and wK ) and siRNA counts were estimated from two studies ( Ghildiyal et al . , 2008; Czech et al . , 2008 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 012prop . TE with epigenetic effectsmean extent of H3K9me2 spreadmean % of increase in H3K9me2 enrichmentp-valueρp-valueρp-valueρpiRNA amount ( w1118 ) 5 . 18E-010 . 1211 . 67E-020 . 4651 . 13E-020 . 493piRNA amount ( wK ) 9 . 99E-010 . 0003 . 41E-030 . 5537 . 09E-030 . 521siRNA counts ( Czech et al . , 2008 ) 2 . 90E-010 . 1934 . 99E-010 . 1421 . 24E-010 . 316siRNA counts ( Ghildiyal et al . , 2008 ) 6 . 08E-010 . 1087 . 46E-01−0 . 0751 . 46E-010 . 329family copy no . 3 . 61E-030 . 4736 . 24E-010 . 0956 . 59E-010 . 085 It has been observed that insertions of abundant TE families are under stronger purifying selection than those of less abundant TE families , and several mechanisms were proposed to account for this copy-number dependency ( reviewed in [Barrón et al . , 2014] ) . Because the generation of piRNAs involves TE transcripts ( Gunawardane et al . , 2007; Brennecke et al . , 2007 ) , it was predicted that for a given TE family the epigenetic effects of TEs , and the associated strength of selection that influences the population dynamics of TEs , should also depend on TE copy number ( Lee and Langley 2010; Lee 2015 ) . Supporting this prediction , TE families with higher copy numbers in a large sample of African flies ( Kofler et al . , 2015 ) have larger proportions of insertions with epigenetic effects ( Table 1 ) . This strong correlation is not driven by TE families with exceptional abundance ( Figure 4—figure supplement 1 ) , because the removal of those TE families does not qualitatively change the results ( Spearman rank ρ = 0 . 46 , p=0 . 0058 ) . In summary , TE families of LTR-type , targeted by larger amounts of piRNAs , or of higher abundance display stronger epigenetic effects on adjacent sequences than other TE families . Given the high density of genes and other functional elements in Drosophila ( modENCODE Consortium et al . , 2010 ) , H3K9me2 spreading from TEs to adjacent sequences is expected to have functional consequences . Accordingly , TE insertions with epigenetic effects should more likely be selected against and have lower population frequencies than TEs without H3K9me2 spreading . Population genomic analysis indicated that Zambia is the likely ancestral origin of D . melanogaster , and Zambian populations have limited admixture from non-African genomes ( Pool et al . , 2012; Lack et al . , 2015 ) . Demographic history should thus have less effect on the analysis of TE population frequencies in the Zambian population compared to non-ancestral populations . Accordingly , we used genome sequences of a Zambian D . melanogaster population ( Lack et al . , 2015 ) to determine the population frequencies of individual TE insertions in the two DGRP strains analyzed ( RAL315 and RAL360 ) , which were first collected in North America . Consistent with previous genome-wide observations that most TE insertions have low population frequencies in D . melanogaster ( González et al . , 2008; Kofler et al . , 2012 , 2015; Cridland et al . , 2013 ) , only 31 . 5% of TE insertions present in either of the two DGRP strains analyzed were found in the Zambian population , and these TEs displayed very low population frequencies ( 0 . 54% ( first quartile ) , 0 . 56% ( median ) , 1 . 61% ( third quartile ) , Figure 5—figure supplement 1 ) . We categorized TE insertions in the two DGRP strains according to their presence in the Zambian population ( ‘high frequency’ – present , ‘low frequency’ – not present ) . Low frequency TEs were more likely to exhibit spreading of H3K9me2 ( Fisher’s Exact Test , p=0 . 039 , odds ratio = 1 . 58 , Figure 5A ) , led to more extensive spreading ( Mann-Whitney U test , p=0 . 011 , Figure 5B and Figure 5—figure supplement 2A ) , and resulted in a larger increase in H3K9me2 enrichment ( Mann-Whitney U test , p=0 . 014 , Figure 5C and Figure 5—figure supplement 2B ) . Consistently , by analyzing the population frequencies of individual TE insertions , we observed significant negative correlations between the strength of a TE’s epigenetic effects and its population frequency ( Spearman rank ρ = −0 . 15 ( extent of H3K9me2 spread ) and −0 . 14 increase in H3K9me2 ) , p<0 . 005 for both , Figure 5—figure supplement 3 ) . 10 . 7554/eLife . 25762 . 013Figure 5 . TEs with different population frequencies show different strength of epigenetic effects . TEs with low population frequencies are ( A ) more likely to show spread of H3K9me2 , ( B ) result in more extensive spread of H3K9me2 , and ( C ) lead to a larger increase in H3K9me2 enrichment . ( Mann-Whitney U test , p<0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 01310 . 7554/eLife . 25762 . 014Figure 5—figure supplement 1 . Histogram for the population frequencies of analyzed TE insertions in the Zambian population . Most TEs have zero ( ‘low frequency’ TEs , see text ) or low population frequencies in the Zambian population . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 01410 . 7554/eLife . 25762 . 015Figure 5—figure supplement 2 . The epigenetic effects of TEs with low and high population frequencies . ( A ) the extent of H3K9me2 spread , and ( B ) the % increase in H3K9me2 enrichment were plotted for individual TE insertion . Median is denoted by horizontal lines . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 01510 . 7554/eLife . 25762 . 016Figure 5—figure supplement 3 . X-Y plot for TE’s epigenetic effects and population frequencies in the Zambian population . ( A and B ) TE’s population frequency vs TE-induced extent of H3K9me2 spread , and ( C and D ) TE’s population frequency vs TE-induced % increase in H3K9me2 . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 016 A potential confounding factor for our observation is that the population frequencies of TE insertions vary between TE families ( i . e . insertions from specific TE families tend to have high/low population frequencies , [Petrov et al . , 2011; Kofler et al . , 2012 , 2015] ) . Thus , ‘low’ and ‘high’ frequency categories of TE insertions could be comprised of insertions from different TE families , whose variation in population frequencies could be due to factors other than the differential strength of selection removing TE insertions ( Blumenstiel , 2011; Blumenstiel et al . , 2014 ) . To address this issue , we performed multiple regression analyses that jointly consider the impact of TE’s epigenetic effects and family identity on the population frequencies of TEs ( see Materials and methods ) . Because most TEs in the two DGRP strains analyzed were not detected in the Zambian population ( Figure 5—figure supplement 1 ) , we treated the frequency of TE insertions ( the number of individuals in which a TE insertion is present in the Zambian population ) also as dichotomous variable ( ‘high frequency’ TE or not , see Materials and methods ) . Even accounting for the effect of TE family identity , the regression coefficients for TE’s epigenetic effects on population frequencies are still negative for all the regression models analyzed , and are statistically significant for a majority of the models ( Table 2 ) , suggesting that TE family identity is unlikely a major contributor for the negative associations between TE’s epigenetic effects and population frequencies . 10 . 7554/eLife . 25762 . 017Table 2 . Regression analysis for the associations between TE’s epigenetic effects and population frequencies while accounting for the influence of TE family identity . Population frequencies of individual TE insertion ( response variable ) were modeled as either dichotomous variable ( ‘high frequency’ TE or not ) or count ( TE count ) . Because the distribution of TE count is overdispersed , TE count was modeled as either ‘quasipoission’ or ‘negative binomial’ in regression analyses . The influence of TE family identity was treated as either fixed or random effect . Also see Table 2—source data 1 for regression coefficients for all TE families . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 01710 . 7554/eLife . 25762 . 018Table 2—source data 1 . Regression coefficients for the epigenetic effects of TEs ( extent of spread and magnitude of spread ) and each TE family . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 018Extent of spreadMagnitude of spreadResponse variableFamily identityp-valueRegression coefficientp-valueRegression coefficient‘high frequency’ TE or notfixed effect4 . 72E-01−0 . 0293 . 37E-01−0 . 246random effect1 . 58E-01−0 . 0494 . 83E-02−0 . 409TE count ( quasipoisson ) fixed effect4 . 00E-03−0 . 1884 . 73E-03−1 . 121random effect3 . 20E-03−0 . 1361 . 71E-04−1 . 400TE count ( negative binomial ) fixed effect5 . 25E-04−0 . 1512 . 31E-04−1 . 041random effect9 . 19E-05−0 . 1385 . 49E-05−0 . 986 An alternative explanation for the observed negative associations between TE’s epigenetic effects and population frequencies is that TEs without epigenetic effects tend to occur in regions of low meiotic recombination . TE insertions in regions with low meiotic recombination are repeatedly observed to have higher population frequencies than TEs in other genomic regions ( Charlesworth and Lapid , 1989; Charlesworth et al . , 1992; Bartolomé and Maside , 2004; Kofler et al . , 2012; Cridland et al . , 2013 ) . A lower probability of recombination between TE insertions at different genomic locations ( ectopic exchange [Langley et al . , 1988; Montgomery et al . , 1991] ) and/or reduced efficacy of selection against TEs due to selective interference ( Hill and Robertson , 1966; Felsenstein , 1974 ) have been proposed to account for these observations ( reviewed in [Charlesworth and Langley , 1989; Lee and Langley 2010; Barrón et al . , 2014] ) . However , we observed that recombination rates do not differ between TEs with or without spreading of H3K9me2 ( Mann-Whitney U test , p=0 . 83 ) . Similarly , neither the extent of H3K9me2 spread nor the increase in H3K9me2 enrichment in flanking sequences was correlated with the local recombination rate for individual TE insertions ( Spearman rank correlation test , p=0 . 62 ( recombination rate vs extent of H3K9me2 spread ) and 0 . 55 ( recombination rate vs % increase in H3K9me2 enrichment ) ) . It is unlikely that variation in recombination rates can account for the observations that TEs with stronger epigenetic effects have lower population frequencies . Overall , these results strongly support the proposed selection against the epigenetic effects of TEs . We hypothesized that selection against TEs with epigenetic effects result from the associated functional consequences , in particular influences on the epigenetic states of adjacent functional elements . To investigate the predicted epigenetic influence of TEs on adjacent genes , we categorized euchromatic protein coding genes according to their shortest distance to a TE ( 0–1 kb , 1–2 kb , 2–5 kb , 5–10 kb , and no TE within 10 kb; see Materials and methods ) . Within each of the two strains analyzed , genes in proximity to TEs are more enriched for H3K9me2 ( Figure 6—figure supplement 1 ) , consistent with previous observations ( Lee 2015 ) . To investigate the influence of TEs on the epigenetic states of homologous alleles , we calculated a z-score that compares the H3K9me2 enrichment of genic alleles with and without TEs located within 10 kb ( see Materials and methods ) . The absolute value of the z-score reports the magnitude of differences in H3K9me2 enrichment between homologous alleles in the two strains , and the sign indicates if the allele with adjacent TEs has higher H3K9me2 enrichment ( positive: yes , negative: no ) . As expected , genes with adjacent TEs in either strain have significantly higher , positive z-scores compared to genes distant from TEs in both strains ( Figure 6A ) . We further investigated if the differential epigenetic states between homologous alleles depend on TE-induced epigenetic effects , or only on the presence of TEs . For all categories of genes within 10 kb from TEs , z-scores are significantly higher for genes whose neighboring TEs exhibit H3K9me2 spreading ( Figure 6B ) . Consistently , there are significant positive correlations between the z-scores of genes and the extent of epigenetic effects from the nearest TEs ( vs . the extent of H3K9me2 spread: Spearman rank ρ = 0 . 31 , p<10−15; vs . % increase in H3K9me2 enrichment: Spearman rank ρ = 0 . 30 , p<10−15 ) . It is worth noting that genes whose nearest TEs did not exhibit epigenetic effects have similar z-scores to genes without TEs within 10 kb ( dashed line , Figure 6B ) . These observations demonstrate that the spread of repressive epigenetic marks from euchromatic TEs leads to substantial epigenetic differences at homologous alleles of adjacent coding genes . 10 . 7554/eLife . 25762 . 019Figure 6 . The epigenetic effects of TEs on adjacent protein coding genes . ( A ) Alleles with adjacent TEs have higher H3K9me2 enrichment compared to homologous alleles in the strain that lacks adjacent TEs , as indicated by positive z-scores ( see text ) , and the strength of the effect decreases with distance from TEs . ( B ) Genes adjacent to TEs with epigenetic effects show stronger differential enrichment for H3K9me2 than genes adjacent to TEs without epigenetic effects . ( C ) Genes adjacent to low frequency TEs with epigenetic effects , which likely experienced stronger selection against them , show stronger differential enrichment of H3K9me2 than genes adjacent to high frequency TEs with epigenetic effects ( Mann-Whitney U test , p<0 . 05 , p<0 . 01 , p<0 . 001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 01910 . 7554/eLife . 25762 . 020Figure 6—figure supplement 1 . H3K9me2 enrichments at genes decreases with distance from TEs in both RAL315 ( left ) and RAL360 ( right ) . Significant levels are comparisons to genes without TEs in 10 kb ( gray ) ( Mann-Whitney U test , p<0 . 05 , p<0 . 01 , p<0 . 001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 020 TE insertions with stronger enrichment of repressive marks in adjacent functional alleles should lead to more deleterious functional consequences . We predict these TEs should be under stronger purifying selection and have lower population frequencies than other TEs . To test this hypothesis , we further restricted the analysis to genes whose nearest TEs show spreading of H3K9me2 . Among these genes , those whose nearest TEs were absent in the Zambian population ( ‘low frequency’ TEs , see above ) have significantly higher z-scores than genes adjacent to ‘high frequency’ TEs ( Mann-Whitney U test , p=0 . 0019 , median: 0 . 95 ( genes near low frequency TEs ) vs 0 . 32 ( genes near high frequency TEs ) ) . The observed differences are most prominent for genes within 1 kb of TEs ( Figure 6C ) . Consistently , there is a significant negative correlation between the z-score of a gene and the population frequency of its nearest TE ( Spearman rank ρ = −0 . 18 , p<10−3 ) . A potential functional consequence of H3K9me2 enrichment is reduced transcript levels of influenced alleles . To address this possibility , we performed RNA-seq of developmental stage-matched embryos . Within either strain , there are indeed significant negative correlations between H3K9me2 enrichment and transcript levels for genes within 10 kb of TEs ( Spearman rank ρ = −0 . 35 ( RAL315 ) and −0 . 33 ( RAL360 ) , p<10−16 for both ) . To compare the differential epigenetic states and transcript levels between homologous alleles , we calculated fold changes in expression and z-scores for H3K9me2 enrichment level using RAL 360 allele as reference ( Figure 7A ) . Note this is different from the z-score used above , which uses the allele without TE as reference . We found an excess number of genes that support the influence of TE’s epigenetic effects on gene expression ( higher H3K9me2 enrichment and lower expression of alleles with adjacent TEs , shaded green area in Figure 7A ) for genes with TEs within 10 kb when compared to other genes in the genome ( Figure 7B , upper 2 × 2 Tables ) . Restricting the analysis to TEs with epigenetic effects produced an even larger proportion of genes whose TE-neighboring alleles have higher H3K9me2 enrichment and lower expression ( Figure 7B , bottom 2 × 2 Tables ) . Intriguingly , the excess number of genes supporting TE-induced epigenetic effects on expression was mainly observed for one of the two strains analyzed ( RAL360 ) . Furthermore , while we found a weak , but significant , negative correlation between z-scores for H3K9me2 enrichment and fold changes in expression for genes without TEs ( Spearman rank ρ = −0 . 035 , p=0 . 0084 ) , there are no such correlations observed for genes with TEs within 10 kb ( Spearman rank test p=0 . 57 ( RAL315 ) and 0 . 16 ( RAL360 ) ) . In fact , there are multiple genes whose alleles associated with TEs have higher enrichment of H3K9me2 , but also higher expression ( e . g . arrows in Figure 7A ) . These observations suggest that the influence of TE-induced epigenetic states on gene expression may be more complex ( see Discussion ) . 10 . 7554/eLife . 25762 . 021Figure 7 . Differential H3K9me2 enrichment and RNA transcript levels of protein coding genes with and without adjacent TE insertions . ( A ) The z-score for H3K9me2 enrichment ( X-axis ) and log2 expression fold change ( Y-axis ) were plotted for euchromatic protein coding genes without TEs within 10 kb ( ‘neither’ ) and for genes with adjacent TEs in either strain . It is worth noting that both H3K9me2 z-score and log2 expression fold change used RAL360 as reference . Shaded green areas are genes displaying the expected negative influence of TE’s epigenetic effects on gene expression ( i . e . alleles adjacent to TEs have higher H3K9me2 enrichment and lower RNA transcript levels ) , while shaded orange areas are all other cases of epigenetic states and transcript levels . For each sub-plot , the numbers of genes ( blue , pink , or gray dots ) in each quarter are shown in black , and the numbers of genes whose nearest TEs with epigenetic effects ( blue dots ) are shown in blue . ( B ) Left: 2 × 2 contingency table for comparing the number of genes supporting the influence of TE’s epigenetic effects on gene expression ( shaded green ) and the number of other genes ( shaded orange ) , against those for genes without TEs within 10 kb ( ‘neither’ ) . Middle and right: 2 × 2 contingency tables for testing if there is an excess number of genes with TEs in RAL315 ( middle ) and in RAL360 ( right ) supporting the influence of TE-induced epigenetic effects on gene expression . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 021 D . simulans diverged from D . melanogaster only four million years ago ( Obbard et al . , 2012 ) , yet is widely observed to harbor fewer TE insertions compared to D . melanogaster ( Dowsett and Young , 1982; Vieira et al . , 1999; Vieira and Biémont , 2004; Kofler et al . , 2015 ) . We hypothesized that variation in the epigenetic effects of TEs , and thus strength of selection against them , contributes to this between-species difference in TE content . To test this hypothesis , we performed H3K9me2 ChIP-seq on 4–8 hr embryos from the D . simulans reference strain . Similar to D . melanogaster , we observed strong H3K9me2 enrichment in sequences flanking TEs ( Figure 8A ) , and genes adjacent to TEs have higher H3K9me2 enrichment than genes distant from TEs ( Figure 8—figure supplement 1 ) . Furthermore , genes adjacent to TEs with epigenetic effects ( see below ) have higher H3K9me2 enrichment than genes adjacent to TEs without epigenetic effects ( Figure 8—figure supplement 1 ) . For genes within 10 kb of TEs , there is also a strong negative correlation between H3K9me2 enrichment and transcript levels ( Spearman rank ρ = −0 . 45 , p<10−16 ) , supporting a functional consequence of H3K9me2 enrichment in D . simulans . 10 . 7554/eLife . 25762 . 022Figure 8 . D . simulans TEs show stronger epigenetic effects than D . melanogaster TEs . ( A ) Enrichment of H3K9me2 is also observed at sequences adjacent to euchromatic TEs in D . simulans . ( B ) Compared to insertions of the same TE family in D . melanogaster , insertions in D . simulans are more likely to show epigenetic effects ( proportion of TE spread ) and a larger increase in relative H3K9me2 fold enrichment in adjacent sequences . FE: fold enrichment , D . mel: D . melanogaster , D . sim: D . simulans . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 02210 . 7554/eLife . 25762 . 023Figure 8—source data 1 . Estimates of epigenetic effects for D . simulans TEs . Estimates of D . melanogaster TEs using the same methods are also included . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 02310 . 7554/eLife . 25762 . 024Figure 8—figure supplement 1 . H3K9me2 enrichment at genes adjacent to TEs in D . simulans . Compared to genes without TEs within 10 kb , genes with adjacent TEs have higher enrichment for H3K9me2 . For genes at equal distance from TEs , genes adjacent to TEs with epigenetic effects ( darker gray ) have higher H3K9me2 enrichment than genes adjacent to TEs without epigenetic effects ( lighter gray ) . ( Mann-Whitney U test , p<0 . 05 , p<0 . 01 , *p<0 . 001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 02410 . 7554/eLife . 25762 . 025Figure 8—figure supplement 2 . IDR plots for replicates of D . simulans ChIP samples . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 025 To compare the epigenetic effects of TEs in D . melanogaster and D . simulans , the H3K9me2 enrichment at sequences flanking TEs were estimated relative to the median fold enrichment level at sequences 20–40 kb away from TEs in both species ( see Materials and methods ) . Current TE annotations are D . melanogaster-centric and it is likely that our analysis missed TE families and/or variants that are D . simulans-specific . Accordingly , we restricted comparisons to TE families that have at least two insertions in both species . While there are no significant between-species differences in the extent of H3K9me2 spread ( paired MWU test , p=0 . 67 ) , TE families in D . simulans display a larger proportion of TEs with epigenetic effects ( paired MWU test , p=0 . 013 ) , and a larger increase in relative H3K9me2 fold enrichment ( paired MWU test , p=0 . 035; Figure 8B ) . It is worth noting that only high confidence TE calls were included in the analysis , and only 14 families had at least two copies in both species . The roo family has the highest proportion of TEs with epigenetic effects in both species ( 94 . 4% and 86 . 5% in D . melanogaster and D . simulans , respectively ) , while the 1360 family has the largest differences between the two species ( 14 . 3% and 77 . 8% in D . melanogaster and D . simulans , respectively ) . These results demonstrate that TEs in D . simulans exhibit stronger epigenetic effects on flanking sequences compared to D . melanogaster . The extent of heterochromatin-mediated gene silencing ( e . g . PEV ) depends on several genetic modifiers , in particular the amount of heterochromatic DNA in a genome ( reviewed in [Girton and Johansen , 2008] ) , and the dosage of several Su ( var ) and E ( var ) genes , whose wildtype proteins enhance and weaken PEV respectively ( Elgin and Reuter , 2013; Swenson et al . , 2016 ) . The prevailing model is that altering the ratio of heterochromatin targets ( heterochromatic DNAs ) and regulators ( Su ( var ) and E ( var ) proteins ) influences heterochromatin nucleation and spreading ( Locke et al . , 1988 ) . For example , lower amounts of heterochromatic DNA result in increased levels of heterochromatic Su ( var ) proteins in other regions , and accordingly enhance PEV . Because both PEV and the epigenetic effects of TEs are mediated through spreading of the same repressive epigenetic marks ( H3K9me2/3 and HP1a ) , the epigenetic effects of TEs may depend on similar PEV modifiers . Indeed , a limited survey using reporter constructs demonstrated that the epigenetic effects of TEs depend on the expression of HP1a ( Su ( var ) 205 ) and Su ( var ) 3–9 , which binds and catalytically generates H3K9me2/3 marks , respectively ( Sentmanat and Elgin , 2012 ) . We thus predicted that variation in the epigenetic effects of TEs within and between species , and accordingly genomic abundance of TE insertions , could be due to differences in the amounts of heterochromatic DNA and/or modifier proteins . We investigated the hypothesis that stronger epigenetic effects of TEs in D . simulans are associated with lower amounts of heterochromatic DNA or altered expression of Su ( var ) s/E ( var ) s . In Drosophila , heterochromatic DNA consists of simple repeats and degenerate TEs ( Hoskins et al . , 2007 , 2015 ) . We first identified short repeats ( 12-mers ) that are enriched in heterochromatic regions by performing K-mer analysis of H3K9me2 ChIP-Seq data ( see above ) , and then quantified the amount of identified H3K9me2-enriched 12-mers in these two species using published genomic sequencing data ( ( Kofler et al . , 2015 ) , see Materials and methods ) . Consistent with previous quantitation of simple repeat content using orthogonal approaches ( melting curves [Lohe and Brutlag , 1987] or flow cytometry [Bosco et al . , 2007] ) , we observed lower amounts of H3K9me2-enriched simple repeats in D . simulans compared to D . melanogaster ( Figure 9A , ANOVA p-value=0 . 00013 ( species ) and <10−6 ( library preparation method ) ) . 10 . 7554/eLife . 25762 . 026Figure 9 . Variation in genetic modifiers of PEV in D . melanogaster and D . simulans . ( A ) D . simulans has higher normalized amounts of H3K9me2-enriched 12-mers than D . melanogaster . Raw amounts of H3K9me2-enriched 12-mers were normalized with sequencing coverage in each sample before comparisons ( see Materials and methods ) . Different library preparation methods ( see [Kofler et al . , 2015] ) are denoted with dots of different colors . ( B ) Compared to genome-wide distributions ( shaded gray ) , known Su ( var ) genes as a group ( orange , 40 genes in total ) have higher expression in D . simulans than in D . melanogaster . Positive z-score represents lower expression rank ( i . e . higher expression ) in D . simulans than in D . melanogaster . Dashed vertical lines represent the top and bottom 5% of transcript level differences genome-wide . ( C ) Z-score for differences in transcript levels of ten known dosage-dependent E ( var ) genes ( green ) , Su ( var ) genes ( orange ) , and histone methyltransferase genes ( also Su ( var ) s ) between D . melanogaster and D . simulans are denoted as vertical lines and compared to genome-wide distributions ( shaded gray ) . Dashed vertical lines indicate top and bottom 5% of transcript level differences genome-wide . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 02610 . 7554/eLife . 25762 . 027Figure 9—source data 1 . Gene expression level of Su ( var ) and E ( var ) genes in D . melanogaster and D . simulans . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 02710 . 7554/eLife . 25762 . 028Figure 9—figure supplement 1 . Differences in transcript levels of Su ( var ) and E ( var ) genes between D . melanogaster and D . simulans . Z-scores , which measure differences in transcript levels between D . melanogaster and D . simulans , for ( A ) 40 Su ( var ) genes and ( B ) five E ( var ) genes are denoted as vertical lines and compared to genome-wide distribution ( shaded gray ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25762 . 028 To test if the expression of Su ( var ) s and E ( var ) s varies between the two species , we estimated z-scores for between-species differences in expression rank ( high expression – low rank ) , using D . simulans as reference ( i . e . positive z-socre: higher expression in D . simulans; negative z-score: higher expression in D . melanogaster ) . Compared to other genes in the genome , 40 known Su ( var ) s , as a group , have higher expression in D . simulans than in D . melanogaster ( Kolmogorov–Smirnov test , p<10−6 , Figure 9B; also see Figure 9—figure supplement 1 and Figure 9—source data 1 for individual genes included in the analysis ) . The small number of known E ( var ) s ( five ) precluded us from drawing any solid conclusions ( Figure 9—figure supplement 1 ) . Several Su ( var ) s/E ( var ) s are known to have dosage-dependent effects on heterochromatin silencing ( Elgin and Reuter , 2013; Swenson et al . , 2016 ) . Among these dosage-dependent Su ( var ) s/E ( var ) s , Su ( var ) 3–9 showed significantly higher expression in D . simulans than in D . melanogaster ( Figure 9C ) . Overall , we found that D . simulans has lower amounts of H3K9me2-enriched simple repeats and higher expression of Su ( var ) s compared to D . melanogaster , both of which could account for the stronger epigenetic effects of TEs observed in D . simulans . Despite the presence of TEs in virtually all eukaryotic genomes surveyed , there is wide variation in euchromatic TE content among species , demonstrated by significant differences in copy number ( Clark et al . , 2007; Biémont , 2010; Chalopin et al . , 2015 ) , frequency spectra ( Lockton and Gaut , 2010; Agren et al . , 2014; Kofler et al . , 2015 ) , predominant types of TEs ( Vieira and Biémont , 2004; Chalopin et al . , 2015; Kofler et al . , 2015 ) , and species-specific TE families ( Daniels et al . , 1990; Lohe et al . , 1995; Mills et al . , 2006 ) . Understanding the causes for such variation is critical for evaluating the impacts of various evolutionary forces on the population dynamics of TEs . Selection against the deleterious effects of TEs has been theoretically proposed ( Charlesworth and Charlesworth , 1983 ) and empirically supported ( reviewed in [Charlesworth and Langley , 1989; Lee and Langley 2010; Barrón et al . , 2014] ) as a dominant force restricting the selfish increase of TEs , and shaping variation of TEs within and between species . Differences in effective population size ( [Lynch and Conery , 2003; Lockton et al . , 2008] , but see [Charlesworth and Barton , 2004; Groth and Blumenstiel , 2017] ) , mating systems ( Charlesworth and Charlesworth , 1995; Wright and Schoen , 1999; Dolgin et al . , 2008; Agren et al . , 2014 ) , and modes of reproduction ( e . g . asexual vs sexual , [Arkhipova and Meselson , 2000] ) , were suggested to influence the efficacy of selection against TEs , and thus result in divergence in euchromatic TE content between species . In addition , differential exposure to opportunities for horizontal transfer might also contribute to variation in TE profiles ( Clark et al . , 2007; Groth and Blumenstiel , 2017 ) . In this study , we analyzed the epigenetic influence of TEs and investigated the role of such effects in the population dynamics of TEs . By comparing the epigenome of two D . melanogaster strains with divergent TE insertion positions , we conclude that the enrichment of repressive epigenetic marks around euchromatic TEs is due to the presence of TEs , instead of the preferential insertions of TEs into genomic regions already enriched for repressive epigenetic marks . Surprisingly , quantification of the epigenetic effects of individual TE insertions in D . melanogaster revealed that more than half of the euchromatic TEs analyzed are associated with at least 1 kb spread of repressive epigenetic marks , with an average spread of 4 . 5 kb from TEs that display epigenetic effects . In contrast , repressive DNA methylation from epigenetically silenced TEs in A . thaliana predominantly spreads only a few hundred base pairs ( Quadrana et al . , 2016; Stuart et al . , 2016 ) . Since ~20% of the euchromatic genes are within 5 kb from at least one TE insertion in the reference D . melanogaster strain ( Kaminker et al . , 2002; Quesneville et al . , 2005; Hoskins et al . , 2015 ) , these estimates suggest that the epigenetic influence of TEs on functional sequences is extensive in D . melanogaster . Indeed , we observed strong positive associations between the presence of TEs with epigenetic effects and the enrichment of repressive epigenetic marks in adjacent genic alleles , when compared to homologous alleles lacking a neighboring TE insertion . This concurs with observations in Arabidopsis that TEs contribute significantly to genic DMRs ( differential methylated regions ) between genomes ( Schmitz et al . , 2013; Quadrana et al . , 2016; Stuart et al . , 2016 ) . In addition , we found substantial variation in the epigenetic effects among TE families , which could be due to differences in TE types , targeting by piRNAs , and/or the abundance of TE families . It was proposed that heterochromatin assembly depends on the concentration of essential heterochromatic enzymatic and structural proteins , whose concentration is highest in heterochromatin and decreases with increasing distance ( ‘mass action model’ , [Locke et al . , 1988] ) . This model can explain the spread of repressive epigenetic marks from pericentromeric or subtelomeric heterochromatin to juxtaposed euchromatic genes in PEV ( reviewed in [Girton and Johansen , 2008; Elgin and Reuter , 2013] ) , and likely the enrichment of repressive epigenetic marks at sequences adjacent to euchromatic TEs . Consistent with predictions of this model , previous ( Lee 2015 ) and current analyses both found that H3K9me2/3 enrichment decays with distance from TEs . The enrichment of repressive epigenetic marks could also result from TE-induced formation of de novo piRNA-generating loci that include TE-flanking sequences ( Shpiz et al . , 2014 ) . piRNAs from these piRNA-generating loci would accordingly initiate epigenetic silencing of TE-flanking euchromatic sequences . TE-induced reduction in expression of neighboring genes has been demonstrated in Drosophila ( Cridland et al . , 2015; Lee 2015 ) , and the spreading of repressive epigenetic marks from TEs is a plausible mechanism for such observations . We observed an excess of TE-flanking alleles with higher H3K9me2 enrichment and lower transcript levels than homologous alleles lacking nearby TE insertions . However , there are apparent exceptions to this pattern , such as alleles adjacent to TEs having higher enrichment of H3K9me2 but also higher expression levels than homologous alleles lacking neighboring TEs ( e . g . arrows in Figure 7A ) . In addition to the epigenetic effects of TEs , cis-regulatory sequences contained within TEs could interfere with gene regulation , leading to increased as well as reduced gene expression ( Naito et al . , 2009; Batut et al . , 2013 ) . In fact , recent studies that jointly analyzed mobilomes and transcriptomes in A . thaliana populations found that TE insertions result in equal frequencies of increased and decreased expression of flanking genes ( Quadrana et al . , 2016; Stuart et al . , 2016 ) . Besides , SNPs and copy number variants ( CNVs ) in regulatory sequences are also identified as significant contributors to differential expression between homologous alleles ( Massouras et al . , 2012 ) . The observed variation in transcripts level is thus the joint consequence of TE’s epigenetic effects and other genetic factors . Importantly , there are known exceptions to the association between higher enrichment of heterochromatic marks and lower gene expression . In fact , enrichment of repressive epigenetic marks and heterochromatin proteins is surprisingly high for active genes normally located in the pericentromeric heterochromatin , and is required for the proper expression of these genes ( Wakimoto and Hearn , 1990; Hearn et al . , 1991; Yasuhara and Wakimoto , 2008; Riddle et al . , 2011 ) . For some of these genes , the association with a local heterochromatin environment was even observed for homologs located in euchromatic regions of other Drosophila species ( Caizzi et al . , 2016 ) . Importantly , our quantifications of the epigenetic effects of TEs and the associated functional consequences on gene expression are likely underestimates of the real effects in natural populations . Screens for genetic mutations repeatedly report an inverse correlation between the dominance of a deleterious allele and its fitness effect ( reviewed in [Simmons and Crow , 1977; Wilkie , 1994; Osada et al . , 2009] ) . If similar trends apply to the deleterious epigenetic effects of TEs , establishment of the wild-derived inbred Drosophila strains used here would have removed the majority of TEs with lethal or sub-lethal epigenetic effects ( i . e . substantial functional consequences ) . The small sample size used here ( two strains ) precluded detecting the subtle functional consequences of TE-induced epigenetic effects that are expected to be prevalent in inbred strains . Future larger scale epigenomic and transcriptomic profiling of multiple , diverse population samples would be necessary to further investigate the functional consequence of TE’s epigenetic effects . It is also worth noting that not every gene included in the analysis is expressed at the embryonic stage studied ( 4–8 hr embryo ) . In fact , we observed a deficiency in the number of genes that both have adjacent TEs and are expressed in 4–8 hr embryos ( using data from modEncode ( Graveley et al . , 2011 ) ; Fisher’s Exact Test , p=0 . 00017 , odds ratio = 0 . 73 ) . This deficiency is even stronger when only considering TEs with epigenetic effects ( Fisher’s Exact Test , p<10−5 , odds ratio = 0 . 58 ) . Epigenetic marks established at embryonic stages are expected to influence both somatic and germline cells , and were experimentally demonstrated to have a long lasting functional effect through development ( Gu and Elgin , 2013 ) . Accordingly , our observations are consistent with selection preferentially removing TEs that result in spreading of repressive epigenetic marks to adjacent genes expressed in embryonic stages . Compared to neutral TE insertions that confer no fitness effects , TEs exerting deleterious fitness effects are more strongly selected against and should appear rare in populations ( reviewed in [Charlesworth and Langley , 1989; Lee and Langley 2010; Barrón et al . , 2014] ) . Consistent with the prediction that the epigenetic effects of TEs have deleterious fitness consequences , we found that TE insertions with stronger epigenetic effects have lower population frequencies , demonstrating the importance of such effects in the population dynamics of TEs . In addition , theoretical work suggests that stable equilibrium of TE copy number in an outbreeding , meiotically recombining population requires synergistic epistasis of the deleterious effects of TEs; specifically host fitness must decrease faster than linear with respect to increases in TE copy number ( Charlesworth and Charlesworth , 1983 ) . However , despite being critical to explaining the population dynamics of TEs , synergistic epistasis has only been empirically supported for deleterious effects mediated by ectopic recombination between nonhomologous TE insertions ( Langley et al . , 1988; Petrov et al . , 2003 , 2011 ) , one of the many proposed deleterious genetic mechanisms for TEs ( reviewed in [Lee and Langley 2010] ) . Because piRNAs are generated through feed-forward cycles that involve TE transcripts ( Gunawardane et al . , 2007; Brennecke et al . , 2007 ) , we previously predicted that the deleterious epigenetic effects of TEs would confer the theoretically required synergistic epistasis for stable containment of TEs ( Lee and Langley 2010; Lee 2015 ) . The observed association between the abundance of a TE family and the propensity of its members to influence the epigenetic states of adjacent sequences supports this prediction , and further extends possible evolutionary mechanisms for stable containment of TE copy number in host populations . An especially interesting observation is the stronger epigenetic effects of TEs in D . simulans compared to D . melanogaster . TE insertions in D . simulans are more likely to show spreading of H3K9me2 and result in larger increase in H3K9me2 enrichment compared to those of the same TE family in D . melanogaster . All else being equal , the stronger epigenetic effects should lead to stronger selection removing TE insertions in D . simulans . If the rate of TE proliferation is also similar in these two species , this could account for the lower genomic TE content ( Clark et al . , 2007 ) and fewer TE insertions ( Dowsett and Young , 1982; Vieira et al . , 1999; Vieira and Biémont , 2004; Kofler et al . , 2015 ) in D . simulans compared to D . melanogaster . Our observations complement previous comparisons of A . thaliana and A . lyrata , which revealed negative associations between genomic TE content and the effectiveness of siRNA targeting ( Hollister et al . , 2011 ) . Overall , these results strongly support the conclusion that variation in the epigenetic effects of TEs contributes to the divergent TE content observed between even closely related species and significantly impacts TE evolution . Finally , we attempted to gain insights into the molecular mechanisms that could account for the between-species differences in the epigenetic effects of TEs . We observed lower amounts of heterochromatic DNA and higher expression of Su ( var ) genes in D . simulans , both of which are known to generate more extensive spread of repressive epigenetic marks from constitutive heterochromatin in D . melanogaster . The amount of heterochromatic DNA was among the first identified dosage-dependent PEV modifiers ( [Dimitri and Pisano , 1989] , reviewed in [Girton and Johansen , 2008] ) . Similarly , Su ( var ) genes were identified through mutants that suppress PEV , demonstrating that the wild type genes play positive roles in heterochromatin establishment and/or maintenance ( reviewed in [Girton and Johansen , 2008; Elgin and Reuter , 2013] ) . In particular , Su ( var ) 3–9 , which encodes the H3K9 methyltransferase , displays significantly higher expression in D . simulans than in D . melanogaster , and its between-species difference in expression level ranks in the top 0 . 75% genome-wide ( see Results ) . H3K9 methylation is critical for suppression of TE expression and transposition ( Penke et al . , 2016 ) , and Su ( var ) 3–9 mutations reduce the epigenetic effects of TEs on adjacent reporter genes ( Sentmanat and Elgin , 2012 ) . Furthermore , Su ( var ) 3–9 is a haploid suppressor and triploid enhancer of PEV ( Schotta et al . , 2002 ) , suggesting that changes in transcript levels of Su ( var ) 3–9 would result in quantitative differences in the epigenetic effects of TEs . Assuming that the epigenetic effects of TEs depend on the amount of heterochromatic DNA and the expression of Su ( var ) genes in D . melanogaster and D . simulans similarly , variation in these two genetic PEV modifiers provides a viable explanation for the observed differences in the epigenetic effects of TEs and more broadly divergent TE profiles between these two species . Our observations support the hypothesis that the host genetic environment contributes to the extent of deleterious epigenetic effects of TEs and influences the population dynamics of TEs , pointing towards a rarely addressed mechanism for the widely observed variation of TEs . Furthermore , PEV is long known to be temperature sensitive ( Gowen and Gay , 1933 ) , and several abiotic factors influence heterochromatin function ( Seong et al . , 2012; Silver-Morse and Li , 2013 ) . Thus , different environmental conditions present in diverse habitats could also contribute to variation in the epigenetic effects of TEs and the widely divergent TE profiles within and between species . The observed significant variation in genetic PEV modifiers between D . melanogaster and D . simulans raises questions about its evolutionary causes . It is worth noting that the deleterious epigenetic effects of TEs are considered as a side effect of host-directed epigenetic silencing of TEs ( Hollister and Gaut , 2009; Lee 2015 ) , and direct positive selection for stronger epigenetic effects of TEs would be unlikely to explain the between-species differences in genetic PEV modifiers . Elevated transcript levels of Su ( var ) genes might have been selected for to silence burst expansions of specific TE families or other types of repetitive sequences . Alternatively , Su ( var ) genes are highly pleiotropic ( reviewed in [Girton and Johansen , 2008; Eissenberg and Reuter , 2009; Elgin and Reuter , 2013] ) , and selection might have acted instead on their essential chromosomal functions , with varying influence on the epigenetic consequences of TEs as a secondary effect . Similarly , changes in the amounts of heterochromatic DNA could have resulted from selfish expansion of repetitive sequences ( Charlesworth et al . , 1994 ) and/or global changes in chromatin landscapes due to karyotype turnover ( Kaiser and Bachtrog , 2010; Vicoso and Bachtrog , 2013 ) . Our findings suggest that the evolution of TEs may be more tightly associated with the evolution of other cellular , chromosomal , and/or genetic processes than previously appreciated . Drosophila strains used in this study are D . melanogaster RAL315 ( Bloomington Drosophila stock center ( BDSC ) #25181 ) , RAL360 ( BDSC #25186 ) , and D . simulans w501 ( Drosophila species stock center ) . Previous analysis showed that these two D . melanogaster inbred wildtype strains have low residual heterozygosity ( Lack et al . , 2015 ) . Flies were cultured on standard medium at 25∘C , 12 hr light/12 hr dark cycles . Before collecting embryos , mated flies were allowed to lay eggs on fresh apple juice agar plates for one hour . Embryos were then collected on fresh apple juice agar plates for 4 hr and aged for 4 hr ( to enrich for 4–8 hr embryos ) . All fly rearing and embryo collections were performed at 25∘C . Chromatin isolation and immunoprecipitation were performed following the modEncode protocol ( http://www . modencode . org/ ) . The antibody used for H3K9me2 ( abcam 1220 ) was validated by modEncode and showed high consistency between lots ( Egelhofer et al . , 2011 ) . For each strain , there were at least two replicates and each IP replicate had a matching input . ChIP-Seq libraries were prepared with NuGen Ovation Ultralow Library Systems V2 ( San Carlos , CA ) and sequenced on Illumina Hi-Seq with 100 bp , paired-end reads . RNAs were extracted from embryos that were collected using the same procedures using the RNeasy Plus kit ( Qiagen ) . There were two replicates for each strain . RNA-Seq libraries were prepared using Illumina TruSeq and sequenced on Illumina Hi-Seq with 100 bp , paired-end reads . We used highly conservative euchromatin-heterochromatin boundaries: 0 . 5 Mb distal from those reported previously for D . melanogaster ( Riddle et al . , 2011 ) . For D . simulans , we used boundaries that are 0 . 5 Mb distal from the sharp transition in H3K9me2 enrichment , based on our ChIP-Seq data . For all the analyses reported , we excluded TEs , genes , and sequences in heterochromatic regions . For D . melanogaster strains , we used TE insertions reported with strong confidence ( coverage ratio greater than or equal to 3; [Rahman et al . , 2015] ) . TEs that are shared between two RAL strains , in shared H3K9me2 peaks in euchromatin ( called by MACS2 and present in both strains , see below ) , and/or in exons were also excluded . TEs in the D . simulans genome were annotated according to ( Chiu et al . , 2013 ) , using blastn ( Camacho et al . , 2009 ) . In brief , we used the blast hit with smallest e-value and excluded a putative insertion when the blast hit had the same smallest e-value for more than one TE family . We required a putative TE call to have at least 100 bp , at least 80% identity to canonical TEs , and merged TE calls of the same family and within 500 bp . TEs of different families but were within 2 kb were called as putative TE clusters and excluded from the analysis . In both species , we excluded INE-1 TEs , most which are relicts of a TE family that experienced an ancient burst of transposition events and are now mostly fixed in populations ( Kapitonov and Jurka , 2003; Singh and Petrov , 2004 ) . Our study included 255 TEs for the Oregon-R strain , 419 TEs for RAL strains , and 349 TEs for the D . simulans strain . Raw reads were processed with trim-galore ( ‘Babraham Bioinformatics - Trim Galore ! ” ) to remove adaptors and low quality sequences . Processed reads were mapped to release six reference D . melanogaster genome ( Hoskins et al . , 2015 ) or release two reference D . simulans genome ( Hu et al . 2013 ) , using bwa mem with default parameters ( v 0 . 7 . 5 ) ( Li and Durbin , 2009 ) . Reads with mapping quality score lower than 30 were filtered using samtools ( Li , 2011 ) and excluded from further analysis . We used Macs2 with a liberal significance threshold ( p=0 . 2 ) to generate peak calls for IDR ( irreproducible rate ) analysis ( Li et al . , 2011 ) , which evaluates the reproducibility of ChIP replicates . Replicates for our samples had low IDRs ( Figure 2—figure supplement 3 and Figure 8—figure supplement 2 ) , and were combined to generate a single H3K9me2 fold enrichment track ( between IP and matching input ) for each sample . Our analyses were based these fold-enrichment tracks . The baseline H3K9me2 enrichment level is slightly different between the two D . melanogaster strains , potentially due to technical and/or biological reasons . As the enrichment of repressive epigenetic marks is generally confined to 10 kb from TEs ( Lee 2015 ) , we used the H3K9me2 enrichment levels 20–40 kb upstream and downstream of each TE insertion to normalize the background levels between the two strains . For each annotated TE insertion , we divided its flanking 20 kb upstream and 20 kb downstream sequences into 20 nonoverlapping 1 kb windows respectively ( Figure 2—figure supplement 1 ) . We then used Mann-Whitney U test to assess if H3K9me2 enrichment in the ith upstream and downstream windows differs significantly between the two strains . The most distant windows considered are 20 kb from TE insertions . The ‘extent of H3K9me2 spread’ is the farthest windows in which the H3K9me2 enrichment is consecutively and significantly higher in the strain with TE . When the farthest windows are different between the left and right sides of a TE insertion , we used the window closer to TE for the ‘extent of H3K9me2 spread’ ( to be conservative ) . The ‘% increase of H3K9me2’ is the difference of median H3K9me2 enrichment between the two strains in the 0–1 kb windows immediately next to TEs ( with TE strain minus without TE strain ) , divided by the enrichment level for the strain without TE . For D . simulans TEs , we calculated relative fold enrichment with respect to the median H3K9me2 fold enrichment at flanking 20–40 kb upstream or downstream sequences , whichever had a higher median ( to be conservative ) . D . melanogaster data were also analyzed using this method to allow between-species comparisons . We again used Mann-Whitney U test to assess if the relative H3K9me2 enrichment in a window is significantly higher than one , the background level of relative fold enrichment . Here , the ‘extent of spread’ is the farthest window in which the relative fold enrichment is consecutively and significantly higher than one . The ‘increase in fold enrichment’ is the median relative fold enrichment in the 0–1 kb window immediately next to TE , minus one . To evaluate the performance of this method , we compared D . melanogaster results using this method to those based on normalization between strains . We found significant correlations between the two approaches for indexes of TE’s epigenetic effects ( Spearman rank ρ = 0 . 63 ( extent of H3K9me2 spread ) and 0 . 68 ( increase in H3K9me2 enrichment ) , p<10−16 for both ) . The calls for the presence of epigenetic effects ( extent of spread at least 1 kb ) were consistent between the two methods for 73 . 3% of TEs . Among TEs with inconsistent results , 67 . 8% ( 18 . 1% of all TEs ) were called as ‘no epigenetic effect’ by the single-genome method but ‘with epigenetic effect’ by the method that incorporate both strains , suggesting that the single-genome method is overall more conservative in estimating the epigenetic effects of TEs . For 80% of the TEs , the estimated extents of spread were either the same or differ within 2 kb between the two methods . We estimated the percentage of sites annotated as simple repeats in a 10 kb window around each TE insertion ( based on the repeat-masked release 6 D . melanogaster genome from https://genome . ucsc . edu/ ) . Recombination rate estimates for TE insertions were interpolated from ( Comeron et al . , 2012 ) , which reported average recombination rate of D . melanogaster in 1 Mb window . For TE-family level analysis , we only considered TE families with at least two observations . Abundance of each TE family is based on ( Kofler et al . , 2015 ) . Ovarian piRNA sequences for two wildtype strains ( w1118 and wK ) were from ( Brennecke et al . , 2008; Kelleher et al . , 2012 ) , and the normalized count estimates of each TE family were from ( Kelleher and Barbash , 2013 ) . We used two endo-siRNA datasets: ( 1 ) the reported counts of endo-siRNA ( excluded pre-microRNAs ) in adult heads for each TE family ( Ghildiyal et al . , 2008 ) , and ( 2 ) endo-siRNAs generated by Ago2 pull-down libraries from ovaries ( Czech and Hannon , 2011 ) . The raw endo-siRNA sequences were processed with trim-galore , mapped with bwa aln to all annotated TEs in D . melanogaster reference genome , and counted for each TE family . For both piRNAs ( from [Kelleher and Barbash , 2013] ) and siRNAs , those that mapped to more than one TE families were excluded from the analysis . For gene-based analysis , we calculated average fold enrichment over gene bodies for each replicate and used quantile-normalization . We calculated a z-score for each gene ( mean H3K9me2 enrichment of allele with nearby TE – mean H3K9me2 enrichment of allele lacking nearby TE ) / ( mean standard deviation of both strains ) . Our analysis excluded genes with ambiguous TE presence/absence status ( such as a gene with TE within 2 kb in one strain but with TE within 5 kb in the other strain ) . We used D . melanogaster annotation 6 . 07 and D . simulans annotation 2 . 01 . ChIP-Seq data from Oregon-R were downloaded from the modEncode website ( http://www . modencode . org/ ) and analyzed with the same procedures . Raw reads were processed with trim-galore , followed by mapping to release six reference D . melanogaster genome ( Hoskins et al . , 2015 ) or release two reference D . simulans genome ( Hu et al . 2013 ) using TopHat with default parameters ( Trapnell et al . , 2009 ) . We used htseq-count ( Anders et al . , 2015 ) to count the number of reads mapping to exons and used DESeq2 ( Love et al . , 2014 ) to normalize and estimate expressional fold change between the two D . melanogaster strains . Estimates of transcript abundance were highly correlated between biological replicates ( Pearson’s r = 0 . 98 ( RAL315 ) , 0 . 97 ( RAL360 ) , and 0 . 88 ( D . simulans ) , p<10−16 for all ) . We only analyzed genes annotated as expressed in 4–8 hr embryos by the modEncode developmental time course study ( Graveley et al . , 2011 ) . Indeed , genes annotated as no or extremely low expression in 4–8 hr embryos in the modEncode study have much fewer mapped reads than other genes in our RNA-seq data ( median for RPKM , RAL315: 0 . 058 ( not expressed ) vs 13 . 16 ( other genes ) , RAL360: 0 . 031 ( not expressed ) vs 12 . 70 ( other genes ) , Mann-Whitney U test , p<10−16 for both ) . To investigate the functional consequence of TE-induced enrichment of repressive epigenetic marks , we categorized protein-coding genes according to their epigenetic states ( RAL 315 or RAL360 higher ? ) and RNA transcript levels ( RAL 315 or RAL360 higher ? ) in the two strains . The proportion of genes with predicted TE-induced epigenetic states and RNA transcript levels ( higher H3K9me2 enrichment and lower expression for alleles adjacent to TEs ) for genes with TEs in 10 kb were compared to other genes in the genome using Fisher’s Exact Test ( also see Figure 7 ) . To compare expression levels between the two species , we used RPKM ( reads per kilobase per million reads ) and ranked genes from highest ( small rank ) to lowest ( large rank ) expression in each library . Z-score was calculated as ( mean rank of D . melanogaster – mean rank of D . simulans ) /mean standard deviation . A negative z-score represents higher expression in D . melanogaster while a positive z-score represents higher expression in D . simulans . Raw reads from Drosophila Population Genomic Project ( DPGP ) 3 ( Lack et al . , 2015 ) were mapped to release six D . melanogaster reference genome using bwa mem with default parameters . Reads mapping within 500 bp upstream or downstream of TE insertion sites were parsed out using samtools . Parsed reads were assembled using phrap ( Ewing and Green , 1998 ) following parameters in ( Cridland et al . , 2013 ) . Assembled contigs were aligned against repeat-masked release six D . melanogaster genome using blastn . If one of the contigs spanned over at least 50 bp on both sides of a TE insertion site , the TE was called absent in the analyzed genome . If no contigs spanned the TE insertion site , contigs were aligned against canonical TE sequences and sequences of all TEs in the reference D . melanogaster genome using blastn . When there were blast hits to TE sequences , a TE was called present if there was a contig aligning at least 30 bp left or right of the TE insertion site without spanning the insertion site . All other scenarios were called as missing data . For population frequency analysis , we only included TEs that have at least 100 alleles ( out of 197 alleles ) called in DPGP3 genomes . A large proportion of the analyzed TE insertions ( 68 . 5% ) has zero population frequencies in the Zambian population ( Figure 5—figure supplement 1 ) . Accordingly , in some analyses , we also categorized TEs into those that are present in the Zambian population ( ‘high frequency’ TEs ) and those that are absent ( ‘low frequency’ TEs ) . To account for the influence of TE family identity on TE’s population frequencies , we performed regression analysis using generalized linear model and generalized mixed linear model . Population frequencies of TE insertions ( response variable ) were treated as either dichotomous variable ( ‘high frequency’ TE or not ) or count ( the number of individuals in which a TE insertion is present ) . Because the distribution of TE count is overdispersed ( i . e . the variance is greater than the mean ) , we modeled the TE count as having either ‘quasipoission’ or ‘negative binomial’ distribution . The influence of TE family identity was modeled as either fixed effect ( generalized linear model ) or random effect ( generalized mixed linear model ) . The two indexes for the epigenetic effects of TEs ( ‘extent of H3K9me2 spread’ and ‘% increase in H3K9me2 enrichment’ ) were analyzed separately . Regression models used were:logit p ∼ TE′s epigenetic effects ( either "extent of H3K9me2 spread" or"% increase in H3K9me2 enrichment" ) +familyTE count ∼ TE′s epigenetic effects+family where logit p is the log odds of whether a TE is observed in the Zambia population ( ‘high frequency’ TEs ) . We used MASS ( Venables and Ripley , 2002 ) for negative binomial regression and lme4 ( Bates et al . , 2015 ) for generalized mixed linear models in R .	The DNA inside an organism encodes all the instructions needed for the organism to develop and work properly . Organisms carefully organize and maintain their DNA ( collectively known as the genome ) so that the genetic information remains intact and the cell can understand the instructions . However , there are some pieces of DNA that are capable of moving around the genome . For example , pieces known as transposable elements can make new copies of themselves and jump into new locations in the genome . Most transposons do not appear to have any important roles , and in fact they are usually harmful to organisms . Despite this , transposons are present in the genomes of almost all species . The number of transposons in a genome varies greatly between individuals and species , but it is not clear why this is the case . Organisms have evolved ways to limit the damage caused by transposons . For example , many cells package regions of DNA containing transposons into a tightly packed structure known as heterochromatin . However , this type of DNA packaging sometimes spreads to neighboring sections of DNA . This is a problem because cells are not usually able to read the information contained within heterochromatin . This means that transposons can prevent some instructions from being produced when they should be . Lee and Karpen used fruit flies to investigate to what extent transposons harm organisms by changing the way DNA is packaged , and whether this influences how transposons evolve . The experiments show that that more than half of the transposons in fruit flies cause neighboring sections of DNA to be packaged into heterochromatin . This can negatively impact up to 20% of genes in the genome . As a result , transposons that have harmful effects on DNA packaging are more likely to be lost from the fly population during evolution than transposons that do not have harmful effects . Fruit fly species containing transposons that tend to package more neighboring sections of DNA into heterochromatin generally have fewer transposons than genomes containing less harmful transposons . The findings of Lee and Karpen provide new insight as to why the numbers of transposons vary among organisms . The next challenge is to find out whether transposons that alter how DNA is packaged are also common in primates and other animals .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "genetics", "and", "genomics" ]	2017	Pervasive epigenetic effects of Drosophila euchromatic transposable elements impact their evolution
Understanding the relationships between different properties of data , such as whether a genome or connectome has information about disease status , is increasingly important . While existing approaches can test whether two properties are related , they may require unfeasibly large sample sizes and often are not interpretable . Our approach , ‘Multiscale Graph Correlation’ ( MGC ) , is a dependence test that juxtaposes disparate data science techniques , including k-nearest neighbors , kernel methods , and multiscale analysis . Other methods may require double or triple the number of samples to achieve the same statistical power as MGC in a benchmark suite including high-dimensional and nonlinear relationships , with dimensionality ranging from 1 to 1000 . Moreover , MGC uniquely characterizes the latent geometry underlying the relationship , while maintaining computational efficiency . In real data , including brain imaging and cancer genetics , MGC detects the presence of a dependency and provides guidance for the next experiments to conduct . Mgc is a multi-step procedure to discover and decipher dependencies across disparate data modalities or properties . Given n samples of two different properties , proceed as follows ( see Materials and methods and ( Shen et al . , 2018 ) for details ) : Computing all local correlations , the test statistic , and the p-value requires O ( n2log⁡n ) time , which is about the same running time complexity as other methods ( Shen et al . , 2018 ) . When , and to what extent , does Mgc outperform other approaches , and when does it not ? To address this question , we formally pose the following hypothesis test ( see Materials and methods for details ) :H0:XandYare independentHA:XandYare not independent . The standard criterion for evaluating statistical tests is the testing power , which equals the probability that a test correctly rejects the null hypothesis at a given type one error level , that is power = Prob ( H0 is rejected \|H0 is false ) . The higher the testing power , the better the test procedure . A consistent test has power converging to 1 under dependence , and a valid test controls the type one error level under independence . In a complementary manuscript ( Shen et al . , 2018 ) , we established the theoretical properties of Mgc , proving its validity and universal consistency for dependence testing against all distributions of finite second moments . Here , we address the empirical performance of Mgc as compared with multiple popular tests: ( i ) Dcorr , a popular approach from the statistics community ( Székely et al . , 2007; Székely and Rizzo , 2009 ) , ( ii ) Mcorr , a modified version of Dcorr designed to be unbiased for sample data ( Szekely and Rizzo , 2013 ) , ( iii ) Hhg , a distance-based test that is very powerful for detecting low-dimensional nonlinear relationships ( Heller et al . , 2013 ) . ( iv ) Hsic , a kernel dependency measure ( Gretton and Gyorfi , 2010 ) formulated in the same way as Dcorr except operating on kernels , ( v ) Mantel , which is historically widely used in biology and ecology ( Mantel , 1967 ) . ( vi ) RV coefficient ( Pearson , 1895; Josse and Holmes , 2013 ) , which is a multivariate generalization of Pearson’s product moment correlation whose test statistic is the sum of the trace-norm of the cross-covariance matrix , and ( vii ) the Cca method , which is the largest ( in magnitude ) singular value of the cross-covariance matrix , and can be viewed as a different generalization of Pearson in high-dimensions that is more appropriate for sparse settings ( Hotelling , 1936; Witten et al . , 2009; Witten and Tibshirani , 2011 ) . Note that while we focus on high-dimensional settings , Appendix 1 shows further results in one-dimensional settings , also comparing to a number of tests that are limited to one dimension , including: ( viii ) Pearson’s product moment correlation , ( ix ) Spearman’s rank correlation ( Spearman , 1904 ) , ( x ) Kendall’s tau correlation ( Kendall , 1970 ) , and ( xi ) Mic ( Reshef et al . , 2011 ) . Under the regularity condition that the data distribution has finite second moment , the first four tests are universally consistent , whereas the other tests are not . We generate an extensive benchmark suite of 20 relationships , including different polynomial ( linear , quadratic , cubic ) , trigonometric ( sinusoidal , circular , ellipsoidal , spiral ) , geometric ( square , diamond , W-shape ) , and other functions . This suite includes and extends the simulated settings from previous dependence testing work ( Székely et al . , 2007; Simon and Tibshirani , 2012; Gorfine et al . , 2012; Heller et al . , 2013; Szekely and Rizzo , 2013 ) . For many of them , we introduce high-dimensional variants , to more extensively evaluate the methods; function details are in Materials and methods . The visualization of one-dimensional noise-free ( black ) and noisy ( gray ) samples is shown in Figure 2—figure supplement 1 . For each relationship , we compute the power of each method relative to Mgc for ~20 different dimensionalities , ranging from 1 up to 10 , 20 , 40 , 100 , or 1000 . The high-dimensional relationships are more challenging because ( 1 ) they cannot be easily visualized and ( 2 ) each dimension is designed to have less and less signal , so there are many noisy dimensions . Figure 2 shows that Mgc achieves the highest ( or close to the highest ) power given 100 samples for each relationship and dimensionality . Figure 2—figure supplement 2 shows the same advantage in one-dimension with increasing sample size . Moreover , for each relationship and each method we compute the required sample size to achieve power 85% at error level 0 . 05 , and summarize the median size for monotone relationships ( type 1–5 ) and non-monotone relationships ( type 6–19 ) in Table 1 . Other methods typically require double or triple the number of samples as Mgc to achieve the same power . More specifically , traditional correlation methods ( Pearson , RV , Cca , Spearman , Kendall ) always perform the best in monotonic simulations , distance-based methods including Mcorr , Dcorr , Mgc , Hhg and Hsic are slightly worse , while Mic and Mantel are the worst . Mgc’s performance is equal to linear methods on monotonic relationships . For non-monotonic relationships , traditional correlations fail to detect the existence of dependencies , Dcorr , Mcorr , and Mic , do reasonably well , but Hhg and Mgc require the fewest samples . In the high-dimensional non-monotonic relationships that motivated this work , and are common in biomedicine , Mgc significantly outperforms other methods . The second best test that is universally consistent ( Hhg ) requires nearly double as many samples as Mgc , demonstrating that Mgc could half the time and cost of experiments designed to discover relationships at a given effect size . Mgc extends previously proposed global methods , such as Mantel and Dcorr . The above experiments extended Mcorr , because Mcorr is universally consistent and an unbiased version of Dcorr ( Szekely and Rizzo , 2013 ) . Figure 2—figure supplement 3 directly compares multiscale generalizations of Mantel and Mcorr as dimension increases , demonstrating that empirically , Mgc nearly dominates its global variant for essen- tially all dimensions and simulation settings considered here . Figure 2—figure supplement 4 shows a similar result for one-dimensional settings while varying sample size . Thus , not only does Mgc empirically nearly dominate existing tests , it is a framework that one can apply to future tests to further improve their performance . Beyond simply testing the existence of a relationship , the next goal is often to decipher the nature or structure of the relationship , thereby providing insight and guiding future experiments . A single scalar quantity ( such as effect size ) is inadequate given the vastness and complexities of possible relationships . Existing methods would require a secondary procedure to characterize the relationship , which introduces complicated ‘post selection’ statistical quandaries that remain mostly unresolved ( Berk et al . , 2013 ) . Instead , Mgc provides a simple , intuitive , and nonparametric ( and therefore infinitely flexible ) 'map’ of how it discovered the relationship . As described below , this map not only provides interpretability for how Mgc detected a dependence , it also partially characterize the geometry of the investigated relationship . The Mgc-Map shows local correlation as a function of the scales of the two properties . More concretely , it is the matrix of ckl’s , as defined above . Thus , the Mgc-Map is an n-by-n matrix which encodes the strength of dependence for each possible scale . Figure 3 provides the Mgc-Map for all 20 different one-dimensional relationships; the optimal scale to achieve t^* is marked with a green dot . For the monotonic dependencies ( 1-5 ) , the optimal scale is always the largest scale , that is the global one . For all non-monotonic dependencies ( 6-19 ) , Mgc chooses smaller scales . Thus , a global optimal scale implies a close-to-linear dependency , otherwise the dependency is strongly nonlinear . In fact , this empirical observation led to the following theorem ( which is proved in Materials and methods ) : Theorem 1 . When ( X , Y ) are linearly related ( meaning that Y can be constructed from X by rotation , scaling , translation , and/or reflection ) , the optimal scale of Mgc equals the global scale . Conversely , a local optimal scale implies a nonlinear relationship . Thus , the Mgc-Map explains how Mgc discovers relationships , specifically , which scale has the most informative pairwise comparisons , and how that relates to the geometry of the relationship . Note that Mgc provides the geometric characterization ‘for free’ , meaning that no separate procedure is required; therefore , Mgc provides both a valid test and information about the geometric relationship . Moreover , similar dependencies have similar Mgc-Maps and often similar optimal scales . For example , logarithmic ( 10 ) and fourth root ( 11 ) , although very different functions analytically , are geometrically similar , and yield very similar Mgc-Maps . Similarly , ( 12 ) and ( 13 ) are trigonometric functions , and they share a narrow range of significant local scales . Both circle ( 16 ) and ellipse ( 17 ) , as well as square ( 14 ) and diamond ( 18 ) , are closely related geometrically and also have similar Mgc-Maps . This indicates that the Mgc-Map partially characterizes the geometry of these relationships , differentiating different dependence structures and assisting subsequent analysis steps . Moreover , in Shen and Vogelstein , 2018 , we proved that the sample Mgc-Map ( which Mgc estimates ) converges to the true Mgc-Map provided by the underlying joint distribution of the data . In other words , each relationship has a specific map that characterizes it based on its joint distribution , and Mgc is able to accurately estimate it via sample observations . The existence of a population level characterization of the joint distribution strongly differentiates Mgc from previously proposed multi-scale geometric or topological characterizations of data , such as persistence diagrams ( Edelsbrunner and Harer , 2009 ) . Geometric intuition , numerical simulations , and theory all provide evidence that Mgc will be useful for real data discoveries . Nonetheless , real data applications provide another necessary ingredient to justify its use in practice . Below , we describe several real data applications where we have used Mgc to understand relationships in data that other methods were unable to provide . There are a number of connections between Mgc and other prominent statistical procedures that may be worth further exploration . First , Mgc can be thought of as a regularized or sparsified variant of distance or kernel methods . Regularization is central to high-dimensional and ill-posed problems , where dimensionality is larger than sample size . Second , Mgc can also be thought of as learning a metric because it chooses the optimal scale amongst a set of n2 truncated distances , motivating studying the relationship between Mgc and recent advances in metric learning ( Xing et al . , 2003 ) . In particular , deep learning can be thought of as metric learning ( Giryes et al . , 2015 ) , and generative adversarial networks ( Goodfellow et al . , 2014 ) are implicitly testing for equality , which is closely related to dependence ( Sutherland et al . , 2016 ) . While Mgc searches over a two-dimensional parameter space to optimize the metric , deep learning searches over a much larger parameter space , sometimes including millions of dimensions . Probably neither is optimal , and somewhere between the two would be useful in many tasks . Third , energy statistics provide state of the art approaches to other problems , including goodness-of-fit ( Székely and Rizzo , 2005 ) , analysis of variance ( Rizzo and Székely , 2010 ) , conditional dependence ( Székely and Rizzo , 2014; Wang et al . , 2015 ) , and feature selection ( Li et al . , 2012; Zhong and Zhu , 2015 ) , so Mgc can be adapted for them as well . Indeed , Mgc can also implement a two-sample ( or generally the K-sample ) test ( Szekely and Rizzo , 2004; Heller et al . , 2016; Shen and Vogelstein , 2018 ) . Specifically , for more than two modalities , one may use summation of pairwise Mgc test statistics , similar to how energy statistic is generalized to K-sample testing from two-sample testing ( Rizzo and Székely , 2010; Rizzo and Székely , 2016; Shen and Vogelstein , 2018 ) , or how canonical correlation analysis is generalized into more than two modalities ( Kettenring , 1971; Tenenhaus and Tenenhaus , 2011; Shen et al . , 2014 ) . Finally , although energy statistics have not yet been explicitly used for classification , regression , or dimensionality reduction , Mgc opens the door to these applications by providing guidance as to how to proceed . Specifically , it is well documented in machine learning literature that the choice of kernel , metric , or scale often has a strong effect on the performance of different machine learning algorithms ( Levina and Bickel , 2004 ) . Mgc provides a mechanism to estimate scale that is both theoretically justified and computationally efficient , by optimizing a metric for a task wherein the previous methods lacked a notion of optimization . Nonlinear dimensionality reduction procedures , such as Isomap ( Tenenbaum et al . , 2000 ) and local linear embedding ( Roweis and Saul , 2000 ) for example , must also choose a scale , but have no principled criteria for doing so . Mgc could be used to provide insight into multimodal dimensionality reduction as well . The default metric choice of Mgc in this paper is always the Euclidean distance , but other metric choices may be more appropriate in different fields , and using the strong negative type metric as specified in Lyons ( 2013 ) guarantees consistency . However , if multiple metric choices are experimented to yield multiple Mgc p-values , then the optimal p-value should be properly corrected for multiple testing . Alternatively , one may use the maximum Mgc statistic among multiple metric choices , apply the same procedure in each permutation ( i . e . in each permutation , use the same number of metric choices and take the maximum Mgc as the permuted statistic ) , then derive a single p-value . Such a testing procedure properly controls the type one error level without the need for additional correction . Mgc also addresses a particularly vexing statistical problem that arises from the fact that methods methods for discovering dependencies are typically dissociated from methods for deciphering them . This dissociation creates a problem because the statistical assumptions underlying the ‘deciphering’ methods become compromised in the process of ‘discoverying’; this is called the ‘post-selection inference’ problem ( Berk et al . , 2013 ) . The most straightforward way to address this issue is to collect new data , which is costly and time-consuming . Therefore , researchers often ignore this fact and make statistically invalid claims . Mgc circumvents this dilemma by carefully constructing its permutation test to estimate the scale in the process of estimating a p-value , rather than after . To our knowledge , Mgc is the first dependence test to take a step towards valid post-selection inference . As a separate next theoretical extension , we could reduce the computational space and time required by Mgc . Mgc currently requires space and time that are quadratic with respect to the number of samples , which can be costly for very large data . Recent advances in related work demonstrated that one could reduce computational time of distance-based tests to close to linear via faster implementation , subsampling , random projection , and null distribution approximation ( Huo and Székely , 2016; Huang and Huo , 2017; Zhang et al . , 2018; Chaudhuri and Hu , 2018 ) , making it feasible for large amount of data . Alternately , semi-external memory implementations would allow running Mgc even as the interpoint comparison matrix exceeds the size of main memory ( Da Zheng et al . , 2015; Da Zheng et al . , 2016a; Da Zheng et al . , 2016b; Da Zheng et al . , 2016c ) . Finally , Mgc is easy to use . Source code is available in MATLAB , R , and Python from https://mgc . neurodata . io/ ( Bridgeford et al . , 2018; experiments archived at https://github . com/elifesciences-publications/MGC-paper ) . Code for reproducing all the figures in this manuscript is also available from the above websites . We showed Mgc’s value in diverse applications spanning neuroscience ( which motivated this work ) and an ’omics example . Applications in other domains facing similar questions of dependence , such as finance , pharmaceuticals , commerce , and security , could likewise benefit from Mgc . This section contains essential mathematical details on independence testing , the notion of the generalized correlation coefficient and the distance-based correlation measure , how to compute the local correlations , and the smoothing technique . A statistical treatment on MGC is in Shen and Vogelstein , 2018 , which introduces the population version of Mgc and various theoretical properties . Six algorithms are presented in order: For ease of presentation , we assume there are no repeating observations of X or Y , and note that Mcorr is the global correlation choice that Mgc builds on . This section provides the 20 different dependency functions used in the simulations . We used essentially the exact same relationships as previous publications to ensure a fair comparison ( Székely et al . , 2007; Simon and Tibshirani , 2012; Gorfine et al . , 2012 ) . We only made changes to add white noise and a weight vector for higher dimensions , thereby making them more difficult , to better compare all methods throughout different dimensions and sample sizes . A few additional relationships are also included . For each sample x∈Rp , we denote x[d] , d=1 , … , p as the dth dimension of the vector x . For the purpose of high-dimensional simulations , w∈Rp is a decaying vector with w[d]=1/d for each d , such that wTx is a weighted summation of all dimensions of x . Furthermore , 𝒰 ( a , b ) denotes the uniform distribution on the interval ( a , b ) , ℬ ( p ) denotes the Bernoulli distribution with probability p , 𝒩 ( μ , Σ ) denotes the normal distribution with mean µ and covariance S , U and V represent some auxiliary random variables , κ is a scalar constant to control the noise level ( which equals 1 for one-dimensional simulations and 0 otherwise ) , and ϵ is a white noise from independent standard normal distribution unless mentioned otherwise . For all the below equations , ( X , Y ) ∼iidFXY=FY\|XFX . For each relationship , we provide the space of ( X , Y ) , and define FY\|X and FX , as well as any additional auxiliary distributions . 1 . Linear ( X , Y ) ∈Rp×R , X∼𝒰 ( −1 , 1 ) p , Y=wTX+κϵ . 2 . Exponential ( X , Y ) ∈Rp×R:X∼𝒰 ( 0 , 3 ) p , Y=exp ( wTX ) +10κϵ . 3 . Cubic ( X , Y ) ∈Rp×R:X∼𝒰 ( −1 , 1 ) p , Y=128 ( wTX−13 ) 3+48 ( wTX−13 ) 2−12 ( wTX−13 ) +80κϵ . 4 . Joint normal ( X , Y ) ∈Rp×Rp: Let ρ=1/2p , Ip be the identity matrix of size p×p , Jp be the matrix of ones of size p×p , and Σ=[IpρJpρJp ( 1+0 . 5κ ) Ip] . Then ( X , Y ) ∼𝒩 ( 0 , Σ ) . 5 . Step Function ( X , Y ) ∈Rp×RX∼𝒰 ( −1 , 1 ) p , Y=I ( wTX>0 ) +ϵ , where I is the indicator function , that is I ( z ) is unity whenever z true , and zero otherwise . 6 . Quadratic ( X , Y ) ∈Rp×R:X∼𝒰 ( −1 , 1 ) p , Y= ( wTX ) 2+0 . 5κϵ . 7 . W Shape ( X , Y ) ∈Rp×R:U∼𝒰 ( −1 , 1 ) p , X∼𝒰 ( −1 , 1 ) p , Y=4[ ( ( wTX ) 2−12 ) 2+wTU/500]+0 . 5κϵ . 8 . Spiral ( X , Y ) ∈Rp×R:U∼𝒰 ( 0 , 5 ) , ϵ∼𝒩 ( 0 , 1 ) X[d]=Usin⁡ ( πU ) cosd⁡ ( πU ) ford=1 , … , p−1 , X[d]=Ucosp⁡ ( πU ) , Y=Usin⁡ ( πU ) +0 . 4pϵ . 9 . Uncorrelated Bernoulli ( X , Y ) ∈Rp×R:U∼ℬ ( 0 . 5 ) ϵ1∼𝒩 ( 0 , Ip ) , ϵ2∼𝒩 ( 0 , 1 ) , X∼ℬ ( 0 . 5 ) p+0 . 5ϵ1 , Y= ( 2U−1 ) wTX+0 . 5ϵ2 . 10 . Logarithmic ( X , Y ) ∈Rp×Rp:ϵ∼𝒩 ( 0 , Ip ) X∼𝒩 ( 0 , Ip ) , Y[d]=2log2⁡ ( \|X[d]\| ) +3κϵ[d]ford=1 , … , p . 11 . Fourth Root ( X , Y ) ∈Rp×Rp:X∼𝒰 ( −1 , 1 ) p , Y=\|wTX\|14+κ4ϵ . 12 . Sine Period 4π ( X , Y ) ∈Rp×Rp:U∼𝒰 ( −1 , 1 ) , V∼𝒩 ( 0 , 1 ) p , θ=4π , X[d]=U+0 . 02pV[d]ford=1 , … , p , Y=sin⁡ ( θX ) +κϵ . 13 . Sine Period 16π ( X , Y ) ∈Rp×Rp: Same as above except θ=16π and the noise on Y is changed to 0 . 5κϵ . 14 . Square ( X , Y ) ∈Rp×Rp: Let U∼𝒰 ( −1 , 1 ) , V∼𝒰 ( −1 , 1 ) , ϵ∼𝒩 ( 0 , 1 ) p , θ=−π8 . ThenX[d]=Ucos⁡θ+Vsin⁡θ+0 . 05pϵ[d] , Y[d]=−Usin⁡θ+Vcos⁡θ , for d=1 , … , p . 15 . Two Parabolas ( X , Y ) ∈Rp×R: ϵ∼𝒰 ( 0 , 1 ) , U∼ℬ ( 0 . 5 ) , X∼𝒰 ( −1 , 1 ) p , Y= ( ( wTX ) 2+2κϵ ) ⋅ ( U−12 ) . 16 . Circle ( X , Y ) ∈Rp×R:U∼𝒰 ( −1 , 1 ) p , ϵ∼𝒩 ( 0 , Ip ) , r=1 , X[d]=r ( sin⁡ ( πU[d+1] ) ∏j=1dcos⁡ ( πU[j] ) +0 . 4ϵ[d] ) ford=1 , … , p−1 , X[p]=r ( ∏j=1pcos⁡ ( πU[j] ) +0 . 4ϵ[p] ) , Y=sin⁡ ( πU[1] ) . 17 . Ellipse ( X , Y ) ∈Rp×R: Same as above except r=5 . 18 . Diamond ( X , Y ) ∈Rp×Rp: Same as 'Square' except θ=−π4 . 19 . Multiplicative Noise ( x , y ) ∈Rp×Rp:u∼𝒩 ( 0 , Ip ) , x∼𝒩 ( 0 , Ip ) , y[d]=u[d]x[d]ford=1 , … , p . 20 . Multimodal Independence ( X , Y ) ∈Rp×Rp:LetU∼𝒩 ( 0 , Ip ) , V∼𝒩 ( 0 , Ip ) , U′∼ℬ ( 0 . 5 ) p , V′∼ℬ ( 0 . 5 ) p . ThenX=U/3+2U′−1 , Y=V/3+2V′−1 . For each distribution , X and Y are dependent except ( 20 ) ; for some relationships ( 8 , 14 , 16-18 ) they are independent upon conditioning on the respective auxiliary variables , while for others they are 'directly' dependent . A visualization of each dependency with D=Dy=1 is shown in Figure 2—figure supplement 1 . For the increasing dimension simulation in the main paper , we always set κ=0 and n=100 , with p increasing . Note that q=p for types 4 , 10 , 12 , 13 , 14 , 18 , 19 , 20 , , otherwise q=1 . The decaying vector w is utilized for p>1 to make the high-dimensional relationships more difficult ( otherwise , additional dimensions only add more signal ) . For the one-dimensional simulations , we always set p=q=1 , κ=1 and n=100 .	If you want to estimate whether height is related to weight in humans , what would you do ? You could measure the height and weight of a large number of people , and then run a statistical test . Such ‘independence tests’ can be thought of as a screening procedure: if the two properties ( height and weight ) are not related , then there is no point in proceeding with further analyses . In the last 100 years different independence tests have been developed . However , classical approaches often fail to accurately discern relationships in the large , complex datasets typical of modern biomedical research . For example , connectomics datasets include tens or hundreds of thousands of connections between neurons that collectively underlie how the brain performs certain tasks . Discovering and deciphering relationships from these data is currently the largest barrier to progress in these fields . Another drawback to currently used methods of independence testing is that they act as a ‘black box’ , giving an answer without making it clear how it was calculated . This can make it difficult for researchers to reproduce their findings – a key part of confirming a scientific discovery . Vogelstein et al . therefore sought to develop a method of performing independence tests on large datasets that can easily be both applied and interpreted by practicing scientists . The method developed by Vogelstein et al . , called Multiscale Graph Correlation ( MGC , pronounced ‘magic’ ) , combines recent developments in hypothesis testing , machine learning , and data science . The result is that MGC typically requires between one half to one third as big a sample size as previously proposed methods for analyzing large , complex datasets . Moreover , MGC also indicates the nature of the relationship between different properties; for example , whether it is a linear relationship or not . Testing MGC on real biological data , including a cancer dataset and a human brain imaging dataset , revealed that it is more effective at finding possible relationships than other commonly used independence methods . MGC was also the only method that explained how it found those relationships . MGC will enable relationships to be found in data across many fields of inquiry – and not only in biology . Scientists , policy analysts , data journalists , and corporate data scientists could all use MGC to learn about the relationships present in their data . To that extent , Vogelstein et al . have made the code open source in MATLAB , R , and Python .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "computational", "and", "systems", "biology", "tools", "and", "resources", "neuroscience" ]	2019	Discovering and deciphering relationships across disparate data modalities
Intracellular lipopolysaccharide ( LPS ) triggers the non-canonical inflammasome pathway , resulting in pyroptosis of innate immune cells . In addition to its well-known proinflammatory role , LPS can directly cause regression of some tumors , although the underlying mechanism has remained unknown . Here we show that secretoglobin ( SCGB ) 3A2 , a small protein predominantly secreted in airways , chaperones LPS to the cytosol through the cell surface receptor syndecan-1; this leads to pyroptotic cell death driven by caspase-11 . SCGB3A2 and LPS co-treatment significantly induced pyroptosis of macrophage RAW264 . 7 cells and decreased cancer cell proliferation in vitro , while SCGB3A2 treatment resulted in reduced progression of xenograft tumors in mice . These data suggest a conserved function for SCGB3A2 in the innate immune system and cancer cells . These findings demonstrate a critical role for SCGB3A2 as an LPS delivery vehicle; they reveal one mechanism whereby LPS enters innate immune cells leading to pyroptosis , and they clarify the direct effect of LPS on cancer cells . The airway is continuously exposed to pathogens , including low levels of gram negative bacteria in the air ( Lundin and Checkoway , 2009 ) . Lipopolysaccharide ( LPS ) is a component of the outer membrane of gram negative bacteria and can cause inflammation in the lung . It was previously thought that toll-like receptor 4 ( TLR4 ) is the sole LPS-specific pattern recognition receptors ( PRRs ) at the cell membrane ( Poltorak et al . , 1998 ) . However , recent studies demonstrated the presence of an TLR4-independent PRRs mechanism to sense LPS in the cytosol via an inflammatory caspase , caspase-11 , in a non-canonical inflammasome pathway ( Hagar et al . , 2013; Kayagaki et al . , 2013 ) . While it is widely known that tumor metastasis is coupled with inflammation in the tumor microenvironment , in many cases , immune cells in the tumor microenvironment no longer exhibit anti-tumor effects , instead they are co-opted to promote tumor growth and metastasis ( Whiteside , 2008 ) . On the contrary , the activity of bacteria or endotoxin for anti-tumor effects has been extensively studied for decades since the first observation by W . B . Coley ( Lundin and Checkoway , 2009; Ribi et al . , 1983 ) . Although ‘Coley’s toxin’ is currently not used for cancer treatment because of its toxicities , accumulating evidence has revealed that his theory was correct and the notion that the enhanced host immune systems by endotoxin could attack some cancer cells has advanced to cancer immunotherapy . However , whether endotoxin has a direct function in attacking cancer cells remains controversial , while the interest in endotoxin as a cancer therapeutic agent waned , despite of many reports for favorable outcomes . A cytokine-like small secreted protein , SCGB3A2 ( secretoglobin 3A2 , also known as UGRP1 and HIN-2 ) , was previously identified that is abundantly and specifically expressed in non-ciliated airway epithelial ( club ) cells of the trachea , bronchus , and bronchioles ( Niimi et al . , 2001 ) and revealing that SCGB3A2 functions to suppress lung inflammation and fibrosis ( Cai and Kimura , 2015; Cai et al . , 2014; Chiba et al . , 2006; Kido et al . , 2014; Kurotani et al . , 2011; Yoneda et al . , 2016 ) . Although specific expression of SCGB3A2 in lung epithelial cells and its role in inflammation may imply a possible important function for SCGB3A2 in the clearance of pathogens , its role in host defense , if any , has not been studied . In addition , while fibrosis is closely related to tumor development ( Coussens and Werb , 2002; Trinchieri , 2012 ) and SCGB3A2 functions as an anti-fibrotic agent , the role of SCGB3A2 in lung cancer development is unknown . To determine whether SCGB3A2 has any influence on cancer cell growth , CCK8 ( cell counting kit 8 ) assay was performed using murine Lewis lung carcinoma ( LLC ) cells . The proliferation of LLC cells was markedly suppressed by mouse recombinant SCGB3A2 ( Figure 1A ) . This in vitro effect of SCGB3A2 was also observed in vivo in the LLC cells intravenous metastasis model using wild-type C57BL/6 mice in conjunction with administration of SCGB3A2 ( Figure 1B–1E ) . To confirm the tumor growth inhibition roles of SCGB3A2 in vivo , Scgb3a2-null mice were subjected to the metastasis model ( Kido et al . , 2014 ) . Mice null for Scgb3a2 developed far greater numbers of lung surface tumors than wild-type littermates when LLC cells were intravenously injected ( Figure 1F ) . Furthermore , administration of recombinant mouse SCGB3A2 to Scgb3a2-null mice clearly rescued the Scgb3a2-null phenotypes of LLC cell lung metastasis ( Figure 1G–1I ) . These results indicate the importance of SCGB3A2 in the suppression of LLC cell tumor development in lungs in vivo . For the above experiments , several preparations of recombinant SCGB3A2 ( mouse and human ) were used from various sources as described in Materials and methods . However , we noticed an unexpected phenomenon where some sources of recombinant SCGB3A2 had almost no effect on LLC cell growth inhibition ( Figure 2—figure supplement 1A ) . This phenomenon was found to be associated with the level of endotoxin ( LPS ) contained in the various preparations ( Supplementary file 1 ) . Moreover , we realized that whenever the endotoxin was removed from the preparation , the final SCGB3A2 protein yield was drastically reduced ( data not shown ) . Because SCGB3A2 is abundantly expressed in airway epithelial cells which are exposed to various microorganisms and LPS derived from bacteria ( Lundin and Checkoway , 2009 ) , we hypothesized that the fundamental function of SCGB3A2 may be related to its binding to and sequestering of LPS . Further , while inflammation is thought to be coupled with cancer metastasis , paradoxically endotoxins or ensuing enhanced immunity may inhibit some cancer growth ( Lundin and Checkoway , 2009; McCarthy , 2006; Ribi et al . , 1983 ) . Indeed , the growth of LLC cells was strongly inhibited by small amounts of LPS ( Figure 2A ) . To test if SCGB3A2 interacts with LPS , imidazole and zinc salt staining was performed ( Figure 2B and C ) ( Rodríguez and Hardy , 2015 ) . Crude LPS ( O111:B4 ) barely migrated into the gel and remained near the well , due to high-molecular mass aggregation ( Figure 2B , lane 1 ( Rodríguez and Hardy , 2015 ) ) . Pre-incubation with SCGB3A2 ( human SCGB3A2 ( C1 ) ; see Supplementary file 1; unless otherwise noted , this lot was mainly used in the current studies ) produced a broad diffuse band in dose dependent manner , indicating that SCGB3A2 interacted with and dramatically enhanced the electrophoretic mobility of LPS ( Figure 2B , lane 2–5 , and Figure 2C ) . Rough A form ( Ra-LPS ) and other serotypes of LPS produced the same results ( Figure 2—figure supplement 1B ) . To further confirm that SCGB3A2 directly binds to LPS , streptavidin pull-down assays were performed using LPS-Biotin and recombinant SCGB3A2 ( Figure 2D ) . The results clearly showed that SCGB3A2 is an LPS binding protein . The ability of SCGB3A2 to bind and disaggregate LPS micelles was further demonstrated by the dynamic light scattering ( DLS ) method ( Figure 2E and Figure 2—figure supplement 1C–1E ) . Thus , SCGB3A2 is an LPS binding protein and has powerful LPS dissociation properties , against both smooth and rough forms of LPS . To determine if LPS alone is sufficient or the combination of LPS+SCGB3A2 is required for the inhibition of growth and metastasis of LLC cells in vivo , LLC cell intravenous metastasis xenograft experiments were carried out , in which various amounts of LPS , estimated in our recombinant protein SCGB3A2 preparations ( see Supplementary file 1 ) , were administered for seven consecutive days in the 1 st week after LLC cells injection ( see Figure 1B ) . The number of tumors obtained was compared with that obtained with administration of recombinant SCGB3A2 without exogenously added LPS . Human SCGB3A2 ( C1 ) alone showed drastic inhibition of LLC cells growth , while the amount of LPS contained in the recombinant SCGB3A2 preparation C1 or C3 , or high concentration did not show any statistically significant differences in tumor numbers compared with PBS administration ( Figure 2F ) . Moreover , LPS-treated lungs showed much larger lesions than did SCGB3A2-treated lung tumors , which sometimes encompassed the entire lobes , demonstrating the fundamental differences between LPS alone and SCGB3A2 administration ( Figure 2G ) . A receptor for SCGB3A2 involved in the SCGB3A2 signaling was unbiasedly identified using human protein microarray analysis ( Figure 3A , Supplementary file 2 and 3 , Figure 3—source data 1 ) . Among the top 116 proteins ( Supplementary file 2 ) , 13 proteins were selected as possible candidates for the SCGB3A2 receptor as a cell surface protein ( Figure 3A and Supplementary file 3 ) . To confirm a direct interaction with SCGB3A2 , pull-down assays were performed , in which syndecan-1 ( SDC1 ) and podoplanin ( PDPN , T1-alpha ) showed positive interaction with SCGB3A2 ( Figure 3B and data not shown ) . PDPN is known as a marker for alveolar type I epithelial cells in lung , while SDC1 was moderately expressed in proximal airway epithelial cells ( Figure 3C ) , suggesting a possible relationship between SCGB3A2 and SDC1 for lung airway homeostasis . Therefore , this study focused on SDC1 . SDC1 was found to be highly expressed on LLC cells surfaces in vitro as well as in metastatic LLC cells in vivo ( Figure 3D and Figure 3—figure supplement 1A ) . In contrast , the B16F10 mouse melanoma cell line , which exhibited less SCGB3A2-dependent growth suppression effects than LLC in vitro ( data not shown ) , showed focal expression of SDC1 near cell nuclei and faint staining at cell-to-cell contact sites ( Figure 3D ) , while the total cell surface staining was low compared to LLC cells . Further analyses supported the robust expression of SDC1 on the surface of LLC cells ( Figure 3E and Figure 3—figure supplement 1B ) , and their binding to SCGB3A2 ( Figure 3F ) . LLC cells stably expressing shRNA-SDC1 ( LLC-sh-SDC1 , Figure 3—figure supplement 1C ) showed diminished SCGB3A2 binding ( Figure 3G ) . In addition , ARH-77 human myeloma cell line , which lacks detectable SDC1 ( Ridley et al . , 1993 ) , and ARH-77 cells over-expressing mouse SDC1 ( ARH-77-mSDC1 ( Dhodapkar et al . , 1998; Liebersbach and Sanderson , 1994 ) , See Figure 3—figure supplement 1D ) verified the SCGB3A2-SDC1 binding interaction . ARH-77-mSDC1 enhanced SCGB3A2 binding compared to the parental cells ( Figure 3—figure supplement 1E ) . Syndecans are a family of transmembrane heparan sulfate proteoglycan ( HSPG ) . To determine the domain of SDC1 that interacts with SCGB3A2 , heparin was used to inhibit the function of heparan sulfate chains ( HS ) . Heparin addition significantly inhibited the binding of SCGB3A2 to both LLC ( Figure 3H ) and ARH-77-mSDC1 cells ( Figure 3—figure supplement 1F ) , suggesting that HS on SDC1 may play a role in SCGB3A2 binding . To reveal the precise binding site of SCGB3A2 on LLC cell surfaces , immunofluorescence analysis was performed using anti-SCGB3A2 and anti-SDC1 ectodomain antibodies ( Figure 4A ) . ARH-77-mSDC1 cells were also used in this analysis . Without stimulation with SCGB3A2 , both LLC and ARH-77-mSDC1 cells had evenly distributed SDC1 on the cell surface ( Figure 4A Control ) . After stimulation with SCGB3A2 , the SDC1 signal became relatively concentrated on cell protrusions equivalent to the uropod structure of myeloma ( Børset et al . , 2000; Yang et al . , 2003 ) , which co-localized with SCGB3A2 ( Figure 4A , +SCGB3A2 ) . Staining of ICAM-1 , a uropod marker ( del Pozo et al . , 1997 ) , confirmed co-localization of SDC1 and SCGB3A2 on the uropods of both LLC and ARH-77-mSDC1 cells . Interestingly , when LLC cells were incubated for a short time with LPS and SCGB3A2 , Alexa-labeled LPS ( LPSA488 , Figure 4—figure supplement 1A ) , SCGB3A2 , and clathrin , a key protein for endocytosis , all co-localized in uropod ( Figure 4B ) . This pattern of clathrin localization was similar to those previously reported using T lymphocyte ( Samaniego et al . , 2007 ) . Upon further incubation , LLC cells appeared to have incorporated SCGB3A2 into the cells as visualized using an HaloTag ( HT ) -SCGB3A2 fusion protein ( Figure 4—figure supplement 1B and C ) . Clathrin expression was localized near the incorporated SCGB3A2 signals ( Figure 4—figure supplement 1C ) , suggesting that the LPS-SCGB3A2 complex enters LLC cells via binding to SDC1 followed by clathrin-dependent endocytosis ( see below ) . Further , live cell imaging clearly showed that SCGB3A2-HT was incorporated into LLC-sh-Control cells after overnight incubation , while very low signals were observed in LLC-sh-SDC1 cells ( Figure 4—figure supplement 1D ) . Computer modeling analysis provided further evidence that SCGB3A2 binds to both LPS and SDC1 when it forms a tetramer ( Figure 4C and Figure 4—figure supplement 2A–2F ) . In fact , SCGB3A2 tends to form oligomers in vitro , demonstrating the validity of the computer modeling ( Figure 4—figure supplement 2G , see Figure 2C CBB staining , also cf: ( Cai et al . , 2014; Niimi et al . , 2001 ) ) . Recent studies demonstrated intracellular LPS triggers caspase-4/11 activation and the non-canonical inflammasome pathway ( Hagar et al . , 2013; Kayagaki et al . , 2013 ) . It’s possible that SCGB3A2 simply enhances TLR4 priming canonical signals via SDC1 binding , transferring LPS to TLR4 . To address this possibility , LLC cells stably expressing sh-TLR4 ( LLC-sh-TLR4 ) were established ( Figure 4—figure supplement 3A ) , and SCGB3A2 binding and uptake were compared with those of LLC-sh-Control and LLC-sh-SDC1 cells ( Figure 4—figure supplement 3B and C ) . SCGB3A2 enhanced binding of LPS to LLC-sh-TLR4 cells at similar level to that of LLC-sh-Control , while LLC-sh-SDC1 cells showed little binding of LPS ( Figure 4—figure supplement 3B ) . In addition , SCGB3A2 and LPS were incorporated into LLC-sh-TLR4 cells and appeared to co-localize within the cytosol ( Figure 4—figure supplement 3C ) . These data , together with data in Figure 4—figure supplement 1D , suggest that SCGB3A2 is important for LPS uptake and that SDC1 , not TLR4 , is required for SCGB3A2-LPS incorporation . To confirm the cytosolic localization of LPS , LLC cells were visualized with Alexa-labeled LPS ( LPSA594 ) , anti-EEA1 ( early endosomes marker ) and anti-Lamp1 ( lysosomal marker ) antibodies ( Figure 4D and E ) . At an early time point , most of the LPS staining was co-localized with EEA1 , which was clearly enhanced when cells were co-incubated with SCGB3A2 ( Figure 4D ) . At a later time point , however , some of the LPS staining did not overlap with either EEA1 or LAMP1 ( Figure 4E ) . With SDC1 staining depicting the plasma membrane , LPS staining was clearly visible inside the membrane , which differed from the EEA1 distribution pattern ( Figure 4F ) . These results suggest that LPS could be transported into the cytosol of LLC cells through an SCGB3A2-dependent mechanism . Next whether LPS transport into cytosol of LLC cells triggers non-canonical inflammasome pathway was examined using LLC-sh-TLR4 cells . This was because TLR4 signaling also enhances pro-caspase-11/NLRP3 expression via the canonical inflammasome pathway ( Kayagaki et al . , 2013 ) . SCGB3A2 + LPS increased pro-caspase-11 and NLRP3 , while caspase-1 expression , the major caspase activated by the canonical inflammasome , was not significantly different ( Figure 4G ) . Caspase-11 processing and IL-1β expression/processing were not detected at the protein level in LLC-sh-TLR4 and LLC-sh-Control cells ( data not shown ) . This might not exclude the possibility that SCGB3A2 promotes pyroptosis of LLC cells with only small amounts of the processed form of caspase-11 that cannot be detected by western blotting based on previous reports ( Hagar et al . , 2013 ) ( see Figure 5A ) . The importance of sensing LPS and triggering caspase-11 and NLRP3 activation in host defense has been mainly studied using macrophage . Moreover , macrophage are key players both for lung homeostasis and the tumor microenvironment . Hence , the effect of SCGB3A2 on mouse macrophage-like RAW264 . 7 cells , which express SDC1 on the cell surface ( Figure 4—figure supplement 4A ) , was next examined . Co-incubation of RAW264 . 7 cells with SCGB3A2 + LPS clearly enhanced expression of caspase-11 and NLRP3 , followed by IL-1β up-regulation and maturation , while SCGB3A2 or LPS alone did not ( Figure 4H ) . Heparin co-incubation abrogated caspase-11/NLRP3/IL-1β expression by SCGB3A2 + LPS . SCGB3A2 enhanced IL-1β secretion from RAW264 . 7 cells , which was inhibited by the addition of heparin ( Figure 4—figure supplement 4B ) . A lactate dehydrogenase ( LDH ) cytotoxicity assay showed that RAW264 . 7 cells exhibited greater cytotoxicity by SCGB3A2 + LPS compared to the individual treatments , demonstrating the critical role of SCGB3A2 as an LPS delivery molecule to macrophage cells as well ( Figure 4—figure supplement 4C ) . In LLC cells under SCGB3A2 + LPS , caspase-11 expression was upregulated in a diffused distribution pattern in the entire area and showed specific foci ( Figure 4I ) . Importantly , the caspase-11 foci overlapped with incorporated LPS ( Figure 4I ) . The expression of NLRP3 was also clearly up-regulated by LPS +SCGB3A2 and accumulated around the caspase-11 foci . We hypothesized that the incorporated LPS triggers formation of caspase-11 foci in LLC cells . As expected , when LPS was introduced into LLC cells using a DNA transfection reagent , LLC cells showed increased intracellular LPS signals and caspase-11 foci , overlapped with LPS ( Figure 4J ) , confirming that the formation of caspase-11 foci is mediated by LPS introduction into the cytosol of LLC cells . We could not detect clear foci of caspase-1 in LLC cells , unlike the case of macrophages as previously reported ( data not shown ) . Caspase-11 foci formation and NLRP3 upregulation driven by LPS + SCGB3A2 were also observed in RAW264 . 7 cells ( Figure 4—figure supplement 4D ) . These results confirm that SCGB3A2 facilitates the delivery of LPS into the cytosol , in concert with the enhancement of non-canonical inflammasome signaling . To confirm the importance of clathrin-mediated endocytosis of LPS via the SCGB3A2-SDC1 pathway for killing of LLC cells , the effect of clathrin inhibitor , Dynasore on the growth of LLC cells was examined in vitro ( Figure 4K–4M ) . LLC cells had strong focal staining of SCGB3A2-HT and LPSA448 at the corresponding locations to each other , while Dynasore potently inhibited the incorporation of SCGB3A2 and LPS into the cytosol of LLC cells ( Figure 4K ) and abrogated the activation of caspase-11 ( Figure 4L ) . LDH release from LLC cells as indication for cytotoxicity was slightly upregulated by LPS +SCGB3A2 , while this upregulation was not observed when cells were treated with either Dynasore or Wedelolactone ( caspase-11 inhibitor ) ( Kobori et al . , 2004 ) ( Figure 4M , Figure 4—figure supplement 5A ) . Furthermore , when LLC-sh-casp-11 cells ( Figure 4—figure supplement 5B ) were subjected to the intravenous metastasis model with or without SCGB3A2 administration , they did not show any significantly different numbers of lung tumors after SCGB3A2 administration , in sharp contrast to the results with control LLC cells ( Figure 4N ) . These results clearly indicate the importance of clathrin-mediated endocytosis of LPS +SCGB3A2 and caspase-11 activation for the killing of LLC cells in vivo . The SCGB3A2 + LPS complex promoted pyroptotic cell death morphology in cultured LLC cells ( membrane swelling; Figure 5A ) . CCK8 assay confirmed the upregulation of pyroptotic cell death of LLC cells by essentially endotoxin-free SCGB3A2 plus a small amount of LPS ( Figure 5B ) . Furthermore , flow cytometry analysis revealed the upregulation of propidium iodide ( PI ) positive cell death by SCGB3A2 + LPS ( Figure 5C ) , demonstrating the formation of cell membrane pores , the characteristic feature of pyroptosis , induced by SCGB3A2 + LPS . Next , the induction of cell death by SCGB3A2 in vivo was examined in the LLC cell intravenous metastasis model ( Figure 5D ) . Large necrotic areas were found in lung tumors from mice treated with early intravenous administration of SCGB3A2 ( 1st and 2nd week ) ( Figure 5D ) . Importantly , these necrotic areas showed enhanced expression of both caspase-11 and NLRP3 , demonstrating that tumor cell death occurred through caspase-11-mediated non-canonical inflammasome activation ( Figure 5E and F ) . These results clearly indicate that SCGB3A2 significantly promotes pyroptotic death of LLC cells both in vivo and in vitro . LLC-sh-SDC1 cells showed reduced susceptibility to the cytotoxic effects of LPS +SCGB3A2 complex in vitro ( Figure 6A ) , accompanied by minimal enhancement of caspase-11 foci formation by LPS +SCGB3A2 ( Figure 6B , see Figure 4I ) . Heparin addition abrogated the increase of caspase-11 foci in LLC-sh-Control cells ( Figure 6B ) , confirming the crucial role of heparin sulfate and SDC1 for caspase-11 foci formation . In vivo sensitivity of LLC-sh-SDC1 cells to SCGB3A2-mediated inhibition of metastasis was next analyzed . Tumor numbers in mice that received LLC-sh-SDC1 cells and SCGB3A2 were not significantly different from those that received LLC-sh-SDC1 cells and PBS , while tumor numbers with LLC-sh-Control cells were significantly reduced by SCGB3A2 co-injection , similar to that observed in Figure 1 ( Figure 6C and D ) . These experiments confirmed a pivotal role for SDC1 in SCGB3A2-mediated inhibition of LLC cell growth and metastasis in vivo . Next , to understand the reason for the differences in response to SCGB3A2 between LLC ( susceptible ) and B16F10 ( resistant ) cells , the baseline mRNA expression from inflammasome-related genes were examined ( Figure 6E ) . As a result , Casp11 , Nlrp3 , Aim2 , Gsdmd , and Il1b mRNAs were highly expressed only in LLC cells , suggesting that LLC cells have the machinery to activate a non-canonical inflammasome pathway driven by caspase-11 in combination with higher expression levels of cell surface SDC1 ( see Figure 3D and E ) . Lastly , the effect of SCGB3A2 on the survival of lung-specific KrasG12D mutant mice was examined using KrasG12D;Scgb3a2 ( fl/fl ) and the littermate KrasG12D;Scgb3a2 ( fl/+ ) mice ( Figure 6F ) . Due to lung-specific activation of the KrasG12D allele , these mice developed lung cancer within 4 months of age . KrasG12D;Scgb3a2 ( fl/fl ) mice clearly showed a poorer survival rate than KrasG12D;Scgb3a2 ( fl/+ ) mice . Based on these results , we propose a new model for SCGB3A2 delivery of LPS and activation of caspase-11 ( caspase-4 ) pathway via SDC1 receptor signaling , leading to pyroptosis of cancer cells ( Figure 6G ) . SCGB3A2 is a member of the secretoglobin family of proteins , which share a common four helical bundle subunit structure , exist as dimers , tetramers , and other oligomers , and some of which have also been implicated in tumor suppression ( Mukherjee et al . , 2007 ) without a clear understanding yet of the mechanistic pathway ( s ) . This work takes a significant step forward to elucidate and describe a new pathway impacted by SCGB3A2 functioning as a tumor suppressor protein . Previously we showed that SCGB3A2 functions as an anti-inflammatory and anti-fibrotic agent in the lung ( Cai and Kimura , 2015; Cai et al . , 2014; Chiba et al . , 2006; Kido et al . , 2014; Kurotani et al . , 2011; Yoneda et al . , 2016 ) . Because SCGB3A2 is mainly secreted by club cells in lung airways , it is reasonable to assume that a primary function of SCGB3A2 is to protect the hosts from pathogens and pathogen-associated molecular patterns such as LPS . The current study demonstrated that SCGB3A2 binds to and facilitates delivery of LPS into the cytosolic compartment through specific binding with SDC1 , resulting in cell death via an inflammatory pathway leading to pyroptosis . This is commonly seen in the macrophage cell line RAW264 . 7 , suggesting a possible conserved role for SCGB3A2 in host defense and enhancing the immune response through the non-canonical inflammasome pathway of pyroptosis . Notably , in the case of LLC cells , the uptake of SCGB3A2-sequestered small amount of LPS triggers inflammatory cell death , probably because of the abundant SDC1 expression on their cell surface . It is noteworthy that caspase-11 and human caspase-4/5 are specific to mammals ( Kayagaki et al . , 2015 ) , while the SCGB superfamily of proteins , including SCGB3A2 , have also evolved in mammalian lineages ( Jackson et al . , 2011 ) , suggesting the co-emergent evolution as an ‘input-output’ for defense from invading pathogens . SDC1 localization to uropods is functionally important as uropods accumulate growth factors and connect them at cell-to-cell contact points or junctions ( Børset et al . , 2000; Yang et al . , 2003 ) . Others demonstrated that the SDC1-specific HS sequence is important for targeting SDC1 to uropods ( Børset et al . , 2000 ) . Our results that heparin treatment dramatically reduces SCGB3A2 and LPS binding and their incorporation into cells , as well as caspase-11 and NLRP3 induction suggest that SCGB3A2 appears to interact with the HS moiety of SDC1 , which is concentrated in the membrane uropods . Recently , it was reported that bacterial outer membrane vesicles ( OMVs ) deliver LPS into the host cell cytosol via clathrin mediated endocytosis ( Vanaja et al . , 2016 ) . OMV is expected to work as a platform vaccine technology because of the potential to deliver small antigens and to modulate the immune system , however , it is highly toxic due to contamination with a large amount of LPS ( Acevedo et al . , 2014 ) . In addition , guanylate-binding proteins ( GBPs ) are reported to have important function for interaction with cytosolic OMV and activation of caspase-11 ( Meunier et al . , 2014; Santos et al . , 2018 ) . Our findings demonstrate that SCGB3A2 is incorporated into cytosol via a clathrin-mediated uptake mechanism , and that SCGB3A2 is a potent LPS disaggregation protein . Hence , SCGB3A2 might be an attractive protein which has a key function similar to OMV , but without any significant toxicity because of its natural occurrence and abundance in lung . It is also interesting to speculate that SCGB3A2 could liberate LPS from OMV to gain access to cell cytosols either from early endosome or from extra cellular spaces , collaborating with other host proteins such as GBPs . Whether this is the case requires further studies . It was reported that the non-canonical inflammasome pathway governed by caspase-4/caspase-11 , intrinsic to intestinal epithelial cells , plays a critical role in antimicrobial defense , causing pyroptotic cell death and shedding of infected cells ( Knodler et al . , 2014 ) . These events could limit pathogen colonization of the intestinal epithelium . Likewise , it’s conceivable that lung airway epithelial cells have an intrinsic non-canonical inflammasome pathway for antimicrobial defense , through the SCGB3A2 and SDC1 interaction . Moreover , the present results suggest that this non-canonical inflammasome pathway is retained in some cancer cells and this property could be used for cancer treatment . Importantly , it was reported that newborn Sdc1 ( -/- ) mouse lungs show marked resistance against P . aeruginosa infection ( Park et al . , 2001 ) . This study was extended to show the biological function of SDC1 in lung epithelial cells from a simple cell membrane receptor for growth factors and chemokines to that of modulating microbial pathogenesis and host defense ( Park et al . , 2001 ) . The role of SCGB3A2 as a chaperon to deliver LPS to cell cytosols may initially be established to protect host cells from infection , while this mechanism may have evolved to protect host from cancer development by activation of the non-canonical inflammasome signaling pathway . Anti-tumor effects of endotoxin/LPS has been known for decades while the effects are still controversial; one reason is because the effects vary depending on different cancers ( Lundin and Checkoway , 2009; Ribi et al . , 1983 ) . Our results could provide one of the reasons for the various sensitivities of different cancer cells to endotoxin . Of note is that the levels of SDC1 expression differ depending on cancer types and are strikingly dysregulated in many cancer cells ( Akl et al . , 2015; Teng et al . , 2012 ) . Because loss of membranous SDC1 increases the mobility of cancer cells , resulting in enhancement of metastasis , in general , loss or weak expression of SDC1 in tumors is thought to be associated with unfavorable outcomes . In lung cancer patients , high serum levels of shed SDC1 and bFGF were associated with poor prognosis ( Joensuu et al . , 2002 ) . Some reports also found cytoplasmic or nuclear localization patterns of SDC/HS in less differentiated malignant cells ( Akl et al . , 2015; Burbach et al . , 2003; Miyake et al . , 2014 ) , however the underlying mechanism for this correlation is largely unknown . Cancer cells are notorious for changing/adapting in order to survive , such as acquisition of the resistance to chemotherapeutic reagents . In addition to the loss of contact with extracellular matrix , the various expression patterns ( reduced , shed , or subcellular ) of SDC1 in many malignant cancer cells might suggest that this could be one of their acquired properties; by losing the expression of SDC1 on their cell surface , they will become refractory to the microorganism/LPS triggering non-canonical inflammasome pathway , thus avoiding their own death . Further studies will be required , particularly regarding which cancer cell types possess the machinery and/or express the necessary genes and protein expression patterns that permit response via the non-canonical inflammasome pathway . Collectively , these findings could be utilized for the recognition of the importance of the inflammasome activation of cancer cells and the innate immune system for cancer targeting and treatment . The currently available cancer immunotherapy is mainly targeted to the host immune cells , whereas our study shows the possibility to directly target the activation of non-canonical inflammasome pathway of cancer cells . Our findings may provide the new clue for the understanding of many cancers that are refractory to cancer immunotherapy mediated by immune cells . Combination of the cancer immunotherapy and the cancer cell self-destructive therapy could greatly advance the treatment of cancer patients . The LLC cells used in this study were the LLC-Mhi cell line ( obtained from Dr . Glenn Merlino , NCI ) , which is a high metastatic subline derived from LLC tumors described previously ( Day et al . , 2012 ) . B16F10 cells were purchased from American Type Culture Collection . ARH-77 and ARH-77-mSDC1 cells were kindly provided by Dr . Ralph D . Sanderson ( University of Alabama at Birmingham ) , and RAW264 . 7 cells by Dr . Raymond P . Donnelly ( FDA ) . LLC , ARH-77 , and RAW264 . 7 cells were all tested negative for mycoplasma ( NCI Core facility ) and authenticated by STR analysis ( IDEXX BioResearch ) . LLC and B16F10 cells were cultured in RPMI 1640 Medium ( LONZA ) with heat-inactivated fetal bovine serum ( FBS ) , supplemented with penicillin/streptomycin ( 1:100 ) at 37 ˚C , 5% CO2 . Culture of LLC cells was carried out under various concentrations of FBS , as indicated in the Figure legends . For LPS stimulation , RAW264 . 7 cells were cultured in OPTI-MEMTM I reduced serum medium ( Thermo Fisher Scientific ) for times indicated in the text . LPS transfection was performed using X-tremeGene HP DNA transfection reagent ( Roche Applied Science ) . SCGB3A2 binding proteins were identified using ProtoarrayTM Human Protein Microarray v5 . 0 Protein-Protein Interaction Kit for biotinylated proteins ( Thermo Fisher Scientific , PAH0525101 , >9000 proteins included ) . Experiments were carried out according to procedures provided by the manufacturer . First , a biotin label was introduced into recombinant human SCGB3A2 protein using Biotin-XX Microscale Protein Labeling Kit ( Thermo Fisher Scientific B30010 ) , which was then used to probe Protoarray Human protein microarrays . The microarrays were washed with washing buffer ( PBS containing 10% Synthetic Block ( included in the kit ) and 0 . 1% Tween 20 ( Thermo Fisher Scientific ) ) , and probed with Alexa Fluor 647 conjugated streptavidin ( included in the kit ) . After washing , the microarrays were dried and scanned by a fluorescent microarray scanner ( Perkin Elmer , Scanarray Express ) to obtain the data . Software for the data analysis ( Protoarray Prospector ) was also provided by the manufacturer . The shRNA constructs were purchased from transOMIC for mouse SDC1 , from ORIGENE for mouse TLR4 and mouse caspase-11 . Retroviral constructs were transfected into Phoenix packaging cells by using X-tremeGene HP DNA transfection reagent ( Roche Applied Science ) . Drug selection and cell cloning were conducted in the presence of 2 μg/ml puromycin by the limited dilution method . shRNA constructs used for mouse Sdc1 knock down are as follows: pMLP-Sdc1-sh1; 5’-CGGGGATGACTCTGACAACTTA-3’ , 5’-TAGTGAAGCCACAGATGTA-3’ , and 5’-TAAGTTGTCAGAGTCATCCCCA-3’ , pMLP-Sdc1-sh2; 5’-ACAGGCAGCTGTCACATCTCAA-3’ , 5’-TAGTGAAGCCACAGATGTA-3’ , and 5’-TTGAGATGTGACAGCTGCCTGG-3’ ) , and pMLP-Sdc1-sh3; 5’-CCAAGACTTCACCTTTGAAACA-3’ , 5’-TAGTGAAGCCACAGATGTA-3’ , and 5’-TGTTTCAAAGGTGAAGTCTTGT-3’ . shRNA sequences used for mouse TLR4 knock down are as follows: 5’-CACTTAGACCTCAGCTTCAATGGTGCCAT-3’ and 5’-TGCCTTCACTACAGAGACTTTATTCCTGG-3’ . shRNA sequences used for mouse Casp11 knockdown are as follows: 5’-TAACAATGCTGAACGCAGTGACAAGCGTT-3’ , 5’-ACAGCACATTCCTGGTGCTAATGTCTCAT-3’ and 5’-ATATTCCTGAAGGTGCAACAATCATTTGA-3’ . COS-1 cells were transfected with 2 . 5 µg each of candidate gene cloned into pcDNA3 . 1/Myc-His vector , the human SCGB3A2 ( NM_054023 ) open reading frame cloned into pcDNA3 . 1 with a C-terminal FLAG tag , or a control plasmid by using X-tremeGene HP DNA transfection reagent . Both cells and media were harvested 48 hr after transfection . The culture media containing cells were centrifuged at 500 g for 10 min at 4°C and the supernatant was collected ( media supernatant ) . Cells were lysed in 400 µL CHAPS IP buffer-1 ( 1% CHAPS , 150 mM NaCl , 50 mM Tris-HCl , pH 7 . 4 , protease inhibitor complete-mini 1 tablet/10 ml ) and sonicated two times for 5 s each on ice . The cell lysates were centrifuged at 15 , 000 g for 10 min at 4°C and the supernatant was collected ( cell lysate supernatant ) . The media supernatant and cell lysate supernatant were combined , which were pre-cleared with Protein G-Agarose ( Santa Cruz Biotechnology ) at 4°C for 3 hr , followed by incubation with FLAG-tagged gel ( 20 µL; #3326 , MBL ) at 4°C overnight . The gel-immunocomplexes were washed twice with CHAPS IP buffer-2 ( 0 . 1% CHAPS , 500 mM NaCl , 50 mM Tris-HCl , pH 7 . 4 ) for 20 min each and then washed twice with CHAPS IP buffer-3 ( 0 . 1% CHAPS , 50 mM Tris-HCl , pH 7 . 4 ) for 20 min each . Immunoprecipitated samples were separated by SDS-PAGE and electroblotted to PVDF membranes . Blocking was carried out with 5% skim milk in TBST ( Tris-buffered saline; Tris-HCl , pH 7 . 4 + 0 . 1% Tween 20 ) and the membrane was subsequently incubated with anti-Myc mouse monoclonal antibody ( 1:1000 , 9B11 , Cell signaling ) at 4°C overnight followed by incubation with sheep anti-mouse IgG HRP-linked F ( ab' ) ₂ fragment ( 1:2000; NA9310 , GE Healthcare ) . Signals were detected as described for western blotting . LPS-Biotin ( 1 mg/ml ) and immobilized Streptavidin agarose gel were incubated for 30 min at 4 ˚C , and after biotin blocking , 1 . 25 mg/ml recombinant human SCGB3A2 was added as a pray protein and incubated for 1 hr at 4 ˚C . Ten % of flow through was used as an input . After washing several times , the gel was boiled for 5 min with SDS sample buffer and the supernatant was used for western blotting . Imidazole-zinc staining was carried out as previously reported ( Rodríguez and Hardy , 2015 ) . Briefly , LPS dissolved and/or SCGB3A2 diluted in water were loaded onto 0 . 8% agarose gel in full in a well to make sure the content reaching to gel surface and run at 50V in TAE ( Tris-acetate-EDTA; 40 mM Tris , 20 mM acetic acid , and 1 mM EDTA , pH 8 . 0 ) buffer until dye reached to the gel bottom . The gel was washed with ddH2O and immersed in 0 . 2 M imidazole for 20 min with gentle agitation . After discarding solution and washing with ddH2O , the gel was placed in the dark and incubated with 0 . 3 N zinc sulfate solution for several minutes . Then the gel was rinsed with ddH2O to stop staining and an image was taken with ChemiDocTM imaging system ( Bio-Rad ) . For double staining experiments , the gel was stained with 0 . 25% Coomassie Brilliant Blue solution after the gel image of Imidazole-zinc staining was scanned . Total RNA was extracted by TRIzol ( Life Technologies ) and reverse transcribed into cDNA by using SuperScript III reverse transcriptase ( Life Technologies ) according to the manufacturer's protocol . Analysis of mRNA levels was performed on a 7900 Fast Real-Time PCR System ( Life technologies ) with SYBR Green-based real-time PCR . The primer sequences used for real-time PCR are as follows: ( sense ) 5’-CTCAGAGCCTTTTGGACAGG-3’ and ( antisense ) 5’TACAGCATGAAAGCCACCAG-3’ for mouse Sdc1; ( sense ) 5-TGTGTACACGGAGAAACATTCAG-3 and ( antisense ) 5- GCAAAGAGAAAGCCGATCAC −3 for mouse Sdc2; ( sense ) 5-AACTGAGGTCTTGGCAGCTC-3’ and ( antisense ) 5’-TACACCAGCAGCAGGATCAG-3’ for mouse Sdc4; ( sense ) 5’-CCAATTTTTCAGAACTTCAGTGG-3’ and ( antisense ) 5’-AGAGGTGGTGTAAGCCATGC 3’ for mouse Tlr4; ( sense ) 5’-GCTGATGCTGTCAAGCTGAG-3’ and ( antisense ) 5’-GAGCCTCCTGTTTTGTCTCG-3’ for mouse Casp11; ( sense ) 5’-CCTCTGTGAGGTGCTGAAAC-3’ and ( antisense ) 5’-TCAGGCTTTTCTTCCTGGAG-3’ for mouse Nlrp3; ( sense ) 5’-TGGGCTGTTTAAAGTCCAGAAG-3’ and ( antisense ) 5’-TTTGTTTTGCTTGGGTTTCC3’ for mouse Aim2; ( sense ) 5’-ACATGGGCTTACAGGAGCTG-3’ and ( antisense ) 5’-ACTCTGAGCAGGGACACTGG-3’ for mouse Asc; ( sense ) 5’-TGTCTGGTGCTTGACTCTGG-3’ and ( antisense ) 5’-CTGGGTTTCACTCAGCACAG-3’ for mouse Gsdmd; ( sense ) 5’-GCTGTGACCCTCTCTGTGAAG-3’ and ( antisense ) 5’-TTTCAGGTGGATCCATTTCC-3’ for mouse Il18; ( sense ) 5’-AAAGCTCTCCACCTCAATGG-3’ and ( antisense ) 5’-AGGCCACAGGTATTTTGTCG-3’ for mouse Il1b; ( sense ) 5’- ACAAGACCCACGTGGAGAAG −3’ . Cells were lysed in RIPA lysis buffer ( Millipore ) and protein concentration was measured by BCA protein assay kit ( Thermo Fisher Scientific ) . Samples were separated by SDS-PAGE and electroblotted to polyvinylidene fluoride ( PVDF ) membranes ( GE Healthcare ) . In the case of SDC1 detection , cell membrane extract was prepared using Subcellular Protein Fractionation Kit for Cultured Cells ( Thermo Fisher Scientific ) according to the manufacturer's protocol , and blotted to cationic nylon membrane ( Immobilon Ny; Millipore ) . Signals were visualized with SuperSignal West Dura Chemiluminescent Substrate ( Thermo Fisher Scientific ) according to the manufacturer's protocol . Chemiluminescence was quantitated using a Bio-Rad ChemiDocTM MP imaging system . For LPS and cell binding assay , cells were washed with PBS and incubated with Alexa 488 or 594-conjugated LPS from E . coli 055:B4 ( L-23351 or L-22353 , 1 µg ) ( Thermo Fisher Scientific ) with or without SCGB3A2 ( 1 µg/ml ) at 4 ˚C for 30 min . After washing with PBS , the cells were analyzed in a FACS Canto II ( Becton Dickinson ) . For the SCGB3A2 and LLC cell binding assay , LLC cells were incubated with recombinant mouse or human SCGB3A2 , washed with PBS , incubated with anti-SCGB3A2 antibody for 30 min followed by PE-rabbit IgG secondary antibody for 30 min . For SDC1 expression analysis , LLC cells were harvested in PBS and stained with PE-rat anti-mouse SDC1 ( clone 281 . 2 , BD Pharmingnen ) for 30 min at 4 ˚C . For Annexin V/PI analysis , Dead Cell Apoptosis Kit with Annexin V FITC and PI , for flow cytometry ( V13242 , Thermofisher Scientific ) was used . Cells were harvested using a scraper and washed with cold PBS and stained with Annexin V-Alexa 488 and PI in 1x Annexin binding buffer for 15 min . As a compensation control , FITC-stained only or PI-stained only cells were prepared by inducing cell death by incubation in 70% EtOH for 10 min . All experiments were carried out in the NCI Flow Cytometry Core Facility . LLC cells ( 2 × 105 cells ) were intravenously administered to C57BL/6N mice ( Charles River , Frederick , MD ) , followed by daily intravenous administration of recombinant mouse or human SCGB3A2 ( 0 . 25 mg/kg/day ) for 7 days starting at day 0 ( 30 min after LLC cells injection ) , 7 , or 14 or during the entire experimental period of 20 days , or PBS injection for 20 days as control . Mice were killed on day 21 and the numbers of lung metastasized tumors evaluated . Some lungs were subjected to histological analysis . Scgb3a2 ( -/- ) mice ( Kido et al . , 2014 ) used in the metastasis model were those 10 times backcrossed to C57BL/6N , and the littermates wild-type mice were used as control . Ccsp-Cre;LSL-KrasG12D conditional mutant mice on the 129SvJ-C57BL/6 mixed background ( Jackson et al . , 2001; Moghaddam et al . , 2009 ) which express the oncogenic KrasG12D gene in lung-specific fashion were provided by Francesco DeMayo ( Baylor College of Medicine , Houston , TX ) . Scgb3a2 ( fl/fl ) mice , previously described ( Kido et al . , 2014 ) , were backcrossed to C57BL/6N mice three times . Ccsp-Cre;LSL-KrasG12D and Scgb3a2 ( fl/fl ) mice were crossed to produce Ccsp-Cre;LSL-KrasG12D;Scgb3a2 ( fl/fl ) ( tentatively named KrasG12D;Scgb3a2 ( fl/fl ) ) and littermate Ccsp-Cre;LSL-KrasG12D;Scgb3 ( fl/+ ) ( tentatively named KrasG12D;Scgb3a2 ( fl/+ ) ) mice , and male mice were used in the study . Mice were maintained under standard specific-pathogen-free conditions , and the studies were carried out according to the guidelines for animal use issued by the National Institutes of Health and after approval by the National Cancer Institute ( NCI ) Animal Care and Use Committee . To construct a HaloTag-mouse SCGB3A2 ( mSCGB3A2-HT ) expression vector , pFC14A HaloTag CMV Flexi Vector ( Promega ) was fused to C-terminal of mouse SCGB3A2 cDNA . Primers for the SCGB3A2 HaloTag plasmid were designed using the Flexi Vector Primer Design Tool web site . A HaloTag Coding Region Control Expression Vector ( Control-HT ) was designed according to the manufacture’s instruction . mSCGB3A2-HT or Control-HT was transfected to HEK293 cells using X-tremeGENE HP DNA Transfection Reagent and after 48 or 72 hr , supernatant was collected and concentrated with Amicon Ultra ( Millipore ) and stored at −80 ˚C until use . The transfection efficiency was confirmed with microscopy using HaloTag TMRDirect ligand . For uptake of HT-mSCGB3A2 into LLC cells , after addition of HT-mSCGB3A2 , cells were stained with HaloTag TMR ligand for short incubation time or HaloTag TMRDirect ligand overnight . After two washes with PBS , the cells were visualized under a microscope . Lung samples were fixed in 10% buffered formalin under 20 cm H2O pressure , embedded in paraffin , sectioned at 4 μm by microtome and performed with Hematoxylin and Eosin staining ( H & E ) . Terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling ( TUNEL ) analysis was performed using DeadEnd Fluorometric TUNEL System ( G3250 , Promega ) according to the manufacturer’s instructions . Total tumor areas and TUNEL positive areas were measured using imageJ software , and a percentage of TUNEL positive areas per total tumor areas was calculated Cells were seeded on glass coverslips ( Nunc Lab-Tek Chambered Coverglass ( 15583PK , Nunc ) . After fixation with 10% buffered formalin for 10 min at room temperature ( RT ) , cells were permeabilized with 100% MeOH at −20˚C for 10 min . Blocking was done with 1% BSA in PBS for 1 hr and cells were stained with primary antibodies for 1 hr at RT . After wash with PBS , cells were stained with secondary antibodies ( 1:200 , Alexa flour , Molecular Probe ) for 45 min at RT . Stained signals were analyzed under confocal microscope ( Zeiss 510/710 ) according to the NCI confocal microscope facility manual or Keyence microscope BZ-X700 . A SCGB3A2 dimer model was build starting from a consensus secondary structure prediction obtained using several procedures including I-TASSER ( https://zhanglab . ccmb . med . umich . edu/I-TASSER/ ) ; LOMETS ( https://zhanglab . ccmb . med . umich . edu/LOMETS/ ) ; RaptorX ( http://raptorx . uchicago . edu ) ; Swissmodel ( https://swissmodel . expasy . org ) ; Phyre2 ( http://www . sbg . bio . ic . ac . uk/phyre2 ) ; BHAGEERATH-H ( http://www . scfbio-iitd . res . in/bhageerath/bhageerath_h . jsp ) and Quark ( https://zhanglab . ccmb . med . umich . edu/QUARK/ ) . The above-mentioned procedures were used as available in their respective web-site implementations as of March 2017 . The methods explored span the spectrum of structure prediction techniques including threading , library-based methods , etc . None of the methods explored produced a compact structure . The helical motifs were properly identified by all models . The consensus helical regions as described in Figure 4—figure supplement 2B were manually aligned against the uteroglobin structure ( PDB ID:1UTG ) identified as the closest homolog of SCGB3A2 for which an experimental structure is currently available . The missing sections connecting the helical motifs were modeled as loops to the sole purpose of connecting the helices in an initial workable model . The model was then refined using Feedback Restrain Molecular Dynamics ( FRMD ) . FRMD is based on a self-consistent procedure to bias molecular dynamics trajectories towards a refined conformation using experimental information from multiple sources including X-ray diffraction or NMR data when available ( Cachau , 1994; Cachau et al . , 1994; González-Sapienza and Cachau , 2003 ) . The procedure is conceptually similar to a reversed molecular replacement protocol when using X-ray data , with the additional advantage that only those regions of the molecule in agreement with the crystallographic data are affected by the crystallographic constrain , as weighted by the FRMD protocol thus preserving the structural homology when available ( Cachau et al . , 1994 ) . FRMD was implemented in QMRx ( Fadel et al . , 2015 ) using X-plor-NIH ( Schwieters et al . , 2003 ) to compute the crystallographic restrains and GROMACS 5 . 1 . 4 ( Abraham et al . , 2015 ) to drive the molecular dynamics ( MD ) calculations using the Amber ff99sb-ildn force field for all MD calculations . All calculations were performed using a time step of 2 fs . All bonds were constrained for all MD calculations . The leapfrog algorithm was used for integration using a velocity rescaling thermostat ( Noose-Hover ) with a 0 . 1 ps coupling constant . Electrostatic forces were computed using a distance criteria , and a cutoff of 10 Å was used for van der Waals interactions . No periodic boundary conditions were used aside from the periodicity resulting from the X-ray constrains . The system was freely equilibrated at T = 300 K for 5 ns without constrains , the purpose of this short run was to relax the initial model without losing the original shape of the model . The model was then fully relaxed using FRMD with X-ray restrains as described in ( Cachau et al . , 1994 ) and Fcalc values computed for PDB ID: 1UTG in-lieu of experimental values not deposited for this entry in the Protein Data Bank , and limited to a 6 Å resolution cutoff . The nature of the FRMD procedure restricts the value of energy-based monitors . The convergence of the model was monitored using a crystallographic R factor and RMSD ( root mean square deviation ) against the reference structure for homologous residues ( see Figure 4—figure supplement 2B ) . The trajectory converges to the structure shown in Figure 4—figure supplement 2 after 350 ns with an R value of 9 . 3 ( 6 Å ) and RMSD 3 . 2 Å . The MD trajectory was continued for another 350 ns without noticeable changes in the structure . The dimer structure was used to explore possible tetrameric arrangements by rolling a dimer against another using GROMACS and the AMBER force field to probe the interaction . A favorable arrangement was detected as described in Figure 4—figure supplement 2F . The number and placement of Cys in 1UTG and SCGB3A2 are different . Thus , SCGB3A2 was modeled replacing Cys 48 by Ala to avoid the possible bias that could have resulted from imposing a disulfide bond during the MD calculation . Ala 48 was then replaced back to Cys in the final dimer model where the two Cys S atoms appear at less than 2 . 5A from each other suggesting a proper placement of the Cys 48 in the dimer . FMRD can be used to estimate the data lost during the modeling procedure by reversing the refinement procedure that is 1UTG was modeled from the final model of SCGB3A2 using an identical protocol as previously used to model SCGB3A2 from 1UTG . The structure of 1UTG thus modeled agrees with the experimental one with an RMSD 3 . 5 Å ( backbone atoms ) . Dynamic light scattering analysis ( DLS ) was performed using DynaPro Nanostar ( Wyatt ) . The radii of LPS , SCGB3A2 , and LPS-SCGB3A2 complex were determined after samples were centrifuged and dissolved in 50 µL of 0 . 22 µm filtered sterile PBS . The evaluation of data was performed by Dynamics V7 software . LPS quantification in each SCGB3A2 recombinant protein was performed using the ToxinSensorTM Chromogenic LAL Endotoxin Assay Kit ( L00350 , GenScript ) . Cells grown in 96 flat bottom well plates were incubated with or without SCGB3A2 and/or LPS ( O111:B4 ) in the media for indicated times as described in the figure legends . Cell supernatants were evaluated for the presence of cytoplasmic enzyme lactate dehydrogenase ( LDH ) using the Pierce LDH Cytotoxicity Assay Kit ( Thermo Fisher Scientific ) . Cytotoxicity was calculated according to the kit instructions; as a percentage of ( experimental LDH − spontaneous LDH ) / ( maximum LDH release − spontaneous LDH ) . Statistical analysis was carried out using GraphPad Prism v7 . Data are shown as means ± SD . Levels of significance for comparison between samples were determined by student’s t-test or one-way ANOVA . For the lung carcinogenesis study , the Kaplan-Meier method was used to estimate survival rates of mice and the log-rank ( Mantel-Cox ) test for comparing survival differences between groups . P values of < 0 . 05 were considered statistically significant .	Inflammation serves to kill invading bacteria and viruses . Certain molecules on the surface of the microbes can trigger an inflammatory cascade , and one example of such a molecule is lipopolysaccharide ( LPS ) . Cells can react to LPS by triggering a process called pyroptosis that causes the cell to burst and die . The released cell contents attract blood and lymphatic cells that in turn kill the LPS-producing bacteria . This prevents the bacteria from multiplying and spreading . LPS was used in the very early days of medicine to treat cancer , although it has fallen out of favor because it causes severe side effects , such as a hyperinflammatory response ( sepsis ) that can result in death . It was not known exactly how LPS kills cancer cells , which has limited its use . Yokoyama et al . now show that a protein called SCGB3A2 , which is produced by the cells that line the lung airways , binds to LPS . Tests on mouse immune cells and lung cancer cells grown in the laboratory showed that the resulting SCGB3A2-LPS complex can then bind to a cell surface protein called syndecan 1 . This enables LPS to enter the cell and trigger pyroptosis and cell death . To confirm the role of SCGB3A2 in pyroptosis , Yokoyama et al . examined tumor growth in mice that are not able to produce SCGB3A2 . These mice developed more tumors than normal mice , but tumor growth was suppressed when mice were injected with SCGB3A2 . The findings presented by Yokoyama et al . could potentially lead to new types of cancer treatments , particularly for lung cancers . However , it remains to be examined whether molecules that trigger pyroptosis , like LPS , could also be used to treat cancers other than those from the lung . Further work is also needed to understand in more detail how SCGB3A2 and LPS work together to cause cancer cell death .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "immunology", "and", "inflammation" ]	2018	A novel pathway of LPS uptake through syndecan-1 leading to pyroptotic cell death
Epithelial morphogenesis and stability are essential for normal development and organ homeostasis . The mouse neural plate is a cuboidal epithelium that remodels into a columnar pseudostratified epithelium over the course of 24 hr . Here we show that the transition to a columnar epithelium fails in mutant embryos that lack the tumor suppressor PTEN , although proliferation , patterning and apical-basal polarity markers are normal in the mutants . The Pten phenotype is mimicked by constitutive activation of PI3 kinase and is rescued by the removal of PDK1 ( PDPK1 ) , but does not depend on the downstream kinases AKT and mTORC1 . High resolution imaging shows that PTEN is required for stabilization of planar cell packing in the neural plate and for the formation of stable apical-basal microtubule arrays . The data suggest that appropriate levels of membrane-associated PDPK1 are required for stabilization of apical junctions , which promotes cell elongation , during epithelial morphogenesis . Phosphoinositides are powerful second messengers in signaling pathways that also control epithelial organization and cell motility , placing them at a unique intersection of signaling and morphogenesis . The lipid phosphatase PTEN , which converts the membrane lipid phosphatidylinositol ( 3 , 4 , 5 ) -trisphosphate ( PtdIns ( 3 , 4 , 5 ) P3 ) to phosphatidylinositol 4 , 5-bisphosphate ( PtdIns ( 4 , 5 ) P2 ) , is the second most commonly mutated gene in human cancers . PtdIns ( 3 , 4 , 5 ) P3 and PtdIns ( 4 , 5 ) P2 act by recruiting specific sets of pleckstrin homology domain-containing proteins to the plasma membrane ( e . g . Lietzke et al . , 2000 ) , where they become active . The best-studied functions of PTEN are as a negative regulator of proliferation and a positive regulator of apoptosis through the PDPK1-AKT-mTOR pathway ( Chalhoub and Baker , 2009; Song et al . , 2012 ) . In addition to its role in tumorigenesis , loss of one copy of the wild-type PTEN gene leads to complex human developmental disorders such as Cowden and Bannayan-Riley-Ruvalcaba syndromes , which are characterized by macrocephaly , benign tumors , arteriovenous malformations , and autism spectrum disorder ( Blumenthal and Dennis , 2008; Zhou and Parada , 2012 ) . Phosphoinositides play important roles in the architecture of epithelia ( Shewan et al . , 2011 ) , consistent with the high frequency of PTEN mutations in carcinomas . Studies on lumen morphogenesis in a three-dimensional culture system showed that PtdIns ( 4 , 5 ) P2 is enriched in the apical membrane , whereas PtdIns ( 3 , 4 , 5 ) P3 is enriched in basolateral membranes ( Martin-Belmonte et al . , 2007 ) , and this was proposed to be important in tumor development ( Shewan et al . , 2011 ) . Mammalian PTEN regulates cellular processes as diverse as collective cell migration ( Bloomekatz et al . , 2012 ) and axon regeneration ( Park et al . , 2008 ) , and some of the effects of PTEN are independent of the AKT pathway ( e . g . Vasudevan et al . , 2009 ) . PTEN is essential for viability and Pten null mouse embryos arrest at midgestation with a complex set of morphological defects ( Suzuki et al . , 1998; Bloomekatz et al . , 2012 ) . We showed previously that PTEN is required for the directional collective migration of a population of extraembryonic cells , the anterior visceral endoderm ( AVE ) , which must move from a distal to proximal position to define the anterior-posterior body axis of the embryo ( Bloomekatz et al . , 2012 ) . PTEN is also required in the cells of the embryo proper: deletion of Pten in cells of the epiblast ( the embryo proper ) using the Sox2-Cre transgene ( Hayashi et al . , 2002 ) ( Pten △Epi ) bypasses the requirement for AVE migration but leads arrest at midgestation ( ~E9 . 0 ) with a syndrome of defects that included cardia bifida , abnormal mesoderm migration , and an abnormal open neural tube ( Bloomekatz et al . , 2012 ) . Mammalian neural tube closure requires more than 100 genes that regulate a sequence of orchestrated morphogenetic processes that transform the neural epithelium into a closed tube ( Copp and Greene , 2010; Harris and Juriloff , 2010; Colas and Schoenwolf , 2001 ) . Failure of any one of these events can cause neural tube defects , the second most common type of human birth defect after cardiac malformations . Most genetic studies of neural tube closure have focused on the cell rearrangements in the ventral midline mediated by the planar cell polarity pathway ( Murdoch et al . , 2003; Ybot-Gonzalez et al . , 2007; Nishimura et al . , 2012; Williams et al . , 2014 ) or on the actin-mediated apical constriction of neural epithelial cells required for neural tube closure ( Suzuki et al . , 2012; Grego-Bessa et al . , 2015 ) . Prior to apical constriction , the neural plate lateral to the midline is transformed from a cuboidal to a tightly packed pseudostratified columnar epithelium , so that by E9 . 5 , up to 8 nuclei are stacked on top of each other , with each cell retaining connections to both the apical surface and the basement membrane of the epithelium . Here we define the cellular and biochemical basis of the neural tube closure defect seen in mouse embryos that lack PTEN . The Pten neural plate phenotype is not the result of changes in proliferation , apoptosis , cell fate or loss of epithelial polarity . Instead , Pten mutants have a novel defect in neural morphogenesis: they fail to form a pseudostratified columnar epithelium . Cells do not elongate along their apical-basal axis; they fail to become compacted along the mediolateral axis of the embryo and they fail to pack into a stable hexagonal array . A combination of genetic and chemical genetic experiments demonstrate that these defects are due to the loss of the lipid phosphatase activity of PTEN and to the activation of 3-phosphoinositide-dependent protein kinase-1 ( PDPK1 ( PDK1 ) ) , but do not depend on the AKT-mTOR tumor suppressor pathway . The data suggest that PTEN activity is required for stabilization of cell packing in the neural plate , which is in turn required for formation of apical-basal microtubule arrays , apical-to-basal trafficking , and cell elongation in the neural plate . We suggest that the role of PTEN in epithelial morphogenesis contributes to the developmental malformations in PTEN mutant syndromes and to the behavior of tumors that lack PTEN . The cephalic neural epithelium in Pten-/- or Pten △Epi embryos does not close to make a neural tube ( Bloomekatz et al . , 2012 ) . At E8 . 5 , scanning electron micrographs showed that the wild-type cephalic neural plate was a smooth structure in which both sides have elevated to begin neural tube closure ( Figure 1A , C ) . In contrast , irregular folds appeared in the Pten mutant neural plate as early as E8 . 0 and the neural plate was dramatically ruffled at E8 . 5 ( Figure 1B , D ) ; the position of the ectopic folds was highly variable between embryos . PTEN protein was strongly expressed in the E8 . 5 wild-type neural plate , where it was enriched both apically and basally ( Figure 1—figure supplement 1A–F ) , consistent with a significant role for PTEN in morphogenesis of the neural tube . Phosphorylated AKT was not detectable in the wild-type neural plate , but was present in all membranes of Pten △Epi neural plate cells ( Figure 1—figure supplement 1G , H ) , consistent with strong activation of the PI3 kinase pathway in Pten mutants . 10 . 7554/eLife . 12034 . 003Figure 1 . Morphological defects in the Pten mutant cephalic neural plate . ( A , B , C , D ) Comparison of neural plate morphology of the dorsal head of wild-type ( WT ) and Pten △Epi mutant embryos at E8 . 0 and E8 . 5 in scanning electron microscope images . Scale bar = 100 μm . ( E , F ) Transverse sections of E8 . 5 WT and Pten △Epi embryos show the absence of pseudostratified columnar organization in the Pten mutant cephalic neural plate . Green is SOX2 , red is phalloidin ( F-actin ) , blue is DAPI . ( G , H ) Z-stack projection of three optical sections ( total of 3 µm ) from transverse sections of the cephalic neural plate of E8 . 5 WT and Pten △Epi mutant embryos stained for phalloidin ( red ) and laminin ( purple ) . Scale bar E–H = 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 00310 . 7554/eLife . 12034 . 004Figure 1—figure supplement 1 . PTEN expression in the cephalic neural plate . Immunodetection of PTEN in transverse sections of E8 . 5 WT ( A , C , E ) and the Pten △Epi embryos ( B , D , F ) shows the specificity of PTEN staining . ( A , B ) Antibody from cell signaling; ( C–F ) antibody from cascade . ( G , H ) Z-stack projections of 3 optical sections taken every 1 μm , showing expression pAKT S473 expression in transverse sections of the E8 . 5 WT ( G ) and Pten △Epi ( H ) neural plate . Red is phalloidin and blue is DAPI . Scale bar in A–D = 100 μm; in E–H = 10 μmDOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 00410 . 7554/eLife . 12034 . 005Figure 1—figure supplement 2 . Normal neural patterning in Pten△Epi embryos . ( A ) In situ hybridization for Engrailed2 , Krox20 , Emx2 and Fgf8 in WT and Pten △Epi embryos at E8 . 5 showed normal anteroposterior regionalization of cephalic neural epithelium . Scale bar = 180 μm . ( B ) Immunodetection of FoxA2 , Nkx2 . 2 and Nkx6 . 1 shows normal dorsal-ventral patterning of the E8 . 5 cephalic neural plate in Pten △Epi embryos . Scale bar = 100 μm . ( C ) The TOPGAL reporter of canonical Wnt signaling was expressed in the normal domains and at normal levels in E8 . 5 Pten △Epi embryos . Scale bar = 180 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 00510 . 7554/eLife . 12034 . 006Figure 1—figure supplement 3 . Apical markers in Pten △Epi mutant embryos . Z-stack projection of three optical sections ( total of 3 µm ) from transverse sections of the cephalic neural plate of E8 . 5 WT and Pten △Epi mutant embryos stained for ( A , B ) N-Cadherin , ( C , D ) pERM , ( E , F ) ZO1 and ( G , H ) aPKC ( green ) and PAR3 ( red ) . ( I ) Comparison of the number of rows of nuclei in the pseudostratified cephalic neural plate of E8 . 5 wild-type and Pten △Epi embryos . n = 27 embryos for each genotype . Blue is DAPI . Scale bar = 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 006 The abnormal morphology of Pten-/- embryos was noted in previous experiments and was attributed to increased proliferation ( Stambolic et al . , 1998 ) ; however we previously showed that proliferation , cell number , and interkinetic nuclear migration are normal in the Pten-/-neural plate ( Bloomekatz et al . , 2012 ) . Previous data suggested that there might be abnormalities in anterior-posterior patterning of cell types in the Pten-/- brain that could account for the abnormal morphology of the anterior neural tube ( Suzuki et al . , 1998 ) . However , we found that anterior-posterior and dorsal-ventral neural patterning were normal in Pten △Epi embryos ( Figure 1—figure supplement 2A , B ) . It has also been reported that loss of Pten activates canonical Wnt signaling ( Chen et al . , 2015 ) , but expression of the canonical Wnt reporter TOPGAL was normal in Pten △Epi embryos ( Figure 1—figure supplement 2C ) . Transverse sections of the cephalic neural plate showed striking differences in organization in the wild-type and Pten △Epi cephalic neural epithelium . ( For simplicity , we refer to Pten △Epi in the text below as Pten . ) The wild-type neural plate is a single-layered columnar epithelium; the cells of the neural epithelium are so tightly packed that the nuclei appear to stack on top of each other , creating a pseudostratified epithelium . Nuclei in the cephalic neural plate , marked by expression of nuclear SOX2 , were stacked in 3–5 rows at E8 . 5 ( Figure 1E ) . In contrast , the SOX2+ nuclei of the E8 . 5 Pten cephalic neural plate were organized in only 1–3 rows ( Figure 1F; Figure 1—figure supplement 3I ) . Apical recruitment of PTEN is required for apical-basal polarity during apical lumen formation by MDCK cells ( Martin-Belmonte et al . , 2007 ) . In contrast , we found that global apical-basal organization in the mouse neural plate was normal in the absence of PTEN . Laminin was basal , and F-actin , N-cadherin , ZO1 , aPKC and Par3 were correctly localized to the apical domain in the mutant neural plate ( Figure 1G , H; Figure 1—figure supplement 3A–H ) . Thus the data indicate that the Pten neural plate phenotype is not caused by abnormalities in proliferation , patterning or global apical-basal polarity; instead PTEN is required for normal morphogenesis of the neural plate . Because cells are very tightly packed in the neural plate , we used the mosaic expression of a cytoplasmic X-linked GFP transgene ( Hadjantonakis et al . , 2001 ) to visualize the shape of individual neural cells . In wild type , neural plate cells were highly elongated along the apical-basal axis , whereas Pten neuroepithelial cells were shorter and wider ( Figure 2A , B ) . Accompanying the lack of pseudostratification , the Pten neural plate was 1 . 5 fold wider than the wild type: the mediolateral apical contour ( from left to right ) at the level of the mid-hindbrain junction in the E8 . 5 wild-type neural plate was 668 ± 242 μm wide ( n = 6 ) and 1023 ± 369 μm wide in Pten ( n = 6 ) . Despite this increase in width , the number of nuclei across the width of the cephalic neural plate was not changed in the mutant ( 275 ± 140 nuclei wide in wild type; 262 ± 88 nuclei in Pten mutants ) , indicating that the same number of cells occupy more area in Pten . 10 . 7554/eLife . 12034 . 007Figure 2 . Cellular defects of Pten △Epi mutant neuroepithelial cells . ( A ) Comparison of WT and mutant cell shape in the E8 . 5 cephalic neural plate , using X-linked GFP-expression to mark individual cells . Schematic representations of individual cells for each genotype are shown ( white box ) . Red is phalloidin . Scale bar is 10 μm . ( B ) Comparison of neural plate height in the cephalic region of WT and mutants . WT E7 . 75 = 23 . 9 ± 4 . 5 μm; Pten △Epi E7 . 75 = 23 . 6 ± 4 . 1 μm: WT and mutant are not different , p = 0 . 86 , by standard t-test . WT E8 . 0 = 32 . 5 ± 1 . 7 μm; Pten △Epi E8 . 0 = 24 . 6 ± 3 . 7 μm: WT is significantly taller than the mutant , p = 0 . 0164 . WT E8 . 5 = 49 . 1 ± 9 . 6 μm; Pten △Epi E8 . 5 = 32 . 6 ± 7 . 4 μm; WT is significantly taller than the mutant , *p < 0 . 0001 . For this and similar analyses below , >100 measurements were made from >3 embryos . ( C ) Comparison of apical cell shape in the cephalic neural epithelium of WT and Pten △Epi embryos viewed en face at E7 . 75 , E8 . 0 and E8 . 5 . Cell borders are marked by expression of ZO1 ( white ) . Scale bar = 20 μm . ( D ) Apical surface of cephalic neural epithelial cells , taken from images like those shown in ( C ) . WT E7 . 75 = 29 ± 17 μm2; Pten △Epi E7 . 75 = 30 ± 16 μm2: WT and mutant are not different , p = 0 . 79 . WT E8 . 0 = 20 ± 10 μm2; Pten △Epi E8 . 0 = 29 ± 15 μm2 . The WT surface area is significantly smaller than in the mutant , p < 0 . 0001 . WT E8 . 5 = 8 ± 4 μm2; Pten △Epi E8 . 5 = 14 ± 9 μm2 . The WT surface area is significantly smaller than in the mutant , p < 0 . 0001 . ( E ) Acetylated microtubule arrays in the neural plate in stage-matched WT and mutant embryos . Transverse sections of cephalic regions of WT and Pten △Epi embryos at E8 . 0 ( 0– 2 somites ) , E8 . 5 ( 5–7 somites ) and E9 . 0 ( 11–13 somites ) . Green is acetylated tubulin; blue is DAPI . Arrows point to the apical surface of neural plate; arrowheads point to the floor plate . The first region of tubulin acetylation in WT is in the floor plate , which is only region of tubulin acetylation in the mutant . Scale bar = 25 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 00710 . 7554/eLife . 12034 . 008Figure 2—figure supplement 1 . Acetylated microtubules in the wild type cranial neural plate . ( A ) α-Tubulin appears to be in apical-basal arrays in E8 . 5 wild-type cranial neuroepithelial cells but is more diffuse in Pten △Epi embryos . Red is phalloidin . Scale bar = 20 µm . ( B ) Western blot analysis shows that of acetylated tubulin increases during maturation of the wild-type neural plate . Each lane represents a pool of the cephalic regions of three embryos . ( C ) Acetylated microtubule arrays increase with time in the cranial neural plate of WT embryos and acetylated tubulin partially colocalizes with PTEN protein beginning at E8 . 5 . Green is PTEN , acetylated tubulin is red; blue is DAPI . Arrows highlight sites of PTEN enrichment and arrowheads show examples of sites of colocalization of PTEN and acetylated tubulin . Scale bar = 25 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 008 We measured the apical surface area of individual neural plate cells by en face imaging , with cell boundaries marked by expression of the tight junction marker ZO1 ( Figure 2C ) . At the onset of neural morphogenesis ( head fold stage , E7 . 75 ) , the apical surfaces of wild-type and Pten mutant cells were both variable in size and shape but had the same average area ( approximately 30 μm2; Figure 2D ) . By ~6 hr later , at E8 . 0 , the average apical surface area of wild-type neural cells had decreased to ~20 μm2 , whereas the apical surface area of Pten mutant cells was unchanged ( Figure 2D ) . At E8 . 5 , the apical surface of wild-type neural plate cells had shrunk further , so that it was ~8 μm2 , ~3 . 5 fold smaller than at E7 . 75 . Between E8 . 0 and E8 . 5 , the surface area of Pten neural plate cells also decreased , but the area of mutant cells was still ~40% greater than that of wild type ( Figure 2D ) . At the same time as the apical surface of wild-type neural plate cells decreased , cell volume remained constant , so the height of the cells increased ~2 fold in WT embryos from ~24 μm at E7 . 75 to ~50 . 0 μm at E8 . 5 ( Figure 2B ) , while the height of Pten mutant cells increased only ~1 . 3 fold , to ~30 μm at E8 . 5 ( Figure 2B ) . Formation of polarized columnar epithelia is accompanied by the formation of arrays of apicobasally polarized stable microtubules , with minus-ends apical ( Bré et al . , 1987; Jaulin and Kreitzer , 2010 ) . For example , in the neural plate of the Xenopus embryo , multiple γ-tubulin-positive apical centrioles nucleate stable arrays of parallel , acetylated microtubules that are thought to drive elongation of the cells along the apical-basal axis ( Lee et al . , 2007 ) . In cells of the mouse embryo neural plate , there is only a single apical centrosome , but noncentrosomal microtubule arrays , marked by expression of α-tubulin , were present parallel to the apical-basal axis of cephalic neural plate cells in both the E8 . 5 wild-type neural plate , although α-tubulin arrays were not apparent in the Pten mutant ( Figure 2—figure supplement 1A ) . Stable microtubules can become acetylated ( Palazzo et al . , 2003 ) ; wild-type microtubule arrays were not acetylated at E8 . 0 ( 0–2 somites ) except in the floor plate but became acetylated by E8 . 5 ( 5–7 somites ) and were strongly acetylated at E9 . 0 ( 11–13 somites ) ( Figure 2E; Figure 2—figure supplement 1B , C ) . In contrast , the Pten neural plate lacked acetylated microtubules at each of these stages; the only acetylated microtubule arrays in the mutant neural plate were located in the floor plate , the cells in the ventral midline ( Figure 2E ) . Because PTEN has both lipid and protein phosphatase activities ( Worby and Dixon , 2014 ) , we tested whether the lipid phosphatase activity of PTEN mediated its role in epithelial morphogenesis . While PTEN dephosphorylates PtdIns ( 3 , 4 , 5 ) P3 to PtdIns ( 4 , 5 ) P2 , phosphoinositide 3-kinase ( PI3 kinase ) carries out the reverse reaction and produces PtdIns ( 3 , 4 , 5 ) P3 . We injected the pregnant mothers of Pten mutant embryos at E7 . 5 with LY294002 , a small molecule inhibitor of PI3 kinase ( Gharbi et al . , 2007 ) and analyzed the embryonic phenotype 24 hr later . The development of wild-type embryos was not affected by this treatment , but the mutant neural plate appeared rescued: it was pseudostratified and showed acetylated microtubules arrays ( Figure 3—figure supplement 1 ) . Thus inhibition of PI3 kinase rescued Pten neural plate phenotype , suggesting that it is the lack of the lipid phosphatase activity that causes the Pten mutant phenotype . We used an independent genetic experiment to test whether increased levels of PtdIns ( 3 , 4 , 5 ) P3 were responsible for the defects in epithelial morphogenesis . Pik3ca encodes the p110 catalytic subunit of PI3 kinase that catalyzes the production of PtdIns ( 3 , 4 , 5 ) P3 . Point mutations in PIK3CA are seen frequently in tumors and approximately 40% of breast cancer PIK3CA mutations are due to a single amino acid substitution allele , PIK3CAH1047R , which causes elevated kinase activity ( Saal , 2005; Carson et al . , 2008 ) . We conditionally expressed a Pik3caH1047R allele in the epiblast under the control of the Sox2 promoter ( Pik3caH1047R-Epi ) . Western blot analysis confirmed that both pAKT Thr308 and pAKT Ser473 , well-characterized targets of the PI3-kinase pathway ( Sarbassov et al . , 2005 ) , were elevated in both Pten and Pik3caH1047R-Epi embryos ( Figure 3A ) . 10 . 7554/eLife . 12034 . 009Figure 3 . Expression of an activated form of PI3 Kinase mimics the Pten mutant neural plate phenotype . ( A ) Loss of Pten ( Pten △Epi ) or expression of the activating mutation Pik3caH1047R-Epi in the epiblast leads to phosphorylation of AKT in E8 . 5 embryos . Representative Western blots ( n = 3 ) show the two phosphorylated forms of AKT in WT , Pten △Epi and Pik3caH1047R–Epi embryos . Numbers indicate approximate MW . ( B ) Pik3caH1047R–Epi embryos phenocopy Pten △Epi embryos . Whole embryos ( inset ) and expanded view of the cephalic region of E8 . 5 WT and Pik3caH1047R-Epi embryos; dorsal view . Scale bar = 120 μm . ( C ) The apical surface of the neural plate , viewed en face; cell borders marked by expression of ZO1 ( white ) ( top row ) , and acetylated tubulin ( green ) in transverse sections of the cephalic neural epithelium of E8 . 5 WT and Pik3caH1047R-Epi embryos . Blue is DAPI . Scale bar = 20 μm . ( D ) Comparison of apical surface area of cephalic neural epithelial cells at E8 . 5 . WT = 8 ± 4 μm2; Pten △Epi = 14 ± 9 μm2; Pik3caH1047R-Epi = 15 ± 10 μm2 . The surface areas of both mutants are significantly larger than wild type , p < 0 . 0001 . ( E ) Cephalic neural plate height at E8 . 5 . WT = 49 . 1 ± 9 . 6 μm; Pten △Epi = 32 . 6 ± 7 . 4 μm; Pik3caH1047R-Epi = 31 . 5 ± 7 . 2 μm . Cells in both mutants are significantly shorter than in wild type , p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 00910 . 7554/eLife . 12034 . 010Figure 3—figure supplement 1 . Inhibition of PI3 kinase restores pseudostratification in the Pten △Epi neural plate . ( A ) Mothers of Pten △Epi mutant embryos were injected with 25 mg/kg of LY294002 24 hr prior to E8 . 5 embryo dissection . This treatment leads to elevation of the neural folds and prevents the formation of abnormal folds in the Pten △Epi neural plate phenotype without affecting wild-type development . ( B ) Immunostaining for acetylated tubulin ( green ) of transverse sections of the cephalic neuroepithelium of treated E8 . 5 WT and Pten △Epi embryos . Blue is DAPI . ( C ) Western blot analysis of untreated and LY294002-treated Pten mutant embryos shows an increase in tubulin acetylation and inhibition of phosphorylation of AKT at Ser473 , indicating that the treatment effectively inhibited PtdIns ( 3 , 4 , 5 ) P3 production . Scale bar in A = 120 μm; in B = 20 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 010 Pik3caH1047R-Epi embryos had an open , ruffled cephalic neural plate , similar to that seen in Pten △Epi ( Figure 3B ) . Transverse sections of the cephalic neural plate showed that Pik3caH1047R-Epi neural plate cells did not become columnar ( height of E8 . 5 neural plate cells = 31 . 5 ± 7 . 2 μm ) , the nuclei failed to become pseudostratified , and there was reduced expression of acetylated tubulin ( Figure 3C ) . The apical surface area of E8 . 5 Pik3caH1047R-Epi neural plate cells was ~15 μm2 , ~40% larger than wild type ( Figure 3C , D ) , and epithelial cell height was ~40% shorter than in wild type , as seen in Pten ( Figure 3C , E ) . The common defects in Pik3caH1047R-Epi and Pten △Epi embryos argue that elevated levels of PtdIns ( 3 , 4 , 5 ) P3 were responsible for the neural plate phenotypes of both mutants . In the PTEN tumorigenesis pathway , elevated PtdIns ( 3 , 4 , 5 ) P3 recruits 3-phosphoinositide-dependent protein kinase-1 ( PDPK1 ) to the plasma membrane through its PH domain , thereby allowing PDPK1 access to specific substrates , including AKT , an important target in tumorigenesis ( Sommer et al . , 2013 ) . Pdpk1 null embryos die at midgestation with defects in morphogenesis of the brain and somites; proliferation and apoptosis are normal in null mutant MEFs , but Pdpk1 mutant cells are small ( Lawlor et al . , 2002 ) . To assess the role of Pdpk1 in neural morphogenesis , we removed the gene in embryonic lineages using a conditional Pdpk1 allele with Sox2-Cre ( Pdpk1 △Epi ) . The general morphology of Pdpk1 △Epi embryos was similar to that previously described for the Pdpk1 null allele ( Lawlor et al . , 2002 ) , although the conditionally deleted embryos appeared more healthy , formed recognizable somites and initiated embryonic turning , unlike the null mutants . The sides of the neural plate in Pdpk1 △Epi failed to elevate at E8 . 5 , but the neural tube closed by E9 . 5 ( Figure 4—figure supplement 1A , B ) . Transverse sections at E8 . 5 and E9 . 5 showed multiple layers of nuclei and strong acetylated tubulin staining in cephalic neural tube ( Figure 4—figure supplement 1A , B ) , indicating that cell elongation and neural plate pseudostratification occurred in absence of PDPK1 . To test whether the neural morphogenesis defects observed in Pten neural plate required the activity of PDPK1 , we simultaneously removed both Pdpk1 and Pten in the epiblast using the Sox2-Cre transgene . While pAKT levels were increased in Pten embryos , the levels of both phosphorylated forms of AKT were decreased in Pdpk1 △Epi single mutants ( hereafter referred to as Pdpk1 ) and were present at approximately normal levels in Pten △Epi Pdpk 1△Epi double mutant embryos ( referred to below as Pten Pdpk1 double mutants ) ( Figure 4A ) . Phosphorylation of the AKT target GSK3β ( Ser9 ) was decreased ( Figure 4A ) , confirming that activation of AKT by removal of PTEN depends on PDPK1 , as in other cell types . We noted that phosphorylation of the downstream target ribosomal protein S6 was not affected in Pten embryos , while phosphorylation of S6 was abolished in Pdpk1 single and Pten Pdpk1 double mutant embryos ( Figure 4A ) . The absence of increased phosphorylation of S6 in Pten embryos probably reflects the high rates of growth and cell division in the wild-type mouse embryo , which are not further increased by removal of PTEN . 10 . 7554/eLife . 12034 . 011Figure 4 . Removal of Pdpk1 rescues the pseudostratified columnar organization of the Pten neural plate . ( A ) Phosphorylation of downstream targets of the PI3 kinase pathway in E8 . 5 wild type , Pten △Epi , Pdpk1 △Epi single mutant and Pten △Epi Pdpk1 △Epi double mutant embryos . Representative western blot shown ( n = 3 ) . Numbers indicate approximate MW . ( B ) Dorsal views of E8 . 5 wild-type , Pten △Epi , Pdpk1△Epi and Pten △Epi Pdpk1 △Epi embryos . The Pten Pdpk1 double mutants are similar in morphology to Pdpk1 single mutants , but are larger . Scale bar = 100 μm . ( C ) The apical surface of the neural plate , viewed en face . Cell borders marked by expression of ZO1 ( white ) ( top row ) and acetylated tubulin ( green ) in transverse sections of cephalic neural epithelium in E8 . 5 wild-type , Pten △Epi , Pdpk1 △Epi and Pten △Epi Pdpk1 △Epi embryos . Blue is DAPI . Scale bar = 20 μm . ( D ) Cephalic neural plate height at E8 . 5 . WT = 49 . 1 ± 9 . 6 μm; Pten △Epi = 32 . 6 ± 7 . 4 μm; Pdpk1 △Epi = 47 . 2 ± 8 . 9 μm; Pten △Epi Pdpk1 △Epi = 48 . 6 ± 8 . 8 μm . Pten △Epi cells are significantly shorter than in wild type , and Pten △Epi Pdpk1 △Epi double mutant cells are significantly taller than in Pten △Epi , p < 0 . 0001 . ( E ) Apical surface area of E8 . 5 cephalic neuroepithelial cells . Wild type = 9 ± 6 μm2; Pten △Epi = 15 ± 9 μm2 . The surface area of Pten △Epi is significantly greater than in wild type , p < 0 . 0001; Pdpk1 △Epi = 8 ± 5 μm2; Pten △Epi Pdpk1 △Epi = 10 ± 7 μm2; the surface area of Pten △Epi Pdpk1 △Epi double mutant cells is significantly less than in Pten △Epi , **p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 01110 . 7554/eLife . 12034 . 012Figure 4—figure supplement 1 . The Pdpk1△Epi phenotype . ( A ) Dorsal view and detail of the cephalic region of E8 . 5 WT and Pdpk1 △Epi embryos . Localization of acetylated tubulin is similar in the neural plate of WT and Pdpk1 mutants , although the shape of the neural plate is abnormal in Pdpk1 mutants . Arrowheads point to somites . ( B ) At E9 . 5 , Pdpk1 △Epi mutant embryos have a closed neural tube . Transverse sections and staining showed similar expression of acetylated tubulin in wild-type and mutant neural plate . Arrows indicate the apical domain of the neural plate . Scale bar = 80 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 01210 . 7554/eLife . 12034 . 013Figure 4—figure supplement 2 . Cell migration phenotypes in Pten Pdpk1 double mutants . ( A ) In situ hybridization of the primitive streak marker Brachyury in E7 . 5 wild-type , Pten-/- , Pdpk1-/- and Pten-/-Pdpk1-/- embryos . Brachyury expression is abnormal in both Pten-/- and Pten-/-Pdpk1-/- , suggesting that removal of Pdpk1 does not rescue the AVE migration and axis specification defects of Pten-/- mutants . ( B ) Ventral views of E8 . 5 wild-type , Pten △Epi , Pdpk1 △Epi and Pten △Epi Pdpk1 △Epi embryos , showing that the cardia bifida phenotype caused by loss of Pten is not rescued by removal of Pdpk1 . Outlines of the hearts are shown below . Scale bar = 100 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 013 The global morphology of the Pten Pdpk1 double mutant embryos resembled that of the Pdpk1 single mutants ( Figure 4B ) . The cells in the E8 . 5 double mutant cephalic neural plate were elongated similar to wild type ( E8 . 5 Pten Pdpk1 neural plate height = 48 . 6 ± 8 . 8 μm ) , pseudostratified , and there were apical-basal arrays of acetylated microtubules in the double mutant neural plate ( Figure 4C , D ) . The apical surface area of cells in E8 . 5 Pten Pdpk1 double mutant neural plate was 50% less than in Pten embryos ( 10 ± 7 μm2 compared to 15 ± 9 μm2 ) , indicating a rescue of cell shape ( Figure 4C , E ) . Thus these aspects of the Pten neural plate phenotype depend on PDPK1 . PTEN acts in extraembryonic tissues to control polarized collective migration of the anterior visceral endoderm that establishes the anterior-posterior body axis and in the epiblast to control movement of cardiac precursor cells to the midline ( Bloomekatz et al . , 2012 ) . In double mutants that lack both Pten and Pdpk1 in all tissues ( Pten-/-; Pdpk1-/- ) , the embryos showed the partial axis duplication seen in Pten single mutants ( Figure 4—figure supplement 2A ) . Pten △Epi Pdpk1 △Epi double mutants showed the cardia bifida phenotype seen in Pten △Epi embryos ( Figure 4—figure supplement 2B ) . Thus these cell migration phenotypes in Pten mutant embryos were not rescued by removal of PDPK1 , in contrast to the PDPK1-dependent phenotype of the Pten neural plate . AKT is a direct substrate for phosphorylation by PDPK1 ( Walker et al . , 1998 ) and the biochemical assays showed that AKT phosphorylation was increased in Pten mutant embryos ( e . g . Figure 3A ) , as expected . There are three Akt genes in the mouse with overlapping functions ( Gonzalez and McGraw , 2009 ) , prohibiting a classical genetic test of the role of Akt in neural morphogenesis . Therefore to test whether pAKT was required for the Pten △Epi phenotype , we injected mothers of Pten mutant embryos with MK-2206 , an allosteric inhibitor that blocks activation of the three AKT isoforms ( Hirai et al . , 2010 ) , 24 and 48 hr before embryo dissection . Western blot analysis showed that the treatment effectively blocked phosphorylation of AKT on both Thr308 and Ser473 ( Figure 5A ) . 10 . 7554/eLife . 12034 . 014Figure 5 . The Pten neural plate phenotype is independent of AKT . ( A ) Effect of the AKT inhibitor MK-2206 treatment on targets of the PI3 kinase pathway in E8 . 5 embryos . Western blot of the two phosphorylated forms of AKT and pS6 S240/4 in WT and Pten △Epi at E8 . 5 in control embryos ( vehicle ) and after 24 or 48 hr of MK-2206 treatment in utero prior to embryo dissection . Numbers indicate approximate MW . ( B ) Dorsal view ( inset ) and enlarged image of the cephalic region of E8 . 5 wild-type and Pten △Epi embryos . There is no change in the morphology of the mutant heads after 24 or 48 hr of MK-2206 treatment in utero . Scale bar = 120 μm . ( C ) The apical surface of the neural plate , viewed en face . Cell borders marked by expression of ZO1 ( white ) ( top row ) ; acetylated tubulin ( green ) in transverse sections of cephalic neural epithelium in wild type and Pten △Epi at E8 . 5 after 24 or 48 hr of MK-2206 treatment in utero . Blue is DAPI . Scale bar = 10 μm . ( D ) Height of the E8 . 5 cephalic neural plate . Wild type , untreated ( control ) = 44 . 9 ± 5 . 7 μm; WT 48 hr treatment = 46 . 5 ± 9 . 9 μm; MK-2206 treatment had no significant effect . Pten △Epi untreated ( control ) = 32 . 4 ± 7 . 3 μm; Pten △Epi 24 hr = 33 . 9 ± 6 . 8 μm2; Pten △Epi 48 hr = 30 . 3 ± 7 . 5 μm . Treated and untreated mutants were all significantly shorter than wild type , but MK-2206 treatment did not significantly rescue cell elongation in the mutant . ( E ) Apical surface area of E8 . 5 cephalic neuroepithelial cells . Control = 8 ± 5 μm2; WT 48 hr = 7 ± 4 μm2; Pten △Epi Control = 14 ± 9 μm2; Pten △Epi 24 hr = 13 ± 9 μm2; Pten △Epi 48 hr = 14 ± 10 μm2 . Treated and untreated mutant cells all had significantly larger surface area than wild type , but MK-2206 treatment did not significantly decrease cell surface area in the mutant . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 01410 . 7554/eLife . 12034 . 015Figure 5—figure supplement 1 . Inhibition of mTORC1 by rapamycin does not rescue the Pten neural plate phenotype . ( A ) Western blot of extracts from control embryos and embryos treated in utero with rapamycin for 48 hr shows that the treatment effectively blocked the expression of pS6 in both WT and Pten △Epi embryos . ( B ) Transverse sections ( acetylated tubulin in green ) and dorsal view of cephalic region of E8 . 5 WT and Pten △Epi embryos after a 48 hr treatment with rapamycin . Scale bar = 10 and 200 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 01510 . 7554/eLife . 12034 . 016Figure 5—figure supplement 2 . Downstream targets of PDPK1 . ( A ) Western blots for pNRDG1 and p-aPKCζ/λ in E8 . 5 WT Pten △Epi , Pdpk1 △Epi and Pten △Epi Pdpk1 △Epi embryos . ( B ) Western blot analysis of pNRDG1 in WT and Pten △Epi embryos treated with MK2206 for 48 hr . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 01610 . 7554/eLife . 12034 . 017Figure 5—figure supplement 3 . Myosin-II distribution and levels appear normal in the Pten neural plate . ( A–D ) En face images of the distribution of MHC-IIB ( green ) . ( A–B ) E8 . 0 embryos . Myosin was anisotropically distributed in the neural plate of all genotypes ( WT , Pten , Pdpk1 and Pten Pdpk1 ) and no preferential enrichment at long or short cell edges was noted . ( C–D ) E8 . 5 embryos . Apical myosin is enriched in all genotypes at E8 . 5 . This is especially prominent in WT , and presumably reflects the formation of the actomyosin rings that mediate apical constriction in the next phase of neural tube closure . Phalloidin is red . Scale bar = 20 μm . ( E ) Western blot detection of phospho-myosin light chain ( pMLC ) and total MLC in E8 . 5 Pten △Epi , Pdpk1 △Epi and Pten △Epi Pdpk1 △Epi embryos . No striking differences between genotypes were apparent . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 017 Despite effective inhibition of AKT activation , treatment with MK-2206 had no detectable effect on the morphology of the neural plate of E8 . 5 Pten embryos ( Figure 5B ) . En face imaging and transverse sections showed that blocking AKT activity did not rescue the neural plate height , pseudostratification or microtubule acetylation ( Figure 5C , D ) . Quantification of apical surface area showed no significant difference between treated and untreated Pten embryos ( Figure 5E ) . An important downstream target of AKT is mTORC1 , which mediates its effects on growth and survival ( Zoncu et al . , 2011 ) . To test whether mTORC1 activity plays a role in morphogenesis of the neural plate , we injected pregnant females with the mTor inhibitor rapamycin . Western blot analysis of treated embryos showed that the rapamycin treatment blocked phosphorylation of ribosomal protein S6 , as expected ( Figure 5—figure supplement 1A ) . Despite its clear biochemical activity , rapamycin did not rescue the cell shape , pseudostratification or tubulin acetylation in the Pten △Epi neural plate ( Figure 5—figure supplement 1B ) . Thus neither AKT nor mTORC1 mediated the effect of PDPK1 on neural morphogenesis . Many other direct substrates for phosphorylation by PDPK1 are known , including more than 20 protein kinases of the AGC family , in addition to AKT ( Pearce et al . , 2010 ) . Atypical PKC ( aPKC ) and PKN family members are PDPK1 targets that are stimulated through PtdIns ( 3 , 4 , 5 ) P3 association ( Balendran et al . , 2000 ) , and aPKC is an important regulator of epithelial polarity . However , we did not detect a change in localization or increased phosphorylation of aPKC in Pten mutants ( Figure 1—figure supplement 3; Figure 5—figure supplement 2A ) . The Serum and Glucocorticoid-induced Kinase ( SGK ) protein family is also activated by phosphorylation by PDPK1 . Phosphorylation of NDRG1 ( T346 ) is mediated by SGK activity ( Murray et al . , 2004 ) , and pNDRG1 was upregulated in Pten embryos and reduced in Pten Pdpk1 double mutants ( Figure 5—figure supplement 2A ) . However , in utero treatment of Pten embryos with the AKT inhibitor MK-2206 blocked phosphorylation of NDRG1 ( Figure 5—figure supplement 2B ) , suggesting that activation of NDRG1 depends AKT and not on the pathway that regulates neural morphogenesis . Evidence suggests that PDPK1 can activate Rho kinase 1 ( ROCK1 ) and phosphorylation of myosin light chain ( Pinner and Sahai , 2008 ) , which should increase the formation of myosin cables . However , myosin-II was anisotropically distributed in neural plate cells of all genotypes ( wild type , Pten-/- , Pdpk1-/- and Pten-/- Pdpk1-/- ) , there was no preferential enrichment of myosin-II at long or short cell edges in E8 . 0 embryos ( Figure 5—figure supplement 3A–D ) and phosphorylation of myosin light chain ( MLC ) was similar in wild type and Pten mutants ( Figure 5—figure supplement 3E ) . To define the cellular processes regulated by PDPK1 in the neural plate , we examined the cellular basis of the Pten mutant phenotype at higher resolution . Pten has been implicated in planar topology of epithelial cells in Drosophila ( Bardet et al . , 2013 ) and cells in the amniote neural plate undergo dynamic cellular reorganization during neural tube closure as cells break and remake junctions with their neighbors ( Schoenwolf and Alvarez , 1989; Nishimura et al . , 2012 ) . In stable epithelia , cells are hexagonally packed into a honeycomb-like array: each cell has six neighbors and three cells converge on each vertex ( Zallen and Zallen , 2004 ) . In dynamic epithelia , this pattern can be disrupted by cell division or by neighbor exchanges , so that each cell has fewer neighbors and a greater number of cells converge on each vertex ( Zallen and Zallen , 2004 ) . Visualizing cell borders with ZO1 ( Figure 2C ) , β-Catenin ( Figure 6A ) or F-actin ( Figure 6D; Figure 6—figure supplement 1A , D ) , cells at the beginning of wild-type neural morphogenesis ( E8 . 0 ) were not hexagonally packed: only ~45% had five or six edges ( Figure 6B ) . Cell arrangements included rosette-like structures where as many as 8 cells converged at a single vertex ( Figure 6A ) , similar to structures in epithelia undergoing active cell rearrangements ( Blankenship et al . , 2006 ) and previously described in the rearranging cells of the neural floor plate in chick and mouse embryos ( Nishimura et al . , 2012; Williams et al . , 2014 ) . The arrangement of cells in the Pten neural plate at E8 . 0 showed the same organization as seen in wild type , where ≥4 cells converging on ∼60% of the vertices ( Figure 6C ) . At E8 . 5 , when pseudostratification was apparent , cells in the wild-type neural plate were packed in a more honeycomb-like arrangement: ∼1 . 8 fold more cells with 5 and 6 edges , and the percentage of cases with ≥4 cells converging on a vertex was reduced by half ( to ∼30% ) , consistent with a more stable epithelium ( Figure 6A–C ) . In contrast , these parameters did not change between E8 . 0 and E8 . 5 in Pten mutants . Thus PTEN appears to promote a more regular , hexagonal organization in the plane of the epithelium at the same stage when the epithelium becomes columnar . Cells in the E8 . 5 neural plate cells of the constitutively activate PI3 kinase mutant ( Pik3caH1047R-Epi ) showed the complex cells arrangements and rosettes seen in Pten mutants ( Figure 6—figure supplement 1A–C ) . 10 . 7554/eLife . 12034 . 018Figure 6 . PTEN promotes stable cell packing in the neural plate . Panels ( A ) and ( D ) show high magnification views of the apical surface of the neural plate embryos , with magnification adjusted so that the cells appear to be approximately the same size , in order to highlight the difference in cell packing in the two genotypes . Scale bars in ( A ) and ( D ) = 15 μm . Orange arrowheads indicate examples of 3 cells/vertex , and yellow arrows indicate vertices formed by ≥4 cells . Cell borders marked by β-catenin ( A ) or F-actin ( D ) expression . ( A ) At E8 . 0 , rosette-like structures are common in both WT and Pten . Fewer rosette-like arrangements are seen in WT at E8 . 5 , but rosettes persist in the E8 . 5 Pten neural plate . ( B ) Quantification of percentage of cells with 3–8 edges . Between E8 . 0 and E8 . 5 , the percentage of cells with 3–4 edges decreases ∼45% , while the percentage with 5–6 edges increases ∼1 . 6 fold in WT embryos , but these parameters are unchanged in E8 . 5 mutants . ( C ) The percentage of vertices plotted against the number of cells meeting at a vertex . In a honeycomb arrangement , 3 cells meet at a vertex; the number of cases where three cells meet at a vertex increases ∼1 . 8 fold between E8 . 0 and E8 . 5 , whereas the Pten neural plate does not changed in this interval . ( D ) At E8 . 5 , Pdpk1 single and Pten Pdpk1 double mutants show packing similar to that in WT , compared to the more rosette-like packing in Pten . Quantification of % of cells with 3–8 edges ( E ) and % of vertices formed by 3–7 cells ( F ) showed similar values in E8 . 5 WT , Pdpk1 and Pten Pdpk1 embryos . Bars indicate % , lines indicate s . d . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 01810 . 7554/eLife . 12034 . 019Figure 6—figure supplement 1 . Cell packing in the neural plate with constitutively active PI3 kinase and when AKT is inhibited with MK-2206 . ( A ) and ( D ) show high magnification views of the apical surface of the neural plate , with magnification adjusted so that the cells appear to be approximately the same size to highlight the difference in cell packing in the two genotypes . Scale bars in ( A ) and ( D ) = 15 μm . Orange arrowheads indicate examples of 3 cells/vertex , and yellow arrows indicate vertices formed by ≥4 cells . Cell borders marked by F-actin expression . ( A ) At E8 . 5 , more rosette-like structures are seen Pik3caH1047R-Epi than in WT . ( B ) Quantification of percentage of cells with 3–8 edges ( neighbors ) . As in E8 . 5 Pten △Epi mutants , the E8 . 5 Pik3caH1047R-Epi neural plate had ∼two fold more cells with 3 and 4 edges , and ∼55% fewer cells with 5 and 6 edges than WT . ( C ) There were ∼45% fewer vertices formed by 3 cells ( the honeycomb arrangement ) and ∼two fold increase in the number vertices formed by ≥4 cells in the E8 . 5 Pik3caH1047R-Epi neural plate compared to WT . ( D–F ) MK-2206 treatment does not affect distribution of cells with 3–8 edges or the percentage of vertices in any category . Bars indicate % , lines indicate s . d . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 019 The organization of E8 . 5 Pdpk1 single and the Pten Pdpk1 double mutant neural plates were similar to wild type , with similar distributions of neighbors per cell ( ∼60% of cells with 5 or 6 edges ) and the percentage of cases with ≥4 cells converging on a vertex was ∼30% ( Figure 6D–F ) . Blocking AKT activity with MK-2206 did not modify cell packing in the Pten neural plate ( Figure 6—figure supplement 1D–F ) . Thus , as with cell elongation and pseudostratification , the failure of Pten mutant neural plate to assume a stable conformation was caused by elevated PtdIns ( 3 , 4 , 5 ) P3 , and depended on PDPK1 but not AKT . The bottle cells of the gastrulating Xenopus embryo share some characteristics with the early neural plate: they begin as cuboidal cells that elongate in an apical-basal direction while forming apical-basal arrays of microtubules and constricting their apical surfaces ( Keller et al . , 2003; Lee and Harland , 2007 ) . During the cuboidal-to-columnar transformation in Xenopus bottle cells , membrane from apical microvilli is endocytosed and trafficked to the basolateral membrane , creating a net movement of membrane from apical to basolateral domains ( Lee and Harland , 2010 ) . Because vesicle trafficking is highly active in dynamic epithelia and stable microtubules failed to form in the Pten mutant neural plate , we tested whether trafficking was affected by the loss of PTEN . Rab5 , a marker of early endosomes , was distributed in an apical-to-basal gradient in the wild type neural plate . In contrast , Rab5+ vesicles were restricted to the most apical domain of the cells in Pten mutants ( Figure 7A , B ) . Clathrin , a marker for coated endocytic vesicles , was also more apically restricted in Pten than in wild-type neural plate cells ( Figure 7C , D ) . The normal distribution of Rab5+ and clathrin+ vesicles was restored in Pten Pdpk1 double mutant neural plates ( Figure 7A–D ) . 10 . 7554/eLife . 12034 . 020Figure 7 . Apical-basal trafficking in PI3 kinase pathway mutants . ( A– D ) Distribution of endosome markers along the apical-basal axis in transverse sections of the cephalic neural plate of E8 . 5 wild-type , Pten △Epi , Pdpk1 △Epi and Pten △Epi Pdpk1 △Epi embryos . ( A ) Localization of Rab5 , an early endosome marker . ( B ) Distribution of Rab5 along the apical-basal axis , normalized to a maximum value of 100 . ( C ) Localization of clathrin . ( D ) Distribution of clathrin along the apical-basal axis , normalized to a maximum value of 100 . ( E ) Uptake of Transferrin-Alexa 647 after 8 hr of embryo culture . Transverse sections of cephalic neural plate of E8 . 5 wild-type , Pten △Epi , Pdpk1 △Epi and Pten △Epi Pdpk1 △Epi embryos . White signal is the native Alexa 647 fluorescence . ( F ) Distribution of Alexa-647 signal along the apical-basal axis . Transferrin-647 accumulates apically in the Pten △Epi but not in Pten △Epi Pdpk1 △Epi double mutants . ( G ) Transverse sections of cephalic neural plate of E8 . 5 wild-type and Pten △Epiembryos treated in utero with MK-2206 for 48 hr and then cultured with 50 μg/ml of Transferrin-647 and MK-2206 for 8 hr . ( H ) Distribution of Alexa-647 along the apical-basal axis is not affected by MK-2206 treatment . Images are Z-projections of 3 optical sections of 1 μm each . Red is phalloidin . Blue is DAPI . Scale bars = 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 020 To test whether the change in vesicle distribution reflected changes in endocytosis or in apical-to-basal trafficking , we cultured E8 . 0 embryos in presence of transferrin coupled to Alexa-647 and analyzed the localization of transferrin-647 after 8 hrours of culture ( Christ et al . , 2012 ) . Total transferrin-647 uptake was similar in wild-type and Pten neural plate cells . However , while transferrin spread along the apical-basal extent of wild-type cells , transferrin accumulated in the apical region in Pten cells ( Figure 7E , F ) , suggesting that defects in basal trafficking are coupled to the failure of Pten mutants to form a pseudostratified columnar neural epithelium . Similar to the other neural plate phenotypes , basal transport of transferrin was rescued in Pten Pdpk1 double mutants , but was not rescued by treatment of Pten with MK-2206 ( Figure 7G , H ) . Mouse embryos that lack PTEN have an unprecedented defect in morphogenesis of the neural tube . In Pten mutant embryos , a SOX2+ neural epithelium forms , shows normal segregation of apical and basal markers , is patterned by developmental signals , and proliferates normally . However , the mutant cephalic neural epithelium fails to undergo the transition from a cuboidal to a tall , columnar pseudostratified epithelium; instead , the mutant neural plate is thin , wide and irregularly folded , and cephalic neural tube closure fails completely . Phosphoinositides have been described as key regulators of apical-basal polarity ( Martin-Belmonte et al . , 2007; Shewan et al . , 2011 ) , and indeed the Pten mutants have a profound defect in the organization of the third ( apical-basal ) dimension of the neural epithelium . However , the traditional markers of apical-basal polarity are localized correctly in the Pten mutant neural plate: Par3 , aPKC , ZO1 , P-ERM , N-cadherin and F-actin are apically localized , and laminin is basally localized . Based on the enrichment of pAKT in both apical and basolateral membranes of the Pten mutant neural plate , apical-basal polarity markers are localized correctly despite high levels of PtdIns ( 3 , 4 , 5 ) P3 throughout cell membranes . Despite the important roles of phosphoinositides in mTOR signaling , endocytic sorting , recycling and trafficking ( Di Paolo and De Camilli , 2006; Shewan et al . , 2011; Dibble and Cantley , 2015 ) , the genetic and chemical genetic data demonstrate that all the phenotypes in the Pten neural plate are mediated by increased activity of PDPK1 . Although phosphorylated AKT is enriched in all cellular membranes in the mutant neural plate , inhibition of the downstream kinases AKT or mTor does not modify the Pten mutant phenotype , whereas removal of Pdpk1 rescues all aspects of the Pten phenotype . We therefore conclude that it is the inappropriate PtdIns ( 3 , 4 , 5 ) P3-stimulated activity of PDPK1 , and not changes in levels of other phosphoinositides or in the activity of AKT or mTorc1 , that mediates all the morphogenetic defects seen in the Pten mutant neural epithelium . Perhaps the most striking cellular difference between the Pten and wild-type neural plate cells is the absence of stable apical-basal microtubule arrays in the mutant . The formation of noncentrosomal apicobasal microtubule arrays , with apical minus-ends and basal plus-ends , is a hallmark of columnar epithelia ( Bré et al . , 1987; Jaulin and Kreitzer , 2010 ) . Consistent with a requirement of microtubule arrays for apical-basal trafficking in columnar epithelia ( Jaulin and Kreitzer , 2010; Rodriguez-Boulan and Macara , 2014 ) , basal trafficking of apically endocytosed transferrin fails in the Pten neural plate . Recent work showed that PTEN can bind directly to microtubule-associated vesicles ( Naguib et al . , 2015 ) , suggesting that PTEN could play a direct role in apical-to-basal trafficking in the neural plate . The data show that the PTEN is required for organization of stable arrays of apical-basally oriented microtubules , which may both stabilize the long axis of the cell and promote the redistribution of membrane from the apical to the basolateral domains of neuroepithelial cells , leading to the transition from a cuboidal to a columnar epithelium . At the same stage ( between E8 . 0 and E8 . 5 ) when wild-type neural cells begin to elongate and form arrays of apical-basal stable microtubules , cells of the neural plate are also reorganizing in the plane of the epithelium to become more hexagonally packed . At E8 . 0 , cell packing in both the wild-type and Pten mutant anterior neural plate is irregular and includes the rosette-like arrangements that are a hallmark of dynamic epithelia ( Blankenship et al . , 2006 ) . By E8 . 5 , wild-type cells have resolved into a more regular packing pattern and fewer rosettes are observed , while the Pten neural plate continues to have many rosette-like cell arrangements . Pten-dependent , Akt-independent changes in cell packing have also been observed in the Drosophila wing disc , where the effect of Pten mutations was attributed to a defects in the remodeling of adherens junctions ( Bardet et al . , 2013 ) . Similar to what we observed in the cephalic neural plate of the mouse Pten mutant , Drosophila Pten mutant wing disc epithelial cells have fewer neighbors than seen in a regular hexagonal array . In the Drosophila case , high levels of myosin-II are preferentially seen on short cell edges of Pten mutant cells . In contrast , myosin-II is anisotropically distributed in the both the wild-type and mutant E8 . 0 mouse neural plate , and it can be enriched at either long or short cell edges . The anisotropic distribution of myosin-II persists in the E8 . 5 Pten mutant , while myosin-II becomes enriched at all cell edges in the E8 . 5 wild-type neural plate , probably in preparation for the next phase of neural tube closure , actomyosin-mediated apical constriction . Thus the loss of PTEN blocks the maturation of cell packing in the neural plate , but there is no simple relationship between the Pten phenotype and the distribution of myosin-II . The abnormal planar cell packing and the absence of apical-basal microtubule arrays in the Pten neural plate appear to be coupled: they occur simultaneously and both depend on regulated activity of PDPK1 . The coupling of these two phenotypes is consistent with known links between apical junctions and microtubule arrays . Apical adherens junctions are sites for anchorage of noncentrosomal microtubule arrays ( Meng et al . , 2008; Gavilan et al . , 2015 ) . Microtubules dynamics , in turn , can regulate the stability of adherens junctions ( Meng et al . , 2008; Waterman-Storer et al . , 2000 ) , supporting the existence of a positive feedback loop that couples stable adherens junctions and microtubule arrays . We propose that a target of PDPK1 in the Pten mutant neural plate inhibits stabilization of apical junctions , which , in turn , blocks the formation of the noncentrosomal microtubule arrays required for elongation of cells in the neural plate ( Figure 8 ) . The direct target of PDPK1 in this process is not known; one possibility is that inappropriate activity of PDPK1 promotes dynamic fluctuations in the activity of aPKC and/or PKN . PtdIns ( 3 , 4 , 5 ) P3-tethered PDPK1 is sufficient to activate these two classes of kinases ( Balendran et al . , 2000 ) and aPKC can regulate both apical junctions and microtubule organization ( Harris and Tepass , 2008; Harris and Peifer , 2007 ) . 10 . 7554/eLife . 12034 . 021Figure 8 . A model for the role of PTEN in the formation of the pseudostratified columnar epithelium . PDPK1 is anchored to the plasma membrane by PtdIns ( 3 , 4 , 5 ) P3 ( PIP3 ) , which is made by PI3 kinase ( PI3K ) and degraded by PTEN . In the Pten mutant , increased PIP3 recruits high levels of PDPK1 to the membrane , where it is activated . Activated membrane-associated PDPK1 has two targets: activated PDPK1 generates high levels of pAKT; in a separate pathway , high levels of membrane-associated PDPK1 inhibit the formation of stable apical junctions . Stable apical junctions are required for the formation of stable apical-basal microtubule arrays , which mediate apical-to-basal trafficking in the neural epithelium , allowing elongation and tight packing of cells in the neural epithelium . In WT , PDPK1 is not required for formation of the pseudostratified neural epithelium , although the delay in neural tube closure in Pdpk1 mutants may reflect a subtle role for the protein in epithelial organization . DOI: http://dx . doi . org/10 . 7554/eLife . 12034 . 021 PTEN has many roles in mammalian brain development , including control of cell size ( Kwon et al . , 2001 ) , neuronal differentiation and migration ( Yue et al . , 2005 ) , synapse structure and synaptic plasticity ( Fraser et al . , 2008; Sperow et al . , 2012 ) and axon regeneration ( Park et al . , 2008 ) . Human mutations in one copy of the PTEN gene are associated with a variety of abnormalities in brain development , including megalencephaly and focal cortical dysplasia , which can lead to autism and pediatric epilepsy ( Hevner , 2015; Jansen , et al . , 2015; Zhou and Parada , 2012 ) . Our findings define a profound , very early role of PTEN in the organization of the brain that is likely to contribute to the human syndromes caused by PTEN haploinsufficiency . PDPK1-dependent changes in epithelial stability could also play an important role in tumors that lack PTEN . Mutations in PI3 kinase pathway are extremely common in tumors: for example , nearly 80% of cases of endometrial carcinoma ( non-ultramutated samples ) have inactivating mutations in PTEN ( Cancer Genome Atlas Research Network et al . , 2013 ) and 45% of human luminal A breast tumors harbor activating mutations in PIK3CA ( Cancer Genome Atlas Network , 2012 ) . Previous studies provided evidence that anchorage-independence and xenograft growth of breast cancer cells carrying the activated H1047R PI3KCA allele depended on PDPK1 but not AKT ( Gagliardi et al . , 2012 ) and phosphoproteomic analysis of cell lines with activating PI3KCA mutations identified cases in which PDPK1 activity , but not AKT activity , was required for tumorigenicity ( Vasudevan et al . , 2009 ) . The data presented here demonstrate that PtdIns ( 3 , 4 , 5 ) P3-dependent PDPK1 activity is an important consequence of the absence of PTEN in vivo , even in the absence of activation of AKT . Our findings highlight the importance of identifying the relevant PDPK1 targets during mouse development , in PTEN-associated developmental syndromes , and in tumors . The mutant alleles used here have been described previously: Ptenflox ( Trotman et al . , 2003 ) , Pdk1flox ( MGI designation: Pdpk1 ) ( Lawlor et al . , 2002 ) , R26-Pik3caH1047R ( Jackson Laboratories , Bar Harvor , ME . Stock #016977 ) . The epiblast specific-expressing CRE line is Sox2-CRE ( Hayashi et al . , 2002 ) . The Wnt-reporter line used was TOPGAL ( DasGupta and Fuchs 1999 ) . The genotype of the Pten ΔEpi ( epiblast-deleted ) embryos is Sox2-Cre/+; Ptenflox/Ptennull . The genotype of the Pdpk1 ΔEpi embryos is Sox2-Cre/+; Pdpk1flox/Pdpk1null . The genotype of the Pten Pdpk1 ΔEpi double mutants is Sox2-Cre/+; Ptenflox/Ptennull; Pdpk1flox/Pdpk1null . We generated the Pten and Pdpk1 deleted ( null ) alleles by crossing conditional mice with Sox2-Cre , taking advantage of Sox2 activity in the female germ line . The X-linked GFP transgene was a gift from Anna-Katerina Hadjantonakis ( Hadjantonakis et al . , 2001 ) . Pten mutants were congenic in CD1 , and all other lines , except R26-Pik3caH1047R ( FVB ) , were backcrossed to CD1 for at least four generations before analysis . For timed pregnancies , noon on the day of the vaginal plug was E0 . 5 . Pregnant females were injected intraperitoneally ( i . p . ) following standard procedures . A final volume of 0 . 5 ml was injected . Treatments were as follows: 25 mg/kg/day of LY294002 ( Selleckchem , Houston , TX ) diluted in DMSO at E7 . 5; 120 mg/kg/day of MK-2206 ( from the Baselga Laboratory; commercially available from Selleckchem ) diluted in Captisol at E7 . 5 or E6 . 5 and E7 . 5; 3 mg/kg/day of Rapamycin ( Sigma , St . Louis , MO ) diluted DMSO at E6 . 5 and E7 . 5 . Embryos were harvested at E8 . 5 . Embryos for SEM were fixed in 2 . 5% glutaraldehyde overnight at 4°C , processed using standard procedures and imaged with a Zeiss Supra 25 Field Emission Scanning Electron Microscope . β-Galactosidase activity was detected using standard described protocols ( Hogan et al . , 1994 ) . Whole-mount in situ hybridization was performed on embryos following standard methods ( Eggenschwiler and Anderson , 2000 ) . The Brachyury ( Wilkinson et al . , 1990 ) , En2 ( Joyner and Martin , 1987 ) , Krox20 ( Wilkinson et al . , 1989 ) , EMX2 ( Simeone et al . , 1992 ) , Fgf8 ( Tanaka et al . , 1992 ) , and Axin2 ( Jho et al . , 2002 ) in situ probes were previously described . The embryos were photographed using an HRC Axiocam ( Zeiss , Germany ) fitted onto a stereomicroscope ( Leica , Germany ) . Embryos were dissected in ice-cold or room temperature PBS/4% BSA and processed for imaging following established protocols ( Lee et al . , 2010 ) . Immunofluorescence staining was performed with Alexa Fluor-conjugated secondary antibodies ( Invitrogen , Waltham , MA ) diluted 1:400 . Sections were counterstained with DAPI ( 1:2000 ) to stain nuclei . All images shown are from the cephalic neural plate . Rhodamine-phalloidin ( Invitrogen ) was used at 1:200 . ARL13b antibody ( Caspary et al . , 2007 ) was used at 1:2000 . Commercial antibodies were: Sigma: γ-tubulin ( T-6557 ) , 1:1000 for immunofluorescence ( IF ) ; α-Tubulin ( T5168 ) 1:1000 for IF , 1:3000 for western blots ( WB ) ; acetylated α-Tubulin ( T7451 ) 1:1000 for IF and 1:3000 for WB . Santa Cruz , Dallas , TX: GAPDH ( sc-32233 ) , 1:5000 for WB . Invitrogen: ZO1 ( 33-9100 ) , 1:200 for IF . Cascade Biosciences , Winchester , MA: Pten ( ABM2052 ) , 1:1000 for IF . Cell Signaling , Danvers , MA: Pten ( 9559 ) 1:500 for IF; S6 ( 2217 ) 1:2000 for WB; pS6 ( 2211 ) 1:1000 for WB; pAKT Ser473 ( 9271 ) 1:1000 for WB; pAKT Thr308 ( 2965 ) 1:1000 for WB; AKT ( 9272 ) 1:1000 for WB; Rab5 ( 3547 ) 1:100 for IF; Clathrin Heavy Chain ( 4796 ) 1:100 for IF; pMLC2 ( 3671 ) , 1:1000 for WB; acetylated α-Tubulin ( 5335 ) 1:3000 for WB . Hybridoma Bank , Iowa City , IA: Nkx2 . 2 ( 74 . 5A5 ) 1:100 for IF; Nkx6 . 1 ( F55A10 ) 1:50 for IF . Covance , Princeton , NJ: MHCIIB ( CMII-23; PRB-445P ) , 1:50 for IF , and 1:1000 for WB . Abcam , Cambridge , MA: FOXA2 ( AB40874 ) 1:800 for IF . Millipore , Billerica , MA: Olig2 ( AB9610 ) 1:200 for IF; SOX2 ( AB5603 ) 1:1000 for IF . For immunofluorescence , samples were mounted using Vectashield ( Vector Labs , Burlingame , CA ) or ProLong Gold ( Life Technologies , Carlsbad , CA ) mounting media , and slides were imaged with SP5 and SP8 confocal microscopes ( Leica ) with a 63 × 0 . 5 NA lens , at a resolution of 1024 × 1024 . In transverse sections , maximum intensity was set in the apical domain , and images with apical non-saturated signal on the neural plate were taken . En face images are Z-projections of 3–5 single optical sections taken every 0 . 3 μm . Images were analyzed using Volocity software ( PerkinElmer , Waltham , MA ) . The immunofluorescence data presented in the figures are representative images of at least three embryos . Pixel intensity along the apicobasal axis of the neural plate was determined on Z-stack projections of 5 optical sections taken every 1 µm ( grayscale ) . Pixel intensity values were taken from lines 20 pixels wide traced with ImageJ . Graphical distribution of pixel intensity average ( n≥3 embryos ) was generated using Prism6 with normalized values . E8 . 0 embryos with intact yolk sac and ectoplacental cone were dissected in 37°C DMEM/F12 containing 10% FBS . After dissection , 5 embryos were transferred to a glass bottle ( Roller Bottle System ) containing 5 ml of 50% rat serum/50% DMEM/F12 and incubated at 37°C with 5% CO2 and 10% O2 . Transferrin-Alexa 674 ( Molecular Probes , Eugene , OR . #Ta3366 ) was diluted in the culture media to 50 μg/ml , as described ( Christ et al . , 2012 ) . After 8 hr , the yolk sac was removed and the embryos were fixed in 4% PFA for 2 hr at 4°C and mounted for cryosectioning following established protocols ( Lee et al . , 2010 ) . Images were taken from transverse sections of the cephalic region using a SP5 Leica confocal microscope collecting the native signal from Transferrin-Alexa 674 . Neural plate height of cephalic region was measured following a previously described method ( Grego-Bessa et al . , 2015 ) . Apical surface area quantification of cephalic neuroepithelial cells was determined from en face images taken with a Leica SP5 inverted confocal microscope and 63 × 0 . 5 NA lens , and analyzed by Volocity software ( >100 measurements per embryo , n≥3 embryos ) . For all analyses , n≥3 embryos . Measurements are average ± s . d . Comparisons were made by standard t-test . Prism6 was used for statistical analysis . For analysis of cell packing , ZO-1 , β-Catenin and Phalloidin-Rhodamine staining delineated the apical domain of cephalic neuroepithelial cells . En face images of the cephalic region were taken by confocal microscope at 63× of magnification . For two-dimensional cell patterns , the number of edges/cell and the number of vertices formed by 3–7 cells were quantitated manually from at least 3 embryos per genotype ( >200 cell vertexes ) . Data analysis was performed with Excel and Prism6 . A pool of three E8 . 5 embryos , after removal of the heart , was lysed in Cell Lysis Buffer ( Cytoskeleton , Denver , CO . GL36 ) plus Complete Protease Inhibitor Cocktail ( Roche , Germany ) . Western blots were performed according to standard protocols , and protein was detected with HRP-conjugated secondary antibodies and ECL detection reagents ( Amersham , UK ) .	In mammals , the brain and spinal cord develop from a flat sheet of cells called the neural plate , which bends around to create a structure known as the neural tube . This bending process occurs through a complex sequence of cell shape changes . The cells in the neural plate are initially short and wide , but transform into long , thin cells as the neural plate forms . Problems that prevent the neural tube from forming correctly are amongst the most common birth defects in humans . Many cancer cells contain a mutation that affects a gene that produces a protein called PTEN . This protein normally activates a tumor suppressor pathway , and so cancer cells that lack PTEN divide and grow uncontrollably . Grego-Bessa et al . have now examined mouse embryos that lack this gene , and found that the neural plate in such embryos forms irregular ruffles rather than a closed tube . Further investigation revealed that the neural tube defects are not due to the inactivation of the traditional tumor suppressor pathway . Instead , correct neural tube formation relies upon the ability of PTEN to remove phosphate groups from a target lipid , which is important for limiting the activity of an enzyme called PDK1 . Unlimited PDK1 activity causes complex changes that prevent the neural plate cells from elongating and packing together correctly . Future work is now needed to investigate the exact molecules targeted by PDK1 and the roles they play in disorders and diseases caused by a lack of the PTEN protein .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology", "cell", "biology" ]	2016	The tumor suppressor PTEN and the PDK1 kinase regulate formation of the columnar neural epithelium
How left-right patterning drives asymmetric morphogenesis is unclear . Here , we have quantified shape changes during mouse heart looping , from 3D reconstructions by HREM . In combination with cell labelling and computer simulations , we propose a novel model of heart looping . Buckling , when the cardiac tube grows between fixed poles , is modulated by the progressive breakdown of the dorsal mesocardium . We have identified sequential left-right asymmetries at the poles , which bias the buckling in opposite directions , thus leading to a helical shape . Our predictive model is useful to explore the parameter space generating shape variations . The role of the dorsal mesocardium was validated in Shh-/- mutants , which recapitulate heart shape changes expected from a persistent dorsal mesocardium . Our computer and quantitative tools provide novel insight into the mechanism of heart looping and the contribution of different factors , beyond the simple description of looping direction . This is relevant to congenital heart defects . Bilateral organisms , such as mammals , are characterised by apparently symmetrical left and right sides . However , left-right patterning of the body is essential for the development of the embryo , and in particular for the formation of visceral organs that are asymmetric . In the case of the heart , the primordium is a tube , which undergoes a process of rightward looping ( Patten , 1922 ) , thereby acquiring a helical shape ( Männer , 2004; see Männer , 2009 ) . Heart looping , which is the first morphological sign of bilateral asymmetry during embryonic development , is required for the correct alignment of cardiac chambers and thus for the establishment of the double blood circulation . Abnormal left-right patterning is associated with human diseases referred to as heterotaxy , with an incidence of about 1/10 000 , including defects in the lung , spleen , liver , stomach , intestine and also complex cardiac malformations , which will determine the prognosis of patients ( Lin et al . , 2014; Guimier et al . , 2015 ) . Experiments in animal models have provided insight into how left-right patterning is established . The left-right organiser , the node ( Nonaka et al . , 1998 ) , is first detectable in the early embryo , at Embryonic day ( E ) 7 . 5 in the mouse . Left-right patterning involves a leftward fluid flow , generated by cilia , which initiates a signalling cascade centred on the left determinant Nodal , a secretory protein of the transforming growth factor-beta ( TGFβ ) superfamily ( Levin et al . , 1995; Collignon et al . , 1996; see Shiratori and Hamada , 2014 ) . This initial left-right biasing mechanism is important to coordinate the morphogenesis of visceral organs according to the same reference . However , it does not explain why morphogenesis is asymmetrical . For example , in Nodal mutants ( Brennan et al . , 2002 ) , the direction of heart looping is randomised , but the process of looping still takes place . Brown and Wolpert ( 1990 ) had hypothesised the existence of an additional mechanism , organ specific , for randomly generating asymmetry . Yet the basis of such a mechanism for the looping of the heart tube has remained enigmatic . Formation of the cardiac tube occurs under the head folds , by fusion of two bilateral heart fields ( Rawles and Rawles , 1943; Kinder et al . , 1999 ) . Heart precursors are detected in two waves , referred to as the first and second heart fields ( see Meilhac et al . , 2014 ) . Formation of the cardiac tube , at E8 in the mouse , is initiated on the ventral side such that the heart remains initially attached to the body dorsally , by a tissue referred to as the dorsal mesocardium , which finally breaks down at E9 . 5 . Elongation of the heart tube progresses cranially and caudally by ingression of heart precursors ( Stalsberg , 1969; de la Cruz et al . , 1977 ) and also by proliferation of cells inside the tube ( de Boer et al . , 2012 ) . Growth of the myocardium inside the tube is coherent ( Meilhac , 2003 ) , suggesting that mixing between cells derived from the right and left heart fields is limited . In agreement with this , cell-labelling experiments have shown that left and right heart precursors contribute to specific regions of the mouse heart tube ( Domínguez et al . , 2012 ) and later , in the fetal heart , to specific veins , atrial regions or specific great arteries ( Lescroart et al . , 2010; Lescroart et al . , 2012 ) . Looping of the tube is concomitant with elongation ( Biben and Harvey , 1997 ) , raising the possibility that the two processes may be linked . Although the general sequence of heart looping has been well studied , the morphological changes of the cardiac tube , particularly in the mammalian embryo , have not been quantified , hindering precise reconstruction of the looping process , as well as description of mutant heart geometries . In principle , looping of a tube may result from intrinsic ( Noël et al . , 2013; Taniguchi et al . , 2011 ) or extrinsic factors ( Davis et al . , 2008; González-Morales et al . , 2015 ) . In the context of the heart , looping mechanisms have been mainly studied in the chick . Early embryologists made the morphological observation that the elongation of the heart tube is constrained ( His , 1868 ) . Measurements indicated that the increase of the tube length is much greater than that of the distance between the attached cranial ( arterial ) and caudal ( venous ) ends ( Patten , 1922 ) , leading to the proposal that looping results from a buckling mechanism , when the tube elongates between fixed poles . This mechanism was challenged by explant experiments , showing curvature of isolated heart tubes ( Manning and McLachlan , 1990 ) , thus pointing to intrinsic rather than extrinsic factors for heart looping . A problem with these analyses lies in the definition of heart looping . Explants generated a C-shape tube , but not a helical shape . In addition , surgical disconnection of a single pole ( the arterial pole ) impaired heart looping ( Kidokoro et al . , 2008 ) . Thus , the roles of intrinsic and extrinsic factors in heart looping have remained unclear . Theoretically , intrinsic factors may contribute to heart looping , for example by differential growth ( Manasek et al . , 1972 ) or oriented deformations ( Itasaki et al . , 1991 ) . To predict which 3D shape might emerge from a combination of forces , computer simulations are required . Simulations with a Finite Element Analysis model , in combination with experiments in the chick , have shown how intrinsic and local variations in tissue stiffness or growth can influence phases of heart looping: the increase in cell size ventrally can generate the initial ventral bulge of the heart tube ( Soufan et al . , 2006; Shi et al . , 2014a ) , whereas increased growth in the left atrial region can bias the direction of heart looping and generate a rightward C-shape ( Kidokoro et al . , 2008; Shi et al . , 2014b; Voronov et al . , 2004 ) . Whether intrinsic factors are dependent on the Nodal left-right signalling cascade remains undetermined , since Nodal is transiently expressed in heart precursors and turned off within the heart tube ( Vincent et al . , 2004 ) , and since the Nodal target Pitx2c , which is expressed in the heart tube , is not required for heart looping ( Lu et al . , 1999 ) . In addition , the concomitant inflation of cardiac chambers , which is associated with differential growth rates ( de Boer et al . , 2012 ) , as well as oriented growth ( Le Garrec et al . , 2013; Meilhac et al . , 2004 ) , would interfere with intrinsic factors of heart looping . Therefore , there is currently no convincing demonstration that intrinsic factors are required to produce a helical heart tube shape . Rather than computer simulations , mechanical simulations with non-biological material such as rubber tubes have explored the theoretical role of extrinsic factors in a buckling mechanism of heart looping , such as a rotation of the tube poles ( Männer , 2004 ) , or tube growth within the confined space of the pericardial cavity ( Bayraktar and Männer , 2014 ) . However , removal of the pericardial membrane does not impair heart looping in the longer term ( Kidokoro et al . , 2008; Nerurkar et al . , 2006 ) , questioning the role of this confinement . The role in heart looping of another mechanical constraint , the attachment by the dorsal mesocardium , was not taken into account in these simulations . Yet , failure of the dorsal mesocardium to break down has been associated with incomplete heart looping , for example in Shh-/- mouse mutants ( Hildreth et al . , 2009 ) or upon inhibition of matrix metalloproteinases in the chick ( Linask et al . , 2005 ) . A structure analogous to the dorsal mesocardium of the heart tube , the dorsal mesentery , has been shown to be involved in the looping of another tube , the gut , as a result of left-right asymmetries in its cellular architecture ( Davis et al . , 2008 ) . Left-right asymmetries of the dorsal mesocardium have also been reported ( Linask et al . , 2003; Linask et al . , 2005 ) , further pointing to this structure as a potential factor of heart looping . Here , we explore further the buckling mechanism and test whether it is sufficient for heart looping . Using the mouse embryo , we have developed a novel procedure to generate qualitative and quantitative datasets of shape changes in 3D of the looping heart , captured by high-resolution episcopic microscopy ( HREM ) . We provide a novel dynamic 3D computer simulation of heart looping which is based on these data . Our combined modelling and experimental approach shows that asymmetries at the fixed heart poles , generating opposite deformations , associated with the progressive release of the heart tube dorsally , are sufficient to generate looping of a tube growing between fixed poles . Our predictive model is validated in four experimental conditions , using cell labelling , time-lapse imaging and molecular deficiencies . The novel model of heart looping that we propose functions as a generator of asymmetric organ morphogenesis , in the sense of Brown and Wolpert ( 1990 ) , able to amplify initial left-right differences between heart precursors . We investigated the process of heart looping in the mouse embryo , whereas it had been previously mainly studied in the chick . In litters dissected at E8 . 5 , we observed a spectrum of heart shapes , which suggests that heart looping is a rapid process . Theiler stages , which report general embryonic landmarks over the whole gestation , fail to account for the rapid progression of heart looping . We grouped the embryos according to heart shapes ( Figure 1A ) . To order them , we used two criteria . The heart tube is known to elongate by ingression of heart precursors at both the cranial , arterial , pole and the caudal , venous , pole ( Domínguez et al . , 2012; Zaffran et al . , 2004 ) . Thus , the addition of novel heart regions was taken as an indication of later stages . The cranial addition of the right ventricle and the extension of the outflow tract were particularly striking . Heart looping is also known to correspond to a repositioning of the right ventricle , from an initial cranial to a final right position ( de la Cruz , 1998 ) . Thus , the position of the right ventricle relative to the left ventricle was taken as another sign of the progression of heart looping . The resulting sequence parallels the addition of somites . However , we found , as others previously ( Kaufman and Navaratnam , 1981 ) , that heart development was not strictly synchronous with somitogenesis . We propose a novel staging system of early heart development , based on the shape of the heart tube . At E8 . 5c , the cardiac crescent is visible but the bilateral heart fields have not fused . At E8 . 5d , the right and left heart fields , adjacent at the midline , bulge separately within the cardiac crescent . At E8 . 5e , the bilateral heart fields have fused , with a visible midline furrow . They form a primitive left ventricle , which bulges ventrally , with the venous and arterial poles caudal and cranial respectively . This corresponds to the initial cardiac tube , taking the shape of an inverted Y , as the venous pole remains bilateral . At E8 . 5f , a right ventricular region appears cranially and the tube still appears bilaterally symmetrical . At E8 . 5g , the first external sign of left-right asymmetry occurs , with the tilting of the tube axis . At E8 . 5h , the outflow region starts to extend , resulting in a more variable curved heart shape . At E8 . 5i , the looped tube is clearly detectable; however , the right ventricle has not yet reached its right position . The right ventricle-left ventricle axis does not parallel the embryonic right-left axis . At E8 . 5j , the right ventricle has reached its final position such that the right ventricle-left ventricle axis tends to be perpendicular to the cranio-caudal axis . To further characterise the shape changes during heart looping , we acquired 3D images ( Video 1 ) by HREM ( Weninger et al . , 2006 ) and , after image segmentation , reconstructed the 3D shape of the myocardium from E8 . 5e to E8 . 5j ( Figure 1B , and Figure 1—source data 1 ) . These 3D reconstructions are essential to extract and quantify geometrical parameters . By plotting the centroid of myocardial sections ( Figure 2A ) , we extracted the axis of the cardiac tube ( Figure 2B ) . We quantified the increase in the length of the cardiac tube from 183 ± 42 µm ( n = 3 ) at E8 . 5e to 800 ± 56 µm ( n = 3 ) at E8 . 5j ( Figure 2C ) . We also quantified the repositioning of the right ventricle , by measuring the right ventricle-left ventricle axis relative to the cranio-caudal axis ( Figure 2D ) , from 7°±7 ( n = 3 ) at E8 . 5f to 72°±1 ( n = 3 ) at E8 . 5j ( Figure 2E ) . These measures validate the ordering of stages and provide quantitative references for the evaluation of the progression of heart looping . We analysed the existence of mechanical constraints that have been proposed to influence heart looping , such as the distance between the heart poles . We found that , while the length of the cardiac tube increases 4 . 4-fold in average between E8 . 5e and E8 . 5j , the distance between the arterial and venous poles is constant ( p=0 . 26 , Student test between E8 . 5e and E8 . 5j ) with a value of 146 ± 29 µm in average ( Figure 2C ) . These measures are consistent with the buckling mechanism initially proposed by Patten ( 1922 ) . To evaluate the shapes that can be generated by a buckling mechanism , we designed a finite element computer model , based on the GPT framework previously used to model flower shapes ( Green et al . , 2010; Kennaway et al . , 2011 ) . The model , in which the growing tissue is simulated as a continuous sheet of material , can capture tissue deformations at a large scale with a reduced number of arbitrary parameters , compared to cell-based simulations ( Osborne et al . , 2017 ) . Tissue deformation depends on the regional growth patterns , modulated in rate and orientation , which are the input parameters of the model . It also depends on the mechanical constraint of maintaining a continuous sheet of tissue elements . In the absence of any asymmetry , the simulation of a tube growing between fixed poles did not lead to buckling , but to a wider tube ( Figure 2F ) . When we introduced a small left-right bias ( burst of 5% increase in growth at one pole ) , which would correspond to stochastic , naturally occurring , left-right variations , the tube was able to curve ( Figure 2G ) . The buckling mechanism is thus able to generate asymmetric morphogenesis by amplifying small left-right variations . However , in this minimal hypothesis , the tube acquired a C-shape . Therefore , a buckling mechanism is insufficient to account for the biological helical shape of the looped heart tube . This prompted us to further analyse mouse hearts , to quantify left-right asymmetries , as well as additional mechanical constraints . The heart tube is initially attached dorsally to the body via the dorsal mesocardium . From a mechanical point of view , this may hinder heart looping . We analysed the dynamics of the dorsal mesocardium in our 3D reconstructions . We observed it in serial embryonic sections acquired by HREM and found variations in its lateral thickness along the cranio-caudal axis and between stages ( Figure 3A–C ) . Thus , we quantified the thickness of the dorsal mesocardium from the arterial to the venous pole at different stages ( Figure 3D ) . The dorsal mesocardium was at least 94 ± 50 µm ( n = 3 ) thick at E8 . 5e and got thinner to a minimum of 28 ± 13 µm ( n = 3 per stage ) between E8 . 5f and E8 . 5h , corresponding to about two cells . Breakdown of the dorsal mesocardium was detectable from E8 . 5i , in the arterial half of the tube and progressed towards the venous pole at E8 . 5j ( see thick lines below Figure 3D ) . Before breaking down , the dorsal mesocardium appeared elongated on the dorsal/ventral axis , at E8 . 5g and E8 . 5h ( Figure 3E–F ) , suggesting stretching of the tissue . The distance to the foregut was higher in the arterial half of the tube , where breakdown of the dorsal mesocardium was initiated . In contrast , the dorsal mesocardium displayed homogenous characteristics in the venous half between E8 . 5f and E8 . 5i . Thus , a clear boundary was apparent between the arterial and venous halves of the tube ( position 90 , Figure 3D , F ) . Our observations show that breakdown of the dorsal mesocardium and heart looping are simultaneous , and we have quantified for the first time the associated spatio-temporal sequence . Heart looping is a directional event , which depends on left-right patterning . Thus , we examined early left-right asymmetries during the formation of the heart tube . The E8 . 5f stage , corresponding to the straight heart tube , had always been considered as a stage of bilateral symmetry , as seen externally ( Figure 1 ) , whereas the chick heart tube undergoes a rightward rotation between stages HH10 and 12 ( de la Cruz et al . , 1977; de la Cruz , 1998 ) . We found on HREM sections , that the mouse heart tube at E8 . 5f was not bilaterally symmetrical , but rather rotated towards the right side ( Figure 4A–B ) . We quantified this asymmetry , by measuring the right and left angles between the heart tube and the dorsal pericardial wall ( Figure 4C ) . We found that this rotation was not present at the previous stage E8 . 5e . At E8 . 5f , this asymmetry was specific to the arterial pole ( Student test , p<0 . 001 ) and not significant in the venous half of the tube ( p>0 . 40 ) . We estimated , at E8 . 5f in the mouse , that the rotation is in the order of 25° ( inset Figure 4C ) . To further follow the rotation of the heart tube , we mapped the position of the limits of the dorsal mesocardium ( Figure 4D–F ) . From a ventral view , the ventral aspect of the dorsal mesocardium was shifted to the right compared to the dorsal aspect . This was visible at E8 . 5f , and amplified at E8 . 5g , in agreement with the dorso-ventral elongation of the dorsal mesocardium . However , this was specific to the arterial pole and barely visible at the venous pole . We then analysed potential left-right asymmetries at the venous pole . Whereas the arterial pole was displaced to the right of the midline ( notochord ) , the dorsal mesocardium at the venous pole appeared displaced in an opposite direction , to the left of the midline ( Figure 4F ) , in agreement with previous reports of a leftward displacement of the venous pole , both in mouse ( Biben and Harvey , 1997 ) and fish ( Chen et al . , 1997 ) . This is not due to an underlying asymmetry in the dorsal pericardial wall: the width of the pericardial wall was found symmetrical throughout the looping process , with a maximum left-sided deviation of 37 µm ± 30 at E8 . 5i , which is not significant ( Student paired test , p>0 . 09 ) ( Figure 4—figure supplement 1 ) . We quantified the position of the venous pole of the heart relative to the notochord and found it significantly left-sided from E8 . 5g , with a minimum 47 µm ± 22 ( E8 . 5g ) and maximum 87 µm ± 10 ( E8 . 5i ) deviation ( Figure 5A–B ) . We analysed further left-right asymmetries , by addressing cell ingression from the posterior second heart field . Symmetrical dye injections were performed with different colours on the right and left sides ( Figure 5C1–D1 ) , and the relative contributions to the heart tube were observed ( Figure 5C2–D2 ) . We evaluated whether there are instances when precursors on one side had been recruited to the heart tube and not that on the other side ( Figure 5E ) . This was observed transiently , at stage E8 . 5g , with a significant higher number of cases when right cells only had been recruited to the heart tube . At a cellular level , left-right differences in cell proliferation have been reported in the sinus venosus at the 7-somite stage ( Galli et al . , 2008 ) , but not in precursor cells . To investigate this further , we analysed cell proliferation with a higher spatio-temporal resolution , using 3D images and our staging system ( Figure 5F–G ) . A significant increase in mitotic cells was observed in right precursors of the second heart field at E8 . 5g , but we did not detect any difference between the anterior and posterior domains . Our dye injections , proliferation assay and measures of the venous pole position are consistent . We show that there is a leftward displacement and a transient asymmetric cell ingression at the venous pole , which takes place at E8 . 5g , i . e . one stage after the rotation of the arterial pole at E8 . 5f . The faster ingression and proliferation of cells on the right side of the venous pole would generate a leftward deformation , opposite and subsequent to the rightward rotation of the arterial pole . To test how the mechanical constraints and left-right asymmetries that we have observed in the mouse can affect the shape of the cardiac tube , we performed computer simulations . The model was initiated as a hollow cylinder of 1800 finite elements , represented by pentahedra ( Figure 6 and Figure 6—figure supplement 1A–D ) . The initial stage represented E8 . 5f but did not take into account the atrial region which is bifid . To account for the mechanical constraints observed in biological samples , the poles of the cylinder were fixed in the cranio-caudal ( z ) axis . In addition , the dorsal mesocardium was simulated by constraints along two vertical lines dorsally , restricting displacement in the dorso-ventral ( y ) and cranio-caudal ( z ) axes . The breakdown of the dorsal mesocardium was simulated by a progressive release of this constraint , starting from the nodes at mid-length of the tube and progressing towards the poles . The orientation of growth was defined relative to the tube , accompanying its deformation , and not relative to embryonic axes . Longitudinal growth parallels the axis of the tube , whereas circumferential growth is perpendicular . Longitudinal growth was implemented with a baseline value accounting for the observed lengthening of the tube ( see Figure 2C ) and with an initial ventral increase to reproduce the ventral bulge of the cardiac tube . Circumferential growth was added to account for the expansion of each ventricle . The simulation ends after 90 steps , corresponding to stage E8 . 5i . To this basal framework , we added growth patterns to simulate left-right asymmetries . The clockwise rotation of the arterial pole was simulated with a left-right difference in circumferential growth , in a tube which is constrained dorsally ( Figure 6B ) . It was calibrated to simulate a 25° rotation , which decreases along the tube from the pole down to mid-length , in agreement with biological observations ( Figure 4C ) . The asymmetric ingression at the venous pole is simulated with a burst of longitudinal growth on the right ( Figure 6C ) . The sequence of growth patterns , which are used as inputs of the model and reflect biological observations , is summarised in Figure 6A . There are six input parameters of growth , of which two ( the basic longitudinal growth and the circumferential growth at the arterial pole ) are set to match observed morphological measurements , three ( the initial ventral longitudinal growth , the left and right ventricle circumferential growth ) are set to match qualitative shape observations , and one ( the longitudinal growth asymmetry at the venous pole ) is set arbitrarily to obtain at the end of the simulation a helical shape similar to that observed at stage E8 . 5i . Simulations ran with this model showed that , in the context of a tube growing between fixed poles , two left-right asymmetries , generating opposite deformations at the poles , together with the progressive breakdown of the dorsal mesocardium , were sufficient to generate a helical shape ( Figure 6D ) , and thus a looped heart tube . The sequence of deformations was compatible with the biological sequence ( Figure 6E–H , Video 2 ) . We tested whether parameters of the model are required for the acquisition of a helical shape . If the constraint of a fixed distance between the poles was released , simulations led to a C-shape heart tube ( Figure 6—figure supplement 1E ) , which is reminiscent of that observed in explant experiments ( Manning and McLachlan , 1990 ) . If the dorsal mesocardium was not taken into account , simulations also produced a C-shape heart tube ( Figure 6—figure supplement 1F ) . If there was no left-right asymmetry , the simulated tube remained straight in a frontal plane , with only the ventral bulge ( Figure 7B abscissa = 0 and ordinate = 0 ) . If there was a single asymmetry at one pole , again the simulated tube acquired a C-shape ( Figure 7B abscissa = 0 or ordinate = 0 ) . This indicates that all parameters of the model are required together for generating a helical shape . Thus , we provide a novel predictive model of heart looping , based on the buckling of the tube , when growing between fixed poles . Buckling is modulated by the progressive breakdown of the dorsal mesocardium and biased by a precise combination of left-right asymmetries at the poles , thus generating a helical shape . We explored how variations in initial left-right asymmetries affect the tube shape . The burst of growth at the venous pole may vary in its position or intensity ( Figure 7A ) . Computer simulations show that there is a narrow window , in which a helical tube shape can be generated . This corresponds to a burst of longitudinal growth located on the right side , that is generating an opposite deformation to the arterial pole , and a burst intensity defined by the ratio of right over left growth around 2 . 8-fold . Outside this window , the tube has a flat S shape ( in case of a burst on the same position , but with a higher fold difference ) , or most frequently a C shape . Reversal of the rotation at the arterial pole ( leftward ) , combined with a reversed position of the burst at the venous pole , creates mirror images . We also tested how the intensities of asymmetries at the arterial and venous poles are combined to shape the heart tube ( Figure 7B ) . Computer simulations indicate that the intensities of asymmetries at the arterial and venous poles have to be proportional to generate a helical shape . The helix becomes flatter , that is S-shape , when the intensity of asymmetry increases ( red dotted diagonal ) . Such flat S-shapes are also generated close to the window of parameter space corresponding to the helix , whereas C-shapes are observed otherwise . These analyses show that the regulation of the position and intensity of left-right asymmetries at the poles of the heart can greatly influence the shape of the looped heart tube . Computer simulations indicate that acquisition of a helical heart tube requires opposite deformations from the arterial and venous poles . However , mechanistically , computer simulations of heart looping are compatible with either a rightward rotation of the arterial pole , or a burst of growth on the left side , similar ( but opposite ) to that observed at the venous pole . The computer model can be used to raise predictions and distinguish these cases . In case of a burst of growth at the arterial pole , the model predicts that the initial ventral line of the tube would remain ventral at the end of the simulations ( Figure 8A ) , whereas it would shift to the right in case of a rotation ( Figure 8B ) . To test these predictions experimentally , we injected DiI at the most cranial ventral midline , at E8 . 5e ( Figure 8C–D ) and E8 . 5f ( Figure 8E–F ) , that is , before or during the stage when we first observed left-right asymmetry at the arterial pole . After 24 hr of culture , in the looped heart , the label was clearly located on the right side . In addition , we performed time-lapse imaging of mouse embryos at the beginning of heart looping , using inducible reporter lines to better track cell movement in a mosaic tissue . We generated two independent movies , in which a cell could be tracked at the arterial pole between E8 . 5e/f and E8 . 5g . In comparison with cells of the headfolds which mainly move caudally , cells of the arterial pole were found to move as much ( Figure 8—figure supplement 1A–C , Video 3 ) or more ( Figure 8—figure supplement 1D–E ) to the lateral right side . These observations reinforce our morphological measurements ( Figure 4A–C ) and together demonstrate rightward rotation of the arterial pole in the mouse . Our morphological observations showed that breakdown of the dorsal mesocardium and heart looping are simultaneous . To further explore how the dorsal mesocardium influences heart shape , we used the computer model to raise predictions . If the dorsal mesocardium is persistent , computer simulations indicate that looping of the heart tube is severely affected ( Figure 9A , Video 4 ) . The right ventricle fails to reach a right position relative to the left ventricle and the curvature of the tube is abnormal . This is reminiscent of Shh-/- mutants , in which heart looping was reported to be incomplete and the dorsal mesocardium persistent ( Hildreth et al . , 2009 ) . Thus , to validate experimentally the prediction of our model , we analysed in more detail the heart tube of Shh-/- mutants , and compared them to our simulations . Qualitatively , the shape observed in Shh-/- mutants was similar to the shape predicted by the computer model ( Figure 9A–B ) . Reconstruction of the axis of the heart tube from HREM images showed that Shh-/- mutant hearts fail to acquire a helical shape , at a stage when control hearts are looped ( Figure 9C and Figure 9—source data 1 ) . This is associated with a persistent dorsal mesocardium ( Figure 9D–E and Figure 9—figure supplement 1 ) . Between 10 and 12 somite stages , whereas all control embryos displayed a broken down dorsal mesocardium , this concerned only 2/5 mutants ( Figure 9—figure supplement 1A , C ) . This was not a delay , as at E9 . 5 , 2/6 mutants had no sign of dorsal mesocardium breakdown , and in the other 4/6 mutants , dorsal mesocardium breakdown was detected over a maximum length of 30 µm , which is much less than a breakdown over a minimum of 124 µm in control littermates ( Figure 9—figure supplement 2 ) . At E8 . 5 , the dorsal mesocardium of mutant embryos was elongated in the dorso-ventral axis , specifically in the arterial half of the tube , however , with higher values in mutant compared to control samples ( Figure 9—figure supplement 1C , D ) . These data indicate that the dorsal mesocardium is regionalised properly in Shh-/- mutants , and potentially able to stretch , but fails to largely break down . As the tube of Shh-/- mutants is not straight , it suggests that some looping has occurred . The direction of the loop was found normally rightward ( n = 5/5 at E8 . 5 , 6/6 at E9 . 5 ) ( Figure 9—source data 1 and Figure 9—figure supplement 2 ) . However , heart looping was severely impaired in Shh-/- mutants ( Figure 9F ) . When we measured the tube length , we found that Shh-/- mutants have a significantly shorter heart tube , compared to control samples ( Figure 9G ) . Lengthening of the tube is not arrested , as the increase in the length of the heart tube relative to the number of somites follows a linear regression , with a slope not significantly different from that of control samples . This suggests that defects in heart looping in Shh-/- mutants are not the result of overall reduced growth . In contrast , our computer simulations show that persistence of the dorsal mesocardium constrains the longitudinal growth of the heart tube , such that enlargement of the tube is observed to account for the same amount of overall tissue growth . Thus , we measured the perimeter of the heart tube in Shh-/- mutants and found a significant 1 . 2-fold increase compared to controls ( Figure 9H , Student test , p=0 . 03 ) . The overall area of the heart tube did not change significantly between mutants and controls ( 0 . 56 ± 0 . 12 mm2 and 0 . 55 ± 1 . 0 mm2 , respectively , n = 5 and 5 , Student test , p=0 . 75 ) , further showing that there is no overall growth defects in Shh-/- mutant hearts . We also confirmed that the distance between the poles is not significantly changed in Shh-/- mutant hearts ( 121 ± 15 µm , compared to 132 ± 17 µm in controls , n = 5 and 5 , p=0 . 31 , Student test ) . Finally , computer simulations predict that persistence of the dorsal mesocardium constrains the curvature of the tube axis in the transverse plane . This prediction is validated in Shh-/- mutant hearts , in which we observed a narrower curvature of the tube axis in the transverse plane ( Figure 9H–J ) . Alternatively , we tested whether the shape of Shh-/- mutant hearts could be explained by impairment of left-right signalling: a reduction by 50% of left-right asymmetries would account for the malposition of the right ventricle , but not for the changes in length and the narrow helix ( see transverse sector ) of the heart tube ( Figure 9—figure supplement 3 ) . In summary , there is a discrepancy between the real shape of Shh-/- mutant hearts and the simulated shape in case of reduced left-right signalling . In contrast , there is a good match between the shape of Shh-/- mutant hearts and the simulated shape in case of persistent dorsal mesocardium , both qualitatively and quantitatively for the change of values of four geometrical parameters . Another case of dorsal mesocardium persistence , without manipulating Shh , was analysed . In the chick , failure of dorsal mesocardium breakdown associated with incomplete heart looping was reported upon inhibition of matrix metalloproteinases ( Mmp ) ( Linask et al . , 2005 ) . We investigated whether this is the case in the mouse . We detected expression of Mmp2 in the foregut of the mouse at the beginning of heart looping ( E8 . 5g ) , with vesicular localisations in the cardiac region , suggesting secretion of Mmp2 ( Figure 10A ) . To test the role of Mmp , we used GM6001 , a broad-spectrum Mmp inhibitor ( Shichi et al . , 2011 ) , in cultures of mouse embryos at E8 . 5d-e ( Figure 10B ) . After 10 hr of culture , the embryos reached stage E8 . 5h-i in the control situation ( DMSO ) ( Figure 10C ) . Whereas all control embryos ( 3/3 ) displayed a broken down dorsal mesocardium , this concerned only 2/4 mutants ( Figure 10D–E ) . Dorsal mesocardium breakdown was detected over a maximum length of 8 µm ( 3/4 ) upon GM6001 treatment , which is much less than a breakdown over 23–125 µm in control cultures ( 3/3 ) . In this situation again , persistence of the dorsal mesocardium was associated with incomplete heart looping . After 24 hr of culture , the right ventricle failed in most cases ( 7/9 ) to reach a right position relative to the left ventricle ( Figure 10F ) , leading to a tube shape similar to that predicted by the computer model ( Figure 9A ) . Taken together , our analyses combining two experimental conditions and computer modelling highlight the important role of the dorsal mesocardium in heart looping . We have reconstructed for the first time heart looping in 3D , taking the mouse as a model . We provide novel tools to quantify the progression of heart looping and analyse the 3D shape . In addition , we have characterised the spatio-temporal sequence of mechanical constraints during looping , including the elongation of the tube , the distance between the poles and the breakdown of the dorsal attachment . With additional left-right asymmetries uncovered at the arterial and venous poles , we propose a novel predictive model of heart looping . Our staging system extends that proposed previously ( Biben and Harvey , 1997 ) . LS-0 is equivalent to E8 . 5e/f , when the tube axis parallels the cranio-caudal axis . LS-I is equivalent to E8 . 5g , when the initial tilting of the tube axis is detected , and the venous pole is displaced leftward . Although we did not use the atrioventricular sulcus as a marker of staging , it can be monitored from our 3D reconstructions ( Figure 1 , Figure 1—source data 1 ) , showing that LS-II is equivalent to E8 . 5g/h . LS-III , which corresponds to ventricular looping , is equivalent to E8 . 5h-j . We now provide 3D reconstructions and quantitative spatio-temporal measures to assist staging of a continuous process . Several geometrical parameters can be used to assess the progression of heart looping , such as the length of the heart tube , the right ventricle/left ventricle orientation , the rotation of the arterial pole , the position of the venous pole , the extent and localisation of the dorsal mesocardium breakdown . These tools will be instrumental to analyse defects of heart looping beyond the simple description of looping direction , as reported previously in mutant embryos . Our model of heart looping predicts that defects in left-right patterning not only result in impaired direction of heart looping , but also in different shapes , from C-shapes to flat S-shapes . Our tools open novel avenues to study heart looping defects with a greater precision and thus , to better understand the mechanism of looping and the respective contribution of different factors . Our model , which is based on analyses in the mouse , could potentially be applied to heart looping in other species . In the mouse , we show that the distance between the poles of the tube is fixed , whereas the tube length increases 4 . 4-fold during the process of looping . We detect a rightward rotation of the arterial pole , which precedes a leftward displacement and deformation of the venous pole . These features are conserved between chick and mouse , despite variations in the morphological sequence of heart looping . The distance between the poles of the chick heart tube has long been reported to be fixed ( Patten , 1922 ) . However , the value is higher in the chick ( 600–800 µm ) compared to the mouse ( 100–200 µm ) , and thus , a straight heart tube is more obvious in the chick . The fold increase in the heart tube length is comparable in the chick and mouse ( Patten , 1922 ) . A rightward rotation of the ventricular region in the chick was shown by labelling with iron oxide particles ( de la Cruz et al . , 1977; de la Cruz , 1998 ) , preceding the leftward displacement of the venous pole ( Kidokoro et al . , 2008 ) . This is compatible with mechanical simulations showing that opposite and sequential rotations of the heart poles are sufficient to generate a helical tube shape ( Männer , 2004 ) . These simulations postulated a 90° rotation , in agreement with the displacement of iron oxide particles in the outer curvature of the chick heart . However , we have measured a 25° rotation of the arterial pole in the mouse . This could be a species difference , because the C-shape of the chick heart tube observed at stage HH12 ( de la Cruz , 1998; Männer , 2000 ) has no equivalent in the mouse . Alternatively , it is possible that confinement in the pericardial cavity increases the apparent rotation of the outer curvature of the heart , generating a transient C-shape heart tube in the chick . This is supported by the reduced displacement of the outer curvature upon removal of the pericardial membrane ( splanchnopleura ) in the chick , with no consequence for heart looping in the longer term ( Voronov and Taber , 2002; Nerurkar et al . , 2006 ) . Rotation of the arterial pole in the chick would need to be measured with the same approach as described here , that is the angle between the heart tube and the dorsal pericardial wall . At the venous pole of the mouse , we do not detect a significant leftward rotation , as proposed by Männer , 2004 , but rather an asymmetric ingression and proliferation of heart precursors , in favour of right cells . This is in keeping with the increased size of the right atrium compared to the left at E8 . 5 ( Meilhac et al . , 2004 ) . Cell labelling experiments in the posterior second heart field at E8 . 5 showed that right cells contribute not only to the right atrium , but also to the ventral aspect of the left atrium ( Domínguez et al . , 2012 ) , indicating a wider deployment of right cells in the mouse . An asymmetric ingression of heart precursors , in favour of right cells , has also been observed in the chick by grafting experiments at the beginning of heart looping ( HH10 , Stalsberg , 1969 ) . These similarities suggest that our model would also apply to the chick embryo , so that the mechanism of heart looping may be conserved in amniotes . In the zebrafish , formation of the heart tube follows a different morphogenetic process , involving the formation of a cone , which telescopes out to form a tube ( Stainier et al . , 1993 ) . The looped heart tube in the zebrafish is a flat-S , distinct from the helix in chick and mouse . The fish tube does not grow between fixed poles , has no equivalent of the dorsal mesocardium , and looping was shown to depend on intrinsic factors ( Noël et al . , 2013 ) , thus ruling out a buckling mechanism . However , left-right asymmetry of the zebrafish heart tube is first seen at the venous pole , with a leftward displacement of the atrial region referred to as cardiac jogging ( Chen et al . , 1997 ) , resulting from asymmetric migration and involution of cells in the cardiac cone ( Baker et al . , 2008; Rohr et al . , 2008; Smith et al . , 2008 ) . It is about a day later that the ventricle , at the arterial pole , bends rightward , in a process referred to as cardiac looping ( Chen et al . , 1997; Stainier et al . , 1993 ) . Thus , as in chick and mouse , opposite and sequential deformations occur at the poles of the zebrafish heart tube . However , the order is reversed , with a venous pole displacement first in fish , whereas it is the arterial pole deformation first in amniotes . Further analyses of the molecular pathways and cell behaviour underlying asymmetric heart morphogenesis in different animal models will be essential to further assess the degree of conservation of the looping mechanism in vertebrates . The molecular and cellular mechanism of heart looping in amniotes remains poorly understood . Left-right asymmetric cell proliferation , in favour of right cells and depending on the Nodal target Pitx2c , has been observed in the sinus venosus of the mouse at the seven somite stage ( Galli et al . , 2008 ) . We extend the observation of asymmetric proliferation to precursor cells of the second heart field , indicating a potential mechanism for the asymmetric cell ingression that we have observed in the venous pole at E8 . 5g . The rotation of the arterial pole that we detect in the early heart tube at E8 . 5f , precedes the well-known rotation of the outflow tract , observed with transgenic markers from E9 . 5 ( Bajolle et al . , 2006 ) and the later spiralling of the aorta and pulmonary trunk . It is possible that a continuous process of rightward rotation at the arterial pole takes place throughout heart morphogenesis . However , we can only speculate about its mechanism , by homology with other examples of asymmetric tubular morphogenesis: movements of myocardial cells in the zebrafish result in a rotation of the cardiac cone ( Baker et al . , 2008; Rohr et al . , 2008; Smith et al . , 2008 ) , tilting of the gut tube in the mouse depends on asymmetries in the cellular architecture of the dorsal mesentery ( Davis et al . , 2008 ) , and in Drosophila , rotation of the hindgut or genitalia is associated with the chiral activity of an atypical myosin , Myo1D , in an organiser tissue ( González-Morales et al . , 2015; Spéder et al . , 2006 ) . The asymmetric ( right > left ) proliferation of precursor cells was observed also in the anterior domain of the second heart field , suggesting potential modulation of the arterial pole deformation at E8 . 5g . This will require further analysis . Breakdown of the dorsal mesocardium was shown , in the chick , not to be associated with apoptosis , but rather to depend on the activity of matrix metalloproteinases ( Linask et al . , 2005 ) . This is in agreement with our mouse embryo cultures treated with the inhibitor GM6001 and suggests a mechanism of degradation of the extra-cellular matrix . Our quantification of the breakdown of the dorsal mesocardium , as well as of the tube rotation , uncovers a sharp boundary between the arterial and venous halves of the mouse cardiac tube at E8 . 5 . This is in keeping with the regionalisation of the second heart field , lying in the dorsal pericardial wall , into anterior and posterior domains . Such regionalisation has been characterised later , at E9 . 5 , based on epithelial markers ( Francou et al . , 2014 ) and differential gene expression , for example of the transcription factors Tbx1 and Tbx5 , respectively ( Rana et al . , 2014 ) . Our results highlight an earlier stage of this boundary , which would provide a context for the differential left-right asymmetries that we observe at the poles of the heart , leading to opposite deformations ( rightward at the arterial pole and leftward at the venous pole ) . Thus , heart looping integrates both left-right and anterior-posterior patterning . Our work identifies Shh as an upstream regulator of the breakdown of the dorsal mesocardium . Hh signalling was shown to be required in the lateral plate mesoderm for the establishment of left-right patterning . However , in this context , Shh and Ihh ligands are redundant , so that it is only in the double mutant of the ligands ( Shhflox/-; Ihhflox/-; Sox2-Cre ) or in the mutant of the receptor ( Smo-/- ) that left markers are absent in the lateral plate mesoderm ( Tsiairis and McMahon , 2009 ) . Shh is also required for the formation of the floorplate , which functions as a barrier for the maintenance of left-right asymmetry ( Meyers and Martin , 1999 ) . In Shh-/- mutants , Nodal expression is initiated correctly , whereas the downstream target Pitx2 is bilaterally expressed ( Hildreth et al . , 2009; Meyers and Martin , 1999 ) . This is in agreement with our observation that the incomplete looping of Shh-/- mutants was biased correctly towards the right side , as also previously reported ( Tsukui et al . , 1999 ) , and distinct from cases of bilateral Nodal expression leading to a randomised looping direction ( Murray and Gridley , 2006; Izraeli et al . , 1999; Furtado et al . , 2008 ) . From these data , we conclude that left-right patterning is not disrupted initially in Shh-/- mutants . Our simulations with reduced left-right signalling fail to reproduce the heart shape observed in Shh-/- mutants . Shh has been shown to regulate cell differentiation in the second heart field ( Goddeeris et al . , 2008; Zhang et al . , 2001 ) , thus affecting the growth of the heart . However , during looping , we have not detected any significant change in the heart growth of Shh-/- mutants , indicating that looping defects are not related to defective growth . The good quantitative match between our computer simulations and mutant shapes rather show that heart looping defects in Shh-/- mutants recapitulate that expected from a persistent dorsal mesocardium . This is reinforced by the observation of similar looping defects associated with dorsal mesocardium persistence in another experimental condition , upon matrix metalloproteinase inhibition . Our model of heart looping extends the buckling mechanism , theoretically proposed by early embryologists ( Patten , 1922 ) and tested mechanically with non-biological materials ( Männer , 2004; Bayraktar and Männer , 2014 ) . We now integrate another mechanical constraint , the dorsal mesocardium , as well as left-right asymmetries taken from biological observations . The direction of the buckling is biased by left-right asymmetries , whereas the degree of buckling depends on the forces applied at the ends , i . e . the magnitude of growth of the heart tube , and restriction from the dorsal mesocardium . The importance of growth of the cardiac tube , as a pre-requisite for the buckling , is supported experimentally by the looping defects observed in mouse mutants with reduced ingression of heart progenitors , when the cardiomyocyte differentiation cascade is affected ( see mutants for Nkx2-5 [Lyons et al . , 1995] , Mef2c [Lin et al . , 1997] , Isl1 [Cai et al . , 2003] , Tbx20 [Stennard et al . , 2005] or Tbx3 [Ribeiro et al . , 2007] ) . The spatio-temporal sequence of breakdown of the dorsal mesocardium , relative to the growth and asymmetries of the heart tube , appears , in our computer model , as an important determinant of embryonic heart shape . This is supported by our analysis of heart looping defects in Shh-/- mutants or GM6001-treated embryos , in which the dorsal mesocardium is persistent . Our computer simulations also show that left-right asymmetries extrinsic to the heart tube , that is in heart precursors , are sufficient for heart looping . This suggests that intrinsic asymmetries , such as complex growth gradients or growth orientations within the heart tube may be largely dispensable for heart looping . This is in agreement with current knowledge of left-right patterning of cardiac cells . Expression of the major left determinant Nodal is detected in heart precursors , and not within the heart tube ( Collignon et al . , 1996; Vincent et al . , 2004 ) , whereas the Nodal target Pitx2c , which is expressed in the heart tube , is not required for heart looping ( Lu et al . , 1999 ) . Our computer simulations show that the position and intensities of left-right asymmetries at the poles of the heart greatly influence heart shape . Thus , our model provides a mechanism for a generator of asymmetric morphogenesis , specific to the heart , able to amplify variations in left-right patterning . The existence of such a mechanism had been postulated by Brown and Wolpert ( 1990 ) as an important element of left-right patterning , in which asymmetric morphogenesis is local , and can be uncoupled from a global left-right biasing mechanism coordinating the position of different organs according to the same reference . This elegant model of left-right patterning is supported by the observation that in the absence ( Brennan et al . , 2002 ) or in case of bilateral expression ( Murray and Gridley , 2006 ) of the left determinant Nodal , the process of asymmetric morphogenesis still takes place , that is , the heart does not remain symmetrical and some heart looping occurs , with a random orientation , probably due to stochastic and spontaneous left-right variations . Our computer simulations indeed show that the heart would only remain straight if there was a complete absence of asymmetry , a very unlikely situation in a noisy biological context . Simulations with our model ( Figure 6 ) predict that very small localised differences in growth rate ( in the order of 1% ) will lead to significant buckling . The curvature is stronger with a progressive breakdown of a dorsal attachment than without ( data not shown ) . The parameter space of left-right asymmetries compatible with the helix shape observed in vivo is narrow . A tight coordination between the intensities and positions of left-right asymmetry at either pole is required . The looping mechanism that we propose for the heart tube shares similarities with that proposed for the looping of the embryonic gut ( Davis et al . , 2008 ) . Left-right cellular asymmetries in the dorsal mesentery have been uncovered , with elegant cell-based computer simulations showing that this is sufficient to generate a leftward tilt of the gut . However , looping of the midgut is more than a tilting . Generation of a S-shape for the midgut has been proposed to depend on a buckling mechanism . Yet , the growth of the tube between fixed poles , and the opposite left-right deformation at the anterior pole have not been quantified . The similarities in the gut and heart raise the possibility of a common framework for the asymmetric morphogenesis of tubular organs . Our model at the tissue level will be generally useful to predict which tubular shape emerges from a given combination of mechanical constraints and left-right asymmetries . Together with the image analysis tools that we have developed to quantify the shape of the heart tube , it will now be possible to explore , in various experimental conditions , the parameter space in vivo and decipher the molecular and cellular determinants of asymmetric tubular morphogenesis . Control embryos ( Figure 1 ) were from a mixed genetic background . The Shh+/- mouse line ( Gonzalez-Reyes et al . , 2012 ) was maintained in a C57Bl6J genetic background . Shh+/+ and Shh+/- were indistinguishable and used together as control embryos . Animal procedures were approved by the ethical committee of the Institut Pasteur and the French Ministry of Research . For imaging , embryos were dissected , incubated in cold 250 mM KCl ( at E9 . 5 ) , fixed in 4% paraformaldehyde or Bouin’s fluid , dehydrated and embedded in methacrylate resin , as previously described ( Weninger et al . , 2006 ) . The number of somites was evaluated from the HREM images . HREM acquires images of the surface of the resin block , in which the embryo is embedded , to produce perfectly registered digital image stacks capturing the 3D tissue architecture at high resolution . Resulting datasets comprise 1000–2000 images of 1 × 1 µm resolution produced by repeated removal of 1–2 µm sections . HREM was performed on E8 . 5 or E9 . 5 embryos as described previously ( Mohun and Weninger , 2012 ) , using the optical high-resolution episcopic microscope ( Indigo Scientific ) . Hearts were segmented from HREM images using the Imaris software ( Bitplane ) . The contour of the myocardium was manually outlined at regular Z intervals , and the Create Surface function was used to reconstruct the 3D surface . The notochord was similarly segmented to serve as a reference longitudinal axis . 3D visualisation was produced with ICY ( Institut Pasteur , Paris ) for raw images , and with Blender ( Blender Foundation , Netherlands ) or DAZ Studio ( Daz Productions Inc . ) for segmented images . 3D PDF files were built with Acrobat Pro ( Adobe Systems Inc . ) after exporting the Imaris file in WRL format , and conversion into a U3D format with Meshlab ( Visual Computing Lab ) . The axis of the cardiac tube was reconstructed from the Imaris surface , using the Oblique Slicer function to intersect the tube perpendicularly , proceeding along its length . On each slice the polygon outlining the myocardium was drawn . The centroids of the successive polygons were computed with Matlab ( geom3d library ) , and the 3D line exported in X3D format ( figure2xhtml function ) . The X3D file was then imported in Blender and superimposed on the segmented myocardium . To quantify the average perimeter of the heart tube , 10 polygons , evenly distributed along the length , were selected and the perimeters of theoretical circular discs of the same area were taken as values . The tranverse sector , in which the tube axis is inscribed , was obtained after rotating the 3D line to superimpose the two extremities of the axis . The sector angle was measured from this view . The orientation of the right ventricle–left ventricle axis was similarly obtained by intersection of the Imaris surface at the level of the interventricular sulcus . The polygon outlining the myocardium was drawn and its centroid computed . The axis was defined as the line perpendicular to this polygon . It was projected on the frontal plane , taken as perpendicular to the dorsal-ventral axis of the embryo . The orientation was measured as the angle between this projection and the notochord axis . The thickness and the dorso-ventral elongation of the dorsal mesocardium , the positions of its ventral and dorsal aspects , as well as the angle between the heart tube and the dorsal pericardial wall , were measured in transverse sections , after aligning the HREM cubic images on the notochord axis with the ICY software ( StackRotationByAngle plugin ) . E8 . 5 embryos from wild-type [Swiss] mice , or from the T4-nlacZ [Swiss] transgenic line ( Biben et al . , 1996 ) were collected , transferred to Hank’s solution and labelled by injection of a lipophilic carbocyanine ( Interchim , France ) as described previously ( Domínguez et al . , 2012 ) . Symmetrical dye injections were done at the right and left venous pole of the embryo using DiO and DiI to distinguish them . Injections at the most cranial midline of the heart tube were performed using DiI . Injected embryos were photographed using a Nikon Digital Sight DS-L1 camera system and a Nikon C-DSS230 stereomicroscope and , then , cultured for 24 hr in 75% rat serum , 25% T6 medium ( Whittingham , 1971 ) , with 5% CO2 , 5% O2 , 90% N2 in rolling bottles in a precision incubator ( BTC Engineering , Milton , Cambridge , UK ) . Two embryos were cultured per bottle , with one identified by an injection of DiI into the left headfold . At the end of the culture , embryos were washed in PBS , fixed 15 min in 4% paraformaldehyde in PBS , washed in PBS and kept at 4°C until examination with a Leica MZ16F fluorescence stereomicroscope . Embryos that showed widespread background heart fluorescence or appeared morphologically abnormal at the end of the culture , were excluded from the analysis . For drug treatment , E8 . 5 embryos from wild-type [C57Bl6J] mice were collected . 10 µM of GM6001 ( Ilomast - Millipore ) , or an equivalent volume of the adjuvant ( DMSO ) , were added to the culture medium , in a 5% CO2-5% O2 atmosphere , and rinsed in PBS after 10 hr . Embryos were processed for HREM imaging or further incubated in culture medium , in a 5% CO2-20% O2 atmosphere , and harvested after 24 hr . Brightfield images were acquired with a Zeiss AxioCamICc5 Camera and a Zeiss StereoDiscovery V20 stereomicroscope . Immunofluorescence on 10-µm cryostat sections was performed with a standard protocol , including permeabilisation in 0 . 75% Triton , blocking in 10% inactivated goat serum and 0 . 5% Triton , quenching of aldehydes in 2 . 6 mg/ml NH4Cl , and using primary antibodies to MMP2 ( 1/50 , sc-13594 ) , Alexa Fluor conjugated secondary antibodies ( 1/500 ) and Hoechst nuclear staining . Multi-channel 16-bit images were acquired with a Zeiss LSM 700 confocal microscope and 20X/0 . 75 or 40X/1 . 3 oil objectives . Immunofluorescence on whole mount E8 . 5 embryos was performed using CUBIC clearing adapted from ( Susaki et al . , 2015 ) . Samples were incubated overnight in the lipid-removing Reagent-1 , then 48 hr with primary antibodies to PH3 ( 1/100 , ab32107 ) and Isl1 ( 1/50 , 39 . 4D5 DSHB ) , and 48 hr Alexa Fluor conjugated secondary antibodies ( 1/500 , Molecular probes ) and Hoechst nuclear staining in TSA Blocking Reagent ( Perkin Elmer ) . Samples were finally incubated 48 hr in Reagent-2 for adjustment of the refractive index and mounted in 0 . 4% agarose in Reagent-2 . Multi-channel 16-bit images were acquired with a Z . 1 lightsheet microscope ( Zeiss ) and a 20X/1 . 0 objective . Automatic detection of mitotic cells was performed with the Spots plugin of Imaris and co-localisation with Isl1 staining was evaluated manually . Live-imaging was performed as described by Ivanovitch et al . , 2017 . For labelling isolated cells , hydroxy-tamoxifen was administered by oral gavage ( 2–4 mg/ml ) in Polr2aCreERT2/+ ( Guerra et al . , 2003 ) ; R26Rtdtomato/YFP ( [Srinivas et al . , 2001]; Ai14 line [Madisen et al . , 2010] ) mouse embryos at E7 . Embryos were cultured under an upright LSM780 two-photon microscope equipped with a 5% CO2 incubator and a 37°C heated chamber , in 50% fresh rat serum , 48% DMEM without phenol red , 1% N-2 neuronal growth supplement and 1% B-27 supplement , covered with mineral oil . Custom plastic holders were used to immobilise embryos during time-lapse acquisition , with the ventral side facing the objective . Multi-channel multi-section eight-bit images were acquired with a 20X/1 objective and MaiTai laser line at 1000 nm , every minute over 4 hr . The size of a scan was 512 × 512 × 19 voxels , with a resolution of 0 . 83 × 0 . 83 × 4 µm . This model is based on the GFtbox software , a MATLAB ( The Mathworks , Inc . , USA ) application developed for the simulation of a growing continuous sheet of tissue ( Kennaway et al . , 2011 ) . The heart tube is represented as a cylindrical mesh , made of 938 nodes and 1800 finite elements , with two outside and inside surfaces and a thickness ( Figure 6 , Figure 6—figure supplement 1A ) . At each successive step during a simulation , each element is deformed according to a growth tensor field specified from the hypotheses of the model . The constraint of continuity of the tissue implies that the resulting growth is different from the input growth , this difference giving rise to residual strain . The output shape of the simulations is computed by minimising the energy derived from this residual strain , under the assumption of linear elasticity ( see [Kennaway et al . , 2011] for a detailed presentation and discussion of this modeling framework and its numerical implementation ) . The simulations of a tube growing between fixed poles , in a minimal hypothesis ( Figure 2F–G ) , were generated with the MATLAB code provided in Source code 1 . In a more refined model , the dorsal mesocardium was simulated as a displacement constraint on a set of nodes situated along two vertical lines on the dorsal side of the tube ( Figure 6 , Figure 6—figure supplement 1B ) . These nodes are not allowed any displacement along the y and z axes ( the axes along which a thin beam would strongly resist deformation ) . This constraint is released to simulate breakdown of the dorsal mesocardium . The fixed distance between the two poles is implemented by forbidding any displacement along the z axis for the nodes situated at both extremities of the tube . The local orientation of growth is defined relative to a reference axis of the tube , called the polarizer axis ( Figure 6—figure supplement 1C–D ) , so that growth in any region may be defined by two components: longitudinal growth along the direction of the polarizer , and circumferential growth in a direction tangent to the surface of the tube and perpendicular to the direction of the polarizer . The direction of the polarizer is defined by the gradient of a morphogen diffusing from the venous pole towards the arterial pole , and thus represents the axis of the tube , which changes in direction and curvature over the course of the simulation . The first 10 steps of the simulation are required to set up initial conditions , and establish the gradients that are used to define the polarizer as well as regions of the tube ( right and left ventricles , venous , arterial , ventral , left regions ) , and thus smoothen the growth profile . The six input parameters of growth were set as follows: Simulations were run for 90 steps . The MATLAB code containing the interaction function of the GFtbox model , and used to generate the shapes in Figure 6 , is provided in Source code 2 . For exploring the parameter space of shapes shown in Figure 7 , the same model was used , excluding only the inflation of the two ventricles . For Figure 7A , the arterial pole rotation was the same as above ( 25° ) and the position of the burst at the venous pole varied between 0° and 360° ( relative to dorsal ) in increments of 90° . For Figure 7B , the position of the burst at the venous pole was fixed as in Figure 6 ( 270° relative to dorsal ) . The arterial pole rotation varied between 0° and 60° by increasing the circumferential growth rate from 0% to 3% per time step . Asymmetry at the venous pole was increased in intensity by variations in the burst growth from 0% to 12% . Simulations were run for 80 steps . The MATLAB code used to generate the shapes is provided in Source code 3 . The simulation for Figure 8A replaced the rotation at the arterial pole by a burst of longitudinal growth , a similar mechanism to that of the venous pole asymmetry , positioned at 120° relative to the burst at the venous pole ( peak of growth in a ventral left position ) . The MATLAB code used to generate the shapes is provided in Source code 4 . In simulations of a persistent dorsal mesocardium in Figure 9 , the only change relative to the basic model was the absence of breakdown of the dorsal attachment , which thus was fixed in yz and free in x throughout the simulation . The MATLAB code used to generate the shapes is provided in Source code 5 . Sample size was checked post-hoc , using the PS software , in order to ensure a power of at least 0 . 8 , with a type I error probability of 0 . 05 , with an effect size of 100% ( Figures 2C , 3F and 4C , Figure 4—figure supplement 1B ) , 50% ( Figure 5E ) or 25% ( Figures 5G and 9F , G and K ) . All sample numbers indicated in the text refer to biological replicates , i . e . different embryos . No outlier was excluded from the data analysis . Comparisons of two centre-values were done on the average , using a Student two-tailed test . A paired Student test was used for comparing left and right angles or lengths at successive positions ( Figure 4C , Figure 4—figure supplement 1B ) . A Mann-Whitney test was used when a normal distribution could not be assumed . A chi-squared goodness-of-fit test , with Yates’s correction for small sample size , was used to compare observed with expected distributions . An ANCOVA ( analysis of covariance ) was used to compare linear regressions . Tests were performed with either Excel or R statistical packages . When assessing whether a distance was significantly different from zero , confidence intervals were calculated assuming a normal distribution of measurements ( Figure 5B ) . The experiments were not randomised and the investigators were not blinded to allocation during experiments and outcome assessment .	The heart is an organ that pumps blood throughout the body to supply oxygen and to remove carbon dioxide and waste products . Its left and right side are shaped differently to circulate blood through two pathways: to the lungs and to all other organs . As the heart develops inside the embryo , it transforms from a simple , straight tube into a helix shape similar to the shell of a snail . During this process called looping , the helix coils anti-clockwise , which determines where the left and right side of the heart form . It is thought that over 20% of heart anomalies in children may be caused by abnormal looping . Much of what is known about heart development is based on studies in chicken and fish . However , despite its medical significance , it was not fully understood how the heart of mammals acquires its helix shape . Now , Le Garrec et al . were able to investigate the looping process more closely by creating 3D images and computer simulations of the developing mouse heart . First , Le Garrec et al . studied the cells that build the heart and found that left and right cells contribute differently . For example , the number of cells differed between left and right side . The computer simulations then showed that looping is caused by mechanical constraints , which occur because of the way the heart attaches to the body . These mechanical constraints amplify the differences between left and right cells and cause the heart to acquire an oriented helix shape . The computer model could predict how the heart shape will change depending on the type of mechanical constraint , or if cells will have varying levels of left/right differences . The model could also accurately reproduce the shape changes observed in the mouse embryo and predict the abnormal shape of embryos with a genetic defect . The tools generated in this study will help to understand how anomalies could appear as the heart develops in the embryo , and may in the future also be applied to other organs like the gut . A next step will be to explore how genes control the looping of the heart and contribute to heart anomalies in children .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology" ]	2017	A predictive model of asymmetric morphogenesis from 3D reconstructions of mouse heart looping dynamics
Transcription occurs in stochastic bursts . Early models based upon RNA hybridisation studies suggest bursting dynamics arise from alternating inactive and permissive states . Here we investigate bursting mechanism in live cells by quantitative imaging of actin gene transcription , combined with molecular genetics , stochastic simulation and probabilistic modelling . In contrast to early models , our data indicate a continuum of transcriptional states , with a slowly fluctuating initiation rate converting the gene between different levels of activity , interspersed with extended periods of inactivity . We place an upper limit of 40 s on the lifetime of fluctuations in elongation rate , with initiation rate variations persisting an order of magnitude longer . TATA mutations reduce the accessibility of high activity states , leaving the lifetime of on- and off-states unchanged . A continuum or spectrum of gene states potentially enables a wide dynamic range for cell responses to stimuli . Transcription of genes is discontinuous , occurring in irregular bursts or pulses of activity , interspersed by irregular intervals of inactivity ( Golding et al . , 2005; Chubb et al . , 2006; Raj et al . , 2006 ) . Bursting transcription is conserved in all forms of life , from prokaryotes ( Chong et al . , 2014 ) to mammalian cells and tissues ( Suter et al . , 2011; Bahar Halpern et al . , 2015; Harper et al . , 2010 ) . The irregular nature of transcriptional bursting is proposed to be a major driver of spontaneous heterogeneity in gene expression , which in turn drives diversity of cell behaviour in differentiation and disease ( Raj and van Oudenaarden , 2008; Eldar and Elowitz , 2010 ) . Bursting reflects the underlying mechanisms of transcriptional regulation , and measures of bursting can reveal the dynamic processes absent from standard population average measures of RNA expression . The standard framework used to describe transcriptional fluctuations compares one state and two state models ( Raj and van Oudenaarden , 2008 ) . In the one state model , transcription occurs with a constant probability , which for moderately and strongly transcribed genes , will generate a low variance in their total transcribed RNA per cell . In some contexts , notably budding yeast ( Zenklusen et al . , 2008 ) , the variance in RNA abundance measured by single molecule RNA fluorescence in situ hybridisation ( smFISH ) ( Femino et al . , 1998; Mueller et al . , 2013 ) can fit this one state scenario , where the distribution of RNA per cell is well characterised by a Poisson distribution . In many other contexts , the one state model does not fit the smFISH data , with measured RNA abundance showing too much variability between cells than can be produced by a constantly active gene . To explain this increased variance , the more complex random telegraph ( or two state ) model is often invoked ( Paulsson , 2005 ) . In this model , the gene switches stochastically between an active state , where mRNA production occurs with constant probability per unit time , and an inactive state , with no mRNA production . The extra state increases the potential variability in output from cells , and can therefore predict the observed extra spread in transcript abundance in the cell population ( Singer et al . , 2014 ) . Use of the two-state model in fitting smFISH and protein distributions allows estimates of the parameters of the transcriptional fluctuations , usually the burst size ( number of transcripts produced in a burst ) and burst frequency ( the frequency with which a burst occurs ) ( Carey et al . , 2013; Dar et al . , 2012 ) . However , these dynamic properties are usually inferred from a population distribution at a single time point , assuming each cell is part of a homogeneous population with fixed values of the switching rates , transcript production rate and transcript lifetime . In other words , the perception has emerged that transcriptional bursting is a product of molecular noise , rather than a process responsive to the demands of the cell . A rethink is required , not least because of recent work demonstrating burst size and frequency are quantities that can be modulated by extracellular signals ( Molina et al . , 2013; Corrigan and Chubb , 2014; Senecal et al . , 2014 ) and cell properties such as volume and cell cycle stage ( Padovan-Merhar et al . , 2015; Muramoto et al . , 2010 ) . These studies challenge the notion , central to the standard two state model , that a population of cells consists of those where the gene of interest is 'off' and those where the gene is 'on' with a constant probability of firing . To make accurate models of transcriptional fluctuations and how they are regulated , it is critical to directly observe and quantify how transcription evolves over time . To directly measure features such as burst size and burst frequency requires data capture of complete sequences of bursts , rather than snapshots . Imaging transcriptional output in living cells is possible using RNA detection systems based upon the binding of a bacteriophage coat protein to stem loops of RNA ( Chubb et al . , 2006; Bertrand et al . , 1998 ) . An array of sequence encoding stem loops , such as MS2 or PP7 , is inserted after the promoter of a gene of interest . When the gene is transcribed , the nascent RNA stem loops are detected with a fluorescent MS2 ( MCP ) or PP7 ( PCP ) coat protein , which is constitutively co-expressed . The system is visualized using time-lapse fluorescence microscopy , with a nuclear spot indicating active transcription of the gene of interest . The intensity of the spot reflects the instantaneous nascent RNA load at the gene and changes in spot intensity reflect how the RNA load fluctuates over time . These tools allow direct observation of the dynamics of transcription regulation , revealing insights into the mechanics of Poissonian transcription ( Larson et al . , 2011 ) and developmental regulation ( Corrigan and Chubb , 2014; Muramoto et al . , 2012; Garcia et al . , 2013; Stevense et al . , 2010; Bothma et al . , 2014; Lucas et al . , 2013 ) . Previous work has interpreted the appearance and disappearance of transcription spots in terms of ‘bursty’ transcription ( Muramoto et al . , 2010; Masaki et al . , 2013 ) . Here , the exponential nature of the 'ON' and 'OFF' time durations of the transcript spot was related to the two state model , with the exponential behaviour proposed to reflect rate-limiting steps in transitions between the active and inactive states . Although this is an appealing inference , how spot fluctuations actually reflect the dynamics of the transcription machinery is unclear . In this paper , we test the current models for explaining transcriptional fluctuations , using a combination of live cell imaging , computational modelling and simulation , and targeted mutations of gene and promoter structure . We use a probabilistic approach to infer dynamics at the molecular level from fluctuations in spot intensity . We make quantitative measurements of the transcription site RNA abundance and the retention time of nascent RNA at the gene . We use these measurements to train candidate hidden Markov models to describe the underlying initiation of RNA polymerases , and find that a spectrum or continuum of initiation rates describes experimental data more accurately than a binary off/on model or discrete levels of activity . Finally , we investigate how the processes of transcription elongation and initiation contribute to the transitions of the gene over this spectrum of activity states . To monitor transcriptional dynamics in living cells , we integrated an array of MS2 stem loops after the promoter of the endogenous actin5 locus , a strongly expressed actin gene in undifferentiated Dictyostelium cells . Transcription continues after the MS2 loops into the coding sequence , then native terminator , to generate a full length transcript of around 2 . 5 kb . We visualized the resulting transcription dynamics using time-lapse fluorescence microscopy and extracted time series of spot intensities using custom-built software integrating both cell tracking and spot detection ( Corrigan and Chubb , 2014 ) . The movie sequence in Figure 1A illustrates the tracking of a typical cell , showing the fluctuations in spot intensity over time . Figure 1B shows the measured transcription spot intensity for the cell in Figure 1A , with a kymograph of the spot fluctuations . For most genes studied , the durations for which a spot is present or absent are often measured to have approximately exponential distributions , which is the case for act5 ( Figure 1—figure supplement 1 ) ( Muramoto et al . , 2012 ) . Exponential timescales have been inferred to represent modulation of gene activity , between the ON and OFF states of the two-state model , with a rate-limiting step determining switching between states ( Golding and Cox , 2006 ) . A more simple possibility is that stochastic fluctuations of a gene with no OFF state - the one state model - could give rise to pulses and intervals between pulses with the experimentally observed lifetimes . 10 . 7554/eLife . 13051 . 003Figure 1 . Measurement and theory of transcriptional fluctuations See also Figure 1—figure supplements 1 and 2 . ( A ) Montage of a cell identified and tracked throughout a time lapse movie showing the transcription spot fluctuating over time . Detected cell ( green ) and nuclear ( red ) boundaries are shown . ( B ) ( Upper ) Spot intensity trace for the cell shown in A . ( Lower ) Kymograph extracted from image , aligned with time axis of upper graph , showing the fluctuations in intensity of the region around the spot . ( C ) Monte Carlo simulation of MS2 system . Binding of polymerases at the start of the gene ( initiation ) and single nucleotide elongation steps are modelled as processes with one rate-limiting step . Additional steps could be added , such as termination/release from the gene . To simulate systems with switches in initiation rate , single rate-limiting steps are used to transition between different initiation states . ( D ) Simulated transcription site intensity fluctuations ( total number of stem loops ) for a promoter with a constant Poisson initiation rate . ( E ) Histogram of pulse durations for different detection thresholds . A pulse is defined as successive frames where the transcription site intensity is above a threshold number of loops . Experimentally , the threshold of detection is the intensity at which a spot is identifiable over background noise , and depends on the imaging conditions . ( F ) Two-dimensional histogram calculated from the bivariate Gaussian theory , showing the probability distribution of the transcription site intensity in two successive frames . Blue region - spot intensity below threshold in current frame; green region - intensity above threshold in both current and next frames; red region - spot intensity above threshold in current frame but below threshold in next frame . The average pulse duration is determined from the probability of the transcription spot disappearing between one frame and the next: P ( off ) = P ( red ) / ( P ( green ) + P ( red ) ) . ( G ) The bivariate Gaussian theory accurately predicts the pulse durations of simulated data . Comparison of theory and simulation are shown for three different initiation rates ( ri ) . Therefore , the duration of a visible transcription pulse depends on properties such as the exposure time , detection sensitivity and frame interval , and does not provide a simple readout of gene activity fluctuations . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 00310 . 7554/eLife . 13051 . 004Figure 1—figure supplement 1 . Experimental pulse durations obtained by applying various thresholds of detection: low - 4000 arbitrary intensity units ( a . u . ) , middle - 8000 a . u . and high 16 , 000 a . u . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 00410 . 7554/eLife . 13051 . 005Figure 1—figure supplement 2 . Agreement between simulations and bivariate Gaussian theory of spot frequency ( fraction ) ( right ) as a function of detection threshold . Circles correspond to different initiation rates and solid lines indicate predictions of the theory , with no free parameters . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 005 To test if a one state model , where the probability of a polymerase initiating is constant over time , can give rise to the distributions of spot durations observed experimentally , we constructed a Monte-Carlo simulation framework describing the MS2 system , incorporating polymerase initiation ( the rate at which polymerases begin transcription ) and elongation ( the velocity of the polymerase while transcribing ) ( Figure 1C , D ) . Additional features , such as the number of gene states , and termination/release rate ( the rate at which the RNA leaves the gene ) were omitted at this stage . By defining a signal detection threshold above which a spot is determined to be present , we calculated the pulse and interval length distributions from the simulated data . The average pulse duration depends strongly on the initiation rate , but was also substantially sensitive to the detection threshold and frame interval specified in the simulations . The pulse duration distribution had exponential distributions at long timescales , with deviations at very short timescales ( Figure 1E ) . This was surprising , as the exponential distribution was previously inferred to support the two-state model . Here , our simulations clearly show such a distribution can arise from a more simple one-state scenario . An exponential distribution of pulse durations has one parameter , a characteristic timescale , and arises from there being a constant probability of the pulse ending at any given time . This probability is independent of the duration for which the pulse has been sustained . It is tempting to infer from this probability the rate of switching from an active to an inactive state of a two state gene . However , with exponential behaviour observed for our simulation of constant polymerase initiation rates ( this is only one state ) , it is necessary to consider additional contributions to the likelihood of a spot disappearing from one imaging frame to the next . To investigate the origins of exponential behaviour , we constructed a theory of spot appearance and disappearance for the live MS2 system . By considering the rate of initiation of polymerases at the start of the gene and the number of RNA stem loops produced by a polymerase as it moves along the gene , we calculate the joint probability distribution linking the spot intensity in the previous frame with the current frame . ( Figure 1F , full details in the appendix ) . The theory successfully predicts the characteristic exponential timescale of pulse duration in terms of the one state model , incorporating the polymerase elongation rate , the frame interval and threshold of spot detection . In simple terms , the pulse duration reflects the balance between polymerase initiation and elongation or termination - in order for a spot to remain present in the next frame , a sufficient number of new polymerases must be initiated to replace the loss of spot intensity from cleaved RNA leaving the transcription site . Intuitively , high initiation rates are likely to give rise to large numbers of initiations per frame , giving long pulses . Increasing the threshold of spot detection decreases the likelihood of sufficient initiations and thus decreases the pulse duration . These predictions are in agreement with the Monte Carlo simulations of the MS2 system ( Figure 1G and Figure 1—figure supplement 2 ) . The initiation and elongation rates have approximately inverse effects , with initiation scaling directly with spot intensity ( as expected intuitively ) and elongation scaling inversely . The effect of slow elongation increasing spot intensity can be thought of in terms of a polymerase 'traffic jam' , with several polymerases building up behind a slower one , causing a build up of nascent RNA on the gene ( Darzacq et al . , 2007 ) . Additionally , the frame interval of observation influences the effective pulse duration , as longer frame intervals increase the likelihood of a gap between pulses being missed between frames . Overall , this theory shows that the length of time for which a spot is visible is not simply related to switches between proposed gene states in two state models and explains the dependence of the pulse duration on the imaging signal-to-noise ( Muramoto et al . , 2010 ) and frame interval ( Masaki et al . , 2013 ) . Since the theory shows that the simple presence of a spot does not provide insight into what activity state the gene is currently in , we must make precise measurements of transcription site intensity in order to detect initiation rate changes ( changes in gene state ) . As depicted by the cartoon in Figure 2A , a fluctuation in intensity might be consistent with either the one state ( Poisson , top ) or multi-state ( bottom ) models . Without calibration of the intensity fluctuations in terms of numbers of RNAs , it is difficult to discount either model . One strategy that can be used is an autocorrelation analysis , which measures the magnitude and timescale of intensity fluctuations ( Larson et al . , 2011 ) . The autocorrelation measures the ratio of intensity fluctuations relative to the mean intensity . For a Poisson ( one state ) gene , where initiations behave independently of the previous or subsequent polymerase , this ratio is related to the average number of bound polymerases , allowing the initiation rate to be estimated . This was previously used to infer the initiation and elongation rates from a POL1 promoter inserted upstream of the GLT1 gene in budding yeast ( Larson et al . , 2011 ) , specifying the time for the correlation to decay to zero as the time for which a single polymerase contributes to the spot intensity . 10 . 7554/eLife . 13051 . 006Figure 2 . Calibration of MS2 system provides quantitative detail of polymerases at the transcription site . See also Figure 2—figure supplements 1 and 2 . ( A ) The correspondence between spot intensity and number of MS2 loops at the transcription site strongly influences the type of model which accurately describes the experimental data . Depending on the actual detection threshold , the blue intensity trace could be generates by either the one state ( top ) or multiple activity state scenarios ( bottom ) . ( B ) Autocorrelation of transcription spot traces . The autocorrelation can be decomposed into three parts: measurement error ( noise ) , polymerase contribution , and longer timescale fluctuations . Classification and distinction between the three parts is discussed in detail in the text . ( C ) FRAP curves showing recovery of TS intensity after photobleaching for different configurations of MS2 loop position . The inset cartoons illustrate the arrangement of loops after the actin5 promoter . Solid line shows best fit to model described in the text . For the 5’ MS2 loop insertion , n=30 cells , for the 3’ loop insertion , n=32 and for the gene replacement loop insertion , n=25 , with each insertion line analysed on 4+ experimental days . ( D ) Grouping of FRAP curves based on the recovery intensity , showing no clear variability in dwell time as a function of intensity . The 5’ MS2 insertion cell line was used here , with data from 56 cells ( captured on 5+ experimental days ) divided into 3 groups for high , medium and low spot intensity ( inset ) . The experimental variability is shown as standard error . ( E ) Intensity distribution of transcription spots measured by smFISH using a probe hybridising to the inter loop region of the MS2 loop array . Plot shows the probability density function . The intensity of one MS2 RNA is calculated from cytoplasmic spots , and used to calibrate the nascent FISH transcription spot intensity in terms of the number of complete MS2 RNA molecules each consisting of 24 loops . For calibration , an average of 53 , 150 cytoplasmic RNA spots were used to measure single molecule fluorescence . 594 transcription spots were measured using smFISH . ( F ) Intensity distribution of transcription spots measured in live cells using MCP-GFP fluorescence . 1449 transcription spots were measured . ( G ) Calibration of MS2 live TS intensity using smFISH measurements . Comparing percentiles of the smFISH ( E ) and live distributions ( F ) , allows the live TS intensity to be interpreted in terms of the number of stem loops present . The colour of the points indicates the percentile of the distribution . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 00610 . 7554/eLife . 13051 . 007Figure 2—figure supplement 1 . Experimental spot data is not consistent with a model of constant activity . Lines show theoretical contours of different initiation rate for a one-state ( Poisson ) model , circles indicate experimental results as a function of threshold for the act5-MS2 wild type ( WT ) cell line . A single contour of initiation rate does not capture the range of spot intensities observed . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 00710 . 7554/eLife . 13051 . 008Figure 2—figure supplement 2 . Experimental spot data is not consistent with a binary on/off model ( two-state ) of transcription initiation . Lines show theoretical contours of different initiation rate , for a two-state model . Circles indicate experimental results for pulse duration ( left ) and spot frequency ( right ) as a function of threshold for the act5-MS2 wild type ( WT ) cell line . In the two state model , the switching rates have been optimized to give best agreement at low thresholds . In all cases , a single contour of initiation rate does not capture the range of spot intensities observed . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 008 Applying a similar analysis to transcription of the Dictyostelium act5 gene ( Figure 2B ) , we find that inference of polymerase properties is complicated by an additional long-timescale decay of several minutes to tens of minutes ( black dotted line ) . The additional slow decay means the RNA level at the gene is correlated beyond the dwell time at the gene of a single RNA . This argues against a Poisson model and indicates a model with two or more transcription states would better describe act5 transcription . If the autocorrelation is recalculated using only frames where a spot is present , the slow decay remains , suggesting that the active periods where a spot is present are not defined by a single constant initiation rate; in other words there are multiple 'ON' states . By subtracting a linear estimate of the long timescale decay from the autocorrelation , we calculated estimates of the RNA dwell time and polymerase load on the gene ( Larson et al . , 2011 ) . The point where the autocorrelation deviates from the long decay gives an approximation of the dwell time as 190 ± 20 s . The average number of polymerases contributing to the intensity is estimated from the magnitude of the deviation at zero lag time as 20–27 polymerases . The cells are predominantly in G2 ( Muramoto and Chubb , 2008 ) , so these autocorrelation-based estimates are for the polymerase load across 2 alleles of the act5 gene comprising the transcription spot . As described above , the observed pulsing properties also depend on the RNA dwell time- the time the RNA spends at the transcription site during elongation and termination before it is released from the gene . As an independent measure of dwell time , we used fluorescence recovery after photobleaching ( FRAP ) measurements on MS2 spots ( Muramoto et al . , 2012 ) . The MCP-RNA interaction is stable ( Maiuri et al . , 2011 ) , so recovery of the spot intensity after bleaching is determined by transcription of new MS2 loops . In the absence of photobleaching , spot intensities fluctuate over time , therefore a bleached spot is not expected to recover to its pre-bleach intensity . We therefore normalized the curves based on the recovered intensity . Recovery of a single endogenous gene is highly stochastic , as expected from simulations of photobleaching and recovery ( see below ) . Therefore , we averaged over 20–30 cells in order to extract the typical RNA dwell time . The average recovery curve was fit to a processive polymerase model of the intensity recovery to estimate the elongation rate and termination time ( full details in the appendix ) . We calculated the dwell time for three different gene constructs , with the MS2 loops integrated into the 5’ gene region , the 3’ region , and replacing the entire actin5 coding region ( Figure 2C ) . The dwell time for the 5’ insertion was longer than for the other insertions , as expected as the polymerase will continue to transcribe after the MS2 array , whereas with the other two cell lines , termination will occur immediately upon completion of synthesis of the array . Using the difference in dwell times and assuming a constant rate of elongation across the gene , we calculate the elongation rate as 22 nt . s-1 and termination time as 60-70 s . This gives a total dwell time of around 170 s , for the 5’ MS2 insertion , roughly in agreement with the autocorrelation-derived approximation on the same allele . As shown by our pulsing theory , spot intensity variations may reflect either fluctuations in the initiation rate or dwell time . If the dwell time is systematically different for spots of different intensity , we would observe this by studying the recovery times of high and low intensity spots . We divided the FRAP data into three equal groups based on their recovery intensity ( inset of Figure 2D ) and calculated the average recovery curve for each group . Figure 2D shows that no significant differences in recovery time are observed for the different intensity groups , suggesting dwell time variations are not the primary source of the spot intensity variability . We then calibrated live RNA spot intensity in terms of the number of MS2 stem loops by comparison with smFISH using a probe for the MS2 RNA . We used FISH-quant ( Mueller et al . , 2013 ) to estimate the intensity of one mature cytoplasmic RNA then used this information to calibrate the number of nascent MS2 loops at the transcription site ( Figure 2E ) . We compared the distribution of these nascent RNA counts with the intensity distribution of transcription site MCP-GFP intensity measurements from live cells immediately prior to fixation for smFISH ( Figure 2F ) . The two distributions are aligned by calculating the percentiles of each distribution and using these values as calibration points ( Figure 2G ) . Due to the differing precision with which the intensity can be calculated in live and fixed samples , there is some deviation from a linear relationship at the extreme ends of the calibration curve , and a kink at low intensities where the detection threshold of live spots is higher than in fixed spots , however the overall trend can be used to calibrate the spot intensities measured in live cells in terms of the number of MS2 stem loops present . After calibrating the system , we used our pulsing theory outlined above to estimate the initiation rate . We measured how the mean pulse duration changes as the detection threshold is varied . For a one-state ( constant activity ) gene , the pulse duration would lie on a contour calculated using our theory . Figure 2—figure supplement 1 shows that the experimental data does not lie on a single contour of constant initiation rate , consistent with the autocorrelation results in Figure 2B . Similarly , contours calculated from a simple two-state implementation , with on- and off-rates chosen to produce the experimentally observed spot frequency ( details in the appendix ) , cannot match the variation of pulse duration and spot frequency as the detection threshold is varied ( Figure 2—figure supplement 2 ) . Instead , the behaviour at high thresholds suggests a short-lived higher initiation rate . Dictyostelium cells are almost exclusively in G2 , owing to a very short S phase and complete absence of G1 ( Muramoto and Chubb , 2008 ) . Therefore almost all cells will have replicated the MS2-tagged gene and have two genes , held together by sister chromatid cohesion , contributing to a single resolved spot . Transcription of the unreplicated gene will not confuse analysis , as act5 transcription is not robustly detected in the first 15 min after mitosis ( Muramoto et al . , 2010 ) , when euchromatin is replicated ( Muramoto and Chubb , 2008 ) . A higher initiation rate state is possible in the situation where both copies are simultaneously active - giving a potential third state with double the initiation rate of a single copy . To test the possibility of this third state , we applied probabilistic modelling to our quantitative data to assess the extent to which three state or higher models can describe the observed transcription dynamics . Since a two-state model of transcriptional activity , with an ‘off’ state and an ‘on’ state of constant initiation rate , does not describe the live data adequately , we asked whether an improvement could be made by including additional active states with different initiation rates . It is not possible to directly observe polymerase initiations using the MS2 system , so we used hidden Markov modelling to infer the likelihoods of different numbers of active states from the measured live cell intensity data . In simple terms , a hidden Markov model ( HMM ) describes a sequence of observations in terms of a sequence of underlying , 'hidden' states . By extension from the two-state model , we define a 'state' as a configuration with a single constant ( Poisson ) initiation rate . In terms of transcription dynamics , the hidden state corresponds to the underlying initiation rate of the system . This rate cannot be observed directly and instead must be inferred from measurements of the transcription site intensity , which is related probabilistically to the initiation rate . Using standard HMM techniques , the magnitudes and dynamics of the underlying initiation rate can be optimized to maximize the probability of generating the experimental data from the model . For the case of the MS2 system , the spot intensity does not depend only on the instantaneous initiation rate , because polymerases initiated in previous frames are still present on the gene and also contribute to the spot intensity . To take account of these polymerase contributions , we constructed a model ( full details in the appendix , architecture depicted in Figure 3A ) with hidden states representing the initiation rate ( blue circles ) and the number of polymerases initiated ( orange circles ) , inferred from the sequence of transcription site intensities ( green squares ) . A benefit of our two-layer HMM framework is that we explicity model the polymerases uncoupled from transitions between states of gene activity . This means that the distribution of polymerases initiated in each state can be estimated ( see below ) . 10 . 7554/eLife . 13051 . 009Figure 3 . A continuum of transcriptional states . See also Figure 3—figure supplements 1 and 2 . ( A ) Architecture of a hidden Markov model ( HMM ) to describe transcription spot intensity in the case where polymerases remain at the transcription site for up to 4 frames . The hidden state at a given point in time consists of the gene-state at the current time ( gt ) and the number of polymerases ( m ) which have been initiated in the previous 4 frames [g , mi , mii , miii , miv] , highlighted by the red background . With approximately processive polymerase behaviour , polymerases initiated in the current frame will be near the start of the gene and thus have transcribed few MS2 loops; polymerases initiated in previous frames have transcribed more MS2 loops by the current frame . The polymerase states , weighted by the expected number of loops per polymerase ( x ) , combine with the measurement error to give the observed state It ( green ) . ( B ) Simulated transcriptional fluctuations based on a 3-state model , with three panels corresponding to different timescales of switching between transcriptional states . The right panel ( timescale of variation 1176 s ) has longer pulses- reflecting the slower switching between initiation rate states . ( C ) Testing the HMM framework on the 3 state simulation from B . As described in the text , the AIC ( Akaike’s Information Criterion ) is reduced for optimal models , while penalizing overly complex models via the number of free parameters . The one state fit has the highest value of AIC , regardless of the switching timescale . The 2-state fit does much better and the 3-state fit better still , with a reduced AIC . A 4-state fit gives no additional improvement over the 3-state fit and is hidden by the 3-state curve . ( D ) Increasing the number of possible initiation rate states improves the likelihood that the model reflects the experimental transcription data . AIC for models of increasing numbers of initiation states . While 1- and 2-state models do not adequately describe the data , the quality of the fit continues to significantly increase as the number of states increases from 3 upwards . The three curves indicate different rules for allowed transitions between states- 'ladder' means the gene can move up or down one state per time , 'jump 1' allows a change of up to 2 states and 'free' is unconstrained switching of the gene between states . These data represent a typical experiment , with data from 145 different cell tracks comprising 6350 individual time points . Three further 3 independent biological replicates gave similar conclusions . A decrease in AIC of 10 ( note: the vertical axis units are scaled by 104 ) is significant at the 1% level ( p=0 . 007 ) . A more extensive treatment of the statistics is included in the Supplementary Material . ( E ) Probability distribution of the number of polymerases initiated per frame for each state of a three-state model , calculated using a modified forward-backward algorithm . Attempted Poisson fits for each state are shown by the dotted lines . The distributions were strikingly non-Poissonian , with χ2 = 5059 ( p=0 ) and 3152 ( p=0 ) for states 2 and 3 . For state 1 , χ2 =10 . 24 , but we cannot reject Ho because of no degrees of freedom . Data from a representative biological replicate are shown . ( F ) The timescale of initiation rate fluctuations revealed by autocorrelation analysis . The curve shows the decay in the correlation as a function of time , with the initiation rate largely uncorrelated with the rate 5–6 min before . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 00910 . 7554/eLife . 13051 . 010Figure 3—figure supplement 1 . Accuracy of measurement of state initiation rates and state switching rate using hidden Markov model methods ( Baum-Welch algorithm ) . Three-initiation-state models were simulated with a range of timescales of state switching , to test the accuracy of Baum-Welch training in measuring the initiation rate for each state and the rate of switching between each state . Left - for all timescales , the fitted initiation rate ( circles , different colours indicate the three states ) estimated using the modified Baum-Welch algorithm is in good agreement with the values inputted into the simulation ( dotted lines ) . The estimated values begin to deviate by small amounts when the timescale of state switching is very short . Right - the calculated state switching timescale shows good agreement with the ground truth ( simulation input ) for slow switching rates ( long timescales ) . When the rate of switching becomes very fast ( left hand side of figures ) the maximum likelihood approach of Baum-Welch fitting misses some fast transitions , and consequently the timescale of state-switching is over-estimated . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 01010 . 7554/eLife . 13051 . 011Figure 3—figure supplement 2 . Cumulative number of polymerases initiated as a function of time , calculated using a custom Gibbs sampling method . Different colours indicate models with different numbers of initiation rate states , with multiple runs per model . The number of polymerases is approximately independent of the number of states used in the model . The initiation rate is given by the gradient of the plot; as such straight lines indicate periods of time for which the initation rate is roughly constant . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 011 To test how well models with discrete states of activity describe the data , we calculate the probability of obtaining the experimental data using the model parameters . This probability is known as the likelihood . We use a standard approach to compare models of different complexities , by calculating the Akaike Information Criterion ( AIC ) from the likelihood ( Singer et al . , 2014 ) . The AIC is used to find the best approximation in situations where the real scenario is likely to be highly complex . Models are penalized by their number of free parameters , preventing overfitting by excessively complex models . The AIC estimates the prediction error of the candidate model , giving a lower numerical value for models which more accurately describe the experimental data . To test the validity of the use of the HMMs and associated algorithms , we tested the framework on simulated data , with known numbers of promoter states , and known timescales of switching between states ( Figure 3B ) . In these cases , the 'hidden' initiation states and numbers of initiated polymerases are known , allowing the accuracy of the probabilistic methods to be measured . Applying the hidden Markov approach to a simulated 3 state model accurately estimated 3 discrete initiation states as the point at which AIC is minimized ( Figure 3C ) . 1 and 2 states give a higher AIC ( lower likelihood ) , whilst 4 shows no improvement over 3 states . In addition , this approach robustly measured the initiation rate of each state , as well as the timescale of switching between the activity states ( Figure 3—figure supplement 1 ) . We next applied the model fitting to experimental data for transcription of the actin5 gene . We carried out 4 independent time lapse experiments , with multiple fields of view in each experiment , capturing data on 44–145 cells per experiment , giving 1686–6350 individual cell-frames , per experiment . Figure 3D shows the AIC for models with discrete numbers of initiation rate states . The 2-state model shows a strong reduction in AIC compared to a 1-state model , with the 3-state model better still . The curve does not plateau after the 3 state model , instead , the fit to the data is continually and significantly improved by adding further initiation states . The continually improving fit does not depend on the permitted mechanisms of switching allowed from state to state ( Figure 3D ) . This continual improvement in fit occurs if the gene constrained to switching between states one step at a time ( Figure 3D- 'ladder' ) , in steps of up to two states ( 'Jump 1' ) , or is unconstrained ( 'Free' ) . The most realistic interpretation of the continually improving fit with more states is that the gene switches in activity over a spectrum or continuum of states , rather than a small number of discrete activity states . In Figure 3D the largest improvement in fitting occurs up to 3 states; therefore , is a model with three initiation rate states appropriate for the act5 gene ? Although the slow rate of improvement in fit for higher numbers of states may suggest three states are adequate , closer analysis of the optimal 3-state fit reveals its inability to capture the full variability of the transcription site intensities . The values of the estimated initiation rates are not uniformly spaced ( as would be expected for two alleles undergoing random telegraph transcription ) , but have a low state and a very high state in addition to an off-state . Furthermore , Figure 3E shows strongly non-Poisson distributions for the predicted number of polymerases initiated per frame in each state of the three state model , inconsistent with a constant initiation rate within each state . In finding the best 3 state fit , the modelling process effectively forced outlying data into non-Poissonian states . The wide polymerase distributions imply either that the initiation rate is varying within a state , or that transitions are occurring between states on a timescale significantly faster than the frame interval . To determine the dynamics of the gene state and number of initiated polymerases , we used a Gibbs sampling algorithm ( see appendix ) . The estimated number of polymerases initiated in each frame is approximately independent of the number of states chosen for the model . The initiation rate , the number of events per unit of time , is difficult to define as an instantaneous measurement; instead , by plotting the cumulative number of polymerase initiations as a function of time the initiation rate can be taken from the gradient . For a typical cell in the experimental data , the cumulative polymerase plot is composed of linear segments ( Figure 3—figure supplement 2 ) representing periods of time over which the initiation rate is constant . We measured the initiation rate using an edge-preserving smoothing filter and found a diversity of such gradients , indicating a spectrum of initiation rates , again implying the transcriptional behaviour is not adequately described by a few discrete levels of activity . The most realistic model to account for this additional complexity in initiation fluctuations is a ladder or continuum of activity states . The timescale of initiation rate variation through this spectrum of activity states can be determined using an autocorrelation analysis of non-zero initiation rates . Figure 3F shows such a plot , which reveals the initiation rate fluctuates with an average timescale of around 5–6 min , although it is possible for a roughly constant initiation rate to be sustained for up to 15 min ( Figure 3—figure supplement 2 ) . To what extent can fluctuations in elongation rate contribute to the complexity of the ladder or continuum of transcriptional states ? A transiently slower rate of elongation may cause a build-up in the number of polymerases on the gene and therefore contribute to non-Poisson variations in spot intensity . To test this possibility , we use simulations with three initiation rate states – with average initiation and elongation rates matched to the experimental estimates – to address whether the additional complexity shown experimentally beyond a three state model ( Figure 3D ) could be accounted for by adding elongation rate fluctuations to the system . In addition to increasing the spot intensity , slower elongation rates lead to increased dwell time , which in FRAP measurements , would lead to the brighter spots taking longer to recover . However , the experimental data ( Figure 2D ) showed no observable difference in recovery between groups of different intensity . It remains possible that fluctuations in elongation rate may occur over a timescale which would not be resolved by FRAP . To investigate the timescale of dwell time fluctuations that might be invisible to FRAP , we developed simulations of the FRAP protocol , matching the experimental procedure as closely as possible . We incorporated temporal fluctuations in the polymerase elongation rate acting either globally ( affecting every polymerase on the gene in the same way ) or in a polymerase-by-polymerase manner . Since very little is known about the type of elongation fluctuations possible in vivo , we implemented a system which switches randomly between 10 and 30 nucleotides/s , and varied the timescale of fluctuation ( how long the system remains in either state ) . The FRAP curves produced by individual simulated cells are treated in the same way as experimental data - dividing the cells into three groups based on their recovery intensity and rejecting cells showing no recovery – to determine the largest fluctuation timescale which shows no difference between intensity groups . As shown in Figure 4A , elongation rates fluctuating independently for each polymerase cannot produce any differences in the recovery curves . For the density of polymerases on the gene for typical spot intensities , fluctuations of individual polymerases are suppressed by catching up and being blocked by a polymerase immediately downstream . This results in a bulk polymerase elongation rate close to the lower speed of 10 nucleotides/s . Thus independent fluctuations in polymerase elongation cannot account for the fluctuations in spot intensity in the continuum model . 10 . 7554/eLife . 13051 . 012Figure 4 . Testing the contribution of elongation rate switching to intensity fluctuations See also Figure 4—figure supplement 1 . A and B Simulated FRAP measurements for a system with three states of initiation rate and two elongation rate states . Initiation rate dynamics are chosen to match those observed experimentally , while the timescale of elongation rate fluctuations is varied from 500 s ( top panel ) to 13 s ( bottom panel ) between 10 bases/s and 30 bases/s . In A , the elongation rate for each polymerase fluctuates independently from other polymerases , whereas in B , all polymerases move with a global fluctuating elongation rate . The simulated data are subdivided equally between three bins of low ( black ) , medium ( orange ) and high ( blue ) spot intensity , as with the experimental data in Figure 2D . Differences between bins are only apparent with global fluctuations . Variability is shown with standard deviations . ( C ) Effects of elongation rate fluctuations on the 3-state simulation . The y-axis shows the increase in complexity produced by adding elongation fluctuations to a three-state simulation , compared with experimental results . Simulated data is slowly varying three-state initiations with fast-varying two-state elongations . Simulations with fast fluctuations ( 13 s ) show a small improvement in fit above three states ( red bar ) . Simulations with 43 s timescale elongation fluctuations ( blue ) show an improvement in fit comparable to experimental data ( grey ) . ( D ) Polymerase distributions in three-state model fit for 3-state simulation with 43 s elongation fluctuations ( solid , straight lines ) , compared with Poisson best fit ( dotted , curved lines ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 01210 . 7554/eLife . 13051 . 013Figure 4—figure supplement 1 . Training a three-initiation-state model on simulated data with three initiation states and no elongation rate fluctuations ( see main text ) . The calculated number of polymerases initiated in each of the three states ( solid blue , green and red lines respectively ) are very close to Poisson distributions , accurately reproducing the states inputted in the simulation . The fitted states are slightly broader than Poisson , as expected due to the probability of making a switch within the last frame interval . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 013 In contrast , global fluctuations in the elongation rate give rise to different recovery curves for different spot intensities ( Figure 4B ) , as the brightest spots tend to be caused by slow-moving polymerases staying at the transcription site for longer , while the intermediate spots have a bias towards faster moving polymerases . As in experiments , the lowest intensity recoveries show a high degree of noise , due to the small normalisation factor . As the timescale of the elongation fluctuations becomes shorter , the difference between the recoveries is reduced , as variability in the elongation rate is blurred out by faster transitions . The point at which the simulated data becomes consistent with the experimental traces ( Figure 2D ) is around the lower two panels of Figure 4B , that is , an elongation fluctuation timescale with an upper limit of around 10-40 s . What this effectively means is that gene-wide fluctuations in the elongation rate may be present on a timescale of up to around 40 s without being apparent in experimental FRAP recovery curves . The exact timescale depends also on the magnitude of the fluctuations , taken here to be around 50% of the mean elongation rate , nevertheless , some degree of elongation fluctuation may contribute to the temporal variation of the transcription spot intensity . We then asked whether adding elongation fluctuations of the magnitude identified above to the three-state model is sufficient to produce the decrease in AIC value beyond 3 states seen in hidden Markov modelling . The difference , ΔAIC , between AIC ( 3-state ) and the minimal AIC value ( for a model up to 8-states ) represents the additional complexity present in the data – essentially the information missing from a three-state fit . We applied our HMM fitting to simulations of the three-state model with different timescales of elongation fluctuation , and measured ΔAIC to estimate if the addition of elongation fluctuations is sufficient to recapitulate the additional complexity measured experimentally . Figure 4C compares the ΔAIC value between a representative experiment and simulations of elongation fluctuations ( adjusted to match experimental sample size ) . For no fluctuations , no improvement is seen as expected , since a three-state model successfully describes the simulated data . For 13 s elongation fluctuations a small increase in complexity is observed . For the 43 s timescale , the magnitude of the improvement is comparable to that observed experimentally . This suggests these elongation fluctuations can potentially recapitulate the variability in spot intensities observed in experiments , with two important caveats . Firstly , in the simulations the 43 s timescale data plateau at 5 states , rather than around 7–8 for the experimental data . Secondly , training a three-state model on experimental data yielded distributions of polymerase initiations in each state with variances greater than expected for Poisson distributions ( Figure 3E ) . This super-Poissonian behaviour of the experimental data is not reproduced by training the same three state model on in silico elongation fluctuation data ( Figure 4D , Figure 4—figure supplement 1 ) , indicating further complexity in the experimental data not fully explained by the simple elongation fluctuations . To test the effects of perturbing the initiation rate on transcriptional bursting , we generated point mutations in the TATA box of the act5 promoter , T1A and A2C ( Figure 5A and Figure 5—figure supplement 1 and 2 ) , which have strong effects on transcriptional output in yeast genes and mammalian expression plasmids ( Patwardhan et al . , 2009; Raser and O'Shea , 2004 ) . Both TATA mutant lines displayed a slight reduction in overall spot intensity ( Figure 5B ) , with FRAP experiments suggesting a similar dwell time to wild type ( Figure 5—figure supplement 3 ) . Analysis of time-lapse experiments using the polymerase HMM framework found that the TATA mutants spend less time in medium and high initiation rate states , and more time at lower initiation rates , compared to control cells ( Figure 5C ) . The overall timescale of initiation rate variability , measured through autocorrelation , was not substantially changed ( Figure 5D ) with both the wild-type and TATA mutations showing fluctuation timescales of several minutes . In addition , we observed no clear difference in switching rates between the inactive and active states . The rate of switching to the inactive state , k ( off ) , was unchanged between wild-type and TATA mutants ( Figure 5E ) . The tendency of spots to appear , k ( on ) , showed a slight impairment in A2C mutants , although this was not statistically significant . This subtle effect might be also interpreted as the enhanced occupancy of active states of unobservably low intensity in the A2C mutation , rather than simply the absence of transcription . We then addressed the likelihood of the gene switching up or down in initiation rate based upon its current state ( Figure 5—figure supplement 4 ) . For all cell lines , both wild-type and TATA mutant , high initiation rates had a tendency to revert to lower initiation rates , and lower initiation rates had a tendency to revert to higher initiation rates . In the TATA mutants , the initiation rate is less likely to switch to a higher activity state , resulting in reduced time spent in high activity states . Together these observations suggest that perturbing the TATA box does not affect the duration or frequency of active states , but rather modulates the initiation rates that are possible . 10 . 7554/eLife . 13051 . 014Figure 5 . The TATA box influences access to the high activity states . See also Figure 5—figure supplements 1–4 . ( A ) TATA box mutations studied for the act5 gene . ( B ) Probability density function of transcription site intensity for TATA mutations T1A and A2C compared to WT . One of four biological replicates is shown . The reduction in intensity in the TATA mutations is slight , but significant ( KS test: p=10–58 for wt vs . T1A and p=10–158 for wt vs . A2C ) . ( C ) Lifetime of constant initiation rate pulses in the active state , as a function of initiation rate for TATA mutants compared to control . The TATA mutants spend longer in lower initiation states and shorter durations at high initiation rates . The curves display mean and S . E . M . from 4 independent experiments ( with 1686–6350 individual frames from 44–145 individual cell tracks , from each cell line , from each of the 4 replicates ) . We used grouped ratio t-tests to compare distributions , pooling the data based upon initiation rate . For low initiation rates ( <0 . 2 s-1 ) gave p=0 . 0083 and 0 . 0015 for T1A and A2C respectively . For high rates ( >0 . 25 s-1 ) gave p=3 . 5 x 10–5 and 0 . 0011 . A breakdown of the data is contained in the Supplementary Material . ( D ) Timescale of initiation rate persistence , as measured by the decay of the autocorrelation of instantaneous initiation rate , is similar for TATA mutants and WT . ( E ) Estimated rates of transition from closed to open state ( k ( on ) ) and from open to closed state ( k ( off ) ) . Values are average of 4 experiments . Error bars are S . E . M . Differences are all non-significant ( p all >0 . 45 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 01410 . 7554/eLife . 13051 . 015Figure 5—figure supplement 1 . Example WT spot intensity traces . The colour-coded arrows denote the traces shown individually in the left panels . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 01510 . 7554/eLife . 13051 . 016Figure 5—figure supplement 2 . Example spot intensity traces for the A2C TATA box mutation cell line . The colour-coded arrows denote the traces shown individually in the left panels . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 01610 . 7554/eLife . 13051 . 017Figure 5—figure supplement 3 . Fluorescence recovery after photobleaching ( FRAP ) curves show no evidence for different RNA dwell times in the TATA mutants ( T1A , A2C ) compared to wild type ( WT ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 01710 . 7554/eLife . 13051 . 018Figure 5—figure supplement 4 . Left - probability of increasing ( dotted lines ) or decreasing initiation rate ( solid lines ) as a function of initiation rate for the act5-MS2 wild type ( WT ) and TATA mutant cell lines . Right - the crossover of the two curves , as an estimate of the equilibrium initiation rate for the three lines . Black - WT , red - T1A , green - A2C . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 018 The standard analysis of transcriptional bursts using smFISH measurements can extract estimates of the burst size and burst frequency . Implicit in the assumptions of the random telegraph ( two state ) model is the idea that cells undergoing a burst in a particular gene all have the same underlying initiation rate . However , it seems unlikely , based on our data , that the active initiation rate will be constant over time during a burst , or that it will be the same for all cells in a population . Rather , the rate of initiation will depend on the binding of molecular factors , which turn over on the timescale of seconds to tens of seconds , based upon measured residence times of transcription factors ( Chen et al . , 2014; Izeddin et al . , 2014 ) . The cytoplasmic lifetime of mRNA means that these fast fluctuations are blurred out in static measurements , such as smFISH . In most such cases only broad differences in overall transcript content between cells can be determined , which accounts for the success of models with a few numbers of discrete states in fitting the data . Measurements using the MS2 system are integrated over the dwell time of the nascent RNA , a timescale of 2–3 min . The dynamic measurements can begin to resolve systematic variability in the initiation rate over time , which is difficult to assign to a small number of discrete states . Our proposed continuum model is summarized in Figure 6A . This model may also be approximated by a discrete state model with more states than can be effectively detected ( Figure 6—figure supplement 1 ) . 10 . 7554/eLife . 13051 . 019Figure 6 . Continuum model . See also Figure 6—figure supplements 1 and 2 . ( A ) Proposed continuum model . In addition to switches to and from a closed state on the timescale of around ten minutes , the initiation rate in the active state fluctuates on a shorter timescale . ( B ) Simulation of the continuum model , resulting in temporal variation in the initiation rate ( upper , green spikes ) . The short integration time of MS2 measurements ( the time for which RNA is retained at the transcription site ) means fluctuations in the active state of the gene can be visualized ( lower ) . ( C ) In simulated smFISH data ( right ) , using the RNA production events from the continuum model ( B ) and a cytoplasmic RNA decay time of 40 min , the distribution is well described by a standard two state bursting ( negative binomial , NB ) model . The long lifetime of cytoplasmic RNA averages out the temporal fluctuations in the initiation rate . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 01910 . 7554/eLife . 13051 . 020Figure 6—figure supplement 1 . Potential mechanisms by which a continuum of activity ( ii ) may arise: ( i ) a ladder containing a large number of discrete states , each with a distinct initiation rate , caused by specific binding of transcription factors or epigenetic marks . The states are too closely spaced to distinguish and count individual states . Alternatively , a model of fast switching between a primed state and an active state ( iii ) on a timescale of seconds or tens of seconds ( shorter than the observation timescale of the MS2 system ) produces a continuum of transcriptional activity . The fraction of time spent in the active state is modulated by the rates of switching into and out of the state , which depend on the local and time-varying concentration of polymerase and transcription factors . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 02010 . 7554/eLife . 13051 . 021Figure 6—figure supplement 2 . Cartoon illustrating the continuum model and the predicted changes caused by TATA sequence modification . Mutation of the TATA box may cause reduced rate of binding or increased rate of unbinding of activator . This results in a lower fraction of time spent in the active state , reducing the upper limit of initiation rates which can be realized . The rate of switching to and from the closed and inaccessible state is unaffected . DOI: http://dx . doi . org/10 . 7554/eLife . 13051 . 021 Our continuum model may be consistent with a recent theoretical model ( Rieckh and Tkačik , 2014 ) which proposed rapid switching between states as an improvement over the standard 2-state model in terms of information transmission ( Figure 6—figure supplement 1iii ) . In this view the initiation rate fluctuations can be thought of as the fraction of time for which the factors required for initiation are bound to the promoter . The rate of binding depends on the spatiotemporal variability in local concentrations , producing a continuum of activities when integrated over the MS2 RNA dwell time . When the TATA box is mutated , the overall decreased initiation rate in the active state may reflect the reduced rate of binding of necessary transcription factors , and therefore reduced occupancy of the initiating state ( Figure 6—figure supplement 2 ) . Illustrating the potential equivalence of this rapid switching model with our continuum model , our data show that the optimal discrete 3-state model consigns polymerase initiations to non-Poissonian states ( Figure 3E ) , which implies either multiple initiation rates within a state ( in other words more than 3 states ) or switching between the discrete states faster than the imaging frame interval . Is this continuum view in conflict with the existing smFISH data on 2-state models ? To answer this question , we simulated steady state levels of RNA derived from a gene where the binding rate varies continuously over time , overlaid with longer periods of inactivity ( Figure 6B , upper ) . We incorporated autocorrelation-derived timescales and initiation rates extracted from the hidden Markov modelling together with a mRNA lifetime of 40 min , similar to measured lifetime values of act5-MS2 using actinomycinD-treated cells . The distribution of cytoplasmic mRNA molecules was well fit to a standard two-state bursting model ( Singer et al . , 2014 ) , using a negative binomial function ( see appendix ) capturing primarily the long timescale activity intervals ( Figure 6C ) . The shorter timescale of the MS2 measurements ( Figure 6B , lower ) is able to discriminate fluctuations within active regions . In other words , when measured in the steady state , using methods such as RNA FISH , a continuum of transcriptional states will appear to consist of far fewer states than can be detected using live cell approaches , highlighting the different timescales measured by different methods . The two-state model approximates the magnitude and broad timescale of transcriptional variability , although it should be noted that the bursting parameters extracted may not directly correspond to the physical quantities of burst size and frequency . We have carried out in-depth measurements of the transcription dynamics of an endogenous , highly-expressed gene in Dictyostelium . This analysis has provided an insight into the dynamics of transcriptional regulation at short timescales , allowing models of the underlying mechanics to be discriminated . We interpreted fluctuations in the intensity of the transcription site in terms of the time-resolved rate of polymerase initiation , and found that the behaviour is not consistent with either constitutive , Poissonian activity or switching between a small number of discrete activity states . Instead , we observed a spectrum of activity states , characterised by an initiation rate varying over timescales of several minutes . This spectrum of activity may be produced by very short periods of transcription initiation , where the activity lifetime or rate of reactivation is dependent on the time-varying local activity of molecular factors , or the gene locus switching between different topological conformations . When integrated over the retention time of nascent RNA at the transcription site , this ‘microbursting’ behaviour would give rise to a continuum of initiation rates . An alternative model might involve each cell having a distinct initiation rate in the active state , thereby producing a spectrum of initiation rates but with a simple two-state model for each cell ( Sherman et al . , 2015 ) . This model is equivalent to a limiting case of our continuum model with infinitesimally slow variation in the activation rate , however , for act5 we observed timescales of variation of minutes by autocorrelation or Gibbs sampling of initiation rates . The differences between our continuum view and models with each cell having it’s own stable transcription rate may reflect differences in experimental system . Alternatively , the differences may arise because live cell analysis of nascent RNA dynamics allows the timescales of fluctuation to be directly extracted from the data , which is not possible from measurements of protein abundance or steady state transcript counts . Bursts of transcriptional activity have been described in a wide range of systems ( Raj and van Oudenaarden , 2008; Chubb and Liverpool , 2010 ) . To investigate the molecular origins of the bursting phenomenon , we constructed a simple theory of Poissonian transcription of the MS2 system . Unexpectedly , this simple system with constant promoter activity predicts exponential distributions of transcriptional pulses and intervals , with the pulse duration determined not only by the promoter initiation rate but also by the frame interval of observations and threshold of spot detection . We confirmed the predictions of the theory using Monte Carlo simulations of the MS2-tagged gene . In general , these simulations provide a framework for studying fluctuations in transcription site intensity , incorporating stochastic events for polymerase initiation , transition to elongation , elongation and termination/release . Additional complexity could be added in the form of the probability of promoter-proximal pausing , premature termination or interactions between adjacent polymerases . This approach allows us to predict the change in the spot intensity due to adding or changing a system parameter , and therefore determine whether competing models can be distinguished experimentally . This is illustrated by our analysis of the fluctuations in the elongation rate , where , in comparison with experimental data , we estimated an upper limit for the timescales of fluctuations at around 40 s . Analysis of elongation rate changes suggested such fluctuations could not account entirely for the complexity of the spectrum of transcriptional states , although they could potentially contribute . Interspersed with the active regions are periods of relative inactivity , where no transcription spot is visible . The number of steps involved in the reactivation of an inactive gene has been inferred from the distribution of the off-durations ( Suter et al . , 2011; Molina et al . , 2013 ) . Due to the current detection threshold of our system , a low level of basal transcription cannot be ruled out in this inactive state . Consistent with early views of bursting ( Raj and van Oudenaarden , 2008 ) , the longer periods of inactivity may reflect slower dynamics of the remodelling of chromatin , rather than binding of specific transcriptional regulators , based upon our perturbation of the core promoter . Mutation of the act5 TATA box reduced the overall amount of transcription , primarily by reducing the amount of time spent at high initiation rates rather than changing the switching dynamics between the off and active states . Importantly , neither TATA box mutation we studied abrogated transcription entirely , as might be expected if the pre-initiation complex can no longer bind the promoter . A reduction in the duration of periods of high activity might suggest an impaired duration or frequency of binding , or disruption of normal promoter conformational switches in response to binding ( Gietl et al . , 2014 ) . The strong effects of TATA mutations in yeast and plasmid systems ( Patwardhan et al . , 2009; Raser and O'Shea , 2004 ) may reflect more simple promoter architectures . In more complex systems , the potential for many inputs to transcriptional regulation will buffer the disruption of any single input ( Perry et al . , 2010 ) . It must be stressed that our quantitative analysis has been carried out on one gene , in steady-state conditions . The gene is strongly expressed and actin is usually put under the umbrella of 'housekeeping' , which is a slight simplification as most of the Dictyostelium actin gene family , including act5 , show some developmental regulation , at least at the transcript level ( Muramoto et al . , 2012; Joseph et al . , 2008 ) . A more strictly induced developmental gene might be expected to show more strict two state ON/OFF behaviour . However , even in cases with more prominent bursting we would argue that the initiation rate in the active state is likely to fluctuate over time and differ from cell to cell . This is supported by recent studies on mammalian transcriptional induction by serum and growth factors , which also suggest more complexity than a standard 2-state model provides , with evidence from luciferase reporter fluctuations ( Molina et al . , 2013 ) and measurements of nascent RNA by smFISH indicating modulation of the transcription rate within the ON state ( Senecal et al . , 2014 ) . A more recent smFISH study quantifying nascent RNA revealed modulation of burst size and frequency by cell size and cycle stage , respectively ( Padovan-Merhar et al . , 2015 ) . Whilst we have argued here for a more complex view of transcriptional regulation , it must be considered that the act5 promoter is less than 700 bp long , the gene contains no introns and around 60% of the Dictyostelium genome encodes protein ( Eichinger et al . , 2005 ) , and so provides little scope for long range regulatory interactions . In the light of these features , we suggest the spectrum of states is likely to have considerably more scope for complexity in a mammalian cell . The continuum of states we infer is likely to be a more realistic view of gene activity fluctuations than the standard views of a small number of fixed discrete states . There are perhaps a hundred different proteins involved in a standard eukaryotic transcription reaction , even ignoring the components of the chromatin template . The likely configurational complexity , in addition to the potential for modulation by protein modification and nuclear context , seems consistent with the continuum view . For targeting of MS2 repeats into the actin 5 gene , we utilized a BsrGI-SpeI restriction fragment containing 24 MS2 repeats ( 1 . 3 kb ) upstream of the blasticidin resistance ( bsr ) cassette ( Faix et al . , 2004 ) . The resistance cassette is flanked by loxP sites for CRE-mediated removal of the marker , allowing transcription to terminate at the natural 3’ sequence of the gene . For the 5’ tagging of act5 , the MS2-bsr was cloned between a promoter fragment of the gene ( -680 to +21 ) and a gene fragment ( +108 to +1313 ) , using BsrGI and SpeI sites . Cells derived using this 5’ tagging vector were used for all experiments described in this paper , unless indicated otherwise . For the gene replacement vector , the MS2-bsr was cloned between the same promoter fragment and a fragment from the 3’ coding sequence and terminator of the gene ( +1092 to +1899 ) . For the 3’ targeting vector , we used this same 3’ region combined with a 5’ region derived from the act5 coding sequence ( +259 to +1113 ) . The ATG corresponds to +1 . The translational STOP , TAA is at +1129 . The first clear polyadenylation motif ( AATAAA ) starts at +1193 . Sequences were checked at each cloning step to ensure plasmid stocks retained the correct sequences ( as specified by dictyBase ) . For TATA mutations , the T1A ( AATAAAT ) and A2C ( TCTAAAT ) mutant promoter fragments were generated by gene synthesis , then spliced into the wild-type promoter sequence using the BstEII site upstream of the TATA box , prior to inserting the MS2-bsr fusion , again using BsrGI and SpeI sites at the same positions . Targeting fragments were released from cloning vectors by digestion with polylinker enzymes ClaI and NotI . These targeting fragments were transformed into a Dictyostelium AX3 clone previously engineered to express a red fluorescent nuclear marker , H2Bv3-Cherry , under the control of the endogenous promoter of the rps30 gene . Targeted clones were identified by PCR , then genomic DNA Southern blotted to ensure MS2 repeat integrity and single correct insertions in the targeted clones . Correct targeting of TATA mutations to the act5 promoter was checked by sequencing of PCR products from recombinant clones . Positive clones were then transiently transfected with a plasmid expressing the CRE recombinase , to remove the bsr cassette , allowing the MS2 RNA to fall under the control of the natural act5 terminator . Clones were then transformed with an extrachromosomal vector , based on pDM1096 ( from Dr . Douwe Veltman ) expressing the MCP-GFP fusion protein , to permit detection of nascent RNA in living cells . Selection used 20 µg/ml G418 . Cell culture preparation for imaging was carried using cells grown in HL5 medium ( FORMEDIUM ) supplemented with penicillin+streptomycin . 20 µg/ml G418 selection was added 72 hr after thawing frozen stocks . 18 hr prior to imaging , cells were split into imaging chambers ( NUNC LabTek-II ) at the appropriate density for imaging the next day at around 20% confluency , drug selection was removed and HL5 was replaced with imaging medium ( 75% LoFlo medium ( Formedium ) , 10% FBS , 15% HL5 ) . Imaging media was refreshed 1 . 5 hr before imaging . Imaging was performed using an UltraView Vox spinning disc confocal microscope . Objective , laser lines , camera ( Hamamatsu C9100-13 EM-CCD ) settings , laser powers and exposure times were optimized to minimize photobleaching and ensure negligible photo-toxicity ( measured in terms of the average transcription spot intensity - in trials we found transcription spots were attenuated before reduced cell motility or cell rounding is observed ) . Data were analysed using custom-built software integrating both cell tracking and spot detection . Code can be accessed at http://www . ucl . ac . uk/lmcb/sites/default/files/Corrigan2016MatlabFiles . zip . Cells were imaged live as described above for a single time point and then immediately fixed and prepared for smFISH measurements following the procedure outlined in Raj et al . ( 2006 ) , using a single probe ( CATGGGTGATCCTCATGT; Biosearch , Petaluma , CA ) against the repeated spacer between the each MS2 loop sequence , end-labelled with Quasar 570 fluorophore . Nuclei were co-stained with DAPI . Image stacks were acquired using the spinning disc using exposure times of 3 s for single molecule sensitivity . Individual cytoplasmic RNA molecules and nascent transcription sites were detected using FISH-quant software v2 ( Mueller et al . , 2013 ) , augmented with a custom-written dual-threshold algorithm to segment nuclei and cytoplasm of cells based on the DAPI signal . A Monte Carlo simulation framework for the MS2 system was constructed using custom-written tools in MATLAB . For simulation of FRAP experiments , binding and potential unbinding of MCP was simulated with first order kinetics . Tools for analysis of nth-order hidden Markov models , including the forward-backward and Baum-Welch algorithms and Gibbs sampling , were custom-written using MATLAB . Full details of the simulation and probabilistic analysis procedures are presented in the appendix .	Understanding how gene activity is regulated relies on accurate measurements of the output of genes . Proteins are generated from genes via a multi-step process . In the first step , called transcription , the DNA of a gene is copied by complex cell machinery to create molecules of mRNA . Subsequently , these mRNA molecules are ‘translated’ into proteins . Previous studies have assayed gene transcription by measuring mRNA production in millions of cells at the same time . The resulting measurements give the impression that transcription occurs as a continuous , smooth process . However , when individual gene transcription is measured in single cells , mRNA production between cells is unexpectedly variable . This challenged the view that transcription is a continuous process . One idea that explains this variability – the "two-state" or "bursting" model – proposes that genes switch between "on" and "off" states with a certain probability . Thus , at any one time , a gene will be off in many cells and on in others . However , the methods used in these experiments measure mRNA in dead cells , and so the dynamic switching of genes between on and off states was presumed , but not accurately measured . Corrigan et al . have now imaged the transcription of a single gene – a gene for a protein called actin – in living cells of an amoeba called Dictyostelium . Genetic techniques and computational modeling were then used to explore what affects the variability in this gene’s activity . These approaches revealed that transcription occurs across a spectrum of activity , rather than in rigid on or off states . The transcription process itself may also contribute to where a gene’s activity sits on this spectrum . Furthermore , Corrigan et al . found that a specific DNA sequence found at the start of the actin gene , that is also found in many genes in complex life-forms , is required for the gene to reach the highest levels of activity on the spectrum . This spectrum of activity states could allow cells to finely tune their responses to the signals they receive . A future challenge will be to assess how the activity of other genes compare to the actin gene and to discover what underlies the variation in the timing of transcription’s different stages .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "chromosomes", "and", "gene", "expression", "computational", "and", "systems", "biology" ]	2016	A continuum model of transcriptional bursting
Nuclear speckles ( NS ) are among the most prominent biomolecular condensates . Despite their prevalence , research on the function of NS is virtually restricted to colocalization analyses , since an organizing core , without which NS cannot form , remains unidentified . The monoclonal antibody SC35 , raised against a spliceosomal extract , is frequently used to mark NS . Unexpectedly , we found that this antibody was mischaracterized and the main target of SC35 mAb is SRRM2 , a spliceosome-associated protein that sharply localizes to NS . Here we show that , the core of NS is likely formed by SON and SRRM2 , since depletion of SON leads only to a partial disassembly of NS , while co-depletion of SON and SRRM2 or depletion of SON in a cell-line where intrinsically disordered regions ( IDRs ) of SRRM2 are genetically deleted , leads to a near-complete dissolution of NS . This work , therefore , paves the way to study the role of NS under diverse physiological and stress conditions . Nuclear speckles ( NS ) are membraneless nuclear bodies ( Banani et al . , 2017; Shin and Brangwynne , 2017 ) in the interchromatin-space of the nucleus that contain high concentrations of RNA-processing and some transcription factors but are devoid of DNA ( Spector and Lamond , 2011 ) . Under normal conditions , they appear as irregularly shaped , dynamic structures that show hallmarks of phase-separated condensates , such as fusion and deformation under pressure in living cells ( Chen and Belmont , 2019; Zhang et al . , 2016 ) . Despite their prevalence , the function of NS remains largely unknown , although they have been proposed to act as reservoirs for splicing factors , and association with NS have been shown to correlate with enhanced transcription and RNA-processing ( Chen and Belmont , 2019; Galganski et al . , 2017 ) . NS have been shown to be involved in replication of herpes simplex virus ( Chang et al . , 2011 ) , processing and trafficking of Influenza A virus mRNA ( Mor et al . , 2016 ) , detaining repetitive RNA originating from the transcription of repeat expanded loci that trigger Huntington's disease , spinocerebellar ataxia and dentatorubral–pallidoluysian atrophy ( Urbanek et al . , 2016 ) , but also repetitive RNA from artificial constructs that produce RNA capable of phase-separation in vitro ( Jain and Vale , 2017 ) . Studying the role of NS involves visualizing them with a fluorescently tagged factor that localizes to NS , or the use of antibodies that show specific staining of NS . Similar to nucleoli and other membraneless bodies of the nucleus , NS disassemble during early stages of mitosis , and assemble back following telophase ( Galganski et al . , 2017 ) . Several protein kinases are thought to be involved in this process , such as DYRK3 , chemical inhibition of which leads to aberrant phase-separation ( Rai et al . , 2018 ) . Overexpression of DYRK3 , or CLK1 on the other hand leads to dissolution of NS in interphase cells , underscoring the importance of phosphorylation in NS integrity ( Rai et al . , 2018; Sacco-Bubulya and Spector , 2002 ) . Unlike several other biomolecular condensates , a specific core necessary for NS formation has not yet been identified , and it has been hypothesized that stochastic self-assembly of NS-associated factors could lead to the formation of NS ( Dundr and Misteli , 2010; Spector and Lamond , 2011; Tripathi et al . , 2012 ) . One of the most frequently used reagents to locate NS is the monoclonal antibody SC35 , which was raised against biochemically purified spliceosomes ( Fu and Maniatis , 1990 ) , and reported to be an antibody against SRSF2 ( Fu and Maniatis , 1992 ) . Testament to the importance of this antibody , NS are also referred to as ‘SC35 domains’ . Although , the name SC35 and SRSF2 are used synonymously and to annotate orthologues of SRSF2 not only in mammalian species but also in species such as D . melanogaster and A . thaliana , mAb SC35 is reported to cross-react with SRSF1 , and potentially with other SR-proteins as well ( Fu et al . , 1992; Neugebauer and Roth , 1997 ) . Furthermore , fluorescently tagged SRSF2 shows staining patterns incompatible with mAb SC35 stainings under identical experimental conditions ( Politz et al . , 2006; Sakashita and Endo , 2010; Sharma et al . , 2010; Tripathi and Parnaik , 2008 ) . Intrigued by these inconsistencies , we undertook a systemic re-characterization of the mAb SC35 and its cellular targets . In order to characterize the cellular targets of the SC35 mAb , we carried out an Immunoprecipitation Mass-Spectrometry ( IP-MS ) experiment . Whole-cell extracts prepared from HAP1 cells were used to immunoprecipitate endogenous targets SC35 mAb , with a matched IgG mAb serving as a control . The immunoprecipitated proteins were then analyzed by mass-spectrometry ( see Methods for details ) . In total , we identified 432 proteins that were significantly enriched in the SC35 purifications compared to controls ( p<0 . 05 , at least two peptides detected in each biological replicate ) . Surprisingly , the most enriched protein in the dataset , both in terms of unique peptides detected , total intensities and MS/MS spectra analyzed , is neither SRSF2 nor one of the canonical SR-proteins ( Manley and Krainer , 2010 ) , but a high-molecular weight RNA-binding protein called SRRM2 ( Figure 1A , Figure 1—figure supplement 1A ) , an NS-associated protein with multiple RS-repeats ( Blencowe et al . , 2000 ) . Analysis of the top 108 targets , corresponding to the third quartile , using the STRING database ( Szklarczyk et al . , 2019 ) shows a clear enrichment for the spliceosome and NS ( Figure 1—figure supplement 1B ) , validating the experimental approach . We were also able to detect all SR-proteins in our dataset , however their scores are dwarfed by that of SRRM2’s ( Figure 1—figure supplement 1C ) . Thus , contrary to initial expectations , the IP-MS results strongly suggest but do not prove that SC35 mAb primarily recognizes SRRM2 , at least in the context of an immunoprecipitation experiment . Before exploring SRRM2 as a potential mAb SC35 target protein , we decided to first take an unbiased look at SR-proteins and the ability of mAb SC35 to recognize them . To this end , we cloned all 12 canonical SR-proteins in humans ( Manley and Krainer , 2010 ) into an expression plasmid , and created stable-cell lines expressing these proteins under the control of a doxycycline-inducible promoter ( Figure 1—figure supplement 2 ) . We used a biotin-acceptor peptide as a tag , and carried out stringent purifications using streptavidin beads to exclude non-specific co-purification of unrelated SR-proteins , and examined the eluates using immunoblotting . Surprisingly , our results show that the main target of mAb SC35 on these immunoblots is SRSF7 ( Figure 1B ) , which , when untagged , runs at approximately ~35 kDa on polyacrylamide gels similar to SRSF2 ( the tag combination that we use shifts both proteins by ~15 kDa ) . To exclude any artefacts that could originate from the use of tagged proteins , we used whole-cell extracts from HEK293 cells and immunoprecipitated targets of mAb SC35 and analyzed the eluates via immunoblotting . Consistent with the results of the IP-MS experiment , and tagged-SRSF1-12 purifications , we observed a very clear enrichment for SRRM2 and SRSF7 , but not for SRSF2 , SRSF1 or other factors ( Figure 1C ) . In order to determine whether the 35 kDa band recognized by mAb SC35 in immunoblots of cellular lysates is composed of multiple proteins , in addition to SRSF7 , we created a cell line where we inserted the FKBP12F36V degron tag ( Nabet et al . , 2018 ) homozygously into the C-terminus of SRSF7 in HEK293 cells . Even without any treatment , it is evident that the 35 kDa band robustly recognized by mAb SC35 in wild-type cells completely disappears in SRSF7FKBP12 cells ( Figure 1D , compare lanes 1 and 2 with 3 and 4 ) , and a new band around ~50 kDa , where the FKBP12F36V-tagged SRSF7 runs , emerges ( Figure 1D , arrowhead ) . Treatment of these cells with 1 µM of dTAG7 for 6 hr lead to the depletion FKBP12F36V-tagged SRSF7 , and to the depletion of the newly-emerged ~50 kDa protein recognized by mAb SC35 . The identity of a fainter band around 37 kDa , which is insensitive to tagging SRSF7 or dTAG treatment remains unknown . These results strongly suggest that the 35 kDa namesake protein revealed by SC35 mAb on immunoblots is SRSF7 and any contribution to this signal from other proteins is negligible to none . Even though our results show that SC35 mAb specifically recognizes SRSF7 rather than SRSF2 , both proteins have significant nucleoplasmic pools in addition to their localization to NS ( Politz et al . , 2006; Prasanth et al . , 2003; Sapra et al . , 2009 ) which is not easily reconciled with the immunofluorescence stainings obtained with the SC35 mAb that are virtually restricted to NS . Intriguingly , SRRM2 , which is by far the most enriched protein in our immunoprecipitations with mAb SC35 , is a relatively large ( ~300 kDa ) protein , that readily co-purifies with spliceosomes ( Bertram et al . , 2017; Bessonov et al . , 2010 ) , shows liquid-like behaviour in cells ( Rai et al . , 2018 ) and co-localizes near-perfectly with mAb SC35-stained NS ( Miyagawa et al . , 2012 ) . In addition , SRRM2 and its yeast counterpart Cwc21/Cwf21 is located in the recent cryo-EM structures of the spliceosome , where it joins the spliceosome at the Bact stage where it seems to support the activated conformation of PRP8’s switch-loop both in humans and yeast ( Jia and Sun , 2018 ) . Predating the recent cryo-EM structures by almost a decade , the yeast orthologue of SRRM2 , Cwf21p , has been shown to directly interact with Prp8p ( PRPF8 ) and Snu114p ( EFTUD2 ) which are also among the most enriched proteins in our mAb SC35 immunoprecipitations ( Grainger et al . , 2009; Figure 1—figure supplement 1A ) . Furthermore , a more recent tandem-affinity purification of the protist Trypanosoma orthologue of SRRM2 revealed Prp8 , U5-200K ( SNRNP200 , also known as Brr2 ) , U5-116K ( EFTUD2 , also known as Snu114 ) and U5-40K ( SNRNP40 ) as major interaction partners ( Silva et al . , 2011 ) . Taking into consideration the fact the mAb SC35 was raised against biochemically purified spliceosomes ( Fu and Maniatis , 1990 ) , together with the aforementioned observations in the scientific literature and our IP-MS results which identified SRRM2 as the top target , we hypothesize that mAb SC35 was most likely raised against SRRM2 , and it recognizes SRRM2 in most if not all immunological assays that utilizes mAb SC35 where SRRM2 is not depleted or unintentionally omitted due to technical reasons . In order to test the veracity of this claim , we designed a series of experiments in human cells . Since , to our knowledge , SC35 mAb has not been shown to recognize SRRM2 on immunoblots , we first created tagged and truncated SRRM2 constructs in living cells . To this end , we generated 11 cell lines that remove between 4 and 2322 amino acids from the SRRM2 protein ( full-length: 2752 a . a . , numbering from Q9UQ35-1 ) by inserting a TagGFP2 ( referred to as GFP for simplicity ) sequence followed by an SV40 polyadenylation signal into 11 positions of the SRRM2 gene in HAP1 cells using a CRISPR/Cas9-based technique called CRISPaint ( Schmid-Burgk et al . , 2016; Figure 2A ) . The deepest truncation removes 84% of SRRM2 , which includes almost all its IDRs , together with two regions enriched for serine and arginine residues , leaving behind 13 RS-dipeptides out of a total of 173 ( Figure 2—figure supplement 1A , B ) . The GFP-tagged , in vivo truncated proteins ( referred to as truncations 0 to 10 , shortened as tr0 - tr10 , Figure 2B ) are then immunoprecipitated using GFP-trap beads and the eluates were analyzed by immunoblotting . This experiment shows that SC35 mAb indeed recognizes SRRM2 on immunoblots ( Figure 2C , lane 2 ) . Interestingly , the signal from SC35 mAb remains relatively stable up until SRRM2tr4 which removes 868 a . a . from the SRRM2 C-terminus , the signal appears to be reduced in SRRM2tr5 which removes 1014 a . a . and becomes completely undetectable from SRRM2tr6 onward ( Figure 2C and Figure 2—figure supplement 1C ) . The same blot was stripped and re-probed with a polyclonal antibody raised against the N-terminus of SRRM2 , common to all truncations , which show that SRRM2 is detectable throughout , and thus indicating that the epitope ( s ) recognized by mAb SC35 reside between amino acids 1 , 360–1884 of SRRM2 . In order to assess the efficiency and the specificity of the GFP-pull-down , we used a wild-type lysate without any GFP insertion , together with lysates made from SRRM2tr0 and SRRM2tr10 cells , which served as the negative control , positive control and the deepest truncation ( tr10 ) we generated , respectively . The immunoblot with mAb SC35 once again clearly shows that near-full-length SRRM2tr0 is recognized by mAb SC35 to the same extent as the SRRM2 polyclonal antibody , while SRRM2tr10 is not detected by mAb SC35 at all but strongly with SRRM2 polyclonal antibody ( Figure 2D ) . These blots also show that full-length SRRM2 co-purifies SRRM1 and to a lesser extent RBM25 , while both interactions are severely compromised in SRRM2tr10 . Furthermore , the absence of any signal in SRRM2tr10 input lane probed with mAb SC35 ( Figure 2D , top left lane 3 ) , and the emergence of a shorter ~100 kDa protein in the complete absence of a ~300 kDa signal in the SRRM2 blot ( Figure 2D , bottom left , lane 3 ) shows that SRRM2tr10 cells have a homozygous insertion of the GFP construct , which was also confirmed by genotyping PCR ( Figure 2—figure supplement 1D ) . This result further indicates that the large IDRs of SRRM2 are not essential for cell viability , at least in HAP1 cells . These results can be puzzling , since we first show that mAb SC35 specifically recognizes a 35 kDa band which we reveal to be SRSF7 ( Figure 1 ) , but later , in a separate set of experiments , we also show that mAb SC35 specifically recognizes a ~300 kDa band , which we reveal to be SRRM2 ( Figure 2 ) , while the original study describing mAb SC35 reports a single 35 kDa band recognized by mAb SC35 on immunoblots ( Fu and Maniatis , 1990 ) . The solution to this conundrum presented itself in the form of altering the immunoblotting technique . Using whole-cell extracts prepared from wild-type cells , together with SRRM2tr0 and SRRM2tr10 cells , in a gel system where we can interrogate both small and large proteins simultaneously , we were able to detect both SRRM2 and SRSF7 on the same blot ( Figure 2E ) . These blots prove that the ~300 kDa band is indeed SRRM2 , since it completely disappears in SRRM2tr10 lysates ( which is accompanied by the appearance of a ~100 kDa band in SRRM2 blots ) while the much fainter 35 kDa band corresponding to SRSF7 ( Figure 1 ) remains unaltered . These experiments provide strong support for our hypothesis that the main target of SC35 mAb is SRRM2 , a protein proven to be part of spliceosomes , against which this antibody was raised , and suggests that a cross-reactivity towards SRSF7 , likely in combination with immunoblotting techniques not suitable to detect large proteins ( Bass et al . , 2017 ) , obscured this fact for more than two decades . mAb SC35 is typically used as an antibody in immunofluorescence experiments that reveals the location of NS in mammalian cells ( Spector and Lamond , 2011 ) . In light of the evidence presented here , it can be assumed that mAb SC35 primarily stains SRRM2 in immunofluorescence stainings , as in immunoblotting experiments . In order to test if this is indeed the case , we took advantage of the SRRM2tr10 cells . These cells are viable and express a severely truncated SRRM2 that is not recognized by mAb SC35 on immunoblots ( Figure 2 ) . SRRM2tr10 cells , together with SRRM2tr0 cells serving as a control , were stained with antibodies against various nuclear speckle markers , including mAb SC35 ( Figure 3A ) . These results show that SC35 signal virtually disappears in SRRM2tr10 cells , while other markers of NS , such as SON , SRRM1 , and RBM25 appear unaltered , ruling out a general defect in NS ( Figure 3A ) . As an additional control , we also mixed SRRM2tr10 cells with SRRM2tr0 cells together before formaldehyde fixation , and repeated the antibody stainings , in order to be able to image these two cell populations side-by-side . SRRM2tr10 and SRRM2tr0 cells are easily distinguished from each other since the latter show a typical nuclear speckle staining whereas the former has a more diffuse , lower intensity GFP signal . These images clearly show that mAb SC35 , obtained from two separate vendors , no longer stains NS or any other structure in SRRM2tr10 cells , whereas other NS markers , including SRSF7 , appear unaltered ( Figure 3B , C , Figure 3—figure supplement 1 ) . Taken together , our results show that mAb SC35 , which was raised against a spliceosomal extract , was most likely raised against SRRM2 , a ~300 kDa protein that , unlike SRSF2 or SRSF7 is present in spliceosomes of both in yeast and humans . We show that mAb SC35 directly recognizes SRRM2 between amino acids 1 , 360–1 , 884 , and that the main signal from mAb SC35 corresponds to SRRM2 both in immunoblots and immunofluorescence images . It is important to note that these results were obtained from unsynchronised human cells , which are mostly at the interphase stage of the cell-cycle . mAb SC35 might recognize additional targets in mitotic cells or cells derived from non-mammalian species . It is also interesting to note that this is not the first time an antibody is serendipitously raised against SRRM2 and was later discovered to recognize SRRM2 only after the fact: In 1994 , Blencowe et al . , 1994 reported three murine monoclonal antibodies , B1C8 , H1B2 , and B4A11 which were raised against nuclear matrix preparations . All three antibodies showed extensive co-localization with NS , although a co-localization between mAb SC35 and B4A11 could not directly be assessed since both mAb SC35 and B4A11 are reported to be IgG mAbs . In a separate work , Blencowe et al . , 1994 showed that B4A11 is an antibody against SRRM2 , suggesting that SRRM2 is present both in spliceosomal purifications and nuclear matrix preparations . During this work , we noticed the remarkable size difference between human SRRM2 protein ( 2752 a . a . ) , and its unicellular counterparts S . cerevisiae Cwc21 ( 133 a . a ) , S . pombe Cwf21 ( 293 a . a ) and T . Brucei U5-Cwc21 ( 143 a . a ) . Moreover , while all three proteins share a conserved N-terminus , which interact with the spliceosome , the serine and arginine-rich extended C-terminus of human SRRM2 is predicted to be completely disordered ( Figure 2—figure supplement 1B ) . Intrigued by this observation , we compiled all metazoan protein sequences of SRRM2 , together with SRRM1 , RBM25 , PNN , SON , PRPF8 and COILIN , and analyzed their size distributions ( Figure 4A ) . This analysis confirmed that , unlike SRRM1 , RBM25 , PNN , PRPF8 or coilin , SRRM2 indeed has a very broad size distribution within metazoa ( Figure 4—figure supplements 1–2 ) . Strikingly , SON follows this trend with orthologues as small as 610 a . a in the basal metazoan sponge A . queenslandica , and as large as 5561 a . a in the frog X . tropicalis . Increase in protein size appears to involve IDR extensions , especially for SRRM2 , but also for SON ( Figure 4B , Figure 4—figure supplements 1–2 ) , suggesting a role in LLPS-mediated condensate formation , which was shown to be the case for both SRRM2 ( Rai et al . , 2018 ) and SON ( Kim et al . , 2019 ) in living cells . Putting together the observation that places SON and SRRM2 at the center of NS ( Fei et al . , 2017 , with the interpretation that SC35 stains SRRM2 in their microscopy work ) , the presence of SRRM2 at the center of collapsed speckles in SON knock-down experiments , and the peculiar variation in the sizes of SON and SRRM2 during evolution involving gain of IDRs , we hypothesize that SON , together with SRRM2 are essential for NS formation , such that SRRM2 continues to serve as a platform for NS-associated proteins in SON-depleted cells . In order to test this hypothesis , we used the SRRM2tr0 and SRRM2tr10 cells as a model , which allowed us to simultaneously detect SON , SRRM2 and an additional NS marker in the same cell . We chose SRRM1 , which is used as a marker for NS in immunofluorescence experiments ( Blencowe et al . , 2000; Blencowe et al . , 1998; Blencowe et al . , 1994; Rai et al . , 2018; Zhang et al . , 2016 ) and located at IGCs in electron microscopy experiments ( Wan et al . , 1994; RBM25 , which is one of two recommended factors to mark NS by the Human Protein Atlas; ‘The Human Protein Atlas version 19 . 3 , 2020’ n . d . ; Thul et al . , 2017 ) ( the other being SRRM2 ) , localizes to NS through its RE/RD-rich mixed-charge domain ( Zhou et al . , 2008 ) that was recently shown to target proteins to NS ( Greig et al . , 2020 ) and PNN , which localizes to NS in human cells ( Chiu and Ouyang , 2006; Joo et al . , 2005; Lin et al . , 2004; Zimowska et al . , 2003 ) . As reported previously ( Ahn et al . , 2011; Fei et al . , 2017; Sharma et al . , 2010 ) , depletion of SON leads to collapsed speckles in SRRM2tr0 cells , with SRRM2 , SRRM1 , PNN , and RBM25 localizing to these spherical NS to different extents ( Figure 5A , Figure 5B , Figure 5—figure supplement 3 , compare SRRM2tr0 cells , control vs SON siRNA treatment ) . In SRRM2tr10 cells on the other hand , where the truncated SRRM2 has a significant nucleoplasmic pool already in control siRNA treated cells , depletion of SON leads to a near-complete diffusion of truncated SRRM2 , which is followed by RBM25 ( Figure 5B ) , SRRM1 ( Figure 5—figure supplement 3A ) and PNN . Using ilastik and CellProfiler , we quantified the signal detected in NS , and compared it to signal detected in the entire nucleus for each cell in every condition for each protein investigated ( Figure 5—figure supplement 2 ) . These results show that truncated SRRM2 shows reduced NS localization ( Figure 5C , right ) , while RBM25 , SRRM1 , and PNN are localized at NS to a similar extent in SRRM2tr0 and SRRM2tr10 cells , although with a broader distribution in SRRM2tr10 cells . Depletion of SON in SRRM2tr0 leads to a significant reduction in NS localization for all proteins , verifying SON’s importance for NS formation . Depletion of SON in SRRM2tr10 cells , however , leads to a more dramatic loss of NS localization for all proteins ( Figure 5 and Figure 5—figure supplement 3 ) , underscoring the essential role of SRRM2’s extended IDR in the formation of NS , especially in SON-depleted cells . Number of Cajal bodies , determined by COILIN staining , remains unaltered in all conditions ( Figure 5—figure supplement 3B ) . Next , to independently verify these observations , we knocked-down SON and SRRM2 , individually and simultaneously in HEK293 cells where we endogenously tagged SRRM2 with TagGFP2 at the C-terminus with the same reagents used to create SRRM2tr0 HAP1 cells . Similar to the HAP1 model , depletion of SON alone leads to collapsed NS where SRRM2 , RBM25 , PNN , and SRRM1 localize to spherical NS to some extent but with a significant non-NS pool in the nucleus ( Figure 5—figure supplement 4 ) . Depletion of SRRM2 alone also leads to delocalization of PNN , SRRM1 , and RBM25 from NS , but not to the extent seen with SON depletion . Co-depletion of SON and SRRM2 leads to near-complete delocalization of all proteins investigated , mirroring the results obtained from the HAP1 model ( Figure 5—figure supplement 4A , B , C and D ) . These results cannot be explained by reduced protein stabilities , as none of the proteins except for SON and SRRM2 show significant changes in their amounts as judged by immunoblotting ( Figure 5—figure supplement 4E ) . Finally , co-depletion of SON together with SRRM1 or RBM25 does not lead to diffusion of spherical NS marked by SRRM2 , indicating that SRRM2 has a unique role in NS formation and cannot be substituted by other NS-associated factors ( Figure 5—figure supplement 5 ) . Taken together , our results show that a widely-used monoclonal antibody to mark NS , SC35 mAb , was most likely raised against SRRM2 and not against SRSF2 as it was initially reported . We speculate that this mischaracterization hindered the identification of the core of NS , without which NS do not form , which we show to consist most likely of SON and SRRM2 . We found that these two factors , unlike other splicing related proteins analyzed , have gone through a remarkable length extension through evolution of metazoa over the last ~0 . 6–1 . 2 billion years , mostly within their IDRs which are typically involved in LLPS and formation of biomolecular condensates . The exact mechanism of NS formation by SON together with SRRM2 , and the evolutionary forces that led to the dramatic changes in their lengths remain to be discovered . Flp-In T-REx HEK293 ( Thermo Fisher Scientific Catalog Number: R78007 ) cells were cultured according to manufacturer’s recommendations . The cells were cultured in DMEM with Glutamax supplemented with Na-Pyruvate and High Glucose ( Thermo Fisher Scientific Catalog Number: 31966–021 ) in the presence of 10% FBS ( Thermo Fisher Scientific Catalog Number: 10270106 ) and Penicillin/Streptomycin ( Thermo Fisher Scientific Catalog Number: 15140–122 ) . Before the introduction of the transgenes cells were cultured with a final concentration of 100 µg/mL zeocin ( Thermo Fisher Scientific Catalog Number: R250-01 ) and 15 µg/mL blasticidin ( Thermo Fisher Scientific Catalog Number: A1113903 ) . To generate the stable cell lines pOG44 ( Thermo Fisher Scientific Catalog Number: V600520 ) was co-transfected with pcDNA5/FRT/TO ( Thermo Fisher Scientific Catalog Number: V652020 ) containing the gene of interest ( GOI are SRSF1 to 12 in this case ) in a 9:1 ratio . Cells were transfected with Lipofectamine 2000 ( Thermo Fisher Scientific Catalog Number: 11668019 ) on a 6-well plate format with total 1 µg DNA ( i . e . 900 ng of pOG44 and 100 ng of pcDNA5/FRT/TO+GOI ) according to the transfection protocol provided by the manufacturer . 24 hr after the transfection cells were split on 3 wells of a 6-well plate at 1:6 , 2:6 and 3:6 dilution ratios to allow efficient selection of Hygromycin B ( Thermo Fisher Scientific Catalog Number: 10687010 ) . The Hygromycin selection was started at the 48 hr after transfection time point with a final concentration of 150 µg/mL and refreshed every 3–4 days until the control non-transfected cells on a separate plate were completely dead ( takes approximately 3 weeks from the start of transfection until the cells are expanded and frozen ) . Induction of the transgene was done over-night with a final concentration of 0 . 1 µg/mL doxycycline . The cells were validated by performing immunofluorescence by FLAG antibody and western blotting of nuclear and cytoplasmic fractions . Human HAP1 parental control cell line was purchased from Horizon ( Catalog Number: C631 ) and cultured according to the instructions provided by the manufacturer . The cells were cultured in IMDM ( Thermo Fisher Scientific Catalog Number: 12440–053 ) in the presence of 10% FBS ( Thermo Fisher Scientific Catalog Number: 10270106 ) and Penicillin/Streptomycin ( Thermo Fisher Scientific Catalog Number: 15140–122 ) . See Supplementary file 1 for the list of sgRNAs used in generation of cell lines with CRISPaint . Cells were co-transfected with three plasmids according to the CRISPaint protocol . Cas9 and sgRNA are provided by same plasmid in 0 . 5 µg final amount , Frame selector plasmid ( depending on the cut site selector 0 , +1 or +2 had to be chosen ) is also in 0 . 5 µg final amount , the TagGFP2_CRISPaint plasmid was provided at a 1 µg final amount . Therefore the total 2 µg DNA was transfected into cells on 6-well plate format using Lipofectamine 2000 . 24 hr after the transfection the cells were expanded on 10 cm culture plates to allow efficient Puromycin ( Thermo Fisher Scientific Catalog Number: A1113803 ) selection . The Puromycin selection is provided in the tag construct and is driven by the expression from the gene locus ( in this case the human SRRM2 gene locus ) . Puromycin selection was started at 48 hr after transfection at 1 µg/mL final concentration and was refreshed every 2 days and in total was kept for 6 days . After the colonies grew to a visible size the colonies were picked by the aid of fluorescence microscope EVOS M5000 . PCR screening of the colonies was performed using genotyping oligos listed in Supplementary file 1 using Quick Extract DNA Extraction Solution ( Lucigen Catalog Number: QE09050 ) according to manufacturer’s protocol in a PCR machine and DreamTaq Green Polymerase ( Thermo Fisher Scientific Catalog Number: K1081 ) using 58°C annealing temperature and 1 min extension time . SRSF7-FKBP12F36V knock-in cells were generated in HEK293T cells ( ordered from ATCC , CRL3216 and cultured according to the protocol provided ) by co-transfecting the sgRNA , Frame selector and mini-circle constructs prepared according to the CRISPaint protocol using Lipofectamine 2000 on a 6-well plate format . This time we used two separate tag donor plasmids to increase the chances of obtaining homozygous clones . The constructs were identical except for the selection antibiotic . Cells are expanded on 10 cm culture plates 24 hr after transfection . At 48 hr after transfection the double selection was initiated . One allele was selected by Puromycin at 1 µg/mL final concentration , whereas the other allele was selected by blasticidin at 15 µg/mL final concentration for 6 days in total . After the removal of selection cells were kept on the same plate until there were big enough colonies . Colonies were picked under a sterile workbench and screened for homozygosity using western blotting with SRSF7 antibody . The degradation of tagged-SRSF7 was induced by adding dTAG7 reagent at a final 1 µM concentration and keeping for 6 hr . SRRM2tr0-GFP Flp-In TREx HEK293 cells were generated using the same strategy as described above for HAP1 cells . Upon Puromycin selection cells were used as a pool ( without sorting or colony picking ) in immunofluorescence experiments . Cell lines are regularly checked for the absence of Mycoplasma using a PCR based detection kit ( Jena Biosciences PP-401 ) . Prior to the seeding of cells , the round glass 12 mm coverslips are coated with poly-L-Lysine hydrobromide ( Sigma P9155 ) for HEK293 cells . The coating is not necessary for the imaging of HAP1 cells . For 1 day of knock-down 40 , 000 cells are plated on coverslips placed into the wells of 24-well plates on the day before the siRNA transfections . Pre-designed silencer select siRNA ( Ambion ) are ordered for SRRM2 ( ID: s24004 ) , SON ( ID: s13278 ) , SRRM1 ( ID: s20020 ) and RBM25 ( ID: s33912 ) . Negative control #1 of the silencer select was used for control experiments . 5 nM ( for double transfections ) or 10 nM ( for single transfections ) of each siRNA is forward transfected using Lipofectamine RNAiMAX Reagent ( Thermo Fisher Scientific Catalog Number: 13778075 ) according to manufacturer’s instructions . The cells were fixed for imaging 24 hr after transfection . Streptavidin-pulldowns ( Figure 1B ) were carried out using stable-cell-line expression SRSF1-12 proteins . Briefly , for each cell line , ~1 million cells ( one well of a 6-well dish , ~90% confluent ) were induced with 0 . 1 µg/mL doxycycline ( final ) for ~16 hr , solubilised with 500 µL of 1xNLB ( 1X PBS , 0 . 3M NaCl , 1% Triton X-100 , 0 . 1% TWEEN 20 ) + 1x PhosSTOP , sonicated with Bioruptor ( 30 s ON/OFF , five cycles on LO ) and centrifuged for 10 min at ~20 . 000 rcf at 4°C to remove cellular debris . Biotinylated target proteins were purified with 25 µL ( slurry ) of MyONE-C1 streptavidin beads ( Thermo Fisher Scientific , 65002 ) , pre-washed with 1x NLB + 1x PhosSTOP , for 2 hr in the cold-room with end-to-end rotation . Beads were washed 3 times with 500 µL of 1x NLB ( 5 min each ) , bound proteins were eluted with 50 µL of 1xLDS sample buffer ( Thermo Fisher Scientific , NP0007 ) + 100 mM beta-mercaptoethanol at 95°C for 5 min . Eluates were loaded on a 4–12% Bis-Tris gel ( Thermo Fisher Scientific , NP0322PK2 ) and transferred to a 0 . 45 µm PVDF membrane ( Merck Millipore , IPVH00010 ) with 10 mM CAPS ( pH 11 ) + 10% MeOH , for 900 min at 20V . Primary antibodies were used at a dilution of 1:1000 in SuperBlock ( Thermo Fisher Scientific , 37515 ) . Membranes were incubated with the diluted primaries overnight in the cold-room . SC35 and IgG immunoprecipitations ( Figure 1C ) were carried out using a whole-cell extract prepared from wild-type HEK293 cells . Briefly , ~10 million cells were resuspended with 600 µL of 1x NLB + 1x cOmplete Protease Inhibitor Cocktail + 1x PhosSTOP , and kept on ice for 15 min . The lysate was cleared by centrifugation at ~20 . 000 rcf for 10 min at 4°C . Clarified lysate was split into two tubes; to one tube 25 µL of control IgG1 ( Santa Cruz , sc-3877 ) was added , to the other 2 . 5 µL of SC35 mAb ( Sigma-Aldrich , S4045 ) , immune-complexes are allowed to form for 3 hr in the cold-room with end-to-end rotation . 40 µL of Protein G Dynabeads ( Thermo Fisher Scientific , 10003D , washed and resuspended with 200 µL of 1x NLB + PI + PS ) was used to pull-down target proteins . Beads were washed three times with 1xNLB , briefly with HSB ( 50 mM Tris . Cl pH 7 . 4 , 1M NaCl , 1% IGEPAL CA-630 , 0 . 1% SDS , 1 mM EDTA ) and finally with NDB ( 50 mM Tris . Cl pH 7 . 4 , 0 . 1M NaCl , 0 . 1% TWEEN 20 ) . Bound proteins were eluted with 50 µL of 1xLDS sample buffer ( Thermo Fisher Scientific , NP0007 ) + 100 mM beta-mercaptoethanol at 80°C for 10 min . Immunoblotting was carried out as described for streptavidin pull-downs , except transfer was carried out with 25 mM Tris , 192 mM glycine , 20% ( v/v ) for 90 min at 90V in the cold-room . Pull-down of truncated SRRM2 proteins ( Figure 2C–E ) were carried out using whole-cell lysate prepared from respective HAP1 cell lines . The protocol is essentially identical to SC35 and IgG immunoprecipitations described above , with these notable differences: ( 1 ) For pull-downs , 25 µL ( slurry ) of GFP-trap agarose beads were used ( Chromotek , gta ) , incubations were carried out overnight in the cold-room ( 2 ) For Figure 2C and Figures 2D , 3-8% Tris-Acetate gels ( Thermo Fisher Scientific , EA0375PK2 ) were used , for Figure 2E a 4–12% Bis-Tris gel was used ( 3 ) Gels were run at 80V for 3 hr ( 4 ) Transfers were carried out with 10 mM CAPS ( pH 11 ) + 10% MeOH for 900 min at 20V . Unless indicated otherwise , all data analysis tasks were performed using Python 3 . 7 with scientific libraries Biopython ( Cock et al . , 2009 ) , pandas ( McKinney , 2010 ) , NumPy ( van der Walt et al . , 2011 ) , matplotlib ( Hunter , 2007 ) and seaborn . Code in the form of Jupyter Notebooks is available in GitHub repository: https://github . molgen . mpg . de/malszycki/SON_SRRM2_speckles . Vertebrate SRRM2 , SON , PRPF8 , SRRM1 , RBM25 , Pinin , and Coilin orthologous protein datasets were downloaded from NCBI’s orthologs and supplemented with orthologues predicted for invertebrate species . For this purpose , OrthoFinder ( Emms and Kelly , 2019 ) was used on a set of Uniprot Reference Proteomes . Invertebrate orthologues were then mapped to NCBI RefSeq to remove fragmentary and redundant sequences . The resulting dataset can be accessed here: https://doi . org/10 . 5281/zenodo . 4065244 and was manually curated to remove evident artefacts lacking conserved domains or displaying striking differences from closely related sequences . Protein lengths were plotted using the seaborn package and descriptive statistics calculated using the pandas package . In order to resolve phylogenetic relationships between species contained in SRRM2 and SON datasets , organism names were mapped to the TimeTree ( Kumar et al . , 2017 ) database . Disorder probability was predicted using IUPred2A ( Mészáros et al . , 2018 ) and MobiDB-Lite ( Necci et al . , 2017 ) and plotted as a heatmap using matplotlib .	Most cells store their genetic material inside a compartment called the nucleus , which helps to separate DNA from other molecules in the cell . Inside the nucleus , DNA is tightly packed together with proteins that can read the cell’s genetic code and convert into the RNA molecules needed to build proteins . However , the contents of the nucleus are not randomly arranged , and these proteins are often clustered into specialized areas where they perform their designated roles . One of the first nuclear territories to be identified were granular looking structures named Nuclear Speckles ( or NS for short ) , which are thought to help process RNA before it leaves the nucleus . Structures like NS often contain a number of different factors held together by a core group of proteins known as a scaffold . Although NS were discovered over a century ago , little is known about their scaffold proteins , making it difficult to understand the precise role of these speckles . Typically , researchers visualize NS using a substance called SC35 which targets specific sites in these structures . However , it was unclear which parts of the NS this marker binds to . To answer this question , Ilik et al . studied NS in human cells grown in the lab . The analysis revealed that SC35 attaches to certain parts of a large , flexible protein called SRRM2 . Ilik et al . discovered that although the structure and sequence of SRMM2 varies between different animal species , a small region of this protein remained unchanged throughout evolution . Studying the evolutionary history of SRRM2 led to the identification of another protein with similar properties called SON . Ilik et al . found that depleting SON and SRRM2 from human cells caused other proteins associated with the NS to diffuse away from their territories and become dispersed within the nucleus . This suggests that SRMM2 and SON make up the scaffold that holds the proteins in NS together . Nuclear speckles have been associated with certain viral infections , and seem to help prevent the onset of diseases such as Huntington’s and spinocerebellar ataxia . These newly discovered core proteins could therefore further our understanding of the role NS play in disease .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "biochemistry", "and", "chemical", "biology", "cell", "biology" ]	2020	SON and SRRM2 are essential for nuclear speckle formation
A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis . Most approaches focused on genes positively selected for mutations that drive carcinogenesis and neglected the role of negative selection . Some studies have actually concluded that negative selection has no role in cancer evolution . We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes . Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations , oncogenes , however , were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations . Significantly , we have identified numerous human genes that show signs of strong negative selection during tumor evolution , suggesting that their functional integrity is essential for the growth and survival of tumor cells . In the last two decades , the rapid advance in genomics , epigenomics , transcriptomics , and proteomics permitted an insight into the molecular basis of carcinogenesis . These studies have confirmed that tumors evolve from normal tissues by acquiring a series of genetic , epigenetic , transcriptomic , and proteomic changes with concomitant alterations in the control of the proliferation , survival , and spread of affected cells . The genes that play key roles in carcinogenesis are usually assigned to two major categories: proto-oncogenes that have the potential to promote carcinogenesis when activated or overexpressed and tumor suppressor genes ( TSGs ) that promote carcinogenesis when inactivated or repressed . Several alternative mechanisms can modify the structure or expression of a gene in a way that promotes carcinogenesis . These include subtle genetic changes ( single nucleotide substitutions , short indels ) , major genetic events ( deletion , amplification , translocation and fusion of genes to other genetic elements ) , as well as epigenetic changes affecting the expression of cancer genes . These mechanisms are not mutually exclusive: there are many examples illustrating the point that multiple types of the above mechanisms may convert the wild-type form of a cancer gene to a driver gene . Exomic studies of common solid tumors revealed that usually several cancer genes harbor subtle somatic mutations ( point mutations , short deletions , and insertions ) in their translated regions but malignancy-driving subtle mutations can also occur in all genetic elements outside the coding region , namely in enhancer , silencer , insulator , and promoter regions as well as in 5'- and 3'-untranslated regions . Intron or splice site mutations that alter the splicing pattern of cancer genes can also drive carcinogenesis ( Diederichs et al . , 2016 ) . A recent study has presented a comprehensive analysis of driver point mutations in non-coding regions across 2658 cancer genomes ( Rheinbay et al . , 2020 ) . A noteworthy example of how subtle mutations in regulatory regions may activate proto-oncogenes is the telomerase reverse transcriptase gene TERT that encodes the catalytic subunit of telomerase . Recurrent somatic mutations in melanoma and other cancers in the TERT promoter cause tumor-specific increase of TERT expression , resulting in the immortalization of the tumor cell ( Heidenreich et al . , 2014 ) . In addition to subtle mutations , tumors also accumulate major chromosomal changes ( Li et al . , 2020 ) . Most solid tumors display widespread changes in chromosome number , as well as chromosomal deletions and translocations ( Lengauer et al . , 1998 ) . Homozygous deletions of a few genes frequently drive carcinogenesis and the target gene involved in such deletions is always a TSG ( Cheng et al . , 2017 ) . Somatic copy-number alterations , amplifications of cancer genes are also widespread in various types of cancers . Amplifications usually contain an oncogene ( OG ) whose protein product is abnormally active simply because the tumor cell contains 10–100 copies of the gene per cell , compared with the two copies present in normal cells ( Beroukhim et al . , 2010; Verhaak et al . , 2019 ) . Chromosomal translocations may also convert wild-type forms of TSGs into forms that drive carcinogenesis if the translocation inactivates the genes by truncation or by separating them from their promoter . Similarly , translocations may activate proto-oncogenes by changing their regulatory properties ( Haller et al . , 2019 ) . Epigenetic mechanisms such as DNA methylation and histone modifications may also alter the activity of cancer genes . It is now widely accepted that genetic and epigenetic changes go hand in hand in carcinogenesis: numerous genes involved in shaping the epigenome are mutated in common human cancers , and epigenetic changes affect many genes carrying driver mutations ( Yang and Yu , 2013; Chen et al . , 2017b; Di Domenico et al . , 2017; Roussel and Stripay , 2018; Chatterjee et al . , 2018 ) . For example , promoter hypermethylation events may promote carcinogenesis if they lead to silencing of TSGs; the tumor-driving role of promoter methylation is obvious in the case of TSGs that are frequently inactivated by mutations in cancer ( Pfeifer , 2018 ) . Conversely , there is now ample evidence that promoter hypomethylation can promote carcinogenesis if it leads to increased expression of proto-oncogenes ( Van Tongelen et al . , 2017 ) . Non-coding RNAs ( ncRNAs ) also play key roles in carcinogenesis ( Slack and Chinnaiyan , 2019 ) . An explosion of studies has shown that – based on complementary base pairing – ncRNAs may function as OGs ( by inhibiting the activity of TSGs ) , or as tumor suppressors ( by inhibiting the activity of OGs or tumor essential genes [TEGs] ) . Alterations in the splicing of primary transcripts of protein-coding genes also contribute to carcinogenesis . Recent studies on cancer genomes have revealed that recurrent somatic mutations of genes encoding RNA splicing factors ( e . g . SF3B1 , U2AF1 , SRSF2 , ZRSR2 ) lead to altered splice site preferences , resulting in cancer-specific mis-splicing of genes . In the case of proto-oncogenes , changes in the splicing pattern may generate active oncoproteins , whereas abnormal splicing of TSGs is likely to generate inactive forms of the tumor suppressor protein ( Dvinge et al . , 2016 ) . There is now convincing evidence that dysregulation of processes responsible for proteostasis also contributes to the development and progression of numerous cancer types ( Mofers et al . , 2017; Chen et al . , 2017c; Voutsadakis , 2017 ) . Recent studies on tumor tissues have revealed that genetic alterations and abnormal expression of various components of the protein homeostasis pathways ( e . g . FBXW7 , VHL ) contribute to progression of human cancers by excessive degradation of tumor-suppressor molecules or through impaired disposal of oncogenic proteins ( Ge et al . , 2018; Bernassola et al . , 2019 ) . Hanahan and Weinberg have defined a set of hallmarks of cancer that allow the categorization of cancer genes with respect to their role in carcinogenesis ( Hanahan and Weinberg , 2011 ) . These hallmarks describe the biological capabilities usually acquired during the evolution of tumor cells: these include sustained proliferative signaling , evasion of growth suppressors , evasion of cell death , acquisition of replicative immortality , acquisition of capability to induce angiogenesis and activation of invasion and metastasis . Underlying all these hallmarks are defects in genome maintenance that help the acquisition of the above capabilities . Additional emerging hallmarks of potential generality have been suggested to include tumor promoting inflammation , evasion of immune destruction and reprogramming of energy metabolism in order to most effectively support neoplastic proliferation ( Hanahan and Weinberg , 2011 ) . Figure 1 summarizes our current view of the cellular processes that play key roles in tumor evolution to emphasize their contribution to the various major hallmarks of cancer . Changes in the maintenance of the genome , epigenome , transcriptome , and proteome occupy a central position because they increase the chance that various constituents of other cellular pathways will experience alterations that favor the acquisition of capabilities that permit the proliferation , survival , and metastasis of tumor cells . In the first phase of carcinogenesis , a cell may acquire a mutation that permits it to proliferate abnormally , and in the next phase , other mutations allow the expansion of cell number and this process of mutations ( and associated epigenetic , transcriptomic and proteomic alterations ) continues , thus generating a primary tumor that can eventually metastasize to distant organs . Recent studies on the chronology and genomic landscape of the events that drive carcinogenesis suggest that complex structural changes of the genome occur early , whereas point mutations occur in later disease phases ( Maura et al . , 2019; Voronina et al . , 2020 ) . According to current estimates , the number of cancer driving mutations needed for the full development of cancer ranges from two-eight depending on cancer type ( Vogelstein and Kinzler , 2015; Anandakrishnan et al . , 2019 ) . A recent integrative analysis of 2658 whole-cancer genomes and their matching normal tissues across 38 tumor types revealed that , on average , cancer genomes contain four to five driver mutations ( Campbell et al . , 2020 ) . Although the temporal order of the mutations affecting genes of key pathways differs among cancer types , it appears that a common feature is that mutations of genes that regulate apoptosis occur in the early phases of tumor progression , whereas mutations of genes involved in invasion pathways occur only in the last stages of carcinogenesis ( Gerstung et al . , 2011 ) . It has been suggested that the reason why the loss of apoptotic control is a critical step for initiating cancer is that the larger the surviving cell population , the higher the number of cells at risk of acquiring additional mutations . Analyses of the mutation landscapes and evolutionary trajectories of various tumor tissues have identified BRAF , KRAS , TP53 , RB , or APC as the key genes whose mutation is most likely to initiate carcinogenesis , permitting the cell to divide abnormally ( Vogelstein and Kinzler , 2015 ) . In the case of ovarian cancers , TP53 mutation is believed to be the earliest tumorigenic driver event , with presence in nearly all cases of ovarian cancer ( Bashashati et al . , 2013 ) . The prevalence of TP53 mutations and BRCA deficiency in these tumors leads to incompetent DNA repair promoting subsequent steps of carcinogenesis . Studies on the evolution of melanoma from precursor lesions have revealed that the vast majority of melanomas harbor TERT promoter mutations , indicating that these immortalizing mutations are selected at an unexpectedly early stage of neoplastic progression ( Shain et al . , 2015 ) . The life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer has been analyzed recently by whole-genome sequencing analysis of 2658 cancers . This study has shown that early oncogenesis is characterized by mutations in a constrained set of driver genes and that the driver mutations that most commonly occur in a given cancer also tend to occur the earliest ( Gerstung et al . , 2020 ) . The prominent role of KRAS and TP53 genes in initiating carcinogenesis has been evident from the observation that their mutation rate in tumors far exceeds those of other genes , suggesting that their mutations are subject to positive selection during tumor evolution . Several types of approaches exploit this principle for the identification of genes that drive carcinogenesis: the rate of mutation of ‘driver genes’ must be significantly higher in the tumor tissue than those of ‘passenger genes’ ( PGs ) that have no role in the development of cancer but simply happen to mutate in the same tumor ( Parmigiani et al . , 2009; Meyerson et al . , 2010 ) . Unfortunately , methods based on mutation frequency alone cannot reliably indicate which genes are cancer drivers because the background mutation rates differ significantly as a consequence of intrinsic characteristics of DNA sequence and chromatin structure ( Michaelson et al . , 2012 ) . Intrinsic mutation hotspots are mutation hotspots that depend on the nucleotide sequence context , the mechanism of mutagenesis and the action of the repair and replication machineries ( Rogozin and Pavlov , 2003 ) . Genes enriched in intrinsic mutation hotspots may accumulate mutations at a significantly higher rate than other genes , creating the illusion of positive selection; based on recurrent mutations they may be mistakenly identified as cancer driver genes ( Carter , 2019; Buisson et al . , 2019 ) . In principle , we can avoid this danger if we compare the mutation pattern of the gene in the tumor tissue with that in the normal tissue the tumor has originated from . However , since the rate of mutation in such hotspots depends not only on the nucleotide sequence but also on the mechanism of mutagenesis and the integrity of DNA repair pathways ( Buisson et al . , 2019; Poulos et al . , 2018 ) mutation hotspots that arise during carcinogenesis could still create the illusion of positive selection . Chromatin organization also has a major influence on regional mutation rates in human cancer cells ( Schuster-Böckler and Lehner , 2012; Gonzalez-Perez et al . , 2019 ) . Since large-scale chromatin features , such as replication time and accessibility influence the rate of mutations , this may hinder the distinction of cancer driver genes whose high mutation rate reflects positive selection and PGs whose high mutation rate is the result of the distinctive features of the chromatin region in which they reside . Moreover , since the cell-of-origin chromatin organization shapes the mutational landscape , rates of somatic mutagenesis of genes in cancer are highly cell-type-specific ( Polak et al . , 2015 ) . Actually , since regional mutation density of ‘passenger’ mutations across the human chromosomes correlates with the cell type the tumor had originated from , this feature may be used to classify human tumors ( Salvadores et al . , 2019 ) . Through the comparison of the exome sequences of 3083 tumor-normal pairs Lawrence et al . , 2013 have discovered an extraordinary variation in mutation frequency and spectrum within cancer types across the genome , which is strongly correlated with DNA replication timing and transcriptional activity . The authors have shown that by incorporating mutational heterogeneity into their analyses , they could eliminate many of the apparent artefactual findings , improving the identification of genes truly associated with cancer . In a more recent study Lawrence et al . , 2014 compared the frequency of somatic point mutations in exome sequences from 4742 human cancers and their matched normal-tissue samples across 21 cancer types and identified 33 genes that were not previously known to be significantly mutated in cancer . They have concluded that 224 genes are significantly mutated in one or more tumor types . However , since background mutational frequency estimates are not sensitive enough , the list of driver genes ( defined as genes with increased somatic mutation rate ) is likely to be incomplete , but may also contain false positives . To overcome these limitations of mutation rate-based approaches , several methods use additional features that may distinguish driver genes and PGs . A major group of such approaches incorporates observations about the impact of mutations on the structure and function of well-characterized proteins encoded by proto-oncogenes and TSGs . Several computational methods aim to identify driver missense mutations most likely to generate functional changes that causally contribute to tumorigenesis ( Kaminker et al . , 2007; Carter et al . , 2009; Nussinov et al . , 2019 ) . In a different type of approach Youn and Simon , 2011 identified cancer driver genes as those for which the non-silent mutation rate is significantly greater than a background mutation rate estimated from silent mutations , indicating that the non-silent mutations are subject to positive selection . The authors have identified 28 genes as driver genes , the majority of the significant matches ( e . g . EGFR , CDKN2A , KRAS , STK11 , TP53 , NF1 , RB1 PTEN , and NRAS ) , were well-characterized OGs or TSGs known from earlier studies . In a more recent study , Zhou et al . , 2017 have identified 365 genes for which the ratio of the nonsynonymous to synonymous substitution rate was significantly increased , suggesting that they are subject to the positive selection of driver mutations . However , an obvious limitation of such approaches is that they implicitly assume that synonymous substitutions are selectively neutral and therefore the ratio of the nonsynonymous to synonymous substitution rate properly monitors selection . This is not necessarily true: some synonymous mutations may have a significant impact on splicing , RNA stability , RNA folding and translation of the transcript of the affected gene and may thus actually act as driver mutations ( Supek et al . , 2014; Hurst and Batada , 2017; Sharma et al . , 2019 ) . Furthermore , some mutation hotspots may significantly increase the rate of synonymous mutations therefore a low ratio of nonsynonymous to synonymous substitution rate does not necessarily indicate the absence of positive selection or the action of purifying selection . Vogelstein et al . , 2013 have used a heuristic approach to identify cancer driver genes . Since the patterns of mutations in the first and best-characterized OGs and TSGs were found to be highly characteristic and nonrandom , the authors assumed that the same characteristics are generally valid and may be used to identify previously uncharacterized cancer genes . For example , since many known OGs were found to be recurrently mutated at the same amino acid positions , to classify a gene as an OG , it was required that >20% of the recorded mutations in the gene are at recurrent positions and are missense . Similarly , since in the case of known tumor suppressors the driver mutations most frequently truncate the tumor suppressor proteins , to be classified as a TSG , it was required that >20% of the recorded mutations in the gene are truncating ( nonsense or frameshift ) mutations . Along these lines , Vogelstein et al . , 2013 have analyzed the patterns of the subtle mutations in the Catalogue of Somatic Mutations in Cancer ( COSMIC ) database to identify driver genes . As a proof of the reliability of this ‘20/20 rule’ , the authors emphasized that all well-documented cancer genes passed these criteria ( Vogelstein et al . , 2013 ) . Although this indicates that the approach detects known cancer genes , it does not guarantee that it detects all driver genes . Acknowledging that additional cancer driver genes might exist , the authors have introduced the term ‘Mut-driver gene’ for genes that contain a sufficient number or type of driver gene mutations to distinguish them from other genes , whereas for cancer genes that are expressed aberrantly in tumors but not frequently mutated they proposed the term ‘Epi-driver gene’ . Based on these analyses , the authors have concluded that out of the 20 , 000 human protein-coding genes , only 125 genes qualify as Mut-driver genes , of these , 71 are TSGs and 54 are OGs ( Vogelstein et al . , 2013 ) . The authors have expressed their conviction that nearly all genes mutated at significant frequencies had already been identified and that the number of Mut-driver genes is nearing saturation . This conclusion may not be justified since the criteria used to identify OGs and tumor suppressors appear to be too stringent and somewhat arbitrary . In search of additional driver genes , Tamborero et al . , 2013 employed five complementary methods to find genes showing signals of positive selection and identified a list of 291 ‘high-confidence cancer driver genes’ acting on 3205 tumors from 12 different cancer types . Bailey et al . , 2018 used multiple advanced algorithms to identify cancer driver genes and driver mutations . Based on their PanCancer and PanSoftware analysis spanning 9423 tumor exomes , comprising all 33 of The Cancer Genome Atlas projects and using 26 computational tools they have identified 299 driver genes showing signs of positive selection . Their sequence and structure-based analyses detected >3400 , 400 putative missense driver mutations and 60–85% of the predicted mutations were validated experimentally as likely drivers . Zhao et al . , 2019a have developed driverMAPS ( Model-based Analysis of Positive Selection ) , a model-based approach for driver gene identification that captures elevated mutation rates in functionally important sites and spatial clustering of mutations . The authors have identified 255 known driver genes as well as 170 putatively novel driver genes . Currently , COSMIC ( the Catalogue Of Somatic Mutations In Cancer , https://cancer . sanger . ac . uk/cosmic ) is the most detailed and comprehensive resource for exploring the effect of subtle somatic mutations of driver genes in human cancer ( Tate et al . , 2019 ) but COSMIC also covers all the genetic mechanisms by which somatic mutations promote cancer , including non-coding mutations , gene fusions , and copy-number variants . In parallel with COSMIC's variant coverage , the Cancer Gene Census ( CGC , https://cancer . sanger . ac . uk/census ) describes a curated catalogue of genes driving every form of human cancer ( Sondka et al . , 2018 ) . CGC has recently introduced functional descriptions of how each gene drives disease , summarized into the cancer hallmarks . CGC describes in detail the effect of a total of 719 cancer-driving genes , encompassing Tier 1 genes ( 574 genes ) and a list of Tier 2 genes ( 145 genes ) from more recent cancer studies that show less detailed indications of a role in cancer . In a different type of approach , Torrente et al . , 2016 used comprehensive maps of human gene expression in normal and tumor tissues to identify cancer related genes . These analyses identified a list of genes with systematic expression change in cancer . The authors have noted that the list is significantly enriched with known cancer genes from large , public , peer-reviewed databases , whereas the remaining ones were proposed as new cancer gene candidates . A recent study has provided a comprehensive catalogue of cancer-associated transcriptomic alterations with the top-ranking genes carrying both RNA and DNA alterations . The authors have noted that this catalogue is enriched for cancer census genes ( Calabrese et al . , 2020 ) . Using transposon mutagenesis in mice , several laboratories have conducted forward genetic screens and identified thousands of candidate genetic drivers of cancer that are highly relevant to human cancer . The Candidate Cancer Gene Database ( CCGD , http://ccgd-starrlab . oit . umn . edu/ ) is a manually curated database containing a unified description of all identified candidate driver genes ( Abbott et al . , 2015 ) . In summary , although a variety of approaches have been developed to identify ‘cancer genes’ , there is significant disagreement as to the number of genes involved in carcinogenesis . Some of the studies argue that the number is in the 200–700 range , other approaches suggest that their number may be much higher . Since the ultimate goal of cancer genome projects is to discover therapeutic targets , it is important to identify all true cancer genes and distinguish them from PGs and candidates that do not play a significant role in the process of carcinogenesis . We must point out , however , that the majority of genomics-based methods were biased as they defined the aim of cancer genomics as the identification of mutated driver genes ( equating them with ‘cancer genes’ ) that are causally implicated in oncogenesis ( Futreal et al . , 2004 ) . In all these studies , the underlying rationale for interpreting a mutated gene as causal in cancer development is that the mutations are likely to have been positively selected because they confer a growth advantage on the cell population from which the cancer has developed . An inevitable consequence of this focus on positive selection was that most studies neglected the possibility that negative selection may also play a significant role in tumor evolution . In principle , with respect to its effect on carcinogenesis , a somatic mutation may promote or may hinder carcinogenesis or may have no effect on carcinogenesis . In cancer genomics , the mutations that promote carcinogenesis ( and are subject to positive selection during tumor evolution ) are called ‘driver mutations’ to distinguish them from ‘passenger mutations’ that do not play a role in carcinogenesis ( and are not subject to positive or negative selection during tumor evolution ) . Mutations that impair the growth , survival , and invasion of tumor cells have received much less attention , although they could also play a significant role in shaping the mutation pattern of genes during carcinogenesis . Hereafter , we will refer to this category of mutations as ‘cancer blocking mutations’ because they are deleterious from the perspective of tumor growth . As discussed above , in cancer genomics , genes are usually assigned to just two categories with respect to their role in carcinogenesis: ( 1 ) ‘PGs’ ( or bystander genes ) that play no significant role in carcinogenesis and their mutations are passenger mutations; ( 2 ) ‘driver genes’ that drive carcinogesis when they acquire driver mutations . The problem with this binary driver gene-PG categorization is that some genes with functions essential for the growth and survival of tumor cells ( hereafter referred to as ‘tumor essential genes’ ) may not easily fit into either category . The coding sequences of driver genes ( TSGs , proto-oncogenes ) , PGs , and TEGs are predicted to experience markedly different patterns of selection during tumor evolution . The mutation patterns of selectively neutral , bona fide PGs are likely to reflect the lack of positive and negative selection , whereas in the case of TEGs purifying selection is predicted to dominate . In the case of TSGs , the mutation pattern is expected to reflect positive selection for inactivating driver mutations . Proto-oncogenes , however , are expected to show signs of both positive selection for activating mutations and negative selection for inactivating , ‘cancer blocking’ mutations as their activity is essential for their oncogenic role . In the coding regions of proto-oncogenes positive selection for driver mutations is expected to favor nonsynonymous substitutions over synonymous substitutions only at sites that are critical for the novel , oncogenic function . For these sites ( and these sites only ) , the ratio of nonsynonymous to synonymous rates is expected to be significantly greater than one reflecting positive selection . If there are many such sites in a protein , or selection is extremely strong the overall nonsynonymous to synonymous ratio for the entire protein may also be significantly higher than one , otherwise the effect of positive selection on the synonymous to nonsynonymous ratio may be overridden by purifying selection at other sites ( Patthy , 1999 ) . In harmony with some of these expectations , using just the ratio of the nonsynonymous to synonymous substitution rate as a measure of positive or negative selection , Zhou et al . , 2017 have shown that in cancer genomes , the majority of genes had nonsynonymous to synonymous substitution rate values close to one , suggesting that they belong to the PG category . The authors have identified a total of 365 potential cancer driver genes that had nonsynonymous to synonymous substitution rate values significantly greater than one ( reflecting the dominance of positive selection ) . Conversely , 923 genes had nonsynonymous to synonymous substitution rate values significantly less than one ( reflecting the dominance of negative selection ) , leading the authors to suggest that these negatively selected genes may be important for the growth and survival of cancer cells . Pyatnitskiy et al . , 2015 have also used the dN/dS ratio ( the ratio of nonsynonymous and synonymous substitution rates ) as an indicator of selective pressure and have identified 91 protein-coding genes ( ’essential cancer proteins’ ) with amino acid sequences under negative selection . Realizing that genes whose wild-type coding sequences are needed for tumor growth are also of key interest for cancer research , Weghorn and Sunyaev , 2017 have also focused on the role of negative selection in human cancers . The authors have used an approach based on the principle that both positive and negative selection can be inferred by comparing the observed mutation rates to the expectation under the sole action of the mutation process . As the authors have pointed out , identification , and analysis of true negatively selected , ’ undermutated’ genes is particularly difficult since the sparsity of mutation data results in lower statistical power , making conclusions less reliable . Although the signal of negative selection was exceedingly weak , the authors have noted that the group of negatively selected candidate genes is enriched in cell-essential genes identified in a CRISPR screen ( Wang et al . , 2015a ) , consistent with the notion that one of the potential causes of negative selection is the maintenance of genes that are responsible for basal cellular functions . Based on pergene estimates of negative selection inferred from the pan-cancer analysis the authors have identified 147 genes with significant negative selection . The authors have noted that among the 13 genes showing the strongest signs of negative selection there are several genes ( ATAT1 , BCL2 , CLIP1 , GALNT6 , CKAP5 , and REV1 ) that are known to promote carcinogenesis . In a similar work , Martincorena et al . , 2017 have used the normalized ratio of non-synonymous to synonymous mutations , to quantify selection in coding sequences of cancer genomes . Using a nonsynonymous-to-synonymous substitution rate value >1 as a marker of cancer genes under positive selection , they have identified 179 cancer genes , with about 50% of the coding driver mutations being found to occur in novel cancer genes . The authors , however , have concluded that purifying selection is practically absent in tumors since nearly all ( >99% ) coding mutations are tolerated and escape negative selection . The authors have suggested that this remarkable absence of negative selection on coding point mutations in cancer indicates that the vast majority of genes are dispensable for any given somatic lineage , presumably reflecting the buffering effect of diploidy and the inherent resilience and redundancy built into most cellular pathways . The key message of Martincorena et al . , 2017 that negative selection has no role in cancer evolution had a major impact on cancer genomics research as reflected by several commentaries in major journals of the field that have propagated this conclusion ( Bakhoum and Landau , 2017; Koch , 2017; Vitale and Galluzzi , 2018 ) . Some more recent studies , however , contradict this conclusion . Although Zapata et al . , 2018 have also used the ratio of nonsynonymous-to-synonymous substitutions to identify genes that are under selection , they have detected significant negative selection in the case of 25 genes . López et al . , 2020 , focusing on dN/dS values for truncating mutations , have shown that purifying selection of essential genes is significant in early phases of tumor evolution ( before whole genome duplications ) , whereas whole-genome doubling allows the accumulation of deleterious alterations . Tilk et al . , 2020 have shown that appreciable negative selection ( dN/dS ~ 0 . 4 ) is present in tumors with a low mutational burden , while the majority of tumors exhibit dN/dS ratios approaching 1 , suggesting that tumors with higher mutational burden do not remove deleterious mutations . Van den Eynden and Larsson , 2017 , however , cautioned that it is crucial to take into account mutational signatures when applying the dN/dS metric to cancer somatic mutation data . For example , the authors have shown that the low dN/dS values observed in malignant melanoma may be due to the predominance of C to T mutations in this tumor and do not necessarily indicate gene essentiality . The authors have also shown that purifying selection is very limited and similar in all tumor types if the dN/dS metric uses mutational signature-derived substitution probabilities . In view of the contradicting conclusions about the significance of negative selection in tumor evolution , in the present work we have reexamined this question using an approach that attempts to overcome some of the problems highlighted by earlier studies . First , most studies used a single dN/dS metric to measure nonsynonymous to synonymous substitution rates as indicators of selective pressure and paid less attention to the fact that the strength of purifying selection is an order of magnitude greater for nonsense mutations than for missense mutations ( Gorlov et al . , 2006 ) . Furthermore , the use of a single dN/dS value for a transcript may preclude the simultaneous detection of positive and negative selection of activating and inactivating mutations , both of which might operate for a given gene . To overcome these limitations , in the present study we have used a clustering-based approach that can detect different signals of selection manifested in rates of nonsense , missense versus silent substitutions in the coding regions of genes . Second , an inherent problem with the detection of purifying selection in tumor tissues is that putative TEGs are likely to be undermutated relative to PGs and driver genes , resulting in low statistical power of their analyses based on dN/dS metrics . We have reduced this problem by combining subtle somatic mutations from different tumors types and limiting our work to transcripts that have at least 100 somatic mutations in tumors . ( Note that the requirement of a minimum number of mutations does not place a theoretical limit on this approach; progress with genome-wide screens and collection of more data is overcoming this limitation . ) In harmony with earlier observations , our analyses have confirmed that the vast majority of human genes are PGs that do not show detectable signals of selection , whereas known TSGs are positively selected for inactivating ( primarily nonsense and frame-shift ) mutations . Known OGs , however , were found to display signals of both negative selection for inactivating ( nonsense , frame-shift ) mutations and positive selection for activating ( missense ) mutations . Improved detection of signals of selection has permitted the identification of a number of novel driver genes that are likely to play important roles in carcinogenesis as TSGs or as OGs . Significantly , we have identified a cluster of human genes that show clear signs of negative selection during tumor evolution , suggesting that their functional integrity is essential for the growth and survival of tumor cells . The group of negatively selected genes includes genes known to play critical roles in the Warburg effect of cancer cells , others are known to mediate invasion and metastasis of tumor cells , indicating that negatively selected TEGs may prove a rich source for novel targets for tumor therapy . The rationale of the analyses described in the present work is that — due to their different roles in carcinogenesis — proto-oncogenes , TSGs , TEGs , and PGs are expected to experience different patterns of selection during tumor evolution and this is reflected in the relative rates of missense , nonsense , and silent mutations of their protein-coding regions . To monitor these differences , we have calculated for each transcript the fraction of somatic substitutions that could be assigned to the silent ( fS ) , misssense ( fM ) , and nonsense ( fN ) category and analyzed their relative rates . ( For details , the reader should consult the Materials and methods section ) . Our analyses have shown that in 3D scatter plots of the fS , fM , and fN values of transcripts the majority of genes are present in a central cluster characterized by fS , fM , and fN values close to those expected assuming no mutation bias and absence of selection , consistent with the view that they correspond to PGs ( Figure 2 ) . Known OGs , however , were found in a separate cluster characterized by higher fM values , reflecting positive selection for missense mutations , whereas the cluster of known TSGs has higher fN values , reflecting positive selection for truncating nonsense mutations ( Figure 2B and C ) . Known cancer genes also separate from the majority of human genes in 3D scatter plots of rSM , rNM , rNS parameters , defined as the ratio of fS/fM , fN/fM , fN/fS , respectively ( Figure 3 ) . In these scatter plots , OGs separate from the central cluster in having lower rSM and rNM values , whereas TSGs have higher rNS and rNM values than those of the central cluster ( Figure 3 ) . The separation of known cancer genes from the majority of human genes is even more manifest in 3D scatter plots of parameters rSMN , rMSN , and rNSM defined as the ratio of fS/ ( fM+fN ) , fM/ ( fS+fN ) , and fN/ ( fS+fM ) , respectively ( Figure 4 ) . In these plots , the transcripts form a three-pronged cluster , with known OGs and TSGs being present on separate spikes of this cluster , the rMSN and rNSM spikes , respectively ( Figure 4 ) . There is , however , a fourth cluster of genes that deviates from the clusters of PGs , OGs , and TSGs ( Figures 2 , 3 and 4 ) . The high fS , rSM , and rSMN values of the transcripts in this group suggest that they are subject to purifying selection during tumor evolution , raising the possibility that this group may contain genes essential for the survival of tumors . The analyses discussed above did not take into account the impact of differences in mutation probability on the fN , fM , and fS values of transcripts . To check the influence of this factor , we have calculated the expected fN* , fM* , and fS* values for all human transcripts using the probabilities of the six substitution classes ( C>A , C>G , C>T , T>A , T>C , and T>G ) observed across tumors ( for deatails the reader should consult the Materials and methods section ) . The various types of observed/expected ratios ( rN* , rM* , rS; rSM , rNM* , rNS; rSMN , rMSN* and rNSM* ) were calculated for each transcript and the data were analyzed in 3D scatter plots as described above for the observed values . As shown in Figures 5 , 6 and 7 , the distribution of transcripts in these 3D scatter plots are similar to those observed in the corresponding Figures 2 , 3 and 4 , indicating that the separation of the clusters of PGs , OGs , TSGs , and TEGs is relatively insensitive to transcript-specific differences in mutation probabilities . We assumed that the genes whose patterns of subtle mutations deviate significantly ( by more than 2SD ) from those of prototypical PGs are enriched in cancer genes that play important role in carcinogenesis . The patterns of subtle mutations of candidate cancer genes assign them to one of the three main clusters that show signs of positive and/or negative selection ( see Figures 2–7 ) . ( A ) Genes positively selected for inactivating ( nonsense and frame-shift ) mutations – putative TSGs; ( B ) genes positively selected for missense mutations and negatively selected for inactivating mutations – putative proto-oncogenes; ( C ) negatively selected genes – putative TEGs . The assumption that the cancer genes assigned to these three clusters play significant roles in carcinogenesis has strong support in the case of the first two categories: the approach used in the present study correctly assigned the known , ‘gold standard’ TSGs and OGs ( Supplementary file 1 ) . In the case of the third category , however , no similar gold standard exists for TEGs . To check the validity and predictive value of the assumption that the genes assigned to the three clusters play critical roles in carcinogenesis , we have selected a number of genes at random from each cluster for further in-depth analyses . We have used three criteria to select genes for detailed analyses from the combined list of candidate cancer genes that deviate from the central clusters of PGs by more than 2SD ( see Materials and methods ) . ( 1 ) The candidate gene is among the genes showing the strongest signals of selection characteristic of the given group . ( 2 ) The candidate gene is novel in the sense that it is not listed among the 145’ gold standard’ OGs and TSGs of Vogelstein et al . , 2013 or among the 719 cancer genes of CGC ( Sondka et al . , 2018 ) . ( 3 ) There is substantial experimental information in the scientific literature on the given gene to permit the assessment of its role in carcinogenesis . The genes discussed below include genes positively selected for truncating mutations ( putative TSGs ) , genes positively selected for missense mutations and negatively selected for inactivating mutations ( putative proto-oncogenes ) and negatively selected genes ( putative TEGs ) . In the main text , we summarize only the major conclusions of our analyses; for annotations of the individual genes , the reader should consult Appendix 1 . We discuss examples of negatively selected genes in the main text in more detail since earlier studies that focussed on positive selection of driver mutations inevitably missed these genes . We also discuss some instructive examples of’ false’ hits , that is cases where the mutation parameters deviate significantly from those of PGs , but this deviation is not due to selection . We have selected genes positively selected for truncating mutations from the combined list of candidate transcripts , that is , transcripts whose parameters deviate from those of PGs by more than 2SD ( for details see Materials and methods ) . We have used the additional restriction that genes with indel_rNSM <0 . 125 were excluded ( Supplementary file 1 ) , thereby removing OGs and TEGs . Out of the 624 genes that satisfy these criteria , we have subjected B3GALT1 , BMPR2 , BRD7 , ING1 , MGA , PRRT2 , RASA1 , RNF128 , SLC16A1 , SPRED1 , TGIF1 , TNRC6B , TTK , ZNF276 , ZC3H13 , ZFP36L2 , and ZNF750 to further analysis . Annotation of the majority of these genes ( BMPR2 , BRD7 , ING1 , MGA , PRRT2 , RASA1 , RNF128 , SLC16A1 , SPRED1 , TGIF1 , TNRC6B , ZC3H13 , ZFP36L2 , and ZNF750 ) has provided convincing evidence for their role in carcinogenesis as tumor suppressors . Interestingly , experimental evidence suggests that TTK , encoding dual specificity protein kinase TTK , is a proto-oncogene that may be converted to an OG by truncating mutations affecting the very C-terminal end of the protein , downstream of its kinase domain ( for further details see Appendix 1 ) . Our annotations suggest that B3GALT1 , ZNF276 are false positives whose apparent mutation pattern deviates significantly from those of PGs , but this deviation is not due to selection . Based on functional annotation of the TSGs identified and validated in the present work ( see Appendix 1 ) , we have assigned them to various cellular processes of cancer hallmarks in which they are involved ( Table 1 ) . Comparison of the list of 624 genes present in this dataset ( CG_SSI2SD rNSM >0 . 125 ) with lists identified by others ( Supplementary file 1 ) revealed that ~60–100 of our candidate TSG-like genes are also found in several gene lists identified by others through analyses of somatic mutations of tumor tissues . Many of the genes selected for annotation are present in at least one of the candidate gene lists identified by others; the genes of MGA , RASA1 , TGIF1 , ZFP36L2 , and ZNF750 are present in multiple cancer gene lists ( Supplementary file 1 ) . It is noteworthy , however , that RNF128 , SLC16A1 , SPRED1 , TNRC6B , and TTK are novel in that they are found only among the candidate cancer genes identified by forward genetic screens in mice ( Abbott et al . , 2015 ) or among the genes whose expression changes in cancer ( Torrente et al . , 2016 ) . We have also analyzed the genes present in dataset CG_SO2SD_rNSM >3 , that is , candidate cancer genes for which the observed rNSM values are more than threefold higher than expected taking into account mutational signature-derived substitution probabilities of tumors ( Supplementary file 2 ) . We have found that 164 ( 100% ) of the 164 genes present in this dataset are also present in the dataset CG_SSI2SD rNSM >0 . 125 . It is noteworthy that the majority of candidate TSGs selected for annotation ( B3GALT1 , BMPR2 , BRD7 , ING1 , MGA , PRRT2 , RASA1 , SLC16A1 , SPRED1 , TGIF1 , ZNF276 , ZFP36L2 , and ZNF750 ) are present among the genes shared by the two datasets that show the strongest signals of positive selection for nonsense substitutions . We have selected genes positively selected for missense and negatively selected for inactivating mutations from the list of candidate transcripts using the restriction that genes with rMSN <3 . 00 ( 440 ) were excluded , thereby removing the majority of TSGs and TEGs ( Supplementary file 1 ) . Out of the 440 genes that satisfy these criteria , we have subjected AURKA , CDK8 , IDH3B , MARCH7 , RIT1 , YAP1 , and YES1 to further analysis . Annotation of these genes has confirmed that they play important roles in carcinogenesis as OGs . Three of these genes encode kinases ( Aurora kinase A , also known as breast tumor-amplified kinase; cyclin-dependent kinase 8; tyrosine-protein kinase Yes , also known as proto-oncogene c-Yes ) but unlike many other oncogenic kinases , these OGs do not show significant clustering of missense mutations . In fact , only in the case of IDH3B and RIT1 did we observe clustering of missense mutations , indicating that recurrent mutation is not an obligatory property of proto-oncogenes . Based on functional annotation of the novel OGs identified and validated in the present work ( see Appendix 1 ) , we have assigned them to various cellular processes of cancer hallmarks in which they are involved ( Table 1 ) . Comparison of this list of 440 genes ( CG_SO2SD rMSN >3 . 00 ) with the lists of cancer genes identified by others ( Supplementary file 1 ) revealed that ~60–100 of our candidate OG-like genes are present in cancer gene lists identified by others through analyses of somatic mutations of tumor tissues . Out of the genes that we have selected for annotation only the RIT1 gene has been identified by others as an OG , based on the analysis of somatic mutations ( Supplementary file 1 ) . AURKA and IDH3B are not present in any of the lists of cancer genes , whereas CDK8 , MARCH7 , YAP1 , and YES1 are found among the more than 9000 candidate cancer genes identified by forward genetic screens in mice ( Abbott et al . , 2015 ) . Interestingly , TTK , identified as a gene positively selected for truncating mutations ( see list CG_SSI2SD rNSM >0 . 125 ) , but annotated as an OG , is also present in the list of genes positively selected for missense mutations ( CG_SO2SD rMSN >3 . 00 ) . We have also analyzed the genes present in dataset CG_SO2SD_rMSN >1 . 50 , that is , genes for which the observed rMSN values are more than 1 . 5-fold higher than expected taking into account mutational signature-derived substitution probabilities of tumors ( Supplementary file 2 ) . We have found that 119 ( 98 . 3% ) of the 121 genes present in this dataset are also present in the dataset CG_SO2SD rMSN >3 . 00 . It should be noted that the majority of candidate OGs selected for annotation ( AURKA , RIT1 , YAP1 , and YES1 ) are found among the genes shared by the two datasets , showing strong signals of positive selection for missense substitutions . We have selected putative TEGs from the list of candidate cancer genes using the restriction that we have excluded genes with rSMN <0 . 5 to eliminate OGs and TSGs ( Supplementary file 3 ) . Out of the 505 genes , we have subjected CX3CR1 , FOXG1 , FOXP2 , G6PD , MAPK13 , MLLT3 , NOVA1 , PNCK , RUNX2 , SLC16A3 , SLC2A1 , SLC2A8 , TBP , TBXA2R , TP73 , and TRIB2 to further analysis . Our analyses have confirmed that in the majority of cases ( CX3CR1 , FOXG1 , G6PD , MAPK13 , NOVA1 , PNCK , SLC16A3 , SLC2A1 , SLC2A8 , TBXA2R , TP73 , TRIB2 ) the high synonymous-to-nonsynonymous and nonsense mutation rates could be interpreted as evidence for purifying selection during tumor evolution . There were , however , several examples ( e . g . DSPP , FOXP2 , MLLT3 , RUNX2 , TBP ) where high synonymous-to-nonsynonymous and nonsense mutation rates were found to reflect increased rates of synonymous substitution ( due to the presence of mutation hotspots ) , rather than decreased rates of nonsynonymous and nonsense substitutions that could be due to purifying selection ( for details see Appendix 1 ) . Annotations of the genes CX3CR1 , FOXG1 , G6PD , MAPK13 , NOVA1 , PNCK , SLC16A3 , SLC2A1 , SLC2A8 , TBXA2R , TP73 , and TRIB2 have confirmed that all of them play important roles in carcinogenesis ( see Appendix 1 ) permitting their assignment to various cellular processes of cancer hallmarks ( Table 1 . ) . As discussed below ( and in Appendix 1 ) , they fulfill pro-oncogenic functions by promoting cell proliferation ( FOXG1 , MAPK13 , PNCK , TRIB2 ) , evasion of cell death ( MAPK13 , PNCK , TP73 ) , replicative immortality ( NOVA1 ) , reprogramming of energy metabolism of cancer cells ( G6PD , SLC16A3 , SLC2A1 , SLC2A8 ) , inducing tumor promoting inflammation ( CX3CR1 , MAPK13 ) and invasion and metastasis ( CX3CR1 , TBXA2R ) . In view of the pro-oncogenic role of these proteins , it is noteworthy , that G6PD , MAPK13 , PNCK , SLC16A3 , and SLC2A1 are among the candidate cancer genes identified by forward genetic screens in mice ( Abbott et al . , 2015 ) . Comparison of our list of 505 negatively selected genes ( CG_SO2SD_rSMN > 0 . 5 ) with those identified by others have revealed very little similarity ( Supplementary file 3 ) . Out of the 147 genes of Weghorn and Sunyaev , 2017 , only one is present in the list of top-ranking negatively selected genes identified in the present study . Similarly , only four of the 25 genes of Zapata et al . , 2018 and only five of the 91 genes of Pyatnitskiy et al . , 2015 are found in our list of negatively selected genes ( Supplementary file 3 ) . We observed a greater similarity when we compared our list of negatively selected genes with that of Zhou et al . , 2017; 32 of the 112 genes identified by Zhou et al . , 2017 are also present among the 505 negatively selected genes identified in the present work ( Supplementary file 3 ) . It is noteworthy that top-ranking genes present in both lists include the ACKR3 , TBP , and MLLT3 genes . As discussed in Appendix 1 , the apparent signals of negative selection ( high synonymous-to-nonsynonymous rates ) of genes like DSPP , FOXP2 , MLLT3 , RUNX2 , and TBP may reflect the presence of mutation hotspots generating silent mutations and not purifying selection . Zhou et al . , 2017 have also noted that "some cancer genes also show negative selection in cancer genomes , such as the OG MLLT3" and that "interestingly , MLLT3 has recurrent synonymous mutations at amino acid positions 166 to 168" . Apparently , the authors did not realize that this observation of recurrent silent substitutions ( in a poly-Ser region of the protein ) questions the validity of the claim that the unusually low nonsynonymous to synonymous rate is due to negative selection ( for more detail see Appendix 1 ) . In summary , the pro-oncogenic , negatively selected genes annotated and validated in the present work are missing from the earlier lists of negatively selected genes ( Zhou et al . , 2017; Pyatnitskiy et al . , 2015; Weghorn and Sunyaev , 2017; Zapata et al . , 2018 ) . A possible explanation for the lack of similarity of top-ranking negatively selected genes identified in the present study with those identified by others is that we have limited our work to transcripts that have at least 100 somatic mutations . It is noteworthy that a large fraction of genes identified by others did not pass this requirement ( see Materials and methods ) . We have also analyzed the genes present in dataset CG_SO2SD rSMN >1 . 50 , that is , candidate cancer genes for which the observed rSMN values are more than 1 . 5-fold higher than expected taking into account mutational signature-derived substitution probabilities of tumors ( Supplementary file 4 ) . We have found that 200 ( 86 . 5% ) of the 231 genes present in this dataset are also present in dataset CG_SO2SD rSMN >0 . 5 . It should be noted that the majority of candidate TEGs selected for annotation ( CX3CR1 , FOXG1 , FOXP2 , MAPK13 , MLLT3 , NOVA1 , RUNX2 , SLC16A3 , SLC2A8 , TBP , TBXA2R , and TRIB2 ) are found among the 200 genes shared by the two datasets and that show the strongest signals of negative selection for missense and nonsense substitutions . As we have emphasized in the Introduction , the conclusions drawn from earlier studies searching for signs of negative selection are highly controversial . A highly publicized study has propagated the conclusion that negative selection has no role in tumor evolution ( Martincorena et al . , 2017; Bakhoum and Landau , 2017; Koch , 2017; Vitale and Galluzzi , 2018 ) . Martincorena et al . , 2017 have argued that the practical absence of purifying selection during tumor evolution is due to the buffering effect of diploidy and functional redundancy of most cellular pathways . A recent study has examined the influence of functional redundancy on the essentiality of genes ( De Kegel and Ryan , 2019 ) . The authors have used CRISPR score profiles of 558 genetically heterogeneous tumor cell lines and converted continuous values of gene CRISPR scores to binary essential and nonessential calls . These analyses have shown that 1014 genes belong to a category of ‘broadly essential genes’ , that is , these genes were found to be essential in at least 90% of the 558 cell lines . De Kegel and Ryan , 2019 have shown that , compared to singleton genes , paralogs are less frequently essential and that this is more evident when considering genes with multiple paralogs or with highly sequence-similar paralogs . In harmony with these conclusions , López et al . , 2020 have found that purifying selection of essential genes is significant in early phases of tumor evolution but in later phases whole-genome doubling allows the accumulation of deleterious alterations . Since the group of negatively selected genes identified by Weghorn and Sunyaev , 2017 were shown to be enriched in cell-essential genes ( Wang et al . , 2015a ) , the authors have proposed that the major cause of negative selection during tumor evolution is the maintenance of genes that are responsible for basal cellular functions . Nevertheless , Weghorn and Sunyaev , 2017 have pointed out that negative selection is also expected to act on neoantigens , expanding the possible scope of purifying selection beyond cell essentiality . Although analyses of negatively selected genes have led Zapata et al . , 2018 to conclude , "Processes that are most strongly conserved are those that play fundamental cellular roles such as protein synthesis , glucose metabolism , and molecular transport" they also emphasized the possible importance of less basic functions . Since the immune system is capable of discriminating cancer cells by recognizing mutated epitope sequences the authors have hypothesized that native epitope sequences would be protected from nonsynonymous mutations during tumor evolution . In harmony with this hypothesis , the authors have observed signals of selection in the immunopeptidome and proteins of the epitope presentation machinery , arguing for their importance in the evasion of immune surveillance by tumors . Gene Ontology analysis of the negatively selected ‘essential cancer proteins’ identified by Pyatnitskiy et al . , 2015 have revealed enrichment of essential proteins related to membrane and cell periphery , leading the authors to speculate that this could be a sign of immune system-driven negative selection of cancer neo-antigens . In summary , there is some disagreement about the significance of purifying selection in tumor evolution and whether tumor essential functions can be equated with basic cellular functions . In order to assess the contribution of cell-essentiality to purifying selection during tumor evolution , we have plotted various measures of negative selection of human genes as a function of their cell-essentiality scores determined by De Kegel and Ryan , 2019 . These analyses have shown that there is a very weak , positive correlation ( Pearson's r = 0 . 05345 , p<0 . 05 ) between rSMN ( a measure of purifying selection ) and the cell-essentiality scores of transcripts ( Figure 8 , Supplementary file 5 ) . Since , by definition , there is a negative correlation between the essentiality of genes and their cell-essentiality scores ( De Kegel and Ryan , 2019 ) , our data indicate that cell essentiality does not contribute significantly to purifying selection during tumor evolution . It is also noteworthy that the cell essentiality scores of negatively selected genes ( CG_SO2SD rSMN >0 . 5 ) are not significantly different from those of PGs ( Figure 8 , Supplementary file 5 ) . Comparison of CRISPR scores ( −0 . 07665 ± 0 . 17269 ) of the cluster of negatively selected genes of CG_SO2SD rSMN >0 . 5 listed in Supplementary file 3 with CRISPR scores ( −0 . 09506 ± 0 . 24168 ) of the cluster of PGs ( PG_SOr3_1SD ) revealed that they are not significantly different ( p>0 . 05 ) . This indicates that basic cell-essentiality per se does not explain the purifying selection observed for this cluster of genes . Comparison of the lists of negatively selected genes identified in the present work with the 1014 ‘broadly essential genes’ defined by De Kegel and Ryan , 2019 has revealed that there is practically no overlap between the two groups . Only six of the 1014 broadly essential genes are included in our list of negatively selected genes ( Supplementary file 3 ) . This observation also suggests that cell-essentiality defined by CRISPR scores determined experimentally on cell lines is not relevant for negative selection during tumor evolution in vivo . Our analyses of cases of strong purifying selection suggest that it has more to do with a function specifically required by the tumor cell for its growth , survival , and metastasis than with general basic cellular functions ( Table 1 ) . It is noteworthy in this respect , that the genes showing the strongest signals of negative selection include several plasma membrane receptor proteins ( e . g . ACKR3 , CCR2 , CCR5 , CX3CR1 , TBXA2R ) that cancer cells utilize to promote migration , invasion , and metastasis ( Appendix 1 ) . Significantly , these proteins exert their biological functions ( in cell migration , inflammation , angiogenesis etc . ) primarily at the organism level , therefore their cell-essentiality scores may have little to do with their overall essentiality for tumor growth and metastasis . Inspection of the data of De Kegel and Ryan , 2019 shows that ACKR3 , CX3CR1 , TBXA2R were not assigned to the essential category in any of the 558 tumor cell lines tested . Negatively selected , TEGs identified in the present study do include proteins involved in cell-level processes: they promote cell proliferation ( FOXG1 , MAPK13 , PNCK , and TRIB2 ) , evasion of cell death ( MAPK13 , PNCK , and TP73 ) , replicative immortality ( e . g . NOVA1 ) , or they are crucial for the reprogramming of energy metabolism in cancer cells ( e . g . GAPD , SLC16A3 , SLC2A1 , and SLC2A8 ) . Nevertheless , their negative selection is unlikely to be a mere reflection of their basic cellular functions . Rather , it reflects the exceptional role of the corresponding cancer hallmarks ( evasion of cell death , replicative immortality , reprogramming of metabolism ) in carcinogenesis ( Figure 1 ) . In harmony with this conclusion NOVA1 , SLC16A3 , SLC2A8 , and TP73 were assigned to the essential category by De Kegel and Ryan , 2019 in less than 10% of the 558 tumor cell lines tested . SLC2A1 ( glucose transporter 1 ) is an exception in as much as it was found to be cell-essential in 41% of the cell lines . Significantly , several nutrient transporter genes ( SLC16A3 , SLC2A1 , and SLC2A8 ) were found among the genes showing the strongest signs of purifying selection . It must be mentioned here that Zapata et al . , 2018 have also noted that the glucose transporters SLC2A1 and SLC2A8 and the lactate transporter SLC16A3 show signs of purifying selection , although they did not list these genes among the 25 genes with significant negative selection . The most likely explanation for the tumor essentiality of transporter protein genes SLC16A3 , SLC2A1 , and SLC2A8 is that tumor cells have an increased demand for nutrients and this demand is met by enhanced cellular entry of nutrients through upregulation of specific transporters ( Ganapathy et al . , 2009 ) . The uncontrolled cell proliferation of tumor cells involves major adjustments of energy metabolism in order to support cell growth and division in the hypoxic microenvironments in which they reside . Otto Warburg was the first to observe an anomalous characteristic of cancer-cell energy metabolism: even in the presence of oxygen , cancer cells limit their energy metabolism largely to glycolysis , leading to a state that has been termed ‘aerobic glycolysis’ ( Warburg , 1956a; Warburg , 1956b ) . Cancer cells are known to compensate for the lower efficiency of ATP production through glycolysis than oxidative phosphorylation by upregulating glucose transporters , such as facilitated glucose transporter member 1 , GLUT1 ( encoded by the SLC2A1 gene ) , thus increasing glucose import into the cytoplasm ( Jones and Thompson , 2009; DeBerardinis et al . , 2008; Hsu and Sabatini , 2008 ) . The markedly increased uptake of glucose has been documented in many human tumor types , by visualizing glucose uptake through positron emission tomography . The reliance of tumor cells on glycolysis is also supported by the hypoxia response system: under hypoxic conditions , not only glucose transporters but also multiple enzymes of the glycolytic pathway are upregulated ( Jones and Thompson , 2009; DeBerardinis et al . , 2008; Semenza , 2010a; Semenza , 2010b; Kroemer and Pouyssegur , 2008 ) . In our view , the central role of GLUT1 in cancer metabolism is reflected by the fact that the SLC2A1 gene encoding this glucose transporter is among the genes that show the strongest signals of purifying selection . The key importance of GLUT1 in cancer may be illustrated by the fact that high levels of GLUT1 expression correlates with a poor overall survival and is associated with increased malignant potential , invasiveness , and poor prognosis ( Wang et al . , 2017a; Deng et al . , 2018; de Castro et al . , 2019 ) . The strict requirement for GLUT1 in the early stages of mammary tumorigenesis highlights the potential for glucose restriction as a breast cancer preventive strategy ( Wellberg et al . , 2016 ) . The tumor essentiality of GLUT1 may also be illustrated by the fact that knockdown of GLUT1 inhibits cell glycolysis and proliferation and inhibits the growth of tumors ( Xiao et al . , 2018 ) . In view of its essentiality for tumor growth , GLUT1 is a promising target for cancer therapy ( Shibuya et al . , 2015; Noguchi et al . , 2016; Chen et al . , 2017d ) . Recent studies suggest that the YAP1-TEAD1-GLUT1 axis plays a major role in reprogramming of cancer energy metabolism by modulating glycolysis ( Lin and Xu , 2017 ) . These authors have shown that YAP1 and TEAD1 are involved in transcriptional control of the glucose transporter GLUT1 , whereas knockdown of YAP1 inhibited glucose consumption , and lactate production of breast cancer cells , overexpression of GLUT1 restored glucose consumption and lactate production . Besides GLUT1 another glucose transporter , GLUT8 ( encoded by the SLC2A8 gene ) also shows strong signals of negative selection , arguing for its importance in tumor survival . In harmony with this interpretation , there is evidence that GLUT8 is overexpressed in and is required for proliferation and viability of tumors ( Goldman et al . , 2006; McBrayer et al . , 2012 ) . Due to abnormal conversion of pyruvic acid to lactic acid even under normoxia , the altered metabolism of glucose consuming tumors must rapidly efflux lactic acid to the microenvironment to maintain a robust glycolytic flux and to prevent poisoning themselves ( Mathupala et al . , 2007 ) . Survival and maintenance of the glycolytic phenotype of tumor cells is ensured by monocarboxylate transporter 4 ( MCT4 , encoded by the SLC16A3 gene ) that efficiently transports L-lactate out of the cell ( Ganapathy et al . , 2009 ) . Significantly , MCT4 , encoded by the SLC16A3 gene also shows strong signals of negative selection , in harmony with its importance in tumor survival . As high metabolic and proliferative rates in cancer cells lead to production of large amounts of lactate , extruding transporters are essential for the survival of cancer cells as illustrated by the fact that knockdown of MCT4 increased tumor-free survival and decreased in vitro proliferation rate of tumor cells ( Andersen et al . , 2018 ) . Using a functional screen Baenke et al . , 2015 have also demonstrated that monocarboxylate transporter four is an important regulator of breast cancer cell survival: MCT4 depletion reduced the ability of breast cancer cells to grow , suggesting that it might be a valuable therapeutic target . In harmony with the essentiality of MCT4 for tumor growth , several studies indicate that expression of the hypoxia-inducible monocarboxylate transporter MCT4 is increased in tumors and its expression correlates with clinical outcome , thus it may serve as a valuable prognostic factor ( Witkiewicz et al . , 2012; Doyen et al . , 2014; Baek et al . , 2014 ) . Consistent with the key importance of MCT4 for the survival of tumor cells , its selective inhibition to block lactic acid efflux appears to be a promising therapeutic strategy against highly glycolytic malignant tumors ( Choi et al . , 2016; Todenhöfer et al . , 2018; Choi et al . , 2018; Zhao et al . , 2019b ) . Interestingly , the thromboxane A2 receptor gene ( TBXA2R ) as well as several chemokine receptor protein genes ( CCR2 , CCR5 , CX3CR1 ) were also found among the genes showing strong signs of purifying selection ( see Appendix 1 ) . ( Note that Pyatnitskiy et al . , 2015 have also identified CCR5 as a negatively selected gene ) . The most likely explanation for their essentiality for tumor growth is that tumor cells rely on these receptors in various steps of invasion and metastasis ( see Appendix 1 ) . It is noteworthy in this respect that another member of the family of chemokine receptors , the atypical chemokine receptor 3 , ACKR3 is also among the genes showing very high values of rSMN , suggesting negative selection of missense and nonsense mutations ( Supplementary file 3 ) . ( Note that Zhou et al . , 2017 have also identified ACKR3 as a negatively selected gene ) . Significantly , ACKR3 is a well-known OG , present in Tier 1 of the Cancer Gene Census . Several studies support the key role of ACKR3 in tumor invasion and metastasis ( Li et al . , 2014; Stacer et al . , 2016; Zhao et al . , 2017; Puddinu et al . , 2017; Melo et al . , 2018; Qian et al . , 2018 ) . Since knock-down or pharmacological inhibition of ACKR3 has been shown to reduce tumor invasion and metastasis , ACKR3 is a promising therapeutic target for the control of tumor dissemination ( for further details see Appendix 1 ) . The data discussed in the previous section indicate that the importance ( ‘essentiality’ ) of a given gene is a question of perspective . Cell-essential genes may be non-essential for tumor growth , whereas TEGs with tumor-specific functions do not necessarily have cell-essential functions . Similarly , we may assume that the importance of a gene might be quite different from the perspective of tumor cells and from the perspective of the entire organism . One could speculate that somatic mutations of genes with functions that have no relevance for tumor growth ( PGs ) experience neutral evolution during tumor growth , whereas germline mutations of the same genes may be subject to purifying selection at the level of organismal evolution , as is true for the majority of genes ( Gorlov et al . , 2006 ) . One may also assume that genes with tumor essential , tumor-specific functions may be subject to purifying selection during both tumor evolution and organism evolution , but the strength of purifying selection of these genes is increased in tumors relative to those of genes that do not have tumor-specific functions . To test these assumptions , we have determined the signals of selection of germline mutations ( Supplementary file 6 ) and compared them with those determined for the same genes in the case of somatic mutations of cancer . Comparison of the patterns of germline and somatic mutations of human transcripts ( Supplementary file 7 ) has revealed that the proportion of silent substitutions is significantly higher for germline mutations than for somatic mutations of tumors ( fSg: 0 . 33900 versus fSs: 0 . 24604 , p<0 . 05 ) . Conversely , the proportions of nonsense and missense mutations are significantly lower for germline mutations than for somatic mutations of tumors ( fNg: 0 . 02329 versus fNs: 0 . 04669 , p<0 . 05; fMg: 0 . 63771 versus fMs: 0 . 70727 , p<0 . 05 ) . These observations are in harmony with the dominance of purifying selection in the human population ( Gorlov et al . , 2006 ) . As shown in Figure 9 , the pattern of the distribution of transcripts in 3D scatter plots of fM , fN , and fS parameters for germline mutations are strikingly different from those observed in the case of fM , fN , and fS parameters of somatic mutations in cancer ( compare Figure 9A and B ) . In addition to a general shift of germline mutations to lower fN and fM and higher fS values , in the case of germline mutations the fN , fM , and fS parameters of transcripts of TSGs , OGs , and TEGs do not separate from those of the central cluster of genes . Similarly , the distribution of transcripts in 3D scatter plots of rS* , rM , and rN parameters for germline mutations are different from those observed in the case of rS* , rM* , and rN* parameters of somatic mutations in cancer ( compare Figure 9C and D ) : cancer genes do not separate from the central cluster of genes . Comparison of the fS , rSM , and rSMN parameters of germline and somatic mutations of transcripts ( Figure 10 , Supplementary file 7 ) has shown that there is only weak correlation between the strength of purifying selection of genes during tumor evolution and organismal evolution . The Pearson's r values for the correlations of the fS , rSM , and rSMN parameters of germline and somatic mutations are 0 . 1127 , 0 . 05757 , and 0 . 02635 , p<0 . 05 , respectively . These comparisons have also revealed that – relative to other genes – the candidate TEGs identified in the present study ( CG_SO2SD_rSMN >0 . 5 ) display significantly stronger signals of purifying selection during tumor evolution than during organismal evolution ( Figure 10 , Supplementary file 7 ) . The fS , rSM , and rSMN parameters of somatic mutations of candidate TEGs are significantly higher than those of other genes ( fSs: 0 . 38322 versus 0 . 24045 , p<0 . 05; rSMs: 0 . 66013 versus 0 . 34375 , p<0 . 05; rSMNs: 0 . 62774 versus 0 . 32356 , p<0 . 05 ) . The fS , rSM , and rSMN parameters of the germline mutations of candidate TEGs , however , differ much less from the corresponding parameters of other genes ( fSg: 0 . 36487 versus 0 . 33831 , p<0 . 05; rSMg: 0 . 64054 versus 0 . 56394 , p<0 . 05; rSMNg: 0 . 61264 versus 0 . 56178 , p<0 . 05 ) . These observations indicate that the negative selection of candidate TEGs during tumor evolution is not a simple reflection of their essentiality at the organism level; it is more likely that they serve tumor-specific functions . In order to assess the contribution of cell-essentiality to purifying selection during organismal evolution we have plotted rSMNg , a measure of negative selection of germline mutations of human genes , as a function of their cell-essentiality scores determined by De Kegel and Ryan , 2019 . These analyses have shown that there is a very weak negative correlation ( Pearson's r = −0 . 03662 , p<0 . 05 ) between the strength of purifying selection of transcripts ( rSMNg ) and their cell-essentiality scores ( Figure 11 , Supplementary file 7 ) . This observation also indicates that essentiality of cell-level functions measured by cell-essentiality scores contribute to , but do not explain the strength of purifying selection observed during organismal evolution . One of the major goals of cancer research is to identify all ‘cancer genes’ , that is genes that play a role in carcinogenesis . In the last two decades , several types of approaches have been developed to achieve this goal , but the implicit assumption of most of these studies was that a distinguishing feature of cancer genes is that they are positively selected for mutations that drive carcinogenesis . As a result of combined efforts , the PCAWG driver list identifies a total of 722 protein-coding genes as cancer driver genes and 22 non-coding driver mutations ( Rheinbay et al . , 2020; Campbell et al . , 2020 ) . In a recent editorial , commenting on a suite of papers on the genetic causes of cancer , Nature has expressed the view that the core of the mission of cancer-genome sequencing projects—to provide a catalogue of driver mutations that could give rise to cancer—has been achieved ( Editorial , 2020 ) . It is noteworthy , however , that , although on average , cancer genomes were shown to contain four to five driver mutations , in around 5% of cases no drivers were identified in tumors ( Campbell et al . , 2020 ) . As pointed out by the authors , this observation suggests that cancer driver discovery is not yet complete , possibly due to failure of the available bioinformatic algorithms . The authors have also suggested that tumors lacking driver mutations may be driven by mutations affecting cancer-associated genes that are not yet described for that tumor type , however , using driver discovery algorithms on tumors with no known drivers , no individual genes reached significance for point mutations ( Campbell et al . , 2020 ) . In our view , these observations actually suggest that a rather large fraction of cancer genes remains to be identified . Assuming that tumors , on average , must have driver mutations affecting at least four or five cancer genes and that known and unknown cancer genes play similar roles in carcinogenesis , the observation that a 0 . 05 fraction of tumors has no known drivers ( i . e . they are driven by four to five unknown cancer drivers ) indicates that about half of the drivers is still unknown . If we assume that ~50% of cancer genes is still unknown 3–6% ( 0 . 55–0 . 54 , i . e . 0 . 03125–0 . 0625 fraction ) of tumors is expected to lack any of the known driver genes , and to be driven by four or five unknown driver mutations . Since the list of known drivers used in the study of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium ( Campbell et al . , 2020 ) comprises 722 driver genes , these observations suggest that hundreds of cancer driver genes remain to be identified . In the present work , we have used analyses that combined multiple types of signals of selection , permitting improved detection of positive and negative selection . Our analyses have identified a large number of novel positively selected cancer gene candidates , many of which could be shown to play significant roles in carcinogenesis as tumor suppressors and OGs . Significantly , our analyses have identified a major group of human genes that show signs of negative selection during tumor evolution , suggesting that the integrity of their function is essential for the growth and survival of tumor cells . Our analyses of representative members of negatively selected genes have confirmed that they play crucial pro-oncogenic roles in various cancer hallmarks ( Table 1 ) . It is important to emphasize that a survey of the group of OGs and pro-oncogenic TEGs reveals that they form a continuum in as much as there are numerous known OGs where negative selection also dominates ( e . g . ACKR3 , BCL2 ) . Although several groups have investigated the role of negative selection in tumor evolution earlier ( Zhou et al . , 2017; Pyatnitskiy et al . , 2015; Weghorn and Sunyaev , 2017; Martincorena et al . , 2017; Zapata et al . , 2018; López et al . , 2020; Tilk et al . , 2020; Van den Eynden and Larsson , 2017 ) , the study that received the greatest attention has reached the conclusion that negative selection has no role in tumor evolution ( Martincorena et al . , 2017; Bakhoum and Landau , 2017; Koch , 2017; Vitale and Galluzzi , 2018 ) . The data presented here contradict the latter conclusion . We believe that the approach reported here will promote the identification of numerous novel OGs , TSGs , and pro-oncogenic TEGs that may serve as therapeutic targets . Cancer somatic mutation data were extracted from COSMIC v88 , the Catalogue Of Somatic Mutations In Cancer ( https://cancer . sanger . ac . uk/cosmic/download ) , which includes single nucleotide substitutions and small insertions/deletions affecting the coding sequence of human genes . The downloaded file ( CosmicMutantExport . tsv , release v88 ) contained data for 29 , 415 transcripts ( Supplementary file 8 ) . For all subsequent analyses we have retained only transcripts containing mutations that were annotated under’ Mutation description’ as substitution or subtle insertion/deletion . This dataset contained data for 29 , 405 transcripts containing 6 , 449 , 721 mutations ( substitution and short indels , SSI ) and 29 , 399 transcripts containing 6 , 141 , 650 substitutions only ( SO ) . Supplementary file 9 contains the metadata for these SO and SSI datasets . Since we were interested in the selection forces that operate during tumor evolution , only confirmed somatic mutations were included in our analyses . In COSMIC such mutations are annotated under’ Mutation somatic status’ as Confirmed Somatic , that is confirmed to be somatic in the experiment by sequencing both the tumor and a matched normal tissue from the same patient . Supplementary file 10 indicates the contribution of major tumor types ( ‘Tumor Primary site’ ) to the somatic mutations of the dataset . As to’ Sample Type , Tumor origin’: we have excluded mutation data from cell-lines , organoid-cultures , xenografts since they do not properly represent human tumor evolution at the organism level . We have found that by excluding cell lines we have eliminated many artifacts of spurious recurrent mutations caused by repeated deposition of samples taken from the same cell-line at different time-points . To eliminate the influence of polymorphisms on the conclusions we retained only somatic mutations flagged 'n' for SNPs . ( Supplementary file 8 ) . To increase the statistical power of our analyses , we have limited our work to transcripts that have at least 100 somatic mutations; Supplementary file 5 contains the metadata for transcripts containing at least 100 confirmed somatic , non polymorphic mutations identified in tumor tissues . Hereafter , unless otherwise indicated , our analyses refer to datasets containing transcripts with at least 100 somatic mutations . This limitation eliminated ~38% of the transcripts that contain very few mutations but reduced the number of total mutations only by 9% ( Supplementary file 8 ) . It should be noted that requiring a higher minimum number of somatic mutations increases the statistical power of the analyses but may disfavor the identification of negatively selected genes that tend to be undermutated . To assess the influence of the cut-off value of the minimum number of mutations on the robustness of the conclusions about negatively or positively selected genes , we have compared the results of analyses in which the minimum number of somatic mutation per gene was set as 0 , 50 , 100 , or 500 ( Supplementary files 11–13 , Figure 12 ) . The choice of the minimum number of somatic mutations was found to have a strong influence on the pattern of observed fN , fS , and fM scores ( Supplementary files 11–13 , Figure 12 ) . In the case of dataset N0 ( no requirement for a minimum number of mutations ) , a large number of transcripts with less than 50 substitutions had scores of zero for one or two of the fN , fS , and fM parameters due to the absence of somatic mutations in those categories ( Figure 12A , Supplementary file 11 ) . Increasing the minimum number of somatic mutations per transcript to 50 , 100 , and 500 resulted in loss of these transcripts and elimination of a diffusely scattered group of transcripts that do not cluster with PGs , known OGs , TSGs , and TEGs ( compare Figure 12A and B , C and D , Supplementary file 11 ) . These observations indicate that we cannot draw valid conclusions about the significance of selection from fN , fS , and fM scores in cases where the number of mutations of a given transcript is too low to permit meaningful analyses . Our analyses have also revealed that a large proportion ( 22% ) of the transcripts unique to the N02SD dataset ( containing fewer than 50 substitutions ) , correspond to short transcript fragments encoding less than 100 amino acids ( Supplementary file 12 ) . This finding suggests that the requirement for a minimum number of somatic mutations would not only increase statistical power , but also increases the biological relevance of the conclusions with the elimination of fragments that do not properly represent the full-length coding sequences . Our analyses , however , have shown that the requirement of more than 500 somatic mutations per transcript ( dataset N500 ) is too stringent . Although the majority of the 86 transcripts of the N5002SD dataset correspond to known OGs ( 25 ) or TSGs ( 48 ) , most OGs and TSGs are not represented in the N5002SD dataset ( Supplementary file 12 ) . Furthermore , none of the negatively selected TEGs validated in the present study is present among the 86 transcripts of the N5002SD dataset or among the 997 transcripts of the N500 dataset ( Figure 12D , Supplementary file 12 ) . This observation is consistent with the view that since negatively selected genes tend to have fewer mutations , they are less likely to pass the requirement for a high number of somatic mutations . Our analyses suggest that the choice of 50 or 100 as the minimum number of somatic mutations per transcript represent acceptable trade-off between statistical power and loss of negatively or positively selected genes . As shown in Supplementary file 12 , both the N502SD dataset ( 1846 transcripts ) and the N1002SD dataset ( 1060 transcripts ) contained the majority of known OGs , known TSGs , and TEGs validated in the present work , but since the choice of 100 offers higher statistical power , we have used this dataset in our analyses . Our choice of 100 as the minimum number of somatic mutation per transcript may also have some relevance for the lack of more extensive similarity of our list of negatively selected genes with those identified by others . As shown in Supplementary file 13 , only 48% , 64% , 77% , and 89% of the negatively selected genes identified by Weghorn and Sunyaev , 2017 , Zapata et al . , 2018 , Zhou et al . , 2017 , and Pyatnitskiy et al . , 2015 , respectively are present in dataset N100 containing 13 , 803 transcripts with at least 100 somatic mutations ( Supplementary file 11 ) . It thus appears that one of the reasons for the differences observed is that , with respect to minimum number of mutations , we have used rather stringent criteria to increase the robustness of our estimates . We wish to point out that , in order to obtain more reliable estimates of purifying selection , Pyatnitskiy et al . , 2015 have also excluded genes from their analysis that carried a low number of mutations but they excluded only those with less than 11 mutations . The COSMIC database of somatic mutations used in the present study contains data obtained by three main types of sequencing: whole-genome sequencing ( WGS ) , whole-exome sequencing ( WES ) and targeted sequencing . As shown in Supplementary file 8 , targeted screens provided substitution mutation data for only 13 , 120 transcripts of human genes , whereas genome wide screens covered 29 , 407 human transcripts as opposed to 29 , 415 transcripts covered by targeted plus genome wide screens . The contribution of targeted screens to somatic point mutations is even more restricted: only 508 , 124 ( 8 . 3% ) of the 6 , 141 , 650 somatic point mutations of the entire COSMIC database were identified by targeted sequencing ( Supplementary file 8 ) . To check the impact of targeted sequencing on the dataset , in some analyzes we have used somatic mutation data only from genome-wide screens , excluding those obtained by targeted sequencing . We have found that omission of the data from targeted screens had no significant effect on the conclusions drawn from our analyses . Several factors may explain this observation . First , targeted screens usually focus on known cancer genes and they usually just reinforce the ‘known cancer gene’ status of the targeted genes . Second , since only a small fraction of the somatic mutations originates from targeted screens their impact is limited even in the case of the targeted genes . Finally , inclusion or omission of data from targeted screens has no impact on the number and pattern of mutations of non-targeted genes identified in genome wide screens . Information on SNPs affecting the coding regions of human genes was downloaded from the dbSNP database ( https://www . ncbi . nlm . nih . gov/snp/ ) . For each SNP , we extracted nucleotide and amino acid variants from the original dbSNP file . In cases where two or three mutant variant was reported for a specific rsID , each variant was treated as an independent polymorphism . The retrieved SNPs were assigned to three functional categories: ( i ) Nonsense or Stop_gained mutations ( N ) , which change an amino acid-encoding codon into a stop codon , ( ii ) Missense mutations ( M ) , which change an amino acid into a mutant amino acid , and ( iii ) Synonymous or silent mutations ( S ) , which do not change the amino acid . We have focused only on SNPs of genes that were also found to contain at least 100 confirmed somatic , non polymorphic mutations in the COSMIC database ( Supplementary file 5 ) . Supplementary file 6 shows the numbers and fractions of SNPs affecting the coding sequences of the various human genes , according to the functional categories of the point mutations . The 61 sense codons can undergo 549 single base substitutions and , depending on the wild type and mutant codon , each substitution can be assigned to the silent , missense or nonsense mutation category . Out of the 549 single-base substitutions , 392 result in missense mutation , 134 lead to silent mutation , and 23 generate nonsense mutation , thus – assuming equal codon frequency , equal probability of the different types of substitutions and neutrality – the expected fractions of nonsense , missense and silent substituions are fN = 0 . 04189 , fM = 0 . 71403 , and fS = 0 . 24408 , respectively . Codons , however , differ significantly in the probability that their mutation would lead to nonsense ( N ) , missense ( M ) , or silent ( S ) mutation ( Supplementary files 14 , 15 , 16 ) and since the 61 sense codons ( amino acids ) do not occur with the same frequency in the coding region of human genes this may have a significant influence on the expected fN , fM , and fS values . We have calculated the probability that a substitution would lead to nonsense , missense or silent mutation taking into account the codon frequency of the proteome of Homo sapiens ( https://www . kazusa . or . jp/codon/cgi-bin/showcodon . cgi ? species=9606 ) . This calculation yielded values of fN = 0 . 0419 , fM = 0 . 7299 , fS = 0 . 2282 for the proteome , slightly different from the values of fN = 0 . 0419 , fM = 0 . 7140 , fS = 0 . 2441 , assuming equal frequency of codons . The amino acid composition and codon usage of some individual proteins ( especially short fragments ) may deviate significantly from average , therefore we have calculated the expected proportion of silent , missense , and nonsense mutations for all transcripts , assuming equal probability of different substitutions classes ( Supplementary file 17 ) . For these calculations , we have downloaded the coding sequences of 53 , 190 transcripts of human protein coding genes ( All_COSMIC_Genes . fasta . gz ) from the COSMIC database ( https://cancer . sanger . ac . uk/cosmic ) and their codon usage and amino acid composition were determined using the SMS server ( https://www . bioinformatics . org/sms2/codon_usage . html , Stothard , 2000 ) . Different classes of substitutions , however , do not occur with equal probability , moreover the various normal and tumor tissues show characteristic differences in the spectrum of substitutions classes ( Alexandrov et al . , 2013; Alexandrov et al . , 2020 ) . Substitutions are assigned to six classes ( C>A , C>G , C>T , T>A , T>C , and T>G ) referred to by the pyrimidine of the mutated Watson–Crick base pair . It is of crucial importance to take differences in the probability of the six mutation classes into account since—due to the unique structure of the genetic code—the six types of substitutions differ markedly in the probability that they would lead to nonsense ( N ) , missense ( M ) , or silent ( S ) mutation of the coding region of protein-coding genes . As shown in Supplementary files 18–25 , there are significant differences in the impact of different substitution classes on the expected proportion of missense , silent , and nonsense mutations of codons ( assuming equal codon frequency ) . For example , the dominance of C>G increases the proportion of missense substitutions , whereas higher rates of C>T and T>C substitutions increase the proportion of silent substitutions . Since mutation bias favoring C>T substitutions is expected to decrease the ratio of missense to silent mutations , decreased dN/dS values may not be taken as evidence for negative selection in the case of tumors , such as malignant melanoma , where the vast majority of all somatic mutations is C>T substitution ( Van den Eynden and Larsson , 2017 ) . To take into account differences in mutation bias , we have calculated the contribution of the C>A , C>G , C>T , T>A , T>C , and T>G mutations to the pattern of single base substitutions in tumors . We have downloaded the files containing ‘Mutational Signatures v3 . 1’ and ‘Attributions of the SBS Signatures to Mutations in Tumors’ from the COSMIC website ( https://cancer . sanger . ac . uk/cosmic/signatures/SBS/index . tt ) . The contributions of C>A , C>G , C>T , T>A , T>C , and T>G mutations to the pattern of Single Base Substitutions in the tumors listed in the PCAWG_sigProfiler_SBS_signatures_in_samples file are summarized in Supplementary file 26 . The C>T substitution accounts for the largest fraction of substitutions in most tumors ( 0 . 3726 ) , followed by T>C ( 0 . 1842 ) , C>A ( 0 . 1583 ) , C>G ( 0 . 1162 ) , T>G ( 0 . 0891 ) , and T>A ( 0 . 0796 ) . There are , however , differences in the relative contribution of the six mutation classes to different tumors . For example , the contribution of C>A mutation is higher than average for colon cancer and lung cancer , the role of C>G mutation is above average for bladder cancer and some breast cancers . The contribution of C>T mutation is very high in the case of skin-melanoma , whereas the T>A mutation contributes significantly to some kidney cancers . The T>C mutation plays a significant role in biliary and liver cancer , whereas the T>G mutation is more significant in colon cancer and esophageal cancer than in other tumors ( see Supplementary file 26 ) . In order to correct for the influence of mutation bias on fN , fM , and fS values of transcripts in tumor tissues , we have calculated the expected fN* , fM* , and fS* values for all human transcripts using the average values of the six substitution types observed across tumors ( Supplementary file 27 ) . It is noteworthy that the average values of expected fN* , fM* , and fS* ( fN=0 . 04483 , fM=0 . 69114 , and fS=0 . 26402 ) are similar to those ( fN = 0 . 04189 , fM = 0 . 71403 , and fS = 0 . 24408 ) assuming equal codon frequency and equal probability of the different types of substitutions . In the case of germline cells , we have also calculated the expected fN* , fM , and fS values for all human transcripts using the mutation probabilities characteristic of these cells ( Supplementary file 28 ) . It has been shown earlier that the human germline mutation spectrum can be recapitulated by a combination of the cancer signatures SBS1 and SBS5 ( Alexandrov et al . , 2015; Rahbari et al . , 2016; Heredia-Genestar et al . , 2020 ) . In the present work , we have combined the effect of mutation signatures SBS1 and SBS5 on the germline mutation spectrum of proteins according to the formula ( 0 . 1 × SBS1 + 0 . 9 × SBS5 ) recommended by Heredia-Genestar et al . , 2020 . It is noteworthy that the average values of expected fN , fM , and fS ( fN=0 . 03791 , fM=0 . 68653 , and fS=0 . 27556 ) are similar to those expected for tumor tissues ( fN=0 . 04483 , fM=0 . 69114 , and fS=0 . 26402 ) . We have used two approaches to determine the observed fM , fS , and fN values of transcripts: one in which we have restricted our analyses to single nucleotide substitutions ( hereafter referred to as SO for 'substitution only' ) and a version in which we have also taken into account subtle indels ( hereafter referred to as SSI for 'substitutions and subtle indels' ) . In the first case , we have calculated for each transcript the fraction of somatic substitutions that could be assigned to the synonymous ( fS ) , nonsynonymous ( fM ) , and nonsense mutation ( fN ) category ( Supplementary file 5 and 9 ) . In the version that also included data for subtle indels , we have calculated the fraction of mutations corresponding to synonymous substitutions ( indel_fS ) , but have merged nonsynonymous substitutions and short inframe indels in the category of mutations that lead to changes in the amino acid sequence ( indel_fM ) . Nonsense substitutions and short frame-shift indels were included in the third category of mutations ( indel_fN ) as both types of mutation lead eventually to stop codons that truncate the protein ( Supplementary file 5 and 9 ) . Analyses of datasets ( Supplementary file 5 ) containing substitutions only have shown that in 3D scatter plots transcripts form a cluster ( Figure 2A ) characterized by values of 0 . 2436 ± 0 . 0619 , 0 . 7090 ± 0 . 0556 , and 0 . 0475 ± 0 . 0322 for fractions of silent , missense , and nonsense substitutions , respectively . The mean fS , fM , and fN values of the transcripts in this cluster are close to those expected if we assume that the structure of the genetic code has the most important role in determining the probability of somatic substitutions during tumor evolution of human genes ( Supplementary file 29 ) . Based on the structure of the genetic code , assuming equal usage of the codons and equal probability of different point mutations , in the absence of selection one would expect that a fraction of 0 . 24408 would be silent , 0 . 71403 of the single-base substitutions would be missense and 0 . 04189 would be nonsense mutations . It is noteworthy , however , that the fS , fM , and fN values of the best known cancer genes ( Vogelstein et al . , 2013 ) deviate from those characteristic of the majority of human genes ( Figure 2B ) . The genes in the central cluster , deviating from mean fM , fS , and fN values by ≤1 SD , are characterized by fraction values of 0 . 24548 ± 0 . 03079 , 0 . 71084 ± 0 . 0274 , and 0 . 04368 ± 0 . 01572 for synonymous , nonsynonymous and nonsense substitutions , respectively . Note that these values are very close to those expected from the structure of the genetic code in the absence of selection , assuming equal frequency of codons and equal probability of the different classes of mutations ( Supplementary file 29 ) . This central cluster of genes ( Supplementary file 5 ) is hereafter referred to as PG_SOf_1SD ( for Passenger Gene_Substitution Only deviating from mean fM , fS , and fN values by ≤1 SD ) because it is likely to be enriched in genes that play no major role in carcinogenesis . In harmony with earlier observations , the values for OGs show a significant ( p<0 . 05 ) shift of fM to higher values ( 0 . 8563 ± 0 . 08224 ) relative to those of PGs ( 0 . 71084 ± 0 . 0274 ) , reflecting positive selection for missense mutations ( Supplementary file 29 ) . On the other hand , the fN values of TSGs are significantly ( p<0 . 05 ) higher ( 0 . 1964 ± 0 . 11063 ) than those of PGs ( 0 . 04368 ± 0 . 01572 ) , reflecting positive selection for truncating nonsense mutations ( Supplementary file 29 ) . The genes ( 1060 transcripts ) with values that deviate from mean values of fS , fM , and fN by more than 2SD , however , are likely to be subject to selection . In harmony with this expectation , this group contains transcripts of the majority of known driver genes ( 62 OG and 119 TSG driver gene transcripts ) . This gene set , defined by 2SD cut-off value , is hereafter referred to as CG_SOf_2SD ( for Cancer Gene_Substitution Only deviating from mean fM , fS , and fN values by more than 2SD ) because it is likely to be enriched in cancer genes ( Supplementary file 29 ) . Out of the 1060 transcripts present in CG_SOf_2SD , 737 transcripts are derived from genes that are not included in the OG , TSG , and CGC cancer gene lists ( Supplementary files 5 and 29 ) . Since the majority of these 737 transcripts have parameters that cluster them with known OGs or TSGs , we assume that they qualify as candidate OGs or TSGs . However , a group of genes deviates from both the central PG cluster and the clusters of OGs and TSGs ( Figure 2C ) . The high fS and low fM and fN values of the genes in this cluster suggest that they experience purifying selection during tumor evolution , raising the possibility that they may correspond to TEGs important for the growth and survival of tumors . Known cancer genes ( OGs and TSGs ) also separate from the majority of human genes in 3D scatter plots of parameters rSM , rNM , rNS defined as the ratio of fS/fM , fN/fM , fN/fS , respectively ( Figure 3 ) . The central cluster of genes that deviate from mean rSM , rNM and rNS values by ≤1 SD is hereafter referred to as PG_SOr2_1SD ( Passenger Gene_Substitution Only deviating from mean rSM , rNM , and rNS values by ≤1 SD ) since it is likely to be enriched in PGs . Conversely , the group of transcripts that deviate from mean rSM , rNM , and rNS values by more than 2SD is referred to as CG_SOr2_2SD ( Cancer Gene_Substitution Only deviating from mean rSM , rNM , rNS values by more than 2SD ) because it is likely to be enriched in cancer genes ( Supplementary file 29 ) . The CG_SOr2_2SD gene set ( 780 transcripts ) contains the majority of driver gene transcripts ( 40 transcripts of OGs , 103 transcripts of TSGs genes ) , 79 transcripts of CGC genes and 558 transcripts derived from 468 genes that are not found in the OG , TSG , and CGC cancer gene lists ( Supplementary file 29 ) . In these scatter plots OGs separate from the central cluster in having significantly ( p<0 . 05 ) lower rSM ( 0 . 13971 ± 0 . 10621 ) and rNM ( 0 . 03936 ± 0 . 0313 ) values than those of the central cluster of PGs ( rSM: 0 . 34523 ± 0 . 06137; rNM: 0 . 0607 ± 0 . 02595 , Supplementary file 29 ) , reflecting positive selection for missense mutations and negative selection of nonsense mutations . Interestingly , in these plots some OGs ( e . g . BCL2 ) have unusually high values of rSM and low values of rNM ( e . g . Figure 3A1 , A2 and Supplementary file 5 ) suggesting that in the case of these OGs purifying selection may dominate over positive selection for amino acid changing mutations . TSGs also separate from the central cluster: they have significantly ( p<0 . 05 ) higher rNS ( 3 . 92588 ± 5 . 66261 ) and rNM ( 0 . 31524 ± 0 . 31575 ) values than those of PGs ( rNS: 0 . 18403 ± 0 . 09138; rNM: 0 . 0607 ± 0 . 02595; Figure 3A1 , A2 . Supplementary file 29 ) , reflecting the dominance of positive selection for inactivating mutations . As mentioned above , the candidate cancer gene set defined by a cut-off value of 2SD also contains 558 transcripts derived from 468 genes that are not found in the OG , TSG , or CGC lists . Since the majority of these 558 transcripts have parameters that cluster them with known OGs or TSGs , they can be regarded as candidate OGs or TSGs . There is , however , a group of genes that deviate from the clusters of PGs , OGs , and TSGs in that they have unusually high rSM values and low rNM and rNS values . Since these values may be indicative of purifying selection , we assumed that they might correspond to TEGs important for the growth and survival of tumors . The separation of known cancer genes from the majority of human genes is even more obvious in 3D scatter plots of parameters rSMN , rMSN , and rNSM defined as the ratio of fS/ ( fM+fN ) , fM/ ( fS+fN ) , and fN/ ( fS+fM ) , respectively ( Figure 4 A1 , A2 ) . In these plots , the gene transcripts are present in a three-pronged cluster , with OGs and TSG being present on separate spikes of this cluster ( Figure 4 ) . We refer to the central cluster of genes , deviating from mean rSMN , rMSN , and rNSM values by ≤1 SD as PG_SOr3_1SD ( Passenger Gene_Substitution Only deviating from mean rSMN , rMSN , and rNSM values by ≤1 SD ) as they are likely to be enriched in PGs . Similarly , we refer to the gene set defined by 2SD cut-off value ( Supplementary files 5 and 29 ) as CG_SOr3_2SD ( Cancer Gene_Substitution Only deviating from mean rSMN , rMSN , and rNSM values by more than 2SD ) as it is likely to be enriched in candidate cancer genes . This gene set has 751 transcripts , containing the majority of transcripts of known driver genes ( 35 OGs , 103 TSGs ) , 80 transcripts of CGC genes and 533 transcripts ( derived from 448 genes ) not found in the OG , TSG , and CGC cancer gene lists ( Supplementary files 5 and 29 ) . The mean parameters of TSGs differ significantly ( p<0 . 05 ) from those of PGs in as much as rNSM values of TSGs are higher ( 0 . 27937 ± 0 . 2783 ) but rSMN ( 0 . 10865 ± 0 . 06128 ) values are lower than those of PGs ( rNSM: 0 . 04812 ± 0 . 02561; rSMN: 0 . 3259 ± 0 . 09265 , Supplementary file 29 ) , reflecting the dominance of positive selection for inactivating nonsense mutations . In the case of OGs the rMSN values are significantly ( p<0 . 05 ) higher ( 15 . 35971 ± 30 . 07472 ) and the rSMN values are significantly lower ( 0 . 13363 ± 0 . 10266 ) than those of PGs ( rMSN: 2 . 58911 ± 0 . 68355; rSMN: 0 . 3259±0 . 09265 Supplementary file 29 ) , reflecting positive selection for missense mutations . The rNSM values of OGs ( 0 . 03394 ± 0 . 02621 ) are also significantly ( p<0 . 05 ) lower than those of PGs ( 0 . 04812 ± 0 . 02561 ) , reflecting purifying selection avoiding nonsense mutations . Interestingly , some OGs have unusually high scores of rSMN ( Figure 4 A1 , A2 , Supplementary file 5 ) suggesting that in these cases ( e . g . BCL2 ) purifying selection dominates over positive selection for amino acid changing mutations . As mentioned above , the candidate cancer gene set defined by a cut-off value of 2SD contains 533 transcripts ( derived from 448 genes ) not found in the OG , TSG , or CGC lists . Since the majority of these genes have parameters that assign them to the clusters containing OGs or TSGs , they can be regarded as candidate OGs or TSGs . There is , however , a group of genes that deviates from the clusters of PGs , OGs , and TSGs ( Figure 4 ) . Their high rSMN and low rMSN and rNSM values suggest that they experience purifying selection during tumor evolution , raising the possibility that this group may be enriched in genes essential for the survival of tumors as pro-oncogenes or TEGs . The three types of analyses described for Substitutions Only ( illustrated in Figures 2–4 ) were also carried out for datasets in which both substitutions and subtle indels ( Substitutions and Subtle Indels , SSI ) were used ( for details of these analyzes see Appendix 2 ) . Comparison of the data obtained by SO and SSI analyses ( Supplementary file 5 ) revealed that inclusion of indels has only minor influence on the separation of the clusters of PGs and CGs . The lists of PGs identified with 1SD cut-off values for SO analyes ( PG_SOf_1SD , PG_SOr2_1SD , PG_SOr3_1SD ) and SSI analyses ( PG_SSIf_1SD , PG_SSIr2_1SD , PG_SSIr3_1SD ) show more than 90% identity in the case of the relevant SO/SSI pairs ( Supplementary file 30 ) . Similarly , the lists of CGs identified with 2SD cut-off values for SO analyses ( CG_SOf_2SD , CG_SOr2_2SD , CG_SOr3_2SD ) and SSI analyses ( CG_SSIf_2SD , CG_SSIr2_2SD , CG_SSIr3_2SD ) show 78% , 87% , and 92% identity , respectively , for the relevant SO/SSI pairs ( Supplementary file 30 ) . The parameters of the 1158 transcripts present in at least one of the various CG_SO2SD lists and the 1333 transcripts present in at least one of the various CG_SSI2SD lists ( Supplementary file 31 ) were used to assign them to three distinct clusters . ( 1 ) Cluster of genes positively selected for missense mutations and negatively selected for nonsense mutations; ( 2 ) Cluster of genes positively selected for nonsense mutations; ( 3 ) Clusters of negatively selected genes ( see Figure 2C , Figure 3 B1 , B2 and Figure 4 B1 , B2 ) . To check the validity and predictive value of the assumption that the genes assigned to these clusters play significant roles in carcinogenesis , we have selected a number of genes for further analyses from the 1457 transcripts present in the combined list ( CG_SO2SD_SSI2SD ) of candidate cancer genes ( Supplementary file 31 ) . The results of these analyses are summarized in the Results section . As outlined in the section on Substitution metrics , a limitation of the analyses discussed above is that they did not take into account the impact of differences in mutation probability on the fN , fM , and fS values of transcripts . In order to eliminate this source of error , we have calculated the expected fN , fM* , and fS* values for all human transcripts using the probability of the six substitution types observed across tumors ( Supplementary file 27 ) . The various types of observed/expected ratios ( rN* , rM* , rS; rSM , rNM* , rNS; rSMN , rMSN* , and rNSM* ) of somatic mutations were calculated for all transcripts ( Supplementary file 32 ) and the data were analyzed in 3D scatter plots as described above for the observed values . As shown in Figures 5–7 , the distribution of transcripts in these 3D scatter plots are similar to those observed in the corresponding Figures 2–4 , in that known OGs , TSGs , and TEGs are separated from the central cluster of PGs as well as from each other ( Supplementary file 32 ) . As a reference , we have carried out similar analyses of the fN , fM , and fS parameters of germline mutations , through the analysis of the human database of human single-nucleotide polymorphisms ( SNPs; Supplementary file 6 ) . Supplementary file 33 contains the various types of observed/expected ratios ( rN , rM , rS; rSM , rNM , rNS; rSMN , rMSN , and rNSM** ) of germline mutations calculated for all transcripts . Data were analyzed in 3D scatter plots as described for somatic mutations . Details of these analyses are presented in the Results section . As the gold standard of 'known' cancer genes we have used the lists of OG and TSGs identified by Vogelstein et al . , 2013 . As another list of known cancer genes we have also used the genes of the Cancer Gene Census ( Sondka et al . , 2018 ) . The statistical package of Origin 2018 was used for all data processing and statistical analysis . We report details of statistical tests in the Supplementary files of the respective sections . Statistical significance was set as a p value of < 0 . 05 .	The DNA in the cells of the human body is usually copied correctly when a cell divides . However , errors ( mutations ) are sometimes introduced during the copying process . Although the majority of mutations have no major impact on cells , many mutations are harmful: they decrease the ability of cells to survive . There are , however , mutations that can lead to cells dividing more frequently or gaining the ability to spread , which can lead to cancer . These mutations are known as ‘driver mutations’ because they drive the growth of tumors . Since such ‘driver mutations’ provide a growth advantage to tumor cells , they are subject to positive selection , this is , their frequency in the tumor increases over time . Because of their selective advantage , driver mutations accumulate at significantly higher rates than the neutral ‘passenger mutations’ that do not play a role in tumor growth . Genes that carry driver mutations are called driver genes , while genes that carry only passenger mutations are known as passenger genes . Certain genes , however , do not fit into either category . For example , some genes that are essential for tumor growth must get rid of harmful mutations to maintain activity . Mutations of such ‘tumor essential genes’ are thus subject to ‘negative’ or ‘purifying selection’ . A major goal of cancer research is to identify genes that play critical roles in tumor growth . Earlier studies have identified numerous driver genes positively selected for driver mutations , exploiting the fact that driver genes show significantly higher mutation rates than passenger genes . Identification of tumor essential genes , however , is inherently more difficult since the paucity of mutations of negatively selected genes hinders the analysis of the mutation data . The failure to provide convincing evidence for negative selection in tumors has led to suggestions that it has no role in cancer evolution . Bányai et al . used a novel approach to address the question of whether negative selection occurs in cancer . Based on characteristic differences in the patterns of mutations in cancer they distinguished clusters of passenger genes , driver genes and tumor essential genes . The group of tumor essential genes includes genes that serve to satisfy the increased demand of rapidly dividing tumor cells for nutrients’ and genes that are essential for cell migration and metastasis ( the spread of cancer cells to other areas of the body ) . The tumor essential genes that Bányai et al . identified may prove to be valuable targets for cancer therapy , illustrating the importance of genome sequencing in cancer research . Identification of additional tumor essential genes is , however , hindered by the fact that they are likely to have low levels of mutations , which can exclude them from meaningful analyses . Progress with genomic sequencing of tumors is expected to overcome this limitation and help identify additional genes that are essential for cancer growth .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "cancer", "biology" ]	2021	Use of signals of positive and negative selection to distinguish cancer genes and passenger genes
The ellipsoid body ( EB ) in the Drosophila brain is a central complex ( CX ) substructure that harbors circumferentially laminated ring ( R ) neuron axons and mediates multifaceted sensory integration and motor coordination functions . However , what regulates R axon lamination and how lamination affects R neuron function remain unknown . We show here that the EB is sequentially innervated by small-field and large-field neurons and that early developing EB neurons play an important regulatory role in EB laminae formation . The transmembrane proteins semaphorin-1a ( Sema-1a ) and plexin A function together to regulate R axon lamination . R neurons recruit both GABA and GABA-A receptors to their axon terminals in the EB , and optogenetic stimulation coupled with electrophysiological recordings show that Sema-1a-dependent R axon lamination is required for preventing the spread of synaptic inhibition between adjacent EB lamina . These results provide direct evidence that EB lamination is critical for local pre-synaptic inhibitory circuit organization . Proper nervous system function relies on precise synaptic connectivity . Laminated distribution of synaptic connections is an organizational feature observed in both vertebrate and invertebrate nervous systems ( Kolodkin and Hiesinger , 2017; Baier , 2013; Sanes and Yamagata , 2009 ) . However , the cellular and molecular mechanisms that control neuronal lamination remain to be fully elucidated , as does how laminar constraint of synaptic organization in particular brain structures contributes to function . The insect brain , although small , is composed of myriad complex synaptic connections organized into multiple neuropil modules ( Ito et al . , 2014 ) , and each module adopts distinct structural features geared toward specific functions . For example , in Drosophila the optic lobes include heavily laminated synaptic connections ( Sanes and Zipursky , 2010 ) , while the antennal lobes include individual glomeruli where distinct olfactory sensory neurons contact to second-order neurons ( Wilson , 2013 ) . Deep within the insect brain , a highly conserved neuropil module called the central complex ( CX ) is composed of several laminated structures critical for sensory integration and motor coordination functions analogous to the vertebrate basal ganglion ( Strausfeld and Hirth , 2013; Strausfeld , 2012; Turner-Evans and Jayaraman , 2016 ) . The CX is composed of four neuropil substructures , from anterior to posterior: the ellipsoid body ( EB ) , the fan-shaped body ( FB ) , the noduli ( NO ) and the protocerebral bridge ( PB ) ( Figure 1A ) . In each CX substructure , neuronal processes elaborate their trajectories in precisely defined regions . They form multiple layers , or laminae , from dorsal-to-ventral within the EB , FB and NO , and other neuronal processes form 16–18 columns , medial-to-lateral , within the EB , PB and FB . More than 50 different types of ‘small-field’ and ‘large-field’ neurons innervate these CX substructures ( Hanesch et al . , 1989; Young and Armstrong , 2010b ) . Every small-field neuron innervates one or two columns and contacts one or multiple laminae in specific CX substructures . On the other hand , each large-field neuron innervates an entire single lamina across all columns in certain CX substructures . Therefore , small- and large-field neurons collaboratively form highly organized wiring patterns and are interconnected in all four CX neuropils ( Lin et al . , 2013; Wolff et al . , 2015; Yang et al . , 2013 ) , allowing for information flow in precisely defined patterns within the CX . 10 . 7554/eLife . 25328 . 003Figure 1 . Large-field ring neuron axons sequentially innervate the ellipsoid body . ( A ) Schematics showing frontal and dorsal views of the central complex ( CX ) in an adult Drosophila brain . The CX is composed of four substructures: from anterior ( ‘A’ ) to posterior ( ‘P’ ) the ellipsoid body ( EB ) , the noduli ( NO ) , the fan-shaped body ( FB ) and the protocerebral bridge ( PB ) . ( B–C ) R15F02-GAL4 ( expressed in R1/R3/R4d neurons ) and R32H08-lexA ( in R2/R4m neurons ) driving GFP and mCherry reporters , respectively , label multiple ring neuron types in an adult fly brain . From laterally located cell bodies ( arrowheads ) , ring neurons axons follow similar trajectories to innervate the bulb ( arrows ) and the ellipsoid body ( dashed square ) ( B ) . Inside the EB , multiple concentric and adjacent rings are formed by dense axon projections from different R neurons ( C ) . A smaller R1 ring is formed at more posterior EB regions and is covered by the R3 ring . ( D ) R32H08-lexA-driving mCherry and R15F02-GAL4-driving CD8-GFP allow for visualization of how R2/R4m and R3/R4d innervation takes place in the EB during pupal development . Strong CadN immunolabeling shows EB morphological changes ( dashed lines ) between 16 hr and 48 hr APF ( hours after puparium formation ) . R2/R4m axons ( red arrows ) extend into the developing EB earlier than R3/R4d axons ( green arrows and arrowheads ) , as can be observed between 16 and 24 hr APF since R2/R4m axons exhibit dense elaboration and co-localization with strong CadN staining prior to R3/R4d axons . R axon projections and elaborations appear complete between 40 and 48 hr APF , and the overall organization of R axons after 48 hr APF shows no difference when compared to the adult brain . ( E ) Schematics highlight changes in EB morphology ( strong CadN staining in dark blue and weak CadN staining in light blue ) , sequential innervation of the EB by pb-eb-gall dendrites ( immunostaining in Figure 1—figure supplement 1K and L ) , R2/R4m and R3/R4 axons during early pupal stages . Scale bars are 50 μm in panels B and D; 20 μm in panel C . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 00310 . 7554/eLife . 25328 . 004Figure 1—figure supplement 1 . R neuron axons and pb-eb-gall dendrites have different morphologies and closely associate with each other in the developing and adult brain . ( A–F’ ) Single-cell MARCM clones show the diverse morphologies of ring neuron axons and pb-eb-gall neuron dendrites in the EB ( frontal views in A–F , dorsal views in A’–F’ ) . ( G ) Schematic depicting a dendritic arbor from a single pb-eb-gall neuron ( red ) overlapping with axons from multiple types of R neuron . Only axons from one R4m ( yellow ) and one R3 ( blue ) are shown here as examples . ( H ) R19G02-GAL4 was used to express pre-synaptic ( Syt-GFP ) and dendritic ( DenMark ) markers in small-field pb-eb-gall neurons . Syt-GFP , but not DenMark , was enriched in the gall , demonstrating that these gall projections are largely pre-synaptic . PB and EB projections were filled by both Syt-GFP and DenMark , suggesting that these projections include both pre- and post-synaptic terminals . ( I–J ) Native GFP fluorescence is reconstituted between pb-eb-gall dendrites and R neuron axons ( R1/R3/R4d in panel I , R2/R4m in panel J ) in GRASP ( GFP reconstitution across synaptic partners ) experiments . CD4-spGFP1-10 was expressed in R1/R3/R4d and R2/R4m neurons by R15B07-GAL4 and EB1-GAL4 , respectively . CD4-spGFP11 and mCherry were expressed in pb-eb-gall neurons by R19G02-lexA in both cases . No GFP fluorescence was observed in control animals in which either GAL4 or lexA driver was not present ( data not shown ) . ( K and L ) R19G02-GAL4-driving CD8-GFP and R19G02-lexA-driving mCherry label pb-eb-gall neuron projections in K and L ( in green ) , respectively . At 24 hr APF , the pb-eb-gall neuron dendrites overlap with R2/R4m axons ( labeled by R32H08-lexA-driving mCherry in panel K , red arrows ) , but not with R3/R4d axons ( labeled by R15F02-GAL4-driving CD8-GFP in panel L , red arrow ) . At 48 hr APF , the pb-eb-gall dendrites have expanded toward the EB canal and overlap with both R2/R4m and R3/R4d axons . Scale bars are: 20 μm in panels A–F’; 50 μm in panels H–L . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 004 The EB in Drosophila is the anterior-most CX substructure and adopts a toroid shape in the central brain ( Figure 1A ) . The major large-field neurons that innervate the EB are called ring ( R ) neurons , named for their circumferential ring-like axonal arborization patterns that form several circular laminae/rings in the anterior shell of the EB ( Hanesch et al . , 1989 ) . Anatomical analyses suggest that ring neurons , through their dendrites located in the bulb ( Figure 1B , arrows ) , receive pre-synaptic inputs from descending neurons of the anterior visual pathway ( Omoto et al . , 2017 ) . Indeed , neuronal activity of some ring neuron dendrites correlates with light stimulation and is organized in a retinotopic fashion within the bulb ( Seelig and Jayaraman , 2013 ) . Further , some ring neurons contribute to the circuits that influence visually evoked memory and learning ( Wang et al . , 2008; Pan et al . , 2009; Neuser et al . , 2008; Ofstad et al . , 2011 ) , and they are also involved in homeostatic regulation of behaviors such as hunger sensation ( Park et al . , 2016; Dus et al . , 2013 ) and sleep homeostasis ( Liu et al . , 2016 ) . One type of small-field neuron , called ‘pb-eb-gall’ or ‘E-PG , ’ elaborates dendrites in the EB that overlap with R neuron axons and presumably serves as their post-synaptic partners . Neuronal activity observed in pb-eb-gall neuron dendrites is tuned to the fly’s head direction coordinates ( Seelig and Jayaraman , 2015; Turner-Evans et al . , 2017; Green et al . , 2017 ) . How laminar organization of R axons influences R and pb-eb-gall neuron functions , however , remains unknown . To gain insight into the logic of EB neural connectivity , we investigate here the developmental principles and molecular mechanisms underlying the formation of R neuron circumferential lamination and pb-eb-gall dendrite targeting within the EB . We observe sequential innervation of the developing EB by pb-eb-gall neuron dendrites and distinct R neuron axons during pupal development , and we uncover different roles for pb-eb-gall dendrites and R axons in EB patterning . The transmembrane semaphorin semaphorin-1a ( Sema-1a ) is expressed in multiple types of R neurons and functions with the transmembrane protein plexin A ( PlexA ) to regulate ring neuron laminar organization in the EB . Disruption of Sema-1a signaling in the EB provides anatomical and functional insight into establishment of local inhibitory connections among R neurons that target adjacent EB laminae . These results show that local axonal interactions , through short-range repellent cues and receptors , regulate the assembly and functional organization of laminated pre-synaptic inhibitory circuits in the central complex within the fly brain . All ring neurons share a common progenitor lineage and follow similar trajectories as their axons approach the brain midline ( Yang et al . , 2013 ) . Multiple R neuron types have been identified based on their different axonal arbor morphologies in the EB and also by analysis of different enhancer trap lines that uniquely express the transcriptional activator GAL4 in R neuron types ( Hanesch et al . , 1989; Renn et al . , 1999 ) . For example , R1 , R2 and R3 neurons have aster-like axon arbors ( Figure 1—figure supplement 1 , A–C’ ) , and R4m and R4d neurons develop ring-shaped axon arbors ( Figure 1—figure supplement 1 , D–E’ ) . Axon arbors from different R types also have different diameters and synapse distribution patterns within the EB . Together , they form multiple concentric circumferential laminae/rings in the EB ( Figure 1B and C ) . N-Cadherin ( CadN ) is a cell adhesion molecule widely expressed in the developing nervous system that consolidates target selection between pre- and post-synaptic neurons ( Iwai et al . , 1997; Lee et al . , 2001 ) . CadN immunostaining shows that the ellipsoid body forms during early pupal stages ( Young and Armstrong , 2010a ) . Our results confirm that the EB emerges at around 16 hr ( 16 hr ) after pupae formation ( APF ) with an open crescent shape that in later developmental stages closes toward the ventral side; the complete circular EB morphology is apparent between 40 and 48 hr APF ( Figure 1D , white dashed lines in panels on right; Figure 1E , light blue schematics ) . To track EB neuron innervation while overall EB morphology develops , we used both GAL4 and lexA drivers to label processes from multiple types of EB neurons simultaneously ( Jenett et al . , 2012; Pfeiffer et al . , 2008 ) . R32H08-lexA and R15F02-GAL4 were first utilized to express mCherry in R2/R4m neurons and CD8-GFP in R3/R4 neurons , respectively , allowing us to trace ring neuron axon extension during early pupal stages ( Figure 1D , red and green arrows ) . At 16 hr APF , the earliest time point when it is possible to identify the EB by CadN expression , many R2/R4m axons have already reached the EB . These R2/R4m axons form a circular structure and the dorsal region of this axon trajectory overlaps with strong CadN staining , suggesting that R2/R4m axons have already established specific contacts with other neuronal processes at this early pupal stage . In contrast , CD8-GFP-labeled R3 axons were barely observed at the midline at 16 and 20 hr APF , and they appeared to have simple linear morphologies ( Figure 1D , green arrows ) . These R3 axons are located in the central canal region of the developing EB defined by very weak CadN staining , suggesting that few CadN-mediated interactions have been established among R3 axons and other neurons at these early stages . At 20–24 hr APF , R2/R4m and R3 axons continue to innervate the developing EB , and R4d axons reach the edge of the EB and circle around the contralateral portion of the EB ( Figure 1D , green arrowheads ) . All R neuron axons continue to extend between 24 and 48 hr APF , forming multiple rings with densely packed R neuron axons . These observations show that the EB is sequentially innervated by axons from different R neuron types . R2/R4m neurons precede R3 neurons , and R4d neuron axons follow the axons of these other R neuron types . Moreover , R2/R4m , R3 and R4d axons do not overlap with one another during all the pupal stages we assessed , suggesting that they are segregated from each other soon after they arrive at the EB . In contrast to R neuron axons , small-field pb-eb-gall neurons have columnar dendritic elaborations in the EB , extending across multiple rings over the entire EB radius ( Figure 1—figure supplement 1F and H ) . When we used the GFP-reconstitution-across-synaptic-partners ( GRASP ) technique to determine the proximity of pb-eb-gall neuron dendrites to R neuron axons , functional GFP was reconstituted between pb-eb-gall dendrites and R2/R4m ( or R1/R3/R4d ) axons in adult fly brains ( Figure 1—figure supplement 1I and J , green fluorescence ) , revealing that un-laminated pb-eb-gall dendrites closely associate with multiple types of laminated R axons ( Figure 1—figure supplement 1G ) . To further characterize the relationship between pb-eb-gall dendrite and R neuron axon development , we used a combination of GAL4 and lexA drivers to label these neurons . Using the R19G02-GAL4 driver line ( Wolff et al . , 2015 ) , we found that many pb-eb-gall neurons , including their PB and Gall projections , are already present at the onset of the metamorphosis ( data not shown ) . However , pb-eb-gall dendrites in the EB appear to develop after the onset of metamorphosis . At 16–24 hr APF , pb-eb-gall dendrites adopt a crescent shape within the EB ( Figure 1—figure supplement 1K and L , blue dashed lines ) . They overlap with R2/R4m axons ( Figure 1—figure supplement 1K , red arrows ) but do not overlap with R3/R4d axons at these stages ( Figure 1—figure supplement 1L , red arrow ) . At later pupal stages , pb-eb-gall dendrites extend ventrally to form a complete circle and also expand inwardly such that pb-eb-gall dendrites occupy the entire radius of the EB and overlap with both R2/R4m and R3/R4d axons , as can be appreciated at 48 hr APF ( Figure 1—figure supplement 1K and L , bottom panels ) . These data suggest that the development of pb-eb-gall dendrites and R axons is highly correlated during the EB formation ( Figure 1E ) . Further , the select association between pb-eb-gall dendrites and R2/R4m axons at early pupal developmental stages suggests an instructive role for pb-eb-gall neurons with respect to R neuron axon targeting and lamina formation . To address whether R neuron and pb-eb-gall neurons rely upon each other for the establishment of their axonal and dendritic elaboration patterns , we conducted a series of cell ablation experiments using specific GAL4 drivers to express a diphtheria toxin subunit ( UAS-DTI ) in select EB neuron types . A temperature-sensitive tub-GAL80ts was used to control expression of UAS-DTI and other UAS transgenes in embryos and larvae , and also to minimize non-specific effects from ablating early developing neurons ( Figure 2A ) . 10 . 7554/eLife . 25328 . 005Figure 2 . The pb-eb-gall and R2/R4m axons are required for EB patterning . ( A ) Genetically encoded diphtheria toxin ( DTI ) was conditionally expressed by GAL4 drivers in targeted neurons upon a temperature shift ( TS ) from 18°C to 29°C , starting from wandering third instar stage ( wL3 ) . Pupae were dissected 24 or 48 hr after the temperature shift ( ATS ) . ( B ) The pb-eb-gall neurons were labeled by R19G02-GAL4-driving CD8-GFP ( green ) . R2/R4m neurons were labeled by R32H08-lexA-driving mtdT ( red ) . In control animals ( n = 8 ) , pb-eb-gall neuron dendrites and R neuron axons ( red circle ) form circumferential innervation at 48 hr ATS . DTI expression by R19G02-GAL4 from wL3 stages ablated most pb-eb-gall neurons ( arrowheads ) and led to pronounced R2/R4m axon patterning defects in the EB ( red oval ) ( n = 14 animals ) . ( C–E ) R54B05-lexA was used to drive myristoylated tdTomato ( mtdT ) in R3 neurons ( green ) while R32H08-GAL4-driving CD8-GFP was used for labeling R2/R4m neurons ( red ) . In panel D , the CD8-GFP-labeled R2/R4m neuron cell bodies were counted at 24 hr ATS ( 73 . 4 ± 10 . 9 cells ( n = 8 brains ) for the control group and 49 . 0 ± 6 . 4 cells ( n = 9 brains ) for DTI expression group , p<0 . 0001 ) and 48 hr ATS ( 121 . 1 ± 15 . 0 cells ( n = 7 brains ) for the control group and 41 . 4 ± 6 . 9 cells ( n = 10 brains ) for DTI expression group , p<0 . 0001 ) . In control animals , R2/R4m and R3 axons projected into different EB rings at 48 hr ATS ( top panels in C ) . After DTI was expressed in R2/R4m neurons from wL3 stages , R3 axon arbors expanded outward in the EB ( green oval in C ) . R3 axon arbor areas were measured and all measurements were normalized to the mean value of the control group shown in panel E: 1 . 000 ± 0 . 052 for controls ( n = 8 brains ) and 1 . 266 ± 0 . 090 for DTI-expressing group ( n = 7 brains ) , p=0 . 0003 . Scale bars are 50 μm in low-magnification images ( ‘Merge’ color panels ) and 20 μm in high-magnification images ( black and white panels ) . Bar graphs are presented as ‘mean’ plus ‘standard deviation ( SD ) ’ here and in following figures unless specified . For the details of statistical methods please refer to Materials and methods and Figure 2—source data 1 . Note that the confocal laser power used to image GFP ( green in B and red in C ) was 10x stronger for DTI-expressing brains ( lower panels in B and C ) compared to control brains ( upper panels ) here , and in all following ablation experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 00510 . 7554/eLife . 25328 . 006Figure 2—source data 1 . Statistical analysis of EB neuron ablation quantification . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 00610 . 7554/eLife . 25328 . 007Figure 2—figure supplement 1 . Ablation of pb-eb-gall and R2/R4m neurons lead to different defects . ( A ) Images shown for the same experiments as in Figure 2B , except here for an earlier time point , 24 hr ATS . ( B ) pb-eb-gall neurons were labeled by R19G02-GAL4-driving CD8-GFP ( green ) . R3/R4d neurons were labeled by R70B04-lexA-driving mtdT ( red ) . Similar to R2/R4m axons , ablation of pb-eb-gall neurons resulted in gross changes in R3/R4d axon patterning in the EB ( Controls: n = 8 brains at 24 hr ATS and n = 9 brains at 48 hr ATS; DTI expressing group: n = 7 brains at 24 hr ATS , n = 14 brains at 48 hr ATS ) . ( C–E ) R2/R4m and pb-eb-gall neurons were labeled by R32H08-GAL4-driving CD8-GFP ( in red ) and R19G02-lexA-driving mCherry ( in green ) , respectively . Conditional expression of DTI in R2/R4m neurons from the wL3 stage led to a significant loss of GFP-labeled R2/R4m axon projections and somas ( red arrows and arrowheads ) , and decreased mCherry-labeled pb-eb-gall dendrite elaboration at 48 hr ATS ( quantification of pb-eb-gall dendrite areas was normalized to the mean pb-eb-gall dendritic area of the control group; 1 ± 0 . 073 for the controls and 0 . 696 ± 0 . 084 for DTI expression group , p<0 . 0001; n = 10 animals for each genotype ) . In panel E , pb-eb-gall axon elaboration in the gall remained similar ( p=0 . 4359 ) between control ( 1 ± 0 . 112 ) and DTI-expressing animals ( 1 . 008 ± 0 . 141; quantified as the sum of left and right side pb-eb-gall neuron axon projection areas and normalized to the mean of the control group; n = 10 brains for each genotype ) . See Figure 2—source data 1 for details of statistical analyses . ( F ) Early time point ( 24 hr ATS ) experiments for Figure 2C . ( G ) Schematics show R3/R4 axon changes before and after pb-eb-gall neuron ablation ( upper panels ) , and pb-eb-gall dendrites before and after R2/R4m ablation ( lower panels ) . Scale bars are 50 μm in low-magnification images ( ‘Merge’ color panels ) and 20 μm in high-magnification images ( black and white panels in A , ( B , C and F ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 007 First , to ask whether small-field neuron dendrites influence R neuron axon patterning , we ablated a subset of pb-eb-gall small-field neurons by using the R19G02-GAL4 driver to express CD8-GFP and DTI . At 24 hr and 48 hr after temperature shift ( ATS ) ( comparable to 24 hr and 48 hr APF under normal animal rearing conditions ) , GFP-labeled pb-eb-gall cell bodies and processes were greatly reduced in number , and the remaining neurons exhibited much weaker GFP expression in DTI-expressing animals compared to control animals ( Figure 2B and Figure 2—figure supplement 1A , compare arrows and arrowheads ) . This demonstrates successful DTI-mediated pb-eb-gall small-field neuron ablation . Following loss of pb-eb-gall neurons , the EB developed major aberrant morphologies , including irregular edges and a loss of the EB central canal ( data not shown ) . Further , R2/R4m neurons , labeled by using R32H08-lexA-driving myristoylated tdTomato ( mtdT ) , no longer extended axons to form coherent ring-shaped structures in the DTI-expressing animals 48 hr ATS ( Figure 2B , red circles and ovals ) . Similar lamination defects were observed in R3/R4d axons , labeled by R70B04-lexA-driving mtdT , when pb-eb-gall neurons were ablated ( Figure 2—figure supplement 1B , red circle and oval ) . Both R2/R4m and R3/R4d axons appear to occupy the entire EB and therefore likely intermingle with one another in DTI-expressing animals . However , ablation of another type of small-field neuron called ‘pb-eb-no’ or ‘P-EN’ ( Wolff et al . , 2015; Omoto et al . , 2017; Turner-Evans et al . , 2017 ) that also elaborates processes in the EB during early pupal stages , using the R83H12-GAL4 driver , did not cause apparent defects to the R2/R4m ring morphologies ( data not shown ) . These results show that pb-eb-gall neurons are selectively required for instructing R neuron axon patterning during EB development . Second , to ask whether R neurons are reciprocally required for pb-eb-gall small-field neuron dendrite elaboration in the EB we ablated R2/R4m neurons , which develop early in EB development , at early pupal stages . We observed a significant loss of R2/R4m GFP-labeled neurons following DTI expression ( 33 . 2% decrease at 24 hr ATS; 65 . 2% decrease at 48 hr ATS ) ( Figure 2C , Figure 2—figure supplement 1C and F , compare red arrows and arrowheads; quantification in Figure 2D ) . Upon the loss of a substantial fraction of R2/R4m neurons , the EB was reduced in size and pb-eb-gall dendrite areas were 29% smaller than in controls 48 hr ATS ( Figure 2—figure supplement 1C , green circles; quantification in Figure 2—figure supplement 1D ) . In contrast , the areas occupied by pb-eb-gall axon terminals in the gall remained unchanged in control and in R2/R4m ablation animals ( Figure 2—figure supplement 1C , blue arrow and arrowhead; quantification in Figure 2—figure supplement 1E ) . These observations show that R2/R4m axons are required locally for pb-eb-gall dendrite growth in the EB . Third , to examine whether or not the ablation of outer R2/R4m axons affects the innervation pattern of inner R3 neuron axons in the EB , we labeled a subset of R3 neurons using R54B05-lexA while simultaneously ablating R2/R4m neurons ( Figure 2C and Figure 2—figure supplement 1F ) . When R2/R4m neurons were ablated , R3 axon arbors occupied a larger area ( 26 . 6% increase ) at 48 hr ATS; Figure 2C and Figure 2—figure supplement 1F , green ovals; quantification in Figure 2E ) . Therefore , R2/R4m ring neurons influence the size of R3 ring neuron axon arborizations . Finally , to test whether other R neurons are similarly required for R2/R4m axon development , we used R15F02-GAL4 to express DTI in R1/R3/R4d neurons . In contrast to what we observed following ablation of R2/R4m neurons , loss of >60% of GFP-labeled R1/R3/R4d neurons ( with the remaining R1/R3/R4d neurons exhibiting >10 fold reduction in GFP expression ) had no observable effects on EB morphology or R2/R4m axon expansion ( data not shown ) . These data show that although R2/R4m axons constrain R3 axon expansion , other R neuron types likely do not serve a reciprocal function with respect to R2/R4m neurons . These neuron ablation experiments suggest that pb-eb-gall dendrites and axons from different R neuron types play distinct roles in EB neuronal process development . The pb-eb-gall neurons are critical for the EB morphogenesis and axon patterning of all R types we examined . In addition , outer R2/R4m axons constrain inner R3 axon arbor expansion . Taken together , analysis of EB development and also EB cell-type-specific ablation experiments show that a series of spatiotemporally regulated interactions among EB neuronal processes facilitates the formation of proper R neuron axon laminae during the development of this central complex structure . To understand how different groups of R neurons and pb-eb-gall neurons regulate the development of EB morphology and lamination , we investigated neuronal guidance molecules that could be involved in these processes . Using antibodies against the transmembrane semaphorin Sema-1a ( Yu et al . , 1998 ) and its receptor plexin A ( PlexA ) ( Sweeney et al . , 2007 ) , we detected expression of both proteins in the central complex during early pupal stages ( Figure 3A and B; Figure 3—figure supplement 1A and B ) . Both proteins are detected in the EB during pupal stages ( Figure 3A and B , arrowheads ) , and Sema-1a is also found in the bulb ( Figure 3A , arrows ) , where R neuron dendrites project . This suggests that EB neurons , particularly R neurons , express Sema-1a and PlexA during EB formation . 10 . 7554/eLife . 25328 . 008Figure 3 . Ellipsoid body ring neurons express Sema-1a . ( A and B ) Antibody staining reveals that Sema-1a and PlexA are both expressed in the EB during early pupal stages ( 24 hr and 48 hr APF ) . Sema-1a is also detected in the bulb ( arrows ) at 48 hr APF . ( C ) Schematic ( modified from: Pecot et al . , 2013 ) shows our strategy for using cell-type-specific flipase ( FLP ) expression to conditionally tag endogenous Sema-1a with V5 epitopes , revealing Sema-1a expression while at the same time conditionally labeling these same neurons with CD2-GFP . ( D–F ) Both CD2-GFP and V5-tagged Sema-1a were conditionally expressed in R neurons using the strategy outlined in panel C when R neuron-specific R11F03-GAL4 was used to express FLP . Low-magnification images show that CD2-GFP and V5 were not detected in animals when R11F03-GAL4 was not present ( D ) . High-magnification images reveal that V5-tagged Sema-1a is specifically enriched in R neuron axonal terminals within the EB at 24 hr APF ( E ) and at 48 hr APF , unlike CD2-GFP , which is uniformly localized in both R neuron soma and axons ( F ) . Sema-1a-V5 exhibits strong expression throughout the EB , and it is also found in R neuron dendritic terminals in the bulb at 48 hr APF . ( G ) Schematic ( modified from: Pecot et al . , 2013 ) showing how Sema-1a-expressing R neurons are labeled by lexA-driving mCherry when FLP is expressed in these same R neurons using GAL4 drivers . ( H–J ) Using the strategy in panel G , multiple GAL4 drivers were used to label different groups of R neurons with CD8-GFP . Sema-1a-expressing R neurons , identified by mCherry expression , are shown at 48 hr APF . Scale bars are 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 00810 . 7554/eLife . 25328 . 009Figure 3—figure supplement 1 . Sema-1a and PlexA are expressed in the central complex . ( A and B ) Antibody staining reveals that Sema-1a ( A ) and PlexA ( B ) are expressed by different CX components at various levels . The ellipsoid body has high Sema-1a but relatively low PlexA expression; however , the fan-shaped body has low Sema-1a but high PlexA expression . The protocerebral bridge exhibits moderate Sema-1a and PlexA expression . Scale bars are 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 009 To examine Sema-1a expression in R neurons , we used a modified endogenous Sema-1a allele ( Sema-1aFSF ) which includes a FLP-stop-FLP cassette inserted at the 3’ end of Sema-1a coding sequence to conditionally tag Sema-1a and label Sema-1a-expressing neurons ( Pecot et al . , 2013 ) . When the flipase ( FLP ) recombinase is expressed , the transcription termination ( ‘stop’ ) sequence is excised . This allows the V5 epitope to be added to the C-terminus of the endogenous Sema-1a protein and also results in co-transcription of lexA along with Sema-1a ( Figure 3C ) . When FLP is expressed in all R neuron types using R11F03-GAL4 , endogenous V5-tagged Sema-1a is detected at high levels in R axon terminals in the EB ( Figure 3E and F , red arrows ) , but at low levels in the R neuron somas ( Figure 3E and F , red circles ) . Consistent with our antibody staining , Sema-1a-V5 is also detected at R neuron dendrite terminals in the bulb ( Figure 3F , red arrowhead ) . The broad distribution of Sema-1a antibody staining and Sema-1a–V5 in the EB at 48 hr APF suggests that Sema-1a expression is not restricted to specific R neuron types . To further assess Sema-1a expression in different R neuron types , Sema-1aFSF was used to visualize Sema-1a expression in neurons following R neuron-specific FLP expression ( Figure 3G ) . Indeed , dual labeling experiments using different GAL4 drivers ( R11F03-GAL4 for R1-R4; R15F02-GAL4 for R1/R3/R4d; EB-GAL4 for R2/R4m ) confirmed that Sema-1a is expressed in most R neurons ( Figure 3I and J , red ) with the exception of a small subset of R2/R4m cells that are labeled only by GFP but not mCherry ( Figure 3J , arrows ) . These results show that Sema-1a is specifically recruited to the axons and dendrites of multiple R neuron types during the EB development , leading us to ask whether or not Sema-1a and PlexA influence R axon lamination in the EB . Since Sema-1a is highly expressed in R neurons and PlexA is also expressed in the EB regions where R axons project , we examined loss-of-function ( LOF ) phenotypes in Sema-1a and PlexA null mutants ( Yu et al . , 1998; Jeong et al . , 2012 ) . Both Sema-1a and PlexA null mutants die during pupal stages , and in homozygous Sema-1a or PlexA mutant pupae the central complex , including the EB , displays severely disrupted morphologies ( data not shown ) . Therefore , to investigate the functions of Sema-1a and PlexA in R neurons , we used GAL4/UAS-based RNA interference ( RNAi ) to knock down the expression of either gene in R neurons . For each gene at least two independent UAS-RNAi lines were used to control for possible off-target effects . We examined the requirement for Sema-1a or PlexA in R2/R4m neurons . R32H08-GAL4 was used to express GFP-tagged synaptotagmin ( Syt-GFP ) in order to reveal the circumferential distribution of R2/R4m presynaptic regions in the EB ( Figure 4Ai , green ) . When Sema-1a-RNAi or PlexA-RNAi ( Pecot et al . , 2013; Sweeney et al . , 2007 ) was expressed using R32H08-GAL4 , the R2/4m ring ( lamina ) was partially disrupted , displaying a more scattered distribution of Syt-GFP labeling in the outer EB ( Figure 4Aii and 4Aiii , green ) . By measuring the areas covered by Syt-GFP immunostaining , we found that the areas including R2/R4m synaptic labeling were increased by 32 . 1% and 27 . 1% in two different Sema-1a-RNAi lines , and by 19 . 3% and 22 . 8% in two different PlexA-RNAi lines , compared to controls ( Figure 4B ) . Although the increase in area covered by R32H08-GAL4>Syt-GFP was less in PlexA-RNAi compared to Sema-1a-RNAi animals , the R2/R4m ring was more severely disrupted in PlexA-RNAi-expressing brains since we observed GFP-negative gaps and holes within the ring ( Figure 4Aiii , arrowheads ) . These data reveal that both Sema-1a and PlexA are required for constraining R2/4m axon arbor growth in what appears to be an EB layer-autonomous manner . 10 . 7554/eLife . 25328 . 010Figure 4 . Sema-1a/PlexA signaling controls R2/R4m axon lamination and synapse localization in the ellipsoid body . ( A and B ) Pre-synaptic R2/R4m axon compartments were specifically labeled using R32H08-GAL4-driving GFP-tagged Synaptotagmin ( Syt-GFP ) in adult fly brains . Knocking down Sema-1a or PlexA in R2/R4m neurons led to aberrant ring neuron axon morphology and inward expansion of the R2/R4m synaptic domain in the EB . Syt-GFP areas were measured on Z-projection images and normalized to the mean area of the control group . Normalized values are compared in panel B for controls ( 1 . 000 ± 0 . 069 [n = 6 brains] ) , the two Sema-1a-RNAi groups ( 1 . 321 ± 0 . 129 [p<0 . 001] and 1 . 271 ± 0 . 140 [p<0 . 001] [n = 10 brains for each group] ) , and for the two different PlexA-RNAi groups ( 1 . 193 ± 0 . 091 [p=0 . 022 , n = 7 brains] and 1 . 228 ± 0 . 127 [p=0 . 002 , n = 12 brains] ) . Resource data for statistical analyses are available in Figure 4—source data 1 . ( C ) MARCM clones were generated using hsFLP . R2/R4m GAL4 drivers were used to label R2/R4m neurons with CD8-GFP in adult flies . HA-tagged synatotagmin ( Syt-HA ) was co-expressed to label pre-synaptic specializations in WT and Sema-1a-/- clones . In a Sema-1a-/- clone containing multiple R2 and R4m cells , Syt-HA-labeled axon terminals expanded inwardly within the EB , recapitulating the Sema-1a-RNAi phenotype in panel A . ( D–E ) Frontal- ( upper panels ) and dorsal- ( lower panels ) view images of axon arbors of single R4m MARCM clones reveal that R4m axons become more complex and some R4m boutons are located closer to the EB center in Sema-1a-/- and Sema-1aEcTM rescue clones than in control and Sema-1a rescue clones . Schematics to the right show the brain coordinates and domain organization of WT Sema-1a and Sema-1aEcTM proteins ( E ) . For rescue experiments , full length Sema-1a or truncated Sema-1a lacking its cytoplasmic domain ( Sema-1aEcTM ) were expressed in Sema-1a-/- clones consisting of R4m neurons . ( F–H ) The pre-synaptic boutons of R4m neurons in the EB were manually determined based on their enlarged morphologies as observed in the reconstructed 3D images of GFP-labeled R4m axon arbor ( see Figure 4—figure supplement 1C ) . The distances between each bouton and the center of EB canal were measured . The smallest and largest distances were plotted and compared to controls in panels F and G , respectively . The percentage of R4m boutons that were no more than 15 μm away from the EB center is shown in panel H . n = 12 Single Cell Clones ( SSCs ) for controls; n = 16 SSCs for Sema-1a-/- group; n = 9 SSCs for Sema-1a rescue; n = 10 SSCs for Sema-1aEcTM rescue . Detailed statistical analyses are available in Figure 4—source data 2 . ( I ) Schematics highlight R4m axon arbor changes shown in panel D . ‘ns’ p>0 . 1234; p<0 . 0332; p<0 . 0021; *p<0 . 0002 . Scale bars are 20 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 01010 . 7554/eLife . 25328 . 011Figure 4—source data 1 . Statistical analysis of R2/R4m syt-GFP quantification . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 01110 . 7554/eLife . 25328 . 012Figure 4—source data 2 . Statistical analysis of single R4m MARCM . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 01210 . 7554/eLife . 25328 . 013Figure 4—figure supplement 1 . Sema-1a is required cell-autonomously for R4m , but not R2 , axon arbor and synapse development . ( A–B ) R2 and R4m neurons have different dendrite locations in the bulb white and yellow circles ) . Syt-HA-labeled R2/R4m synapse ring ( red ) was disrupted in multiple-cell MARCM clones containing both R2 and R4m neurons ( A ) , but remained largely normal in multiple-cell MARCM clones containing only R2 neurons ( B ) . Scale bars are 20 μm . ( C ) High-magnification stack images were processed in Imaris . See details in the Materials and methods . ( D–E ) The total bouton number , axonal branch length and branch point numbers in each single R4m axon arbors in the EB are plotted . n = 12 SSCs for the control; n = 16 SSCs for Sema-1a-/- group; n = 9 SSCs for Sema-1a rescue; n = 10 SSCs for Sema-1aEcTM rescue; ‘ns’ p>0 . 1234; p<0 . 0332; p<0 . 0021; p<0 . 0002 . Statistical analyses are included in Figure 4—source data 2 . Scale bars are 20 μm in A and B . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 01310 . 7554/eLife . 25328 . 014Figure 4—figure supplement 2 . PlexA and Sema-1a are differentially required in R1–3 neurons for EB morphogenesis and R axon lamination . ( A–B ) R4m ( green in A ) and R3 ( red in B ) axons were labeled by R34D03-lexA and R54B05-lexA-driving mtdT in adult fruitfly brains , respectively . Knocking down Sema-1a or PlexA in R1–3 neurons using R40G10-GAL4 leads to distinct changes in EB morphology and R axon elaboration . Top panels are projections of low-magnification Z-stack images and lower panels are high-magnification images of single optic sections . ( G ) Schematics showing R2m and R3 axon trajectory alterations following Sema-1a-RNAi and PlexA-RNAi expression in R1–3 neurons . Scale bars are 50 μm in low-magnification images ( top panels ) and 20 μm in high-magnification images ( other lower panels in A and B ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 014 To directly address whether Sema-1a is cell-autonomously required for regulating R2 and/or R4m axonal arbor elaboration , we generated hsFLP mosaic analysis with a repressible cell marker ( MARCM ) clones . First , we generated MARCM clones containing multiple ( > or = to 5 ) R2 and/or R4m cells . Despite the presence of intermingled R2 and R4m axons in the EB , the composition of multiple-cell clones can be determined by the different locations of R2 and R4m dendrites in the bulb ( Figure 4—figure supplement 1A and B , white and yellow circles ) . In control clones , R2/R4m synaptic terminals , labeled by HA-tagged synaptotagmin ( Syt-HA ) , form a regular ring within the peripheral EB ( Figure 4C , Control ) , displaying a clear inner boundary ( Figure 4Ci , dashed line ) . However , in about half of Sema-1a mutant clones , the R2/R4m ring was partially deformed ( Figure 4C , Sema-1a-/- ) . A significant fraction of Syt-HA-positive boutons were mis-localized to regions closer to the EB central canal ( Figure 4Cii , arrows ) and beyond the normal R2/R4m inner boundary ( Figure 4Cii , dashed lines ) . This is in line with the lamination defects observed when Sema-1a is knocked down in these same R neuron types ( Figure 4A ) . Interestingly , this outer EB lamination phenotype was only observed in Sema-1a-/- clones consisting of R4m neurons ( Figure 4—figure supplement 1A ) , but not in clones composed of only R2 cells ( Figure 4—figure supplement 1B ) . These results show that R2/R4m axon lamination defects in Sema-1a LOF experiments are largely due to lack of Sema-1a in R4m neurons , underscoring the select interactions among ring neurons that are required to generate EB laminar organization . To confirm the cell-autonomous function of Sema-1a , we generated smaller clones in order to label single R4m neurons ( Figure 4D ) . Indeed , compared to control R4m neurons ( Figure 4Di ) , single Sema-1a-/- R4m neurons had aberrant axon arbor morphology ( Figure 4Dii ) . Further , some synaptic boutons , identified by both GFP-labeled varicosities and Syt-HA labeling ( Figure 4Dii , small panels , arrowheads ) , were localized to more central EB regions ( Figure 4Dii , arrows ) . To confirm that these R4m phenotypes resulted from loss of Sema-1a function , we expressed a full-length Sema-1a ( Sema-1a rescue ) or a truncated Sema-1a ( Sema-1aEcTM rescue ) transgene in Sema-1a-/- R4m cells ( Figure 4E ) . Expression of Sema-1a , but not Sema-1aEcTM , in Sema-1a-/- R4m cells rescued R4m axon arbor phenotypes ( Figure 4Diii and 4Div ) . To quantify changes in R4m synapse localization , we counted R4m pre-synaptic boutons based on the presence of CD8-GFP+ varicosities in reconstructed 3D images ( Figure 4—figure supplement 1Ciii and 1Cvii , white dots ) . We then measured the distance from each bouton to the EB canal center ( Figure 4—figure supplement 1Civ and 1Cviii , color-coded bouton distance ) , grouped the smallest and largest distances between R4m boutons and the EB center in single R4m clones , and compared them across all four genotypes . The smallest distances , which indicate how close R4m boutons are to the EB center , were significantly decreased in Sema-1a-/- ( 11 . 85 ± 2 . 36 μm , n = 16 singe-cell clones [SSCs] ) and in Sema-1aEcTM rescue ( 9 . 34 ± 1 . 64 μm , n = 10 SSCs ) neurons but showed no difference compared to control R neurons ( 15 . 95 ± 1 . 93 μm , n = 12 SSCs ) or Sema-1a rescue R neurons ( 14 . 62 ± 1 . 64 μm , n = 9 SSCs ) ( Figure 4F ) . In contrast , the largest distances from the EB center , which are a measure of how wide the R4m ring is , were the same across all four genotypes ( Figure 4G ) . R2/R4m synapses are normally excluded from the central EB region , which is innervated by R3 axons that have arbors with a minimum diameter of about 30 μm ( Figure 5—figure supplement 1G , ‘R3 Diameter’ ) . We scored central R4m boutons ( defined by their locations within a 15 μm radius originating at the EB center ) and calculated the percentage ratio of central bouton number vs total bouton number for each R4m neuron ( Figure 4H ) . A significantly larger fraction of R4m boutons was found in the central EB region in both Sema-1a-/- ( 4 . 66% , n = 16 SSCs ) and Sema-1aEcTM rescue ( 10 . 52% , n = 10 SSCs ) compared to the control group ( 0 . 62% , n = 12 SSCs ) . The Sema-1a rescue R4m neurons ( 2 . 43% , n = 9 SSCs ) , although they exhibited 32 . 3% fewer total synapses ( Figure 4—figure supplement 1D ) , showed no significant change in central synapse distribution compared to controls . In addition to these synapse distribution changes , R4m axon arbor morphology changes were also quantified by tracing R4m axon branches ( Figure 4—figure supplement 1Cii and 1Cvi ) . Total branch length was increased in Sema-1a-/- ( 11 . 6% increase ) and Sema-1aEcTM rescue ( 16 . 5% increase ) ( Figure 4—figure supplement 1E ) , and the total number of branch points was decreased by 26 . 9% in Sema-1a rescue ( Figure 4—figure supplement 1F ) , compared to controls , suggesting that Sema-1a regulates R4m axon branch formation and extension . Taken together , these results demonstrate that full length Sema-1a , but not truncated Sema-1a lacking its cytoplasmic domain , is required cell-autonomously to regulate R4m axon branch growth , lamination and synapse distribution within the EB . Therefore , these functions are likely mediated through a Sema-1a ‘reverse signaling’ pathway in which the Sema-1a protein functions as a receptor in R4m neurons and PlexA protein servers as its ligand in other EB neurons ( Battistini and Tamagnone , 2016 ) . Since lamination defects were observed when either Sema-1a or PlexA was knocked down in R2/R4m ( Figure 4A ) , we suspected that R2 neurons express PlexA and therefore regulate R4m axon lamination through Sema-1a reverse signaling . Unfortunately , all the available R2 drivers we characterized also drive expression in R4m neurons . However , R40G10-GAL4 drives expression in R1 , R2 and R3 neurons throughout pupal and adult stages ( Lovick et al . , 2017 ) . Therefore , we used this driver to knock down Sema-1a or PlexA in R1–3 neurons , and we also used R34D03-lexA to label a subset of R4m axons . Down-regulation of Sema-1a or PlexA using R40G10-GAL4 resulted in different EB morphology and R axon elaboration phenotypes . In Sema-1a-RNAi–expressing animals , the EB partially lost its circular shape . R4m axons formed an irregular ring but still appeared organized in a laminar fashion in EB outer regions ( Figure 4—figure supplement 2 , Aii and Av ) . More severe phenotypes were observed in PlexA-RNAi expressing animals . In many R40G10>PlexA-RNAi animals , the EB remained completely open and R4m axons expanded their axon projections to cover most EB regions ( Figure 4—figure supplement 2 , Aiii and Avi ) . Taken together , our data suggest that PlexA in R1–3 , and most likely in R2 alone since knocking down PlexA in R1/R3 with multiple drivers did not generate similar defects ( data not shown ) , plays an important role in regulating R4m axon lamination formation through Sema-1a reverse signaling pathway . From our EB neuron ablation experiments during pupal development , we learned that R2/R4m axons constrain R3 axon expansion within the EB . To further investigate how the disruption of R2/R4m axon lamination in Sema-1a or PlexA LOF mutants affects R3 axons , we used the R54B05-lexA driver to express mtdT and label a subset of R3 neurons , while we also used the R32H08-GAL4 driver to express CD8-GFP and Sema-1a-RNAi ( or PlexA-RNAi ) in R2/R4m neurons ( Figure 5A ) . In controls , R2/R4m and R3 axon arbors exhibit characteristic sizes and are adjacent to one another with a clear boundary separating these two R neuron arbors ( Figure 5Ai and 5Aiv , yellow dashed line ) . However , in either R2/R4m Sema-1a-RNAi or PlexA-RNAi–expressing fly lines , R3 axon arbors expand outward within the EB ( Figure 5Av and 5Avi ) , and this is accompanied by inward expansion of R2/R4m axons ( Figure 5Aii and 5Aiii ) . This results in disruption of the boundary between these two laminae , with the outer edge of the R3 axon arbors displaying a jagged morphology and R3 axons extending outward to more peripheral EB regions . Further , the smallest distance between R3 axons and the outer edge of the R2/R4m ring was greatly decreased ( Figure 5Aiv , 5Av and 5Avi; double-headed arrows ) . These changes in R axon patterning appear to develop gradually during EB formation ( Figure 5—figure supplement 1A and B ) . 10 . 7554/eLife . 25328 . 015Figure 5 . Sema-1a and PlexA are required in R2/R4m neurons to constrain R axon growth within the ellipsoid body . ( A–D ) R2/R4m neurons were labeled by R32H08-GAL4-driving CD8-GFP , while R3 axons were labeled using R54B05-lexA-driving mtdTomato ( mtdT ) in adult fly brains . Knocking down Sema-1a or PlexA in R2/R4m neurons did not change axon trajectory or cell numbers of either R3 or R2/R4m ( panels C and D ) , but resulted in non-cell autonomous R3 axon arbor expansion in the EB ( panel B ) . Seven to eight brains for each genotype were used for quantification . ‘ns’ p>0 . 1234; p<0 . 0332; p<0 . 0021; p<0 . 0002 . See Figure 5—source data 1 for detailed statistical analyses . ( E ) Schematics showing that both R3 ( red ) and R2/R4m ( green ) axons expand when Sema-1a or PlexA is down-regulated in R2/R4m , leading to intermingled R3 and R2/R4m axons . Scale bars are 50 μm in low-magnification images in top panels and 20 μm in high-magnification images in other panels . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 01510 . 7554/eLife . 25328 . 016Figure 5—source data 1 . Statistical analysis of R3 axon quantification in R2/4m RNAi . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 01610 . 7554/eLife . 25328 . 017Figure 5—source data 2 . Statistical analysis of R3 RNAi quantification . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 01710 . 7554/eLife . 25328 . 018Figure 5—source data 3 . Statistical analysis of single R3 MARCM quantification . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 01810 . 7554/eLife . 25328 . 019Figure 5—figure supplement 1 . R2/R4m Sema-1a , but not R3 Sema-1a/PlexA , is required for R axon lamination . ( A–B ) R2/R4m and R3 axons were differentially labeled by R32H08-GAL4-driving CD8-GFP ( green ) and R54B05-lexA-driving mtdT ( red ) , respectively . Knocking down Sema-1a in R2/R4m neurons led to gradual changes in R axon elaboration during early pupal stages . ( C–D ) R46D01-GAL4 was used to express CD8-GFP ( green ) and UAS-RNAi in a group of R3 neurons . Knockdown of either Sema-1a or PlexA did not change R3 axon elaborations . The R3 axonal arbor area was measured and compared to controls in panel D . Eight to ten animals were used for quantification of each genotype . All p-values are > 0 . 21 . Detailed statistical analyses are available in Figure 5—source data 2 . ( E ) The mushroom body ( MB ) , the ellipsoid body ( EB ) and the fan-shaped body ( FB ) are adjacent to one another along the anterior-posterior axis in the adult Drosophila brain , shown here using Nc82 immunostaining . ( F–G ) R84H09-GAL4 was used to express CD8-GFP in R3 neurons in MARCM clones . In Sema-1a-/- single-cell MARCM clones , the radial elaboration of R3 axons within the EB is generally normal as indicated by no change of R3 axon ‘Diameter’ compared to control clones ( 34 . 83 ± 2 . 46 μm for the control ( n = 24 SSCs ) and 34 . 49 ± 2 . 53 μm for the Sema-1a-/- ( n = 21 SSCs ) , p=0 . 826 ) . However , some Sema-1a-/- R3 neurons extended a few axon branches posteriorly into the FB , which was quantified by assessing the increase in axon arbor ‘Thickness’ as observed in dorsal view images ( 10 . 83 ± 3 . 17 μm for the control ( n = 24 SSCs ) and 22 . 51 ± 9 . 78 μm for the Sema-1a-/- ( n = 21 SSCs ) , p<0 . 001 ) . See Figure 5—source data 3 for details of statistical analyses . Scale bars are 20 μm in panels A , B and F , and in right high-magnification images in panel C ( black and white panels ) ; 50 μm in left low-magnification images ( color panels ) in panel C . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 01910 . 7554/eLife . 25328 . 020Figure 5—figure supplement 2 . Sema-1a and PlexA are not required for pb-eb-gall dendrite elaboration and synaptogenesis in the EB . ( A ) R2/R4m and pb-eb-gall neurons were differentially labeled using R32H08-GAL4-driving CD8-GFP ( green ) and R19G02-lexA-driving mtdT ( red ) , respectively . Knocking down Sema-1a or PlexA in R2/R4m neurons led to changes in R2/R4m axon lamination , but no changes in pb-eb-gall dendrite elaboration in the EB . ( B ) R32H08-GAL4 and R19G02-lexA were used to express nSyb-spGFP1-10 and CD4-spGFP11 in R2/R4m and pb-eb-gall neurons , respectively . Sema-1a-RNAi was co-expressed in R2/R4m neurons in experimental conditions . And mtdT was co-expressed to labeled pb-eb-gall projections in all animals . ( C–D ) pb-eb-gall neurons were labeled by R19G02-GAL4-driving CD8-GFP ( green ) . R2/R4m ( red in C ) and R3/R4d ( red in D ) neurons were labeled by R32H08-lexA and R70B04-lexA , respectively , driving mtdT . Knocking down Sema-1a or PlexA in pb-eb-gall neurons alters pb-eb-gall axon projections , but does not change pb-eb-gall dendrite elaboration and R axon lamination in the EB . Scale bars are 50 μm in low-magnification images in all panels; 20 μm in high-magnification images in panel A ( i–vi ) and B ( i–iv ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 020 We quantified the expansion of R3 axon arbors by measuring the area covered by mtdT immunostaining within the EB in adult brain Z-projection images . Following normalization to controls ( n = 8 animals ) , the area occupied by R3 axon arbors increased 48 . 3 ± 15 . 6% ( n = 8 brains ) and 42 . 8 ± 12 . 4% ( n = 7 brains ) for the two independent Sema-1a-RNAi lines , and 34 . 8 ± 28 . 7% ( n = 8 brains ) and 50 . 4 ± 32 . 6% ( n = 8 brains ) for the two independent PlexA-RNAi lines ( Figure 5B ) . To address whether Sema-1a or PlexA are required for controlling R neuron numbers , we quantified R3 and R2/R4m cell numbers using mtdT and CD8-GFP labeling , respectively . R3 numbers were similar to controls in all of the RNAi lines except for a small increase ( 17 . 6% ) in one Sema-1a-RNAi line ( Figure 5C ) . Further , we found that R2/R4m cell numbers remained unchanged in all RNAi experiments compared to the controls ( Figure 5D ) . To further examine the role of R2 and R4m in constraining R3 axon growth , R40G10-GAL4 was used to express Sema-1a or PlexA-RNAi in R1–3 neurons . R54B05-lexA>mtdT-labeled R3 axon arbors were mildly altered but remained within their territory in the inner EB in R40G10>Sema-1a-RNAi animals ( Figure 4—figure supplement 2 , Bii ) . However , R3 axons generally increased their elaboration within the EB in R40G10>PlexA-RNAi animals ( Figure 4—figure supplement 2 , Biii ) . Taken together , these results support the idea that Sema-1a/PlexA-dependent R2/R4m axon patterning is required to control R3 axon expansion through a local repellent signal , thereby regulating R axon lamination . Both Sema-1a and PlexA can signal as receptors to mediate repulsion ( Jongbloets and Pasterkamp , 2014; Battistini and Tamagnone , 2016 ) . We wondered whether Sema-1a or PlexA , given their expression patterns , act as signals or receptors between R2/R4m and R3 axons . Therefore , we next tested whether Sema-1a or PlexA function cell-autonomously in R3 neurons to control R3 axon expansion . First , we found that knocking down Sema-1a or PlexA in R3 neurons did not change the size or morphology of the R3 rings ( Figure 5—figure supplement 1C; quantification in Figure 5—figure supplement 1D ) . Second , MARCM experiments show that single R3 axon ring diameters are not different between control and Sema-1a-/- R3 neurons ( Figure 5—figure supplement 1F; ‘Diameter’ quantification in Figure 5—figure supplement 1G ) . However , in Sema-1a-/- single R3 MARCM clones , many R3 neurons elaborate axon branches that grow more posteriorly within the EB , and for some R3 neurons ( 6 out of 21 cells ) out of the EB and into the posterior FB ( Figure 5—figure supplement 1E and F; quantification of ‘R3 Thickness’ in Figure 5—figure supplement 1G ) . This suggests that Sema-1a controls longer range R axon extension to secure the overall separation of neighboring brain structures . We were unable to use MARCM to test the cell autonomy of PlexA in R neurons since PlexA is located on the fourth chromosome and tools are not presently available for this experiment . Together , these results show that it is unlikely PlexA or Sema-1a act in R3 as receptors for R2/R4m Sema-1a or PlexA to directly regulate R3 axon growth . Although we find that Sema-1a and PlexA function in R2/R4m neurons to organize axon lamination , our data suggest that R2/R4m axons use other molecules to constrain R3 axons and refine axon patterning and lamination during pupal development . Our analyses of central complex development show that pb-eb-gall dendrites play an important role in controlling R axon lamination ( Figure 1—figure supplement 1K and Figure 2B ) . Is R axon lamination also required for pb-eb-gall dendrite elaboration , and do Sema-1a or PlexA play a role in pb-eb-gall development ? To address these issues , we first used R19G02-lexA to drive mtdT expression in pb-eb-gall neurons in combination with R32H08-GAL4 to knock down Sema-1a or PlexA in these neurons . The pb-eb-gall dendrites appeared normal when R2/R4m axons expanded their elaboration within the EB in either Sema-1a-RNAi or PlexA-RNAi–expressing animals ( Figure 5—figure supplement 2 , Aiv-vi ) . Secondly , we used the syb:GRASP technique ( Macpherson et al . , 2015 ) to detect potential synaptic connections between R2/R4m axons and pb-eb-gall dendrites . The spGFP1-10-tagged neuronal synaptobrevin ( syb-spGFP1-10 ) was expressed in R2/R4m by R32H08-GAL4 while CD4-spGFP11 and mtdT were expressed in pb-eb-gall neurons . Reconstituted GFP ( rcGFP ) was detected in the EB in both control and UAS-Sema-1a-RNAi-expressing animals , even though different GFP fluorescence patterns were observed ( Figure 5—figure supplement 2B ) . These data suggest that R axon lamination defects do not affect pb-eb-gall dendrite elaboration or synaptogenesis between R axons and pb-eb-gall dendrites . Finally , R19G02-GAL4 was used to drive Sema-1a-RNAi or PlexA-RNAi in pb-eb-gall neurons . Knocking down Sema-1a or PlexA in pb-eb-gall neurons partially or completely re-directed their axon projections from the gall ( Figure 5—figure supplement 2 , Ci-iii and Di-iii , arrows ) to a region close to the EB ( Figure 5—figure supplement 2 , Ci-iii and Di-iii , arrowheads ) . However , loss of Sema-1a or PlexA in pb-eb-gall neurons did not affect pb-eb-gall dendrite elaboration or R2/4 m and R3/R4d axon lamination ( Figure 5—figure supplement 2 , Civ-vi and Div-vi ) , suggesting that Sema-1a and PlexA in pb-eb-gall neurons are not required for pb-eb-gall dendrite or R axon development in the EB . In invertebrate and vertebrate visual systems , pre- and post-synaptic neurites co-stratify to facilitate appropriate connections for visual system function ( Zipursky and Sanes , 2010; Zhang et al . , 2017 ) . For example , co-stratification of axons from select On cone bipolar cells with dendrites of On-Off direction selective ganglion cells in specific sub-laminae of the inner plexiform layer ( IPL ) is critical for functional direction-selective responses in mice ( Duan et al . , 2014 ) . In the EB , multiple different types of laminated R axons converge onto pb-eb-gall neuron dendrites , and so this raises the question: what function does R axon lamination serve , and might it play a role in specifying distinct synaptic connections among R neurons ? To understand the functional significance of Sema-1a/PlexA-mediated R neuron axon lamination , we first investigated the neurochemical properties of R neurons . Abundant protein expression of gamma aminobutyric acid ( GABA ) and RDL ( a GABA-A receptor subunit ) has been observed in the EB and appears to be closely associated with R neuron axons ( Kahsai et al . , 2012; Martín-Peña et al . , 2014; Enell et al . , 2007 ) . However , the sources of GABA and RDL and their functions in the EB are largely unknown , although a group of R2/R4m neurons are known to be GABAergic and thermogenetic activation of these neurons impairs medium-term olfactory memory ( Zhang et al . , 2013 ) . To address this issue , we took advantage of newly developed protein-trap GAL4 and QF2 drivers ( Diao et al . , 2015 ) . These drivers were obtained by converting a MiMic insertion in a coding intron to an exogenous exon containing coding sequences for the transcriptional activators GAL4 or QF2 ( Venken et al . , 2011 ) . This strategy reliably represents the expression of target genes , often more faithfully than traditional enhancer-trap GAL4 driver lines ( Diao et al . , 2015 ) . We found that fluorescent reporters driven by Gad1MI-QF2 and RdlMI-GAL4 ( Diao et al . , 2015 ) were both expressed in a large number of R neurons , displaying characteristic axon trajectories into the bulb and EB ( Figure 6A and D , arrowheads and arrows ) . Expression of either UAS-Gad1-RNAi or UAS-Rdl-RNAi in R2/R4m neurons using R32H08-GAL4 decreased GABA and RDL immunoreactivity , respectively , in the peripheral EB , the location where R2/R4m axons project ( Figure 6B arrowhead; Figure 6E , arrows ) . Loss of GABA or RDL following RNAi knockdown was quantified by measuring the area of strong immunostaining revealed by each antibody in the EB ( Figure 6B and E , circles; quantification in Figure 6C and F ) . Expression levels of GABA and RDL were also reduced in central EB regions when R3 Gal4 drivers were used to express these corresponding RNAi transgenes ( data not shown ) . We also examined three other common neurotransmitter pathways ( acetylcholine , glutamate and dopamine ) in the EB using antibody staining and similar genetic labeling strategies as we used for GABA . Many fewer cholinergic , glutamatergic or dopaminergic inputs were observed in the EB , and most of these were preferentially localized to posterior EB regions ( Figure 6—figure supplement 1 , arrows ) . Taken together , these results show that multiple types of R neurons are GABAergic and also that they recruit GABA-A receptors to their axon terminals in the EB , raising the question of whether R axon lamination influences EB inhibitory synaptic properties . 10 . 7554/eLife . 25328 . 021Figure 6 . Ring neurons transport GABA and GABA-A receptors to their axonal terminals in the ellipsoid body . ( A and D ) MiMic-based GAL4 and QF2 drivers ( Diao et al . , 2015 ) were used to express fluorescent reporters and to access Gad1 ( A ) and Rdl ( D ) expression in adult fruitfly brains . Both drivers clearly express in a large group of ring neurons , which project from the lateral regions into the bulb ( arrowheads ) and EB ( arrows ) . ( B , C , E , F ) GABA and Rdl immunostaining is enriched in the EB in adult fruitfly brains . Knocking down Gad1 and Rdl in R2/R4m neurons using R32H08-GAL4 significantly decreased GABA ( arrowheads in panel B ) and Rdl ( arrows in panel E ) immunostaining in the peripheral regions of the EB . The area covered by strong GABA and Rdl immunostaining in the EB was measured using maximal intensity Z-projection images . All measurements were normalized to the mean of the control groups , and they were compared between control and RNAi groups as shown in panels C and F . For GABA staining in the control group , normalized area was 1 . 000 ± 0 . 083 ( n = 5 ) , and for the RNAi group the normalized area was 0 . 563 ± 0 . 028 ( n = 5 ) and significantly reduced , compared to the control ( p=0 . 0079 ) . For Rdl staining , the normalized area was 1 . 000 ± 0 . 087 ( n = 5 ) and for the RNAi group the normalized area was 0 . 587 ± 0 . 052 ( n = 5 ) , again , significantly reduced compared to the control ( p=0 . 0079 ) . Detailed statistical analyses are available in Figure 6—source data 1 . Scale bars are 50 μm in panels A and D , and 20 μm in panels B and E . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 02110 . 7554/eLife . 25328 . 022Figure 6—source data 1 . Statistical analysis of Gad1 and Rdl RNAi in R2/R4m quantification . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 02210 . 7554/eLife . 25328 . 023Figure 6—figure supplement 1 . Ellipsoid body ring neurons are not cholinergic , glutamatergic or dopaminergic . The neurochemical identities of EB projection neurons were analyzed using either antibody staining or fluorescent reporter proteins ( CD8-GFP and mTdt ) driven by GAL4 and QF2 drivers . All drivers , except for TH-GAL4 , are MiMic ( MI ) -based protein trap lines ( Diao et al . , 2015 ) . ChaT ( A ) , vGluT ( C ) and TH ( E ) stainings show that cholinergic , glutamatergic and dopaminergic neurons preferentially innervate the EB posterior shell , but not the anterior shell where ring neuron axons elaborate . ChaTMI-QF2 ( B ) , vGluTMI-QF2 ( D ) and TH-GAL4 ( F ) are expressed in small-field EB neurons and extrinsic ring neurons . Scale bar is 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 023 The co-recruitment of GABA and GABA-A receptors to R neuron axon terminals suggests that local inhibitory circuits are formed among R axon terminals . R neuron axons are precisely organized into discrete rings/laminae , suggesting that inhibitory synaptic connections among R neuron axons may also be organized in a laminated fashion such that co-stratified R neuron synaptic terminals inhibit each other . This leads to the prediction that R neuron axons that project within adjacent rings , such as R2/R4m and R3 , do not exhibit coupled inhibitory interactions . To test this idea , we took advantage of the R axon lamination phenotype we observed in Sema-1a LOF mutants . We used the R32H08-GAL4 driver to express UAS-mCherry in R2/R4m neurons in order to locate their R2/R4m cell bodies for electrophysiological recording , and also to express Sema-1a-RNAi in these same R neurons and disrupt R axon lamination . Additionally , the R54B05-lexA driver was used to express lexAop2-CsChrimson-mVenus in a subset of R3 neurons , allowing us to control optogenetic stimulation of R3 neurons ( Figure 7B ) . Consistent with the anatomical defects we observed in R neurons in the absence of Sema-1a ( Figures 4 and 5 ) , expression of Sema-1a-RNAi in R2/R4m resulted in general lamination defects of both R2/R4m and R3 rings ( Figure 7A , red and green ) and also morphological changes in the axon arbors of single R2/R4m neurons that we could identify with dye-injections following electrophysiological recordings ( Figure 7Ai and 7Aii , white ) . 10 . 7554/eLife . 25328 . 024Figure 7 . Loss of Sema-1a in R2/R4m neurons increases R2/R4m inhibition in response to R3 neuron activation . ( A ) In the photo-stimulation and recording experiments , R2/R4m neurons were labeled by R32H08-GAL4-driving mCherry ( red ) and R3 neurons expressed CsChrimson-Venus ( green ) driven by R46D01-LexA in adult fly brains . Note the separation of R3 and R2/R4m rings in the control brain ( left ) and intermingling of R3 and R2/R4m rings in the R32H08-GAL4-driving Sema-1a-RNAi brain ( right ) . Injection of Biocytin after recording and Alexa647-Streptavidin staining revealed the morphology and identify of recorded R2 and R4m neurons . Scale bars are 50 μm in large panels and 20 μm in small panels . ( B ) Diagrams highlight the experimental design of photostimulation of R3 neurons and patch-clamp recording of single R2 or R4m neurons in ex vivo cultured adult fruitfly brains . Control and RNAi flies share all the transgenes for R3 neuron optogenetic manipulation and R2/R4m neuron labeling , except that Sema-1a-RNAi was expressed in R2/R4m neurons in RNAi flies . ( C ) Example traces show that current injections evoked neuronal spiking in R2/R4m neurons in a control fruitfly brain . Blue lines indicate voltage trace without LED light and red lines indicate voltage trace with LED light . ( D ) F-I curve of the R2/R4m cells from control R2/R4m cells with ( red ) or without ( blue ) LED light stimulation of R3 neurons ( n = 4 cells from 4 animals ) . Data are shown as mean±SEM . ( E ) Comparisons of F-I slopes in D . Each symbol represents one cell ( n = 4 ) . LED-off: 0 . 142 ± 0 . 032 Hz/pA; LED-on: 0 . 125 ± 0 . 032 Hz/pA; p=0 . 391 . ( F–H ) Similar to C-E for Sema-1a-RNAi expressing R2/R4m cells ( n = 4 ) . In panel H , LED-off: 0 . 208 ± 0 . 074 Hz/pA; LED-on: 0 . 050 ± 0 . 019 Hz/pA; p=0 . 0234 . Raw recording data and statistical analyses are available in the Zip file for Figure 7—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 02410 . 7554/eLife . 25328 . 025Figure 7—source data 1 . Raw data and statistical analysis of electrophysiological recording . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 02510 . 7554/eLife . 25328 . 026Figure 7—figure supplement 1 . Electrophysiological characterization of R3 and R2/R4m axon-axon inhibition . ( A and C ) Comparisons among the resting membrane potentials ( Vm ) of control ( A ) and Sema-1a-RNAi ( C ) R2/R4m cells with ( red ) or without ( blue ) LED light stimulation of R3 neurons . In panel A , LED-off: −56 . 45 ± 5 . 16 mV; LED-on: −57 . 14 ± 6 . 33 mV; p=0 . 546 . In panel C , LED-off: −58 . 60 ± 1 . 20 mV; LED-on: −63 . 26 ± 2 . 68 mV; p=0 . 0307 . ( B and D ) Comparisons among the input resistence ( Rin ) of control ( B ) and Sema-1a-RNAi ( D ) R2/R4m cells with ( red ) or without ( blue ) LED light stimulation of R3 neurons . In panel B , LED-off: 1 . 105 ± 0 . 157 GOhm; LED-on: 1 . 120 ± 0 . 148 GOhm; p=0 . 822 . In panel D , LED-off: 1 . 001 ± 0 . 299 GOhm; LED-on: 0 . 803 ± 0 . 365 GOhm; p=0 . 0139 . Statistical analyses are included in the Zip file Figure 7—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 25328 . 026 To examine inhibitory currents among R3 and R2/R4m axons , we used optogenetic stimulation coupled with electrophysiological recording in an ex vivo fly brain preparation ( Figure 7B ) ( Inagaki et al . , 2014; Liu et al . , 2016 ) . CsChrimson depolarizes R3 neurons upon LED light stimulation , which we first confirmed by field recordings near R3 somas ( data not shown ) . To detect inhibitory input onto R2/R4m neurons from R3 neurons , we performed current-clamp recordings on the cell bodies of single R2 or R4m neurons . In control animals ( n = 4 ) , injection of current above a certain threshold ( 10 . 5 ± 5 . 7 pA ) successfully evoked trains of action potentials ( APs ) in R2/R4m neurons ( Figure 7C , blue line ) . We plotted AP frequencies against the injected current amplitudes ( Figure 7D , F–I curves ) and observed that above the current threshold the AP frequency in R2/R4m neurons was positively correlated with the depolarization current amplitudes . More importantly , LED stimulation of R3 neurons had little effect on evoking APs in R2/R4m neurons in these control animals ( Figure 7C and D , red vs blue lines ) . The slopes of the f-I curves were no different when the LED light was off or on in control brains ( 0 . 142 ± 0 . 032 Hz/pA for LED-off vs 0 . 125 ± 0 . 032 Hz/pA for LED-on , p=0 . 391 ) ( Figure 7E ) , showing that the excitability of R2/R4m neurons is not coupled to R3 activation when R2/R4m and R3 axons are physically separated into two adjacent laminae in WT animals . In Sema-1a-RNAi animals ( n = 4 ) , APs were also successfully evoked when depolarization currents above the threshold ( 9 . 8 ± 6 . 6 pA ) were injected into the recorded R2/R4m neurons ( Figure 7F and G , blue lines ) . However , following R3 neuron activation ( LED on ) in these Sema-1a-RNAi animals , R2/R4m neurons generated many fewer APs when evoked with the same depolarization currents above the current threshold ( Figure 7F and G , red vs blue lines ) . In contrast to controls , the slopes of f-I curves were significantly decreased in Sema-1a-RNAi expressing animals when the LED light was on ( 0 . 208 ± 0 . 074 Hz/pA for LED-off vs 0 . 05 ± 0 . 019 Hz/pA for LED-on , p=0 . 023 ) ( Figure 7H ) , showing that it is more difficult to evoke APs in R2/R4m neurons when light-activated R3 axons are intermingled with R2/R4m axons in Sema-1a-RNAi brains . To further assess how optical R3 neuron activation inhibits R2/R4m neurons , we calculated the resting potential ( Vm ) and input resistance ( Rin ) at the R2/R4m membrane . Consistently , LED stimulation of R3 resulted in no change of Vm ( −56 . 45 ± 5 . 16 mV for LED-off vs −57 . 14 ± 6 . 33 mV for LED-on , p=0 . 546 ) or Rin ( 1 . 105 ± 0 . 157 GOhm for LED-off vs 1 . 120 ± 0 . 148 GOhm for LED-on , p=0 . 822 ) in R2/R4m neurons in control animals ( Figure 7—figure supplement 1A and B ) . However , in Sema-1a-RNAi-expressing animals , R3 activation significantly hyperpolarized the Vm ( −58 . 60 ± 1 . 20 mV for LED-off vs −63 . 26 ± 2 . 69 mV for LED-on , p=0 . 031 ) and decreased the Rin of R2/R4m neurons ( 1 . 001 ± 0 . 299 GOhm for LED-off vs 0 . 803 ± 0 . 365 GOhm for LED-on , p=0 . 014 ) ( Figure 7—figure supplement 1C and D ) . These results indicate that R3 activation acutely altered the electrophysiological properties of R2/R4m cell membranes when Sema-1a-mediated axon lamination was disrupted . Taken together , these data support the idea that when Sema-1a-mediated R axon lamination is disrupted , R3 and R2/R4m axons exhibit ectopic contacts , aberrantly coupling their activity through ectopic inhibitory synapses . Therefore , laminar organization of R axons plays an important role in specifying inhibitory synapse formation between different types of R axons in the EB . Previous anatomical analyses show that small-field pb-eb-gall neurons and large-field R neurons elaborate their dendrites and axons , respectively , in the anterior region of the EB ( Wolff et al . , 2015 ) . How small- and large-filed neuron process elaboration is coordinated during pupal development so as to establish laminar and columnar organization during EB formation was not known . Our findings suggest that small-field pb-eb-gall neuron dendrites and large-field R neuron axons locally interact during the EB lamination . The elaboration of pb-eb-gall dendrites can be separated into at least two discrete steps: initial growth with R2/R4m axons shortly after the onset of metamorphosis and a second expansion phase from the peripheral R2/R4m axon regions to the outermost R4d axon and central R3 axon regions of the EB during the second day after pupae formation . This two-step dendrite growth strategy by pb-eb-gall neurons may direct R axon lamina/ring formation since R axon lamination is greatly compromised following pb-eb-gall neuron ablation . Selective association between pb-eb-gall dendrites and early extending R2/R4m axons could serve to localize R2/R4m axons in the peripheral EB and constrain late R3/R4d axon growth in the central and outermost EB through short-range attraction and repulsion . Future studies will determine the underlying molecular mechanisms that regulate pb-eb-gall dendrite/R axon local interactions . R neuron axons also influence pb-eb-gall dendrite development . When we ablated R2/R4m axons , pb-eb-gall neurons show delayed dendrite development at early times and developed smaller dendritic fields within the EB at later stages . However , ablation of either R2/R4m or R1/R3/R4d neurons did not change the general innervation pattern of pb-eb-gall dendrites in the EB , indicating that different R types are independently required for pb-eb-gall dendrite development . In the mouse cerebellum , the expansion of Purkinjie cell dendrites is regulated by TrkC signaling initiated by granule cell-derived neurotrophin-3 ( Joo et al . , 2014 ) . At present , it is unclear whether R axons merely serve as a substrate for pb-eb-gall dendrite growth or whether they play an active role in pb-eb-gall dendrite expansion . Though pb-eb-gall dendrites appear to form a scaffold upon which different R axons extend , interactions among R axons further enhance their laminar organization . Ablation of R2/R4m neurons and their axons , which extend early during EB development , leads to ectopic expansion of R3 axon arbors . Knocking down Sema-1a or PlexA in R2/R4m neurons results in expansion of both R2/R4m and R3 axon rings . These results show that R2/R4m axons constrain R3 axons and separate different R neuron axon layers . However , Sema-1 or PlexA knockdown in R3 neurons does not affect R neuron axon lamination , suggesting that other unidentified molecules mediate direct interactions between R2/R4m and R3 axons , or that R2/R4m neurons regulate R3 axons indirectly through other cells , including pb-eb-gall dendrites . Within the R2/R4m ring , MARCM analysis shows that Sema-1a in R4m neurons , but not in R2 neurons , cell-autonomously regulates axon morphogenesis and synapse localization . Further , knocking down PlexA , but not Sema-1a , in R2 ( along with R1 and R3 ) resulted in severe R axon lamination defects similar to those observed following expression of Sema-1a-RNAi or PlexA-RNAi in R2/R4m . Thus , Sema-1a in R4m and PlexA in R2 could act together to modulate R4m and R2 axon branch growth and targeting . In Drosophila , Sema-1a was initially identified as a repulsive axon guidance cue that signals through its receptor PlexA ( Yu et al . , 1998; Winberg et al . , 1998 ) . More recent studies revealed that Sema-1a also functions as a transmembrane receptor to mediate both axon repulsion and attraction ( Pecot et al . , 2013; Hsieh et al . , 2014; Komiyama et al . , 2007; Lattemann et al . , 2007; Yu et al . , 2010; Hernandez-Fleming et al . , 2017; Cafferty et al . , 2006; Jeong et al . , 2012 ) . In the mouse visual system , Sema6A is expressed in a group of On direction-selective ganglion cells ( On DSGC ) and acts as the receptor for brain-derived PlexA2 and PlexA4 to mediate target recognition of On DSGC axons ( Sun et al . , 2015 ) . This suggests that transmembrane semaphorin reverse signaling is conserved across phyla ( Battistini and Tamagnone , 2016 ) . In the Drosophila ellipsoid body , Sema-1a is broadly expressed in most , if not all , major R neuron types . Single-cell MARCM analysis shows that Sema-1a is cell-autonomously required for multiple R neuron types to direct their axon growth within correct EB rings . In addition , axon patterning phenotypes in Sema-1a-/- mutants are rescued in R4m neurons when full length , but not truncated , Sema-1a is re-introduced into Sema-1a mutant R neurons , further supporting the idea that Sema-1a reverse signaling controls R axon growth . However , loss of Sema-1a in different R neuron types and in pb-eb-gall neurons has distinct consequences . In R2/R4m neurons , loss of Sema-1a results in disruption of lamination , whereas in R3 neurons it leads to ectopic axon projections to other brain regions . In pb-eb-gall neurons , both Sema-1a and PlexA act cell-autonomously to guide axon projections outside of the EB but they are not required for dendrite development within the EB . Thus , how Sema-1a forward and reverse signaling shape axon growth and targeting is context dependent . Other parameters , including developmental timing and spatial localization of R neuron axons , also are important for establishing EB ring neuron axon organization . For example , R3 axons develop later than R2/R4m axons and they do not make close contacts with pb-eb-gall dendrites until the second day after puparium formation . Thus , a developmental time window may exist when R3 axons rely on EB-extrinsic repulsive guidance cues , such as fan-shaped body or mushroom body PlexA , to constrain their axon growth through Sema-1a signaling . In addition , other axon guidance and adhesion molecules likely act redundantly or collaboratively with Sema-1a to regulate R axon organization , since we observed that R axon innervation was only partially disrupted in Sema-1a and PlexA LOF mutants . We expect that many additional molecules contribute to EB ring organization and to the development of other CX structures . Our initial observations on Sema-1a- and PlexA-dependent and independent functions within the developing EB will facilitate their identification . For example , one secreted semaphorin , Sema-2b , and its PlexB receptor exhibit complementary expression patterns in the CX ( data not shown ) . Our preliminary data suggest that Sema-2b/PlexA signaling also plays important roles in EB morphogenesis and FB lamination . Is neuronal lamination essential for synapse organization or a consequence of developmental programs that produce nervous system wiring ? In the mouse retina , heterophilic adhesive interactions between bipolar cell axons and their layer-specific targets in the inner plexiform layer are critical for correct lamination and direction selective visual system responses ( Duan et al . , 2014 ) . However , although establishment of strong synaptic specificity between W3B RGCs and VGT3 amacrine cells ( ACs ) is dependent upon the homophilic Ig superfamily adhesion molecule sidekidk2 , loss of sidekick2 does not result in apparent loss of W3B RGC/VGT3 laminar organization ( Krishnaswamy et al . , 2015 ) . Further , in zebrafish astray mutants , RGC axons do not show clear lamination in the tectum , yet they do display visual system directional tuning responses similar to wild-type ( Nikolaou and Meyer , 2015 ) , and in reeler mutant mice sensory maps in barrel cortex are normal despite disorganization of cortical laminar patterning ( Guy et al . , 2015 ) . These and additional results ( Zhang et al . , 2017 ) suggest that lamination may not always be required for functional circuit assembly . Our results on the role played by Sema-1a in EB axon targeting show that disruption of R axon lamination does indeed result in ectopic inhibitory synaptic connections between R2/R4m and R3 neurons , arguing that lamination is required for the organization of inhibitory synapses among R axons in adjacent EB laminae/rings . How does R axon inhibition and laminar organization of R axon inhibitory synapses contribute to the brain functions and behaviors known to involve R neurons and other EB neurons ? Previous studies show that R neurons are functionally heterogeneous , mediating behaviors that include learning and memory involving visual and olfactory sensory responses ( Neuser et al . , 2008; Ofstad et al . , 2011; Pan et al . , 2009; Wang et al . , 2008; Zhang et al . , 2013 ) and homeostatic regulation of hunger and sleep ( Dus et al . , 2013; Liu et al . , 2016 ) . R neuron functions can differ from ring to ring , as exemplified by R3 neurons , but not R4 neurons , being required for visual place learning ( Ofstad et al . , 2011 ) . Even within each ring , R neurons can serve different functions . For example , only a subset of R4m neurons regulate hunger sensation ( Dus et al . , 2013 ) , and a small number of R2 neurons encode sleep drive ( Liu et al . , 2016 ) . Therefore , among co-stratified R neuron axons residing in the same ring , lateral inhibition from one neuron type to another may help fine tune circuit output , and mutual inhibition may serve to shift bipartite behavioral choices from one choice to another: for example , increasing locomotion to forage , or decreasing locomotion to rest . Physical separation of R neuron axons into different rings/laminae is one way to ensure that GABA-mediated inhibition is effectively transmitted from one R neuron axon to others within the same ring , and to minimize GABA-mediated inhibition to R neuron axons in other rings . Lamination may also be involved in separating different types of inhibitory synapses . We found that inotropic GABA-A receptors and GABA are homogenously distributed in R axon terminals throughout the EB . However , metabotropic GABA-B receptors ( GABA-B-R ) are preferentially localized in the peripheral region of the EB ( Kahsai et al . , 2012 ) . This raises questions as to whether and how GABA-B receptor-expressing neurons form close contacts with select types of R neuron axons and also how intra-ring R neuron connectivity mechanisms are influenced by different inhibitory circuits . In conclusion , we have investigated the spatiotemporal innervation by R neuron axons and pb-eb-gall dendrites during EB formation , and we have uncovered different roles played by these cellular components during the formation of the EB circular lamination pattern . Further , the transmembrane proteins Sema-1a and PlexA play critical roles in the development of R neuron axon patterning in the EB . These results provide evidence for the functional significance of R axon lamination in the context of inhibitory synapse organization within and between EB laminae/rings , suggesting that similar developmental strategies are employed by the EB in flies and in other laminated neural systems in both invertebrates and vertebrates . These include step-wise innervation by pre- and post-synaptic processes and a gradual expansion of laminae during neural development as more neuronal processes undergo directed targeting ( Kolodkin and Hiesinger , 2017 ) . This multi-step process involving neurite growth , guidance , and targeting uses many phylogenetically conserved guidance cues and receptors , and our analysis of EB lamination sets the stage for studying how highly organized neuronal structural features , including lamination , facilitate select synapse formation , circuit organization and behavior . All GMR GAL4 and lexA lines purchased from the Bloomington Drosophila Stock Center ( BDSC ) at Indiana University were generated at Janelia farm ( Pfeiffer et al . , 2008; Jenett et al . , 2012 ) . The following transgenes were used: UAS-CD8::GFP ( RRID: BDSC_5130 ) ( Lee and Luo , 1999 ) , UAS-mCherry ( a kindly gift from Rui Duan in Elizabeth Chen laboratory ) , 13xlexAop2-6xmCherry-HA ( RRID: BDSC_52272 ) ( Shearin et al . , 2014 ) , 3xUAS-IVS-Syt::smGFP-HA ( Aso et al . , 2014 ) , UAS-Syt::GFP , UAS-DenMark ( RRID: BDSC_33064 ) ( Nicolaï et al . , 2010 ) , UAS-CD4::spGFP1-10 , lexAop-CD4::spGFP11 ( RRID: BDSC_57321 ) ( Gordon and Scott , 2009 ) , UAS-nSyb::spGFP1-10 , lexAop-CD4::spGFP11 ( RRID: BDSC_64314 ) ( Macpherson et al . , 2015 ) , UAS-DTI ( Lin et al . , 2015 ) , tub-GAL80ts ( RRID: BDSC_7108 ) , UAS-Dicer2 ( RRID: BDSC_24644 ) ( Dietzl et al . , 2007 ) , 8xlexAop2-IVS-GAL80 ( RRID: BDSC_32215 ) , 13xlexAop2-IVS-CsChrimson::mVenus ( RRID: BDSC_55138 ) ( Klapoetke et al . , 2014 ) , UAS-Sema-1a ( RRID: BDSC_65734 ) ( Jeong et al . , 2012 ) , UAS-Sema-1a . mEC-5xmyc ( renamed UAS-Sema-1aEcTM in this paper , RRID: BDSC_65739 ) ( Jeong et al . ( 2012 ) . The following RNAi lines were ordered from Vienna Drosophila Resource Center ( VDRC ) ( Dietzl et al . , 2007 ) : UAS-Sema-1a-RNAi ( GD36148 and KK104505 ) , UAS-PlexA-RNAi ( GD27238 ) , UAS-Gad1-RNAi ( GD32344 ) . Additional RNAi lines were ordered from BDSC ( Perkins et al . , 2015 ) : UAS-Sema-1a-RNAi ( HMS01307 , RRID: BDSC_34320 ) , UAS-PlexA-RNAi ( HM05221 , RRID_ BDSC_30483 ) , UAS-Rdl-RNAi ( 8–10 j ) ( Liu et al . , 2007 ) . The following mutant alleles were used for gene expression analysis or LOF experiments: Sema-1aFSF ( Pecot et al . , 2013 ) , Sema-1aP1 ( RRID: BDSC_11097 ) ( Yu et al . , 1998 ) , PlexAMB09499 ( RRID: BDSC_61741 ) ( Jeong et al . , 2012 ) , Gad1MI09277-QF2 ( RRID: BDSC_60323 ) ( Diao et al . , 2015 ) , RdlMI02957-GAL4 ( RRID: BDSC_60328 ) ( Diao et al . , 2015 ) , ChaTMI04508-QF2 ( RRID: BDSC_60320 ) ( Diao et al . , 2015 ) , vGluTMI04979-QF2 ( RRID: BDSC_60315 ) ( Diao et al . , 2015 ) . Flies were reared at 25°C for general purposes . For genetic ablation experiments , eggs were laid and maggots were kept at 18°C . In about 8–10 days after egg laying , the late third instar larvae crawled out of the food to prepare for pupariation . These wandering third instar larvae of desired genotypes were collected and transferred to new vials kept at 29°C ( 0 hr after temperature shift ) . In 24 or 48 hr after the transferring , pupae in the new vials were dissected and brains were stained for analysis . The pupae at 24 and 48 hr after temperature shift are comparable to normal-raised animals at 24 and 48 hr after pupae formation . For MARCM analyses , the hsFLP was used to generate mosaic clones as previously described ( Lee and Luo , 1999 ) with small modifications . To generate small R neuron clones , middle and late 3rd instar larvae were heat shocked twice , for 60 min at 37°C each time , in two consecutive days . Adult male flies were dissected for immunohistochemistry analysis . For most of RNAi experiments , parental flies were kept at 25°C to lay eggs . One day after egg laying , larvae were transferred to and raised at 29°C until adult F1 flies were dissected . Except for RNAi experiments using UAS-Sema-1a-RNAi ( HMS01307 ) , both parental and F1 animals were kept at 25°C . Fly brains were quickly dissected from pupae in cold PBS or from adult flies in cold PBS with 0 . 1% Triton X-100 ( 0 . 1% PBT ) , and immediately transferred into fixation buffer ( 4% paraformaldehyde in 0 . 1% PBT ) . Brains were notated in fixation buffer for 20 min at room temperature ( RT ) . After washing with 0 . 1% PBT , fly brains were incubated with blocking buffer ( 5% normal goat serum in 0 . 3% PBT ) for 1 hr at RT . Then brains were incubated with primary and secondary antibodies for 2 days at 4°C or 1 day at RT for each antibody . Brains were washed intensively ( 20 min , 3 times in 0 . 3% PBT at RT ) after the primary and secondary antibody incubation . After final wash , brains were incubated with a drop of Vectashield mounting medium ( Vector Laboratories , H-1000 ) overnight at 4°C . Then brains were loaded onto glass slides ( Superfrost Plus , Fisherbarnd ) prepared with silicon spacers of 120 μm depth ( Grace Biolabs ) , covered by glass coverslip ( 1 oz . , Fisherbrand ) and were ready for imaging analysis . The following primary antibodies were used: chicken-anti-GFP ( 1:1000 , AVES , RRID: AB_10000240 ) , rabbit-anti-GFP ( 1:1000 , Thermofisher , RRID: AB_221569 ) , mouse-anti-GFP ( 1:100 , Sigma G6539 , RRID: AB_259941 ) ( good for GRASP ) , rabbit-anti-DsRed ( 1:1000 , Clontech , RRID: AB_10013483 ) , rabbit-anti-Sema-1a ( 1:200 ) ( Yu et al . , 1998 ) , rabbit-anti-PlexA ( 1;200 , RRID: AB_2569773 ) ( Sweeney et al . , 2007 ) , rat-anti-HA ( 1:500 , Roche , 3F10 , RRID: AB_390915 ) , mouse-anti-Brp ( 1:50 , Developmental Studies Hybridoma Bank ( DSHB ) , Nc82 , RRID: AB_2314868 ) , rat-anti-CadN ( 1:50 , DSHB , DN-Ex#8 , RRID: AB_2619582 ) , rabbit-anti-GABA ( 1:500 , Sigma , A2052 , RRID: AB_477652 ) , rabbit-anti-RDL ( 1:100 , RRID: AB_2568660 ) ( Liu et al . , 2007 ) , mouse-anti-ChAT ( 1:100 , DSHB 4B1 , RRID: AB_528122 ) , rabbit-anti-vGluT ( 1:5000 , RRID: AB_2567386 ) ( Daniels et al . , 2004 ) , mouse-anti-TH ( 1:100 , EMD Millipore MAB318 , RRID: AB_2201528 ) . The secondary antibodies were raised in goat against rabbit , chicken , mouse and rat antisera ( Life Technology ) , conjugated to Alexa 488 ( 1:1000 ) , Alexa 555 ( 1:1000 ) or Alexa 647 ( 1:300 ) . Antibodies are prepared in the blocking buffer with 0 . 02% NaN3 and primary antibodies can be reused for several times . All images were taken on a LSM700 confocal microscope ( Zeiss ) using either a 20X air lens ( N . A . 0 . 8 ) or a 63X oil immersion lens ( N . A . 1 . 4 ) . Most of the image stacks were taken under 1X zoom , in a 512 × 512 configuration , and have 1 μm ( 20X lens ) and 0 . 5 μm ( 63X lens ) Z resolution . Unless specified , images stacks were processed with Fiji ( imagej ) and Adobe Photoshop CS6 . Figures are composed with Adobe Illustrator CS6 . Image stacks with CD8-GFP-labeled R4m axon arbors were first reconstructed into three dimension ( 3D ) images in imaris 7 . 7 . 3 ( Bitplane ) . The experimenter was blinded to the genotype and did the following analyses . Axonal branches were semi-automatically traced ( Figure 4—figure supplement 1Cii and 1Cvi , yellow lines ) using ‘Filament’ functions . Pre-synaptic boutons were manually defined based on their enlarged morphologies ( Figure 4—figure supplement 1Ciii and 1Cvii , white dots ) . At last , the center of EB canal was semi-manually determined based on Nc82 staining ( Figure 4—figure supplement 1Civ , white cord ) using ‘Filament’ function . The minimal distance between each bouton to the EB center cord was automatically measured and transformed into voxel intensity using ‘Matlab extension’ , and each bouton is differentially color-coded based on their distance to the EB center ( Figure 4figure supplement 1Civ and 1Cviii , colorful dots ) . Experiments were performed on 3- to 6-day-old female flies , with the experimenter blinded to the genotype . Perforated patch-clamp recordings with β-escin were performed as previously described with minor modifications ( Liu et al . , 2016 ) , in order to measure action potentials ( APs ) from EB R2/R4m neurons . Brains were removed and dissected in a Drosophila physiological saline solution ( 101 mM NaCl , 3 mM KCl , 1 mM CaCl2 , 4 mM MgCl2 , 1 . 25 mM NaH2PO4 , 20 . 7 mM NaHCO3 , and 5 mM glucose; pH 7 . 2 ) , which was pre-bubbled with 95% O2 and 5% CO2 . To better visualize the recording site , the perineuronal sheath surrounding the brain was focally and carefully removed after treating with an enzymatic cocktail , collagenase ( 0 . 4 mg/ml ) and dispase ( 0 . 8 mg/ml ) , at 22°C for 1 min and cleaning with a small stream of saline pressure-ejected from a large diameter pipette using a 1-mL syringe . In addition , prior to recording , cell surfaces were cleaned with saline pressure-ejected from a small diameter pipette , using a 1-ml syringe connected to the pipette holder . The recording chamber was placed on an X-Y stage platform ( PP-3185–00; Scientifica , UK ) , and the cell bodies of the targeted EB ring neurons were visualized with tdTomato fluorescence on a fixed-stage upright microscope ( BX51WI; Olympus , Japan ) and viewed with a 40× water immersion objective lens ( LUMPlanFl , NA: 0 . 8 , Olympus ) . Patch pipettes ( 8–12 MΩ ) were fashioned from borosilicate glass capillary without filament ( OD/ID: 1 . 2/0 . 68 mm , 627500 , A-M systems , WA ) by using a Flaming-Brown puller ( P-1000; Sutter Instrument ) , and further polished with a MF200 microforge ( WPI ) prior to filling internal pipette solution ( 102 mM potassium gluconate , 0 . 085 mM CaCl2 , 0 . 94 mM EGTA , 8 . 5 mM HEPES , 4 mM Mg-ATP , 0 . 5 mM Na-GTP , 17 mM NaCl; pH 7 . 2 ) . Biocytin hydrazide ( 13 mM; Life Technologies ) was added to the pipette solution before the recording . Recordings were acquired with an Axopatch 200B amplifier ( Molecular Devices ) , and sampled with a Digidata 1440A interface ( Molecular Devices ) . These devices were controlled via pCLAMP 10 software ( Molecular Devices ) . The signals were sampled at 20 kHz and low-pass filtered at 2 kHz . Junction potentials were nullified prior to high-resistance ( GΩ ) seal formation . Cells showing evidence of ‘mechanical’ breakthrough , as assessed by the abrupt generation of a large capacitance transient ( as opposed to the more progressive , gradual one generated by chemical perforation ) were excluded . One neuron per brain was recorded . During the recording , the bath solution was slowly but continuously perfused with saline by means of a gravity-driven system ( approximate flow rate of 1–2 ml/min ) . APs were elicited in response to current injections with 300 ms stepping pulses at 20 pA increments up to 100 pA . Electrophysiological analysis was performed using custom MATLAB-based software ( software code is included in Source code 1 ) . APs were detected automatically by identification of local maxima and were then manually curated to remove excitatory post-synaptic potentials using minimum voltage threshold criteria . Frequency of detected APs was quantified as mean firing rate during current injection . Several key parameters were calculated or measured from the original data , such as: the slope of the f-I curve , the current threshold and and input resistance and resting membrane potential . All-trans retinal ( ATR ) ( R2500 , Sigma ) was prepared as a 35 mM stock solution dissolved in ethanol , and this stock was mixed into rehydrated fly food flakes ( Nutri-Fly Instant , 66–117 , Genesee Scientific ) at a final concentration of 400 μM . A high-powered red LED with peak wavelength at 627 nm ( LXM2-PD01-0050 , Lumileds ) and Buckpuck driver ( RapidLED , Randolph , Vermont ) was used to stimulate CsChrimson-expressing EB R3 neurons . The LED was mounted directly underneath the preparation and light was presented at 0 . 276 mW/mm2 as measured by light meter ( Fieldmate power meter , Coherent , CA ) . Photostimulation began 300 ms before the onset of current injection and ended 300 ms after termination of the current step , which also lasted 300 ms . The timing of photostimulation from LED and current injection from electrode were synchronized using an Arduino Uno board . After recording the physiological responses of EB ring neurons , biocytin hydrazide was iontophoresed into the cell with a constant hyperpolarizing current of 0 . 9–1 . 2 nA passed for at least 5 min . The brain was then fixed in 4% paraformaldehyde in PBS overnight at 4°C . After washing for 1 hr in several changes of PBST ( 0 . 3% Triton X-100 in PBS ) at room temperature , brains were incubated with Alexa-647 conjugated Streptavidin ( 1:300 , Molecular Probes , ThermoFisher Sci . ) in PBST overnight at 4°C . After extensive washing ( 15 min , 3 times in PBST ) , brains were processed following standard fluorescent immunostaining protocol . All statistical tests were performed by Prism 6 or 7 ( GraphPad ) ; methods are listed below . Post hoc power analysis was conducted on existing datasets by GPower 3 . 1 ( Universität Düsseldorf ) . All tests that have p<0 . 05 also have power >0 . 8 except Figure 7—figure supplement 1C , which has a power value of 0 . 728 . FiguresStatistic methodsFigure 2 , E; Figure 2—figure supplement 1 , D and E; Figure 6 , C and FTwo-tailed unpaired t-test with Mann-Whitney test . ‘’ ‘’Figure 2 , D; Figure 5—figure supplement 1 , GMultiple t-tests with the Holm-Sidak correction . ‘’Figure 4 , B , F–H; Figure 4—figure supplement 1 , D–F; Figure 5 , B–D; Figure 5—figure supplement 1 , DOne-way ANOVA with multiple comparisons ( Tukey test ) ‘’ ‘’ ‘’Figure 7E and H; Figure 7—figure supplement 1 , A–DTwo-tailed paired t-tests ‘’	The human brain contains around one hundred billion nerve cells , or neurons , which are interconnected and organized into distinct layers within different brain regions . Electrical impulses pass along a cable-like part of each neuron , known as the axon , to reach other neurons in different layers of various brain structures . The brain of a fruit fly contains fewer neurons – about 100 thousand in total – but it still establishes precise connections among neurons in different brain layers . In both flies and humans , axons grow along set paths to reach their targets by following guidance cues . Many of these cues are conserved between insects and mammals , including proteins belonging to the semaphorin family . These proteins work together to steer growing axons towards their proper targets and repel them away from the incorrect ones . However , how neurons establish connections in specific layers remains poorly understood . In the middle of the fruit fly brain lies a donut-shaped structure called the ellipsoid body , which the fly needs to navigate the world around it . The ellipsoid body contains a group of neurons that extend their axons to form multiple concentric rings . Xie et al . have now asked how the different “ring neurons” are organized in the ellipsoid body and how this sort of organization affects the connections between the neurons . Imaging techniques were used to visualize the layered organization of different ring neurons and to track their growing axons . Further work showed that this organization depends on semaphorin signaling , because when this pathway was disrupted , the layered pattern did not develop properly . This in turn , caused the axons of the ring neuron to wander out of their correct concentric ring and connect with the wrong targets in adjacent rings . Together these findings show that neurons rely on evolutionarily conserved semaphorins to correctly organize themselves into layers and connect with the appropriate targets . Further work is now needed to identify additional proteins that are critical for fly brains to form layered structures , and to understand how this layered organization influences how an animal behaves .	[ "Abstract", "Introduction", "Results", "Discussion", "Materials", "and", "methods" ]	[ "developmental", "biology", "neuroscience" ]	2017	The laminar organization of the Drosophila ellipsoid body is semaphorin-dependent and prevents the formation of ectopic synaptic connections

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